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Centering and decentering of cellular components is essential for internal organization of cells and their ability to perform basic cellular functions such as division and motility . How cells achieve proper localization of their organelles is still not well-understood , especially in large cells such as oocytes . Here , we study actin-based positioning mechanisms in artificial cells with persistently contracting actomyosin networks , generated by encapsulating cytoplasmic Xenopus egg extracts into cell-sized ‘water-in-oil’ droplets . We observe size-dependent localization of the contraction center , with a symmetric configuration in larger cells and a polar one in smaller cells . Centering is achieved via a hydrodynamic mechanism based on Darcy friction between the contracting network and the surrounding cytoplasm . During symmetry breaking , transient attachments to the cell boundary drive the contraction center to a polar location . The centering mechanism is cell-cycle dependent and weakens considerably during interphase . Our findings demonstrate a robust , yet tunable , mechanism for subcellular localization .
The proper localization of cellular components is essential for a variety of cell functions ( Rafelski and Marshall , 2008 ) . Depending on the cellular context , some components need to be positioned at the center of the cell , whereas other components must assume a polar , decentered localization ( van Bergeijk et al . , 2016 ) . For example , depending on the cell type and its stage in the cell cycle , the cell nucleus has to be positioned at the cell center or asymmetrically localized , whereas abnormal nuclear positioning can lead to various pathologies and disease ( Gundersen and Worman , 2013 ) . Similarly , the centrosome is located at the geometrical center of cells under many conditions , yet in other cases it is required at the cell periphery where it serves as the base of the primary cilium ( Letort et al . , 2016 ) . Cells use diverse mechanisms to control their internal organization and dynamically regulate the positioning of their organelles in response to various internal and external cues . While much research has been devoted to the study of the various cellular positioning mechanisms , many basic questions remain open . This is particularly true in large cells such as oocytes ( Mitchison , 2012 ) , where movements must be coordinated over large scales and it is often still unclear what drives movement in the first place and how the proper localization is stabilized . Transport of cellular components depends on biophysical processes that rely on the cell’s cytoskeleton . The mechanisms involved are diverse , typically employing either microtubules , the actin cytoskeleton , or both , together with their respective molecular motors ( Gundersen and Worman , 2013; Mitchison , 2012; Mullins , 2010; Xie and Minc , 2020 ) . The mechanisms also vary in terms of the relevant length scales at which they operate , as the physical requirements for positioning objects in a small cell are different from those in an extremely large oocyte ( Mitchison , 2012; Wühr et al . , 2009; Mogessie et al . , 2018 ) . Many positioning mechanisms rely on microtubule asters radiating from the organelle being positioned , impinging on the cortex at the cell boundary and generating pushing and/or pulling forces against it ( Wühr et al . , 2009 ) . For example , the spindle in dividing cells is often centered by astral microtubules that emanate radially from the centrosomes and interact with dynein motors at the cortex ( Grill and Hyman , 2005 ) . However , this mechanism depends on the availability of long enough microtubules that can directly interact with the cell boundary . In large cells such as oocytes , this is often not the case and various actin-based localization schemes are found . These include , for example , nuclear centering in mouse oocytes which depends on gradients in actin-dependent active diffusion ( Almonacid et al . , 2015 ) , chromosome congression in starfish oocytes where a contracting actin network carries the chromosomes like a fishnet ( Lénárt et al . , 2005 ) , or ooplasm segregation in zebrafish oocytes where differential friction with a contracting actin network drags the ooplasm toward the animal pole ( Shamipour et al . , 2019; Ierushalmi and Keren , 2019 ) . The study of cellular localization mechanisms has greatly benefitted from in vitro work on cell-free systems , that make it possible to study the localization schemes in a simplified , well-controlled environment , and isolate the basic biophysical and biochemical processes involved ( Mullins , 2010; Holy et al . , 1997; Laan et al . , 2012; Abu Shah and Keren , 2014 ) . A notable example is the work on localization of microtubule asters in micro-fabricated , cell-sized compartments . Early work showed that microtubule assembly and disassembly dynamics are sufficient for centering of microtubules asters ( Holy et al . , 1997 ) , while more recently the influence of cortex-bound microtubule motors was studied by attaching motors to the compartment’s interface ( Laan et al . , 2012 ) . These experiments , together with theoretical modeling ( Grill et al . , 2001; Vogel et al . , 2009 ) , showed how the interaction between the tips of microtubules and the cell boundary generates pushing and pulling forces that lead to robust centering under a variety of conditions . Localization in large cells where the cytoskeletal elements do not span the entire system , cannot rely on direct interaction between the cytoskeleton and the cell boundary . Rather , the centering mechanisms must involve indirect sensing of the cell boundary , to be able to define the cell center without directly interacting with it ( Wühr et al . , 2009 ) . Here we use a recently developed in vitro system that self organizes to form persistently contracting bulk actin networks within cell-sized compartments ( Malik-Garbi et al . , 2019 ) , to demonstrate a hydrodynamic centering mechanism that can function in very large cells in the absence of any direct interaction between the cytoskeleton and the cell boundary . Our system is based on encapsulation of Xenopus egg extracts in cell-sized water-in-oil emulsions ( Abu Shah and Keren , 2014; Malik-Garbi et al . , 2019; Pinot et al . , 2012; Tang et al . , 2018 ) . The system self-organizes to form persistently contracting actomyosin networks surrounding an aggregate that forms around the contraction center ( Malik-Garbi et al . , 2019 ) . We observe size-dependent localization of the aggregate: large droplets are symmetric with the aggregate positioned at the center , whereas smaller droplets are polar with the aggregate near the boundary . The centering and decentering of the contraction center resemble cellular centering and decentering as seen for example during nuclear centering and spindle migration in mammalian oocytes ( Almonacid et al . , 2018; Uraji et al . , 2018 ) and plant eggs ( Ohnishi and Okamoto , 2017 ) , and can serve as a simplified model to study actin-based localization in large cells . We show that the centered state is stable against large perturbations and suggest a hydrodynamic active centering mechanism that is based on an imbalance of the Darcy friction forces between the contracting actomyosin network and the cell’s cytoplasm . We use mathematical modeling to show how the displacement of the contraction center from the center of the droplet is translated into an asymmetry in the actin network density , and how this in turn leads to an effective centering force with spring-like properties . We further show that the model correctly predicts how the network properties affect the centering dynamics under various conditions , including different cell-cycle states and biochemical conditions . The size-dependent localization of the contraction center arises from a competition between the hydrodynamic centering force , and a decentering force due to engagement between the contracting network and the boundary , which is more prominent in smaller droplets . Finally , we discuss the implication of these findings for intracellular centering and symmetry breaking , and suggest future experiments to examine if the proposed mechanisms are at play in cellular processes .
Persistently contracting actin networks are generated in cell-like compartments by encapsulating cytoplasmic Xenopus egg extract in water-in-oil emulsion droplets ( Abu Shah and Keren , 2014; Malik-Garbi et al . , 2019; Pinot et al . , 2012; Tang et al . , 2018 ) . Endogenous actin nucleation activities induce the formation of bulk actin networks , which undergo continuous myosin-driven contraction ( Malik-Garbi et al . , 2019 ) . A dense ‘exclusion zone’ forms around the contraction center within minutes , as the network contracts and accumulates particulates from the ( crude ) extract into a dense aggregate . We find that the droplets are typically in one of two configurations: a symmetric state or a polar state ( Figure 1 ) . In the symmetric state , the aggregate is localized near the middle of the droplet and the network exhibits spherically symmetric density and flow patterns ( Figure 1a–c , Video 1 ) . In the polar state , the aggregate is positioned near the droplet’s boundary and the network displays a flow pattern that is skewed toward the side ( Figure 1a–c , Video 2 ) . The observed configurations , both the symmetric one and the polar one , reflect a dynamic steady-state in which the system self-organizes into persistent contractile flow patterns , which remain nearly stationary over time scales that are considerably longer than the characteristic time scale for network contraction and turnover ( ~1 min ) ( Malik-Garbi et al . , 2019 ) . The network dynamics arise from distributed actin network assembly and disassembly processes , coupled to myosin-generated forces that drive global network contraction . The network contracts toward a single point , which is located at the center in the symmetric configuration or near the boundary in the polar configuration , generating characteristic centripetal flow patterns around the contraction center ( Figure 1c ) . The system is able to reach a dynamic steady-state thanks to the presence of rapid actin network disassembly , which limits the accumulation of network around the contraction center and enables the system to reach a stationary network density distribution ( Malik-Garbi et al . , 2019 ) . We have previously shown that the network contracts at a homogenous , density independent rate ( Malik-Garbi et al . , 2019 ) , with an inward flow velocity that increases linearly in magnitude as a function of distance from the contraction center , and approaches zero on the surface of the aggregate surrounding the contraction center ( Figure 1c ) . The symmetry state of individual droplets was found to be strongly correlated with their size ( Figure 1d–f ) . The aggregate is typically centered in larger droplets , whereas in smaller droplets the aggregate is in a polar position near the boundary . The localization of the contraction center in spherical droplets of varying sizes was determined by measuring the displacement of the centroid of the aggregate from the droplet center ( Materials and methods ) . Characterization of the symmetry states 40 min after sample preparation is shown in Figure 1e , with small droplets ( R < 31 µm ) exhibiting a polar state and large droplets ( R > 40 µm ) predominantly in a symmetric state . Intermediate-sized droplets ( 31 µm < R < 40 µm ) exhibit a bimodal distribution whereby both polar and symmetric droplets are observed . This size-dependent localization pattern depends on actin dynamics , whereas disrupting microtubule assembly with Nocodazole has no effect ( Figure 1—figure supplement 1 ) . The characteristics of this localization pattern persists for more than an hour ( i . e . smaller droplets are polar whereas larger droplets are primarily in a symmetric state ) , but over time the fraction of droplets in the polar state increases ( Figure 1—figure supplement 2 ) , suggesting that the centered state is metastable . To gain more insight into the mechanisms for centering and symmetry breaking of the contraction center , we followed the dynamics of aggregate position over time in different-sized spherical droplets . We measured the 3D positions of the aggregate centroids in time lapse movies ( Materials and methods ) and analyzed the dynamics of their displacement over time ( Figure 2 ) . Small droplets were already in a polar state ~5 min after sample preparation . However , we could find droplets with R > 25 µm that were initially symmetric and underwent a symmetry breaking process , whereby the system transitioned from a symmetric state into a polar state ( Figure 2a , b; Video 3 ) . In these cases , the aggregate was initially positioned near the middle of the droplet , exhibiting limited fluctuations , and subsequently , at some moment , started moving in a directional manner toward the droplet boundary ( Figure 2b , f ) . The timing of the transition varied between different droplets , whereas the duration of the transition from the center to the boundary was similar , τ = 12 ± 4 min ( N = 18 ) . In droplets with R > 35 µm , the symmetric state could remain stable for more than 1 hr ( Figure 2c ) . We characterized the centered state by analyzing the movements of aggregates in droplets that remained symmetric throughout the experiment ( 1 hr ) . The aggregates exhibit random movement around the droplet centers . Analysis of this movement shows that the mean squared displacement ( MSD ) of the aggregate positions in individual droplets scales nearly linearly with time ( Figure 2d ) , so that MSD=⟨ Δr→ ( t ) 2 ⟩≈6Dtα with α=0 . 93±0 . 07 ( Mean ± STD; N = 12 droplets ) and D=0 . 32±0 . 1μm2/min ( Mean ± STD ) for t < 5 min . Over longer time scales ( t > 10 min ) , the movement appears confined , as the MSD is bounded and does not continue to increase with time . Thus , the aggregate motion in the centered state can be effectively described as a confined random walk . We can estimate the extent of confinement of the aggregate positions by measuring the mean squared distance from the droplets centers Rc=⟨ d2 ⟩=3 . 4±1 . 6μm ( Mean ± STD ) which is ~10% of the system size . The velocity autocorrelation function decays immediately at the temporal resolution of our measurements ( 0 . 5 min; Figure 2e ) , indicating that the aggregates motion is uncorrelated temporally on these time scales . The characteristics of the symmetry breaking process were analyzed in N = 18 droplets that displayed a transition from the symmetric state to a polar state within one hour . Initially , the aggregates exhibited random fluctuations that were similar to the behavior of centered aggregates in the symmetric state ( Figure 2—figure supplement 1 ) . The onset of symmetry breaking was abrupt , with the aggregate starting to move in a directional manner toward the boundary ( Figure 2b ) . This movement was characterized by a mean squared displacement that increased with time as , MSD=⟨ Δr→ ( t ) 2 ⟩~tα with α=1 . 5±0 . 1 ( Mean ± STD ) , indicating that the movement of the aggregate at this stage is directed rather than random . The average outward radial velocity during the symmetry breaking process was V=1±1μm/min ( Mean ± STD ) . During the symmetry breaking process , the aggregates’ velocity becomes temporally correlated as it moved in a directional manner . This is reflected in the velocity autocorrelation function , which in contrast to the symmetric state , exhibits clear temporal correlations over several minutes ( Figure 2h ) , comparable to the duration of the symmetry breaking process . Once the aggregate reached the boundary it remained there , in a stable polar state . To probe the stability of the centered state and gain insight on the centering mechanism , we developed a methodology to apply external forces on the aggregate that allow us to transiently displace the aggregate from the middle of the droplet and then follow its recovery dynamics ( Figure 3; Videos 4 and 5 ) . This is done by introducing micron-sized superparamagnetic beads into the extract mix ( Figure 3—figure supplement 1; see Materials and methods ) . During the first few minutes after sample preparation , the magnetic beads are swept together with other particulates in the crude extract into the aggregate that forms around the contraction center . The magnetic beads then allow us to move the contraction center with an external magnet ( Tanimoto et al . , 2018 ) . To that end , we introduce the droplets into rectangular capillaries and use a micromanipulator to position a magnetic needle from the side ( Figure 3a and Figure 3—figure supplement 1 ) . In this configuration , the magnetic force and hence the displacement of the aggregate are nearly horizontal ( i . e . parallel to the imaging plane ) , allowing us to follow the dynamics within a single imaging plane . To displace an aggregate from the center of a droplet in the symmetric state , we placed a magnetic needle ~200 µm from the droplet ( Figure 3b ) . The magnetic force on the beads pulls the aggregate toward the needle . As the aggregate is connected to the cytoskeletal network surrounding it , this causes the complex of the aggregate and the surrounding network to move from the middle of the droplet toward the side . The magnetic needle was kept in place for ~1 min while monitoring the aggregate’s position . We estimate that the net pulling force acting on the aggregate is of the order of ~50 pN ( Materials and methods ) . The needle is then removed , and the recovery dynamics of the aggregate are followed . Typically , we observed that shortly after removing the magnetic needle , the aggregate recentered , moving in a directional manner back toward the center of the droplet ( 26 out of 31 droplets examined; Figure 3 , Video 4 ) . Occasionally , we observed droplets that transitioned into a polar state after the perturbation ( 5 out of 31 droplets examined; Figure 3—figure supplement 2 , Video 5 ) . We followed the recentering dynamics of the aggregates following large displacements in N = 19 droplets ( Figure 3 ) , measuring the recentering velocity as a function of time ( Figure 3c , f ) and as a function of distance from the center of the droplet ( Figure 3g ) . We find that the aggregates move toward the center of the droplet and resettle there in a symmetric configuration . The centering velocity reaches a peak value of 5–10 µm/min after an initial reorganization phase . Subsequently , the recentering process proceeds at a velocity that decreases as a function of the displacement of the aggregate from the droplet center in a characteristic concave-down fashion , which is similar among different droplets ( Figure 3g ) . These results show that the centered position of the aggregate is actively maintained by a centering force and is stable against large perturbations . The recentering process is accompanied by a dynamic reorganization of the actin network ( Figure 3d and Figure 3—figure supplement 3 ) . While the contractile network flows persist throughout the recentering process , the network density distribution and flow pattern undergo substantial rearrangements . During the reorganization phase , following the large displacement of the aggregate from the droplet center , the network distribution around the aggregate becomes skewed with a higher density toward the middle of the droplet . As the aggregate moves toward the center of the droplet , the asymmetry in the network distribution becomes less prominent until eventually , when the aggregate reaches the center of the droplet , the network distribution becomes symmetric again ( Figure 3—figure supplement 3 ) . To understand the centering mechanism , we model our system as a two-phase system made up of an active actomyosin network immersed in the surrounding fluid ( cytosol ) , both enclosed within a spherical droplet with the aggregate as an excluded region ( Figure 4a ) . Mathematically , we describe the system using dynamical equations for the actin network density ( ρ ) and the coupled flows of the network ( V ) and the fluid cytosol ( U; see Appendix 1 ) . Many studies ( reviewed in Mogilner and Manhart , 2018; see also Shamipour et al . , 2019 ) demonstrated that the cytosol can be considered as a viscous fluid that flows in the cell with a low Reynolds number , and squeezes through effective pores formed by the cytoskeletal mesh . Physically , the relative movements of the fluid cytosol and the cytoskeletal mesh lead to the so-called Darcy friction , which is proportional to the relative local movement between the mesh and the cytosol . Given the network density and movement rates , a well-defined system of equations ( Mogilner and Manhart , 2018 ) allows one to calculate the Darcy friction and the resulting pressure distribution and flow in the cytosol . The Darcy friction between the contracting network and the fluid cytosol depends on the relative velocity between them , the fluid viscosity , and the network permeability coefficient which is a function of the network density ( Charras et al . , 2005; Schmidt et al . , 1989 ) . Based on our previous work ( Malik-Garbi et al . , 2019 ) , we assume that the actin network exhibits centripetal flow with a speed that increases linearly with the distance from the aggregate ( Figure 1c ) , and that the network turns over with constant assembly and disassembly rates . Two fundamental laws – conservation of mass and momentum – govern the dynamics and mechanics of the network and fluid flow . We numerically solve the respective coupled equations and obtain the actin network density distribution , the fluid velocity and pressure distributions , and the position of the aggregate ( Appendix 1 ) . The net centering force on the aggregate is obtained by integrating the friction forces due to the relative movement between the contracting network and the fluid phase ( Appendix 1 ) . To estimate the effective force on the aggregate , we performed simulations in which the aggregate is held fixed in place at a given position , and the network is allowed to reach a dynamic steady-state for this configuration ( Figure 4b , c ) . A ‘fountain’-like cytosolic flow emerges when the aggregate is decentered: if the aggregate is shifted to the left ( Figure 4b , c ) , more network accumulates to its right and this body of network flows to the left . The cytosol is pulled to the left with the contracting network , and then , due to the incompressibility , escapes around the aggregate to the left curving away from the aggregate . The cytosol returns to the right near the boundaries of the droplets , where the network density is low and the resistance to fluid flow is smaller ( Figure 4c ) . We calculate the net hydrodynamic force on the aggregate by integrating the Darcy friction force over the whole droplet , as a function of aggregate position and droplet radius ( Figure 4d ) . We find that the hydrodynamic centering force behaves like a Hookean spring with a force that increases linearly as a function of the displacement from the droplet center , and an effective spring constant that scales with the volume of the droplet ( Figure 4d , e ) . This scaling arises because the network density depends weakly on the radius , so each volume element contributes a certain force and the net force scales with the droplet’s volume . The hydrodynamic centering force is strongly dependent on the contraction rate of the actin network: faster contraction increases the relative movement between the network and the surrounding fluid , and hence enhances the Darcy friction forces which generate the centering force ( Figure 4f ) . Intuitively , the appearance of a centering force can be understood as follows . The contracting network flows through the cytosol . When the aggregate is displaced from the center , for example to the left , then the network distribution becomes skewed with more network on the right ( it has more space to assemble on the right ) ( Figure 4b ) . As the network permeability decreases with network density , the Darcy friction force is higher on the right , and also the force is integrated over a greater volume on the right . The direction of the force from every element of volume is opposed to the network flow ( Figure 4c ) , so the net force on the aggregate will be directed toward the droplet center . Importantly , this centering force does not involve a direct interaction with the droplet boundary ( e . g . push/pull ) . Rather , the centering force arises from hydrodynamic interactions of the network with the fluid cytosol at low Reynolds number . The presence of this effective centering force also explains the confined nature of the aggregate motion observed in the centered state ( Figure 2c , d ) . To model the dynamics of the magnetic recentering experiments ( Figure 3 ) , we performed additional simulations in which the aggregate was free to move ( Figure 4g–h , k ) . To emulate the experimental configuration , we assume initial conditions in which the network distribution is equal to the steady-state distribution in the centered state that is then displaced toward the side ( Figure 4g; Appendix 1 ) . We simulate the evolution of the system by numerically solving the coupled equations for the network and fluid dynamics iteratively , whereby at each step the aggregate moves in response to the net force acting on it ( details in Appendix 1 ) . The simulated aggregate recenters to the middle of the droplet with a centripetal velocity that decreases as a function of its displacement from the droplet center . The movement of the aggregate in the simulation is correlated with the extent of asymmetry in the actin network distribution , which agrees with our experimental observations ( Figure 3—figure supplement 3 ) . The model further predicts that the centering dynamics will be strongly enhanced by the rate of network contraction , but will be nearly independent of the fluid viscosity and the meshwork assembly and disassembly rates ( Figure 4—figure supplement 1 ) . These trends can be easily understood intuitively; both the hydrodynamic driving force for recentering and the opposing drag are dependent on the fluid viscosity and the meshwork permeability in a similar manner , so the effect of changing these properties cancels out and as a consequence has little influence on the centering dynamics . In contrast , the contraction rate of the network is directly related to the magnitude of the hydrodynamic centering force , but does not affect the resisting drag force . As such , a decrease ( increase ) in the network contraction rate is expected to cause a corresponding slow down ( speed up ) of the centering process ( Figure 4—figure supplement 1 ) . To test these predictions experimentally , we modulated the behavior of the system by changing the cell cycle state of the extract or by varying the system’s composition , and examined the relation between the centering dynamics and the network contraction rate ( Figure 4i-k , Figure 4—figure supplement 2 and Figure 4—figure supplement 3 ) . Cycling the meiotic ( M-phase ) extract into interphase ( I-phase ) has a dramatic influence on actin-myosin dynamics ( Field et al . , 2011 ) and results in a sparser network with a ~ 3 fold slower contraction rate ( Figure 4i , j ) . We find that this substantial reduction in the network contraction rate is accompanied by slower centering dynamics . These observations are in agreement with the predictions of the model , taking into account the measured changes in the network contraction rate with no additional fit parameters ( Figure 4j , k and Figure 4—figure supplement 2 ) . Similar results are obtained with networks supplemented with ActA that promotes branched filament nucleation , and has recently been shown to induce changes in network behavior , and in particular slow down network contraction ( Malik-Garbi et al . , 2019 ) . Again , we find that the slower contraction rate is accompanied by slower centering dynamics as predicted by the model ( Figure 4—figure supplement 3 ) . While both perturbations likely influence the meshwork permeability , the effect on the centering dynamics can be quantitatively accounted for solely based on the observed reduction in the contraction rate . Moreover , the weaker centering force in interphase-extracts also leads to cell-cycle dependent changes in the localization pattern of the contraction center , with more droplets assuming a polar configuration ( Figure 4—figure supplement 4 ) . The hydrodynamic interaction generates a centering force that explains the stability of the centered state . But how does the system break symmetry and become polar ? Why is the symmetry state of the system size-dependent ? We posit that the decentering is driven by an attractive interaction between the actin network and the droplet boundary , for example by crosslinking of the network filaments to proteins on the boundary . In this scenario , the symmetry state of the system is determined by a competition between the hydrodynamic centering force and the attractive interaction with the boundary ( Figure 5a ) . To test this idea experimentally , we modulated the interaction between the actin network and the boundary by adding low concentrations of Bodipy-conjugated ActA , which is an actin nucleation promoting factor that has been engineered to localize to the water-oil interface ( Abu Shah and Keren , 2014; Tan et al . , 2018 ) . The presence of ActA at the interface activates the actin nucleator Arp2/3 and promotes the nucleation of actin filaments at the surface . We reasoned that the addition of low levels of ActA ( much lower than the amounts required to form a continuous cortical network [Abu Shah and Keren , 2014] ) would have a negligible influence on the bulk actin network , but would increase the likeliness of transient network attachment to the boundary via the surface nucleated filaments . We find that indeed as the concentration of ActA-bodipy is increased , the system shifts toward a polar configuration ( Figure 5b ) . These changes were related to the localization of ActA at the interface , as control experiments with the same concentration of ActA in the cytoplasm did not induce symmetry breaking ( Figure 5—figure supplement 1 ) . To model the size-dependent symmetry state of the system , we considered a simple effective model which incorporates the hydrodynamic centering force ( Figure 4d , e ) and also assumes that the network can stochastically engage with the boundary ( Appendix 2 ) . While the exact nature of the interaction between the bulk actin network and the boundary is not known , we assume the network interacts by a stochastic ‘clutch’ mechanism , with a characteristic on-rate ( kon ) and off-rate ( koff ) , that when engaged pulls the aggregate to the closest boundary . We further assume that the probability to engage with the boundary depends on the actin network density near the boundary , reasoning that the on and off-rates should increase or decrease , respectively , as a function of the network density close to the boundary . Since the network density falls off as a function of the distance from the contraction center , we assume the on-rate increases when the aggregate is closer to the boundary ( since the network density near the boundary becomes larger ) while the off rate decreases . Specifically , for simplicity , we assume that the off-rate is proportional to the distance between the aggregate and the closest boundary , while the on-rate is inversely proportional to that distance . Finally , we assume that when the aggregate is engaged , it moves at a constant speed toward the closest boundary , to mimic the directed motion we observe during symmetry breaking ( Figure 2 ) . This simple model recapitulates the size-dependent symmetry states in the system , predicting that small droplets will be polar and large droplets will be centered , with a transition zone for intermediate-sized droplets . The centered state in this model is metastable; while larger droplets tend to stay centered for long periods because the probability to engage the clutch is lower , eventually , due to the stochastic nature of the surface interaction , they can break symmetry . As a result , the size-dependence of the symmetry states in the system evolves over time , with larger and larger droplets becoming polar as observed experimentally ( Figure 1—figure supplement 2 ) . The size-dependent localization pattern that arises from a competition between the hydrodynamic centering and the surface interactions can be tuned by varying the properties of the contracting network or the boundary . For example , when we enhance the interaction with the boundary by increasing the probability of clutch engagement and/or decreasing the probability for disengagement , to mimic the increase in cortical actin following the addition of ActA at the interface ( Figure 5b ) , the model predicts that the transition between the symmetry states occurs at larger droplet sizes , which is aligned with our experimental observations ( Figure 5c ) . We can further test the model predictions by varying the geometry of the droplets . The model predicts that for non-spherical droplets , symmetry breaking will be biased toward the boundary closest to the droplet’s center . Indeed , we find that in squished , pancake-shaped droplets , symmetry breaking occurs preferentially toward the top or bottom interface , in contrast with the more homogenous angular distribution in spherical droplets ( Figure 5—figure supplement 2 ) . Note , that the alternative to the clutch model would be assuming the presence of a constant interaction force with the boundary that is a function of the distance between the aggregate and the boundary . However , such a model would predict that following the magnetic force pulling experiments , which bring the aggregate close to the boundary , the interaction force with the boundary would either be stronger than the hydrodynamic centering force , in which case we would not expect the aggregate to recenter , or weaker than the hydrodynamic centering force , whereby decentering would never take place . This contradicts our observations ( Figures 2 and 3 ) . The clutch model , on the other hand , is consistent with the system’s behavior following the magnetic perturbations , and also accounts for the observed time-dependence: 1 ) Larger droplets remain centered over a finite time interval because the frequency of the clutch engagement decreases with radius . 2 ) After sufficient time , decentering occurs even in large droplets , because when the clutch is engaged , the interaction with the boundary overcomes the hydrodynamic force . While the quantitative predictions of our symmetry breaking model ( Appendix 2 ) depend on the specific realization of the surface interaction and the parameters used , the qualitative localization pattern does not . The essential feature of the surface interaction is their transient nature , whereas any model that assumes a continuous interaction is unable to account for the observed phenomenology ( i . e . a bimodal distribution of cells in a centered and polar configuration , where the centered state is robust against large perturbations yet meta-stable over time ) .
We developed a model system to study actin-based localization in artificial cells . The system exhibits two stable configurations; a centered configuration in which the contraction center is actively maintained at the middle of the cell and a decentered one where the contraction center is near the boundary . While the contracting network dynamics are largely determined by the interplay between internal forces , namely the myosin induced contractile force and the opposing force which is primarily due to the viscosity of the network ( Malik-Garbi et al . , 2019 ) , the size-dependent localization of the contraction center is determined by the smaller friction forces between the network and the fluid it is immersed in and the interactions between the network and the droplet boundary . The observed dependence of intracellular localization on cell size and cell cycle state can have important implications for early embryonic development , where cycles of rapid cell division with limited growth typically lead to a rapid decrease in cell size . Our results illustrate that changes in cell size and cell-cycle state can have a substantial effect on the symmetry state of the system . In particular , our results suggest that simply reaching a certain cell size can be sufficient for inducing a transition from a symmetric configuration to symmetry breaking at a certain developmental stage . By combining experimental measurements and theoretical modeling , we show that a simple and robust hydrodynamic centering mechanism is responsible for actively maintaining the contraction center at the middle of the droplet ( Figure 4 ) . The hydrodynamic centering is based on the presence of a persistent centripetal network flow , that is subject to Darcy friction with the surrounding cytoplasm . A net centering force arises from an imbalance in the friction forces when the network distribution is asymmetric , providing an indirect way to sense the shape of the cell as the boundary of the domain in which the reaction-diffusion-convection dynamics of the network take place . The net force that arises can lead to centering of cellular components even without generating appreciable intracellular cytoplasmic flows ( Figure 4c ) . Since the hydrodynamic centering force scales with cell volume ( Figure 4e ) , the contribution of the hydrodynamic centering mechanism is expected to become more prominent in large cells such as oocytes . The main control parameter for the centering mechanism is the network contraction rate ( Figure 4—figure supplement 1 ) , a property that can be modulated by changing the properties and concentrations of various cytoskeletal regulators ( Malik-Garbi et al . , 2019 ) , e . g . as a function of the cell cycle or developmental state of the cell . Importantly , the centering mechanism is extremely robust; the network naturally self-organizes into an asymmetric configuration when the contraction center is skewed , allowing cells to dynamically respond and adapt to changes in their shape , and the centering dynamics are insensitive to variations in the structure of the meshwork and/or the viscosity of the cytoplasm ( Figure 4—figure supplement 1 ) . Furthermore , unlike other cytoskeletal-based cellular centering mechanisms ( Tanimoto et al . , 2016; Wu et al . , 2017 ) , the hydrodynamic centering mechanism does not involve any direct interactions with the boundary . Notably , the hydrodynamic centering mechanism proposed is generic; the centering force does not depend on the specific molecular components involved . Rather , the same centering mechanism would function in the presence of continuous flow of any cellular component . The important features are the presence of persistent flow patterns that generate directed Darcy friction forces , and a dynamic density of the flowing components that changes in response to its cellular localization . Our system can also break symmetry , and transition from the centered state to an asymmetric polar state in a size-dependent manner . Our results suggest that this transition reflects a competition between the hydrodynamic centering force and attractive interactions with the boundary , that can be enhanced by the presence of a cortical actin layer at the interface ( Figure 5 ) . While the hydrodynamic drag originating from the bulk cytoplasmic flows generates a continuous net centering force , the attractive interaction with the boundary ( likely generated when a part of the network becomes anchored to the interface ) can be transient – as it depends on the presence of a mechanical link between the contracting network and the boundary . Filament turnover and active forces that cause local network rupture events can break this link and thwart the pulling forces towards the boundary . The detachment of the anchor between the contracting network and the interface reverses the direction of force exerted on the aggregate: an anchored contracting network pulls the aggregate toward the boundary , whereas an unanchored network pushes the contraction center away from the closest boundary via the hydrodynamic centering mechanism . Our analysis ( Appendix 2 ) indicates that the transient character of the surface interaction is essential for the time and size-dependent localization pattern observed , facilitating a centered state that is robust against large fluctuations , yet susceptible to symmetry breaking over time . While the exact nature of the surface interactions is not well characterized , our results suggest that the tendency to polarize in smaller droplets arises primarily from the higher probability for the contracting network to engage with the boundary in these droplets . We model the interaction between the contracting network and the boundary as a transient ‘clutch’ . While the actual interactions are likely more complicated than assumed in this simplified model , the agreement between our experimental results under different conditions and the predictions of this model , suggest that the simplified model captures , at least qualitatively , the main features responsible for the size-dependent centering and decentering of the contraction center . Importantly , the strong dependence of the centering efficiency on the contraction rate implies that cells can regulate the localization of cellular components via modulations of the actin network dynamics . In particular , our results show that changes in the cell cycle state which modulate the contraction rate have a large influence on the observed localization pattern ( Figure 4—figure supplement 4 ) . This novel symmetry breaking mechanism joins an array of different actin-myosin-based symmetry breaking mechanisms that have been previously discovered ( van Oudenaarden and Theriot , 1999; van der Gucht et al . , 2005; Kozlov and Mogilner , 2007; Mullins , 2010; Abu Shah and Keren , 2014; Barnhart et al . , 2015 ) , demonstrating yet another way in which these active networks can break symmetry and generate large scale motion . In all these examples , the symmetry breaking mechanisms reflect an inherent mechanical instability in the system , which can be biased by the presence of directional cues , allowing cells to polarize in response to various signals ( Mullins , 2010 ) . Similarly , in our system the transition from a centered state to a polar one is spontaneous and occurs in a random direction , yet the same mechanism could allow cells to respond to a directional signal , for example if nucleation of cortical actin was enhanced locally , in a particular region of the cell boundary , the system would polarize in that direction . The development of a simplified reconstituted model system , allowed us to discover and characterize in detail novel actin-based mechanisms for cellular localization . The characteristics of our in vitro system , such as the size of the cells and the typical velocities , are comparable to those found in oocytes and large embryonic cells , so we expect that the localization mechanisms explored here can be relevant for localization of various sub-cellular components in vivo . To assess the contribution of the actin-based hydrodynamic mechanism proposed here for the localization of organelles within living cells will require measuring the intracellular forces on these components in vivo ( Tanimoto et al . , 2018 ) , and examining how local or global disruptions of the actin network flow influence the forces and the resulting localization patterns . Since the mechanisms for cellular localization in vivo are diverse , especially during early embryogenesis , and also involve additional force-generating systems such as the microtubule cytoskeleton ( Gundersen and Worman , 2013; Mitchison , 2012; Xie and Minc , 2020; Wühr et al . , 2009; Mogessie et al . , 2018 ) , we expect the contribution of the hydrodynamic centering to vary considerably among organisms and between different cellular contexts . For example , nuclear positioning in Xenopus embryos appears to be primarily microtubule-dependent ( Wühr et al . , 2009 ) , and hence is unlikely to involve the actin-based mechanisms discussed here . In contrast , localization of cellular components in other systems such as mammalian oocytes is known to be actin-dependent ( Xie and Minc , 2020; Almonacid et al . , 2018; Uraji et al . , 2018 ) . In these systems , we expect the mechanisms discussed here will contribute to the positioning of organelles when the actin networks exhibit large-scale flows . While bulk actin network flows are often masked by cortical dynamics and hence difficult to detect ( Field and Lénárt , 2011 ) , they have been observed in various systems including starfish oocytes ( Lénárt et al . , 2005 ) and zebrafish oocytes ( Shamipour et al . , 2019; Ierushalmi and Keren , 2019 ) . The involvement of persistently flowing actin networks in cellular centering and decentering is seen in diverse cellular contexts . Examples include the localization of the nucleus in migrating cells that transition from a centered state in stationary cells to a polar localization in motile cells in fibroblasts ( Gomes et al . , 2005 ) and keratocytes ( Yam et al . , 2007 ) . Note also that this decentering and polarization is often the hallmark of motility initiation in cells , both in 2D ( Barnhart et al . , 2015 ) and in 3D ( Callan-Jones and Voituriez , 2016 ) , adding to the significance of the mechanism that we uncovered . Future research will show if Darcy forces could be part of the motility initiation phenomena in cells . An important feature of the localization mechanisms based on actin network flow is their ability to operate across scales and drive transport even over macroscopic scales . The transport of cellular components can be directly driven by the network flow , as seen for example during chromosome congression in star fish oocytes ( Lénárt et al . , 2005 ) , or indirectly via friction based mechanisms as observed recently during ooplasm segregation in zebrafish oocytes ( Shamipour et al . , 2019; Ierushalmi and Keren , 2019 ) . In our system , the movement of the aggregate during decentering is directly coupled to the flow of the contracting actin network , whereas the centering is indirectly mediated by a hydrodynamic interaction between the contracting network and the surrounding cytoplasm . There is an analogy between the localization mechanism we observed and a general class of mechanisms that can regulate switches between centering and decentering , based on shifting the balance between the interaction of the network periphery with the cell boundary and forces that act along the network length ( Tanimoto et al . , 2016; Zhu et al . , 2010; Fogelson and Mogilner , 2018 ) . The novelty of our mechanism is that the centering force along the network length is hydrodynamic in nature . There was , in fact , a previous proposal that cytoplasmic flow generated by drag from dynein-driven cargo on astral microtubules can position organelles in large cells ( Shinar et al . , 2011; Niwayama et al . , 2011 ) . Involvement of an actin-network-generated cytoplasmic flow in decentering of meiotic spindle in mouse oocytes was also proposed ( Yi et al . , 2013 ) . In general , the appreciation for the presence of cytoplasmic flow in cells , driven by either actin-myosin ( Keren et al . , 2009 ) or microtubule-kinesin-dynein networks ( Monteith et al . , 2016 ) , is increasing . The contribution of our study is that we demonstrate the significance of such flow for subcellular localization in a minimal in vitro system . The mechanistic understanding of the processes responsible for the localization of cellular components in vivo and the force generation involved , especially in large cells , is still in many cases surprisingly limited ( Xie and Minc , 2020 ) . The development of in vitro model systems to study centering and decentering mechanisms in cells , as exemplified by this work , provides important insights to understand the complex dynamics that determine the internal cellular organization , which is an essential step toward deciphering the underlying operation principles of the living cell .
Concentrated M-phase extracts were prepared from freshly laid Xenopus laevis eggs as previously described ( Abu Shah and Keren , 2014; Malik-Garbi et al . , 2019; Abu-Shah et al . , 2014 ) . Briefly , Xenopus frogs were injected with hormones to induce ovulation and laying of unfertilized eggs for extract preparation . The eggs from the different frogs were collected and washed with 1X MMR ( 100 mM NaCl , 2 mM KCl , 1 mM MgCl2 , 2 mM CaCl2 , 0 . 1 mM EDTA , 5 mM Hepes , pH 7 . 8 , 16°C ) . The jelly envelope surrounding the eggs was dissolved using 2% cysteine solution ( in 100 mM KCl , 2 mM MgCl2 , and 0 . 1 mM CaCl2 , pH 7 . 8–7 . 9 ) . Finally , eggs were washed with CSF-XB ( 10 mM K-Hepes pH 7 . 7 , 100 mM KCl , 1 mM MgCl2 , 5 mM EGTA , 0 . 1 mM CaCl2 , and 50 mM sucrose ) containing protease inhibitors ( 10 µg/ml each of leupeptin , pepstatin and chymostatin ) . The eggs were then packed using a clinical centrifuge and crushed by centrifugation at 15000 g for 15 min at 4°C . The crude extract ( the middle yellowish layer out of three layers ) was collected , supplemented with protease inhibitors ( 10 µg/ml each of leupeptin , pepstatin and chymostatin ) and 50 mM sucrose , snap-frozen in liquid N2 as 10 µl aliquots and stored at −80◦C . Typically , for each extract batch a few hundred aliquots were made . Different extract batches exhibit similar behavior qualitatively , but the values of the contraction rate and disassembly rate vary ( Malik-Garbi et al . , 2019 ) . All comparative analysis between conditions was done using the same batch of extract . I-phase extract was prepared by adding CaCl2 and cycloheximide to M-phase extract to concentrations of 0 . 4 µM and 2 µg/ml , respectively , and incubating at room temperature for a few minutes ( Field et al . , 2011; Field et al . , 2014 ) . ActA-His was purified from strain JAT084 of Listeria monocytogenes ( a gift from Julie Theriot , Stanford University ) expressing a truncated actA gene encoding amino acids 1–613 with a COOH-terminal six-histidine tag replacing the transmembrane domain , as previously described ( Abu Shah and Keren , 2014; Abu-Shah et al . , 2014 ) . Purified proteins were aliquoted , snap-frozen in liquid N2 , and stored at −80°C until use . ActA-His-Cys was purified from strain DP-L4363 of Listeria monocytogenes ( a gift from Julie Theriot , Stanford University ) and conjugated with ∼6–8 molecules of Bodipy FL-X-SE ( #D6102 , Molecular Probes ) per protein as previously reported ( Abu Shah and Keren , 2014; Abu-Shah et al . , 2014 ) . ActA-Bodipy was stored at a concentration of ∼30 µM at −80°C . Before use , ActA-Bodipy was sonicated on ice for 15 min and centrifuged at 4°C for 15 min at 16 , 000 g to remove aggregates . Actin networks were labeled with GFP-Lifeact purified from transformed E . coli ( gift from Chris Field , Harvard Medical School ) . The purified protein was concentrated to a final concertation of 252 µM in 100 mM KCL , 1 mM MgCl2 , 0 . 1 mM CaCl2 , 1 mM DTT and 10% Sucrose , and stored at −80°C until use . An aqueous mix was prepared by mixing the following: 8 µl crude extract , 0 . 5 µl 20 × ATP regeneration mix ( 150 mM creatine phosphate , 20 mM ATP , 20 mM MgCl2 and 20 mM EGTA ) 0 . 5 µM GFP-Lifeact and any additional components as indicated . The final volume was adjusted to 10 µl by adding XB ( 10 mM Hepes , 5 mM EGTA , 100 mM KCl , 2 mM MgCl2 , 0 . 1 mM CaCl2 at pH 7 . 8 ) . The concentration of the components of the actin machinery in the mix can be estimated based on Wühr et al . ( 2014 ) . The total actin concentration is estimated to be ∼20 µM . The ATP regeneration mix enables the system to continuously flow for more than 1–2 hr . Emulsions were made by adding ∼3% by volume extract mix to degassed mineral oil ( Sigma ) containing 4% Cetyl PEG/PPG-10/1 Dimethicine ( Abil EM90 , Evonik Industries ) and stirring for 1 min at 4°C . The emulsions were put in 30 µm or 100 µm thick chambers for imaging , or in 100 µm thick glass capillaries . 30 µm or 100 µm thick chambers were prepared by separating two passivated coverslips ( Abu-Shah et al . , 2014 ) with double sided tape , and sealing with VALAP ( 1:1:1 mix of vaseline , lanolin and paraffin ) . In 30 µm thick samples the imaged droplets were squished , allowing for better imaging of the actin network due to the flat glass-droplet surface , while in the 100 µm samples all but the largest droplets ( R > 50 µm ) were spherical , minimizing interactions with the interface and maximizing boundary symmetry . Nocodazole treatment was performed by adding Nocodazole to a final concentration of 33 µM in the extract mix . Bulk ActA assays were performed by adding Acta-His to the extract mix to the specified concentrations of 100 nM or 0 . 5 µM . Modulation of the interaction with the interface were performed by adding 25-100 nM of ActA-Bodipy to the extract mix . 1 µm diameter Dynabeads MyOne Sterptavidin C1 superparamagnetic beads ( Invitrogen ) were washed 3 times and resuspended in XB to 200 µg/ml . The washed beads were incubated for 30 min with 0 . 6 µm biotin , washed 3 more times and resuspended in XB to 5 mg/ml ( 3–5 × 106 beads/µl ) . For magnetic manipulation assays , 0 . 5 µl magnetic beads ( at 3 or 5 mg/ml ) were added to the extract mix . For droplets in the size range examined ( R ~ 50–100 µm ) we estimate the number of beads in the aggregate to be 10–100 beads . Emulsions prepared as described above were loaded into rectangular glass capillaries ( cross-section: 100 µm x 2000 µm ) by capillary forces . The samples were incubated at room temperature for 10–15 min to allow the network to reach a steady state of contraction with a well-defined aggregate in the contraction center . Droplets in a symmetric configuration that were positioned near the capillary wall were identified and a horizontal magnetic force was applied by placing a magnetic needle ( the tip of a steel sewing needle attached to a K and J Magnetics D14-N52 neodymium magnet 1/16’ dia . x 1/4’ thick ) , mounted on a three-axis micrometer manipulator , at a distance of 100–300 µm from the droplet ( Figure 3—figure supplement 1 ) . At this distance , the magnetic force on a single bead is ∼0 . 1-2pN ( depending on the distance of the magnetic needle ) . The magnetic needle was held in place for 40–120 s during which the contraction center moved toward the side of the droplet . Since the droplets are not anchored to the surface , the external force also leads to some movement of the entire droplet toward the capillary wall . The magnetic needle was then removed to allow the contraction center to reposition under the influence of internal forces . Bright-field and confocal images of the process were acquired using a spinning disk microscope as described below . The proximity to the capillary wall introduces some optical artifacts due to the glass curvature . The magnetic force on a single bead was estimated by imaging beads in water in the capillaries and measuring their velocity toward the magnetic needle . Under the influence of the magnetic field , the beads formed straight chains that moved axially toward the magnet . Each chain was tracked and measured automatically using MATLAB . The drag force on each chain was estimated as the force on a cylinder , and the magnetic force per bead obtained by dividing by the estimated number of beads in the chain . Bright-field Images were acquired on a Zeiss Observer Z1 microscope using a Photometrics CoolSNAP HQ2 CCD camera or a QuantEM camera . Confocal images were acquired using a Yokogawa CSU-X1 spinning disk attached to a Zeiss Observer Z1 microscope and acquired with an EM-CCD ( QuantEM; Photometrics ) . Symmetry state statistics images of emulsions in 100 µm thick chambers were taken using 40x ( NA = 1 . 3 ) or 63x ( NA = 1 . 4 ) objectives . For each sample , 20–50 positions were chosen , containing droplets 30–120 µm in diameter , and z-stacks of bright-field images at a 3–5 µm separation of the sample were taken at several time-points between 15 to 60 min after sample preparation . Magnetic manipulation experiments and aggregate tracking experiments were imaged by bright-field and spinning disk confocal microscopy in glass capillaries using a 20x ( NA = 0 . 5 ) or a 10x ( NA = 0 . 5 ) objective . Magnetic manipulation experiments were imaged at a single plane , with 2 s time interval for up to 15 min , starting several minutes after sample preparation . Aggregate tracking experiments were performed by following 3–5 droplets at a time , using 100 µm thick samples , 5–9 z-planes at 5 µm separation , at 30 s intervals . The imaging was initiated several minutes after sample preparation and lasted for 1 hr . Fluorescence spinning-disk images were taken using 488 nm and 561 nm lasers and appropriate emission filters . Device controls and image acquisition were carried out using Slidebook software . Image analysis was carried out using custom-written code in Matlab . 2D and 3D positions of the droplets and aggregates were extracted from bright-field or confocal images and z-stacks , either manually or in an automated fashion . For the statistical analysis of symmetry states , the x-y positions of the droplet centers were determined automatically from the bright-field images using a circle detection built-in Matlab function based on the Circular Hough Transform . The x-y positions of the aggregate centers were determined by fitting a circle to the aggregates in the images manually . The z-position of the droplet and aggregate centers were determined manually from the z-stack . Three droplet size ranges – small ( polar; denoted red in the figures ) , intermediate ( transition; yellow ) and large ( symmetric; green ) – were determined for each data set as follows: A threshold of aggregate displacementdroplet radius=0 . 4 was used to categorize droplets as centered ( 0 ) or polar ( 1 ) , and a symmetry breaking curve was defined as a moving Gaussian mean over droplet diameter ( using a Gaussian window with a variance , σ=5μm2 ) of these binary symmetry states . A cumulative mean of the symmetry breaking curve was used to determine the border between the small ( polar ) and the intermediate droplet size ranges , where the cumulative mean drops below 0 . 95 of its max-min , and a reverse cumulative mean of 1 minus the symmetry breaking curve was similarly used to determine the border between the large ( centered ) and the intermediate droplet size ranges . In the aggregate tracking experiments , the position of the contraction centers was determined from movies taken with 5–9 z planes at 5 µm separation at each time-point as follows . The initial droplet and aggregate positions were marked by hand . Subsequently in each frame the droplet position was determined by scanning the vicinity of the droplet position from the previous frame for a circle of the same size with maximum intensity . The vicinity of the aggregate from the previous frame was scanned for the brightest or highest-gradient elliptical ring in all z-planes , and the x-y position of the aggregate was defined as the centroid of this ring . The z-position was determined to sub-pixel resolution in the z-direction using a 3-point Gaussian interpolation over the ellipses scores ( brightness or gradient ) in the different z-planes . The z-position was determined as the center of the Gaussian . Droplets that broke symmetry were defined as droplets which reached aggregate displacementdroplet radius≥0 . 3 during the movie . An initial estimate of the initiation time of the symmetry breaking process was defined to be the last time at which the displacement was 0 . 05 of the droplet radius ( point A ) . The end of the symmetry breaking process in each droplet was determined as the first time the aggregate reached 0 . 97 of its maximum displacement ( point C ) . The time of highest velocity-velocity correlation at 2 s interval between A and C was determined ( point B ) , and the last point between A and B that had a negative radial velocity was taken as the start of the symmetry breaking process . If no such point exists , point A was considered instead . The duration of the symmetry breaking process was defined as the interval between the start and end points described above . The mean squared displacement and velocity-auto correlation of the contraction centers of droplets that remained symmetric , and of the symmetry breaking process in droplets that broke symmetry , were analyzed from the tracks of the aggregates centers using MSDanalyzer MATLAB package described in Tarantino et al . ( 2014 ) . The magnetic experiments were imaged in a single z-plane . The aggregate and droplet x-y positions were determined as detailed above . For analysis of the different recentering experiments as a function of time , the time in each experiment was defined by taking t = 0 to be the time at which the aggregate moved with zero velocity , or reached its minimal recorded velocity . The actin network flow patterns were extracted by PIV analysis of time lapse movies of droplets as described previously ( Malik-Garbi et al . , 2019 ) . Mathematical modeling and numerical analysis of the Darcy flow , actomyosin network dynamics , forces on the network and aggregate , and aggregate positioning were done by solving Darcy equations , force balance equations and stochastic differential equations , as outlined in detail in the Appendices . | In order to survive , cells need to react to their environment and change their shape or the localization of their internal components . For example , the nucleus – the compartment that contains the genetic information – is often localized at the center of the cell , but it can also be positioned at the side , for instance when cells move or divide asymmetrically . Cells use multiple positioning mechanisms to move their internal components , including a process that relies on networks of filaments made of a protein known as actin . These networks are constantly remodeled as actin proteins are added and removed from the network . Embedded molecular motors can cause the network of actin filaments to contract and push or pull on the compartments . Yet , the exact way these networks localize components in the cell remains unclear , especially in eggs and other large cells . To investigate this question , Ierushalmi et al . studied the actin networks in artificial cells that they created by enclosing the contents of frog eggs in small droplets surrounded by oil . This showed that the networks contracted either to the center of the cell or to its side . Friction between the contracting actin network and the fluid in the cell generated a force that tends to push the contraction center towards the middle of the cell . In larger cells , this led to the centering of the actin network . In smaller cells however , the network transiently attached to the boundary of the cell , leading the contraction center to be pulled to one side . By developing simpler artificial cells that mimic the positioning processes seen in real-life cells , Ierushalmi et al . discovered new mechanisms for how cells may center or de-center their components . This knowledge may be useful to understand diseases that can emerge when the nucleus or other compartments fail to move to the right location , and which are associated with certain organs developing incorrectly . | [
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] | 2020 | Centering and symmetry breaking in confined contracting actomyosin networks |
The new concept of mammalian sex maintenance establishes that particular key genes must remain active in the differentiated gonads to avoid genetic sex reprogramming , as described in adult ovaries after Foxl2 ablation . Dmrt1 plays a similar role in postnatal testes , but the mechanism of adult testis maintenance remains mostly unknown . Sox9 and Sox8 are required for postnatal male fertility , but their role in the adult testis has not been investigated . Here we show that after ablation of Sox9 in Sertoli cells of adult , fertile Sox8-/- mice , testis-to-ovary genetic reprogramming occurs and Sertoli cells transdifferentiate into granulosa-like cells . The process of testis regression culminates in complete degeneration of the seminiferous tubules , which become acellular , empty spaces among the extant Leydig cells . DMRT1 protein only remains in non-mutant cells , showing that SOX9/8 maintain Dmrt1 expression in the adult testis . Also , Sox9/8 warrant testis integrity by controlling the expression of structural proteins and protecting Sertoli cells from early apoptosis . Concluding , this study shows that , in addition to its crucial role in testis development , Sox9 , together with Sox8 and coordinately with Dmrt1 , also controls adult testis maintenance .
Sox genes encode an important group of transcription factors with relevant roles in many aspects of pre- and post-natal development of vertebrates and other animal taxa . There are 20 Sox genes in vertebrates , which are classified into 9 groups . Sox8 , Sox9 , and Sox10 ( SoxE group ) are involved in many developmental processes ( reviewed in Lefebvre et al . , 2007 ) . All three SoxE genes are expressed during testis development , Sox9 being essential for testis determination and Sox9/Sox8 necessary for subsequent embryonic differentiation ( Chaboissier , 2004 , Barrionuevo et al . , 2006 , Barrionuevo et al . , 2009 ) . Sox10 can substitute for Sox9 during testis determination ( Polanco et al . , 2010 ) . Undifferentiated gonads have the inherent potential to develop into two completely different organs , either as testes or as ovaries . The decision as to which fate to follow depends on the presence/absence of sex-specific factors . In the male , the Y-linked , mammalian sex-determining factor , SRY , upregulates SOX9 which triggers testis differentiation , whereas in the female , the WNT/β -catenin signaling pathway becomes active and induces ovarian development ( Sekido and Lovell-Badge , 2008; reviewed in Svingen and Koopman , 2013; Sekido and Lovell-Badge , 2013 ) . Both pathways antagonize each other: the loss of either SRY or SOX9 leads to the formation of XY ovaries ( Berta et al . , 1990; Foster et al . , 1994; Wagner et al . , 1994 ) whereas the absence of WNT-signaling molecules such as WNT4 or RSPO1 causes XX sex reversal ( Vainio et al . , 1999; Parma et al . , 2006 ) . Similarly , gain-of-function experiments confirmed this antagonism , as either upregulation of the testis promoting genes Sox9 or Dmrt1 in the XX bipotential gonad ( Bishop et al . , 2000; Vidal et al . , 2001; Zhao et al . , 2015 ) or ectopic activation of the canonical WNT signaling pathway in the XY bipotential gonad ( Maatouk et al . , 2008 ) leads to XX and XY sex reversal , respectively . Furthermore , Sertoli cell-specific conditional inactivation of Sox9 on a Sox8-/- background at embryonic day 13 . 5 ( E13 . 5 ) , two days after the sex determination stage , leads to Dmrt1 downregulation with upregulation of the ovarian-specific genes Wnt4 , Rspo1 and Foxl2 ( Barrionuevo et al . , 2009; Georg et al . , 2012 ) . Similarly , Sertoli cell-specific ablation of Dmrt1 at the same stage ( E13 . 5 ) results in ectopic expression of Foxl2 by postnatal day 14 ( P14 ) and to Sox9 downregulation by P28 , including male-to-female genetic reprogramming , as revealed by mRNA profiling ( Matson et al . , 2011a ) . Again , gain-of-function experiments confirmed the existence of sexual antagonism after the sex determination period , as conditional stabilization of β-catenin in differentiated embryonic Sertoli cells ( E13 . 5 , Amh-Cre ) resulted in testis cord disruption ( Chang et al . , 2008 ) . The male-vs-female genetic antagonism also persists in the adult ovary . The finding that in adult fertile females granulosa cells transdifferentiate into Sertoli-like cells after Foxl2 ablation revealed that terminally differentiated female somatic cells require permanent repression of the male-promoting factors to maintain correct identity and function ( Uhlenhaut et al . , 2009 ) . Furthermore , transgenic expression of Dmrt1 in the adult ovary silenced Foxl2 and transdifferentiated granulosa cells into Sertoli-like , Sox9-expressing cells ( Lindeman et al . , 2015 ) . Regarding the adult testis , a similar phenomenon appears to occur in fully functional Sertoli cells after Dmrt1 ablation ( Matson et al . , 2011a ) . In addition to cells with a Sertoli cell morphology expressing both SOX9 and FOXL2 , some cells with typical granulosa cell features were also observed , including the absence of SOX9 and the presence of FOXL2 . However , Sertoli-to-granulosa cell transdifferentiation was not unambiguously documented , as the authors used an inducible ubiquitous promoter ( UBC-CreERT2 ) for Dmrt1 ablation in adult Sertoli cells and the possible existence of genetic reprogramming was not investigated as no mRNA profiling was performed in adult mutant testes . Nothing is known on the role of SOX9 in the adult testis , where it is expressed by Sertoli cells in a spermatogenic stage-dependent manner in several mammalian species ( Fröjdman et al . , 2000; Dadhich et al . , 2011; Massoud et al . , 2014 ) . Here we report the use of two Sertoli-cell-specific Cre lines ( Wt1-CreERT2 and Sox9-CreERT2 ) to induce Sox9 ablation on a Sox8-/- background in the adult testis , starting at postnatal day 60 ( P60 ) . We show that Sox9/8 Sertoli cell-specific knockout ( SC-DKO ) testes undergo testis-to-ovary genetic reprogramming and Sertoli-to-granulosa cell transdifferentiation . The process is retinoic acid ( RA ) -mediated and occurs as a consequence of Dmrt1 downregulation . SOX9/8 are necessary to maintain Dmrt1 expression and thus to prevent Foxl2 expression in the adult testis . Furthermore , double mutant testes exhibited complete degeneration of the seminiferous tubules and increased apoptosis , indicating that SOX9/8 are continually required for the maintenance of testis integrityy .
To investigate the function of Sox9 and Sox8 in the adult testis , we induced the Sertoli cell-specific ablation of Sox9 in adult Sox8 null mutant mice using the tamoxifen ( TX ) -inducible Cre-loxP mutagenesis system . We used two different CreERT2 mouse lines , a Wt1 knock-in line ( Wt1-CreERT2; Zhou et al , 2008 ) , and a Sox9 BAC-transgenic line ( Sox9-CreERT2; Kopp et al , 2011 ) . To check the Cre recombination efficiency , we introduced the R26R-EYFP allele in both Sox9-CreERT2 and Wt1-CreERT2 double homozygous Sox9/8 knockout ( DKO ) mutants . Sox8/9 DKO mice fed with a TX-supplemented diet for a maximun of 30 days starting at P60 ( Figure 1A ) showed EYFP expression in a reduced number of Sertoli cells already 10 days after the beginning of TX administration ( 10 datx , P70 ) in the two CreERT2 lines . The number of EYFP+ Sertoli cells increased in both lines at later time-points , the Sox9-CreERT2 line showing always a higher number of positive cells than the Wt1-CreERT2 line . From P150 ( 90 datx ) on , the EYFP signal occupied the whole area of the seminiferous tubule section ( Figure 1Ba , Figure 1—figure supplement 1A ) . However , the fact that the cytoplasm of Sertoli cells is very large and complex in shape , together with the severe shrinkage that Sox9/8 SC-DKO seminiferous tubules have undergone by this time , made it very difficult to identify individual EYFP+ cells in these testes . Hence we performed immunofluorescence for SOX9 and counted the number of SOX9+ cells per transversal testis cord section . At P90 ( 30 datx ) all seminiferous tubules still contained many positive cells , but the number was clearly reduced by P120 ( 60 datx ) and even more by P150 ( 90 datx ) , when some testis cords were completely devoid of SOX9+cells ( Figure 1Bb–c , Figure 1 —figure supplement 1B ) . At this later stage , the number of SOX9+ cells per seminiferous tubule cross section decreased to 15 . 39 ± 3 . 36 ( 37% reduction ) in the testes of the Wt1-CreERT2; Sox9f/f; Sox8-/- [Sox9/8 DKO ( Wt1 ) ] mice and to 7 . 49 ± 3 . 61 ( 69% reduction ) in those of the Sox9-CreERT2; Sox9f/f; Sox8-/- [Sox9/8 DKO ( Sox9 ) ] mice , when compared to controls ( 24 . 31 ± 2 . 94 ) ( Figure 1Bd , Figure 1—source data 1 ) . The fact that the number of recombinant Sertoli cells lacking Sox9 in these mutant mice continues decreasing for several weeks after the end of the period of TX administration ( 30 days ) suggests that many newly recombined cells appear after that time ( persistence of TX in the body ) and that perhaps either the Sox9 transcript or the protein , or both , are very stable in adult Sertoli cells , so that the gene product may remain for days or weeks in the cell after the gene ablation event . We also found that the reduction of SOX9+ cells varied among testis cords and among animals . We selected the most affected regions of the most affected individuals for further analyses . 10 . 7554/eLife . 15635 . 003Figure 1 . Sox9 and Sox8 maintain the function and integrity of the adult mouse testis . ( A ) Diagram illustrating the time course of TX administration . Mice were fed with a TX-supplemented diet during one month , between P60 ( 0 datx ) and P90 ( 30 datx ) . After this period , mice were fed with a normal diet . The main stages studied in this work are depicted . ( B ) Analysis of the Cre-recombination efficiency in Sox9/8 DKO ( Sox9 ) mice at p150 ( 90 datx ) . ( a ) EYFP is widely expressed in SC-DKO testis cords . At the same stage , the number of SOX9+ cells in the control ( Sox9f/f;Sox8-/- ) ( b ) is clearly higher than in the mutant ( c ) . ( d ) Comparisons of the mean number of SOX9+ cells per transversal testis tubule section in control ( Sox9f/f;Sox8-/- ) and mutant testes . All pairwise comparisons provided statistically significant differences ( two tail test , p<0 . 001 in all cases ) . ( C ) Time-course of testis regression in Sox9/8 DKO ( Sox9 ) mice . Representative micrographs are shown for both TX-treated controls ( Sox9f/f;Sox8-/- ) ( a–f ) and Sox9/8 DKO ( Sox9 ) mice ( g–l ) between p70 ( 10 datx ) and P180 ( 120 datx ) . T , normal seminiferous tubules; arrows indicate desquamated germ cells; asterisks mark testis tubules showing signs of degeneration ( from enlarged lumen to Sertoli cell-only condition ) ; STC , shrunken testis cords; ATC acellular testis cords . ( D ) Analysis of somatic ( a–i and m–n ) and germ cell ( j–l ) molecular markers . Immunofluorescence for LAMININ ( a–c ) , ACTA2 ( d–f ) and CLAUDIN11 ( g– i ) in both P150 ( 90 datx ) TX-treated control ( Sox9f/f;Sox8-/- ) ( a , d and g ) and SC-DKO testes at P150 ( 90 datx ) ( b , e and h ) and P180 ( 120 datx ) ( c , f and i ) . Arrows mark seminiferous tubule expression of ACTA2 ( d and e ) and Claudin11 ( g and h ) ; arrowheads mark arterial expression of ACTA2 ( d–f ) . Asterisks mark testis cords lacking ACTA2 ( f ) or Claudin11 ( i ) expression . Double immunofluorescence for PCNA and DMC1 showing the time-course of spermatogenesis reduction in the testes of both P150 ( 90 datx ) TX-treated control ( Sox9f/f;Sox8-/- ) ( j ) and P90 ( 30 datx ) and P150 ( 90 datx ) SC-DKO mice ( k , l ) . Arrows mark spermatocytes showing colocalization of the two proteins; arrowheads mark proliferating spermatogonia expressing PCNA but not DMC1 . Expression of P450SCC ( green fluorescence ) in Leydig cells of both TX-treated control ( Sox9f/f;Sox8-/- ) ( m ) and SC-DKO ( n ) testes at P150 ( 90 datx ) . Scale bars in Bc , Cl and Dn represent 100 µm for pictures in B , 50 µm for those in C and 50 µm for those in D , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 00310 . 7554/eLife . 15635 . 004Figure 1—source data 1 . Comparison of the number of SOX9+ cells per testis tubule in both SC-DKO mutants and TX-treated controls ( Sox9f/f;Sox8-/- ) . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 00410 . 7554/eLife . 15635 . 005Figure 1—figure supplement 1 . Analysis of the CRE-recombination efficiency in SC-DKO mice . ( A ) Immunostaining of the EYFP protein in mutant mice at different times-points after the initiation of the TX treatment . The number of cells expressing EYFP increases with time ( from a to e and from f to j ) . ( B ) Immunostaining for SOX9 in control ( Sox9f/f;Sox8-/- ) ( a , b and c ) and SC-DKO testes ( d–i ) at different time points after the initiation the TX treatment . The number of SOX9+ cells decreases with time . Scale bar in Aj and Bi represents 100 μm in A and B respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 00510 . 7554/eLife . 15635 . 006Figure 1—figure supplement 2 . Redundant role for Sox9 and Sox8 in the maintenance of adult testis cord architecture . Haematoxilin-eosin stained transversal sections of P120 testes ( left column ) and epididymides ( right column ) from control mice ( a–b ) and four different types of Sox9/8 DKO ( Wt1 ) mice differing in the number of Sox9/8 mutant alleles 60 days after TX administration during 5 days with a feeding-gauge needle ( c–j ) . Mutant mice exhibited various degrees of testis regression correlating with the number of mutated alleles . Double homozygote mutants exhibited the highest degree of testicular degeneration , with all seminiferous tubules transformed into sterile , solid testis cords ( Ai ) . Seminiferous tubules from control testes were normal , whereas those from mutant mice showed several abnormalities including enlarged lumen ( arrow in c ) , and different degrees of germ cell depletion ( asterisks in c , e , g , and i ) . Only control epididymides contained abundant sperm ( arrows in b ) , whereas those from mutant mice were completely sterile ( d , f , h , and j ) . Correspondingly , the testis mass of mutant mice decreased as the number of Sox9/8 mutant alleles increased in their genotypes ( k ) . Scale bars shown in j represents 50 μm for all micrographs . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 00610 . 7554/eLife . 15635 . 007Figure 1—figure supplement 3 . Relative abundance of the most relevant morphological features observed in the testes of P120 control and Sox9/8 mutant mice differing in the number of Sox9/8 mutant alleles 60 days after TX administration during 5 days with a feeding-gauge needle . Categories of relative abundance: ( − ) not present , ( + ) sporadic , ( ++ ) scarce , ( +++ ) abundant , ( ++++ ) generalized . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 00710 . 7554/eLife . 15635 . 008Figure 1—figure supplement 4 . Time course of the testis phenotype of control and SC-DKO ( Wt1 ) mice . Representative micrographs of H&E stained sections of non-treated Sox8-/- ( a–f ) , TX-treated Sox8-/- ( g–l ) and TX-treated Sox8/9 DKO ( Wt1 ) ( m–r ) mouse testis at different time-points . T , normal testis tubules; arrows indicate desquamated germ cells; asterisks mark testis tubules showing some degree of degeneration; double asterisk mark acellular interstitial space; STC shrunken testis cords . Scale bars in Ar represents 50 μm for all micrographs . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 00810 . 7554/eLife . 15635 . 009Figure 1—figure supplement 5 . Relative abundance of the most relevant morphological features observed throughout the timecourse of testis regression in double Sox8/Sox9 mutant mice . Categories of relative abundance: ( − ) not present , ( + ) sporadic , ( ++ ) scarce , ( +++ ) abundant , ( ++++ ) generalized . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 00910 . 7554/eLife . 15635 . 010Figure 1—figure supplement 6 . Functional status of the BTB in Sox9/8 DKO testes . The location of a biotin tracer ( red fluorescence ) shows that the BTB is impermeable in the control ( Sox9f/f;Sox8-/- ) testis ( a ) as it did not enter into the adlumninal compartment of the seminiferous tubules , whereas it is permeable in the mutant ( b ) , where the tracer can be seen also inside the tubules . Scale bar in b represent 50 μm for a and b . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 01010 . 7554/eLife . 15635 . 011Figure 1—figure supplement 7 . Expression bar plots of two adult Leydig cell markers . Hsd3b1: 3β-hydroxysteroid dehydrogenase; Insl3: Insulin-Like 3 . Data obtained from our transcriptome analysis . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 011 Consistent with the situation reported for embryonic stages of development ( Barrionuevo et al . , 2009 ) , we observed that the testis phenotype of the different Sox9/8 compound mutants increased in severity with the number of Sox9/8 mutant alleles ( Figure 1—figure supplement 2 and 3 ) . O'Bryan et al . ( 2008 ) reported a Sox8-/- mouse line in which a progressive deregulation of spermatogenesis occurred and where male mice became sterile by P150 . In contrast , our Sox8 mutants ( Sock et al . , 2001 ) do not show such a severe testicular phenotype and males are normally fertile even at P180 . At the histological level , our Sox8-/- mice appeared normal until P120 , but showed signs of germ cell desquamation ( sloughing ) afterwards ( Figure 1—figure supplement 4a–f ) . Genetic background differences between the two Sox8-/- lines may explain these phenotypic discrepancies . TX-treated controls were similar to untreated males , except between P80 ( 20 datx ) and P120 ( 60 datx ) and mainly at P90 ( 30 datx ) , when they showed some degenerating seminiferous tubules , but recovered afterwards ( Figure 1Ca–f , Figure 1—figure supplement 4a–l ) . Testes in Sox9/8 DKO ( Sox9 ) mice were similar to the TX-treated controls at P70 ( 10 datx ) except for a few testis tubules with enlarged lumen ( Figure 1Cg ) . At P80 ( 20datx ) , only few seminiferous tubules showed signs of degeneration ( shrinkage and germ cell depletion ) , whereas this was more frequent by P90 ( 30 datx ) . In many cases , Sertoli cell-only tubules were visible ( Figure 1Ch , i ) . By P120 ( 60 datx ) , tubules had become solid testis cords whose diameter appeared even more reduced at P150 ( 90 datx ) ( Figure 1Cj , k ) . While some mice continued to exhibit this phenotype at P180 ( 120 datx ) , a subset of mice in this group was more affected . In these latter mice Sertoli and germ cells had disappeared completely ( Figure 1Cl ) . At later time points , all mice showed this severe testicular phenotype . This progressive degeneration of the testicular phenotype in Sox9/8 SC-DKO mice was evident when we analyzed the relative abundance of the most relevant testicular morphological features between P70 ( 10 datx ) and P180 ( 120 datx ) ( Figure 1—figure supplement 5 ) . In contrast , Leydig cells appeared morphologically normal in mutant testes . Sox9/8 DKO ( Wt1 ) mice exhibited a similar testicular phenotype ( Figure 1—figure supplement 4m–r ) . These results show that Sox8 and Sox9 alleles act redundantly in adult Sertoli cells and are necessary to maintain the integrity of the seminiferous tubules of functional testes . To better define the mutant phenotype , we next studied the expression of several somatic and germ cell markers . Laminin , a principal component of the basement membrane ( Richardson et al . , 1995 ) persisted in both P150 ( 90 datx ) and P180 ( 120 datx ) testes of SC-DKO mice ( Figure 1Db , c ) . Alpha smooth muscle actin ( Acta2 ) expressed by both peritubular myoid ( PM ) cells and arterialmuscle fibers was detected in the testes of both TX-treated controls and P150 ( 90 datx ) SC-DKO mice ( Figure 1Dd , e ) . In contrast , at P180 ( 120 datx ) , strong arterial ACTA2 signal persisted but that of PM cells was almost undetectable ( Figure 1Df ) . This shows that acellular cords in severely affected SC-DKO testes have lost not only Sertoli and germ cells , but also PM cells . Claudin11 is a principal component of tight junctions , the main junctional structures forming the blood-testis barrier ( BTB ) . Cldn11 ( the claudin11 gene ) expression was similar between controls and double mutants before P150 ( 90 datx ) ( not shown ) , but it was severely reduced by P150 ( 90 datx ) and completely absent in P180 ( 120 datx ) Sox9/8 mutant testes ( Figure 1Dg–i ) , indicating that the BTB is not functional in these testes . To proof this assumption , we tested the permeability of the BTB of P120 ( 60 datx ) mice with a biotin tracer experiment revealing that control testes had a functional BTB , whereas that of the mutant testes had become permeable ( Figure 1—figure supplement 6 ) . We also performed immunofluorescence for both PCNA , which is expressed in mitotic spermatogonia as well as in zygotene and early pachytene , but not leptotene spermatocytes ( Chapman and Wolgemuth , 1994 ) , and DMC1 , a meiotic recombination protein marking zygotene-pachytene spermatocytes ( Yoshida et al . , 1998 ) . At P60 ( 30 datx ) , most mutant seminiferous tubules exhibited a clear reduction of spermatogenic activity and some spermatocytes were abnormally located in the inner region of the tubules ( Figure 1Dk ) and not at the periphery , as seen in TX-treated control testes ( Figure 1Dj ) . In P120 ( 60 datx ) testes , spermatocytes were scarce and only proliferating spermatogonia were seen in most testis tubules ( not shown ) , while at P150 ( 90 datx ) , both spermatogonia and spermatocytes had disappeared in most tubules ( Figure 1Dl ) . These results indicate that spermatogenesis becomes disrupted in testes with Sertoli cells deficient for both Sox9 and Sox8 . Unlike other somatic cells , Leydig cells appear not to be seriously affected in testes from Sox9/8 SC-DKO mice . These cells do not transdifferentiate into theca cells , as they never express Foxl2 ( as theca cells do; not shown ) , and maintain the steroidogenic function for a long time after Sox9 ablation , as deduced from the expression of P450scc , a cytochrome involved in the synthesis of testosterone ( Figure 1Dm , n ) . Consistently , the testosterone-producing enzyme HSD17b3 and the marker for adult functional Leydig cells Insl3 are expressed at high levels in the mutant testes ( Figure 1—figure supplement 7 ) . The loss of Foxl2 in adult granulosa cells results in a somatic ovary-to-testis genetic reprogramming with granulosa-to-Sertoli cell transdifferentiation which includes Sox9 upregulation ( Uhlenhaut et al . , 2009 ) . Contrarily , Foxl2 is upregulated when Sox9 is ablated in embryonic Sertoli cells of Sox8 null mutants after the sex-determination stage ( Georg et al . , 2012 ) . To test whether a similar phenomenon took place in our Sox9/8 SC-DKO mice , we carried out immunofluorescence for FOXL2 . At P90 ( 30 datx ) , FOXL2 protein was almost completely absent from mutant testes . However , by P105 ( 45 datx ) , positive cells were present in almost all testis cords , and by P150 ( 90 datx ) , the most severely affected mice showed many FOXL2-positive cells within almost all testis cords ( Figure 2A , Figure 2—figure supplement 1 ) . These results show that transdifferentiation also occurs in adult Sox9/8 DKO Sertoli cells . Accordingly , we performed a genome-wide transcriptome analysis of P150 untreated control testis , P150 ( 90 datx ) control and mutant testis and control ovary . Our results show that SC-DKO testes exhibit a striking feminization of the testicular transcriptome . Figure 2B shows a Log2-fold-change heat map including the 12 , 380 genes detected to have significant differential expression between the five sample conditions ( the complete list of genes with differential expression is shown in Figure 2—source data 1 ) . With the exception of a few gene clusters , most genes in mutant testes adopted an ovary-like expression pattern ( Figure 2B , Figure 2—figure supplement 2 , Supplementary file 1 ) . Cluster analyses of all genes , both by replicates and by conditions , showed that mutants are clustered together , with no clear distinction between Sox9-CreER and Wt1-CreER lines ( Figure 2—figure supplement 3 ) . Similarly , pairwise gene sets with significant differential expression at α < 0 . 05 demonstrated that the number of differentially expressed genes is higher when mutants were compared with testis controls than when compared with ovary ( Figure 2—figure supplement 4A ) . Accordingly , the distance map is higher between mutant and control testis than between mutant testis and ovary ( Figure 2—figure supplement 4B ) . The same results were obtained when comparing isoforms , transcription start sites or coding DNA sequences ( not shown ) . Expression heat maps for selected 39 ovarian somatic cell-specific genes and 33 oocyte-specific genes selected using bioGPS ( biogps . gnf . org ) revealed that the cell reprogramming observed in the SC-DKO testes only affects somatic cells ( Figure 2C ) . Notably , bar plots for six genes known to be adult granulosa cell markers showed that these genes were upregulated in the mutant Sertoli cells , revealing an ovary-like expression pattern ( Figure 2D ) . In addition , within the seminiferous cords of SC-DKO testes we found a few FOXL2+ cells expressing the enzyme aromatase ( Figure 2E ) . This is evidence that , in addition to Foxl2 , other genes normally expressed by granulosa cells are also transcribed and translated in Sox9/8 SC-DKO testes . 10 . 7554/eLife . 15635 . 012Figure 2 . Genetic reprogramming in somatic cells of adult Sox9/8 SC-DKO ( Sox9 ) . ( A ) Expression of FOXL2 ( green fluorescence ) in P150 ( 90 datx ) TX-treated control ( Sox9f/f;Sox8-/- ) ( a ) and in Sox9/8 SC-DKO ( Sox9 ) mouse testes analyzed at P90 ( 30 datx ) ( b ) , P105 ( 45 datx ) ( c ) , and P150 ( 90 datx ) ( d ) as well as in a P 90 control ovary ( e ) . ( B ) Heatmap showing the 12 , 380 genes found to be differentially expressed at alpha < 0 . 005 when comparing control ( Sox9f/f ) and mutant adult gonads . The log2 ( FPKM+1 ) of each gene in each condition has been divided by the corresponding value in control testis . Gene expression has not been altered by the TX treatment . Red colors indicate genes upregulated with respect to their expression levels in control testis and green colors indicate downregulated genes . ( C ) Expression heatmaps of selected ovarian somatic-specific and oocyte-specific genes . ( D ) Expression bar plots of six relevant ovarian somatic-specific genes upregulated in mutant testes . ( E ) Aromatase ( red ) and FOXL2 ( green ) immunofluorescence staining of TX-treated control ( Sox9f/f;Sox8-/- ) testis ( a ) , mutant testes ( b–d ) , and control ovary ( e ) . Arrows mark reprogrammed Sertoli cells showing simultaneous expression of Aromatase and FOXL2 . Scale bar shown in Ae represents 150 µm in A and 75 µm in E . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 01210 . 7554/eLife . 15635 . 013Figure 2—source data 1 . Genes with significant differential expression among untreated controls , TX-treated controls , Sox8/9 SC-DKO mutants and control ovary at P150 ( 90 datx ) identified from the bioinformatic analysis of our transcriptome . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 01310 . 7554/eLife . 15635 . 014Figure 2—figure supplement 1 . Expression of Foxl2 in somatic cells of adult Sox9/8 DKO ( Wt1 ) . Immunofluorescence for FOXL2 ( green fluorescence ) in P150 ( 90 datx ) TX-treated control ( Sox9f/f;Sox8-/- ) ( a ) and Sox9/8 DKO ( Wt1 ) mouse testes analyzed at P90 ( 30 datx ) ( b ) , P105 ( 45 datx ) ( c ) , and P150 ( 90 datx ) ( d ) as well as in a control ovary ( e ) . Scale bar shown in e represents 150 µm for all the micrographs . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 01410 . 7554/eLife . 15635 . 015Figure 2—figure supplement 2 . Heatmaps showing the expression of genes involved in 8 selected pathways ( A–H ) , relative to their expression in control testes . Gene sets where obtained from the Pathway Unification Database except for the Myosin genes Family that where obtained from the list of differentially expressed genes . The pathways , URL , and number of genes of each paths are as follow: ( A ) Sertoli-Sertoli Cell Junction Dynamics ( http://pathcards . genecards . org/pathway/543 ) 213 genes ( B ) Germ Cell-Sertoli Cell Junction Dynamics ( http://pathcards . genecards . org/pathway/1181 ) 140 genes ( C ) Epithelial Tight Junctions ( http://pathcards . genecards . org/pathway/448 ) 329 genes ( D ) Regulation of Microtubule Cytoskeleton ( http://pathcards . genecards . org/pathway/792 ) 44 genes ( E ) Regulation of actin cytoskeleton SuperPath ( http://pathcards . genecards . org/card/regulation_of_actin_cytoskeleton ) 230 genes ( F ) Myosin Family 26 genes ( G ) Cell-extracellular matrix interactions ( http://pathcards . genecards . org/pathway/286 ) 18 genes ( H ) Cell adhesion molecules ( http://pathcards . genecards . org/pathway/1823 ) 145 genes The complete lists of genes are included in Supplementary file 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 01510 . 7554/eLife . 15635 . 016Figure 2—figure supplement 3 . Cluster analysis of ( A ) replicates and ( B ) conditions . The global gene expression patterns of mutants are closer to the expression in ovaries than to that in normal testes . Both controls with and without Tamoxifen treatment also group together as expected . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 01610 . 7554/eLife . 15635 . 017Figure 2—figure supplement 4 . Quantification of genes with differential expression and Jensen–Shannon ( JS ) distances between conditions . ( A ) Number of genes showing differential expression in pairwise comparisons of the different conditions . ( B ) Jensen–Shannon ( JS ) distances heatmap between conditions . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 017 We next investigated the origin of the granulosa-like , FOXL2+ cells present in the Sox9/8 SC-DKO testes . Several pieces of evidence show that FOXL2+ cells in our mutant testes originate from Sox9/8 null Sertoli cells . The two gene promoters we used to drive Cre expression ( Sox9 and Wt1 ) are Sertoli cell-specific in the testis , indicating that transdifferentiation originates directly from this cell type . Importantly , we found that FOXL2+ cells always located inside testis cords with strong expression of the Cre-recombination reporter EYFP ( Figure 3A ) . We also analyzed the expression of WT1 , a Sertoli cell marker whose expression is maintained after Sox9/8 ablation in embryonic mouse Sertoli cells ( Barrionuevo et al . , 2009 ) , and that it is co-expressed with FOXL2 in granulosa cells of immature , but not mature , follicles ( Chun et al . , 1999; Figure 3Ce ) . At P90 ( 30 datx ) we observed many WT1+ Sertoli cells that have already lost SOX9 ( green cells , Figure 3Bb ) . The number of WT1+ SOX9- cells decreased by P150 ( 90 datx ) ( Figure 3Bc ) , indicating that recombined Sertoli cells were being lost . This decrease in the number of WT1+ SOX9- Sertoli cells coincides with an increase in the number of FOXL2+ cells which either retain weak WT1-staining or are WT1- ( Figure 3C ) , suggesting that FOXL2+ cells originate from cells previously expressing WT1 , that is Sertoli cells . Altogether , these results indicate that Sox9/8 SC-DKO testes experience a cell-autonomous Sertoli-to-granulosa cell transdifferentiation which triggers the observed testis-to-ovary genetic reprogramming in these gonads . 10 . 7554/eLife . 15635 . 018Figure 3 . Identification of the somatic cells expressing FOXL2 . ( A ) Double immunofluorescence for FOXL2 ( green , nuclear ) and EYFP ( red , cytoplasmic ) ( B ) Double immunofluorescence for SOX9 and WT1 in P150 ( 90 datx ) TX-treated control ( Sox9f/f;Sox8-/- ) testes ( a ) and Sox9/8 SC-DKO testes analyzed at P90 ( 30 datx ) ( b ) and P150 ( 90 datx ) ( c ) . Dashed lines delineate the seminiferous tubules contour . Arrows mark mutant cells expressing WT1 but not SOX9 . ( C ) Double immunofluorescence for FOXL2 and WT1 in both TX-treated control ( Sox9f/f;Sox8-/- ) ( a ) and Sox9/8 SC-DKO mutant testes analyzed at P120 ( 60 datx ) ( b–d ) , as well as in a control ovary ( e ) . Arrows point to mutant cells expressing both proteins . Scale bar in Ac represents 25 μm in A; scale bar in Bc represents 25 μm in B; scale bar in Cd represents 25 μm in Ca–d and scale bar in Ce represents 100 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 018 Since Sox9 is upregulated after Foxl2 ablation in adult granulosa cells ( Uhlenhaut et al . , 2009 ) and downregulated after Dmrt1 ablation in embryonic Sertoli cells ( Matson et al . , 2011a ) , we investigated the expression pattern of these two genes in the testes of the Sox9/8 SC-DKO mutants . We found that cells coexpressing SOX9 and FOXL2 were rare at any stage analyzed [12 out of 203 FOXL2+ cells co-expressed SOX9 at P120 ( 60 datx ) ] ( Figure 4A , Figure 4—figure supplement 1A ) , indicating that Foxl2 upregulation requires previous elimination of both SOXE proteins . Next , we examined the expression of both Sox9 and Dmrt1 in Sox9/8 SC-DKO testes . As Dmrt1 is expressed in both Sertoli cells and spermatogonia of adult testes ( Raymond et al . , 2000 ) , we used a third marker , PCNA , that labels spermatogonia as well as zygotene and early pachytene spermatocytes . Whereas all Sertoli cells in control testes showed strong staining for both DMRT1 and SOX9 ( SS , Figure 4Ba ) , mutant Sertoli cells showed varying degrees of both SOX9 and DMRT1 staining intensity , although they normally paralleled each other in intensity . Therefore Sertoli cells with a weak staining for both DMRT1 and SOX9 ( WS ) were also visible in these testes . Consistent with this , we found a very reduced number of cells expressing only DMRT1 at P90 ( 30 datx ) ( Figure 4Bb–d , red cells ( arrow ) ; SOX9- DMRT1+ PCNA- ) and almost none at P120 ( 60 datx ) ( Figure 4Be–g ) . Furthermore , in P150 ( 90 datx ) testes , which are almost devoid of germ cells , DMRT1 immunoreactivity was almost exclusively restricted to SOX9+ cells ( Figure 4—figure supplement 1B ) . Double WT1-DMRT1 staining confirmed that as early as at P90 ( 30 datx ) many WT1+ cells ( Sertoli cells ) have already lost DMRT1 expression ( green cells in Figure 4Cb ) , showing that Dmrt1 is downregulated after Sox9 ablation and before Wt1 downregulation occurs in SC-DKO testes . In addition , as observed for SOX9 and FOXL2 ( see above ) , DMRT1 and FOXL2 only colocalize in a reduced number of cells in the testes of our Sox9/8 SC-DKO mice [16 out of 127 FOXL2+ cells co-expressed DMRT1 at P120 ( 60 datx ) ] ( Figure 4D , Figure 4—figure supplement 1C ) . Overall , these findings support the notion that SOX9 and SOX8 are necessary for the maintenance of Dmrt1 expression in adult Sertoli cells and that these testis-promoting factors negatively regulate Foxl2 . 10 . 7554/eLife . 15635 . 019Figure 4 . Role of Dmrt1 in Sertoli-to-granulosa cell transdifferentiation . ( A ) Double immunofluorescence for SOX9 and FOXL2 in TX-treated control ( Sox9f/f;Sox8-/- ) testis ( a ) , Sox9/8 SC-DKO mutant testes analyzed at P105 ( 45 datx ) and P150 ( 90 datx ) ( b , c ) and a control ovary ( d ) ( b' is a higher magnification of the area marked in b ) . Colocalization of SOX9 and FOXL2 was rare and the few observed cells showed weak fluorescence for both proteins ( arrowheads in b' ) . ( B ) Triple immunofluorescence for SOX9 , DMRT1 and PCNA ( germ cell marker ) in P150 ( 90 datx ) TX-treated control ( Sox9f/f;Sox8-/- ) testes ( a ) and Sox9/8 SC-DKO mutant testes at P90 ( 30 datx ) ( b–d ) and P120 ( 60 datx ) ( e-g ) . Different cell types can be identified: SS: Sertoli cells with strong staining for both DMRT1 and SOX9 ( SOX9+ DMRT1+ PCNA-; strong yellow ) ; WS: Sertoli cells with weak staining for both DMRT1 and SOX9 ( SOX9+ DMRT1+ PCNA-; pale yellow ) ; SP: spermatocytes ( SOX9- DMRT1- PCNA+; blue ) ; SG: proliferating spermatogonia ( SOX9- DMRT1+ PCNA+; purple ) , arrow ( SOX9- DMRT1+ PCNA-; red ) . Non-proliferating spermatogonia could be confused in Sox9/8 SC-DKO mice with recombined DMRT1+ SOX9- Sertoli cells in which SOX9 already disappeared , but the number of the former cell type is so low that they can be ignored . ( C ) Double immunofluorescence for DMRT1 and WT1 in P90 ( 30 datx ) TX-treated control ( Sox9f/f;Sox8-/- ) ( a ) and mutant testes ( b ) . ( D ) Double immunofluorescence for DMRT1 and FOXL2 in P150 ( 90 datx ) TX-treated control ( Sox9f/f;Sox8-/- ) testis ( a ) , Sox9/8 SC-DKO mutant testes ( b–c ) and control ovary ( d ) ( b' is a higher magnification of the area marked in b ) . Colocalization of both proteins was rare ( arrowheads in b' ) . Scale bar in Dd represent 50 µm in A and D; scale bar in Bg represents 25 µm in B; scale bar in Cb represents 50 µm in C . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 01910 . 7554/eLife . 15635 . 020Figure 4—figure supplement 1 . Role of Dmrt1 in Sertoli-to-granulosa cell transdifferentiation . Double immunofluorescence for SOX9 and FOXL2 ( A ) , and for FOXL2 and DMRT1 ( C ) as shown in Figure 4 but showing separated the single color channels ( a' , c' and e' are higher magnifications of the areas marked in a , c and e in both A and C ) . Triple immunofluorescence for SOX9 , DMRT1 and PCNA ( B ) of P150 ( 90 datx ) Sox9/8 DKO ( Sox9 ) testis . Arrowheads in A and C indicate colocalization of both proteins . Scale bar in Cf represent 50 μm in A and C; scale bar in Bc represents 25 μm in B . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 020 To further test this hypothesis , we compared the microarray data from P28 SC-Dmrt1 KO testes reported by Matson et al . ( 2011b ) with the RNA-seq data from our P150 ( 90 datx ) SC-Sox9/8 DKO testes , and plotted all mRNAs that resulted either downregulated or upregulated when compared to control males in both datasets ( Figure 5A , small blue dots ) . Nearly all genes strongly affected by the loss of Dmrt1 were also affected by the loss of Sox9/8 ( Figure 5A , Figure 5—source data 1 ) . This finding suggests that both Dmrt1 and Sox9/8 act in the same pathway , although the possibility also exists that this coincidence between both gene expression patterns could be a secondary effect of the change in relative numbers of cell types in the SC-Sox9/8 DKO testes . Among the genes upregulated in both experiments ( upper right quadrant in Figure 5A ) , we found 29 somatic ovarian-specific genes including female promoting genes such as Foxl2 , Wnt4 , Rspo1 , Fst , Fshr ( Figure 5A , red triangles ) . Also , a set of genes were upregulated in Dmrt1 mutants and downregulated in Sox9/8 mutants ( upper-left quadrant in Figure 5A ) , which may be a consequence of 1 ) the age-differences between the two compared sample sets , 2 ) the incomplete efficiency of Sox9 inactivation of our conditional SC-Sox9 KO , or 3 ) the existence of additional roles for Sox9/8 and/or Dmrt1 in the adult testis . 10 . 7554/eLife . 15635 . 021Figure 5 . Sox9 and Dmrt1 act in the same pathway during Sertoli-to-granulosa transdifferentiation . ( A ) Log2 fold change scatterplot comparing the microarray data from the P28 SC-Dmrt1 KO testes reported by Matson et al . , 2011b; GEO accession: GSE27261 ) with the RNA-seq data from our P150 ( 90 datx ) SC-Sox9/8 DKO testes , including 8910 genes showing significant differential expression respect to normal testis in both Dmrt1 and Sox9/8 mutants ( blue dots ) . Among these , green dots represent 24 genes belonging to the all-trans-retinoic acid-mediated apoptosis path and RA receptors-mediated signaling from the PathCards database . Red triangle show 29 of the ovarian somatic specific genes included in Figure 2C . The names of some relevant genes are indicated . Regression line is shown in red ( intecept = 0 . 7112 , slope = 1 . 0883 ) . ( B ) Effect of the treatment of Sox9/8 SC-DKO mice with WIN 18 , 644 on Sertoli-to-granulosa cell transdifferentiation . FOXL2-positive cells ( green fluorescence ) were much more abundant in untreated ( a ) than in treated mutant testes ( b ) . The number of positive cells per testis/cord section was 3 . 5-fold higher in untreated mice ( c ) . The ACTA2 signal ( red fluorescence ) permitted to delineate the testis cords in a and b . Scale bar in Bb represents 100 µm in Ba–b . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 02110 . 7554/eLife . 15635 . 022Figure 5—source data 1 . List of 8910 genes showing significant differential expression respect to normal testis in both Dmrt1 and Sox9/8 mutants . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 02210 . 7554/eLife . 15635 . 023Figure 5—source data 2 . Log2 fold change of expression of both Dmrt1 and Sox9/8 mutants respect to controls in a set of genes belonging to the all-trans-retinoic acid-mediated apoptosis path and RA receptors-mediated signaling from the PathCards database . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 02310 . 7554/eLife . 15635 . 024Figure 5—source data 3 . Comparison of the number of FOXL2+ cells per transversal testis cord section in Sox9/8 DKO ( Sox9 ) WIN 18 , 446-treated mice and vehicle-injected controlsDOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 024 It was recently reported ( Minkina et al . , 2014 ) that DMRT1 functions by protecting male gonadal cells from retinoid acid ( RA ) -dependent sexual transdifferentiation and that this process could be inhibited by blocking intra-tubular RA synthesis in the Dmrt1-mutant testes . By comparing the mRNA profiling of SC-Dmrt1 KO and SC-Sox9/8 DKO testes , we found a set of genes belonging to the RA-signaling pathway showing similar misexpression in both mutants ( Figure 5A , green dots , Figure 5—source data 2 ) . As Dmrt1 is downregulated in the Sox9/8 SC-DKO testes , we hypothesized that reducing RA levels in our SoxE mutants should also affect the transdifferentiation process . To test this , we treated adult SC-DKO mice with the retinaldehyde dehydrogenase inhibitor WIN 18 , 446 just when the first FOXL2-positive cells are detected . We found a 3 . 5-fold reduction in the number of FOXL2-positive cells per testis cord section in the WIN 18 , 446-treated mice ( 1 . 80 ± 2 . 03 ) , compared to the vehicle ( DMSO ) -injected controls ( 6 . 57 ± 3 . 52; p<0 . 001; Figure 5B , Figure 5—source data 3 ) . Hence , as reported for Dmrt1 SC-KO mice ( Minkina et al . , 2014 ) , the process of Sertoli-to-granulosa cell transdifferentiation seems to be also inhibited when RA levels were reduced in our study model . Coinciding roughly with the end of TX treatment , Sox9/8 SC-DKO testes begin to progressively degenerate , as evidenced by shrinkage of the seminiferous tubules , which in the most severely affected mice reach an extreme degree of tubular involution and become acellular testis cords . A possible explanation for the loss of tubular somatic cells is that apoptosis is operating in these testes . TUNEL assay revealed apoptotic cells mainly inside the testis tubules/cords , showing that interstitial cells ( mostly Leydig cells ) are not seriously affected . The numbers of TUNEL-positive cells counted in a total area of 11 . 55 mm2 between P90 ( 30 datx ) and P120 ( 60 datx ) in both SC-DKO mutants ( 370 cells for the Wt1-CreERT2 line and 488 cells for the Sox9-CreERT2 line ) were significantly higher than those found in TX-treated control testes ( 120 cells; goodness of fit test p<2 . 2e-16 in both cases; Figure 6Aa–c , Figure 6—source data 1 ) . The presence of abundant apoptotic bodies at P150 ( 90 datx ) ( Figure 6Ad ) documents the massive cell death that had occurred during previous stages in the Sox9/8 SC-DKO mice . 10 . 7554/eLife . 15635 . 025Figure 6 . Incidence of apoptosis in Sox9/8 SC-DKO testes . ( A ) TUNEL staining in testis sections of TX-treated control ( Sox9f/f;Sox8-/- ) at P120 ( 60 datx ) ( a ) and Sox9/8 SC-DKO at different time-points ( b–d ) . ( B ) Double immunofluorescence for TUNEL and three molecular markers: SOX9 [a , ( P90 ( 30datx ) ) and b , ( P150 ( 90 datx ) ) ] , FOXL2 ( c , P120 ( 60 datx ) ) , and WT1 ( d , P90 ( 30 datx ) ) . Arrows in b and d mark cells showing coexpression of the two proteins . Scale bar in Ac represents 100 µm for Aa–c; scale bar in Ad represents 50 µm; scale bar in Bd represents 50 µm in B . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 02510 . 7554/eLife . 15635 . 026Figure 6—source data 1 . Comparison of the number of TUNEL-positive cells per section area unit in both SC-DKO mutants and TX-treated controls . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 026 To identify the cell types undergoing apoptosis , we combined TUNEL staining with immunofluorescence for several molecular markers . Neither SOX9- nor FOXL2-expressing cells were observed to be apoptotic in mutant testes before P120 ( 60 datx ) ( Figure 6Ba , c ) , but SOX9+ cells were found to be apoptotic in the P150 ( 90 datx ) testes ( Figure 6Bb ) . In contrast , we observed apoptotic cells expressing WT1 as early as P90 ( 30 datx ) ( Figure 6Bd ) , indicating that apoptosis mainly affects recombined Sertoli cells in which Sox9 had been ablated but Foxl2 had not yet been upregulated . Altogether , these findings suggest that testis regression in Sox9/8 mutants occurs in two different stages . During the first two months after the initiation of TX administration , both non-recombined Sertoli cells ( SOX9+ ) and transdifferentiated cells ( FOXL2+ ) remain alive , whereas recombined but not yet transdifferentiated cells ( SOX9− , WT1+ ) do undergo apoptosis . In the second stage ( P180 and older mice ) , massive apoptosis affects all cell types , including the remaining Sertoli cells and granulosa-like cells .
There is now compelling evidence that the bipotential nature of the genital ridge at the beginning of gonad development is not completely lost once either testes or ovaries acquire their final adult morphology and functionality . During embryonic development the newly formed Sertoli cells can transdifferentiate to their ovarian counterparts when the testis promoting factors Sox9 or Dmrt1 are lost ( Georg et al . , 2012; Matson et al . , 2011a ) . The finding that Foxl2 in the adult ovary was necessary to prevent granulosa-to-Sertoli cell transdifferentation revealed that this antagonism also operates in the adult gonad . In the adult testis , the same antagonism also appears to exist , as FOXL2+ cells were observed when Dmrt1 was ubiquitously deleted ( Matson et al . , 2011a ) . Here we show that Sertoli-to-granulosa cell transdifferentiation can be induced as well in the adult mouse testis by just deleting two SoxE genes , Sox9 and Sox8 . These results evidence that Sox9 has a crucial role , not only during sex determination and testis differentiation , but also in adult testis maintenance , where , together with Sox8 and coordinately with Dmrt1 , it prevents male-to-female genetic reprogramming . The regulatory relationship between Dmrt1 and Sox9 requires further discussion . At the sex determination stage of the mouse ( E11 . 5 ) , both Sox9 and Dmrt1 are expressed in the early embryonic testis ( Kent et al . , 1996; Raymond et al . , 1999 ) , but whereas early embryonic Sox9 mutants show sex reversal ( Chaboissier et al . , 2004; Barrionuevo et al . , 2006 ) , early embryonic Dmrt1 KO mice have testes that express Sox9 and appear histologically normal until P7 ( Raymond et al . , 2000 ) . Thus , Sox9 expression is independent of DMRT1 during sex determination and some time thereafter . Similarly , Sertoli cell-specific inactivation of Sox9/8 at E13 . 5 , shortly after the sex determination stage , leads to a rapid downregulation of Dmrt1 that becomes already visible four days later , at E17 . 5 ( Georg et al . , 2012 ) . In contrast , Dmrt1 ablation at E13 . 5 results in a very delayed Sox9 downregulation , which is seen at P14 ( one month later ) , coinciding with Foxl2 upregulation ( Matson et al . , 2011a ) . This suggests again that Sox9 expression is independent of Dmrt1 in newly differentiated Sertoli cells and that the loss of Sox9 after Dmrt1 ablation is a secondary consequence of the upregulation of ovarian genes ( s ) , such as Foxl2 , in the same cells . On the other hand , several observations suggest the transactivation of SOX9 by DMRT1: 1 ) DMRT1 binds near the Sox9 locus in P28 mouse testes ( Matson et al . , 2011a ) , 2 ) ectopic expression of Dmrt1 in embryonic XX gonads causes XX sex reversal with upregulation of Sox9 ( Zhao et al . , 2015 ) and 3 ) FOXL2-/- sex reversed polled goats undergo a process of transdifferentiation in which DMRT1 expression precedes the upregulation of SOX9 ( Elzaiat et al . , 2014 ) . In the latter two cases , however , female-promoting genes , including FOXL2 , are either downregulated or not expressed , and thus , SOX9 upregulation could be again an indirect consequence of the downregulation of female-promoting genes . Here we provide evidence that in the adult gonad , mutant Sox9/8 Sertoli cells lose DMRT1 , and that FOXL2 protein appears concomitant with the loss of DMRT1 , consistent with the notion that Dmrt1 expression is SOX9/8-dependent and that DMTR1 represses Foxl2 . Additional observations support this view: 1 ) nearly all the genes strongly affected by the loss of DMRT1 were also affected by the loss of SOX9/8; 2 ) Sertoli-to-granulosa cell transdifferentiation observed in the testes of our Sox9/8 mutant mice may be reduced by decreasing levels of RA , a signaling pathway known to be blocked by DMRT1 in Sertoli cells to prevent Foxl2 expression and transdifferentiation into granulosa-like cells ( Minkina et al . , 2014 ) ; 3 ) DMRT1 can silence Foxl2 in the absence of SOX9 and SOX8 ( Lindeman et al . , 2015 ) ; and 4 ) Sox9 is upregulated in the adult ovary after the ectopic expression of Dmrt1 , coinciding with Foxl2 downregulation ( Lindeman et al . , 2015 ) . Altogether , available data suggest that , like at earlier stages , a main role for SOX9/8 in adult male sex maintenance is to keep Dmrt1 actively expressed , this latter gene having a fundamental role in repressing female-specific genes . However , these observations do not rule out the possibility that DMRT1 is also necessary for the maintenance of Sox9 expression in the adult testis and that a feed-forward regulatory loop between Sox9/8 and Dmrt1 exists that ensures testis maintenance and antagonizes the feminizing action of Foxl2 . Additional experiments ( e . g . a time course of Sox9 expression in adult SC-DKO Dmrt1 mice ) will help to clarify this issue . There is evidence that Wt1 acts upstream of both Sox9 and Sox8 during the early stages of embryonic testis development ( Gao et al . , 2006; Barrionuevo et al . , 2009 ) . In the adult testis , we have seen that Sox9/8-depleted Sertoli cells initially maintain WT1 expression , but this expression becomes progressively downregulated coinciding with the time-point at which Foxl2 is upregulated . This suggests that Wt1 retains its hierarchical position also in the adult testis , and that female-specific factors , including Foxl2 , may be involved in its silencing . Consistent with previous studies ( Chun et al . , 1999; Schmidt et al . , 2004 ) , we detected two types of granulosa cells in the normal adult ovary ( Figure 3Ce ) : 1 ) those located in antral ( mature ) follicles express FOXL2 but not WT1 , and 2 ) those located in pre-antral follicles express both proteins . Thus , considering these two molecular markers , transdifferentiation of Sox9/8 SC-DKO Sertoli cells seems to give rise to mature follicle-type granulosa cells . This expression pattern also suggests that WT1 may play an anti-feminizing role in adult Sertoli cells . We have reported here that the functional redundancy between the Sox9 and Sox8 alleles observed in embryonic Sertoli cells ( Barrionuevo et al . , 2009 ) and other embryonic cell types ( Chaboisier et al . , 2004; Stolt et al . , 2004; Reginensi et al . , 2011 ) is also maintained in adult Sertoli cells . The phenotype of mutant testes becomes ever more severe as the numbers of null alleles increase in their genotype , with extreme phenotypes observed in homozygous DKO testes 4 months after the beginning of TX treatment , at which stage seminiferous tubules have literally disappeared . As Sertoli cell proliferation stops once they obtain their adult appearance ( Kluin et al . , 1984 ) , programmed cell death in Sox9/8 mutants may explain their reduction in number . Consistently , we found no SOX9+ apoptotic cell by P90 ( 30 datx ) , indicating that Sox9/8 initially protects Sertoli cells from apoptosis , a role previously shown for this gene in other developing organs ( Akiyama et al . , 2002; Cheung et al . , 2005; Seymour et al . , 2007 ) . Similarly , newly differentiated FOXL2+ cells did also not apoptose , showing that reprogrammed granulosa-like cells are also protected from apoptosis . However , the situation was substantially different in P150 ( 90 datx ) mutant testes , where apoptosis was intense . At these late stages of testis regression , cord structure was dramatically compromised and even Sertoli cells still expressing Sox9 were seen to undergo apoptosis . It is well known that the number of Sertoli cells must reach a critical threshold to organize embryonic testicular cords ( Palmer and Burgoyne , 1991; Schmahl and Capel , 2003 ) . Accordingly , our results suggest that adult testis tubules also require the presence of a minimum number of Sertoli cells to be maintained . The progressive loss of Sertoli cells after Sox9/8 ablation , either by apoptosis or by transdifferentiation into granulosa-like cells , appears to reach a point of no return at which the remaining normal Sertoli cells are unable to support the tubular structure and are also induced to apoptose . Hence , our results show that SOXE factors are necessary to maintain Sertoli cell identity and seminiferous tubule integrity , as these cells maintain all the other cell types forming the tubules , which become completely disorganized in their absence . Several findings suggest that deregulation of important structural proteins controlled by SoxE genes could be involved in the process . SOX9 controls , either directly ( Bell et al . , 1997 ) or indirectly ( Barrionuevo et al . , 2008; Georg et al . , 2012 ) , the expression of extracellular matrix proteins , which contribute importantly to the tubular structure . Sox8-/- mice show increased BTB permeability and greatly reduced levels of α-tubulin acetylation , suggesting that impairment of the Sertoli cell cytoskeleton may have modified the microenvironment of the seminiferous epithelium ( Singh et al . , 2013 ) . Also , after Sox9 ablation in Sox8 mutants , both developing ( Barrionuevo et al . , 2009; Georg et al . , 2012 ) and adult testes ( present paper ) experience downregulation and/or abnormal distribution of several important proteins required for the formation of Sertoli–Sertoli and/or Sertoli–germ cell adhesion complexes ( Figure 2—figure supplement 2 ) . In this context , it is noteworthy that spermatogenesis is halted when the functionality of the BTB is impaired ( Meng et al . , 2005; Dadhich et al . , 2013 ) . Thus , in our Sox9/8 SC-DKO mouse testes , BTB permeation and cytoskeleton impairment may give rise to a damaged intra-tubular microenvironment in which spermatogenesis is not supported anymore , germ cells undergoing both apoptosis and desquamation . Altogether , available data strongly suggest that failure of Sox9/8 double mutant Sertoli cells to sustain testis tubule architecture is a direct consequence of altered expression of cell adhesion molecules and probably of other structural elements such as components of the cytoskeleton or the extracellular matrix . Regarding the somatic cells of the testis , PM cells disappear in Sox9/8 mutant testes , whereas Leydig cells appear not to be affected , as they express the Leydig cell markers HSD17b3 and Insl3 . Although PM and Leydig cell specification is induced by Sertoli cells during early testis development ( reviewed by Svingen and Koopman , 2013 ) , at later stages of testis development ( E14 . 5 and onward ) Leydig cells do not require Sertoli cells for proliferation and synthesis of testosterone ( Gao et al . , 2006 ) . Our results in the adult testis show that adult PM cells retain their original dependence from Sertoli cells , whereas maintenance of adult Leydig cells is again Sertoli cell-independent . Further research is required to unravel the actual functional status of Leydig cells in Sox9/8 mutant testes . According to the above considerations , we propose a model for the maintenance of Sertoli cell fate in the adult testis . In this model , Sox9/8 play a central role in maintaining active Dmrt1 , which prevents expression of ovary promoting genes , including Foxl2 , which in turn negatively regulates Sox9/8 and/or Dmrt1 . Dmrt1 inhibits RA signaling which promotes the expression of Foxl2 , although an interference of Sox9 on this signaling pathway , through a Dmrt1-independent mechanism , cannot be ruled out . Wt1 positively regulates Sox8/9 and is negatively regulated by Foxl2 and/or other ovarian-specific genes . Sox9/8 are also needed for maintaining the expression of important testis structural genes and for protecting Sertoli cells from apoptosis ( Figure 7 , solid lines ) . It is also possible that Dmrt1 may establish feed-forward regulatory loop with Sox9/8 and that Sox9/8 repress the expression of ovary-specific mRNAs through Dmrt1-independent mechanisms , although these interactions are less strongly supported by available data ( Figure 7 , dashed lines ) . 10 . 7554/eLife . 15635 . 027Figure 7 . Model for the regulation of mammalian sex maintenance . Positive regulation is indicated by arrows . Negative regulation is indicated by perpendicular lines . See text for a detailed explanation . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 027 In conclusion , we have shown Sox9/8 have important DMRT1-dependent and independent functions in the maintenance of the adult testis . In their absence , phenotypically normal , fertile testes are genetically reprogrammed and Sertoli-to-granulosa cell transdifferentiation occurs . Nevertheless , this is a mere transient stage of mutant adult Sertoli cells in the irreversible degenerative process the seminiferous tubules face in the absence of Sox9 and Sox8 .
Previously generated Sox9f/f; Sox8-/- mice ( Barrionuevo et al . , 2009; Kist et al . , 2002; Sock et al . , 2001 ) were bred to Wt1-CreERT2 mice ( Zhou et al . , 2008 ) and the resulting double heterozygous offspring harboring the Cre allele was backcrossed to Sox9f/f; Sox8-/- mice to obtain heterozygous and homozygous compound Sox9; Sox8 conditional mutants . The same mating scheme was followed with the Sox9-CreERT2 mouse line ( Kopp et al . , 2011 ) . To report CRE activity , the R26R-EYFP reporter allele ( Srinivas et al . , 2001 ) was crossed into Wt1-CreERT2; Sox9f/f; Sox8-/- and Sox9-CreERT2; Sox9f/f; Sox8-/- mice . For genotyping we performed PCR and qPCR with DNA purified from tail tips . Primers and PCR conditions for Sox9flox , Sox8- , Cre , and R26R-EYFP were used as described Barrionuevo et al . ( 2009 ) . Mouse housing and handling , as well as laboratory protocols , were approved by the University of Granada Ethics Committee for Animal Experimentation . Tamoxifen ( Sigma , T5648 ) dissolved in corn oil ( Sigma , C8267 ) at a concentration of 30 mg/ml and 0 . 16 mg of TX per gram of body weight was initially administered orally to mice with a feeding needle for 5 consecutive days . With this treatment Sox9/8 double mutants displayed a 90% lethality , so we reduced the dose of TX ( down to 0 . 07 mg TX / gr of body weight ) and 90% of Sox9/8 double mutants survived , but the efficiency of CRE recombination fell then to below 20% . Then , we tried to feed mice with a TX-supplemented diet ( 40 mg TX/100 g Harlan 2914 diet ) for one month . This treatment resulted in a 100% survival rate . TX administrations were started at 2 months ( P60 ) and finished 30 days after the beginning of TX administration ( P90 [30 datx] ) ( Figure 1A ) . All results presented here , except those included in Figure 1—figure supplement 2 and 3 , were obtained from mice fed with the TX-supplemented diet . Gonads were dissected out , weighted and prepared for standard histological methods , including haematoxylin and eosin staining . Single and multiple immunofluorescence were performed as previously described ( Dadhich et al . , 2013 ) . Table 1 summarizes the antibodies used . 10 . 7554/eLife . 15635 . 028Table 1 . Antibodies used in this study . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 028Gene productRaised inWorking dilutionReferencesLamininrabbit1:100Sigma L9393ACTA2mouse1:200Sigma A2547Claudin11rabbit1:100Santa Cruz Biotechnology , CA sc-25711DMC1goat1:100Santa Cruz Biotechnology , CA sc-8973PCNAmouse1:100Santa Cruz Biotechnology , CA sc-56CYP14A1 ( P450scc ) goat1:200Santa Cruz Biotechnology , CA sc-18043SOX9rabbit1:100Santa Cruz Biotechnology , CA sc-20095SOX9goat1:10Santa Cruz Biotechnology , CA sc-17341WT1rabbit1:100Santa Cruz Biotechnology , CA sc-192FOXL2goat1:100Abcam ab5096GFPrabbit1:100Novus Biologicals NB600-308WT1mouse1:30DAKO M3561 ( clone 6F-H2 ) CYP19A1 ( Aromatase ) mouse1:10GeneTex GTX41561DMRT1rabbit1:400Gift from Dr . Silvana Guioli To perform the TUNEL technique we used the Fluorescent In Situ Cell Death Detection Kit ( Roche , Mannheim , Germany ) according to the manufacturer's instruction . The in vivo test to analyze the permeability of the BTB in the testes of control and mutant mice was performed using a biotin-labelled tracer compound ( EZ-Link Sulfo-NHS-LC-Biotin tracer , Thermo Scientific ) as described ( Dadhich et al . , 2013 ) . TX-treated Sox9-CreERT2; Sox9f/f; Sox8-/-mice were injected subcutaneously either with 40 μg/μl WIN 18 , 446 ( Tocris , Biotechne , UK , Cat . No 4736 ) , dissolved in 50 μl dimethyl sulfoxide or with the vehicle alone for 8 days , 4 days before and 4 days after the end of the 30 days diet TX treatment . Fifteen days after the end of WIN 18 , 446 treatment , gonads were collected and processed for double immunofluorescense for ACTA2 and FOXL2 as described above . The number of FOXL2+ cells per transversal ST section was counted in 20 tubules of 5 WIN 18 , 446-treated and 5 control animals . Only circular or ellipsoid tubular sections in which the major/minor axis ratio was lower than two were used for counts . Both testes were extracted from six P150 ( 90 datx ) mutant males ( three Wt1-CreERT2; Sox9f/f; Sox8-/- and three Sox9-CreERT2; Sox9f/f; Sox8-/- ) . As controls , both gonads were also extracted from two P150 ( not treated ) and two P150 ( 90 datx ) Sox9f/f male mice as well as from two 4–5 months old normal females . All TX-treated mice were euthanized three months after the initiation of diet TX-treatment for one month . The two gonads of each individual were pooled , homogenized in 1 ml of RNAzol ( Molecular Research Center , Inc . ) per 100 mg of tissue and the total RNAs were then individually purified from the twelve samples following the RNAzol manufacturer’s instructions . After successfully passing Macrogen Inc . quality check , the twelve RNAs were paired-end sequenced separately in an Illumina HiSeq 2000 platform at that company and the quality of the resulting sequencing reads was assessed using FastQC ( http://www . bioinformatics . bbsrc . ac . uk/projects/fastqc/ ) . RNAseq data were processed with the Tuxedo tools ( Trapnell et al . , 2012 ) . Alignments were done with Tophat/Bowtie2 against the mm10 UCSC annotated mouse genome . Differential expression analyses where done with Cuffdiff . Analysis of the resulting data were performed with the CummeRbund Bioconductor package . The quality of RNA-seq was checked as described in the package documentation . Briefly , by comparing FPKM scores across samples , and looking for outliers replicates , by analyzing squared coefficient of variation which allows visualization of cross-replicate variability between conditions and by analyzing the dispersion plots ( Figure 8 ) . 10 . 7554/eLife . 15635 . 029Figure 8 . Pairwise scatterplots comparing log10 FPKM between different conditions . Dispersion is lower when comparing similar conditions ( controls , mutants ) and higher when comparing mutant with control conditions . Notice that dispersion observed when mutants are compared with ovary is lower than that observed comparing them with any of the testis controls . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 029 To explain the presence of both Sox9 and Sox8 transcripts in the transcriptome of double homozygotes for the null allele , their transcripts where visualised with the IGV genome browser ( Robinson et al . , 2011 ) . Recombinant Sox9 locus is seen by Cuffkinks and IGV as an alternative spliced transcript . Sashimi plots show that the CRE recombination is not 100% effective as transcripts with the correct splicing still remain in both mutant conditions but a high proportion of the Sox9 genes are efficiently deleted . These plots also show that in the absence of the 2nd and 3rd exons after recombination , alternative intron donor and acceptor sites downstream of Sox9 can be used for splicing . Sox8 transcripts only include the 5’ untranslated portion of the transcripts demonstrating that these individuals are actually Sox8−/− ( Figure 9 ) . 10 . 7554/eLife . 15635 . 030Figure 9 . Sashimi plots of the Sox9 and Sox8 genes in mutant and control conditions . Vertical lines indicate coverage and curved lines indicate splicing . The mm10 row shows the positions of the exons and introns and the translated region as annotated in the mm10 UCSC mouse genome . Aberrant splicing sites where found in mutant but not in control samples . Notice that normal splicing also occurred in mutant animals showing that CRE-recombination efficiency was not 100% . DOI: http://dx . doi . org/10 . 7554/eLife . 15635 . 030 For Sox9/8 DKO and Dmrt1 KO transcriptome comparison CEL files corresponding to the Dmrt1 conditional knockout expression analysis of P28 testes by Matson et al . ( 2011a ) were downloaded from the GEO database ( Acc: GSE27261 ) . Files were processed with the simpleaffy ( Miller , 2016 ) package from Bioconductor and normalized with gcrma ( Wu and Gentry , 2016 ) . Uninformative data , control probes and genes with low variation or close to background were filtered out . Data were grouped in two conditions , Control and Mutant . Differential expression was analyzed with the limma package ( Richie et al . , 2015 ) and annotated with the Affymetrix Mouse Genome 430 2 . 0 Array annotation data . Genes with log2FC having p values less than 0 . 05 for differential expression tests respect to normal testes where selected . These genes list was then selected from our transcriptome data and those showing non-significative log2FC where filtered out . The remaining 8910 genes showing significant differential expression in both experiments are included in Figure 5—source data 1 . | Scientists thought for years that the ovaries and testes are fully developed , stable organs that cannot change their structure and function in mature mammals . However , more recent studies have shown that a gene called Foxl2 is active throughout life to prevent ovary cells from becoming more like the Sertoli cells present in the testes . Similarly , a gene called Dmrt1 keeps Sertoli cells from becoming more like ovary cells after birth . Scientists don’t yet know all the details about how Dmrt1 prevents testes from becoming more ovary-like . For example , do genes that help testes develop in the embryo ( which include two genes called Sox8 and Sox9 ) play a role in maintaining the adult testes ? Barrionuevo , Hurtado , Kim et al . have now genetically engineered adult male mice to lack the Sox8 and Sox9 genes . The Sertoli cells in the testes of these mice gradually lost their key characteristics and ultimately died . During this process , the testes cells took on certain characteristics that made them more ovary-like: for example , the ovary-maintaining Foxl2 gene was activated in the Sertoli cells . Eventually , the structures in the testes that produce sperm degenerate and are replaced by empty space in the genetically engineered mice . This happens because the Sox8 and Sox9 genes control the production of proteins that maintain these structures . In addition , these genes also protect the Sertoli cells from self-destructing , and the testes-maintaining Dmrt1 gene is not active when Sox8 and Sox9 are missing . More studies are now needed to determine how Sox8 and Sox9 work with Dmrt1 to maintain the testes . | [
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] | 2016 | Sox9 and Sox8 protect the adult testis from male-to-female genetic reprogramming and complete degeneration |
We show that a cage-shaped F-actin network is essential for maintaining a tight spatial organization of Cav1 . 3 Ca2+ channels at the synaptic ribbons of auditory inner hair cells . This F-actin network is also found to provide mechanosensitivity to the Cav1 . 3 channels when varying intracellular hydrostatic pressure . Furthermore , this F-actin mesh network attached to the synaptic ribbons directly influences the efficiency of otoferlin-dependent exocytosis and its sensitivity to intracellular hydrostatic pressure , independently of its action on the Cav1 . 3 channels . We propose a new mechanistic model for vesicle exocytosis in auditory hair cells where the rate of vesicle recruitment to the ribbons is directly controlled by a synaptic F-actin network and changes in intracellular hydrostatic pressure .
Auditory hair cells convert tiny variations of sound pressure through the displacement of their apical hair bundles into analogous voltage waveforms . Neural encoding of these microphonic potentials occurs at the ribbon synapses of inner hair cells ( IHCs ) by mechanisms involving Cav1 . 3 channels ( Platzer et al . , 2000; Brandt et al . , 2003; Brandt et al . , 2005 ) and otoferlin-dependent exocytosis of synaptic vesicles ( Roux et al . , 2006; Beurg et al . , 2010 , Vincent et al . , 2014 ) . Unlike most neuronal central synapses , IHCs have the extraordinary property to sustain indefatigably high rates of exocytosis during continuous sound stimulation ( Safieddine et al . , 2012 ) . The precise molecular mechanisms underlying this fast and massive recruitment of synaptic vesicles to the IHC ribbon active zones still remain elusive . The implication of an unconventional molecular regulation of synaptic vesicle fusion and replenishment of the releasable pool of vesicles has been proposed ( Nouvian et al . , 2011; Vogl et al . , 2015 ) . In other secretory cells such as neuroendocrine cells , a network of sub-membranous cortical F-actin is known to influence exocytosis greatly by tightly regulating plasma membrane tension and the access of the granules to the secretory sites ( Apodaca , 2002; Torregrosa-Hetland et al . , 2011; Gutierrez and Gil , 2011 ) . In central neuronal synapses , F-actin is also involved in maintaining vesicle pools and regulating vesicle mobility ( Cingolani and Goda , 2008 ) . Electron-tomography evidence for F-actin and microtubules near the synaptic ribbons has been observed in bullfrog hair cells ( Graydon et al 2011 ) . Whether IHC synaptic exocytosis is modulated by F-actin remains unknown . Other factors affecting membrane tension and exocytosis in many cell types , such as mast cells , include hydrostatic pressure across the membrane ( Solsona et al . , 1998 ) . This latter factor has also been shown to influence the spontaneous and evoked activity of vestibular hair cells of the dogfish by mechanisms that remain unknown ( Fraser and Shelmerdine , 2002 ) . In the present study , we investigated whether F-actin and intracellular hydrostatic pressure regulate synaptic exocytosis in mouse IHCs .
Direct labeling of F-actin with fluorescent phalloidin revealed the presence of a dense F-actin network that surrounded the IHC ribbon synaptic zones ( Figure 1A ) . This network extended underneath the plasma membrane and formed intracellular dense cages beneath the synaptic ribbons . These cages displayed a mean size diameter of 0 . 8 ± 0 . 1 µm ( n = 186 actives zones; Figure 1B ) . Overlapping with otoferlin , each F-actin cage was generally associated with one ribbon and one Cav1 . 3 co-immunoreactive patch . Similarly , Ca2+ channels and the secretory machinery have been shown to be associated with the borders of F-actin cytoskeletal cages in chromaffin cells ( Torregrosa-Hetland et al . , 2011 ) . 10 . 7554/eLife . 10988 . 003Figure 1 . Confocal imaging of the synaptic F-actin cages in IHCs . ( A ) Confocal images from averaged Z-stack projection ( 20 slices of 0 . 25 µm ) of P13-IHCs labeled in blue with otoferlin-immuno-reactivity . Directly visualized with fluorescent-phalloidin ( purple ) , F-actin intensively labelled the cuticular plate and the stereocilia but also in a punctated manner the synaptic basal pole of the IHCs . In this latter area , at higher magnification ( averaged Z-stack projection of 8 slices of 0 . 25 µm ) , the synaptic F-actin forms a mesh of cages ( see right panel where the blue channel of otoferlin is omitted; the cages are indicated by the white asterisks ) . At each border of the synaptic F-actin cages was generally attached one synaptic ribbon ( red ) and one associated Cav1 . 3 patch ( green ) as indicated in the lower left panel . The graph represents an example of fluorescent intensity profile through the white dashed line crossing the ribbon and the associated Cav1 . 3 . ( B ) The graph indicates the Gaussian distribution of the larger axis ( double white arrow head ) of each F-actin cage . ( C ) A 45 min treatment with extracellular latrunculin-A disrupted the synaptic F-actin cages . The black holes at the base of the IHCs likely indicated swollen IHC active zones produced by the synaptic F-actin disorganization . At higher magnification ( right panel ) , note also the disorganization of the Cav1 . 3 clusters ( green ) at the ribbons , as indicated by a larger distance in their respective fluorescent intensity profile distribution ( bottom graph ) . ( D ) Comparative Gaussian distribution of the center mass distance between Cav1 . 3 and ribbon in controls ( black , n = 71 active zones ) and latrunculin-treated ( orange , n = 102 active zones ) IHCs . The inset histogram indicates the mean ± SEM distance in both conditions . *p<0 . 05 . DOI: http://dx . doi . org/10 . 7554/eLife . 10988 . 003 A 45-min treatment of the organ of Corti in vitro with 1 µM extracellular latrunculin-A completely disorganized the synaptic F-actin cages and the Cav1 . 3 immunoreactive-patches at the IHC ribbons ( Figure 1C , D ) . The mean distance between the Cav1 . 3 immunoreactive patches and the ribbons increased from 218 ± 11 nm ( n = 71 ) in controls to 260 ± 12 nm with latrunculin-A treatment ( n = 102; p<0 . 05 ) . In the latter conditions , surprisingly , whole-cell patch clamp recordings revealed a largely facilitated exocytosis as compared to controls , while voltage-gated Ca2+ currents were unchanged ( Figure 2A , B ) . After 100 ms depolarization , from -80 to -10 mV , the exocytotic response in control IHCs reached a maximum amplitude of 22 . 0 ± 2 . 9 fF ( n = 11 ) . Considering that a 40 nm diameter synaptic vesicle corresponds to 37 aF ( Lenzi et al . , 1999 ) , we estimated a RRP size of about 590 vesicles , i . e 33 vesicles per ribbons if we assume a number of 18 ribbons per IHCs . This number of RRP vesicles fit well with previous findings ( Johnson et al . , 2008; Vincent et al . , 2014 ) . In latrunculin-treated IHCs , the exocytotic responses reached , at 100 ms , 32 . 4 ± 3 . 3 fF ( n = 18 ) , a value significantly larger as compared to control IHCs ( p<0 . 05; Figure 2B ) . Remarkably , while the exocytotic response saturated at 90–100 ms in controls , the response did not show saturation in latrunculin-treated IHCs . Since the exocytotic response was unchanged for short impulses below 30 ms ( Figure 2B ) , these results suggested that the disruption of F-actin did not affect the steps of vesicle docking and priming but facilitated the replenishment of the RRP . In bassoon mutants with abnormal number of anchored ribbons and reduced Ca2+ currents both short ( 20 ms ) and long ( 100 ms ) impulse activated-exocytosis were affected ( Jing et al . , 2013 ) . 10 . 7554/eLife . 10988 . 004Figure 2 . Latrunculin-A treatment facilitated exocytosis in IHCs . ( A ) Ca2+ currents , evoked by a voltage-ramp protocol , were not significantly affected by latrunculin-A ( orange ) . The parameters of the Boltzman fit are indicated by the histograms ( mean ± SEM ) . Rising intracellular osmotic pressure from 310 mOsm to 390 mOsm did not affect the Ca2+ currents in latrunculin-treated IHCs ( pink ) . For each curves , the darker line indicates the mean responses and the light filled area the standard error . ( B ) Exocytosis evoked by voltage-steps from -80 mV to -10 mV was largely facilitated after latrunculin-A treatment . At right , examples of Ca2+ currents and capacitance jumps ( △Cm ) are shown for a control ( black traces ) and latruncutin-treated IHC ( orange traces ) . In 5 mM intracellular EGTA condition , the facilitation was greatly reduced ( blue points ) . Data are represented as mean ± SEM . *p<0 . 05 . ( C ) Exocytosis under Ca2+ uncaging was also facilitated after latrunculin-A treatment . The jump in the concentration of intracellular free Ca2+ was similar in control and latrunculin-treated IHCs ( inset; p = 0 . 9 ) . ( D ) Left , comparative exocytotic rates of control and latrunculin-treated IHCs obtained from the first derivative ( dCm/dt ) of the curves in C . Right , comparative maximum exocytotic amplitude and peak rate histograms ( mean ± SEM ) . Increasing intracellular hydrostatic pressure from 310 to 390 mOsm ( pink bars ) did not affect exocytosis in latrunculin-treated IHCs . Numbers of cells are indicated in the histogram . *p<0 . 05 and n . s . as non significative . DOI: http://dx . doi . org/10 . 7554/eLife . 10988 . 004 The exocytotic facilitation in latrunculin-treated IHCs was greatly reduced to 21 . 8 ± 2 . 5 fF at 100 ms ( n = 7 ) and comparable to controls ( p=0 . 9 ) when intracellular Ca2+ buffering was increased with 5 mM EGTA ( Figure 2B ) . This sensitivity to EGTA suggested a spatial disorganization of the Ca2+ channel clusters in regards to the release sites in latrunculin-treated IHCs , in good agreement with confocal imaging ( Figure 1C , D ) . Rising the EGTA concentration could also affect the extent to which neighboring calcium sources interact and summate to produce a global effect on free calcium . The intriguing question now is: why is the disruption of the synaptic F-actin with latrunculin-A facilitating exocytosis in IHCs ? One explanation is that the synaptic F-actin network , in addition to organizing Ca2+ microdomains , also acts as a diffusion barrier for synaptic vesicles limiting the access to the site of release , as suggested in some central synapses ( Cingolani and Goda , 2008 ) . Its disruption with depolymerising agents would therefore facilitate vesicle replenishment of the release sites , as shown in a large variety of secretory cells ( Malacombe et al . , 2006 ) , by increasing the number of available vesicles for docking and priming . Alternatively , a disrupted F-actin could facilitate the diffusion of Ca2+ from its sites of entry and stimulate replenishment . To directly test these hypothesis , we studied the effect of F-actin depolimerization on exocytosis triggered by intracellular Ca2+ uncaging , i . e . independently of the activation and the organization of the Ca2+ channels . In these experiments , a large exocytotic facilitation was also observed in latrunculin-treated IHCs ( Figure 2C , D ) . The peak exocytotic rate , obtained by measuring the first derivative function of the curves in Figure 2C , was nearly two fold larger in latrunculin-treated IHCs as compared to controls ( Figure 2D; 25 . 9 ± 2 . 7 pF/s ( n = 14 ) and 13 . 7 ± 1 . 7 pF/s ( n = 16 ) , respectively; p<0 . 05 ) . The levels of the peak intracellular Ca2+ concentration ( [Ca2+]free ) reached upon UV-flash Ca2+ uncaging were verified to be similar in control ( n = 7 ) and latrunculin-treated IHCs ( n = 5 ) , respectively 59 ± 7 µM and 57 ± 5 µM ( p=0 . 9; Figure 2C-inset ) . These Ca2+ uncaging experiments again suggested that a synaptic F-actin network controls the diffusion rate of the synaptic vesicles to the sites of release in IHCs . Furthermore , since intracellular hydrostatic pressure has been suggested to influence membrane tension and exocytosis through the F-actin network in many cell types such as mast cells ( Solsona et al . , 1998 ) , we probed Ca2+-evoked exocytosis under various intracellular osmotic pressures in auditory IHCs . We first found that increasing intracellular osmotic pressure from 310 to 390 mOsm with sucrose produced a significant increase in the resting membrane capacitance of IHCs . The resting size of IHCs , voltage-clamped at -80 mV for a period of 2 min after break-in , was 9 . 93 ± 0 . 27 pF ( n = 11 , 310 mOsm ) and 11 . 10 ± 0 . 34 pF ( n = 10 , 390 mOsm; p<0 . 05 ) , respectively . This augmentation of the IHC resting membrane capacitance was about 50 times larger than the size of the RRP evoked by membrane depolarization ( Figure 2B; RRP = 22 fF ) . Where does this large addition of membrane come from ? One possible explanation was that high intracellular hydrostatic pressure triggers the fusion of a large amount of extrasynaptic vesicles to the plasma membrane , as previously suggested for Ca2+ uncaging ( Vincent et al . , 2014 ) . Remarkably , in these latter conditions of intracellular hyperosmotic stress at 390 mOsm , voltage-dependent Ca2+ currents displayed larger amplitude ( Figure 3A , C ) and accelerated activation kinetics as compared to control conditions at 310 mOsm ( Figure 3E ) . A shift in their voltage-dependence toward more negative potentials was also observed ( Figure 3A , D ) . The hydrostatic pressure effects on Ca2+ currents were no longer visible when IHCs were pre-treated with latrunculin-A ( Figure 2A , pink line ) and were greatly reduced in IHCs lacking otoferlin ( Otof-/-; Figure 3B , C ) . This indicated that the Cav1 . 3 channels of IHCs are mechanosensitive , like the Cav1 . 2 channels in smooth muscle cells ( Lyford et al . , 2002 ) . We cannot exclude the addition of Ca2+ channels to the plasma membrane during the massive vesicular fusion caused by increased hydrostatic pressure . However , this mechanism appears unlikely in regards to results obtained in non-secretory cells such as smooth muscle and HEK cells where similar effect on Ca2+ channels were obtained ( Lyford et al . , 2002 ) . In our study , the sensitivity to membrane tension in IHCs required an intact synaptic F-actin network and otoferlin , a proposed synaptic Ca2+ sensor thought to interact physically with the Cav1 . 3 II-III loop ( Ramakrishnan et al . , 2009 ) . Notably , an increase in Ca2+ current amplitude in low bath hydrostatic pressure ( equivalent to increasing intracellular pressure ) was also reported in dissociated guinea pig vestibular hair cells ( Duong Dinh et al . , 2009; Haasler et al . , 2009 ) . In these latter studies , the change in Ca2+ currents could be interpreted as due to a pressure activation of K+ currents leading to less pronounced depolarization . Although pressure effects on K+ currents have also been shown in guinea-pig IHCs ( Kimitsuki , 2013 ) , we don't think that these currents would influence our Ca2+ current measurements since we were working in conditions where most K+ currents are blocked . Overall , our results suggested here that the mechanosensitivity of the Cav1 . 3 channels is mediated through an intact synaptic F-actin network . 10 . 7554/eLife . 10988 . 005Figure 3 . Intracellular hydrostatic pressure modulates Ca2+ currents and exocytosis . ( A ) Comparative Ca2+ currents evoked by a voltage-ramp protocol in Otof+/- and Otof+/+IHCs ( expressing otoferlin ) . Cells were recorded with intracellular osmotic pressure at 310 mOsm ( n = 10 ) or at 390 mOsm ( n = 9 ) . The darker line indicates the mean responses and the light filled area the standard error . ( B ) Ca2+ currents in Otof-/- IHCs ( lacking otoferlin ) recorded in conditions similar to A . ( C-D ) Both maximum amplitude and half-maximum voltage-activation of Ca2+ currents were maximally affected at 390 mOsm . Note that the slopes of the Boltzman fit of the activation curve in A and B were found slightly affected in IHCs expressing otoferlin ( 310 mOsm: K = 5 . 90 ± 0 . 14 pA/mV , n = 10 and 390 mOsm: K = 5 . 36 ± 0 , 09 pA/mV , n = 9; p < 0 . 05 ) but not in Otof-/-IHCs ( 310 mOsm: K= 6 . 18 ± 0 . 20 pA/mV , n=6 and 390 mOsm: K= 5 . 89 ± 0 . 28 , n = 6; p = 0 . 71 ) ( data not shown ) . Data are represented as mean ± SEM , with the number of cells indicated above each point and ( * ) indicating p < 0 . 05 . ( E ) Activation kinetics of Ca2+ currents , evoked by voltage-steps from -80 mV to different voltage levels , were significantly faster at 390 mOsm in hair cells expressing otoferlin but not in Otof-/- IHCs . For unknown reason the activation kinetics were faster in Otof-/-IHCs as compared to IHCs expressing otoferlin . Data are expressed as mean ± SEM . * p < 0 . 05 . The number of cell is similar to D . ( F ) Ca2+ efficiency of exocytosis was plotted as the change in membrane capacitance ( ΔCm ) against the integral of the calcium current ( QCa ) when depolarizing IHCs at a constant voltage-step from -80 to -10 mV with increasing duration from 10 to 120 ms . Data points at 310 mOsm and 390 mOsm were fitted with a power function with a Ca2+ efficiency slope A = 2 . 47 ± 0 . 45 fF/pC and 9 . 30 ± 2 . 5 fF/pC ( p < 0 . 05 ) and a ( power cooperative index ) = 0 . 33 and 0 . 51 ( p = 0 . 52 ) , respectively . The supralinear power cooperative index of 0 . 3 and 0 . 51 found here was somewhat lower to previous values reported by Cho et al . ( 2011 ) and Johnson et al ( 2005 ) . ( G ) The exocytotic efficiency of each data point in F ( ΔCm/QCa ) was plotted for each depolarizing time at 310 mOsm and 390 mOsm . Data were fitted with an exponential function with 22 . 2 ± 10 . 4 ms and 50 . 1 ± 11 . 9 ms , respectively ( p < 0 . 05 ) . ( H ) Ca2+ efficiency of exocytosis ( recorded in conditions similar to F ) was unaffected when rising osmotic pressure from 310 to 390 mOsm in Otof-/-IHCs . Data were best fitted with a linear function with a similar slope of 0 . 12 ± 0 . 1 fF/pC and 0 . 48 ± 0 . 1 fF/pC ( p = 0 . 2 ) at 310 and 390 mOsm , respectively . Data are represented as mean ± SEM . * p < 0 . 05 . DOI: http://dx . doi . org/10 . 7554/eLife . 10988 . 005 Exocytosis triggered by voltage-activation of Ca2+ channels from -80 to -10 mV ( a voltage at which Ca2+ current amplitude is maximum , Figure 3A ) also showed maximum facilitation when intracellular pressure was increased from 310 to 390 mOsm ( Figure 3F , G ) . Notably , on the contrary to what observed when disrupting the synaptic F-actin , exocytotis increased for short impulses ( 10–20 ms ) at 390 mOsm ( Figure 3F , G ) , suggesting that the last steps of vesicle exocytosis ( docking , priming and/or fusion ) were here accelerated . Exocytosis for longer steps up to 90 ms also increased , from 1 . 40 ± 0 . 23 fF/pC ( n = 7 , 310 mOsm ) to 2 . 58 ± 0 . 49 fF/pC ( n = 5 , 390 mOsm; p<0 . 05 ) at 50 ms ( Figure 3G ) . These latter results suggested that the replenishment rate of the RRP was also accelerated . This facilitation of exocytosis , under intracellular hyperosmotic stress , appeared unrelated to the effects on Ca2+ channels since it was also observed when directly uncaging intracellular Ca2+ ( Figure 4 ) . The levels of the peak [Ca2+]free reached upon UV-flash Ca2+ uncaging were verified to be similar in 310 mOsm ( n = 7 ) and 390 mOsm ( n = 5 ) conditions , 59 ± 7 µM and 64 ± 6 µM ( p=0 . 6; Figure 4A-inset ) . In these latter conditions of intracellular hyperosmotic stress , the strength of exocytotic facilitation was maximal at 390 mOsm and abruptly decreased at 410 mOsm , a high pressure at which the intrinsic organization of the IHC exocytotic machinery may have been damaged . At 390 mOsm , the maximum amplitude of the exocytotic response was 1 . 5 ± 0 . 15 pF ( n = 9 ) as compared to 1 . 0 ± 0 . 1 pF ( n = 16 ) in controls at 310 mOsm ( p<0 . 05; Figure 4A , C ) . The peak exocytotic rate was largely increased at 390 mOsm as compared to 310 mOsm ( 25 . 0 ± 4 . 5 pF/s ( n = 9 ) and 13 . 7 ± 1 . 7 pF/s ( n = 16 ) , respectively; p<0 . 05 , Figure 4D ) . Interestingly , the facilitation of exocytosis by high intracellular hydrostatic pressure was not observed in IHCs treated with latrunculin-A ( Figure 2D , pink bars ) and in Otof-/- IHCs ( Figure 3H and Figure 4B–D ) . The peak [Ca2+]free reached upon UV-flash Ca2+ uncaging was unchanged in Otof-/- IHCs as compared to control IHCs ( Vincent et al . , 2014 ) . 10 . 7554/eLife . 10988 . 006Figure 4 . Exocytosis triggered by Ca2+ uncaging is sensitive to intracellular hydrostatic pressure . ( A ) Exocytosis in IHCs expressing otoferlin was largely potentiated when rising osmotic pressure from 310 mOsm ( light grey ) to 390 mOsm ( dark grey ) . The darker line in each condition indicates the mean responses and light filled area the standard error . The jump in the concentration of intracellular free Ca2+ was similar in 310 and 390 mOsm conditions ( inset; p = 0 . 6 ) . ( B ) Exocytosis evoked in Otof-/- IHCs in conditions similar to A . ( C-D ) Comparative maximum amplitude and peak exocytotic rate at various intracellular osmotic pressure . The results showed a maximum facilitation at 390 mOsm . These pressure effects were not seen on the residual slow exocytosis of Otof -/- IHCs . Data points are means ± SEM . The number of IHCs is indicated above each point . * p < 0 . 05 . DOI: http://dx . doi . org/10 . 7554/eLife . 10988 . 006 Overall , the facilitation of exocytosis by high intracellular hydrostatic pressure could be explained by an increased membrane tension that impacts on membrane fusion ( Kozlov and Chernomordik , 2015 ) but also by an increased vesicular mobility , possibly as the result of reduced molecular crowding and loosened vesicle interactions , accelerating the replenishment rate . In the same way , the mobility of vesicles in pancreatic cells and primary hepatocytes was shown to be affected by hydrostatic pressure , a process related to molecular crowding and microfilaments polymerization ( Nunes et al . , 2015 ) . Do large changes in hydrostatic pressure occur in the cochlea during physiological or pathological conditions ? In the cochlea , large intercompartmental osmotic gradients from 289 to 322 mOsmol/kg H20 are present between the perilymphatic and endolymphatic compartments , respectively ( Sterkers et al . , 1984 ) . These osmotic gradients are likely regulated by aquaporins present in outer sulcus cells ( Eckhard et al . , 2015 ) and genetic deletion of aquaporin-4 in mice leads to impaired hearing ( Li and Verkman , 2001 ) . Osmolarity changes in these inner fluid compartments have long been suspected to be contributing factors to inner ear disorders such as tinnitus and fluctuating hearing loss , inclusive of Menière's disease ( Angelborg et al . , 1982 ) . The outer hair cells , producing the electro-mechanical amplification of sound in the cochlea , have been shown to be mechanically sensitive to extracellular osmotic variation ( Dulon , et al . , 1987 ) , a factor that greatly influence hearing ( Choi and Oghalai , 2008 ) . Our study , showing that IHC exocytosis is sensitive to osmotic forces , suggests that a defect in synaptic transmission at the auditory ribbon synapses may also underlie some hearing disorders associated with Menière's disease . In conclusion , we propose that a synaptic F-actin network tightly controls the flow of synaptic vesicles during exocytosis at the IHC ribbons . This synaptic F-actin network also influences the sensitivity of exocytosis to hydrostatic pressure changes , presumably through a regulation of membrane tension at the active zones . Such actin-based regulation of intracellular hydrostatic pressure and membrane tension , recently shown in a variety of physiological processes such as in the secretory properties of neuroendocrine cells ( de Wit , 2010; Gutiérrez and Gil , 2011 ) , the mitotic cell rounding during cell division ( Stewart et al . , 2011 ) , the photomechanical responses of photoreceptors ( Hardie and Franze , 2012 ) to name a few , is here described for the first time in auditory hair cells .
Experiments were performed in accordance with the guidelines of the Animal Care Committee of the European Communities Council Directive ( 86/609/EEC ) and were approved by the ethics committee of the University of Bordeaux ( animal facility agreement number C33-063-075 ) . All mice ( C57BL6 of either sex ) were anesthetized by intraperitoneal injection of xylazin ( 6 mg/ml ) and ketamine ( 80 mg/ml ) mixture ( Sigma Aldrich , St Louis , USA ) diluted in physiological saline . The organs of Corti were dissected as previously described ( Vincent et al . , 2014 ) . Electrophysiological recordings were obtained from littermate Otof+/+ , Otof+/- or from knock-out ( KO ) otoferlin ( Otof-/- ) C57BL6 mice at postnatal day 12–16 ( P12-P16 ) inner hair cells ( IHCs ) in whole-mount organs of Corti in the apical cochlear area coding for frequencies ranging from 8 to 16 kHz . The organ of Corti ( OC ) was freshly dissected under binocular microscopy in an extracellular solution maintained at 4°C containing ( in mM ) : NaCl 135; KCl 5 . 8; CaCl2 1 . 3; MgCl2 0 . 9; NaH2PO4 0 . 7; Glucose 5 . 6; Na pyruvate 2; HEPES 10 , pH 7 . 4 , 305 mOsm . The tectorial membrane was carefully removed and the OC was placed in a recording chamber and visualized under a 60x water immersion objective ( CFI Fluor 60X W NIR , WD = 2 . 0 mm , NA = 1 ) attached to an upright Nikon FN1 microscope . The extracellular solution was complemented with 0 . 5 µM of apamin ( Latoxan , Valence , France ) and 0 . 2 µM of XE-991 ( Tocris Bioscience , Lille , France ) to block SK channels and KCNQ4 channels , respectively . All Ca2+ current and capacitance recordings were performed in the presence of 5 mM extracellular Ca2+ and carried out at room temperature ( 20–24°C ) . All patch clamp experiments were performed with an EPC10 amplifier controlled by pulse software Patchmaster ( HEKA Elektronik , Germany ) . Patch pipettes were pulled with a micropipette Puller P-97 Flaming/Brown ( Sutter Instrument , Novato , CA , USA ) and fire-polished with a Micro forge MF-830 , ( Narishige , Japan ) to obtain a resistance range from 3 to 5 MΩ . Patch pipettes were filled with an intracellular cesium-based solution containing ( in mM ) : CsCl 145; MgCl2 1; HEPES 5; EGTA 1; TEA 20; ATP 2 and GTP 0 . 3; pH 7 . 2 , 310 mOsm . To increase intracellular osmotic pressure , the cesium-based solution was complemented with different concentrations of sucrose ( 17g/L for 360 mOsm; 27g/L for 390 mOsm and 34 g/L for 410 mOsm ) . Current-voltage ( I-V ) curves were recorded using two different protocols . First , cells were maintained at -80 mV and depolarized with a ramp protocol ( 1 mV/ms ) from -90 mV to +30 mV in 120 ms . The voltage parameters ( half max activation potential and the slope ) of the IV curves were given by fitting the IV curves from -70 mV to -10 mV with a Boltzmann sigmoidal function: ( 1 ) y=A1−A21+exp ( V−V1/2 ) /K Where A1 and A2 are the minimum and the maximum y value . V is the voltage value and V1/2 is the half max voltage activation , K is the slope of the IV curve . Second , cells were step-depolarized from -80 mV to -5mV in 5mV increments for a constant time duration of 100 ms . Activation kinetics of the Ca2+ currents were determined at different potentials with the latter protocol by using a single exponential fit: ( 2 ) y=y0+A1exp ( −x/t ) Where yo is the offset of activation , A1 the amplitude of the Ca2+ current and t the time constant . Real-time capacitance measurements ( Cm ) were performed using the Lock-in amplifier Patchmaster software ( HEKA ) by applying a 1 kHz command sine wave ( amplitude 20 mV ) at holding potential ( -80 mV ) before and after the pulse experiment , as previously described ( Vincent et al . , 2014 ) . The time interval between each depolarization was set at 10 seconds to allow full replenishment of the RRP . The exocytosis efficiency was determined by fitting the data points with a power function: ( 3 ) ΔCm=y0+A[x−xc]a Where yo is the initial value ( fF ) , A the Ca2+ efficiency slope ( fF/pC ) , a the power cooperative index , xc the Ca2+ charge ( QCa in pC ) threshold of the response and x ( pC ) the QCa value for each stimulation . The relationship between the Ca2+ efficiency and the time was fitted with a exponential function: ( 4 ) y=y0+A1e− ( x−x0 ) /t Where yo et x0 are the y and x offset , A1 the amplitude and t the time constant . Only cells with stable membrane resistance ( Rm ) , leak current below 50 pA at holding potential ( -80 mV ) and stable series resistance below 15 MΩ were considered in the study . All Ca2+ currents were leak-subtracted . To trigger a fast increase in intracellular Ca2+ concentration from the caged Ca2+ chelator DM-nitrophen ( Interchim , France ) , we used 200 ms brief flashes from a UV LED light source ( Mic-LED 365 , 128mW , Prizmatix , Givat Shmuel , Israel ) . The UV LED was directly connected to the epi-illumination port at the rear of our upright Nikon FN1 microscope and illumination was focalized through the 60X objective ( CFI Fluor 60X W NIR , WD = 2 . 0 mm , NA=1 ) . Hair cells were loaded with in mM , CsCl 145; HEPES 5; TEA 20; DM-nitrophen 10; CaCl2 10 . In some experiments , in order to check that similar Ca2+ release occurs in all our conditions , we added 50 µM OGB-5N in the intracellular solution . To determine the intracellular Ca2+ concentration ( [Ca2+]free ) , we used the following formula: ( 5 ) [Ca2+]free=KD× ( F−Fmin ) ( Fmax−F ) Where [Ca2+] free is the caged Ca2+ released by the UV flash , KD the constant dissociation of OGB-5N . We used the value of 23 . 3 µM determined by our previous in vivo calibration ( Vincent et al . , 2014 ) . F the over time fluorescence , Fmin the minimum fluorescence and Fmax the maximal fluorescence . Fluorescence signals were plotted and compared as ΔF/Fmin where ΔF is equal to: F-Fmin . To vary the intracellular osmotic pressure from 310 to 360 , 390 or 410 mOsm , the DM-nitrophen solution was complemented with sucrose as described above . P13 organs of Corti ( OC ) were rapidly fixed with 100% methanol at -20°C for 30 min and washed with cold phosphate buffered saline ( PBS ) . The tectorial membrane was carefully removed . Tissues were first incubated with PBS containing 30% normal horse serum for 1 hr at room temperature ( RT ) . Then they were incubated with primary antibodies diluted with PBS ( 1:200 ) containing 5% horse serum and 0 . 1% triton X-100 . The organization of the sub-membranous actin-F network was visualized using Phalloidin Fluoprobe 405 ( 1:100 , Interchim , Montlucon , France; cat # FP-CA9870 ) . Synaptic ribbons ( CtBP2 ) , Cav1 . 3 channels and otoferlin were simultaneously labeled with anti-CtBP2 ( Goat polyclonal , Santa Cruz , USA; cat # SC-5966 ) , anti-Cav1 . 3 ( Rabbit polyclonal , Alomone labs , Jerusalem , Israel; cat # ACC-005 ) and anti-otoferlin ( Mouse monoclonal , Abcam , Paris , France; cat # ab53233 ) antibodies , respectively . The organ of Corti was then washed with PBS and incubated in two steps with secondary antibodies at 1:500: first , Donkey anti-Goat Fluoprobe 547H ( Interchim; cat # FP-SB2110 ) and Donkey anti-Mouse Fluoprobe 647 ( Interchim; cat # FP-SC4110 ) , second , after a PBS rinse with Goat anti-Rabbit Alexa Fluor 488 ( Invitrogen; cat # A-11008 ) . To disrupt F-actin , the organs of Corti were incubated prior to fixation with 1 µM latrunculin-A ( Interchim , France; cat # FP-47143A ) for 45 min at RT . Confocal imaging was performed with a Leica SP8 confocal laser-scanning microscope with a 63X oil immersion objective ( NA = 1 . 4 ) and white light laser ( 470 to 670 nm ) ( Bordeaux Imaging Center ) . Phalloidin was imaged by using a diode laser at 405 nm also mounted on the microscope . Stack images were acquired with the following parameters: laser power 40% , scan rate 700 Hz , scans averaged per XY section 4 times , step size 250 nm , pixel size 80 nm giving an X-Y image size of 41 x 41 µm ( 512 x 512 pixels ) . Images were then processed for three-dimensional ( 3D ) blind deconvolution with AutoQuant X2 ( MediaCybernetics ) . After deconvolution , images were processed with ImageJ software ( W . S . Rasband , NIH , Bethesda , USA ) . Distance measurements between CtBP2 ( ribbon ) and the Cav1 . 3 channels were performed using the Image J software after 3D blind deconvolution , as previously described ( Vincent et al . , 2014 ) . The plot profile tool of Image J was used to determine the mean distance between the ribbon and the Cav . 3 patch ( center mass distance ) . Electrophysiological results were analyzed with Patchmaster ( HEKA Elektronik , Germany ) , OriginPRO 9 . 1 ( OriginLab , Northampton , USA ) and IgorPro 6 . 3 ( Wavemetrics , Oregon , USA ) . Results are expressed as mean ± SEM . Statistical analyses were performed by using the non-parametric Mann-Whitney U test with OriginPRO 9 . 1 software . The number of samples for each condition was indicated on graphs or in legends . The limit of significance was set at 0 . 05 ( p<0 . 05 ) . When the statistical tests were found to be non-significant , the p value was given . | To hear a sound , the pressure produced by sound waves must be converted into an electrical nerve signal . The cells inside the ear that perform this transformation are called hair cells , which are so named because they have hundreds of hair-like structures on their upper surface . Pressure from sound waves causes movements in the inner ear that bend these ‘hairs’ . This causes the hair cells to release chemical signals to neighboring nerve cell terminals that ultimately transmit information about the sound to the brain . The chemical signals are stored inside the hair cells in bubble-like compartments called vesicles . To release the chemicals from the cell , the vesicles merge with the membrane that surrounds the hair cell . Most cells that communicate in this way are limited in how long they can transmit such messages . However , hair cells can continuously fuse vesicles to the membrane even when a sound lasts for a long time . This suggests that the hair cells have a different way of producing vesicles and getting them to the membrane than other cell types . Inside the hair cells , vesicles are stored in regions called active zones . Each active zone contains a “ribbon” ( attached to which are hundreds of vesicles ) and also ion channels that allow calcium ions to flow into the cell . ( An increase in calcium ion concentration inside the cell is necessary for the vesicle to fuse with the cell membrane and so release its chemical content ) . Now , Vincent et al . show that in hair cells , a cage-like network made from a protein called actin surrounds each active zone . This network helps to position the calcium ion channels . Treating the hair cells with a compound that disorganized the actin networks speed up the process of vesicle movement , which suggests that the actin network also controls the rate at which vesicles reach the membrane . Next , it will be important to identify how the actin network interacts with other molecules that help vesicles to release their contents; in particular a protein called otoferlin , which is thought to act as a calcium ion sensor . | [
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] | 2015 | A synaptic F-actin network controls otoferlin-dependent exocytosis in auditory inner hair cells |
Msp1 is a conserved AAA ATPase in budding yeast localized to mitochondria where it prevents accumulation of mistargeted tail-anchored ( TA ) proteins , including the peroxisomal TA protein Pex15 . Msp1 also resides on peroxisomes but it remains unknown how native TA proteins on mitochondria and peroxisomes evade Msp1 surveillance . We used live-cell quantitative cell microscopy tools and drug-inducible gene expression to dissect Msp1 function . We found that a small fraction of peroxisomal Pex15 , exaggerated by overexpression , is turned over by Msp1 . Kinetic measurements guided by theoretical modeling revealed that Pex15 molecules at mitochondria display age-independent Msp1 sensitivity . By contrast , Pex15 molecules at peroxisomes are rapidly converted from an initial Msp1-sensitive to an Msp1-resistant state . Lastly , we show that Pex15 interacts with the peroxisomal membrane protein Pex3 , which shields Pex15 from Msp1-dependent turnover . In sum , our work argues that Msp1 selects its substrates on the basis of their solitary membrane existence .
Tail-anchored ( TA ) proteins are integral membrane proteins with a single C-terminal transmembrane segment ( TMS ) . In the budding yeast Saccharomyces cerevisiae , the majority of TA proteins are captured post-translationally by cytosolic factors of the conserved Guided Entry of TA proteins ( GET ) pathway , which deliver them to the endoplasmic reticulum ( ER ) membrane for insertion by a dedicated insertase ( Denic et al . , 2013; Hegde and Keenan , 2011 ) . TA proteins native to the outer mitochondrial and peroxisomal membranes are directly inserted into these membranes by mechanisms that are not well defined ( Chen et al . , 2014a; Papić et al . , 2013 , and reviewed in Borgese and Fasana , 2011 ) . Gene deletions of GET pathway components ( getΔ ) result in reduced cell growth , TA protein mistargeting to mitochondria , and cytosolic TA protein aggregates ( Jonikas et al . , 2009; Schuldiner et al . , 2008 ) . Two recent studies identified the ATPase associated with diverse cellular activities ( AAA ATPase ) Msp1 as an additional factor for supporting cell viability in the absence of GET pathway function ( Chen et al . , 2014b; Okreglak and Walter , 2014 ) . Specifically , they observed that msp1Δ cells accumulate mislocalized TA proteins in the mitochondria and that double msp1Δ getΔ cells have synthetic sick genetic interactions . This sick phenotype is associated with disruption of mitochondrial function and is exacerbated by overexpression of TA proteins prone to mislocalization ( Chen et al . , 2014b ) . Msp1 is a cytosolically-facing transmembrane AAA ATPase which resides on both mitochondria and peroxisomes ( Chen et al . , 2014b; Okreglak and Walter , 2014 ) . Closely-related members of Msp1’s AAA ATPase subfamily form hexamers that bind hydrophobic membrane substrates and use the energy of ATP hydrolysis to extract them from the membrane for protein degradation ( Olivares et al . , 2016 ) . Several lines of evidence are consistent with the working model that Msp1 operates by a similar mechanism: ATPase-dead mutations of Msp1 are unable to complement msp1Δ mutant phenotypes; mitochondrial mistargeting of TA proteins leads to their enhanced co-immunoprecipitation with ATPase-dead Msp1; cells lacking Msp1 have increased half-lives of mistargeted TA proteins; and lastly , a complementary analysis of the mammalian Msp1 homolog ATAD1 ( Chen et al . , 2014b ) established a conserved role for Msp1 in correcting errors in TA protein sorting . Substrate selectivity mechanisms of many AAA proteins have been successfully dissected by bulk cell approaches for measuring substrate turnover . These approaches are resolution-limited , however , when used to study Msp1 in getΔ cells because TA proteins mistargeted to mitochondria co-exist with a dominant TA population that remains correctly localized in the same cell . Previous studies overcame this issue through two different approaches that increased the ratio of mistargeted to properly localized substrates . In one case , cells were engineered to produce a Pex15 deletion mutant ( Pex15ΔC30 ) that is efficiently mistargeted to mitochondria because it lacks its native peroxisomal targeting signal ( Okreglak and Walter , 2014 ) . A major limitation of this approach , however , is its inherent unsuitability for establishing if native Pex15 is a latent Msp1 substrate because of undefined peroxisomal factors . Second , a cell microscopy pulse-chase approach was used to monitor turnover of mitochondrial signal from transiently expressed fluorescently-labeled wild-type Pex15 made susceptible to mistargeting by deletion of GET3 ( Chen et al . , 2014b ) . In this approach , expression of Pex15 was transcriptionally controlled by the inducible GAL promoter in cells expressing wild-type , ATPase-dead , or no Msp1 . Comparison of mitochondrial Pex15 clearance following GAL promoter shut-off revealed that cells lacking functional Msp1 had a reduced fractional rate of substrate clearance ( Chen et al . , 2014b ) ; however , these cells also had a larger starting population of mitochondrial Pex15 . Thus the presence of Msp1 during Pex15 pulse periods ( Chen et al . , 2014b; Okreglak and Walter , 2014 ) leaves open the possibility that Msp1 does not mediate substrate extraction from the mitochondrial outer membrane but instead blocks substrate insertion into this membrane . Distinguishing between these possibilities requires better tools for temporally controlling and accurately measuring Msp1 activity in cells . Substrate recognition by AAA proteins can be controlled by a variety of intrinsic substrate determinants and extrinsic factors ( Olivares et al . , 2016 ) . Some insight into Msp1 substrate selectivity comes from negative evidence showing that native mitochondrial TA proteins are inefficient Msp1 substrates ( Chen et al . , 2014b ) . Thus , substrates might contain intrinsic Msp1 recognition determinants or native mitochondrial TA proteins might be protected from Msp1 recognition by extrinsic mitochondrial factors . Similarly , the potential existence of extrinsic peroxisomal factors might explain why Pex15 ( a native peroxisomal TA protein ) appears to stably co-reside with Msp1 at peroxisomes but is a substrate for Msp1 at mitochondria ( Chen et al . , 2014b; Okreglak and Walter , 2014 ) .
To generate a defined Msp1 substrate population prior to initiation of Msp1 activity , we utilized two established synthetic drug-inducible gene expression systems to orthogonally control expression of Pex15 and Msp1 . Briefly , we created a yeast strain genetic background with two transcriptional activator-promoter pairs: 1 . the doxycycline ( DOX ) -activated reverse tetracycline trans-activator ( rTA ) ( Roney et al . , 2016 ) for controlling expression of fluorescently-labeled Pex15 ( YFP-Pex15 ) from the TET promoter; and 2 . the β-estradiol-activated synthetic transcription factor Z4EV ( McIsaac et al . , 2013 ) for controlling Msp1 expression from the Z4EV-driven ( ZD ) promoter ( Figure 1—figure supplement 1A–C ) . Next , we pre-loaded mitochondria with Pex15 in the absence of any detectable Msp1 ( Figure 1—figure supplement 1A ) by growing cells for 2 hr in the presence of a high DOX concentration ( 50 μg/ml ) necessary to induce sufficient mitochondrial mistargeting ( Figure 1A and see below ) . This was followed by 2 hr of DOX wash-out to allow for mitochondrial maturation of newly-synthesized YFP-Pex15 ( Figure 1A ) . Using confocal microscopy , we could resolve the relatively faint mitochondrial YFP fluorescence from the much brighter punctate YFP fluorescence ( corresponding to peroxisomes , see below ) by signal co-localization with Tom70-mTurquoise2 ( a mitochondrial marker; Figure 1B ) ( see Figure 1—figure supplement 2 , Videos 1 and 2 , and Materials and methods for computational image analysis details ) . Lastly , we monitored changes in mitochondrial YFP-Pex15 fluorescence density by timelapse live-cell imaging in the presence or absence of β-estradiol to define the effect of de novo induction of Msp1 activity ( Figure 1A ) . Starting with the same pre-existing mitochondrial Pex15 population , we found that de novo Msp1 induction significantly enhanced mitochondrial YFP signal decay ( Figure 1B–C ) . We reached a similar conclusion when we used a deletion variant of Pex15 ( Pex15ΔC30 ) that is efficiently mistargeted to mitochondria because it lacks a C-terminal peroxisome targeting signal ( Okreglak and Walter , 2014 ) ( Figure 2A–C ) . To establish if Pex15ΔC30was fully membrane-integrated prior to Msp1 induction , we harvested cells after DOX treatment . Following cell lysis , we isolated crude mitochondria by centrifugation and treated them with Proteinase K ( PK ) . Immunoblotting analysis against a C-terminal epitope engineered on Pex15 revealed the existence of a protected TMS-containing fragment that became PK-sensitive after solubilizing mitochondrial membranes with detergent ( Figure 2D ) . Taken together , these findings argue that Msp1 can extract a fully-integrated substrate from the mitochondrial outer membrane and gave us a new tool for mechanistic dissection of Msp1 function in vivo . While performing the previous analysis , we observed that β-estradiol also enhanced YFP-Pex15 signal decay at punctate , non-mitochondrial structures . To test if these punctae corresponded to peroxisomes , we used a strain with mCherry-marked peroxisomes ( mCherry-PTS1 ) and induced YFP-Pex15 expression with a lower DOX concentration ( 10 μg/ml ) . Indeed , we saw robust YFP and mCherry signal co-localization with little apparent Pex15 mistargeting to mitochondria ( Figure 3A–B ) . As we initially surmised , β-estradiol-driven Msp1 expression enhanced YFP-Pex15 signal decay at peroxisomes ( Figure 3A–C ) . Immunoblotting analysis of lysates prepared from comparably-treated cells provided further support for our conclusion that de novo induction of Msp1 activity enables degradation of peroxisomal Pex15 ( Figure 3D ) . To our knowledge , Msp1-induced turnover of peroxisomal Pex15 had not been reported previously . We found two pieces of evidence that this unexpected phenotype was the product of Pex15 overexpression . First , treatment of pTET-YFP-PEX15 cells with 5 μg/ml DOX concentration still induced a > 10 fold higher YFP fluorescence at peroxisomes relative to steady state levels of YFP-Pex15 expressed from its native promoter ( Figure 3—figure supplement 1A–B ) . Second , we could detect no difference in natively-expressed peroxisomal Pex15 levels when we compared wild-type and msp1Δ cells ( Figure 3E , left panel ) . This is unlikely a signal detection problem because we could robustly detect the accumulation of natively-expressed Pex15ΔC30 at mitochondria in msp1Δ cells ( Figure 3E , right panel ) . Why does Msp1-dependent turnover of peroxisomal Pex15 necessitate excess substrate when the same AAA machine clears mitochondria of even trace amounts of mistargeted Pex15 ? In search of an answer to this question , we repeated our analysis at higher temporal resolution and found a major difference between the kinetic signatures of mitochondrial and peroxisomal Pex15 turnover by Msp1 ( Figure 4A and see below ) . Specifically , while mitochondrial Pex15 turnover showed simple exponential decay ( i . e . linear decay after log-transformation ) , the decay of peroxisomal Pex15 appeared to be more complex , comprising faster and slower kinetic components . We detected no major kinetic differences between Msp1 targeting to mitochondria and peroxisomes that could explain this phenomenon ( Figure 1—figure supplement 1B–C ) but found a potential clue from a proteome-wide pulse-chase study showing that while most proteins decay exponentially , some exhibit non-exponential decay that can be explained by their stoichiometric excess over their binding partners ( McShane et al . , 2016 ) . Since peroxisomal membranes have unique residents that interact with native Pex15 ( Eckert and Johnsson , 2003 ) , we hypothesized that non-exponential decay of overexpressed peroxisomal Pex15 arises due to the existence of an Msp1-sensitive ‘solitary’ Pex15 state and an Msp1-insensitive ‘partner-bound’ Pex15 state . This solitary state would be minimally populated by endogenously expressed Pex15 under steady-state conditions , but a significant fraction of overexpressed Pex15 molecules would be solitary because of stoichiometric excess . By contrast , since mitochondria are unlikely to have Pex15-binding partners , mitochondrial Pex15 would exist in an obligate solitary state and would therefore decay exponentially . To test this hypothesis , we fit our microscopic YFP-Pex15 decay data against two competing stochastic models , which were previously used to describe proteome-wide protein decay data ( see Materials and methods for modelling details ) ( McShane et al . , 2016 ) . In the 1-state ( exponential ) model ( Figure 4B , left ) , we posit that all Pex15 molecules have the same probability of decay ( kdecay ) . In the 2-state ( non-exponential ) model ( Figure 4B , right ) , we introduce the probability ( kmat ) of nascent Pex15 maturation , alongside distinct probabilities for decay of the nascent ( kdecay , 1 ) and mature ( kdecay , 2 ) Pex15 states . Depending upon the determined fit parameters , the 2-state model can approximate a 1-state model by minimizing the contribution of one of the two states ( Sin et al . , 2016 ) . To quantify the difference between the 1-state and 2-state models for each sample , and therefore to assess the contribution of a distinct second substrate state to turnover , we measured the area between the 1-state and 2-state fit curves ( see Materials and methods ) . To analyze mitochondrial Msp1 substrate turnover , we chose YFP-Pex15ΔC30 over wild-type Pex15 to avoid measuring weak mitochondrial signals juxtaposed to strong peroxisomal signals ( compare Figure 1B and Figure 2B ) . We also restricted our analysis to the first 45 min of β-estradiol treatment because longer Msp1 induction times led to a significant fraction of mitochondria with no detectable YFP signal , which would interfere with turnover fitting ( Figure 2B , later timepoints ) . In both the presence and absence of Msp1 , our measurements could be similarly explained by both 1-state and 2-state models . The fits from these two models were almost identical ( Figure 4C–D , Figure 4G , and Figure 4—figure supplement 1A ) . Thus , we parsimoniously concluded that Msp1 enhances Pex15 clearance from mitochondria as part of a simple exponential process . Turning to overexpressed YFP-Pex15 at peroxisomes , where YFP-Pex15 persisted at peroxisomes for over 3 hr ( Figure 3B , later timepoints ) , we could undertake quantitative analysis on a longer timescale . We again found that the 1-state model and 2-state were indistinguishable in the absence of Msp1 . By contrast , the 1-state and 2-state models yielded markedly different fits for our measurements taken after inducing expression of Msp1 ( Figure 4E–G and Figure 4—figure supplement 1A–B ) . The fit parameters from the 2-state model , which more closely approximated measured Pex15 turnover , revealed that Pex15 in the nascent state decayed ~4 fold faster ( kdecay , 1 = 3 . 45 hr−1 ) than Pex15 in the mature state ( kdecay , 2 = 0 . 87 hr−1 ) ( Figure 4—figure supplement 1A ) . The 1-state and 2-state models of peroxisomal Pex15 turnover make distinct predictions about the effect of Msp1 expression on the age of Pex15 molecules . Specifically , in the 1-state model , transient Msp1 overexpression in cells with constitutive Pex15 expression should equally destabilize all Pex15 molecules , thus rapidly reducing their mean age over time ( Figure 5B , top left panel ) . By contrast , in the 2-state model , Pex15 age should be buffered against Msp1 overexpression because of two opposing forces ( Figure 4B and Figure 5B , top right panel ) : At one end , there would be an increase in kdecay , 1 leading to less nascent Pex15 , which would drive down the mean age over time . However , there would also be an opposing consequence of rapid depletion of new peroxisomal Pex15 by Msp1: the mature population of Pex15 would receive fewer new ( younger ) molecules , which would drive up the mean age over time . Notably , both models predict that transient Msp1 expression would result in a decrease in peroxisomal Pex15 levels , albeit with differing kinetics ( Figure 5B , bottom panels ) . We simulated Pex15 levels and age following transient Msp1 activation in the 1- and 2-state models with a set of possible half-lives that ranged from our microscopically determined value of 58 min to as slow as 143 min , as reported in the literature ( Belle et al . , 2006 ) ( Figure 5B ) . Since our half-life value includes decay due to dilution from cell division , it is likely an underestimate of the actual value . To measure the effect of Msp1 overexpression on the age of Pex15 molecules , we N-terminally tagged natively-expressed Pex15 with a tandem fluorescent timer ( tFT-Pex15 ) ( Figure 5—figure supplement 1A and Khmelinskii et al . , 2012 ) comprising a slow-maturing mCherry and a rapidly-maturing superfolder YFP ( sfYFP ) . On a population level , the mean ratio of mCherry to sfYFP fluorescence is a hyperbolic function of tFT-Pex15 age ( Figure 5—figure supplement 1B and Khmelinskii et al . , 2012 ) . In this strain background , we marked peroxisomes using mTurquoise2-PTS1 and induced overexpression of Msp1 from a ZD promoter using β-estradiol ( Figure 5A ) . Live-cell confocal microscopy combined with computational image analysis revealed a progressive reduction in peroxisomal sfYFP signal following Msp1 overexpression consistent with the predictions of both models , though with kinetics more akin to the predictions of the 2-state model ( Figure 5B–C , bottom panels ) . More strikingly , the peroxisomal mCherry:sfYFP fluorescence ratio was insensitive to β-estradiol treatment , consistent with the prediction of the 2-state model ( Figure 5B–C , top panels ) . Collectively , our experimental evidence and theoretical analysis strongly support the existence of a Pex15 maturation process at peroxisomes that converts newly-synthesized Pex15 molecules from an Msp1-sensitive to an Msp1-insensitive state . To gain insight into the molecular basis of Pex15 maturation at peroxisomes , we hypothesized the existence of peroxisomal proteins that interact with Pex15 and whose absence would reveal that natively-expressed Pex15 is a latent substrate for Msp1 . The cytosolic AAA proteins Pex1 and Pex6 are two prime candidates for testing this hypothesis because they form a ternary complex with Pex15 ( Birschmann et al . , 2003 ) . However , we did not observe the expected decrease in YFP-Pex15 levels in pex1Δ or pex6Δ cells that would be indicative of enhanced turnover by Msp1 ( Figure 6—figure supplement 1A ) . To look for additional Pex15 binding partners , we noted that the Pex1/6/15 complex is a regulator of peroxisome destruction by selective autophagy ( Kamber et al . , 2015; Nuttall et al . , 2014 ) . This process is initiated by Atg36 , a receptor protein bound to the peroxisomal membrane protein Pex3 ( Motley et al . , 2012 ) . Consistent with a previously published split-ubiquitin assay for detecting protein-protein interactions ( Eckert and Johnsson , 2003 ) , we found that Pex15 interacts with Pex3 by co-immunoprecipitation analysis ( Figure 6A ) . Before we could test if Pex3 protects Pex15 from Msp1-dependent turnover , we had to overcome a major technical challenge . Specifically , Pex3 is essential for targeting of numerous peroxisomal membrane proteins , which is why pex3Δ cells lack functional peroxisomes ( Fang et al . , 2004 ) . Since Pex3 is normally turned over very slowly ( Figure 6—figure supplement 1D and Belle et al . , 2006 ) , promoter shut-off is not a suitable method for acutely depleting Pex3 . Instead , we exploited an established Auxin-inducible degradation system to rapidly eliminate Pex3 from peroxisomes in situ . First , we appended a tandem V5 epitope tag followed by an Auxin-inducible degron sequence ( Nishimura et al . , 2009 ) to the cytosolic C-terminus of Pex3 ( Pex3-V5-AID ) . Next , we overexpressed an E3 ubiquitin ligase from rice ( OsTir1 ) that binds and ubiquitinates Auxin-bound AID to enable degradation of AID fusions by the proteasome ( Nishimura et al . , 2009 ) . Immunoblotting analysis for the V5 epitope revealed that Auxin addition induced rapid Pex3 destruction , which was dependent on OsTir1 expression and independent of Msp1 ( Figure 6—figure supplement 1B–E ) . Importantly , microscopic analysis of cells co-expressing Pex3-GFP-AID and mCherry-PTS1 revealed that peroxisomes persisted for hours following Pex3 destruction ( Figure 6—figure supplement 1B ) . We next introduced the Pex3 AID system into either wild-type or msp1Δ cells with endogenously expressed tFT-Pex15 . To monitor changes in peroxisomal sfYFP fluorescence density after Pex3 depletion we again used live-cell confocal microscopy combined with computational image analysis ( Figure 6B ) . Strikingly , we observed that Pex3 degradation immediately increased the rate of Msp1-dependent Pex15 turnover ( Figure 6C ) , thus unmasking endogenous Pex15 as a latent substrate . By contrast , Pex3 degradation did not result in Msp1-dependent destabilization of Pex11 and Pex12 , two peroxisomal membrane proteins we analyzed as controls for the substrate specificity of Msp1 ( Figure 6—figure supplement 1I–J ) . We observed a similar phenomenon in cells overexpressing YFP-Pex15 , albeit to a lesser extent , possibly because of excess YFP-Pex15 relative to endogenous Pex3 prior to Auxin addition ( Figure 6—figure supplement 1F–H ) . Consistent with this idea , constitutive overexpression of Pex3 from the strong TDH3 promoter blunted the effect of de novo Msp1 induction on transiently overexpressed YFP-Pex15 ( Figure 6D–E ) . Taken together , these data argue that Pex3 stoichiometrically protects Pex15 from Msp1 recognition at peroxisomes . A recent study showed that GFP fused to the TMS of the mammalian Msp1 homolog ATAD1 is targeted to both mitochondria and peroxisomes ( Liu et al . , 2016 ) . This suggests that the TMS of Msp1 is an ambiguous targeting signal whose function is to localize the rest of Msp1 into proximity with its substrates . To explore this issue , we first attempted to restrict Msp1 to either mitochondria or peroxisomes by replacing Msp1’s TMS with the signal anchor of Tom70 ( Tom70TMS-Msp1 ) , a mitochondrial outer membrane resident , or the transmembrane peroxisomal targeting signal of Pex22 ( Pex22TMS-Msp1 ) , respectively ( Figure 7A ) . Indeed , Tom70TMS-Msp1-YFP produced from the MSP1 promoter is primarily localized to mitochondria with some residual localization to peroxisomes , whereas Pex22TMS-Msp1-YFP was exclusively localized to peroxisomes ( Figure 7B and Figure 7—figure supplement 1A ) . Next , we monitored the ability of these Msp1 chimeras to suppress mitochondrial accumulation of tFT-Pex15ΔC30 in cells lacking wild type Msp1 and found that Tom70TMS-Msp1 was fully functional , whereas Pex22TMS-Msp1 was unable to complement the msp1Δ phenotype ( Figure 7C and Figure 7—figure supplement 1B ) . Lastly , we monitored clearance of excess peroxisomal YFP-Pex15 following de novo induction of Msp1 chimaeras ( Figure 7D ) . This analysis revealed that Pex22TMS-Msp1 enhanced substrate turnover more robustly than Tom70TMS-Msp1 ( Figure 7E ) , which we can simply explain by its relatively higher peroxisome abundance ( Figure 7B ) . These data lead us to speculate that the Msp1 AAA domain ( with its juxtamembrane region ) initiates substrate clearance by directly binding to substrate regions at the interface between the aqueous cytosol and the lipid core .
Errors in TA protein targeting by the GET pathway pose a constant threat to mitochondrial health . Two recent studies revealed that yeast Msp1 ( ATAD1 in humans ) , a AAA membrane protein resident on the surface of mitochondria and peroxisomes , is part of a conserved mechanism for preventing mistargeted TA proteins from accumulating in mitochondria ( Chen et al . , 2014b; Okreglak and Walter , 2014 ) . At the same time , this pioneering work raised an important question about Msp1’s substrate selectivity: What distinguishes TA proteins mistargeted to mitochondria from TA proteins native to mitochondria and peroxisomes ? Here , we answer this question as it pertains to Pex15 , a native peroxisomal TA protein known to be an Msp1 substrate when mistargeted to mitochondria ( Chen et al . , 2014b; Okreglak and Walter , 2014 ) . As our starting point , we coupled live-cell quantitative microscopy with two orthogonal drug-inducible gene-expression systems to show that de novo induction of Msp1 activity clears a fully-integrated Pex15 variant from mitochondria ( Figure 7 ) . This result solidifies the working model in the literature that Msp1 is a mechanoenzyme capable of extracting its substrates from the membrane ( Chen et al . , 2014b; Okreglak and Walter , 2014; Wohlever et al . , 2017 ) . We were also able to reveal that peroxisomal Pex15 is a latent Msp1 substrate at peroxisomes . The key starting observation that led us to this conclusion was that Pex15 overexpressed at peroxisomes was turned over by an unusual non-exponential process , which depended on Msp1 induction . By model fitting of these data and comparative analysis with the exponential decay of mitochondrial Pex15 , we found evidence for a Pex15 maturation mechanism unique to peroxisomes . By positing that this mechanism converts newly-resident peroxisomal Pex15 from an initial Msp1-sensitive state to an Msp1-resistant state , we were able to account for the non-exponential decay kinetics ( Figure 8 ) . Moreover , we validated a key prediction of this mechanism by showing that Msp1 selectively removes peroxisomal Pex15 from the young end of its molecular age distribution . More broadly , a testable hypothesis that emerges as an extension of our work is that native mitochondrial TA proteins are latent substrates normally shielded from Msp1 by maturation mechanisms specific to mitochondria . The precise molecular mechanism by which Pex15 matures into an Msp1-resistant state remains to be worked out . However , our evidence strongly argues that complex assembly between Pex15 and the peroxisomal membrane protein Pex3 is a critical component of this process . Pex3 has been previously shown to play a role in the insertion of peroxisomal membrane proteins ( Fang et al . , 2004 ) . Thus , it is possible that loss of Pex3 function leads to indirect loss of another membrane protein that itself blocks Msp1-dependent turnover of Pex15 . We cannot formally exclude this possibility but we find it unlikely for three reasons . First , we showed that Pex3 co-immunoprecipitates with Pex15 . Thus , in principle , Pex3 could physically occlude an Msp1 binding site on Pex15 or make Pex15 structurally more resistant to mechanodisruption . Second , we showed that rapid degradation of Pex3 causes a near-instantaneous increase in the rate of Msp1-dependent Pex15 clearance from peroxisomes without destabilizing two control peroxisomal membrane proteins . Third , we found that overproduction of Pex3 increased protection of overexpressed Pex15 from Msp1-dependent turnover at peroxisomes . Our results do not rule out the possibility that additional binding partners of Pex15 , such as certain components of the importomer for peroxisomal matrix proteins ( Rosenkranz et al . , 2006 ) , confer protection from Msp1 . More broadly , a simple extension of our working model for Msp1 substrate selectivity leads to the intriguing hypothesis that native mitochondrial TA proteins are shielded from Msp1 by their binding partners . The microscopy methodology we have described here will facilitate testing of this idea in the near future . Lastly , our work adds Msp1 to the growing class of proteostasis pathways that mediate degradation of excess subunits of soluble ( Sung et al . , 2016 ) and transmembrane complexes ( Kihara et al . , 1995; Lippincott-Schwartz et al . , 1988; Westphal et al . , 2012 ) . Interestingly , Msp1 is expressed at a relatively low level ( Ghaemmaghami et al . , 2003 ) and its prolonged overexpression induces severe growth defects ( data not shown ) . This raises the possibility that superphysiological levels of Msp1 are detrimental because they reduce the abundance of undefined protein complexes via hypervigilant membrane clearance of immature subunits and complex assembly intermediates . Future tests of this idea using proteome-wide approaches have the potential to define the full breadth of Msp1’s role in maintaining protein complex homeostasis .
All S . cerevisiae gene deletion and tagged strains were constructed using standard homologous recombination methods ( Longtine et al . , 1998 ) and are listed in the Key resources table . Cassettes for fluorescent protein tagging at genes’ endogenous loci were PCR amplified from the pKT vector series ( Sheff and Thorn , 2004 ) . Tandem fluorescent timer-tagged Pex15 was expressed from a transgene integrated at the ura3 locus . Fluorescent peroxisome markers , expressed as transgenes from the TRP1 locus , were generated by creating pKT plasmid variants containing the S . cerevisiae TDH3 promoter upstream of a gene encoding a fluorescent protein with an engineered PTS1 sequence ( Serine-Lysine-Leucine-stop ) . Strains with β-estradiol-induced Msp1 expression were made by homologous recombination of a 5’ LEU2-marked Z4EV expression cassette with a 3’ Z4EV-driven ( ZD ) promoter ( McIsaac et al . , 2013 ) upstream of the endogenous MSP1 ORF . Similar cassettes were constructed for yeast expression of Pex221-35-Msp132-362 protein and Msp11-12-Tom7012-29-Msp128-362 from the endogenous MSP1 locus . Strains with doxycycline-induced expression of Pex15 variants were made by homologous recombination of a 5’ CgTRP1-marked expression cassette the G76V variant of the reverse tetracycline transactivator ( rTA ) ( Roney et al . , 2016 ) with a 3’ GAL1 promoter variant altered for control by rTA driving expression of the YFP ORF ( lacking a stop codon ) fused to the PEX15 ORF or mutant variant , and followed by the PEX15 terminator . This cassette was integrated into the ura3 locus of strains as indicated in the strain table . PEX3-FLAG was generated by integrating a previously described C-terminal 3 × FLAG tagging cassette ( Denic and Weissman , 2007 ) . Yeast cultures were grown overnight to 0 . 8 OD600 units at 30°C in YEPD ( 1% yeast extract ( BD Biosciences , San Jose , CA ) , 2% bacto-peptone ( BD Biosciences ) , 2% glucose ( Sigma , St . Louis , MO ) ) and treated with 3-indoleacetic acid ( auxin , 500 μM ) ( Sigma ) , cycloheximide ( 100 μg/mL ) ( Sigma ) or DMSO vehicle as indicated . Cells were pelleted by 3000 × g centrifugation for 1 min , resuspended in ice cold 0 . 2 M NaOH and incubated on ice for 10 min . Cells were then pelleted by 10 , 000 × g centrifugation for 1 min and boiled in SDS-PAGE sample buffer ( 50 mM Tris-HCl pH 6 . 8 , 2 . 5% sodium dodecyl sulfate , 0 . 008% bromophenol blue , 10% glycerol , 5% β-mercaptoethanol ) . Following centrifugation to remove any insoluble cell debris , supernatant samples were resolved by SDS-PAGE ( 70 min at 195V ) using Novex 4–20% Tris-Glycine gels ( Thermo Fisher Scientific , Waltham , MA ) and electroblotted onto nitrocellulose membranes . Blocking and antibody incubations ( mouse anti-FLAG M2 ( Sigma ) , mouse anti-V5 R960-25 ( Thermo Fisher Scientific ) , mouse anti-GFP ( Sigma ) , mouse anti-Pgk1 22C5D8 ( Thermo Fisher Scientific ) , rabbit anti-Hsc82 ab30920 ( Abcam ) , and rabbit anti-Sdh4 ( gift of N . Pfanner ) ) were performed in 5% milk in TBST ( 10 mM Tris-HCl pH 7 . 4 , 150 mM NaCl , 0 . 25 mM EDTA , 0 . 05% Tween-20 ) . HRP-conjugated secondary antibodies ( BioRad , Hercules , CA ) were detected following incubation with SuperSignal West Femto Substrate ( Thermo Fisher Scientific ) using a ChemImager ( AlphaInnotech , San Jose , CA ) . Fluorescent secondary antibodies ( Thermo Fisher Scientific ) were detected using a Typhoon Trio imager ( GE Healthcare , Chicago , IL ) . VDY3412 cells were pre-grown to late log phase ( 1 OD600 ) in 100 mL YEPD and then diluted to 0 . 1 OD600 in 1 L YEPD . Cells were grown with shaking at 30°C to 1 OD600 and then treated with 50 μg/ml doxycycline ( Sigma ) for 4 hr at 30°C with shaking . Cells were harvested by centrifugation . Crude mitochondria were isolated from harvested cells as described previously ( Meisinger et al . , 2006 ) . 100 μg of crude mitochondria was treated with 10 μg Proteinase K ( Roche , Basel , Switzerland ) or mock treated in the presence or absence of 1% Triton X-100 ( Sigma ) at room temperature for 30 min . Phenylmethanesulfonyl fluoride ( PMSF ) ( Sigma ) was added to each sample to a final concentration of 5 mM to inhibit Proteinase K and samples were incubated 10 min on ice . Samples were mixed with boiling SDS-PAGE sample buffer and subjected to SDS-PAGE and immunoblotting analysis as described earlier . Cells were inoculated into 2 mL of complete synthetic media with glucose ( 0 . 67% yeast nitrogen base ( BD Biosciences ) , 2% glucose , 1 × CSM ( Sunrise Sciences , San Diego , CA ) ) and grown overnight at 30°C on a roller drum . The following morning , cells were back-diluted to 0 . 05 OD600 in fresh media and grown to mid-to-late log phase ( 0 . 5–1 OD600 ) for imaging with drug treatments as indicated in figure schematics . β-estradiol ( Sigma ) was used at 1 μM for all experiments; doxycycline was used at concentrations indicated in figure legends . Cells in culture media were applied directly to the well of a concanavalin A ( MP Biomedicals , Santa Ana , CA ) -coated Lab-Tek II chambered coverglass ( Thermo Fisher ) and allowed to adhere for 5 min at room temperature . Culture media was removed and adhered cells were immediately overlaid with a 1% agarose pad containing complete synthetic media with glucose and supplemented with drugs when applicable . The agarose pad was overlaid with liquid media for timelapse imaging experiments . Live-cell imaging was performed at 25°C on a TI microscope ( Nikon , Tokyo , Japan ) equipped with a CSU-10 spinning disk ( Yokogawa , Tokyo , Japan ) , an ImagEM EM-CCD camera ( Hamamatsu , Hamamatsu , Japan ) , and a 100 × 1 . 45 NA objective ( Nikon ) . The microscope was equipped with 447 nm , 515 nm and 591 nm wavelength lasers ( Coherent , Santa Clara , CA ) and was controlled with MetaMorph imaging software ( Molecular Devices , Sunnyvale , CA ) . Z-stacks were acquired with 0 . 2 µm step size for 6 µm per stack . Camera background noise was measured with each Z-stack for normalization during timelapse imaging . For quantitative microscopy experiments , the number of cells present in each sample was manually counted in brightfield images and indicated in the associated figure legend . Each experiment was repeated the number of times indicated in the associated figure legend . Replicates represent technical replicates in which the same strains were subjected to repetition of the entire experiment , often on different days . All fluorescence images were normalized to background noise to compensate for uneven illumination and variability in camera background signal . To identify peroxisomes and mitochondria , images of their respective markers were processed by an object segmentation script . Briefly , images were smoothed using a Gaussian filter and then organelle edges were identified by processing each slice with a Canny edge detector ( Canny , 1986 ) implemented in the Python package scikit-image . Enclosed objects were filled and individual three-dimensional objects were identified by locally maximizing Euclidean distance to the object border . Individual objects were identified and separated by watershed segmentation as implemented in scikit-image . For mitochondria , contiguous but separately segmented objects were merged to form one mitochondrion . For YFP-Pex15 quantitation at mitochondria , regions of mitochondria that overlapped with peroxisomes were removed by eliminating segmented mitochondria pixels that overlapped with segmented peroxisomes . Segmentation code is available at http://www . github . com/deniclab/pyto_segmenter ( Weir , 2017a ) and sample implementation is available at www . github . com/deniclab/Weir_2017_analysis ( Weir , 2017b ) ( copies archived at https://github . com/elifesciences-publications/pyto_segmenter and https://github . com/elifesciences-publications/Weir_2017_analysis respectively ) . Raw source images are available on the Dryad data repository associated with this manuscript . Following organelle segmentation , total fluorescence intensity for Pex15 was determined in each segmented object by summing intensities in the corresponding pixels for YFP fluorescence images ( and mCherry images for mCherry-sfYFP-Pex15 and mCherry-sfYFP-Pex15ΔC30 in Figure 5C ) . Fluorescence density was calculated by dividing total pixel intensity by object volume in pixels . Background was calculated empirically by measuring Pex15 fluorescence intensity in peroxisomes and/or mitochondria in cells lacking fluorescently labeled Pex15 , and the mean background density was subtracted from each segmented object’s fluorescence density . Because Pex15 fluorescence density was approximately log-normally distributed , mean and standard error of the mean were calculated on logarithmically transformed fluorescence densities when applicable . Plotting was performed using R and the ggplot2 package . See www . github . com/deniclab/Weir_2017_analysis for tabulated data and analysis code . For 1-state and 2-state model fitting , organelle fluorescence density means were first normalized to the sample’s mean at time 0 . For the 1-state model , log-transformed mean fluorescence densities at each time point were fit to a linear model using least squares fitting in R . For the 2-state model , logarithmically transformed data was fit to a logarithmically transformed version of a previously derived 2-state degradation model ( Sin et al . , 2016 ) using the Levenberg-Marquardt algorithm ( Levenberg , 1944 ) for non-linear least squares fitting as implemented in the R package minpack . lm . Error for fit parameters was obtained from fit summary statistics . The difference between the 1-state and 2-state model fits was determined by integrating the difference between the two fit equations over the measured time interval , then dividing by the time interval to normalize across timecourse experiments of different lengths . See www . github . com/deniclab/Weir_2017_analysis for tabulated data and R code . Observed half-life was determined by converting the peroxisomal YFP-Pex15 –Msp1 kdecay ( Figure 4—figure supplement 1 ) using the equation half-life = ln ( 2 ) /kdecay , and then multiplied by 60 to convert from hours to minutes . Error bars represent standard error of the mean . To stochastically model peroxisomal Pex15 levels and age following transient Msp1 expression , we used a Gillespie algorithm approach ( Gillespie , 1977 ) . In brief , this approach cycles through the following steps: 1 . Model the expected time until the next ‘event’ takes place ( import , degradation , or maturation of a Pex15 molecule ) by summing event rates and drawing from an exponential distribution based on the summed rate constant , 2 . Age all simulated Pex15 molecules according to time passage , 3 . Determine which of the possible events took place by weighted random draws based on each event’s probability of occurring , 4 . Execute that event , and then repeat these steps until the simulation’s time has expired . Based on our observation that Pex15 turnover in the absence of Msp1 occurs with exponential decay kinetics ( Figure 4F ) , we established starting conditions by drawing 1000 ages from an exponential distribution with half-life indicated in Figure 5B . For the rest of the simulation we used this rate constant to predict import of new molecules and as a steady-state degradation rate constant ( and as kdecay , 2 in 2-state simulations ) . We treated this vector of 1000 ages as a single peroxisome containing 1000 Pex15 molecules ( this is likely an over-estimation of Pex15 amounts in many cases , but over-estimating Pex15 levels improved statistical robustness of the analysis and did not alter simulation mean outcomes ) . When simulating steady state 2-state behavior using the calculated kmat value , we found that ~60% of the elements existed in the ‘unstable’ form at steady state ( data not shown ) and therefore used this as a starting value . For 2-state simulations we randomly drew 600 of the vector elements to be ‘unstable’ at the start of the simulation , weighting probabilities of each draw using an exponential distribution with kmat as the decay rate constant . After validating that our starting conditions represented a stable steady state by simulating without perturbing rate constants , we began the reported simulations with kdecay set to 2 . 82 hr−1 , the best linear fit for turnover from the first three time points ( for 1-state simulations ) , or with kdecay , 1 ( for 2-state simulations ) set to the calculated value from Figure 4F . Simulations ran for 4 hr of simulated time and values for particle age and abundance were recorded at every simulated minute . 100 simulations were performed with each set of parameters and the mean particle age and abundance at each minute were calculated across the 100 simulations . Finally , we modeled maturation of sfYFP fluorescence and mCherry fluorescence based on established maturation half-times ( Hansen and O'Shea , 2013; Khmelinskii et al . , 2012 ) , respectively ) and calculated the mean population tFT ratio at each minute . We normalized these data to the value at the simulation’s starting point . See the www . github . com/deniclab/Weir_2017_analysis for Gillespie simulation R code . Yeast cultures were grown overnight in synthetic medium to 0 . 5 OD600 and treated with 3-indoleacetic acid ( Auxin , 1 mM ) ( Sigma ) or DMSO vehicle as indicated . Following concentration of cells by centrifugation , cells were imaged at room temperature on an Axiovert 200M microscope body ( Carl Zeiss , Oberkochen , Germany ) equipped with a CSU-10 spinning disk ( Yokogawa ) and 488 nm and 561 nm lasers ( Coherent ) using an oil-immersion 100 × 1 . 45 NA objective ( Carl Zeiss ) . Images were acquired using a Cascade 512B EM-CCD detector ( Photometrics , Tuscon , AZ ) and MetaMorph acquisition software ( Molecular Devices ) . 1 L yeast cell culture was grown to 1 . 8–2 . 2 OD600 in YEP +5% glucose at 30°C with shaking . Cells were collected by centrifuging 20 min at 3000 × g , 4°C , then washed once with 50 ml sterile H2O . Cells were resuspended in 1 ml ice-cold lysis buffer ( 50 mM HEPES-KOH pH 6 . 8 , 150 mM KOAc , 2 mM MgCl2 , 1 mM CaCl2 , 0 . 2 M sorbitol , 2x cOmplete protease inhibitors ( Sigma ) ) per 6 g wet weight , and dripped into liquid nitrogen to flash-freeze . Cells were lysed cryogenically using a PM100 ball mill ( Retsch , Haan , Germany ) and stored at −80°C . 0 . 4 g lysed cell powder was thawed on ice and mixed with 1 . 6 mL IP buffer ( 50 mM HEPES-KOH pH 6 . 8 , 150 mM KOAc , 2 mM Mg[OAc]2 , 1 mM CaCl2 , 15% glycerol , 1% NP-40 , 5 mM sodium fluoride , 62 . 5 mM β-glycerophosphate , 10 mM sodium vanadate , 50 mM sodium pyrophosphate ) . Lysates were detergent solubilized at 4°C for 1 hr with nutation and then subjected to low-speed centrifugation ( twice at 3000 × g , 4°C for 5 min ) to remove any unlysed cells and cell debris . The supernatants were further cleared by ultracentrifugation ( 100 , 000 × g , 4°C for 30 min ) before adding 40 µL protein G Dynabeads ( Sigma ) conjugated to anti-FLAG M2 monoclonal antibody ( Sigma ) . Following incubation for 3 hr at 4°C with nutation , Dynabeads were washed four times with IP buffer and bound proteins were eluted at room temperature with two sequential rounds of 10 µl 1 mg/mL 3 × FLAG peptide ( Sigma ) in IP buffer . Immunoblotting analysis was performed as described above . A complementary structure-function analysis of Msp1 was published while this work was under review ( Wohlever et al . , 2017 ) . | The phrase “finding a needle in a haystack” refers to the difficulty of locating a specific target among a large number of very similar objects . Living cells face a comparable challenge whenever they carry out seek and destroy missions aimed at broken or otherwise undesirable molecules . Scientists are still figuring out how these quality control systems can quickly and accurately pick out the few unwanted molecules that occasionally appear in crowds of normal molecules . Msp1 is a quality control protein that resides on the outer surfaces of two compartments within cells: mitochondria and peroxisomes . Previous work showed that when a protein called Pex15 , which is normally found in peroxisomes , is mistakenly sent to mitochondria it is rapidly eliminated by Msp1 . Weir et al . set out to understand if Msp1 can distinguish incorrectly localized Pex15 from correctly localized Pex15 . Fluorescence microscopy was used to watch how Msp1 eliminates Pex15 from compartments within living yeast cells . Although Msp1 did not normally recognize Pex15 at peroxisomes , when Weir et al . attempted to over-load peroxisomes with Pex15 they saw that Msp1 provided a counterforce . Comparing how quickly cells eliminated excess Pex15 at peroxisomes with predictions from mathematical models showed that Pex15 “matures” from an Msp1-sensitive to an Msp1-insensitive state . Further experiments revealed that Pex15 binds to another protein found in peroxisomes , called Pex3 , which protects Pex15 from Msp1 . By contrast , occasional Pex15 molecules that reached mitochondria remained immature and sensitive to Msp1 . Proteins similar to Msp1 are also found in humans , and Weir et al . hope that a better understanding of how Msp1 works in yeast will help scientists studying human disorders caused by defects in similar quality control systems . This could help to combat disease like cancer , neurodegenerative diseases and cystic fibrosis – which have all been linked to quality control systems that have started to target too few or too many proteins . | [
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] | 2017 | The AAA protein Msp1 mediates clearance of excess tail-anchored proteins from the peroxisomal membrane |
We tested a novel hypothesis , generated from representational accounts of medial temporal lobe ( MTL ) function , that the major white matter tracts converging on perirhinal cortex ( PrC ) and hippocampus ( HC ) would be differentially involved in face and scene perception , respectively . Diffusion tensor imaging was applied in healthy participants alongside an odd-one-out paradigm sensitive to PrC and HC lesions in animals and humans . Microstructure of inferior longitudinal fasciculus ( ILF , connecting occipital and ventro-anterior temporal lobe , including PrC ) and fornix ( the main HC input/output pathway ) correlated with accuracy on odd-one-out judgements involving faces and scenes , respectively . Similarly , blood oxygen level-dependent ( BOLD ) response in PrC and HC , elicited during oddity judgements , was correlated with face and scene oddity performance , respectively . We also observed associations between ILF and fornix microstructure and category-selective BOLD response in PrC and HC , respectively . These striking three-way associations highlight functionally dissociable , structurally instantiated MTL neurocognitive networks for complex face and scene perception .
Recent studies suggest that substructures in the medial temporal lobe ( MTL ) contribute to processes beyond memory , with the hippocampus ( HC ) and perirhinal cortex ( PrC ) necessary for accurate perceptual discrimination of conjunctive scene and face stimuli , respectively ( Graham et al . , 2010 ) . It has been shown , for example , that HC damage leads to impairments in perceiving , learning , and remembering complex scenes , whereas broader MTL damage , affecting both the HC and PrC , results in perceptual and mnemonic deficits for scene , but also face , stimuli ( Barense et al . , 2005; Lee et al . , 2005b; Mundy et al . , 2013 ) . Critically , however , these same patients are able to perform perceptual discriminations on the basis of simple visual features ( e . g . , size and colour ) and can learn to discriminate between complex dot patterns ( Mundy et al . , 2013 ) . These findings have been complemented by functional MRI ( fMRI ) studies showing ( a ) differential recruitment of the HC and PrC for scene and face discriminations , respectively , including on tasks analogous to those affected in lesion patients ( Barense et al . , 2010 ) , and ( b ) modulations of blood oxygen level-dependent ( BOLD ) response by visual feature overlap in both MTL ( high > low ) and occipito-temporal ( low > high ) regions ( Mundy et al . , 2012 ) . This body of work challenges the idea that the MTL is involved exclusively in mnemonic processes ( Squire et al . , 2007 ) and highlights the need to understand how different types of representational content may drive recruitment of the HC and PrC in perceptual , but also memory , tasks ( Graham et al . , 2010 ) . Emerging hierarchical , representational theories provide a framework for such investigations ( Graham et al . , 2010; Saksida and Bussey , 2010 ) ; they propose that perceptual difficulties in patients with MTL involvement reflect damage to conjunctive scene and face/object representations stored within the HC and PrC , respectively ( Murray et al . , 2007; Graham et al . , 2010; Saksida and Bussey , 2010 ) . Any task , whether perceptual or mnemonic , that places demand on these conjunctive representations , such as discriminating between exemplars with many overlapping features ( Saksida and Bussey , 2010 ) , is predicted to lead to impairment . By contrast , processing of visual exemplars with minimal overlap can be supported by posterior visual cortical regions ( Barense et al . , 2010; Mundy et al . , 2012 ) . The strongest evidence for these accounts has come from studies using oddity judgement tasks in which participants detect an ‘odd-one-out’ stimulus from an array of same-category items presented from different viewpoints ( Lee et al . , 2005a , 2006 ) . For example , macaque monkeys with lesions to the PrC are impaired on oddity tasks when presented with face and object arrays ( Buckley et al . , 2001 ) ; this pattern is also seen in amnesic patients when damage to the MTL includes PrC ( Lee et al . , 2005a ) . Conversely , patients with selective damage to the HC perform as well as controls on face and object oddity judgements , but show impairments for scene oddity decisions ( Lee et al . , 2005a ) . While studying the functional dissociation between MTL regions has been revealing , a fundamental question is how these distinct representations might emerge via wider , distributed , and interactive brain networks ( Mesulam , 1990 , 1994 ) . More specifically , it has been proposed that , ‘functional specialization is not simply an intrinsic property of individual regions that compute specific representations in isolation , but rather , is an emergent property of the interactions between a set of spatially distributed nodes and their functional and structural connections’ ( Behrmann and Plaut , 2013 , p . 211 ) . The fornix—a major input and output pathway of the HC—has long been considered to form part of an extended hippocampal system , both in terms of its role in episodic memory ( Aggleton and Brown , 1999; Metzler-Baddeley et al . , 2011 ) , but also in spatial processing ( Bird and Burgess , 2008 ) . For example , the impact of fornix transection in non-human primates suggests that exchange of information between HC and diencephalic regions is necessary for learning both object-in-place associations and conjunctions of spatial features ( Gaffan , 1994; Buckley et al . , 2004 ) . Fornix damage in humans also impairs episodic memory , as assessed by standardised neuropsychological tasks ( Tsivilis et al . , 2008 ) , and fornix microstructure has been shown to correlate with scene recollection ( Rudebeck et al . , 2009 ) , suggesting a potential convergence between findings in human and non-human primates . While these studies support the notion that the HC-diencephalic connection established by the fornix is important in memory , there is no evidence that inter-individual variability in this pathway in humans is associated with spatial perception , specifically where tasks modified from the animal literature are applied ( Buckley et al . , 2001 ) , and where mnemonic demands are minimised through use of concurrent stimulus presentation and trial-unique stimuli . If the HC contains conjunctive scene representations that can sub-serve both scene memory and perception , as predicted by representational accounts , then inter-individual variability in fornix tissue microstructure may partly support interactions between the HC and its interlinked brain regions within the limbic system that , in concert , underlie successful scene discrimination . A second relevant white matter ( WM ) pathway is the inferior longitudinal fasciculus ( ILF ) . This tract serves as the primary input pathway to the antero-medial temporal lobe , including PrC ( Catani et al . , 2003 ) , and is comprised mainly of long association fibres connecting extrastriate visual areas with regions in ventral anterior temporal ( vATL ) cortex ( Latini , 2015 ) . Representational accounts propose that brain regions within the antero-medial temporal lobe are part of an extended representational system in the visual ventral stream ( Murray et al . , 2007; Saksida and Bussey , 2010 ) ; in these hierarchical views , object discriminations requiring complex and conjunctive visual representations are supported by the PrC and those based on lower-level perceptual features are dependent on early visual areas ( Mundy et al . , 2012 , 2013 ) . Given this , microstructural properties of the ILF—a major WM tract of this extended visual stream ( Mishkin et al . , 1983; Yeterian and Pandya , 2010 ) —could influence representations in PrC that are required to differentiate between complex object and/or face stimuli . Indeed , there is evidence suggesting that ILF microstructure is related to performance in tasks involving face stimuli; for example , individuals with congenital prosopagnosia ( CP , a syndrome characterised by impairments in identifying faces ) have altered WM microstructure and macrostructure ( i . e . , volume ) relative to matched controls in ILF ( Thomas et al . , 2009; Gomez et al . , 2015 ) . These studies suggest that the ILF may be a key tract in a network supporting the perceptual processing of face stimuli and may , through its antero-medial temporal connections , mediate performance in conjunctive face discriminations impacted directly by damage to vATL regions , such as PrC ( Lee et al . , 2005a , 2006 ) . These studies imply , therefore , that the ILF and fornix may underpin structurally distinct distributed neural circuits that are specialised for information processing of different types of visual representations . Unfortunately , however , such a conclusion is limited by two key weaknesses in the literature . First , there has been no double dissociation of the functional contributions of the ILF and the fornix within the same participants . Consequently , it is possible that any purported cognitive dissociations reflect the different tasks and/or methodological approaches used to investigate the fornix/ILF independently . Second , no study has yet manipulated representational content within the same experimental paradigm where cognitive demand and difficulty were also matched , as well as ensuring provision of a stringent control condition in which no statistical association is predicted with either WM tract . Here , we addressed these issues by using diffusion tensor imaging ( DTI ) in healthy individuals to test our primary hypothesis that the fornix and ILF are differentially associated with complex scene and face perception , respectively . This prediction was based on the distinct structural connections these tracts establish with the HC and vATL , and generated from published theoretical/computational accounts of representational models ( Cowell et al . , 2010; Elfman et al . , 2014 ) . To draw correspondence with lesion studies in animals and humans , and to provide the strongest test of representational accounts , we used a modified odd-one-out task in which mnemonic demand was minimised ( see ‘Materials and methods’ ) . By applying diffusion tractography methods , we extracted two microstructural measures: mean diffusivity ( MD ) and fractional anisotropy ( FA ) . MD ( 10−3 mm2 s−1 ) reflects a combined average of axial diffusion ( diffusion along the principal axis ) and radial diffusion ( diffusion along the orthogonal direction ) , and FA reflects the extent to which diffusion within biological tissue is anisotropic , or constrained along a single axis , and can range from 0 ( fully isotropic ) to 1 ( fully anisotropic ) . Decreases in MD ( and also increases in FA ) are associated , typically , with microstructural properties that are considered to support the efficient transfer of information along WM , such as increased myelination and axon density ( Beaulieu , 2002; but see; Jones et al . , 2013 ) . Based on this , we predicted that individual success on scene oddity would correlate negatively with fornix MD and positively with fornix FA . Conversely , we predicted that accuracy in the face oddity condition would correlate negatively with ILF MD and positively with ILF FA . Furthermore , we hypothesised that measures obtained from these pathways would not be associated with performance on an equally difficult size oddity control condition , which is unaffected following damage to MTL ( Buckley et al . , 2001 ) . A second analysis investigated the relationship between HC and PrC BOLD response during oddity judgement and task accuracy . Based on a potential representational hierarchy along the ventral visual stream ( Murray et al . , 2007; Saksida and Bussey , 2010 ) , we also studied the face-selective ventral occipitotemporal fusiform cortex ( fusiform face area , FFA ) . This region is known to receive ILF inputs ( Gschwind et al . , 2012; Pyles et al . , 2013 ) , and in combination with the PrC , supports complex face perception ( O'Neil et al . , 2012 , 2014 ) . In this analysis , we predicted that inter-individual variation in BOLD response ( in a category-specific manner , within the key regions-of-interest , ROIs ) would be associated with oddity performance . Finally , we used a mediation analysis ( Hayes , 2013 ) to examine the three-way relationship between regional functional activity , WM microstructure , and oddity performance . If broader MTL neural circuits support complex visual discrimination , then the relationship between regional activity in HC and PrC/FFA and scene and face oddity accuracy , respectively , may be partially mediated by inter-individual variation in fornix and ILF WM microstructure , respectively .
To ensure that behavioural performance ( proportion correct ) was matched across stimulus types , and that no learning was shown across task runs , we conducted a 3 ( task: scenes , faces , size ) × 2 ( run number: run 1 , run 2 , run 3 ) analysis of variance ( ANOVA ) . This analysis revealed no effect of task ( F ( 2 , 28 ) = 1 . 47 , p = 0 . 24 ) or run number ( F ( 2 , 28 ) = 1 . 37 , p = 0 . 26 ) on behavioural performance and no significant interaction between these two factors ( p = 0 . 27 ) . These results indicate that performance was matched across the three conditions and that there was no improvement in accuracy across task runs ( i . e . , learning ) . Descriptive statistics for the behavioural task are shown in Table 1 . 10 . 7554/eLife . 07902 . 005Table 1 . Descriptive statistics for the three behavioural conditionsDOI: http://dx . doi . org/10 . 7554/eLife . 07902 . 00510 . 7554/eLife . 07902 . 006Table 1—source data 1 . Raw behavioural data from the oddity task . DOI: http://dx . doi . org/10 . 7554/eLife . 07902 . 006AccuracyRTMeanSDMeanSDFace0 . 870 . 072932 . 40479 . 68Scene0 . 850 . 063147 . 86557 . 49Size0 . 840 . 122464 . 10668 . 31Mean and standard deviation ( SD ) are reported for both accuracy ( proportion correct ) and reaction time ( RT ) . Raw behavioural data for the three oddity categories are available in Table 1—source data 1 . As MD and FA are associated with properties that affect the efficiency of information transfer along axons ( Beaulieu , 2002 ) , it is highly likely that inter-individual variation in these measures will impact on , and constrain , BOLD activity in specific ROIs ( Behrens and Johansen-Berg , 2005 ) . Here , we used probabilistic atlases to define bilateral ROIs of PrC , FFA , and HC and employed a general linear model ( GLM ) to test for voxel-wise linear associations between WM microstructure and category-selective BOLD ( see ‘Materials and methods’ ) . Given that increased MD reflects greater diffusion along both the axial and radial diffusion directions , we predicted a negative association with BOLD activity . For FA , we predicted a positive association with BOLD , as this metric reflects the extent to which diffusion within biological tissue is highly directional , or constrained along a single axis . The data from four participants were excluded from the analysis due to excessive movement during the functional run ( >3 mm ) , and a further participant removed due to scanner error , resulting in a sample of n = 24 for all subsequent analyses . As shown in Figure 3A , the statistical map reflecting a negative association between inter-subject BOLD for F > S and ILF MD revealed significant bilateral clusters in FFA ( left: −28 , −52 , −18 , Z = 2 . 99 , 25 voxels; right: 44 , −52 , −18 , Z = 3 . 07 , 41 voxels ) . There was also a cluster of 11 voxels in left PrC ( −32 , −12 , −34 , Z = 2 . 81 ) , which did not quite reach our PrC cluster extent threshold ( cluster >17 voxels , p = 0 . 05; Figure 3B ) . No clusters were found for F > S and ILF FA . As these functional associations with MD could arise from between-subject variability in the scene oddity baseline , we also conducted this analysis for F > rest; as above , we identified significant bilateral clusters in FFA associated with ILF MD , with the larger , stronger , cluster located in right hemisphere ( left: −28 , −52 , −18 , Z = 2 . 98 , 19 voxels; right: 42 , −54 , −18 , Z = 4 . 21 , 210 voxels; Figure 3A ) . We also identified bilateral face-sensitive clusters in the PrC associated with ILF MD ( left: −32 , −14 , −34 , Z = 3 . 52 , 19 voxels; right: 28 , −16 , −32 , Z = 3 . 52 , 27 voxels; Figure 3B ) . There were no supra-threshold clusters in FFA or PrC for ILF FA . Across two BOLD contrasts ( F > S and F > rest ) , therefore , inter-individual differences in ILF microstructure ( MD ) were correlated with BOLD response to faces in FFA and PrC ( Pyles et al . , 2013 ) . 10 . 7554/eLife . 07902 . 011Figure 3 . Voxel-wise linear associations between WM microstructure and category-sensitive blood oxygen level-dependent ( BOLD ) response . A group-level region-of-interest ( ROI ) analysis of the fMRI data was conducted to identify clusters reflecting a significant relationship between BOLD response for faces and scenes and tissue microstructure of the ILF and fornix , respectively . ( A ) Fusiform face area ( FFA ) : significant bilateral clusters reflecting a negative association between BOLD response during face oddity judgements ( F > S , left; F > rest , right ) and ILF MD . ( B ) Perirhinal cortex ( PrC ) : bilateral clusters reflecting a significant negative association between face-sensitive BOLD ( F > rest ) and ILF MD ( right ) . A sub-threshold cluster for F > S is shown on the left . ( C ) Hippocampus ( HC ) : a significant cluster was identified in the intermediate HC that corresponds to a positive association between task-induced scene deactivations ( against rest ) and fornix FA . DOI: http://dx . doi . org/10 . 7554/eLife . 07902 . 011 In the HC , there were no significant clusters for our two scene contrasts ( S > F or S > rest ) and either measure of fornix microstructure . Given evidence for a decoupling between HC BOLD and underlying neuronal activity ( Ekstrom , 2010 ) , in particular that negative BOLD changes in the HC are often accompanied by increased spike rate or synaptic input ( see ‘Discussion’ for further details ) , we also investigated whether fornix MD or FA might be associated with inter-individual differences in scenes compared to rest ( S < rest , i . e . , task-induced hippocampal deactivations ) . This analysis revealed a significant cluster in left intermediate HC ( −26 , −24 , −16 , Z = 3 . 1 , 32 voxels; Figure 3C ) that was strongly associated with fornix FA . The fornix MD analysis revealed a cluster in right anterior HC ( 20 , −16 , −16 , Z = 2 . 24 ) albeit at a lower voxel-wise threshold ( p = 0 . 05 ) . To test whether this deactivation effect was specific to scene oddity , we conducted the same analysis for face deactivations against rest baseline ( F < rest ) ; this revealed no voxels in HC associated with either fornix MD or FA . To test whether fornix microstructure is associated with scene-selective BOLD in other scene-selective cortical regions ( Epstein , 2014 ) , we conducted an additional voxel-wise analysis within anatomically defined , independent ROIs sampling the posterior parahippocampal gyrus ( PHG ) , retrosplenial cortex ( RSC ) , and transverse occipital sulcus ( TOS; see ‘Materials and methods’ ) . No significant clusters were found that showed a significant positive or negative association between scene-selective BOLD ( S > F , S > rest ) and fornix microstructure ( MD or FA ) in any of the additional scene-selective ROIs . We next correlated mean individual percentage BOLD signal change values from each probabilistic anatomical ROI with face and scene oddity performance ( one-tailed , Figure 4 ) . A significant positive relationship was found between face-sensitive BOLD ( F > rest ) in FFA and face oddity accuracy ( r = 0 . 36 , p = 0 . 04 , 95% CI [0 . 05 , 0 . 65]; Figure 4A ) . A significant positive correlation was also observed between BOLD for faces ( F > rest ) in PrC and face oddity performance ( r = 0 . 42 , p = 0 . 02 , 95% CI [0 . 06 , 0 . 69]; Figure 4B ) . Neither ROI was correlated with scene oddity performance ( all ps > 0 . 2 ) . 10 . 7554/eLife . 07902 . 012Figure 4 . Correlations between mean percentage BOLD signal change from each probabilistic anatomical ROI ( shown above each graph ) and oddity performance . ( A ) Scatter plot displaying the relationship between inter-individual variation in percentage signal change for faces ( relative to rest ) and face oddity performance ( proportion correct ) in the pre-defined FFA ROI . ( B ) The relationship between inter-individual variation in face-related activations ( against rest ) and face oddity performance in the PrC ROI . ( C ) The relationship between task-induced scene deactivations ( relative to rest ) and scene oddity performance in the pre-defined HC ROI . A total of 24 data points are shown on each graph . Individual percentage signal change values for each ROI are contained in Figure 4—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 07902 . 01210 . 7554/eLife . 07902 . 013Figure 4—source data 1 . Individual percentage BOLD signal change values for the fMRI contrasts . DOI: http://dx . doi . org/10 . 7554/eLife . 07902 . 013 There was also a strong trend evident between scene-related deactivations ( S < rest ) and scene oddity accuracy in the HC ROI ( r = 0 . 30 , p = 0 . 08 , 95% CI [−0 . 039 , 0 . 606]; Figure 4C ) ; an association that was not evident for the face oddity condition ( p = 0 . 20 ) . From a mediation analysis conducted using ordinary least squares path analysis ( Preacher and Hayes , 2008 ) , FFA BOLD activity indirectly influenced face oddity performance through its relationship with ILF MD . As seen in Figure 5A , individuals with a higher FFA BOLD response for F > rest had significantly lower ILF MD values , and participants with lower ILF MD values showed significantly better face discrimination ability . A bootstrap 95% CI ( based on 1000 bootstrapped samples ) for the indirect effect was entirely above zero ( see Figure 5A ) . Thus , there was no evidence that FFA BOLD activity influenced face oddity performance independent of its relationship with ILF MD . Further analyses revealed that this effect was predominantly evident in the right hemisphere ( see Supplementary file 2 ) . An alternative model in which FFA activity mediated the influence of ILF MD on face oddity performance revealed no evidence for an indirect effect of ILF MD on face discrimination accuracy through its effect on FFA BOLD activity ( 95% CI [−1017 . 19 , 1361 . 64]; Supplementary file 2 ) . 10 . 7554/eLife . 07902 . 014Figure 5 . Statistical mediation ( path ) analysis examining the three-way relationship between regional functional BOLD activity , WM microstructure , and oddity performance . Mediation models are presented for ( A ) FFA , ( B ) PrC , and ( C ) HC . These models test the extent to which the relationship between BOLD response ( in the a priori FFA , PrC , and HC ROIs ) and odd-one-out accuracy is mediated by WM microstructure . The left , middle , and right boxes in each model represent the independent variable ( IV ) , mediator ( M ) , and dependent variable ( DV ) , respectively . Unstandardised coefficients and their corresponding one-tailed p values are depicted for each path of interest ( a , b , a*b , c′ , and c ) . In a simple mediation model , these paths reflect the following: path c represents the total effect of the IV on the DV; path a quantifies the effect of the IV on the M; path b reflects the causal effect of the M on the DV; and path c′ is the direct effect of the IV on the DV that also partials out the effect of the M . Significant paths are indicated by dashed lines and significant terms are indicated by bold font . The bootstrap 95% CI is displayed for the indirect effects ( a*b ) . See Supplementary file 2 for further details . DOI: http://dx . doi . org/10 . 7554/eLife . 07902 . 014 A comparable analysis for PrC revealed a significant direct effect of BOLD response for faces ( F > rest ) on face oddity success ( Figure 5B ) . A bootstrap 95% CI for the indirect effect was found to cross zero and was thus non-significant ( 95% CI [−0 . 01 , 0 . 1] ) . This finding indicates that PrC BOLD and ILF MD are independent predictors of face oddity performance . As with FFA , this effect was predominantly evident in the right hemisphere ( see Supplementary file 2 ) . The alternative mediation model in which PrC BOLD activity mediated the influence of ILF MD on face oddity ( see Supplementary file 2 ) revealed no evidence for an indirect effect of ILF MD on face discrimination via its effect on PrC activity . Analysis of the HC ROI revealed a significant direct effect of BOLD deactivation for S < rest on scene oddity ( Figure 5C ) . As the indirect effect of HC BOLD , via fornix MD , crossed zero ( −0 . 19 , −0 . 01 ) , these measures independently predicted inter-individual variations in scene oddity accuracy . The alternative model in which HC scene deactivations mediated the influence of fornix MD on scene discrimination performance ( see Supplementary file 2 ) revealed no evidence for an indirect effect of fornix MD on scene oddity via its effect on HC BOLD .
Understanding network-level contributions to cognition is fundamental for modelling the relationship between brain structure and function and in elucidating the mechanisms underpinning inter-individual differences in behaviour . Here , we contribute new knowledge relevant to this goal by testing whether the distinct WM pathways converging on the PrC and HC , as measured by assessment of tissue microstructure in the ILF and the fornix , respectively , would be correlated with performance on face and scene odd-one-out discriminations that are differentially sensitive to PrC and HC damage in humans ( Lee et al . , 2005a ) and monkeys/rats ( Buckley et al . , 2001; Bartko et al . , 2007 ) . We found that MD of the fornix , a principal tract linking the HC with surrounding cortical and subcortical areas ( Saunders and Aggleton , 2007; Aggleton et al . , 2015 ) , was strongly ( negatively ) associated with perceptual performance on scene , but not face , oddity judgements ( Figure 1 ) . Conversely , MD for the ILF , the main ventral visual input pathway to antero-medial temporal cortex ( Catani et al . , 2003; Latini , 2015 ) , correlated with face , but not scene , oddity performance . None of the microstructural measures obtained , in either pathway , were significantly associated with accuracy on a difficulty-matched size oddity condition , consistent with the preservation of size oddity after MTL lesions ( Buckley et al . , 2001 ) . Going beyond this striking double dissociation , we also demonstrated selective relationships between WM microstructure and the magnitude of BOLD response in key , category-selective , ROIs situated along these tracts ( Figure 3 ) . Specifically , ILF MD was strongly associated with BOLD response for faces in both PrC and FFA—two regions linked anatomically by the ILF ( Pyles et al . , 2013 ) . In turn , average percentage signal change from our predefined FFA and PrC ROIs was related to face discrimination success . Notably , WM microstructure ( MD ) of the ILF mediated the relationship between BOLD activity in FFA and oddity performance for faces; this was not evident for PrC . By contrast , fornix FA was positively associated with HC scene deactivations . Furthermore , inter-individual differences in HC BOLD percentage signal reduction , within our independently defined HC ROI , were related to performance in the scene oddity task . A mediation analysis revealed that fornix WM and HC BOLD deactivations made independent contributions to scene discrimination performance . Our data , therefore , make a novel and important contribution to the literature by demonstrating that inter-individual differences in fornix and ILF microstructure may play an integral role in determining performance on complex visual discriminations for different visual categories . Further , while previous studies in both animals and humans have linked fornix connectivity with both spatial learning ( O'Keefe et al . , 1975; Buckley et al . , 2001; Hofstetter et al . , 2013; Dumont et al . , 2015 ) and memory ( Gaffan , 1994; Rudebeck et al . , 2009; Vann et al . , 2009; Bennett et al . , 2014 ) , this is the first demonstration that inter-individual variability in this pathway in humans is associated with spatial scene perception . The striking convergence between the findings from our DTI analyses , and similar functional divisions evident in complementary fMRI and animal/human MTL lesion studies ( e . g . , Buckley et al . , 2001; Lee , Bussey , et al . , 2005 ) , provides strong evidence for HC and PrC contributions to perceptual discriminations involving complex visual stimuli , via their role as key nodes within distinct , distributed , functionally specialised neural networks . According to hierarchical representational accounts , PrC is best understood as the apex of the visual ventral processing stream ( Murray et al . , 2007 ) . Several regions along this stream have been identified as important for face processing ( Tsao et al . , 2008 ) , including FFA ( Kanwisher et al . , 1997 ) , but also more recently PrC , which seems to play a critical role in face perception via its contribution to processing of complex feature conjunctions ( Barense et al . , 2005 , 2007 ) . For example , monkeys with PrC lesions are impaired on oddity tasks when presented with face and object arrays ( Buckley et al . , 2001 ) ; this profile is also seen in patients when damage to the MTL includes PrC ( Lee et al . , 2005a ) . By using a similar oddity task here ( Barense et al . , 2005; Lee et al . , 2005a ) , our results indicate that the ILF , through its interactions with more posterior face processing regions ( e . g . , FFA ) , may be important in facilitating the formation of complex representations ‘downstream’ in the MTL . This extends previous findings that highlight a functional coupling between anterior temporal lobe regions , such as PrC , and face processing regions of fusiform cortex ( Moeller et al . , 2008; O'Neil et al . , 2012 , 2013 , 2014; Anzellotti et al . , 2014 ) . The results presented here also go beyond such findings by presenting a ‘structural realisation’ of this functional connectivity ( Kosslyn and Van Kleeck , 1990; Behrmann and Plaut , 2013 ) , that is , a direct relationship between the WM bundle connecting these distributed regions and complex face discrimination . Moreover , we demonstrate that ILF tissue microstructure is associated with face-related BOLD activity in both PrC and FFA . Together , these analyses provide clear links between structure , function , and behaviour , and support the idea that the anatomical connection linking antero-medial temporal cortex ( including PrC ) and FFA is a critical structure in complex face perception , as suggested by neuropsychological studies ( Thomas et al . , 2009; Grossi et al . , 2014 ) . An important question that emerges from these results is how inter-individual variation in behaviour emerges from the interplay between ILF microstructure and functional activity in the face-processing network . Studies exploring the role of functional nodes in antero-medial temporal and occipital temporal cortex suggest a number of possible mechanisms for how these regions might together support face processing . For instance , both antero-medial temporal cortex ( Barense et al . , 2010; Freiwald and Tsao , 2010; Collins and Olson , 2014; Yang et al . , 2014; Anzellotti and Caramazza , 2015 ) and ventral occipital temporal cortex ( Winston et al . , 2004; Nestor et al . , 2011; Anzellotti et al . , 2014 ) have been shown to contain representations that are invariant to facial transformations ( e . g . , viewpoint or emotional expression ) , indicating that these discrete regions may be involved jointly in the online maintenance of viewpoint-invariant face representations ( Freiwald and Tsao , 2010; Nestor et al . , 2011; Collins and Olson , 2014 ) . Another study that manipulated the visual similarity between face stimuli found that PrC exhibits a greater response to highly overlapping faces , whereas FFA shows the opposite pattern ( Mundy et al . , 2012 ) , a finding consistent with a posterior-to-anterior hierarchy in which face representations become increasingly complex ( Saksida and Bussey , 2010 ) . Furthermore , a recent study reported response suppression for different images of matching identities in antero-medial temporal cortex but not more posterior face-processing regions ( Yang et al . , 2014 ) . Strikingly , this adaptation effect in anterior temporal lobe was preserved in a prosopagnosic patient with ipsilateral lesions of FFA and ‘occipital face area’ , indicating a potential top–down role of this region in processing face identity . By observing a specific relationship between the ILF and performance on our oddity task in which highly overlapping faces must be discriminated across multiple viewpoints , our results suggest that the connection established by the ILF may be integral for both ( a ) an effective iterative feedback mechanism between PrC and ventral occipital temporal cortex that allows for online maintenance of identity across visual transformations ( Fox et al . , 2008; Freiwald and Tsao , 2010; Nestor et al . , 2011; O'Neil et al . , 2013; Yang et al . , 2014 ) and ( b ) the efficient feed forward of stimulus information from fusiform gyrus and extrastriate cortex to antero-medial temporal cortex , which permits fine-grained discrimination across multiple viewpoints ( Graham et al . , 2010; Saksida and Bussey , 2010; Fox et al . , 2013 ) . This proposed relationship is confirmed by evidence of a strong coupling between ILF MD and face-sensitivity in both FFA and PrC , as well as recent evidence suggesting that patterns of WM connectivity are better predictors of an individual's FFA location than group-derived functional ROIs ( Saygin et al . , 2012; Osher et al . , 2015 ) . These results may also address some inconsistencies in the literature , where associations have been found between face recognition and locally defined ventral temporal pathways but not with long-range ILF ( Tavor et al . , 2014; Gomez et al . , 2015 ) . As indicated above , it may be the case that tasks probing complex antero-medial temporal representations ( e . g . , perceptual identity ) will reveal stronger associations with long-range ILF connectivity ( see also Postans et al . , 2014 ) . The importance of WM in driving cognitive performance was made explicit by the finding that ILF microstructure mediated the relationship between BOLD activity in FFA and accuracy on face discrimination . This result converges with data from individuals with CP , in whom the FFA functions normally , but who show disrupted ILF microstructure and macrostructure and a reduction in posterior–anterior temporal lobe connectivity under both task-related and resting conditions ( Thomas et al . , 2009; Avidan et al . , 2014 ) . Notably , ILF undergoes protracted development well into adulthood , and these developmental changes in ILF MD are tightly and specifically linked with an age-related increase in the size of the FFA ( Scherf et al . , 2014 ) . Our findings , together with the refinement of the ILF over an extended developmental period , and its compromise in CP , all point to a potential mechanism in which an extended face network , over the course of experience and maturation , becomes progressively organised and optimised . This may occur via neural activity-dependent mechanisms that can stimulate myelination or myelin remodelling , thereby leading to increased network specialisation ( Scherf et al . , 2014; McKenzie et al . , 2014 ) . Involvement of the ILF in face perception was further confirmed by a complementary whole-brain TBSS analysis . This augmented our deterministic approach by highlighting additional associations between face oddity performance and microstructural variation in the WM tracts linking occipital and temporal lobe structures ( including PrC ) with the frontal lobes ( e . g . , IFOF , SLF , and the cingulum bundle [Yeterian and Pandya , 2010] ) . These WM pathways may be necessary for linking perceptual processing of faces in occipital cortex and vATL with prefrontal cortex face representations ( Moeller et al . , 2008; Tsao et al . , 2008 ) , again highlighting the critical nature of broadly distributed circuits in face perception . By contrast , inter-individual differences in fornix microstructure were associated with performance on a complex scene discrimination task and scene-related BOLD deactivations in HC; these findings support the notion that the HC—as part of a broader anatomical network of which the fornix is a key component—is involved in spatial processing ( Aggleton et al . , 2015 ) . While the importance of the HC in spatial navigation has long been established at the neurophysiological level ( Ono et al . , 1991; O'Keefe et al . , 1998; Rolls , 1999 ) , recent studies have since indicated that the HC is behaviourally important when tasks place a demand on complex spatial representations ( Bird and Burgess , 2008; Graham et al . , 2010; Mundy et al . , 2012 ) . With this in hand , therefore , it is worth considering exactly how the fornix , as a pathway between distributed regions , contributes to spatial scene perception and the complex spatial representations contained in HC . In particular , the reciprocal interplay between HC and surrounding neocortical and subcortical regions ( Saunders and Aggleton , 2007; Aggleton et al . , 2015 ) —that is afforded partly by fornical connections—appears critical for the formation of flexible spatial representations in the HC ( i . e . , those that maintain the coherent layout of a spatial environment across multiple viewpoints ) . For example , efferent connections from the HC to both the mammillary bodies and the anterior thalamus , via the fornix , have been shown to play a role in scene processing ( Gaffan et al . , 2001 ) and object-in-place learning ( Gaffan , 1994; Parker and Gaffan , 1997; Buckley et al . , 2004 ) . Fornix lesions also cause object-in-place learning impairments above and beyond combined lesions to frontal and inferior temporal ( i . e . , ventral stream ) regions ( Wilson et al . , 2008 ) . This suggests that visual-spatial inputs from dorsal visual areas ( e . g . , parahippocampal and posterior cingulate cortices ) , via the subiculum of the HC , may underpin aspects of scene processing that are independent of interactions between inferotemporal and frontal cortices . These dorsally mediated inputs may , for example , convey spatial , rather than object , feature information , such as orientation , position , and size ( Buckley et al . , 2004; Wilson et al . , 2008; Nasr et al . , 2014 ) . Interestingly , this may also account for the moderate , though non-significant , association between size oddity and fornix microstructure ( see ‘Results’ ) . Given the effect of fornix lesions on these various forms of spatial processing , it is plausible that tasks that tap these emergent flexible scene representations , either by the use of different viewpoints ( as in oddity tasks ) or where there is a need to discriminate between scenes or objects with unique conjunctions of spatial features ( Buckley et al . , 2004 ) , may be particularly sensitive to the HC and the functional network it forms via the fornix . Consistent with this , patients with HC damage only show scene oddity impairments when items are presented from different viewpoints ( Lee et al . , 2005b ) , and individuals with Alzheimer's disease exhibit greater deficits on scene odd-one-out tasks when different , rather than same , viewpoint scenes are presented ( Lee et al . , 2006 ) . Likewise , HC damage leads to short-term memory deficits in matching rotated scenes based on topographical information ( Bird and Burgess , 2008 ) . As representational accounts also propose that episodic retrieval is predominantly driven by reimagining the rich spatial context in which a particular memory event occurred ( Gaffan , 1994; Hassabis and Maguire , 2007; Graham et al . , 2010 ) , this may explain why individual differences in fornix microstructure have also been associated with non-spatial , episodic memory tasks ( Metzler-Baddeley et al . , 2011 ) . Interestingly , the reported association between fornix microstructure ( FA , and to a lesser extent MD ) and scene-related BOLD activity in HC was in the opposite direction to that observed between ILF microstructure and face selectivity in PrC/FFA , with fornix FA positively correlating with HC scene deactivations . Further , this surprising association was localised in more anterior/intermediate HC ( Figure 3C ) . In contrast , posterior HC is more often recruited during visual perception ( i . e . , oddity; Lee et al . , 2005a; Mundy et al . , 2013; Zeidman et al . , 2014 ) . Given differences in subfield organisation ( Duvernoy , 1988; Poppenk et al . , 2013 ) and fornical axon fibre contributions ( Saunders and Aggleton , 2007; Aggleton , 2012 ) along the long axis , it is likely that the more anterior HC plays a different functional role compared to posterior HC ( Nadel et al . , 2013; Poppenk et al . , 2013; Duarte et al . , 2014; Zeidman et al . , 2014 ) . Notably , using a similar scene oddity paradigm , Lee et al . ( 2008 ) reported significant scene activations in posterior HC but scene deactivations ( relative to baseline ) in anterior HC . Negative HC BOLD response has also been localised to anterior rather than posterior HC during spatial encoding and retrieval , consistent with the results reported here ( Figure 3C; Duarte et al . , 2014 ) . This interpretation of scene deactivations ( relative to baseline ) should be treated with caution given that baselines are difficult to define in functional neuroimaging studies ( Gusnard and Raichle , 2001 ) , particularly in MTL regions such as the HC ( Stark and Squire , 2001 ) . For instance , it is possible that the reported association emerges from variability in anterior HC activity during rest , rather than during scene oddity judgements . Further , not only is it difficult to define a baseline in HC , but there is also a particularly complex relationship between HC neural activity and the BOLD response ( Ekstrom , 2010 ) . Studies in both rats ( Schridde et al . , 2008; Angenstein et al . , 2009 ) and humans ( Ekstrom et al . , 2009 ) have reported increases in neural activity that are associated with negative changes in the HC BOLD signal . The BOLD signal itself is dependent on the relationship between cerebral blood flow and oxygen metabolism ( Fox and Raichle , 1986 ) , and the assumption that , during neural activity , blood flow outmatches metabolic demands ( leading to a relative increase in oxygenated haemoglobin ) . One possibility , therefore , is that sparser blood supply in the HC ( e . g . , lower capillary density; Borowsky and Collins , 1989 ) leads to a decoupling between neuronal activity and BOLD , that is , where oxygen metabolism exceeds local blood flow . Whilst HC BOLD deactivations were related to both fornix microstructure and scene discrimination accuracy , fornix microstructure did not mediate the relationship between HC BOLD and behavioural performance ( and vice versa ) . More specifically , HC activity and fornix microstructure independently contribute to individual variability in scene discrimination performance . Further , in the BOLD-DTI analyses , fornix microstructure was only found to correlate with HC BOLD ( Figure 3C ) when the contrast was between scenes and rest , not scene and faces . The distinct contributions of HC neuronal activity and fornix microstructure are consistent with several lines of evidence from lesion work in rats . First , that non-fornical HC pathways are also critical for spatial processing ( Dumont et al . , 2015 ) . Second , that fornix lesions , which impair HC-dependent spatial memory , do not necessarily suppress HC neuronal activity but rather disrupt longer term HC cellular plasticity mechanisms ( Fletcher et al . , 2006 ) . Third , that fornical fibres may mediate some spatial functions not attributable to the HC ( Whishaw and Jarrard , 1995 ) . In summary , our findings confirm that scene processing ( and by extension episodic memory ) is an emergent property of the functional and structural connectivity between the HC and key cortical and subcortical regions ( Graham et al . , 2010 ) , mediated in part , but not exclusively , by the fornix . It is notable that , overall , we observed stronger statistical effects with MD compared to FA , although FA did show a similar profile to that seen in MD ( e . g . , for fornix FA and scene oddity ) , and was associated with a cluster in ILF for face oddity in our whole brain TBSS analysis . As different attributes of WM ( e . g . , axon density , axon diameter , myelin [Beaulieu , 2002] , and the manner in which axons are laid out within a given voxel [Jones et al . , 2013] ) can influence the hindrance and restriction of water , as has been described elsewhere ( Jones et al . , 2013 ) , the interpretation of DTI and its specific metrics ( including MD and FA ) is not straightforward . Thus , while we found stronger associations with MD than with FA , we are not yet able to say whether a particular aspect of WM microstructure ( e . g . , myelin ) underpins these differences . Consistent with this , MD and FA are often reported jointly in the literature ( Metzler-Baddeley et al . , 2011; Gschwind et al . , 2012; Scherf et al . , 2014 ) , and like our reported findings , sometimes MD has been shown to have stronger effects than FA . For example , MD can decrease following spatial learning ( Sagi et al . , 2012 ) and appears to be more sensitive than FA to age-related changes in ILF and fornix WM ( Scherf et al . , 2014; Wendelken et al . , 2014 ) . Further , MD and FA metrics are not orthogonal , meaning that changes in one of these measures will be potentially reflected in the other ( O'Donnell and Pasternak , 2015 ) . Based on representational accounts of MTL function that assume dissociable roles for the HC and PrC in scene and face processing , respectively ( Graham et al . , 2010; Saksida and Bussey , 2010 ) , we provide a compelling demonstration that WM tracts connecting to the HC and PRC may be critical pathways in networks that support the successful discrimination of complex places and faces , respectively . More specifically , complex face perception is not just a property of the FFA or PrC but emerges from interactions within large-scale integrated neurocognitive networks ( Mesulam , 1990 , 1994; Behrmann and Plaut , 2013 ) . Likewise , spatial impairments in amnesic individuals may not simply disrupt local HC processing per se , but rather the extent to which communication in the broader , functional networks supporting formation of flexible spatial representations is interrupted ( Graham et al . , 2010; Murray and Wise , 2010; Baxter , 2012 ) . By providing a structural framework that may underpin how category-selective perception emerges in the MTL , these findings add to an emerging literature that challenges the long-held view that the MTL is an exclusive unitary memory system . Rather , our results indicate that these MTL substructures , through their distinct anatomical connections ( including fornix and ILF ) , comprise broader neurocognitive networks that are dissociable in the types of stimulus representations they support . In this context , higher level perception , rather than depending on the isolable properties of individual MTL regions , arises from the dynamic interplay within integrated and specialised neurocognitive circuits ( Gaffan , 2002; Graham et al . , 2010; Saksida and Bussey , 2010 ) .
30 undergraduates from Cardiff University participated in this study ( 2 male; aged 18–22 years; mean = 19; SD = 0 . 96 ) and were paid for taking part . The experiment was undertaken with the understanding and written consent of each subject . Cardiff University School of Psychology Research Ethics Committee approved the research project . In the oddity task , participants were presented with three stimuli on each trial ( top centre; bottom left; bottom right ) and instructed to select the odd-one-out as quickly and as accurately as possible . Two of these stimuli were the same item from different viewpoints , and the third stimulus was a different item . In this article , we analysed the behavioural performance on scene and face oddity with a size oddity condition acting as a single feature baseline . Example trials for the scene and face oddity conditions are shown in Figure 1 . The scene stimuli were real-world photographs of outdoor environments . On each trial , participants viewed two images of a single locale from different viewpoints and one different locale . Face stimuli were greyscale photographs of human faces , half of which were male . Individual faces were overlaid on a black background ( 170 × 216 pixels ) . Two faces were the same individual presented from different viewpoints , and the target was a different face presented from a different viewpoint . For the size task , three black squares were presented . The position of the squares on the screen was jittered so that none of the edges lined up along vertical or horizontal axes . On each trial , two of the squares were identical in size and a third square was either slightly larger or smaller . The difference in length between target and non-targets could vary between 9 and 15 pixels . All stimuli were trial-unique ( i . e . , never repeated once shown in the task ) . Each trial was presented for 6 s with a jittered inter-trial interval of 500–4000 ms . The task was administered in the scanner over three functional imaging runs . Within each run , trials for a given category ( scene , face , size ) were presented in mini-blocks of three successive trials . The order in which category ‘triplets’ were presented was counterbalanced across participants . Overall , 18 trials were presented per category per run resulting in 54 trials per condition overall . An equal number of targets appeared at each screen position ( i . e . , top centre; bottom left; bottom right ) within each stimulus condition . Stimuli were presented in the scanner using ePrime ( Psychology Software Tools , Inc . , Sharpsburg , PA ) and projected onto the screen behind the participant using a Canon SX60 LCOS projector system combined with the Navitar SST300 zoom converter lens . Button responses in the scanner were acquired using a right-hand MR compatible button box . Imaging data were collected at the Cardiff University Brain Research Imaging Centre ( CUBRIC ) using a GE 3-T HDx MRI system with an 8-channel receive-only head coil . Whole-brain high angular resolution diffusion image data were acquired using a diffusion weighted single-shot spin-echo echo-planar imaging ( EPI ) pulse sequence with the following parameters: TE = 87 ms; voxel dimensions = 2 . 4 × 2 . 4 × 2 . 4 mm3; field of view = 23 × 23 cm2; 96 × 96 acquisition matrix; 60 contiguous slices acquired along an oblique–axial plane with 2 . 4-mm thickness ( no gap ) . Acquisitions were cardiac gated using a peripheral pulse oximeter . Gradients were applied along 30 isotropic directions ( Jones et al . , 1999 ) with b = 1200 s/mm2 . Three non-diffusion-weighted images were acquired with b = 0 s/mm2 . Functional BOLD data were acquired using an EPI pulse sequence with the following scan parameters: TR/TE = 3000/35 ms; FOV = 240 mm; 64 × 64 data matrix; ASSET ( acceleration factor ) ; 90° flip angle . 42 interleaved slices were collected per volume for whole-brain coverage . Each slice was 2 . 8-mm thick with a 1-mm inter-slice gap ( 3 . 75 × 3 . 75 × 2 . 8-mm voxels ) . Slices were acquired with a 30° axial-to-coronal tilt relative to the AC-PC line ( anterior upwards ) . The first four volumes of each run were discarded to allow for signal equilibrium . Two 3D SPGR images were acquired to improve registration and reduce image distortion as a result of magnetic field inhomogeneity ( TE = 7 ms and 9 ms , TR = 20 ms , FOV = 384 × 192 × 210 mm , 128 × 64 × 70 data matrix , 10° flip angle ) . The SPGR used the same slice orientation as the functional acquisition . High-resolution anatomical images were acquired using a standard T1-weighted 3D FSPGR sequence comprising 178 axial slices ( TR/TE = 7 . 8/3 . 0 s , FOV = 256 × 256 × 176 mm , 256 × 256 × 176 data matrix , 20° flip angle , and 1 mm isotropic resolution ) . ExploreDTI ( Leemans and Jones , 2009 ) was used to correct for subject motion and eddy current distortions . In order to correct for partial volume artefacts arising from voxel-wise free water contamination , the two-compartment ‘free water elimination’ procedure was implemented ( Pasternak et al . , 2009 ) . Following free water correction , corrected diffusion indices were computed: MD and FA . The resulting free water-corrected maps were inputs for both the tractography and the TBSS analyses . Deterministic whole-brain WM tractography was performed using ExploreDTI . Tractography was based on constrained spherical deconvolution ( CSD; see Tournier et al . , 2004; Jeurissen et al . , 2011 ) , which extracts peaks in the fibre orientation density function ( fODF ) at each voxel . The ‘diffusion tensor’ model is not sufficient when modelling the distribution of water displacement in more complex fibre configurations , such as crossing or kissing fibres ( e . g . , as seen where the anterior pillars of the fornix meet the anterior commissure ) . The fODF—which is estimated directly by CSD—quantifies the proportion of fibres in a voxel pointing in each direction and so information about more complex fibre configurations can be extracted ( Jones , 2008 ) . Each streamline was reconstructed using an fODF amplitude threshold of 0 . 1 and a step size of 1 mm and followed the peak in the fODF that resulted in the smallest step-wise change in orientation . An angle threshold of 30° was used and any streamlines exceeding this threshold were terminated . To generate three-dimensional reconstructions of each tract , ‘way-point’ ROIs were manually drawn onto whole-brain FA maps in the diffusion space of individual subjects ( Metzler-Baddeley et al . , 2011 ) . In accordance with Boolean logic , these way-point ROIs can specify that: ( a ) tracts passing through multiple ROIs are retained for analysis ( i . e . , ‘AND’ ROIs ) and ( b ) tracts passing through certain ROIs are omitted from analysis ( i . e . , ‘NOT’ ROIs ) . Depending on the specific tract , or the anatomical plausibility of initial reconstructions , such ROIs can be combined; for example , a tract may pass through ROI-1 ‘AND’ ROI-2 but ‘NOT’ ROI-3 ( Figure 6 ) . The ROI approaches described below will adopt this Boolean terminology when describing the ROIs that were drawn for each tract . Following the reconstruction of each pathway in each subject , mean MD and FA were calculated by averaging the individual values at each 1-mm step along the tracts , and in the case of the ILF , across hemispheres . The placement of ROIs for each tract is depicted in Figure 6 . 10 . 7554/eLife . 07902 . 015Figure 6 . Example reconstructions for the fornix and ILF . Tractography ROIs are shown for three participants ( SEED ROI , blue; AND ROI , green; NOT ROI , red ) . DOI: http://dx . doi . org/10 . 7554/eLife . 07902 . 015 Voxel-wise statistical analysis of the DTI data was carried out using TBSS ( for a full description , see Smith et al . , 2006 ) . TBSS permits voxel-wise correlations by non-linearly projecting all subjects' DTI data ( MD or FA ) onto a mean tract skeleton before applying voxel-wise cross-subject statistics . To investigate the relationship between microstructure ( MD , FA ) and performance on the behavioural tasks , we implemented a GLM with two explanatory variables: proportion correct for ( 1 ) scene oddity and ( 2 ) face oddity . Two main contrasts were carried out to compare our two categories of interest: Faces > Scenes ( F > S ) , and Scenes > Faces ( S > F ) . To address the possibility of reporting false-positives , clusters were extracted using Threshold-Free Cluster Enhancement ( Smith and Nichols , 2009 ) with a FWE-corrected threshold of p = 0 . 05 . All reported co-ordinates are in Montreal Neurological Institute ( MNI ) 152 space . Functional MRI data were preprocessed using FEAT ( FSL , www . fmrib . ox . ac . uk/fsl ) and involved: motion correction ( Jenkinson et al . , 2002 ) , removal of non-brain tissue using BET ( Smith , 2002 ) , spatial smoothing using a 5 mm full-width at half-maximum ( FWHM ) Gaussian kernel , mean-based intensity normalisation , and high-pass temporal filtering ( Gaussian-weighted least squares straight line fitting , with sigma = 50 . 0 s ) . Phase information from the two SPGR images was unwrapped using PRELUDE ( Jenkinson , 2003 ) . These phase images were then subtracted and the resulting fieldmap used to unwarp the EPI data using FUGUE . Time-series statistical analysis was carried out using FMRIB's Improved Linear Model ( FILM ) with local autocorrelation correction ( Woolrich et al . , 2001 ) . Registration to high-resolution 3D anatomical T1 scans ( per participant ) and to a standard MNI 152 template image ( for group averaging ) was carried out using FLIRT ( Jenkinson and Smith , 2001; Jenkinson et al . , 2002 ) . Following pre-processing , analyses were first conducted at the single-subject level using FILM . The BOLD signal was modelled using a standard model of haemodynamic response function . Co-ordinates of significant effects are reported in MNI space . Two explanatory variables comprising correct scene and face oddity trials were used to model the time-course data . A GLM was implemented to examine the BOLD response associated with three main contrast groups: ( a ) each separate stimulus category compared to each other ( S > F; F > S ) , ( b ) each stimulus category against rest baseline ( S > rest; F > rest ) , and ( c ) rest baseline against scenes and faces ( i . e . , negative BOLD activation for scenes and faces ) . The three individual runs for each participant were combined using a fixed-effects model in FEAT . Group-level ROI analyses were carried out using the FMRIB Local Analysis of Mixed Effects tool ( Beckmann et al . , 2003; Woolrich et al . , 2004 ) . A bilateral ROI of the HC was extracted from the Harvard–Oxford subcortical atlas ( Jovicich et al . , 2009 ) using FSL and was defined using a probability threshold of 50% . The PrC was based on a probabilistic mask from the literature ( Devlin and Price , 2007 ) and likewise defined using a probability threshold of 50% . The FFA was approximated by intersecting an anatomical mask of the fusiform gyrus ( from the Harvard–Oxford cortical atlas ) with a probabilistic map of face activations thresholded at 50% ( Atlas of Social Agent Perception , http://www . andrewengell . com/wp; see Engell and McCarthy , 2013 ) . Anatomically defined , independent ROIs were also used for the DTI-BOLD analysis of other scene-selective regions . For the PHG , we used posterior PHG from the Harvard–Oxford Cortical Atlas , defined using a probability threshold of 50% . For RSC , we extracted Brodmann area 29 dilated by a single voxel ( Bluhm et al . , 2009 ) . TOS was taken from the International Consortium for Brain Mapping ( ICBM ) Sulcal atlas using a probability threshold of 25% ( Mazziotta et al . , 1995 ) . DTI metrics ( MD and FA ) for the fornix and ILF were demeaned and added as covariates for the HC and PrC/FFA group-level analyses , respectively . The resulting statistical maps were thresholded at p = 0 . 01 ( voxel-wise , uncorrected ) , where supra-threshold voxels reflect a significant positive association between BOLD response for a given contrast ( see above ) and a particular microstructural measure . To determine negative associations with MD , individuals' MD value was multiplied by minus 1 to derive inverse values . To control for false positives in each of the ROIs , we used Monte-Carlo simulation ( AFNI's 3dClustSim , http://afni . nimh . nih . gov/pub/dist/doc/program_help/3dClustSim . html ) to determine cluster significance at our voxel-wise alpha level ( p = 0 . 01 , see above ) ; all reported clusters correspond to cluster-corrected threshold of p < 0 . 01 . To correlate BOLD response and behaviour , percentage signal change values for the four main contrasts ( S > F , F > S , S > rest , F > rest ) were extracted from the ROIs using Featquery in FSL . These percentage signal change values ( extracted from our main probabilistic anatomical ROIs ) were also used in the mediation analyses . | Perceiving an object or picture stimulates activity in the regions of the brain that make up the visual system . Some of these regions respond differently depending on what is being viewed: for example , some areas are more active when looking at faces , and others respond more when viewing places . One theory is that , rather than working in a self-contained fashion , category-sensitive brain regions are elements or ‘nodes’ within more complex brain networks that are specialised for processing different types of visual stimuli . The inside of the brain contains regions of dark and light tissue . The lighter regions are known as ‘white matter’ and contain fibres that allow information to travel between different parts of the brain . These fibers may play an important role in how widely distributed brain regions communicate . To investigate this , Hodgetts , Postans et al . used a technique called diffusion MRI to measure the structure , or coherence , of white matter fibers in healthy volunteers . Brain activity was also measured while volunteers completed a task in which they needed to spot the odd-one-out from images of either faces or places . Hodgetts , Postans et al . investigated the fine structure of a white matter fibre bundle known as the inferior longitudinal fasciculus ( ILF ) . This fibre links two parts of the brain involved in face perception , called the occipital and anterior temporal lobes . Strikingly , ILF structure predicted both face-related brain activity in these regions and how well an individual could discriminate between faces , but not place stimuli . By contrast , the ability of volunteers to tell apart different places ( but not faces ) was related to the structure of the fornix . The fornix is a bundle of white matter fibres that carries information to and from the hippocampus , a region that is important for finding one's way around an environment and remembering such journeys afterwards . Hodgetts , Postans et al . 's findings suggest that the systems that process different visual categories are best thought of as large-scale distributed networks rather than a set of individual , specialised regions in the brain . In the future , research will be needed to further understand how white matter contributes to the perception of different visual categories , and to investigate in finer detail how visual experience influences the structure of white matter pathways . | [
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] | 2015 | Dissociable roles of the inferior longitudinal fasciculus and fornix in face and place perception |
Every movement we make represents one of many possible actions . In reaching tasks with multiple targets , dorsal premotor cortex ( PMd ) appears to represent all possible actions simultaneously . However , in many situations we are not presented with explicit choices . Instead , we must estimate the best action based on noisy information and execute it while still uncertain of our choice . Here we asked how both primary motor cortex ( M1 ) and PMd represented reach direction during a task in which a monkey made reaches based on noisy , uncertain target information . We found that with increased uncertainty , neurons in PMd actually enhanced their representation of unlikely movements throughout both planning and execution . The magnitude of this effect was highly variable across sessions , and was correlated with a measure of the monkeys’ behavioral uncertainty . These effects were not present in M1 . Our findings suggest that PMd represents and maintains a full distribution of potentially correct actions .
Each motor action we perform reflects only one of the many available or considered actions . In some situations , the full set of potential actions comprises a set of discrete choices ( e . g . , which of these three apples should I pick ? ) . In these cases , the task for the sensorimotor system is to evaluate each option and decide which will lead to the most favorable outcome . However , these 'target selection' situations represent only one type of motor related decision-making . In many other scenarios the sensorimotor system cannot simply select between multiple explicit options , but instead must estimate the best action based on continuous – and often noisy – sensory information and learned experience . Reaching toward a familiar object seen only in the peripheral vision , or under poor illumination is one such example . Though target selection represents only one type of sensorimotor task , it dominates the current literature on neural correlates of motor-related decision making . This is true for both eye movements ( Basso and Wurtz , 1997; Britten et al . , 1996; Fetsch et al . , 2011; Newsome and Britten , 1989; Shadlen and Newsome , 2001 ) and reaching ( Bastian et al . , 2003; Cisek and Kalaska , 2005; Coallier et al . , 2015; Messier and Kalaska , 2000; Thura and Cisek , 2014 ) . These studies vary significantly in the methods by which they provide cues to elicit a motor response . The cues may indicate different parameters of the action , such as the direction or extent of the movement ( Bastian et al . , 2003; Crammond and Kalaska , 1994; Gail et al . , 2009; Messier and Kalaska , 2000; Welsh and Elliott , 2005 ) . They can be discrete ( Meegan and Tipper , 1998; Thura and Cisek , 2014; Wood et al . , 2011 ) or continuous ( Gold and Shadlen , 2001; Hernández et al . , 2010; Resulaj et al . , 2009 ) , and can even span different sensory modalities ( Hernández et al . , 2010; Romo et al . , 2004 ) . However , all share a common characteristic: the action is directed towards one of several mutually exclusive targets . This mutual exclusivity is a constraint specific to the task of target selection and does not exist in target estimation , since no explicit options are presented . It is therefore not obvious how the results from target selection tasks may or may not extend to the case of target estimation . In both target selection and estimation , there is some degree of uncertainty in the decision making process as well as in the final decision itself . This uncertainty largely depends on the ambiguity of the available cues . If the task includes a completely unambiguous cue indicating the correct choice , the decision will contain practically no uncertainty whatsoever . For example , one standard multiple-target selection task used in non-human primate reaching studies ( e . g . , Bastian et al . , 2003; Cisek and Kalaska , 2005 ) briefly presents a monkey with two or more potential reach targets before indicating the correct one . In this situation the animal may be initially uncertain about which target is correct , but that uncertainty vanishes with the disambiguating cue . Variants of this task provide more ambiguous cues and allow the animal to choose one of two targets while still unsure about the correct choice ( Coallier et al . , 2015; Thura and Cisek , 2014 ) , which results in decisions that are made despite a lingering uncertainty . Studies of reach-related brain areas during target selection tasks have suggested that the dorsal premotor cortex ( PMd ) plays a significant role in sensorimotor decision-making . Historically , PMd has been viewed as a movement planning area , displaying activity consistent with a representation of upcoming movements to visual targets ( Cisek et al . , 2003; Shen and Alexander , 1997; Weinrich and Wise , 1982 ) . Later studies showed that these pre-movement representations can include multiple simultaneous potential targets ( Cisek and Kalaska , 2005 ) and reflect motor plans even in the absence of visual targets ( Klaes et al . , 2011 ) . Furthermore , the representations during multiple-target tasks are modulated by decision-related variables ( Coallier et al . , 2015; Pastor-Bernier and Cisek , 2011 ) . These more recent results are consistent with an interpretation that activity in PMd modulates with the complexity ( or uncertainty ) of a motor decision . In general , sensorimotor decision-making should take into account the uncertainty present in all task-relevant information sources – namely the current sensation and prior experience . When sensation provides a highly reliable action cue ( e . g . , when reaching toward a well-lit , foveated object ) , it can be used exclusively to plan and execute the appropriate motor output . However , as uncertainty in sensation increases , it becomes more beneficial to combine sensory information with information learned through prior experience . The optimal method for integrating sensory and prior information was formulated centuries ago as Bayes’ theorem ( Bayes and Price , 1763 ) . A direct application of Bayes’ theorem states that cues should be weighted in inverse proportion to their variance ( Knill and Saunders , 2003; Körding and Wolpert , 2006 ) . The Bayes optimal decision will lead to better results than either cue alone , but will still contain a degree of uncertainty . Bayesian models have been used to describe human behavior in a wide array of psychophysical studies , including visual ( Knill and Saunders , 2003; Mamassian and Landy , 2001; Weiss et al . , 2002 ) , auditory ( Battaglia et al . , 2003 ) , somatosensory ( Goldreich , 2007 ) , cross-modal ( Alais and Burr , 2004; Ernst and Banks , 2002; Gu et al . , 2008; Rowland et al . , 2007 ) , and sensorimotor ( Greenwald and Knill , 2009; Körding and Wolpert , 2004; Trommershäuser et al . , 2008; van Beers et al . , 2002 ) applications . In these tasks , behavior generally matched the predictions of various Bayesian models of optimal performance , which has been taken as evidence that the brain does indeed incorporate information about the relative uncertainty of various cues when planning and executing movements . To probe the effect of target estimation uncertainty on M1 and PMd , we designed a task in which monkeys estimated the location of reach targets using knowledge of the average target location ( learned through experience ) and noisy visual cues . Although M1 activity appeared to reflect only the direction of the executed reach , we found that the monkeys’ uncertainty about where to reach correlated with changes in PMd activity during both movement planning and execution . The magnitude of these uncertainty-related effects in PMd was spatially tuned . Neurons whose strongest response direction ( their preferred direction , or PD ) was aligned with the planned reach direction remained largely unchanged , while neurons with PDs opposite the reach direction experienced a significant increase in activity with increased uncertainty . Neurons with intermediate PDs displayed somewhat smaller uncertainty-related effects . The uncertainty-related change in this off-direction neural activity varied considerably across sessions , not only because of experimentally altered prior and likelihood uncertainty , but also apparently because of the monkeys’ own subjective uncertainty in their final action decisions . We found that the magnitude of these cross-session activity differences correlated with estimates of the monkey’s decision-related uncertainty .
Our goal in this study was to understand the effect of uncertainty on arm movement representations in the motor system . To this end , we designed a behavioral task in which monkeys ( one rhesus macaque , one cynomolgus macaque ) made decisions about where to reach using a planar robotic manipulandum , based on the learned history of target distributions and uncertain visual cues . During the first block of trials , the monkeys made center-out reaches with an instructed delay to well-specified ( zero uncertainty ) targets that were randomly distributed across eight locations ( Figure 1A , top ) . In the second block of trials , the target locations were randomly drawn from a circular normal ( von Mises ) prior distribution centered on a single direction that remained constant for the remainder of the session . Additionally , the monkey did not receive veridical feedback about the location of the target , but instead saw a noisy distribution of five ( monkey M ) or ten ( monkey T ) lines ( Figure 1A , bottom ) . These lines were drawn from a likelihood distribution – also von Mises – centered on the correct target location , providing the monkey with noisy information about the target location . Each session contained at least two likelihood distributions of low and high variance , randomly interleaved across trials . 10 . 7554/eLife . 14316 . 003Figure 1 . Experimental setup and behavior . ( A ) Monkeys made planar center-out reaches with instructed delay to visual targets . Illustrations on right show target locations ( black ) and reach trajectories ( gray ) for trials in the center out and uncertainty blocks for an example session . In the center-out block , targets were distributed uniformly across eight directions and were cued with no uncertainty . In the uncertainty block , targets were sampled from a von Mises distribution and cued with stochastically sampled lines with either low or high variance . ( B ) Scatter plots of cue centroid versus reach direction for three sessions , with each dot representing a single trial . Under high uncertainty , the endpoints reflected an increased bias toward the average target location – indicated by a reduction in slope – and increased variability surrounding the fit line . ( C ) With the exception of two datasets from monkey M , fits to the behavioral scatter plots reveal reduced slope ( negative ∆cue weighting ) for higher uncertainty targets . All datasets show greater residual variance with greater uncertainty . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 00310 . 7554/eLife . 14316 . 004Figure 1—source data 1 . Experimental details for all sessions . In some instances we obtained multiple sessions from the same day ( sessions 3–4 , 5–7 , 8–10 , 11–12 , 13–14 , 16–17 , and 26–27 ) . In these cases , the sessions shared the same sorted neurons and center out trials . Uncertain trial blocks could differ in either target distribution or visual cue properties . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 004 Therefore , during uncertainty trials , the monkey had two pieces of information available to estimate the target location: ( 1 ) the noisy visual cue and ( 2 ) a learned estimate of the distribution of previous target locations . According to Bayes’ rule , optimal performance on the task would require the monkey to use the centroid of the displayed line segments ( its likelihood estimate ) and the average target location ( prior estimate ) , weighted according to the inverse of their variances . In general , this means that using an appropriately weighted sum of both the likelihood and prior estimates will , on average , result in smaller errors than either cue alone . Fits to the scatter plot between the centroid of the visual cue and the reach direction reveal the monkey’s relative weighting of the visual cue ( the likelihood ) and its estimate of the average target location ( the prior; see Materials and methods for more information ) . A fitted line with a slope of zero would indicate complete reliance on the prior , while a slope of one would indicate reliance only on the likelihood . Panel B of Figure 1 shows several representative sessions . In each , the monkey relied more on the visual cue when its uncertainty was low ( blue symbols ) than when it was high ( red symbols ) . We summarized the difference in visual cue weighting between the uncertainty conditions ( ∆cue weighting ) for each session by subtracting the slopes of the fitted lines . The negative values of ∆cue weighting in Figure 1C reveal that both monkeys almost always relied less on the visual cue during high uncertainty trials . This indicates that the monkeys combined information from both the displayed lines and the average target location in a Bayesian-like manner to estimate the location of the required reach target . Although there was a general tendency towards lesser weighting of the visual cue when it was more uncertain , there was a great deal of variability in that trend across sessions . In some instances , fits to the two uncertainty conditions revealed large differences in visual cue weighting ( Figure 1B red and blue fitted slopes , session 14 ) while in others the relative weighting was nearly identical ( Figure 1B , session 18 ) . Similarly , the uncertainty in the final estimate ( as measured using the variance of the fit residuals ) was sometimes very different between two conditions ( Figure 1 inset distributions , session 5 ) and sometimes nearly identical ( Figure 1 , session 14 ) . We characterized the total difference in this behavioral uncertainty between the two conditions ( ∆behavioral uncertainty ) for each session by subtracting the angular dispersion of the residuals . These two within-session metrics ( ∆cue weighting and ∆behavioral uncertainty ) were very weakly correlated for monkey M and negatively correlated for monkey T ( Figure 1C ) . This variability provided a diverse set of uncertainty-related behavioral effects on which to examine neural activity . During the center out block of trials ( zero uncertainty , eight discrete targets ) many neurons in PMd displayed a robust burst of activity directly following presentation of the visual cue , followed by a more moderate , tonic response for the remainder of the delay period ( e . g . , Figure 2B ) . We more formally described the population trends by calculating the percentage of neurons tuned in the visual ( V ) , delay ( D ) , and movement ( M ) time periods . The results for each session are shown in Figure 2C . We performed the same analysis for M1 neurons ( Figure 2C , right ) . In general , M1 displayed a bias toward delay and movement period tuning while PMd showed about equal percentages of tuned neurons for each time period . 10 . 7554/eLife . 14316 . 005Figure 2 . Neural recordings and directional tuning . ( A ) Each monkey was implanted with two 96-channel microelectrode arrays , targeting the primary motor cortex ( M1 ) and dorsal premotor cortex ( PMd ) . ( B ) An example raster of a neuron in PMd displaying directional tuning , summarized below in three temporal periods: visual ( V ) , delay ( D ) and movement ( M ) . ( C ) Percentage of neurons from each session with significant tuning in each of the temporal periods . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 005 During the remaining experimental blocks consisting of uncertain targets , we found many neurons in PMd to be more active during high uncertainty trials than low uncertainty trials ( red vs . blue in Figure 3 ) . This effect was most prominent during the delay ( D ) period , with some carryover into movement ( M ) . Some neurons that had been essentially inactive during the block of zero-uncertainty reaches became strongly active during the delay period of high-uncertainty trials ( e . g . , c77u1 and c29u1 , Figure 3 ) . We also noted that there was a greater tendency for increased activity in neurons with PDs not aligned to the direction of movement ( e . g . , c31u1 and c87u1 , Figure 3 ) . Importantly , we found that greater uncertainty only ever led to increased activity . 10 . 7554/eLife . 14316 . 006Figure 3 . Single unit activity in PMd . ( A ) Raster plot for an example neuron . Activity is aligned to either the visual cue appearance ( left ) or movement onset ( right ) . Colors indicate zero ( black ) , low ( blue ) , and high ( red ) uncertainty conditions . Dark black points indicate target onset , go cue , and movement onset ( B ) Directional tuning for other example neurons . Due to the nature of the task , reaches made during uncertain conditions with a non-uniform prior did not span all directions . Many neurons showed an increase in delay ( D ) or movement ( M ) activity as a function of uncertainty . Bounds on the tuning plots represent bootstrapped 95% confidence of the mean estimate . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 006 M1 neurons did not display nearly the same degree of modulation with uncertainty as PMd neurons ( Figure 4 ) . We observed neurons with strong directional tuning in all time periods , but this tuning was consistent across all uncertainty conditions . In general , analysis of single unit behavior suggested that M1 activity reflected only the reach direction and was largely unaffected by uncertainty . 10 . 7554/eLife . 14316 . 007Figure 4 . Single unit activity in M1 . ( A ) Raster plot for an example neuron with same conventions as Figure 3 . ( B ) Directional tuning for other example neurons . In general , M1 activity was well-modulated by reach direction , but appeared to be largely unaffected by the uncertainty condition . Bounds on the tuning plots represent bootstrapped 95% confidence of the mean estimate . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 007 The anecdotal observations in Figures 3 and 4 strongly suggest that higher uncertainty leads to increased neural discharge in PMd but not in M1 . Additionally , the magnitude of the uncertainty-related effect in individual PMd neurons was dependent on the neurons’ tuning characteristics . A neuron experienced the greatest uncertainty-related activity increase when the reaches were directed away from its preferred direction . To further examine this relationship between tuning and uncertainty-related activity changes , we created spatiotemporal activity maps for both cortical areas in the manner of Cisek and Kalaska ( 2005 ) ( Figure 5 ) . We binned each neuron’s responses based on the angle between its PD and the reach direction . We then averaged across trials , resulting in population activity profiles centered on reach direction . 10 . 7554/eLife . 14316 . 008Figure 5 . Tuning-related changes in activity with uncertainty . ( A ) Spatiotemporal activity maps for PMd and M1 . Neurons were binned on each trial by the distance between their preferred directions and the reach direction . Color indicates average change in firing rate from baseline in spikes per second . Left and right plots in each panel are aligned to target onset ( T ) and reach onset ( R ) respectively . ( B ) Average change from baseline for SD and OD neurons in the initial center-out block ( zero uncertainty; top ) and subsequent blocks with low ( bue ) and high ( red ) uncertainty targets ( bottom ) . High uncertainty trials resulted in reduced early activity for both SD and OD neurons in PMd , but an increase in OD activity for the remainder of the delay and movement phases . ORTH neurons were omitted for visibility . Error bars represent bootstrapped 95% confidence bounds on the mean estimate . For all plots , PDs were calculated separately for visual , delay , and movement epochs . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 008 In the zero-uncertainty condition , many PMd neurons displayed a burst of activity directly following cue appearance . This quickly resolved into a clear , maintained representation of the upcoming reach direction throughout the remainder of the delay and movement periods ( Figure 5A , top left ) . In contrast , M1 activity built more slowly as the trial evolved , ultimately producing a strong spatial representation of the executed reach direction ( Figure 5A , top right ) . However , while the recruitment of M1 neurons during low and high uncertainty conditions was similar ( Figure 5A , right ) , the representation in PMd differed significantly across these conditions . During high uncertainty trials , the representation of the reach direction in the delay period was present but significantly less distinct , most notably due to increased activity in neurons with PDs far away from the reach direction ( Figure 5A , bottom left ) . We partitioned the neurons into three groups for each trial: same direction ( SD; preferred direction within 45 degrees of the reach direction ) , opposite direction ( OD; preferred direction within 45 degrees of the anti-reach direction ) , and orthogonal direction ( ORTH; preferred direction within 45 degrees orthogonal the reach direction ) . After averaging the activity of these populations , it became clear that while both SD and OD neurons in PMd were less active immediately after target appearance in high uncertainty trials , the OD neurons showed higher activity in the subsequent D and M periods . Thus the main delay-period effect of higher target uncertainty was an increase in the PMd activity in neurons with preferred directions away from the reach direction . To summarize this uncertainty effect over sessions , we calculated the difference in average firing rates between low and high uncertainty conditions for SD , ORTH , and OD neurons . In most sessions , ORTH and OD activity during the delay and movement periods was significantly greater in the high uncertainty condition , while SD activity showed little change ( Figure 6A – monkey M; Figure 7A – monkey T ) . However , the increase in OD activity varied considerably across sessions . We reasoned that the sessions with the greatest OD activity differences might correspond to the sessions with the greatest differences in the monkeys’ uncertainty . To test this , we calculated the difference in behavioral uncertainty ( ∆behavioral uncertainty ) between uncertainty conditions for each session ( see Materials and methods: behavioral task ) . By plotting the activity differences as a function of ∆behavioral uncertainty , we found strong positive correlations for OD activity , but none for SD ( Figure 6B – monkey M; Figure 7B – monkey T ) . For monkey M , the slope of the relation increased from SD to ORTH to OD neurons ( Figure 6B ) , consistent with the single-session example shown in Figure 5 . We found very similar effects of uncertainty among OD neurons for monkey T ( Figure 7B ) . These findings suggest that as the monkeys became less certain about their decision of where to reach , the representations of less likely reach directions increased . 10 . 7554/eLife . 14316 . 009Figure 6 . Relationship between PMd activity and behavioral uncertainty . ( A ) Thin lines indicate the average difference in firing rate between high and low uncertainty trials for individal sessions . Heavy lines mark the mean across sessions . While SD neurons displayed an average change near zero , activity for ORTH and OD neurons was consistently higher for high uncertainty trials ( B ) Differences in firing rate between high and low uncertainty conditions as a function of the difference in behavioral uncertainty for a single time window 500–700 ms after target appearance . The correlation was weak for same-direction neurons , but strongly positive for orthogonal- and opposite-direction neurons . Thus , the greater the difference in behavioral uncertainty , the larger the difference in activity for ORTH and OD neurons . Marker size indicates the number of contributing neurons for each session ( C ) The slopes from B calculated during the visual period ( 50–250 ms after target appearance; left ) and for 100 ms time windows throughout the delay ( middle ) and movement ( right ) periods . The larger effect of behavioral uncertainty on OD and ORTH activity compared to SD activity persisted throughout planning and execution . ( D ) R2 values for the linear fits in C . Filled symbols in C and D represent significant correlations , p<0 . 05 . All error bars represent bootstrapped 95% confidence bounds on the mean estimates . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 00910 . 7554/eLife . 14316 . 010Figure 7 . Summary of uncertainty related effects in PMd for Monkey T . All conventions as in Figure 6 . Although we had only five sessions for monkey T , by splitting larger sessions into multiple blocks we obained 11 total data points . Specifics are given in Figure 7—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 01010 . 7554/eLife . 14316 . 011Figure 7—source data 1 . Subsampling of sessions for monkey T . Due to low sample size for monkey T , we subdivided larger sessions to obtain separate blocks of 100+ trials each . Here we show the trials contributing to each trial block and the subsequent numbers of low- and high-uncertainty trials . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 011 We also found that the tuning-related effect of uncertainty persisted throughout the entirety of movement planning and even after the initiation of the reach . We applied the analysis in Figure 6B to different time periods throughout the trial and plotted the slopes ( Figure 6C ) and R2 ( Figure 6D ) relating ∆behavioral uncertainty to changes in SD , ORTH , and OD activity . For both monkeys , the difference in OD activity first displayed a significant correlation with ∆behavioral uncertainty during the visual period ( Figures 6 , 7 , panels C and D ) . This effect persisted throughout the remainder of the delay period and the initiation of movement . ORTH activity displayed a similar trend but with a consistently shallower slope , indicating a weaker effect of uncertainty . SD neurons never displayed any significant correlation with uncertainty . For monkey T , only OD activity was consistently correlated with uncertainty throughout the delay and movement periods ( Figure 7C , D ) . Thus it appears that movement representations in PMd remain affected by decision-related uncertainty leading up to and throughout execution of a movement . There was also substantial cross-session variability in the M1 firing rates between high and low uncertainty . For monkey M , SD activity was generally lower for high uncertainty trials and OD activity was slightly higher ( Figure 8A ) . However , there was rarely any correlation between the firing rate difference and the difference in behavioral uncertainty . For monkey M , SD activity was negatively correlated with uncertainty at the beginning of the delay period ( 300–400 ms following target appearance; Figure 8C ) . This effect dissipated quickly and was never observed for monkey T . As a result , we conclude that behavioral uncertainty had no significant effect on M1 activity during movement planning or execution . 10 . 7554/eLife . 14316 . 012Figure 8 . Summary of uncertainty-related actvity in M1 for both monkeys . All conventions as in Figures 6 , 7 . Specifics of how we obtained datapoints for monkey T in panels B and D are given in Figure 7—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 012 Although the correlations between behavioral uncertainty and OD activity in PMd were significant , we considered the possibility that the neural effects were actually driven by the monkeys’ relative weighting of the visual and prior information . To disassociate these two possibilities , we examined the independent correlations of OD activity with each of the two metrics in selectively subsampled groups of sessions . When we chose sessions that caused ∆behavioral uncertainty and ∆cue weighting to be highly correlated ( further exaggerating their normal relation ) , both metrics explained the change in OD activity ( Figure 9A ) . However , for subsampled groups of sessions with poor correlation between the two metrics , only ∆behavioral uncertainty remained well correlated with OD activity ( Figure 9B ) . In fact , differences in OD activity correlated better with ∆behavioral uncertainty than with ∆cue weighting for almost any randomly subsampled group of sessions for either monkey ( Figure 9C , D ) . This suggests that the firing rate changes in PMd actually reflect differences in the monkeys’ uncertainty about their decisions , rather than the weights applied to either visual or prior information leading to those decisions . 10 . 7554/eLife . 14316 . 013Figure 9 . Differences in PMd activity correlate with differences in behavioral uncertainty rather than differences in the weighting of the visual cue . ( A ) Eighteen sessions ( filled symbols ) selected for monkey M in order to increase the correlation between ∆behavioral uncertainty and ∆cue weighting ( top ) . Across these select sessions both metrics could explain the observed differences in OD activity ( bottom ) . ( B ) Alternate subsampling that minimized the correlation between the two behavioral metrics ( top ) . This resampling did not change the correlation between changes in OD activity and ∆behavioral uncertainty ( lower left ) . However , it eliminated the correlation between ∆cue weighting and OD activity ( lower right ) . ( C ) Correlations of OD differences with ∆behavioral uncertainty ( filled ) and ∆cue weighting ( open ) for 1000 unique 18-session subsamples . Each is plotted against the correlation between ∆behavioral uncertainty and ∆cue weighting . The correlation with ∆behavioral uncertainty was consistently stronger than with ∆cue weighting . The correlation with ∆cue weighting was only strong when ∆cue weighting and ∆behavioral uncertainty were well correlated with each other . ( D ) Same as in C , but for monkey T . Each subsample contains six trial blocks . Unlike monkey M , ∆cue weighting and ∆behavioral uncertainty were negatively correlated across sessions . Regardless , OD activity in PMd was still positively correlated with ∆behavioral uncertainty . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 013 Another way of examining the evolution of target-related information in M1 and PMd is to use the neural activity to predict the monkey’s choice of reach direction . For a representative session , we found that although it was possible to predict the monkey's reach direction from PMd activity , the predictions were consistently less accurate for high uncertainty trials than for low uncertainty trials ( Figure 10A , left ) . Accuracy rather rapidly reached these levels within about 200 ms of target appearance , but then increased more slowly throughout the remainder of the trial . On the other hand , the ability to decode reach direction from M1 improved steadily through the delay period ( Figure 10A , right ) . This was true for both high and low uncertainty trials , with only slightly higher delay-period decoding accuracy for low uncertainty trials . At the time of movement initiation , the M1 decoder was equally accurate for both conditions . 10 . 7554/eLife . 14316 . 014Figure 10 . Decoding reach direction from neural activity measured on single trials from PMd and M1 , based on PDs computed during center-out ( zero-uncertainty ) reaches . ( A ) The performance of PMd ( left ) and M1 ( right ) decoders as a function of time for one example session . Performance is defined as one minus the circular variance of the decoder error . ( B ) PMd decoder performance in low v . high uncertainty conditions for four 200 ms time windows spanning target appearance to movement in all sessions for both monkeys . Each point represents a single session from monkey M ( closed ) or monkey T ( open ) ( C ) Same as in B , but for M1 . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 014 Across all sessions , we observed results similar to the single session example . The PMd decoder nearly always performed better during low uncertainty trials than high uncertainty trials ( Figure 10B ) , especially during the visual and delay periods . PMd decoding generally did improve at the time of movement , however the difference in decoder performance between low and high uncertainty conditions remained significant . T-Tests on the performance difference between low and high uncertainty revealed significantly better low-uncertainty performance in all behavioral periods for monkey M ( p<0 . 05 ) and all except the movement time period for monkey T ( discounting sessions with overall poor decoding , see Materials and methods ) . In M1 , decoding performance was also slightly better for low uncertainty trials during the visual and delay periods ( t-test , p<0 . 05 ) , although only for monkey M ( Figure 10C ) . This effect of uncertainty was much smaller than that observed in PMd . At the time of movement there was no bias in performance between low and high uncertainty trials . In general , we found decoding from M1 to be more accurate than from PMd , and less affected by uncertainty – especially at the time of movement . We considered several alternate explanations for the effects of uncertainty , including differences in the visual stimuli , inhomogeneous distribution of the target prior over sessions , and variations in the kinematics of reaching . To test for possible visual effects , we performed three control sessions with a single monkey ( monkey M ) in which half of the high-uncertainty trials contained an additional , different colored line segment at the correct target location ( Figure 11A ) . These sham trials had almost exactly the same visual properties as high uncertainty trials , but did not actually induce any uncertainty . The monkey learned to rely entirely on the new cue line ( Figure 11B ) . Comparing the difference in activity between actual high uncertainty and sham uncertainty trials , we found that OD ( and to some extent ORTH ) activity was greater only for the actual high uncertainty condition ( Figure 11C ) . This suggests that our main finding of uncertainty-related changes in ORTH and OD activity cannot be explained simply as the result of differences in the visual information . 10 . 7554/eLife . 14316 . 015Figure 11 . Neural effects cannot be explained by either the visual qualities of the target cue or changes in the average reach direction across sessions . ( A ) Design of a control experiment to test whether the uncertainty-related effect could be explained solely by differences in the visual stimuli between conditions . Half of the trials contained a high-uncertainty cue ( top left ) and the other half contained sham high-uncertainty trials that included an additional line of a different color to indicate the veridical target location ( top right ) . ( B ) Reaching errors were much smaller for the sham trials , indicating that the monkey learned to rely on the veridical cue . ( C ) Thin lines indicate the average difference in firing rate between actual and sham uncertainty trials for individal sessions . Heavy lines mark the mean across sessions . OD activity was higher during actual high uncertainty trials , despite the nearly equivalent visual properties . ( D ) Control to test whether the neural effects could be explained by differences in the average target location across sessions . We selected two groups of sessions that each contained a consistent average reach direction . ( E ) Correlations between changes in OD and ORTH activity and ∆behavioral uncertainty for the two groups of sessions , 500–700 ms after target appearance . OD and ORTH activity within each group of sessions still correlated with ∆behavioral uncertainty . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 01510 . 7554/eLife . 14316 . 016Figure 11—figure supplement 1 . Kinematic controls . ( A ) Example distribution of peak reach speed for high and low uncertainty conditions . ( B ) Distribution of peak speeds for same session as in A , subsampled to reverse the condition-dependent difference . ( C ) Plot showing the difference in average peak speed between low and high uncertainty conditions for the full and reverse-sampled datasets . ( D ) Thin lines indicate the average difference in firing rate between high and low uncertainty conditions for an individal session , subsampled to reverse the trend of peak speed . Heavy lines mark the mean across sessions . ( E ) Example distribution of reaction time for high and low uncertainty conditions . ( F ) Distribution of reaction times for same session as E , subsampled to reverse the condition-dependent difference . ( G ) Plot showing the difference in average reaction time between low and high uncertainty conditions for the full and reverse-sampled datasets . ( H ) Same as in D . Average differences in peak speed and reaction time cannot explain the neural effect . DOI: http://dx . doi . org/10 . 7554/eLife . 14316 . 016 We also considered the possibility that the effects on neural activity resulted from changes in the average target location ( and subsequently the average reach direction ) across sessions . We tested this possible explanation by separately analyzing groups of sessions with a shared average target direction . Figure 11D shows the distribution of reach directions for two groups of sessions for Monkey M in which the average target location was at either 0 or 90 degrees . Analyzing these two sets of sessions separately revealed a positive correlation between changes in OD/ORTH activity and ∆behavioral uncertainty ( Figure 11E ) that was very similar to the full data set ( Figures 7 and 8 ) . We anticipated that both the reaction time and peak speed might be affected by the target uncertainty , and might indirectly give rise to the firing rate changes we observed in PMd . In fact , these differences were rather small , but to test this possibility , we resampled the trials in each session to reverse the sign of the uncertainty effect on either reaction time or peak speed ( Figure 11—figure supplement 1 ) . These manipulations had no effect on the correlation between PMd activity and ∆behavioral uncertainty , indicating that the difference was not simply driven by kinematics .
In this study , we set out to examine the neural effects of uncertainty on the motor system during a target estimation task . We showed that when visual cues of target location were made less informative , monkeys biased their reach direction toward the average target location that they had learned over the course of previous trials ( their prior estimate ) in a Bayesian-like manner . Activity in dorsal premotor cortex ( PMd ) changed systematically as a function of the resulting uncertainty in the monkeys’ final estimate of target location , with higher uncertainty leading to higher activity in PMd neurons . This effect was not present in primary motor cortex ( M1 ) . The extent to which uncertainty affected the activity of PMd neurons depended on their directional tuning properties . Neurons with preferred directions aligned to the ultimate reach direction showed no correlation with uncertainty , while those with orthogonal or opposite direction tuning displayed significant increases in activity with increased uncertainty . This can be interpreted as an increase in uncertainty causing in increase in the representation of less likely movements directions . The uncertainty-related effect in PMd was present not only during movement planning , but also during execution – a result not readily predicted from previous studies . Several studies have recorded from PMd neurons as monkeys chose between multiple potential reach options ( Cisek and Kalaska , 2005; Coallier et al . , 2015; Klaes et al . , 2011; Pastor-Bernier and Cisek , 2011; Thura and Cisek , 2014 ) . Some even included ambiguous cues ( Coallier et al . , 2015; Thura and Cisek , 2014 ) , which we might expect to induce uncertainty in the monkeys’ decisions . The resulting representations of potential actions in PMd did , in some sense , reflect the monkey's uncertainty in the choice prior to movement execution . However , in no studies before ours did the activity changes induced by an ambiguous cue persist throughout movement execution . One study that used gradually accumulating evidence to trigger movement choice ( Thura and Cisek , 2014 ) found that prior to movement , greater ambiguity in the cue resulted in a stronger representation of the target that was ultimately not selected . They observed no effect on activity corresponding to the selected target , which reached a consistent peak about 300 ms prior to movement initiation . These observations are well in line with our own results . However , at the time of movement initiation they found no ambiguity-related effects on activity , for either the neurons tuned to the selected target or the non-selected target . This is at odds with our finding of a persistent effect of uncertainty on the representation in PMd throughout the execution of movement . That we did not observe a resolution in the reach representation prior to movement execution may reflect a difference in the decision-making processes associated with target estimation and target selection . Inherent to target selection is the knowledge that the correct action will only be one of several mutually exclusive options . This constraint represents additional task-relevant information that can ( and should ) be integrated into the decision-making processes within sensorimotor areas like PMd . In a target selection task , reaching anywhere that is not an explicit target will lead to failure . It is therefore reasonable for the system to enforce a policy that before initiating a reach , the representation must only reflect one of the explicit target options . However , in target estimation tasks there are no such constraints on the executed action , allowing for a broader movement representation . The different neural responses observed during target selection and target estimation has important implications for the assumed roles of M1 and PMd . Results from target selection tasks suggest that movement decisions are made within PMd as the result of a biased neural competition between potential actions ( Cisek , 2007; Gallivan et al . , 2016; Pastor-Bernier and Cisek , 2011 ) . This interpretation – that PMd ultimately decides on the action – is especially convincing given the previous observations that decision-related variables ( e . g . , cue ambiguity , uncertainty , etc . ) had no effect on movement representations in PMd at the time of movement initiation ( Thura and Cisek , 2014 ) . However , we observed uncertain representations that persisted throughout movement execution , which indicates that PMd may not necessarily be the final step in the motor decision-making process . Our results suggest that PMd does not actually decide which movement to execute , but rather that it maintains a continuously updated estimate of the distribution of potentially useful actions . This distribution is likely dependent on a number of factors , including current sensory information and prior experience , as well as constraints specific to the task ( e . g . , selecting between mutually exclusive targets ) . In our study , the distribution of actions represented in PMd changed according to the monkey's behavioral uncertainty , but was relatively static within a single trial . At no point did the distribution resolve into a single unambiguous reach representation . M1 , on the other hand , seemed relatively unaffected by uncertainty and consisently reflected the direction of the executed reach . These findings imply that PMd is not solely responsible for 'deciding' which movement to execute , but instead contains only a noisy representation of potential reach directions that must be interpreted in some way by downstream areas like M1 . Thus , we suggest that the processing that occurs in the connections between PMd and M1 'denoises' the PMd representation to provide a single , unambiguous movement decision . The reach decoding results ( Figure 10 ) support the interpretation that action decisions do not arise solely from PMd . Decoding performance at the time of movement initiation was significantly higher compared to delay-period levels , especially for high uncertainty trials . However , we still observed a consistent bias towards better decoding performance on low uncertainty trials . M1 , on the other hand , showed a steady increase in reach-related information leading up to movement initiation that was slightly skewed towards better accuracy under low uncertainty . At the time of movement initiation , M1 was able to decode reach direction with high accuracy , regardless of uncertainty condition . From these observations , we speculate that the decision about where to reach is not explicitly determined in PMd , but rather in the connections between PMd and M1 . The noisy representation of potential actions in PMd appears to be interpreted in some way by M1 , ultimately producing a single unambiguous motor command . The process by which M1 obtains a final movement representation could potentially occur through a maximum a posteriori ( MAP ) readout of the PMd representation . This kind of mechanism is not only consistent with the results of the current and previous studies , but could potentially explain the neural basis of sensorimotor learning . For example , we would expect that in very high uncertainty conditions ( e . g . , a novel behavioral task ) , PMd might retain nearly equal representations of all possible movements . As a consequence , small fluctuations due to noise within PMd would cause large variability in a downstream readout , driving exploration of the environment . As learning progressed and uncertainty decreased , the distribution in PMd would narrow and motor output would begin to converge on the optimal movement decision . Our task varied the monkeys’ uncertainty in target estimation by manipulating both the history of target distribution and the noise in visual cues . We found that PMd activity changed not as a function of the weighting of either of those two pieces of information , but rather in proportion to the total uncertainty in the final decision . Thus PMd contains uncertainty-related information pertaining to the final action , which encompasses more than just the reliability of the visual cue . Additionally , if uncertainty in visual information were the sole driving force of changes in PMd planning- and execution-related activity , we would have observed very little difference in activity across sessions , since the visual cue properties were largely equivalent for all sessions . Instead , we found that activity modulated with the total behavioral uncertainty , which is a combination of visual uncertainty and prior expectation . This suggests that PMd likely reflects the combined uncertainty of all information sources relevant to a movement decision . There exist a number of theoretical models that address the potential neural representation of uncertainty ( Deneve , 2008; Hinton and Sejnowski , 1983; Hoyer and Hyvarinen , 2003; Ma et al . , 2006; Zemel et al . , 1998 ) . The predictions from these models encompass a wide range of neural behaviors , including temporal dynamics ( Deneve , 2008 ) and variability in spike timing ( Deneve , 2008; Hoyer and Hyvarinen , 2003 ) . Unfortunately , our experimental design prevents us from performing fair and comprehensive tests of these model predictions . For example , our use of a static visual cue and instructed delay limits the potential interpretations regarding dynamic uncertainty codes . For these reasons , we hesitate to make any strong statements about the validity of any given model . Despite the limitations of our experimental design , our results do bear some resemblance to admittedly simplistic interpretations of a few theoretical models . A probabilistic population code ( PPC ) model predicts that firing rates across a population should reflect the probability distribution – high uncertainty should therefore result in lower peak activity and higher non-peak activity ( Ma et al . , 2006 ) . We did indeed observe an increase in non-peak activity with increased uncertainty , and the spatiotemporal activity plots in Figure 5 do convincingly resemble probability distributions of reach direction . However , we did not see any consistent decrease in the peak activity with increasing uncertainty , which prevents us from interpreting the population activity as representing a true probability distribution . Our findings also argue against the concept of divisive normalization , in which the total activity remains equivalent when representing multiple potential targets ( Cisek and Kalaska , 2005; Pastor-Bernier and Cisek , 2011 ) , at least in the context of target estimation . Our results provide new insight into the behavior of PMd during movement planning . It is already well established that PMd can simultaneously represent all potential actions when faced with multiple , mutually exclusive visual targets ( Bastian et al . , 2003; Cisek and Kalaska , 2005 ) . Our results provide the additional observation that PMd also represents and retains a distribution of potential motor plans that are not explicitly presented , but arise as possibilities during uncertain target estimation . The question of why this representation is maintained for the problem of target estimation but not target selection is an interesting one . One possibility is that it is simply an unavoidable result of noisy inputs to PMd . That is , in the absence of explicit reach targets , the fidelity of the representation in PMd may be limited by the quality of available information . On the other hand , maintaining heightened representations of alternative movements in high uncertainty conditions may be useful to the sensorimotor system for more rapid error correction or to drive subsequent motor learning . Experiments designed to test these alternatives could help to further our understanding of the role of PMd in movement planning .
The monkeys were seated in front of a vertical monitor and controlled an on-screen cursor using a planar robotic manipulandum . The behavioral task involved two or more blocks of trials . In the first block , monkeys performed a basic center-out reaching task with an instructed delay period . The monkey held the cursor within a central target for a random length center-hold period ( 700–1000 ms ) , after which a target ( 15 degrees wide ) appeared in one of eight well-defined locations , distributed equally around an outer ring ( Figure 1A , top ) . Following an additional random delay period ( 700–1000 ms ) the center target disappeared and the monkey received an auditory signal cueing him to reach to the outer target . Upon reaching the outer ring , the cursor froze . If the cursor was within the target , the monkey heard a success tone and received a small amount of juice . Otherwise , the monkey heard a failure tone and received no juice reward . In the remaining ( uncertainty ) trial blocks , the target locations θ were not distributed uniformly among eight locations as before , but were instead selected randomly from a von Mises ( circular normal ) prior ( 1 ) f ( θ ) = eκ cos ( θ− μ ) 2π I0 ( κ ) The mean of this prior distribution ( μ ) was always fixed for the duration of a session , but could vary in width ( κ ) across trial blocks . Additionally , during uncertainty trials the monkeys did not receive veridical visual cues about the target until the end of the trial . Instead , during planning and execution they were only shown several small lines ( five for monkey M , ten for monkey T ) sampled from a likelihood distribution ( also von Mises ) centered on the target location ( Figure 1A , bottom ) . These lines gave the monkey information about the target location , but with different levels of uncertainty depending on the variance of the distribution . Each session contained two different likelihood distributions , which were randomly interspersed across trials . The exact parameters used for each session are provided in Figure 1—source data 1 . Upon reaching to the outer ring , the cursor froze and the ambiguous cue lines were replaced with the actual target ( 15 degrees , all conditions ) . The monkey subsequently received ( or did not receive ) reward as in the center-out trial block . Although we directly specified the variance ( and therefore uncertainty ) in the target distribution and the visual cue , the monkeys’ subjective estimates of those parameters could deviate considerably from their true values . We therefore used the monkeys’ actual responses throughout the session to estimate two values: the monkeys’ weighting of the current visual cue , and the total uncertainty remaining in the monkeys’ final estimate of the required reach direction . To do this , we assumed a Bayesian-like model of cue integration in which the final estimate was the product of likelihood ( visual cue ) and prior ( distribution of target locations ) probability distributions . We modeled both of these as von Mises distributions . The product of two von Mises distributions can be approximated by a third , with mean ( 2 ) μ3= μ1+tan−1 ( sin ( μ2− μ1 ) k1k2 +cos ( μ2− μ1 ) ) To obtain an estimate of the relative weighting of the visual cue for each uncertainty condition , we substituted the true target centroid location for μ2 , the true average target location for μ1 , and then fit ( k1k2 ) to minimize the sum of the squared residuals between the model outputs and the monkeys’ actual reach directions . The resulting equation for μ3 describes the general function relating the centroid of the visual cue and the reach direction . ( red and blue lines; Figure 1B ) . Except for cases in which |μ2− μ1| is very large , this can be suitably approximated by the linear function ( 3 ) μ3≅ μ1+ k2k1+k2 ( μ2− μ1 ) In all further analysis , we use the slope term ( k2k1+k2 ) as a proxy for our estimate of the monkeys’ relative weighting of the visual cue with respect to the summed prior and likelihood uncertainty . Slopes close to one represent high reliance on the visual cue , while slopes close to zero represent high reliance on the average prior target location . The slope metric described above reveals only the monkeys’ relative uncertainties in the likelihood and prior . It does not contain any information about the total magnitude of uncertainty present in the monkeys’ decisions . We estimated this total uncertainty from the monkeys’ behavior , by calculating the angular dispersion of the residuals from each behavioral fit like those shown in Figure 1B . It is important to note that the behavioral uncertainty can be affected by uncertainty in the estimate of average target location , uncertainty in the visual cue , and potentially other internal variables affecting the monkeys’ behavior that we did not control ( e . g . , motivation , attention ) . Throughout the experiments we recorded from neurons in M1 and PMd ( Figure 2A ) using chronically implanted 96-channel microelectrode arrays ( Blackrock Microsystems , Salt Lake City UT ) . We identified single neurons from each session using offline sorter by isolating clusters within a principle component space projected from the waveform shapes of putative neurons ( Plexon Inc . , Dallas TX ) . There was likely significant overlap between sessions of the populations of recorded neurons , but we made no effort to track the identity of neurons across sessions . On each session , we used the activity from the center-out block of trials ( zero uncertainty , eight target locations ) to characterize the directional tuning characteristics of all neurons . Since many neurons ( especially those in PMd ) can have complex temporal profiles , we calculated preferred directions ( PDs ) in three distinct time periods: visual ( 50–250 ms after target appearance ) , delay ( 300–700 ms post-target ) , and movement ( 0–200 ms after initiation of the reach ) using a generalized linear model with Poisson noise:λ= exp[α+ βcos ( θ−θ∗ ) ] where λ is a vector of firing rates across trials , θ is a vector of reach directions , θ* is the preferred direction , and α , β are scaling parameters . For each neuron , we also obtained confidence bounds on the fit parameters through bootstrapping . A neuron was only considered to be significantly tuned if 95% of the bootstrapped estimates of θ* were within forty-five degrees of the mean estimate . Due to the lower neuron count for monkey T , we relaxed this constraint to accept neurons with bootstrapped PDs within ninety degrees of the mean . For all analyses , we used only the preferred directions calculated within the appropriate time period ( for example , delay-period tuning for all delay-period analyses ) . When analyzing a given time period , we excluded neurons without significant tuning in that period . Full details on the numbers of tuned neurons for all sessions is provided in Figure 1—source data 1 . We used a simple decoding approach based on each neuron’s PD , computed from data collected during the center-out ( zero-uncertainty ) task . We first divided neurons according to their PDs , creating sixteen bins of 22 . 5 degrees each . We then averaged the activity of all neurons within each bin ( after first subtracting pre-target baseline activity levels ) and fit a cosine to the resulting activity profile . The peak of this cosine defined the decoded reach direction . We characterized decoder performance for each uncertainty condition as one minus the circular variance of decoder error . Circular variance is bounded by 0 and 1 . Therefore , a performance of 0 represents that the decoder did no better than random guessing , and a performance of 1 represents perfect decoding of the reach direction . This metric is similar to VAF , except it is not normalized by the total variance of the reach distribution . This is important for our dataset , which contained very non-uniform distributions of reach directions . It provides a fair comparison of decoder performance regardless of differences in distributions between sessions or uncertainty conditions . We assessed the effect of uncertainty condition on decoding performance by performing t-tests on the distributions of differences between low and high uncertainty conditions for each monkey and time period . For monkey T , low neuron counts made decoding on a trial-by-trial basis much less accurate . Therefore , when assessing biases , we only included sessions in which the decoder performance on low uncertainty trials was greater than 0 . 5 . | Whether it is trying to find the light switch in a dimly lit room or reaching for your glasses when you wake in the morning , we often need to reach toward objects that we cannot see clearly . In these situations , we plan our movements based both on the limited sensory information that is available , as well as what we have learned from similar situations in the past . The brain areas involved in using information to decide on the best movement plan appear to be different from those involved in actually executing that plan . One area in particular , called the dorsal premotor cortex ( or PMd ) , is thought to help a person decide where to reach when they are presented with two or more alternative targets . However , it was not known how this brain area is involved in choosing a direction to reach when the targets are fuzzy , or unable to be seen clearly . Dekleva et al . trained Rhesus macaque monkeys to reach in various directions , towards targets that were represented by fuzzy , uncertain visual cues . These targets were not simply positioned randomly; instead they were more likely to require reaches in certain directions over other directions . Because there were many such training and experimental sessions , the monkeys were able to learn where targets were more likely to be located . Dekleva et al . found that , like humans , the monkeys combined this knowledge from previous experience with the fuzzy visual information; like people , the monkeys also weighted each source of information based on how well they trusted it . For example , blurrier targets were treated as less trustworthy . Further analysis showed that neurons in the PMd signaled the chosen direction well before the monkey began to reach . However , throughout the entire time the monkey was reaching , the same neurons also seemed to hold in reserve the other , less likely reach directions . In contrast , neurons in the area of the brain that directly controls movement – the primary motor cortex – only ever signaled the direction in which the monkey actually reached . Further work is now needed to understand the decision-making process that appears to start in the PMd and resolve in the primary motor cortex . In particular , future experiments could explore why the retained information about other possible reach decisions persists throughout the movement , including if this helps the individual to rapidly correct errors or to slowly improve movements over time . | [
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] | 2016 | Uncertainty leads to persistent effects on reach representations in dorsal premotor cortex |
Herpesviruses acquire their membrane envelopes in the cytoplasm of infected cells via a molecular mechanism that remains unclear . Herpes simplex virus ( HSV ) −1 proteins pUL7 and pUL51 form a complex required for efficient virus envelopment . We show that interaction between homologues of pUL7 and pUL51 is conserved across human herpesviruses , as is their association with trans-Golgi membranes . We characterized the HSV-1 pUL7:pUL51 complex by solution scattering and chemical crosslinking , revealing a 1:2 complex that can form higher-order oligomers in solution , and we solved the crystal structure of the core pUL7:pUL51 heterodimer . While pUL7 adopts a previously-unseen compact fold , the helix-turn-helix conformation of pUL51 resembles the cellular endosomal complex required for transport ( ESCRT ) -III component CHMP4B and pUL51 forms ESCRT-III–like filaments , suggesting a direct role for pUL51 in promoting membrane scission during virus assembly . Our results provide a structural framework for understanding the role of the conserved pUL7:pUL51 complex in herpesvirus assembly .
Herpesviruses are highly prevalent human and animal pathogens that cause life-long infections and result in diseases ranging from cold sores and genital lesions ( herpes simplex virus , HSV ) to viral encephalitis ( HSV-1 ) , congenital birth defects ( human cytomegalovirus , HCMV ) and cancer ( e . g . Kaposi’s sarcoma associated herpesvirus , KSHV ) ( Evans et al . , 2013; Virgin et al . , 2009 ) . Herpesviruses share conserved virion morphology , their DNA genome-containing capsids being linked to glycoprotein-studded limiting membranes via a proteinaceous layer called tegument , and a conserved assembly pathway whereby final envelopment of the DNA-containing capsids occurs in the cytoplasm ( reviewed in Mettenleiter et al . , 2009; Owen et al . , 2015 ) . While herpesviruses are known to extensively remodel the intracellular architecture of infected cells ( Das et al . , 2007 ) , the molecular mechanisms by which they direct intracellular membranes to envelop nascent virions remain unclear . HSV-1 tegument proteins pUL7 and pUL51 promote virus assembly by stimulating the cytoplasmic wrapping of nascent virions ( Albecka et al . , 2017; Roller and Fetters , 2015 ) . pUL7 and pUL51 form a complex that co-localizes with Golgi markers both during infection and when co-transfected into cells ( Albecka et al . , 2017; Roller and Fetters , 2015; Nozawa et al . , 2003 ) , palmitoylation of residue Cys9 being required for pUL51 membrane association ( Nozawa et al . , 2003 ) . Deletion of pUL7 , pUL51 , or both proteins from HSV-1 causes a 5- to 100-fold decrease in virus replication ( Albecka et al . , 2017; Nozawa et al . , 2005; Roller et al . , 2014 ) and cells infected with HSV-1 lacking pUL7 and pUL51 accumulate unenveloped capsids in the cytoplasm ( Albecka et al . , 2017 ) . Similar results have been observed in other α-herpesviruses . pORF53 and pORF7 , the pUL7 and pUL51 homologues from varicella-zoster virus ( VZV ) , co-localize with trans-Golgi markers in infected cells ( Selariu et al . , 2012; Wang et al . , 2017 ) and deletion of pORF7 causes a defect in cytoplasmic envelopment ( Jiang et al . , 2017 ) . Similarly , deletion of pUL7 or pUL51 from pseudorabies virus ( PrV ) causes defects in virus replication and the accumulation of cytoplasmic unenveloped virions ( Klupp et al . , 2005; Fuchs et al . , 2005 ) , and PrV pUL51 co-localizes with Golgi membranes during infection ( Klupp et al . , 2005 ) . Homologues of pUL7 and pUL51 can be identified in β- and γ-herpesviruses , although pUL51 homologues lack significant sequence similarity with α-herpesvirus pUL51 and their homology is inferred from their conserved positions in virus genomes ( Lenk et al . , 1997; Campadelli-Fiume et al . , 2007 ) . The putative pUL51 homologue pUL71 from HCMV , a β-herpesvirus , associates with the Golgi compartment when expressed in isolation and with Golgi-derived virus assembly compartments during infection ( Dietz et al . , 2018 ) . Deletion of pUL71 causes defects in HCMV replication , characterized by aberrant virus assembly compartments ( Womack and Shenk , 2010 ) and defects in secondary envelopment ( Schauflinger et al . , 2011 ) . Similarly , the HCMV pUL7 homologue pUL103 co-localizes with Golgi markers when expressed alone or during infection , and deletion of pUL103 causes a loss of assembly compartments , reductions in virus assembly and defects in secondary envelopment ( Ahlqvist and Mocarski , 2011 ) . Relatively little is known about the pUL7 and pUL51 homologues from γ-herpesviruses . Both the pUL7 and pUL51 homologues from murine γ-herpesvirus 68 are essential for virus replication ( Song et al . , 2005 ) . The putative pUL51 homologue BSRF1 from Epstein-Barr virus associates with Golgi membranes and siRNA knock-down of BSRF1 in B95-8 cells prevents virion production ( Yanagi et al . , 2019 ) . The KSHV homologue of pUL7 , pORF42 , is similarly required for efficient virion production ( Butnaru and Gaglia , 2019 ) . While a direct interaction has not been shown for the pUL7 and pUL51 homologues from β- or γ-herpesviruses , the EBV homologues BBRF2 and BSRF1 have been shown to co-precipitate from transfected cells ( Yanagi et al . , 2019 ) . Definitive molecular characterization of pUL7 and pUL51 function in HSV-1 or other herpesviruses has been hampered by their lack of homology to any proteins of known structure or function . However , a recent study of HCMV hypothesized that the pUL51 homologue pUL71 may act as a viral endosomal sorting complex required for transport ( ESCRT ) -III component ( Streck et al . , 2018 ) . We characterized the pUL7:pUL51 complex by solution scattering and solved the atomic-resolution structure of the proteolysis-resistant core of this complex using X-ray crystallography . pUL7 comprises a single globular domain that binds one molecule of pUL51 via a hydrophobic surface , a second molecule of pUL51 being recruited to the solution complex via the N-terminal region of pUL51 . While the fold of pUL7 is not similar to any known structure , the α-helical pUL51 protein shares unanticipated structural similarity to components of the ESCRT-III membrane-remodeling machinery . Like cellular ESCRT-III component CHMP4B , pUL51 is capable of forming long filaments . Furthermore , we show that formation of the pUL7:pUL51 complex and its association with the trans-Golgi network is conserved across α- , β- and γ-herpesviruses , consistent with a conserved function for this complex in herpesvirus assembly .
Full-length HSV-1 pUL7 and pUL51 were co-expressed in Escherichia coli , the palmitoylation site of pUL51 ( Cys9 ) having been mutated to serine to avoid aberrant disulfide bond formation ( Figure 1—figure supplement 1 ) . Proteins were co-expressed and co-purified because pUL51 ( 25 . 5 kDa ) formed large soluble aggregates when purified alone ( Figure 1—figure supplement 1 ) and pUL7 ( 33 . 0 kDa ) was extremely prone to aggregation upon removal of the GST purification tag when purified in the absence of pUL51 . Multi-angle light scattering ( MALS ) analysis showed the complex to elute from size-exclusion chromatography ( SEC ) as two peaks with molecular masses of 79 . 0 ± 1 . 8 kDa and 165 . 5 ± 1 . 1 kDa ( Figure 1A ) , consistent with pUL7 and pUL51 forming a 1:2 heterotrimer in solution ( calculated mass from amino acid sequence 84 . 5 kDa ) that dimerizes at higher concentrations to form a 2:4 heterohexamer ( calculated mass 169 kDa ) . However , pUL51 of the co-purified complex was prone to degradation , frustrating crystallization attempts ( Figure 1A ) . Prior sequence analysis ( Oda et al . , 2016; Nozawa et al . , 2003 ) and our bioinformatics ( Figure 1—figure supplement 2 ) suggested that the C-terminal region of pUL51 lacks regular secondary structural elements and is disordered . Consistent with this prediction , SEC with inline small-angle X-ray scattering ( SAXS ) showed the pUL7:pUL51 complex to be extended . The 1:2 and 2:4 complexes have radii of gyration ( Rg ) of 4 . 3 and 4 . 8 nm , with maximum particle dimensions ( Dmax ) of ~18 nm and 20 nm , respectively ( Figure 1B , J , K and Supplementary file 1–Table S1 ) . Ab initio shape analysis was performed by fitting the 2:4 scattering curve to a dummy-atom model , or simultaneously fitting both scattering curves to a dummy-residue model , with the imposition of P2 symmetry . The models thus obtained are consistent with the pUL7:pUL51 complex comprising a folded core with an extended region of poorly-ordered amino acids ( Figure 1C and D ) . In agreement with this , dimensionless Kratky plots of the 1:2 and 2:4 complex SAXS data shows both to have maxima above sRg = √3 ( Figure 1L ) with extended tails observed in the corresponding probable frequency of real-space distances ( p ( r ) profiles ) at longer vector-length distances ( Figure 1K ) . Previous truncation analysis had shown residues 29–170 of pUL51 to be sufficient for pUL7 binding ( Albecka et al . , 2017 ) . However , neither pUL7 in complex with pUL51 residues 29–170 , nor with pUL51 residues 1–170 , proved amenable to crystallization . Mass spectrometry analysis identified a smaller protein species , evident whenever the pUL7:pUL51 ( 1–170 ) was analyzed by SDS-PAGE , as pUL51 residues 8–142 . On the assumption that this represented the proteolysis-resistant fragment of pUL51 , pUL7 was co-expressed and co-purified with pUL51 ( 8–142 ) . This protein complex could be readily purified and was monodisperse in solution , SEC-MALS showing the pUL7:pUL51 ( 8–142 ) complex to have a mass of 61 . 5 ± 3 . 1 kDa , consistent with a 1:2 complex ( calculated mass 63 . 1 kDa ) as observed for full-length pUL7:pUL51 ( Figure 1E ) . SEC-SAXS analysis ( Figure 1G ) showed the pUL7:pUL51 ( 8–142 ) complex to be much more compact ( Rg = 3 . 0 nm; Dmax = 11 . 5 nm; Figure 1K; Supplementary file 1–Table S1 ) . The Gaussian-like appearances of a dimensionless Kratky plot of the pUL7:pUL51 ( 8–142 ) scattering data , which is centered on sRg of √3 ( Figure 1L ) , and of the corresponding p ( r ) profile ( Figure 1K ) are consistent with the protein having a globular fold . Ab initio shape analysis of this data reveals that the pUL7:pUL51 ( 8–142 ) complex visually resembles the folded core of the full-length complex ( Figure 1H and I ) . The pUL7:pUL51 ( 8–142 ) complex was crystallized and its structure was solved by four-wavelength anomalous dispersion analysis of a mercury acetate derivative . The structure of native pUL7:pUL51 ( 8–142 ) was refined to 1 . 83 Å resolution with residuals R = 0 . 195 , Rfree = 0 . 220 and excellent stereochemistry , 99% of residues occupying the most favored region of the Ramachandran plot ( Supplementary file 1–Table S2 ) . The asymmetric unit contained four copies of pUL7 residues 11–234 and 253–296 plus eight residues from the C-terminal purification tag ( see below ) and four copies of pUL51 residues 24–89 and 96–125 , the remaining residues of pUL7 and pUL51 ( 8–142 ) being absent from electron density and presumably disordered . Strikingly , the molecules of pUL7 and pUL51 in the structure were arranged as a hetero-octamer , with single β-strands from each pUL7 and pUL51 molecule in the asymmetric unit forming a central β-barrel ( Figure 2A ) . Closer inspection revealed that the pUL7 strands in this β-barrel comprised the C-terminal amino acids encoded by the restriction site and from the human rhinovirus 3C protease recognition sequence that remained following proteolytic removal of the GST purification tag . We therefore suspected that this hetero-octameric pUL7:pUL51 arrangement was an artefact of crystallization . SEC-MALS of a pUL7:pUL51 ( 8–142 ) construct where the purification tag was moved to the N terminus of pUL7 , and would thus be unlikely to form the same β-barrel observed in the crystal structure , yielded the same 1:2 pUL7:pUL51 heterotrimeric stoichiometry as observed with C-terminally tagged pUL7 ( Figure 2—figure supplement 1A ) . Removal of residues 8–40 from pUL51 , including residues 24–40 that form part of the β-barrel , yielded a 1:1 heterodimeric complex of pUL7 and pUL51 ( 41–142 ) as determined by SEC-MALS ( Figure 2—figure supplement 1B ) , although we note that this truncated complex had reduced solubility . Taken together , these results suggest that pUL7 and pUL51 residues 41–142 assemble to form a heterodimeric ‘core’ complex and that recruitment of the additional pUL51 molecule in the native heterotrimeric complex is mediated by the N-terminal region ( residues 8–40 ) of pUL51 . The core heterodimeric complex formed by pUL7 residues 11–296 and pUL51 residues 41–125 is shown in Figure 2B . pUL7 comprises two short N-terminal α-helices followed by a compact globular fold with a mixed α-helical and β-sheet topology containing a central anti-parallel β-sheet and two mostly-buried α-helices that are surrounded by a β-hairpin and additional helices ( Figure 2—figure supplement 2 ) . Structure-based searches of the Protein Data Bank did not reveal any other domains with a similar fold , which we will henceforth refer to as the Conserved UL7 ( Seven ) Tegument Assembly/Release Domain ( CUSTARD ) fold . pUL51 comprises a hydrophobic loop region followed by a helix-turn-helix . The interaction with pUL7 is extensive and largely hydrophobic in nature ( Figure 2 ) : The hydrophobic loop of pUL51 ( residues 45–50 , sequence LLPAPI ) interacts with pUL7 helix α2 and with a hydrophobic pocket formed by sheets β1 and β6 , helices α4 and α7 and the C-terminal tail of pUL7; hydrophobic residues of pUL51 helix α1 interact with a hydrophobic face of pUL7 helix α8; and hydrophobic residues from the C-terminal portion of pUL51 helix α2 interact with hydrophobic residues from pUL7 helices α8 and α9 ( Figure 2C–E ) . Chemical cross-linking and mass spectrometry was used to further characterize the interaction between pUL7 and pUL51 in solution . As shown in Figure 2—figure supplement 3A , incubation of the pUL7:pUL51 ( 8–142 ) complex with either disuccinimidyl sulfoxide ( DSSO ) or disuccinimidyl dibutyric urea ( DSBU ) yielded species with masses corresponding to 1:1 or 1:2 pUL7:pUL51 complexes , plus some higher-order species . Analysis of these cross-linked complexes by MS3 mass spectrometry identified multiple cross-links between pUL7 and pUL51 residues ( Supplementary file 1–Table S3 ) . Five of these crosslinks were not compatible with the heterodimer crystal structure , suggesting that they were formed by the other molecule of pUL51 in the heterotrimer , whereas other cross-links could have been formed by either pUL51 molecule . Multiple pseudo-atomic models of the 1:2 pUL7:pUL51 ( 8–142 ) solution heterotrimer were thus generated by fitting the SAXS profile using the core heterodimer structure , a second copy of pUL51 residues 41–125 , and permutations of the feasible chemical cross-linking restraints . Of the 80 models thus generated , half could not simultaneously satisfy all crosslinking restraints and were discarded . The other models all fit the pUL7:pUL51 ( 8–142 ) SAXS profile well ( χ2 < 1 . 26 ) . These models showed the second copy of pUL51 to have the same general orientation relative to pUL7 , binding near pUL7 helices α1 , α2 , α6 , α7 , and the loop between helices α7 and α8 ( Figure 2—figure supplement 3C ) . However , the precise orientations of this second pUL51 copy differed , as did the locations of the pUL51 termini . The observed variability is within the resolution limits provided by SAXS , although it may also point to co-existence of alternate conformations , i . e . that the second copy of pUL51 does not adopt one well-defined conformation in solution . The α- , β- and γ-herpesvirus families diverged approximately 400 million years ago ( McGeoch and Gatherer , 2005 ) . Homologues of pUL7 from α- , β- and γ-herpesviruses can be readily identified by their conserved amino acid sequences , despite relatively low sequence identities ( HCMV and KSHV homologues share 17% and 16% identity , respectively , with HSV-1 pUL7 ) . The predicted secondary structures of pUL7 homologues from representative α- , β- and γ-herpesviruses that infect humans are very similar to the experimentally-determined secondary structure of HSV-1 pUL7 , strongly suggesting that these proteins will adopt the CUSTARD fold ( Figure 1—figure supplement 2 ) . Similarly , the predicted secondary structures of putative β- and γ-herpesvirus pUL51 homologues closely match the prediction for HSV-1 pUL51 ( Figure 1—figure supplement 2 ) despite low sequence identity ( HCMV and KSHV homologues sharing 16% and 13% identity , respectively , with HSV-1 pUL51 ) . As the pUL7 and pUL51 homologues conserve secondary structure and , where tested , conserve function in promoting virus assembly , we sought to determine whether the formation of a pUL7:pUL51 complex is conserved across the α- , β- and γ-herpesvirus families . GFP-tagged pUL7 homologues from human herpesviruses HSV-1 , VZV , HCMV or KSHV were co-transfected with mCherry-tagged pUL51 homologues from the same virus into human embryonic kidney ( HEK ) 293 T cells . In all cases , pUL51-mCherry or the relevant homologue could be readily co-precipitated with the GFP-pUL7 homologue , whereas pUL51-mCherry homologues were not efficiently co-precipitated by GFP alone ( Figure 3A ) . The association of pUL7 and pUL51 homologues is therefore conserved across the herpesvirus families . Given the large evolutionary distance between α- , β- and γ-herpesvirus pUL7 and pUL51 homologues , and consequent sequence divergence , it was unclear whether the molecular details of the interaction between these proteins would be conserved . GFP-tagged pUL7 was thus co-transfected with mCherry-tagged pUL51 from HSV-1 or with mCherry-tagged homologues from VZV , HCMV or KSHV . Co-precipitation was observed for HSV-1 pUL51 and for pORF7 from VZV , an α-herpesvirus , but not for the homologues from HCMV or KSHV ( Figure 3B ) . This suggested that the pUL7:pUL51 molecular interface is partially conserved within the α-herpesvirus family , but not across families . VZV pORF53 and pORF7 share 33% and 35% identity with HSV-1 pUL7 and pUL51 , respectively . Mapping the conservation of α-herpesvirus pUL7 sequences onto the HSV-1 pUL7 structure reveals several regions of conservation that overlap with the binding footprint in pUL51 in the core heterodimeric complex ( Figure 3E ) . However , capture of pUL51-mCherry did not result in co-precipitation of the VZV pUL7 homologue pORF53 , nor did capture of GFP-pORF53 result in co-precipitation of HSV-1 pUL51 ( Figure 3C and D ) . We therefore conclude that , while the pUL7:pUL51 interface is partially conserved across α-herpesviruses , there has been co-evolution of pUL7 and pUL51 homologues such that the interaction interfaces are distinct at a molecular level . To test whether the core heterodimeric pUL7:pUL51 interaction interface is subject to co-evolutionary change , a matrix of 63 interacting pairs of residues ( one from each protein ) was generated by manual inspection of the binding interface . The amino acids carried at these sites across an alignment of pUL7 and pUL51 homologues from 199 strains of α-herpesvirus were tested for correlated changes . Initially , 35 of the 63 interacting-residue pairs where homology could be confidently assigned were analyzed , results being compared to a null distribution determined from 106 data sets where interacting sites were paired at random . True pairings showed more correlated change than 94% of the randomized pairings and the results were little changed when different subsets of the data , including fewer strains and more interactions , were analyzed ( Supplementary file 1–Table S4 ) . This is suggestive evidence for co-evolution of the interaction interface across the α-herpesviruses . Similar analysis was attempted to probe for co-evolution of the core pUL7:pUL51 interaction interface across all herpesviruses , but the extensive sequence divergence confounded the confident assignment of interacting amino acid pairs ( only 12 pairs could be confidently assigned ) and so the subsequent analysis was underpowered . In addition to interacting with pUL7 , it has previously been shown that HSV-1 pUL51 is able to interact with HSV-1 pUL14 ( Oda et al . , 2016 ) and that mutation of pUL51 amino acids Ile111 , Leu119 and Tyr123 to alanine disrupts this interaction . Residues Leu119 and Tyr123 are completely buried in the interface between pUL7:pUL51 in the core heterodimer structure , interacting with residues from pUL51 helix α1 and from pUL7 helices α8 and α9 ( Figure 3—figure supplement 1A ) . Such burial would preclude simultaneous binding of these residues to pUL7 and pUL14 . However , the second copy of pUL51 in the solution heterotrimer may be capable of binding pUL14 , or pUL14 may compete with pUL7 for binding to pUL51 . To test these hypotheses , pUL51-mCherry was co-transfected into mammalian cells together with GFP-pUL7 and/or myc-pUL14 and then captured using mCherry affinity resin . While GFP-pUL7 was readily co-precipitated , we could not detect co-precipitation of myc-pUL14 with pUL51-mCherry either in the presence or absence of GFP-pUL7 ( Figure 3—figure supplement 1B ) . As the pUL51:pUL14 interaction was previously demonstrated using infected cells or infected-cell lysates ( Oda et al . , 2016 ) it seems likely that this interaction is not direct , but is instead mediated by other herpesvirus proteins and that it may require binding of pUL51 to pUL7 . In addition to the roles of the pUL7 and pUL51 in promoting virus assembly , which appear to be conserved across herpesviruses , the HSV-1 pUL7:pUL51 complex has been shown to interact with focal adhesions to stabilize the attachment of cultured cells to their substrate during infection ( Albecka et al . , 2017 ) . To probe whether focal adhesion binding is a conserved property of pUL7:pUL51 homologues , GFP-tagged pUL7 and mCherry-tagged pUL51 ( or homologous complexes ) were co-transfected into HeLa cells . As previously observed , HSV-1 pUL7:pUL51 complex co-localizes with both TGN46 , a trans-Golgi marker , and with paxillin and zyxin at the cell periphery , markers of focal adhesions ( Figure 4; Figure 4—figure supplement 1; Figure 4—figure supplement 2 ) . VZV pORF53:pORF7 , HCMV pUL103:pUL71 and KSHV pORF42:pORF55 all co-localize with TGN46 at trans-Golgi membranes ( Figure 4 ) . However , these homologues do not co-localize with paxillin or zyxin at focal adhesions ( Figure 4—figure supplement 1; Figure 4—figure supplement 2 ) . While the pUL7 CUSTARD fold has not been observed previously , frustrating attempts to infer function by analogy , the helix-turn-helix fold of pUL51 residues 41–125 is a common feature of many proteins . Of the proteins identified by structure-based searches , the similarity to human CHMP4B , a component of the ESCRT-III membrane-remodeling machinery , is particularly notable given the role of pUL51 and homologues in stimulating virus wrapping ( Albecka et al . , 2017; Jiang et al . , 2017; Klupp et al . , 2005; Schauflinger et al . , 2011 ) . CHMP4B and homologues are required for the efficient fusion of membrane leaflets during vesicle budding into organelle lumens , cytokinetic abscission , nuclear envelope closure , and budding of some enveloped viruses ( McCullough et al . , 2018 ) . Helices α1 and α2 of pUL51 superpose onto human CHMP4B ( Martinelli et al . , 2012 ) with 1 . 2 Å root-mean-squared deviation across 59 Cα atoms ( Figure 5A ) . pUL51 also resembles the structures of yeast and fly CHMP4B homologues Snf7 ( Tang et al . , 2015 ) and Shrub ( McMillan et al . , 2016 ) , and pUL51 can be superposed onto either structure with 1 . 5 Å root-mean-squared deviation across 57 Cα atoms ( Figure 5B and C ) . A conserved feature of cellular ESCRT-III components like CHMP4B is their ability to form filaments that line the neck of nascent membrane buds and act in concert with VPS4 to promote membrane scission ( McCullough et al . , 2018; Maity et al . , 2019 ) . Formation of such filaments is accompanied by a conformational switch from a closed , auto-inhibited form to an open , polymerization-competent form where helix α3 of the ESCRT-III core domain is continuous with helix α2 ( Tang et al . , 2015; McMillan et al . , 2016; McCullough et al . , 2015 ) . The region of pUL51 immediately following helix α2 is predicted to be helical ( Figure 1—figure supplement 2 ) . We therefore sought to investigate whether pUL51 can form ESCRT-III–like filaments . As the C-terminal region of ESCRT-III components promote stabilization of the closed , auto-inhibited form ( Bajorek et al . , 2009; Henne et al . , 2012 ) , we used a truncated form of pUL51 spanning residues 1–170 that is predicted to be largely α-helical in nature ( Figure 5D; Figure 1—figure supplement 2 ) . When expressed in E . coli in the absence of pUL7 this protein was insoluble ( Figure 5—figure supplement 1A ) . However , the protein could be readily purified from inclusion bodies and refolded in vitro by rapid dilution ( Figure 5—figure supplement 1A ) . Circular dichroism spectroscopy of the refolded protein confirmed it to be largely α-helical ( Figure 5—figure supplement 1B ) . While the refolded protein was soluble at low salt concentrations ( ≤200 mM ) , it rapidly aggregated to form visible precipitates at higher salt concentrations . Negative stain electron microscopy analysis of the pUL51 ( 1–170 ) protein prepared in buffers lacking salt showed it to form filaments in vitro . The form of these filaments varied with pH , concentration and incubation time on the electron microscopy grid: Short proto-filaments with diameters of 20–28 nm were formed by 100 µM pUL51 ( 1–170 ) at pH 8 . 5 in the absence of salt incubated on grids for 30 s before staining ( Figure 5E–G ) , whereas longer 12–15 nm wide filaments were formed by 10 µM pUL51 ( 1–170 ) in pH 7 . 5 HEPES incubated on grids for 1–2 min before staining ( Figure 5H and I ) . These pUL51 ( 1–170 ) filaments resemble the filaments observed in vitro for purified Snf7 ( Henne et al . , 2012 ) and CHMP4B ( Pires et al . , 2009 ) . Therefore , in addition to sharing structural similarity to cellular ESCRT-III components , pUL51 shares the ability to form filaments in vitro .
We present here the structure of HSV-1 pUL7 in complex with pUL51 . In solution this complex forms a 1:2 heterotrimer that is capable of forming higher-order oligomers ( Figure 1 ) . The C-terminal region of pUL51 is predicted to be disordered ( Figure 1—figure supplement 2 ) and is extended in solution ( Figure 1C , D and L ) , consistent with this region having little intrinsic structure . The crystal structure of pUL7 in complex with pUL51 ( 8–142 ) shows pUL7 to comprise a single compact globular domain that adopts a previously-unobserved CUSTARD fold ( Figure 2; Figure 2—figure supplement 2 ) . A single molecule of pUL51 is bound to pUL7 in this crystal structure via an extended hydrophobic interface that is largely conserved across α-herpesviruses ( Figure 3 ) and there is evidence that residues at the interface are co-evolving ( Supplementary file 1–Table S4 ) . Most of the pUL7-interacting residues lie within the hydrophobic loop and helix α1 of pUL51 ( residues 45–88 ) , consistent with a recent report that pUL51 residues 30–90 are sufficient for the interaction with pUL7 in transfected cells ( Feutz et al . , 2019 ) . Recruitment of the second copy of pUL51 to the pUL7:pUL51 complex in solution requires pUL51 residues 8–40 ( Figure 1; Figure 2—figure supplement 1 ) , consistent with observations that the equivalent N-terminal region of the HCMV pUL51 homologue pUL71 is required for its self-association both in vitro and in cultured cells ( Meissner et al . , 2012 ) , and that VZV pUL51 homologue pORF7 can also form higher-order oligomers ( Wang et al . , 2017 ) . Furthermore , we showed that pUL51 ( 1–170 ) can form long filaments that are reminiscent of those formed by cellular ESCRT-III components ( Figure 5 ) . The interaction between pUL7 and pUL51 homologues is conserved across all three families of herpesvirus ( Figure 3A ) , as is the association of these complexes with trans-Golgi compartments in cultured cells ( Figure 4 ) , but of the complexes tested only HSV-1 pUL7:pUL51 associates with focal adhesions in cultured cells ( Figure 4—figure supplement 1; Figure 4—figure supplement 2 ) . The conserved association of pUL7:pUL51 complexes with trans-Golgi membranes is consistent with a conserved role for this complex in herpesvirus assembly . Assembly of HSV-1 occurs at juxtanuclear membranes that contain cellular trans-Golgi and endosomal marker proteins ( Owen et al . , 2015; Henaff et al . , 2012 ) and that are derived , at least in part , from recycling endosomes ( Hollinshead et al . , 2012 ) . Similarly , HCMV assembly occurs at viral assembly compartments that contain trans-Golgi marker proteins ( Das et al . , 2007; Sanchez et al . , 2000 ) and mutation of the pUL71 Yxxϕ motif , which mediates recycling from the plasma membrane via recognition by AP2 ( Kelly et al . , 2014 ) , causes re-localization of pUL71 to the plasma membrane and prevents efficient HCMV assembly ( Dietz et al . , 2018 ) . Given the conservation of the pUL7:pUL51 interaction , the conserved localization of this complex to trans-Golgi membranes , and the established evidence supporting roles for pUL7 or pUL51 homologues in virus assembly ( Albecka et al . , 2017; Nozawa et al . , 2005; Jiang et al . , 2017; Klupp et al . , 2005; Womack and Shenk , 2010; Schauflinger et al . , 2011; Ahlqvist and Mocarski , 2011; Yanagi et al . , 2019; Butnaru and Gaglia , 2019 ) , we propose that pUL7 and pUL51 form a complex that is conserved across herpesviruses and functions to promote virus assembly by stimulating cytoplasmic envelopment of nascent virions . pUL51 forms large aggregates when expressed in the absence of pUL7 ( Figure 5E–G and Figure 1—figure supplement 1 ) , suggesting that the binding of pUL7 physically interferes with the ability of pUL51 to self-associate . The helix-turn-helix conformation of pUL51 resembles the cellular ESCRT-III component CHMP4B ( Figure 5A ) and , like CHMP4B , pUL51 ( 1–170 ) can form long filaments in vitro ( Figure 5H and I ) . Polymerization of CHMP4B is known to be regulated by association with CC2D1A in humans ( Martinelli et al . , 2012 ) and in flies the protein Lgd regulates polymerization of the CHMP4B-homologue Shrub ( McMillan et al . , 2017 ) . Superposition of the pUL7:pUL51 core heterodimer onto Shrub shows that pUL7 occupies the space that would be occupied by the adjacent Shrub molecule of a putative Shrub homopolymer ( Figure 5J; McMillan et al . , 2016 ) . Similarly , the DM14-3 domain of Lgd , which is sufficient to bind Shrub in vitro and prevent Shrub polymerization ( McMillan et al . , 2017 ) , occupies a similar space to helices α8 and α9 of pUL7 ( Figure 5K ) . Taken together , these observations suggest that polymerization of pUL51 may utilize equivalent molecular surfaces as cellular CHMP4B homologues . We propose that pUL7 acts as a chaperone of pUL51 , regulating its polymerization by physically inhibiting its self-association . The N-terminal region of pUL51 is palmitoylated and this modification is required for its membrane association ( Nozawa et al . , 2003 ) . These properties are shared by the N-terminally myristoylated cellular ESCRT-III component CHMP6 ( Yorikawa et al . , 2005 ) . Activity of ESCRT-III components and VPS4 , the AAA-ATPase that dissociates ESCRT-III and promotes bud scission ( McCullough et al . , 2018; Maity et al . , 2019 ) , are known to be required for efficient assembly of HSV-1 ( Crump et al . , 2007; Pawliczek and Crump , 2009 ) . During cellular budding events ESCRT-III proteins are recruited to sites of membrane deformation via direct interactions with components of the ESCRT-I and ESCRT-II complexes , or with the Bro1-domain containing proteins Alix , HD-PTP or BROX ( Christ et al . , 2017 ) . However , these proteins are not required for HSV-1 assembly ( Pawliczek and Crump , 2009; Barnes and Wilson , 2020 ) . The lack of requirement for cellular initiators of ESCRT-III polymerization , combined with the ability of pUL51 to bind directly to membranes and to form filaments , suggests that pUL51 may directly promote membrane deformation and virus budding – effectively performing the roles of multiple cellular ESCRT-III components . This proposal is consistent with observations made in HCMV , where mutation of the pUL51 homologue pUL71 results in the accumulation of HCMV particles in membrane buds with narrow necks ( Read et al . , 2019 ) that are reminiscent of the stalled budding profiles observed for HIV-1 when ESCRT-III activity is perturbed ( von Schwedler et al . , 2003 ) or HSV-1 in cells expressing a dominant negative form of VPS4 ( Crump et al . , 2007 ) . The mechanism by which herpesviruses recruit ESCRT-III to tegument-wrapped capsids in order to catalyze cytoplasmic envelopment remains poorly characterized ( Barnes and Wilson , 2019 ) . Based on the structural and functional homology between pUL51 and CHMP4B/CHMP6 we propose that pUL51 and homologues act as viral ESCRT-III components . The interaction between pUL7 and pUL51 homologues is conserved across herpesviruses , and we propose that this interaction regulates polymerization of pUL51 homologues in infected cells . It remains unclear whether there exists a trigger that would promote pUL7 dissociation from pUL51 , or whether high local concentrations of pUL51 at sites of virus assembly would be sufficient to stimulate pUL51 polymerization . Furthermore , as deletion of pUL51 or its homologues does not completely abolish virus replication ( Albecka et al . , 2017; Nozawa et al . , 2005; Roller et al . , 2014; Jiang et al . , 2017; Klupp et al . , 2005; Schauflinger et al . , 2011; Yanagi et al . , 2019 ) it is likely that herpesviruses use multiple , redundant mechanisms to ensure efficient wrapping of nascent virions .
Full-length herpes simplex virus ( HSV ) −1 strain KOS protein pUL51 ( UniProt ID D3YPL0 ) , either with the wild-type sequence or where residue Cys9 ( the palmitoyl group acceptor ) had been substituted with serine , was expressed with an N-terminal MetAlaHis6 tag and purified by Ni2+ affinity capture and size-exclusion chromatography as described in Albecka et al . , 2017 . pUL51 ( 1-170 ) was expressed with an N-terminal MetAlaHis6 tag and residue Cys9 substituted to serine in the Escherichia coli strain T7 express lysY/Iq ( New England BioLabs ) . Bacteria were cultured in 2 × TY medium , recombinant proteins being expressed overnight at 25°C following addition of 0 . 4 mM isopropyl β-d-1-thiogalactopyranoside . The complex of HSV-1 strain KOS proteins pUL7 ( UniProt ID A0A110B4Q7 ) and pUL51 , or truncations thereof , were co-expressed in the E . coli strain T7 express lysY/Iq ( New England BioLabs ) using the polycistronic vector pOPC ( Tan , 2001 ) . The nucleotide sequence of pUL7 had been optimized to enhance recombinant expression ( GeneArt ) and , where present , residue Cys9 of pUL51 had been substituted with serine . For all experiments except Figure 2—figure supplement 1 , pUL7 was fused to a C-terminal human rhinovirus 3C protease recognition sequence and GST purification tag . For Figure 2—figure supplement 1A , the GST and 3C recognition sequence were fused to the N terminus of pUL7 . Bacteria were cultured in 2 × TY medium , recombinant proteins being expressed overnight at 22°C following addition of 0 . 4 mM isopropyl β-d-1-thiogalactopyranoside . Bacterial cell pellets were resuspended in lysis buffer ( 50 mM sodium phosphate pH 7 . 5 , 500 mM NaCl , 0 . 5 mM MgCl2 , 1 . 4 mM β-mercaptoethanol , 0 . 05% Tween-20 ) supplemented with 400 U bovine pancreas DNase I ( Merck ) and 200 μL EDTA-free protease inhibitor cocktail ( Merck ) at 4°C . Cells were lysed using a TS series cell disruptor ( Constant Systems ) at 24 kPSI and the lysate was cleared by centrifugation at 40 , 000 × g for 30 min at 4°C . For soluble proteins , cleared lysate was incubated with glutathione sepharose 4B resin ( GE Healthcare ) equilibrated in GST wash buffer ( 50 mM sodium phosphate pH 7 . 5 , 300 mM NaCl , 1 mM dithiothreitol ( DTT ) ) for 1 hr at 4°C before being applied to a column and washed with >10 column volumes ( c . v . ) of GST wash buffer . To remove contaminating nucleic acids , pUL7:pUL51 complexes were resuspended in 25 mM sodium phosphate pH 7 . 5 , 150 mM NaCl , 0 . 5 mM DTT , 1 mM MgCl2 and incubated with 2000 U of benzonase ( Merck ) for 1 hr at room temperature before being applied to a column , washed with 8 c . v . of 50 mM sodium phosphate pH 7 . 5 , 1M NaCl , and then washed with 4 c . v . of GST wash buffer . Bound protein was eluted using GST wash buffer supplemented with 25 mM reduced l-glutathione , concentrated , and further purified by size-exclusion chromatography ( SEC ) using an S200 16/600 column ( GE Healthcare ) equilibrated in 20 mM tris pH 7 . 5 , 200 mM NaCl , 1 mM DTT . The GST tag was removed by supplementing the pooled SEC fractions containing pUL7:pUL51 complex with 0 . 5 mM EDTA and then incubating with 40 μg of GST-tagged human rhinovirus 3C protease . Free GST and uncleaved GST-tagged pUL7 were captured using glutathione sepharose resin and the cleaved complex was again subjected to SEC using S200 16/600 or 10/300 columns ( GE Healthcare ) equilibrated in 20 mM tris pH 7 . 5 , 200 mM NaCl , 1 mM DTT , 3% ( v/v ) glycerol . Purified pUL7:pUL51 was concentrated , snap-frozen in liquid nitrogen as small aliquots , and stored at −80°C . Protein concentrations were estimated from absorbance at 280 nm using calculated extinction coefficients ( Wilkins et al . , 1999 ) where pUL7 and pUL51 were assumed to be present in 1:2 molar ratios for all complexes except for pUL7:pUL51 ( 41-142 ) , where an equimolar ratio was assumed . pUL51 ( 1-170 ) was purified from inclusion bodies and refolded by rapid dilution as described previously for vaccinia virus CrmE ( Graham et al . , 2007 ) . Briefly , cells were lysed and the lysates clarified as above . Insoluble pellets were then washed four times by resuspension in inclusion body wash buffer ( 50 mM tris pH 7 . 5 , 100 mM NaCl , 0 . 5% Triton X-100 ) using a Dounce homogenizer , followed by centrifugation at 25 , 000 × g for 10 min at 4°C . Pellets were washed once with inclusion body wash buffer without Triton X-100 , then resuspended in solubilization buffer ( 50 mM tris pH 7 . 5 , 100 mM NaCl , 6 M guanidine hydrochloride , 10 mM EDTA , 10 mM DTT ) for 3 hr at 4°C . Protein concentration was estimated from absorbance at 280 nm using a calculated extinction coefficient ( Wilkins et al . , 1999 ) and the unfolded protein was stored at −20°C . To refold , 20 mg aliquots of pUL51 ( 1-170 ) were thawed and supplemented with 10 mM DTT , then subjected to a rapid 1:100 ( v/v ) dilution into refold buffer ( 200 mM tris pH 7 . 5 , 10 mM EDTA , 1 M l-arginine , 1% ( v/v ) EDTA-free protease inhibitor cocktail ( Merck ) ) that was briskly stirred for 2 hr at 4°C . Refolded pUL51 ( 1-170 ) was buffer-exchanged into 20 mM phosphate buffer pH 7 . 5 or 20 mM HEPES pH 7 . 5 using a Sephadex PD-10 gravity column ( GE Healthcare ) or into 20 mM tris pH 8 . 5 by exhaustive dialysis overnight at 4°C . Multi-angle light scattering ( MALS ) experiments were performed immediately following SEC ( SEC-MALS ) by inline measurement of static light scattering ( DAWN 8+; Wyatt Technology ) , differential refractive index ( Optilab T-rEX; Wyatt Technology ) , and UV absorbance ( 1260 UV; Agilent Technologies ) . Samples ( 100 μL ) were injected onto an S200 Increase 10/300 column ( GE Healthcare ) equilibrated in in 20 mM tris pH 7 . 5 , 200 mM NaCl , 3% ( v/v ) glycerol , 0 . 25 mM tris ( 2-carboxyethyl ) phosphine ( TCEP ) at 0 . 4 mL/min . Molecular masses were calculated using ASTRA 6 ( Wyatt Technology ) and figures were prepared using Prism 7 ( GraphPad ) . Continuous flow small-angle X-ray scattering ( SAXS ) experiments were performed immediately following SEC with in-line MALS and dynamic light scattering ( SEC-SAXS-MALS-DLS ) , at EMBL-P12 bioSAXS beam line ( PETRAIII , DESY , Hamburg ) ( Blanchet et al . , 2015; Graewert et al . , 2015 ) . Scattering data ( I ( s ) versus s , where s = 4πsinθ/λ nm−1 , 2θ is the scattering angle , and λ is the X-ray wavelength , 0 . 124 nm ) were recorded using a Pilatus 6M detector ( Dectris ) with 1 s sample exposure times for a total of 3600 data frames spanning the entire course of the SEC separation . 90 μL of purified pUL7:pUL51 ( 8 mg/mL ) or pUL7:pUL51 ( 8–142 ) ( 4 . 5 mg/mL ) was injected at 0 . 5 mL/min onto an S200 Increase 10/300 column ( GE Healthcare ) equilibrated in 20 mM HEPES pH 7 . 5 , 200 mM NaCl , 3% ( v/v ) glycerol , 1 mM DTT ( pUL7:pUL51 ) or 20 mM tris pH 7 . 5 , 200 mM NaCl , 3% ( v/v ) glycerol , 0 . 25 mM TCEP ( pUL7:pUL51 ( 8–142 ) ) . Data presented in Figure 1 are representative of three replicate SEC-SAXS experiments . SAXS data for the pUL7:pUL51 complex , which eluted as two peaks , were recorded from macromolecule-containing and -free fractions as follows: heterohexamers ( frames 1236–1283 s ) , heterotrimers ( frames 1382–1416 s ) and solvent blank ( spanning pre- and post-sample elution frames ) . For the pUL7:pUL51 ( 8–142 ) complex , the SEC-SAXS experiment was performed three times as follows: heterotrimers ( frames 1779–1886 s , 1785–1876 s and 1781–1880 s for the three experiments , respectively ) and solvent blanks ( spanning pre- and post-sample elution frames ) . Primary data reduction was performed using CHROMIXS ( Panjkovich and Svergun , 2018 ) and 2D-to-1D radial averaging was performed using the SASFLOW pipeline ( Franke et al . , 2012 ) . Buffer frames were tested for statistical equivalence using all pairwise comparison CorMap p values set at a significance threshold ( α ) of 0 . 01 ( Franke et al . , 2015 ) before being averaged to generate a final buffer scattering profile and subtracted from the relevant macromolecule elution peaks . Subtracted data blocks producing a consistent Rg through the elution profile ( as evaluated using the Guinier approximation ) ( Guinier , 1939 ) were scaled and checked for similarity using CorMap before being averaged to produce the final reduced 1D scattering profiles . For the pUL7:pUL51 ( 8–142 ) construct , the averaged scattering profiles obtained for the three repeated SEC-SAXS measurements underwent additional scaling and final combined averaging . Primary data processing , including all CorMap calculations , was performed in PrimusQT of the ATSAS package ( Petoukhov et al . , 2012 ) . Molecular weight estimates were calculated using the datporod ( Porod volume ) ( Petoukhov et al . , 2012 ) , datmow ( Fischer et al . , 2010 ) , datvc ( Rambo and Tainer , 2013 ) and Bayesian consensus modules ( Hajizadeh et al . , 2018 ) of the ATSAS package . Indirect inverse Fourier transform of the SAXS data and the corresponding probable real space-scattering pair distance distributions ( p ( r ) versus r profile ) were calculated using GNOM ( Svergun , 1992 ) , from which the Rg and Dmax were determined . In addition , the a priori assessment of the non-uniqueness of scattering data was performed using AMBIMETER ( Petoukhov and Svergun , 2015 ) . SAXS data collection and analysis parameters are summarized in Supplementary file 1–Table S1 . Ab initio modeling was performed using GASBOR ( Svergun et al . , 2001 ) and DAMMIN ( Svergun , 1999 ) . For pUL7:pUL51 , reciprocal space intensity fitting accounting for oligomeric equilibrium with P2 symmetry imposed ( GASBORMX ) was used to simultaneously fit the 1:2 ( heterotrimer ) and 2:4 ( heterohexamer ) pUL7:pUL51 SAXS profiles . The two SEC-elution peaks contained heterohexamer:heterotrimer volume fractions of 1 . 0:0 . 0 and 0 . 2:0 . 8 , respectively , as determined by GASBORMX . Because SAXS data can be ambiguous with respect to shape restoration , DAMMIN and GASBOR were run 20 times and the consistency of the individual models was evaluated using the normalized spatial discrepancy ( NSD ) metric ( Volkov and Svergun , 2003 ) . Dummy-atom models were clustered using DAMCLUST ( Volkov and Svergun , 2003 ) , averaged using DAMCLUST ( pUL7:pUL51 ) or DAMAVER ( pUL7:pUL51 ( 8–142 ) ) , and refined using DAMMIN . For the pUL7:pUL51 heterohexamer three clusters were identified , which visually corresponded to parallel or anti-parallel dimers of heterotrimers , whereas for the pUL7:pUL51 ( 8–142 ) heterotrimer all models formed a single cluster . The refined dummy-atom models that best fit the SAXS profile ( lowest χ2 ) are shown in Figure 1 . Purified pUL7:pUL51 ( 8–142 ) at 1 mg/mL ( 16 . 4 μM ) in HEPES SAXS buffer was incubated with 20- to 100-fold molar excess of disuccinimidyl sulfoxide ( DSSO; ThermoFisher ) or disuccinimidyl dibutyric urea ( DSBU; ThermoFisher ) dissolved in DMSO , or with DMSO carrier alone ( the final DMSO concentration remaining below 2% ( v/v ) in all cases ) . Reactions were incubated at room temperature for 30 min before quenching by addition of 1 M tris pH 7 . 5 to a final tris concentration of 20 mM . Samples were separated by SDS-PAGE using a 4–12% Bolt Bis-Tris gel ( ThermoFisher ) in MOPS running buffer and stained with InstantBlue Coomassie Protein Stain ( Expedeon ) according to the manufacturers’ instructions . Cross-linked samples corresponding to pUL7:pUL51 ( 8–142 ) heterodimers ( 1:1 ) or heterotrimers ( 1:2 ) were excised , reduced , alkylated and digested in-gel using trypsin . The resulting peptides were analyzed using an Orbitrap Fusion Lumos coupled to an Ultimate 3000 RSLC nano UHPLC equipped with a 100 µm ID × 2 cm Acclaim PepMap Precolumn and a 75 µm ID × 50 cm , 2 µm particle Acclaim PepMap RSLC analytical column ( ThermoFisher Scientific ) . Loading solvent was 0 . 1% formic acid ( FA ) with analytical solvents A: 0 . 1% FA and B: 80% ( v/v ) acetonitrile ( MeCN ) + 0 . 1% FA . Samples were loaded at 5 µL/min , loading solvent for 5 min before beginning the analytical gradient . The analytical gradient was 3% to 40% B over 42 min , rising to 95% B by 45 min , followed by a 4 min wash at 95% B , and finally equilibration at 3% solvent B for 10 min . Columns were held at 40°C . Data were acquired in a DDA fashion with MS3 triggered by a targeted mass difference . MS1 was acquired from 375 to 1500 Th at 60 , 000 resolution , 4 × 105 AGC target and 50 ms maximum injection time . MS2 used quadrupole isolation at an isolation width of m/z 1 . 6 and CID fragmentation ( 25% NCE ) . Fragment ions were scanned in the Orbitrap with 5 × 104 AGC target and 100 ms maximum injection time . MS3 was triggered by a targeted mass difference of 25 . 979 for DSBU and 31 . 9721 for DSSO with HCD fragmentation ( 30% NCE ) and fragment ions scanned in the ion trap with an AGC target of 2 . 0 × 104 . Raw files were process using XLinkX 2 . 2 in Proteome Discoverer 2 . 2 . 0 . 388 ( ThermoFisher ) . MS2 or MS3 spectra were selected based on the identification of either DSSO ( K +158 . 004 Da ) or DSBU ( K +196 . 085 Da ) and then processed in two workflows in parallel with the following parameters . Workflow 1: XlinkX Search against a database containing an HSV-1 proteome ( downloaded 04 . 04 . 2016 ) , E . coli proteome ( downloaded 06 . 09 . 2019 with OPGE removed ) and 246 common contaminants; full trypsin digestion; carbamidomethyl static modification of cysteines; oxidation variable modification of methionines; 1% FDR using XlinkX validator Percolator . Workflow 2: spectra filtered for either MS2 or MS3 scans with each set searched separately using Mascot against a database containing an E . coli proteome ( downloaded 06 . 09 . 2019 with OPGE removed ) with 246 common contaminants , and HSV-1 proteome ( downloaded 04 . 04 . 2016 ) ; PSM validator Max . Delta Cn = 0 . 05 . Statistical validation of identified cross-link peptides from both workflows was carried out by a joint consensus workflow . Pseudo-atomic modelling of the pUL7:pUL51 ( 8–142 ) heterotrimer was performed using CORAL ( Petoukhov et al . , 2012 ) . The core heterodimer structure , comprising pUL7 and pUL51 ( 41–125 ) , was fixed in this model and a second copy of pUL51 ( 41–125 ) was free to move . To include a priori information about predicted secondary structure ( Figure 1—figure supplement 2 ) , the pUL51 ( 8–142 ) sequence was modelled by I-TASSER ( Yang et al . , 2015 ) using pUL51 residues 41–125 from core heterodimer structure as a template . Secondary structural ( helical ) elements from the I-TASSER model were included for regions of pUL51 that were disordered in the crystal structure ( residues 8–23 and 126–142 ) or involved in the artefactual interaction with the pUL7 purification tag ( residues 24–40 ) . DSSO and DSBU cross-links were used to generate maximal inter-residue distance restraints of 26 . 1 and 28 . 3 Å , respectively ( Merkley et al . , 2014 ) . Cross-links between residues of pUL7 and pUL51 that are not feasible based on the core heterodimer structure were assumed to be between pUL7 and the additional copy of pUL51 . Cross-links that could not be assigned unambiguously ( e . g . cross-links between pUL51 residues that could be either inter- or intra-molecular ) were permuted and all possible restraint geometries were tested by modelling against the pUL7:pUL51 ( 8–142 ) SAXS profile ( s < 3 . 2 nm−1 ) . The final distribution of target function ( F ) values was clearly bimodal: models from the cluster with higher F values were unable to simultaneously satisfy the provided cross-link restraints and the SAXS data , and were thus discarded . Remaining models were assessed for fit to the SAXS profile ( χ2 ) using CRYSOL . pUL7:pUL51 ( 8–142 ) was crystallized in sitting or hanging drops by mixing 1 µL of 5 . 3 mg/mL protein with 0 . 5 µL of 0 . 5 M benzamidine hydrochloride and 1 µL of reservoir solution containing 0 . 15 M sodium citrate pH 5 . 5 , 12% ( v/v ) 2-methyl-2 , 4-pentanediol , 0 . 1 M NaCl and equilibrating against 200 µL reservoirs at 16°C for at least one week . Crystals of pUL7:pUL51 ( 8–142 ) were cryoprotected by brief immersion in reservoir solution supplemented with 20% ( v/v ) glycerol before flash cryo-cooling by plunging into liquid nitrogen . For multiple-wavelength anomalous dispersion ( MAD ) phasing experiments , 1 μL of 1 mM mercury ( II ) acetate in reservoir solution was added to the mother liquor and incubated at 16°C for 4 hr before cryoprotection and cryo-cooling as described above . Diffraction data were recorded at 100 K on a Pilatus3 6M detector ( Dectris ) at Diamond Light Source beamline I03 . Images were processed using DIALS ( Winter et al . , 2018 ) , either using the DUI graphical interface ( Fuentes-Montero et al . , 2016 ) for the native dataset or the xia2 automated processing pipeline ( Winter , 2010 ) for the mercury derivative datasets . Scaling and merging was performed using AIMLESS ( Evans and Murshudov , 2013 ) and data collection statistics are shown in Supplementary file 1–Table S2 . Four-wavelength anomalous dispersion analysis of the mercury derivative ( space group P 4 21 2 ) was performed using CRANK2 ( Skubák and Pannu , 2013 ) , followed by iterative density modification and automated model building using parrot ( Cowtan , 2010 ) and buccaneer ( Cowtan , 2012; Cowtan , 2006 ) , part of the CCP4 program suite ( Winn et al . , 2011 ) . An initial model comprising a single pUL7:pUL51 ( 8–142 ) core heterodimer was used as a molecular replacement model to solve the structure of the native complex ( space group P 21 ) using MolRep ( Vagin and Teplyakov , 2010 ) , identifying four core heterodimers in the asymmetric unit with pseudo four-fold non-crystallographic symmetry . Density modification and automated model building were performed using parrot and buccaneer , respectively , followed by cycles of iterative manual rebuilding in COOT ( Emsley et al . , 2010 ) and TLS plus positional refinement using Refmac5 ( Murshudov et al . , 1997 ) with local NCS restraints . The building was assisted by the use of real-time molecular dynamics-assisted model building and map fitting with the program ISOLDE ( Croll , 2018 ) . Final cycles of refinement following manual rebuilding were performed using autoBUSTER ( Bricogne et al . , 2017 ) with local NCS restraints and TLS groups that were identified with the assistance of the TLSMD server ( Painter and Merritt , 2006 ) . The quality of the model was monitored throughout the refinement process using MolProbity ( Chen et al . , 2010 ) and the validation tools in COOT . Molecular graphics were produced using PyMOL ( Schrodinger LLC , 2015 ) . Conservation of pUL7 and pUL51 residues across the α-herpesviruses was mapped onto the structure using the CONSURF server ( Ashkenazy et al . , 2016 ) and the sequence alignment used for co-evolutionary analysis ( Data set 2 , below ) . Circular dichroism spectra were recorded on a Jasco J‐810 spectropolarimeter at 20°C using 1 mg/mL pUL51 ( 1–170 ) in 20 mM phosphate buffer , pH 7 . 5 . A total of 20 spectra were recorded per sample at 50 nm/min with 1 nm bandwidth between 260–190 nm . Spectra were converted to mean residue ellipticity , averaged , and smoothed ( Savitzky and Golay method , second order smoothing , 5 nm sliding window ) using Prism 7 ( GraphPad ) . Spectra were decomposed using CDSSTR ( Sreerama and Woody , 2000 ) as implemented by DichroWeb ( Whitmore and Wallace , 2008 ) using a 1 nm wavelength step and reference set 7 . Copper grids ( 300 mesh ) coated with formvar and continuous carbon ( EM Systems Support ) were glow discharged in air for 20 s . Three microlitres of 10–100 μM pUL51 ( 1-170 ) in 20 mM HEPES pH 7 . 5 or 20 mM tris pH 8 . 5 was applied to the grid and allowed to adsorb ( 30 s to 2 min ) before wicking away excess solvent with filter paper ( Whatman ) . Grids were sequentially applied to two 30 µL drops of 2% ( w/v ) uranyl acetate for approximately 3 s and then 30 s , respectively . Excess stain was wicked away using filter paper ( Whatman ) and grids were allowed to air dry . Images were obtained using a Tecnai Spirit transmission electron microscope ( FEI ) operating at 120 kV , equipped with an Ultrascan 1000 CCD camera ( Gatan ) . Images were acquired at 30 , 000–120 , 000 × magnification with −1 μm defocus and a total electron dose of 20–40 e–/A2 across 1 s exposures . Protein sequences of pUL7 and pUL51 homologues from representative α- , β- and γ-herpesviruses that infect humans were as follows ( Uniprot ID ) : HSV-1 pUL7 ( A0A110B4Q7 ) and pUL51 ( D3YPL0 ) , VZV pORF53 ( P09301 ) and pORF7 ( P09271 ) , HCMV pUL103 ( D3YS25 ) and pUL71 ( D3YRZ9 ) , human herpesvirus 7 ( HHV-7 ) U75 ( P52458 ) and U44 ( P52474 ) , KSHV pORF42 ( F5HAI6 ) and pORF55 ( F5H9W9 ) , Epstein-Barr virus ( EBV ) BBRF2 ( P29882 ) and BSRF1 ( P0CK49 ) . Secondary structure prediction was performed using the NetSurfP-1 . 1 server ( Petersen et al . , 2009 ) , disorder prediction was performed using moreRONN version 4 . 6 ( Ramraj , 2014 ) and palmitoylation sites were predicted using CSS-Palm 4 . 0 ( Ren et al . , 2008 ) using the confidence threshold ‘High’ . Structure-based database searches for proteins with similar folds to pUL7 or pUL51 were performed using PDBeFOLD ( Krissinel and Henrick , 2004 ) , DALI ( Holm and Laakso , 2016 ) and CATHEDRAL ( Redfern et al . , 2007 ) . Clustal Omega ( Sievers and Higgins , 2018 ) was used to generate seed alignments for Alphaherpesvirinae ( HSV-1 , VZV ) or across all sub-families ( HSV-1 , VZV , HCMV , HHV7 , KSHV , EBV ) . Seed alignments were used to generate hidden Markov models ( HMMs ) using the HMMER ( Eddy , 2011 ) program hmmbuild . HMMs were subsequently used to extract and align homologue sequences from UniProt using HMMER ( Eddy , 2011 ) program hmmsearch locally ( for Alphaherpesvirinae ) or using the HMMER web server ( Finn et al . , 2011 ) ( for all Herpesviridae ) . We mapped the proteins thus identified to the source virus genomes , discarding any protein sequences from partial genome sequences where pUL7 or pUL51 were absent . Our initial alignments comprised distinct pairs of pUL7 and pUL51 sequences from 205 Alphaherpesvirinae , 147 Betaherpesvirinae and 78 Gammaherpesvirinae , and the alignments for homologues in each subfamily were improved by manual correction . The structure of the core pUL7:pUL51 heterodimer was inspected to compile a table of 63 pairwise interactions between amino acids in the two proteins , 59 of which involved side chain atoms . These interactions arose from 33 distinct residues in pUL7 and 29 residues in pUL51 . Using the alignments generated above , we compiled a matrix of amino acid pairs ( one in each pUL7 and pUL51 homologue ) that are predicted to interact . For each pair of interacting sites , we calculated the strength of the correlation between its amino acid states across the alignment . For this purpose , we followed Zaykin and colleagues ( equation 3 of Zaykin et al . , 2008 ) . For a single pair of sites , whose alignments contain , respectively , k and m amino acid states , then the correlation between two of those states , i and j , isrij=pij−pipjpi ( 1−pi ) pj ( 1−pj ) where pi is the proportion of strains that carry amino acid i at the relevant site in pUL7 , pj is the proportion that carry amino acid j in pUL51 , and pij is the proportion of strains that carry both . The total strength of correlation at the pair , T , isT= ( k-1 ) ( m-1 ) kmN∑i=1k∑j=1mrij2where N is the number of strains , and the test statistic , z , is this quantity summed across all interacting pairsz=∑i=1ITwhere I is the number of interactions . To test whether z , the signature of coevolution , was significantly greater than would be expected by chance , we compared the measured test statistic to a null distribution comprised of 106 data sets for which the interacting partner sites were randomly permuted . The p value for each test was the proportion of randomly permuted data sets for which the test statistic was greater than or equal to the value for the real data . Under our permutation scheme , each randomized data set resembled the true data in terms of the total number of interactions , the number of interactions involving each site , and the allele frequencies at each putatively interacting site . The test also controls for shared evolutionary history , which can generate spurious evidence of coevolution ( Horner et al . , 2008 ) . As a consequence , however , the test is expected to be highly conservative , because many of the randomized interactions might resemble the true interactions ( not least because single sites were involved in multiple putative interactions ) and because , under plausible evolutionary scenarios , multiple interacting pairs might evolve in concert . Of this set of interactions , not all could be analyzed for all sequences , either because of missing amino acids in some sequences ( due to both deletions and missing data ) , or because we could not be confident in the alignment of some sites . There was thus an inherent trade-off between maximizing the number of interactions and maximizing the number of strains in the test . We initially examined alignments across the Herpesviridae , but the low sequence identity meant that we could not confidently assign homology for most sites involved in putative interactions . Across the family as a whole , only 12 conserved interacting pairs could be analyzed , and this led to an underpowered test . Accordingly , we restricted our analyses to the Alphaherpesvirinae . From our initial alignments we excluded six very short sequences ( one pUL51 homologue: A0A2Z4H851 , and five pUL7 homologue: A0A120I2R6 , A0A097HXP5 , A0A286MM74 , A0A2Z4H5E9 , A0A120I2N0 ) . This led to an alignment containing 199 strains , for which the amino acids of 35/63 interacting sites could be confidently aligned across all strains . These 35 interactions involved 21 sites from pUL51 and 19 sites from pUL7 ( main text and Data set 1; Supplementary file 1–Table S4 ) . Because so many interactions were missing from this analysis , we next excluded two further pUL7 homologue sequences ( B7FEJ7 , A0A0X8E9M8 ) where many of the interacting sites could not be confidently aligned . This led to an alignment of 197 strains , for which 54/63 interactions could be tested ( involving all 29 putatively interacting sites from pUL51 and 29/33 sites from pUL7 ) . Despite the increase in the size of the data set , results were little changed ( Data set 2; Supplementary file 1–Table S4 ) . Results were similarly little changed when we considered only interactions involving side chain atoms ( Data set 3; Supplementary file 1–Table S4 ) , and when we restricted our analysis to the subset of better conserved positions , as found in the regions of aligned sequence returned by HMMER ( Data set 4; Supplementary file 1–Table S4 ) . R code for performing the analysis is available as file Source code 1 . Sequence alignments and table of interacting residues are available in Source data 1 . Mycoplasma-free human embryonic kidney ( HEK ) 293 T and HeLa cells were maintained in Dulbecco’s modified Eagle’s medium ( DMEM; ThermoFisher ) supplemented with 10% ( v/v ) heat-inactivated fetal bovine serum ( FBS ) and 2 mM l-glutamine ( ThermoFisher ) . Cells were maintained at 37°C in a humidified 5% CO2 atmosphere . Plasmids for GFP-pUL7 ( N-terminal tag ) and pUL51-mCherry ( C-terminal tag ) were as used in Albecka et al . , 2017 . Homologues from other herpesviruses were cloned into pEGFP-C2 , encoding an N-terminal GFP tag , or pmCherry-N1 , encoding a C-terminal mCherry tag , as follows . pUL103 and pUL71 were cloned from HCMV strain Toledo cDNA , pORF42 and pORF55 were cloned from KSHV strain JSC-1 cDNA , and VZV pORF53 and pORF7 were cloned from codon-optimized synthetic genes ( GeneArt ) to boost their otherwise-poor expression in cultured cells . For co-precipitation experiments , 5 × 106 HEK 293 T cells were transfected by adding 1 µg total DNA ( split evenly by mass between the plasmids indicated ) and 1 . 5 µg of branched polyethylenimine ( PEI; average MW ~25 , 000 , Merck ) that had been diluted in Opti-MEM ( ThermoFisher ) and incubated together for 20 min before addition to cells . For immunocytochemistry , 7 . 5 × 104 HeLa cells/well were seeded in six-well plates containing four sterile no . 1 . 5 coverslips/well and grown overnight before being transfected by addition of 625 ng total DNA ( split evenly by mass between the plasmids indicated ) and 6 µL/well TransIT-LT1 ( Mirus ) that had been diluted in Opti-MEM and incubated together for 20 min before addition to cells . Cells were harvested 24 hr post-transfection by scraping in phosphate buffered saline ( PBS; 137 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , 1 . 8 mM KH2PO4 ) , and washed twice in PBS . Cell pellets were resuspended in lysis buffer ( 10 mM tris pH 7 . 5 , 150 mM NaCl , 0 . 5 mM EDTA , 0 . 5% IGEPAL CA-630 ( a . k . a . NP-40 , Merck ) , 1% ( v/v ) EDTA-free protease inhibitor cocktail ( Merck ) ) and incubated at 4°C for 30 min before clarification by centrifugation at 20 , 000 × g , 4°C for 10 min . The protein concentration in each lysate was normalized after assessment using the bicinchoninic acid assay ( ThermoFisher ) according to the manufacturer’s instructions . Normalised lysates were incubated for 1 hr at 4°C with GFP-Trap or RFP-Trap bead slurry ( Chromotek ) that had been pre-equilibrated in wash buffer ( 10 mM tris pH 7 . 5 , 150 mM NaCl , 0 . 5 mM EDTA ) . Following incubation , beads were washed three times , the supernatant was completely removed , beads were resuspended in SDS-PAGE loading buffer and the samples were heated at 95°C for 5 min to liberate bound proteins before removal of the beads by centrifugation . Samples were separated by SDS-PAGE using 12% or 15% polyacrylamide gels and transferred to Protran nitrocellulose membranes ( Perkin Elmer ) using the Mini-PROTEAN and Mini-Trans-Blot systems ( BioRad ) following the manufacturer’s protocol . After blocking in PBS with 5% ( w/v ) non-fat milk powder , membranes were incubated with primary antibody overnight at 4°C and then secondary antibody for 1 hr at room temperature . Dried blots were visualized on an Odyssey CLx infrared scanner ( LI-COR ) . Cells were transferred onto ice 24 hr post-transfection . Coverslips were washed with ice-cold PBS and incubated with cold 250 mM HEPES pH 7 . 5 , 4% ( v/v ) electron microscopy-grade formaldehyde ( PFA , Polysciences ) for 5 min on ice before being incubated with 250 mM HEPES pH 7 . 5 , 8% ( v/v ) PFA at room temperature for 10 min . Coverslips were washed with PBS before quenching of residual PFA by addition of 25 mM NH4Cl for 5 min at room temperature . After washing with PBS , cells were permeabilized by incubation with 0 . 1% saponin in PBS for 30 min before being incubated with blocking buffer ( 5% ( v/v ) FBS , 0 . 1% saponin in PBS ) for 30 min . Primary antibodies ( below ) were diluted in blocking buffer and incubated with coverslips for 2 hr . Coverslips were washed five times with blocking buffer before incubation for 1 hr with the relevant secondary antibodies ( below ) diluted in blocking buffer . Coverslips were washed five times with blocking buffer , three times with 0 . 1% saponin in PBS , three times with PBS , and finally with ultrapure water . Coverslips were mounted using Mowiol 4–88 ( Merck ) containing 200 nM 4′ , 6-diamidino-2-phenylindole ( DAPI ) and allowed to set overnight . Images were acquired using a Zeiss LSM780 confocal laser scanning microscopy system mounted on an AxioObserver . Z1 inverted microscope using a 64× Plan Apochromat objective ( NA 1 . 4 ) . Images were processed using Fiji ( Rueden et al . , 2017; Schindelin et al . , 2012 ) . Primary antibodies used for immunoblotting were rabbit anti-GFP ( Merck , G1544 ) , rat anti-RFP ( Chromotek , 5F8 ) , or mouse anti-GAPDH ( ThermoFisher , AM4300 ) . Secondary antibodies for immunoblotting were LI-COR IRDye 680T conjugated goat anti-rat ( 926–68029 ) , donkey anti-rabbit ( 926–68023 ) or goat anti-mouse ( 926–68020 ) , or LI-COR IRDye 800CW conjugated donkey anti-rabbit ( 926–32213 ) or goat anti-mouse ( 926-32210 ) . Primary antibodies used for immunocytochemistry were anti-TGN46 ( Bio-Rad , AHP500G ) , mouse anti-Paxillin ( BD Biosciences , 610051 ) , rabbit anti-Zyxin ( abcam , ab71842 ) , and secondary antibodies were Alexa Fluor 647 conjugated donkey anti-sheep ( A-21448 , ThermoFisher ) , goat anti-mouse ( A-21236 , ThermoFisher ) or goat anti-rabbit ( A-21245 , ThermoFisher ) . Crystallographic coordinates and structure factors have been deposited in the Protein Data Bank , www . pdb . org ( accession code 6T5A ) , and raw diffraction images have been deposited in the University of Cambridge Apollo repository ( https://doi . org/10 . 17863/CAM . 44914 ) . SAXS data , ab initio models and pseudo-atomic models have been deposited into the Small-Angle Scattering Biological Data Bank ( SASBDB ) ( Valentini et al . , 2015 ) under the accession codes SASDG37 ( pUL7:pUL51 ( 8–142 ) heterotrimer ) , SASDG47 ( pUL7:pUL51 heterohexamer ) and SASDG57 ( pUL7:pUL51 heterotrimer ) . Mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE ( Perez-Riverol et al . , 2019 ) partner repository with the dataset identifier PXD015941 . | Most people suffer from occasional cold sores , which are caused by the herpes simplex virus . This virus causes infections that last your entire life , but for the most part it lies dormant in your cells and reactivates only at times of stress . When it reactivates , the virus manipulates host cells to make new virus particles that may spread the infection to other people . Like many other viruses , herpes simplex viruses also steal jelly-like structures known as membranes from their host cells to form protective coats around new virus particles . In cells from humans and other animals , proteins belonging to a molecular machine known as ESCRT form filaments that bend and break membranes as the cells require . Many viruses hijack the ESCRT machinery to wrap membranes around new virus particles . However , herpes simplex viruses do not follow the usual rules for activating this machine . Instead , they rely on two viral proteins called pUL7 and pUL51 to hot-wire the ESCRT machinery . Previous studies have shown that these two proteins bind to each other , but it remained unclear how they work . Butt et al . used a combination of biochemical and biophysical techniques to solve the three-dimensional structures of pUL7 and pUL51 when bound to each other . The experiments determined that the structure of pUL51 resembles the structures of different components in the ESCRT machinery . Like the ESCRT proteins , pUL51 formed filaments , suggesting that pUL51 bends membranes in cells and that pUL7 blocks it from doing so until the time is right . Further experiments showed that the equivalents of pUL7 and pUL51 in other members of the herpes virus family also bind to each other in a similar way . These findings reveal that herpes simplex viruses and their close relatives have evolved a different strategy than many other viruses to steal membranes from host cells . Interfering with this mechanism may provide new avenues for designing drugs or improving vaccines against these viruses . The pUL7 and pUL51 proteins may also inspire new tools in biotechnology that could precisely control the shapes of biological membranes . | [
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] | 2020 | Insights into herpesvirus assembly from the structure of the pUL7:pUL51 complex |
DNA double-strand breaks ( DSB ) are the most deleterious type of DNA damage . In this work , we show that SIRT6 directly recognizes DNA damage through a tunnel-like structure that has high affinity for DSB . SIRT6 relocates to sites of damage independently of signaling and known sensors . It activates downstream signaling for DSB repair by triggering ATM recruitment , H2AX phosphorylation and the recruitment of proteins of the homologous recombination and non-homologous end joining pathways . Our findings indicate that SIRT6 plays a previously uncharacterized role as a DNA damage sensor , a critical factor in initiating the DNA damage response ( DDR ) . Moreover , other Sirtuins share some DSB-binding capacity and DDR activation . SIRT6 activates the DDR before the repair pathway is chosen , and prevents genomic instability . Our findings place SIRT6 as a sensor of DSB , and pave the road to dissecting the contributions of distinct DSB sensors in downstream signaling .
DNA safekeeping is one of the most important functions of the cell , allowing both the transfer of unchanged genetic material to the next generation and proper cellular functioning . Therefore , cells have evolved a sophisticated array of mechanisms to counteract daily endogenous and environmental assaults on the genome . These mechanisms rely on the recognition of the damaged DNA and its subsequent signaling . This signaling cascade triggers responses such as checkpoint activation and energy expenditure , and initiates the DNA repair process ( Bartek and Lukas , 2007; Bartek and Lukas , 2003; Ciccia and Elledge , 2010; San Filippo et al . , 2008; Hoeijmakers , 2009; Iyama and Wilson , 2013; Jackson and Bartek , 2009; Lieber , 2008; Madabhushi et al . , 2014 ) . If DNA damage is not properly recognized , all downstream signaling will be impaired . Among the various types of DNA damage , the most deleterious are double-strand breaks ( DSBs ) , which can cause translocations and the loss of genomic material . Until now , very few DSB sensors have been identified , among them poly ADP-ribose polymerase-1 ( PARP1 ) , the MRN complex ( MRE11 , RAD50 , NBS1 ) and Ku70/80 complex . All of these sensors initiate downstream signaling cascades which usually lead to the activation of specific repair pathways , such as homologous recombination ( HR ) or classical non-homologous end joining ( C-NHEJ ) ( Andres et al . , 2015; Sung et al . , 2014; Woods et al . , 2015 ) . How a specific repair pathway is chosen is not fully understood , but it is known that the identity of the DSB sensor influences the outcome . For example , the MRN complex is associated with HR , whereas Ku70/80 is associated with C-NHEJ . Once DNA damage is recognized , transducers from the phosphoinositide 3-kinase family ( e . g . , ATM , ATR , and DNA-PK ) are recruited to the sites of damage . They initiate a broad cascade , recruiting and activating hundreds of proteins which regulate the cellular response , including cell cycle progression , transcription , and metabolism . Ultimately , this response will determine whether the cell will live , senesce , or die . Failure to recognize and repair DSBs may lead to tissue ageing and disease ( Ciccia and Elledge , 2010; San Filippo et al . , 2008; Gasser et al . , 2017; Ribezzo et al . , 2016; Shiloh , 2014 ) . Sirtuin 6 ( SIRT6 ) is a chromatin-bound protein from a family of NAD+-dependent deacylases and ADP-ribosylases . Through these functions , SIRT6 regulates DNA damage repair ( DDR ) , telomere maintenance , and gene expression ( Feldman et al . , 2013; Jiang et al . , 2013; Kugel and Mostoslavsky , 2014 ) . The importance of SIRT6 to DNA maintenance is exemplified in SIRT6-KO mice phenotypes , which include accelerated ageing , cancer and neurodegeneration ( Kaluski et al . , 2017; Stein and Toiber , 2017; Tasselli et al . , 2017; Zorrilla-Zubilete et al . , 2018; Zwaans and Lombard , 2014 ) . SIRT6-deficient cells exhibit genomic instability , increased aerobic glycolysis and defects in DNA repair , among other phenotypes ( Kugel and Mostoslavsky , 2014; Stein and Toiber , 2017; Tasselli et al . , 2017 ) . Moreover , it was recently shown that the capacity of SIRT6 to repair DSB , but not to perform nucleotide excision repair ( NER ) , is directly linked to longevity ( Tian et al . , 2019 ) . We have shown previously that SIRT6 is one of the earliest factors recruited to DSBs , arriving at the damage site within 5 seconds and allowing the opening of chromatin at these sites by recruiting the chromatin remodeler SNF2H ( Toiber et al . , 2013 ) . In addition , the silencing of SIRT6 resulted in impaired downstream signaling , affecting the recruitment of key repair proteins such as Ku80 , BRCA1 and 53BP1 , among others , which are involved in both NHEJ and HR ( Bunting et al . , 2010; Chen et al . , 2017; Daley and Sung , 2014; Escribano-Díaz et al . , 2013; Gupta et al . , 2014; McCord et al . , 2009; Tang et al . , 2013; Toiber et al . , 2013 ) . These studies indicate that SIRT6 plays important roles at very early stages of the DDR . The prominent role of SIRT6 in the early steps of DNA damage signaling raises the fascinating possibility that it is also directly involved in DSB sensing . In this work , we show that SIRT6 is indeed a DSB sensor , able to detect broken DNA and to activate the DNA damage signaling , revealing its key role in DNA repair initiation .
First , we set out to investigate the relationship between SIRT6 and the three known DSB sensors , PARP1 , MRE11 ( of the MRN complex ) , and Ku80 ( of the Ku complex ) . PARP proteins are among the fastest known enzymes to arrive at DSBs , and their absence is known to impair the recruitment of DSB repair enzymes such as MRE11 , NBS1 and Ku80 ( Haince et al . , 2008; Yang et al . , 2018 ) . We inhibited PARP activity by supplementing cells with Olaparib , and tracked SIRT6 recruitment to sites of laser induced damage ( LID ) by live-cell imaging . Interestingly , SIRT6 recruitment was found to be independent of PARP activity . SIRT6 arrived at the damage sites even when PARP proteins were inhibited , while the recruitment of the macro-H2A macro domain , which was used as a control , depended entirely on PARylation ( Figure 1A–C , Figure 1—figure supplement 1A–C ) . Subsequently , we silenced MRE11 and observed impaired NBS1 recruitment but no effect on SIRT6 ( Figure 1D–F , Figure 1—figure supplement 1D–F ) . Ku80 silencing resulted in the expected defects in Ku70 recruitment , but did not impair SIRT6 arrival , in fact even larger amounts of SIRT6 were recruited to the site of damage ( Figure 1D–F , Figure 1—figure supplement 1 G-I ) . Moreover , when we tested the effect of SIRT6-KO ( Figure 1—figure supplement 1J ) on the recruitment of MRE11 and Ku80 , we found that while MRE11 recruitment was defective ( Figure 1G–I ) , Ku80 was not affected by the lack of SIRT6 ( Figure 1J–L ) . This suggests that SIRT6 may have a role in MRN recruitment or residency at DSB , but that the Ku complex is independent of it . Next , we silenced ATM and H2AX , which are both involved in DDR signaling ( Figure 1—figure supplement 2A ) . Even though this produced defective signaling , as shown by decreased DDR signaling ( Figure 1—figure supplement 2B–D ) , SIRT6 arrived at the sites of damage independently of these factors ( Figure 1—figure supplement 2E–G ) . These results indicate that SIRT6 recruitment is independent of known DSB sensors and is upstream of ATM and H2AX phosphorylation . To understand whether SIRT6 is recruited through by signaling initiated at the sites of damage themselves , we tested whether it can be recruited by the initiation of a DNA damage response in the absence of actual DNA damage ( lack of DSBs ) . To answer this question we took advantage of a tethering assay in which we used U2OS cells containing 256x lactose operator ( LacO ) repeats in their genome ( Shanbhag et al . , 2010; Tang et al . , 2013 ) . We transfected these cells with chimeric proteins containing lactose repressor ( LacR ) conjugated to known DDR-initiating repair enzymes ( scheme in Figure 2A; Soutoglou and Misteli , 2008 ) . In this system , the mere presence of ATM ( ATM-LacR-Cherry ) on chromatin initiates the DDR , as shown by H2AX ser-139 phosphorylation ( γH2AX ) ( Figure 2—figure supplement 1A–B; Soutoglou and Misteli , 2008 ) . However , in this system with no actual DNA damage , ATM failed to recruit SIRT6 to the LacO site , even though signaling was taking place and H2AX was phosphorylated ( Figure 2B–C ) . As a control , we showed that known interactors such as SNF2H and Ku80 ( McCord et al . , 2009; Toiber et al . , 2013 ) did recruit SIRT6 to the tethering sites ( Figure 2B–C , Figure 2—figure supplement 1C–D ) . Moreover , MRE11 and NBS1 also recruited SIRT6 to the LacO site ( Figure 2—figure supplement 1C–D ) , suggesting that there is either direct interaction between these sensors and SIRT6 or that they work together in a DDR complex . Taken together , these results indicate that SIRT6 arrives at the sites of damage independently of MRE11 , Ku80 and PARP activity , and that signaling itself is not sufficient to bring SIRT6 to the damage sites in the absence of actual DNA damage . The findings described so far suggest that SIRT6 responds selectively to the actual damage , and that silencing or inhibiting key factors in the DDR do not affect its fast recruitment . Therefore , we tested whether SIRT6 could detect the actual DNA break on its own . We first measured SIRT6 capacity to bind naked DNA by electrophoretic mobility shift assay ( EMSA ) . We found that SIRT6 was able to bind naked DNA without preference for a sequence ( we tested different oligos and restricted sites , see Table 1 ) ( Figure 3A–B , Figure 3—figure supplement 1A ) . We studied the preference of SIRT6 for several DNA damage structures , including dsDNA with blunt or overhanging ends as well as RNA . SIRT6 has the ability to bind them all , but it binds RNA with much lower affinity ( Figure 3—figure supplement 1B ) . SIRT6 exhibits the highest affinity towards ssDNA ( Kd = 1 . 39 μM ) , showing binding affinity values similar to those for MRE11 ( Kd ~1 μM ) ( Williams et al . , 2008 ) and Ku80 ( Kd = 0 . 4 μM ) ( Arosio et al . , 2002 ) . Interestingly , on the basis of the curve fitting , SIRT6 seems to bind ssDNA at one site as a monomer . By contrast , there seems to be a cooperative effect when testing blunt and sticky-end DNA ( Hill Slope greater than 1 ) , suggesting that for open-ended dsDNA , two molecules of SIRT6 participate in binding , each SIRT6 molecule binding one DNA strand ( Figure 3A–B , Figure 3—figure supplement 1A , scheme in Figure 3C ) . As all of the DNAs used in the EMSA were open-ended , we developed an additional DNA-binding assay based on the co-immuno-precipitation of a plasmid ( IP-qPCR ) . In brief , flag-tagged repair proteins were purified and incubated with DNA , then immunoprecipitated along with the DNA that they bound . The DNA was later purified and its enrichment was measured by qPCR . Proteins were incubated either with a circular plasmid or with the same plasmid presenting blunt or sticky ends . As expected , NBS1 , which does not bind DNA by itself ( Myler et al . , 2017 ) , did not bind either plasmid ( open or closed ends ) . By contrast , SIRT6 and MRE11 had high affinity to liner DNA , but they showed almost no binding to closed plasmids ( Figure 3—figure supplement 1C ) . Moreover , SIRT6 exhibited a higher affinity for sticky ends , structures that show a high resemblance to DSBs , over blunt ends ( Figure 3D ) . In addition , it did not distinguish between 3' or 5' overhangs ( Figure 3—figure supplement 1D ) . These assays indicate that SIRT6 does not function by binding intact DNA or a particular sequence , but rather by binding to open DNA ends , and particularly to ssDNA . It is important to note that this capacity is independent of the presence of NAD+ , the known cofactor of SIRT6 ( Figure 3E ) , and the binding of DNA per se , does not activate SIRT6 catalytic activity ( Figure 3—figure supplement 1E ) . Moreover , SIRT6 was able to protect the open ends of DNA from exonuclease activity ( ExoI ) , preventing exonuclease cleavage just as in the case of MRE11 and implying that SIRT6 specifically binds to DNA ends ( Figure 3—figure supplement 1F–H ) . Our EMSA results indicate that SIRT6 binds ssDNA with no cooperativity , suggesting a single binding site . By contrast , when the substrates were dsDNA oligos , we found the Hill coefficient to be greater than 1 , indicating cooperativity ( Figure 3A–B , Figure 3—figure supplement 1A ) . These results suggest that a single molecule of SIRT6 binds ssDNA . Even so , given two ssDNAs , such as would be present at an open-ended DSB , one SIRT6 molecule will interact with another , allowing a dimer of SIRT6 to bind a single molecule of dsDNA that has two open ends on a single side , 5′ and 3′ ( see schematic Figure 3C ) . Together , the two SIRT6 molecules show cooperativity . Interestingly , the known crystal structure of SIRT6 presents a dimer conformation ( Jiang et al . , 2013; You et al . , 2017 ) . To further characterize the structure of SIRT6 in a solution , we used size exclusion chromatography-multi-angle light scattering ( SEC-MALS ) and small-angle X-ray scattering ( SAXS ) . Importantly , both methods showed that SIRT6 tends to aggregate; however , when using SEC-MALS , we noted that the aggregation was significantly reduced by the presence of DNA oligomers ( Figure 3—figure supplement 2A ) , which suggests that SIRT6 is stabilized by ( and favors ) DNA interactions . SAXS data provide a low-resolution structure of SIRT6 , presumably corresponding to a tetramer ( Figure 3—figure supplement 1B–D ) , supporting the model suggested by the EMSA results ( with dimers at the 5′ and 3′ , a tetramer ) . The result obtained by SAXS does not exclude the presence of SIRT6 dimers or trimers in solution ( see Table 2 ) . Last , we measured dimerization in vivo by taking advantage of SIRT6-LacR-GFP localization at LacO sites and the recruitment of SIRT6-RFP , observing a significant co-localization of both SIRT6 molecules ( Figure 3F–G ) , indicating that the bound SIRT6-GFP recruits the soluble SIRT6-RFP . Overall , our predictions suggest that the SIRT6-DNA complex is organized in dimers , probably at each end of the DNA oligomers . Moreover , on the basis of the reconstructed SAXS structure , we show a compaction of SIRT6 in the presence of DNA , suggesting a conformational change ( Figure 3—figure supplement 1B–D ) . SIRT6 has not been previously reported in the literature to be a DNA binding protein , so we aimed to identify the domain involved in ssDNA binding . To this end , we first analyzed the SIRT6 structure to find a potential DNA-binding domain . We found a region within the core domain ( 28 a . a . ) that had potential to bind DNA ( Figure 4A–C ) . We purified full-length SIRT6 ( SIRT6 FL ) and a fragment of the core domain alone ( core: from a . a . 34 to 274 ) . Both were able to bind DNA with similar affinities , indicating that the core domain is the main domain responsible for DNA binding ( Figure 4D ) . To understand which amino acids could be involved in the DSB binding , we mapped them to the known structure of SIRT6 ( http://dnabind . szialab . org/ ) . The model points to a subset of amino acids that are more likely to be involved in DNA binding . Surprisingly , these amino acids are concentrated near a physical structure that resembles a tunnel ( Figure 4A ) . This tunnel is narrow and could accommodate ssDNA ( Figure 4E ) , but not larger dsDNA . Without an open end , normal undamaged DNA could not enter this tunnel , but broken DNA ends could . Therefore , we hypothesized that the destruction or disruption of the tunnel would impair SIRT6 DNA-binding capacity . To test this hypothesis , we generated several point mutations of the amino acids in the tunnel-like structure of SIRT6 ( Figure 4—figure supplement 1A–B ) . Purified SIRT6-MBP point-mutants were tested by EMSA to estimate their DNA-binding ability . As predicted , single point mutations in key amino acids at the tunnel ( including the catalytic dead mutant H133Y ) impaired the DNA- binding capacity ( Figure 4F–G ) . The only mutant that showed no effect on binding was D63Y , in which the mutated amino acid did not impair the charge as strongly as the D63H mutation . Interestingly , mutations in D63 had previously been reported to provoke the loss of SIRT6 function in cancer , and have recently been shown to be lethal in humans ( Ferrer et al . , 2018; Kugel et al . , 2015 ) . As our prediction shows that the SIRT6 DNA-binding domain is in close proximity to its catalytic domain , we set out to examine how these mutations would affect SIRT6 catalytic activity . We performed a Fluor-de-lys assay to assess the mutant deacylation activity , using a H3K9-myristolatted peptide . Most mutants showed a decrease in SIRT6 activity compared to SIRT6-WT; however , A13W mutation showed increased SIRT6 activity ( Figure 4—figure supplement 1C ) . This finding indicates that DSB binding and SIRT6 deacylation activity are not completely linked . However , given the close proximity of the two domains , they may share some of their functions because of the similarity of ssDNA and NAD+ molecules ( ssDNA is a polymer of nucleotides; NAD+ consists of two nucleotides joined through their phosphate groups ) . The core domain of SIRT6 , where its DNA-binding domain is located , is conserved among all Sirtuins . Therefore , we tested whether other mammalian Sirtuins could bind DSB as well . Our results indicate that all Sirtuins have some capacity to bind broken-ended DNA , but some do it with a significantly lower affinity ( Figure 4H , Figure 4—figure supplement 1D ) . Only SIRT7 showed binding capacity towards circular DNA , as previously described ( Gil et al . , 2013 ) . It is also important to note that we tested mouse and human SIRT6 ( mSIRT-Flag , hSIRT6-His ) and found that both bind linear , but not circular DNA ( Figure 4H , Figure 4—figure supplement 1D ) . As shown above , SIRT6 directly recognizes DNA breaks and arrives at the sites of damage independently of DDR signaling . Nonetheless , DNA damage recognition per se cannot activate the DDR . Therefore , we set out to examine whether SIRT6 also has the capacity to initiate the DDR through downstream signaling . To that aim , we took advantage of the previously described tethering assay using SIRT6-LacR-GFP/Cherry chimeras . Remarkably , SIRT6 has the same ability to induce the activation of the DDR as MRE11 , measured by its capacity , compared to that of LacR-GFP/Cherry , to activate the phosphorylation of H2AX at the LacO site . Interestingly , the SIRT6 catalytic mutant SIRT6-HY was also able to initiate the DDR , raising the possibility that SIRT6 DDR initiation capacity is independent of its catalytic activity ( Figure 5A–B ) . Nevertheless , because SIRT6 can generate dimers , endogenous SIRT6 could dimerize in the cells with SIRT6-HY-LacR , allowing the activation of the DDR . To test this possibility , we used nicotinamide ( NAM ) to inhibit endogenous SIRT6 activity ( Figure 5—figure supplement 1A ) . However , even when the endogenous SIRT6 was inhibited ( shown by the increase in H3K56ac ) , LacR-SIRT6-HY was still able to activate the DDR , supporting the evidence of DDR initiation that is independent of SIRT6 catalytic activity ( Figure 5—figure supplement 1B ) . It is important to highlight that SIRT6-HY has 50% less DNA-binding capacity to DSB than wildtype SIRT6 ( Figure 4F–G ) ; however , in this assay , it is forced to bind to the DNA through the LacR domain . In fact , we predicted that SIRT6-HY would fail to bind DNA if it was not tethered to chromatin through the LacR domain . To prove this hypothesis , we tested SIRT6-HY recruitment to DSBs in vivo using laser-induced damage in SIRT6-KO U2OS cells . Using SIRT6-KO cells rules out any contribution that an interaction with the endogenous SIRT6 might have . As expected , we found that unlike SIRT6-WT , SIRT6-HY does not arrive at sites of damage ( Figure 5C–E ) . This finding strengthens our hypothesis that DNA binding is an important step in the role of SIRT6 in DSB repair , and that the residue that is defective in the SIRT6-HY mutant is critical for SIRT6-DSB binding . To study whether SIRT6 activity and initiation capacity are separate , we tested Core-LacR-GFP , which has an active catalytic domain but lacks the C and N terminus of SIRT6 ( Tennen et al . , 2010 ) . We observed that Core-LacR-GFP failed to activate the DDR ( Figure 5F , Figure 5—figure supplement 1C ) , suggesting that other domains play a more prominent role in initiating signaling . Moreover , we tested the initiation capacity of LacR-SIRT1 , SIRT2 and SIRT7 in the tethering assay , because all of these Sirtuins have the ability to localize to the nucleus and have been associated with DNA repair ( Jeong et al . , 2007; Li et al . , 2016; Paredes and Chua , 2016; Rifaï et al . , 2018; Vazquez et al . , 2017; Zhang et al . , 2016 ) . Remarkably , SIRT2 and SIRT7 could initiate the DDR , but SIRT1 could not ( see note in 'Materials and methods' ) ( Figure 5G , Figure 5—figure supplement 1D ) . Although other Sirtuins have some binding activity and some initiation capacity , SIRT6 is unique for having both . Taken together , these experiments indicate that although SIRT6 binds DNA through its core domain , the activation of downstream signaling does not require the catalytic activity of SIRT6 , but its N and C terminus are required for DDR activation . Last , we tested whether SIRT6 could recruit repair factors of the DDR cascade and whether it shows a preference for a certain repair pathway . Although we observed a more prominent effect of SIRT6 on the recruitment of the HR initiator MRE11 rather than that of the NHEJ initiator Ku80 ( Figure 1G–L ) , it was previously reported that SIRT6 affects both repair pathways ( Chen et al . , 2017; Mao et al . , 2011; McCord et al . , 2009; Tian et al . , 2019; Toiber et al . , 2013 ) . Indeed , we noticed that SIRT6 deficiency results in impaired recruitment of both 53BP1 and BRCA1 to the sites of laser-induced DSBs , suggesting impaired activation of both NHEJ and HR ( Figure 6—figure supplement 1A ) . In order to test SIRT6's ability to recruit these and other DDR factors to the sites of damage , we took advantage of the tethering system once more . Our results show that SIRT6 can recruit proteins that are involved in HR , such as MRE11 , NBS1 , ATM and BRCA1 , as well as proteins that are involved in NHEJ , such as Ku80 , Ku70 and 53BP1 ( Figure 6A–B ) . As a control , we tested co-localization with CDT1 , a nuclear protein that does not participate in the DDR . As expected , CDT1 was neither recruited by SIRT6 nor by GFP alone . As SIRT6 DDR activation is independent of its catalytic activity , we further examined whether it is needed for DDR protein recruitment . Taking advantage of the tethering assay , we observed that both SIRT6-WT and SIRT6-HY recruited 53BP1 and BRCA1 , meaning that the recruitment is independent of SIRT6 catalytic activity ( Figure 6—figure supplement 2A–B ) . 53BP1 and BRCA1 can antagonize each other , and a change in their concentration within the cell may influence the recruitment capacity . Therefore , we used IF to test whether overexpression of these proteins results in a different outcome from that produced by the endogenous proteins . However , the results were very similar , suggesting that the recruitment is independent of the amount of protein in the cell , and that an additional layer of regulation would influence the recruitment ( Figure 6—figure supplement 2C–D ) . The tethering assay can detect both protein–protein interaction or recruitment through signaling . To differentiate these two possibilities , we inhibited DDR signaling by supplementing the media of the cells with Wortmannin , thus inhibiting ATM , ATR and DNA-PKc ( scheme in Figure 6—figure supplement 3A ) . Our results indicate that when these kinases are inhibited ( shown by a reduction in γH2AX levels ) , the recruitment of both 53BP1 and BRCA1 to the LacO site is reduced ( Figure 6—figure supplement 3B–D ) . However , the recruitment of the DDR initiators Ku80 and MRE11 is not affected by Wortmannin , suggesting that their recruitment is based on protein–protein interactions and not on downstream signaling alone ( Figure 6—figure supplement 3E ) . These results indicated that SIRT6 participates in DDR activation through the initiation of signaling and the recruitment of various proteins , which lead to the different DNA-repair pathways .
In this work , we discovered a novel function for the chromatin factor SIRT6 as a DSB sensor that is able to bind DSBs and initiate the cellular DDR . We showed that SIRT6 can bind DNA with high affinity for ssDNA and open-ended dsDNA . We believe that the binding occurs through a tunnel-like structure in the protein core domain , close to its catalytic site . This structure could only fit ssDNA , and whereas other proteins require resection for ssDNA identification , 3–4 bases are enough for SIRT6 . By generating several point mutations in the hypothesized DNA-binding site , we managed to reduce the DNA-binding capacity of SIRT6 , also reducing the catalytic activity . However , A13W and D63Y mutations raise the possibility that , despite the proximity of these sites , these abilities are distinct ones . The D63Y mutation had no effect on DNA binding , but it caused a significant reduction in SIRT6 catalytic activity . A13W mutation , on the other hand , resulted in an increase in catalytic activity along with a slight reduction in DNA binding . In addition , we showed that SIRT6 can arrive at the sites of DSBs independently of the known sensors MRE11 and Ku80 and of PARP activity , and can activate the DDR on its own . We also observed that its catalytic activity is not necessary for DDR initiation when it is already bound to the DNA , as shown by the ability of SIRT6-HY-LacR to initiate the DDR . However , because the binding capacity in the HY mutant is reduced , we believe that SIRT6-HY is not able to bind and remain attached to the DNA ( as shown by its impaired recruitment to laser-induced damage sites ) , and therefore that all DDR initiation would be impaired by this mutant . Interestingly , even though the initiation of the DDR occurs when SIRT6 is catalytically inactive , it cannot be initiated by the active core-domain alone . These results suggest a complex relationship between binding capacity and activation , in which binding per se cannot result in DDR signaling . Given that the core domain , which contains both the catalytic domain and the DNA-binding domain of SIRT6 , is conserved among Sirtuins , we also showed that other Sirtuins share the ssDNA-binding capacity ( but with different affinities ) . This is especially interesting as Sirtuins are present in the cell at different cellular locations ( cytoplasm , nucleus and mitochondria ) and have different catalytic activities ( deacetylases , deacylases , and ADP ribosylases ) ( Liszt et al . , 2005 ) . This suggests that the DSB-binding capacity could be relevant in other cellular compartments , for example , in mitochondrial DNA repair . When nuclear SIRT2 and SIRT7 were forced to localize to the DNA by the LacO-LacR tethering assay , they were also able to initiate the DDR . However , SIRT7 lacks the broken-DNA binding specificity and SIRT2 has a poor binding capacity , which would impair their roles as DNA damage sensors . These findings open new possibilities for the cellular functions of the Sirtuin family; nevertheless , we believe in the uniqueness of SIRT6 as it possesses all of these abilities at once . The placing of SIRT6 as a sensor of DSBs might explain why the lack of SIRT6 gives rise to one of the most striking phenotypes in humans , monkeys and mice , including phenotypes that are typically associated with genomic instability such as premature ageing , accelerated neurodegeneration , tissue atrophy and cancer ( Kugel and Mostoslavsky , 2014; Tasselli et al . , 2017 ) . In particular , SIRT6 is involved in several repair pathways . As a sensor and DDR initiator , its absence would have deleterious effects on the whole downstream DDR signaling . Our results point out that its role begins as a DSB sensor ( although it may recognize other DNA lesions ) , recognizing and initiating the DDR independently of other factors . SIRT6 has multiple functions in the context of chromatin ( Kugel and Mostoslavsky , 2014 ) , including transcriptional regulation . Thus , it might seem somewhat paradoxical that it can initiate the DDR response by merely binding to damage sites . It is not particularly clear how SIRT6 can selectively activate the DDR when bound to DNA damage sites but not when bound to sites of transcription regulation . A possible explanation could rely on the fact that transcription factors are very dynamic , and they usually bind chromatin transiently ( Hager et al . , 2009 ) . Therefore , we speculate that SIRT6 , similarly to MRE11 , probes the DNA transiently , and that even though it is constantly present in chromatin , its binding to unbroken DNA is not as tight as when it encounters broken DNA ( as seen in the binding assays ) ( Myler et al . , 2017 ) . Tighter binding of SIRT6 might allow stabilization through protein interactions and modifications , analogous to the processes that occur with MRE11 , NBS1 , ATM and other DDR proteins . It is also possible that SIRT6 undergoes a conformational change when bound to broken DNA . However , our tethering system suggests that its continuous presence in chromatin ( in the absence of broken DNA to bind ) is sufficient to initiate the DDR cascade . Interestingly , unlike other factors , SIRT6 recruitment and kinetics are not affected by PARP activity , making it independent of PARylation and giving it an advantage over other factors that require PARylation for fast recruitment ( Mao et al . , 2011 ) . This feature could be relevant as an adjuvant therapy in cancer treatment ( Beck et al . , 2014; Haince et al . , 2008 ) . It is also important to note that although SIRT6 can recruit proteins of both HR and NHEJ and its deficiency affects both pathways , SIRT6 KO impaired the recruitment of MRE11 , but not Ku80 , to sites of laser-induced damage . It is possible that the Ku complex does not require SIRT6 for recognition , yet it may require SIRT6 chromatin remodeling activity in later repair steps as NHEJ repair is affected by the lack of SIRT6 . Alternatively , as in the case of PARP1 and the MRN complex , SIRT6 might compete with the Ku complex for DSB binding and DDR initiation ( Myler et al . , 2017; Yang et al . , 2018 ) . Our findings place SIRT6 at the beginning of the DDR response as a novel DSB sensor , but how it affects the DSB repair pathway choice still needs to be investigated . Nevertheless , as there is significant cross-talk between the pathways ( seen , for example , by the involvement of the HR initiator MRE11 in NHEJ [Xie et al . , 2009] ) , it is possible that it has roles in both . In conclusion , we have demonstrated that SIRT6 has a role as an independent DNA damage sensor . This is critical for the initiation of the DSB-DNA damage response and hence for the support of genomic stability and health .
All cells were cultured in DMEM and 4 . 5 g/l glucose , supplemented with 10% fetal bovine serum , 1% penicillin and streptomycin cocktail and 1% L-glutamine . Cells were cultured with 5% CO2 at 37°C . All lines were confirmed to be Mycoplasma-free using a hylabs Hy-mycoplasma Detection PCR Kit with internal control ( Cat No . KI 5034I ) . Cells were authenticated by the Biochemical Core Facility of the Genomics Center at the Technion-Israel Institute of Technology . To prepare pQCXIP-msirt6-GFP-LacR , mouse sirt6 without a stop codon was amplified by PCR and introduced in frame with GFP-LacR into the AgeI site of plasmid pQCXIP-GFP-LacR ( Addgene , 59418 ) . pQCXIP-mSIRT6-H133Y-GFP-LacR was prepared by Quick Change Site-directed mutagenesis of mSIRT6 flanked by AgeI sites in pGEM , and after sequencing , introduced to the AgeI site in frame with the fused GFP-LacR of pQCXIP-GFP-LacR ( Addgene , 59418 ) . pQCXIP-Cherry-LacR was prepared by excision of the AgeI/XhoI GFP fragment of pQCXIP-GFP-LacR and exchanged with AgeI/XhoI mCherry amplified from pDEST-mCherry-LacR-BRCA1 ( Addgene , 71115 ) . pQCXIP-mSIRT6-Cherry-LacR was prepared by introducing the AgeI mSIRT6 from pQCXIP-KU80-GFP-LacR and by introducing KU80 , amplified from pEGFP-C1-FLAG-Ku80 ( Addgene , 46958 ) , into the AgeI site of pQCXIP-GFP-LacR in frame with GFP . pQCXIP-hSIRT1-GFP-LacR was prepared by inserting the amplified SIRT1 from SIRT1-Flag ( Mostoslavsky Lab ) with the AgeI site in frame with the GFP-LacR of plasmid pQCXIP-GFP-LacR ( Addgene , 59418 ) . pQCXIP-hSIRT2-GFP-LacR was prepared by inserting the amplified SIRT2 from SIRT2-Flag ( Addgen #13813 ) with the AgeI site in frame into the GFP-LacR of plasmid pQCXIP-GFP-LacR ( Addgene , 59418 ) . pQCXIP-hSIRT7-GFP-LacR was prepared by inserting the amplified SIRT7 from SIRT7-Flag ( Addgen #13818 ) with the AgeI site in frame into the GFP-LacR of plasmid pQCXIP-GFP-LacR ( Addgene , 59418 ) . pQCXIP-Core hSIRT6-GFP-LacR was prepared by inserting the amplified 233 amino acid ( aa ) core region from aa 43 to aa 276 of human SIRT6 and introducing it into the AgeI site of pQCXIP-GFP-LacR ( Addgene , 59418 ) with an additional methionine before aa 43 and in frame with the GFP-LacR of the plasmid . pMal-C2-hSIRT6 A13W , D63H , D63Y , W188A , D190Wand I217A were prepared by Quick Change Site-directed Mutagenesis on pMal-C2-hSIRT6 . The mutation was affirmed by sequencing . All PCRs were performed with Hot start , KAPA HiFi #KM 2605 or abm Kodaq #G497-Dye proofreading polymerases . All clones were sequenced for validation , and expression of the fluorescent fusion proteins were checked by transfection into cells . All transfections were performed using PolyJet In Vitro Transfection ( SignaGen , SL100688 ) , according to the manufacturer's instructions . U2OS cells were washed with PBS and fixed with 2% paraformaldehyde for 15 min at room temperature , followed by an additional wash . Quenching was then performed with 100 mM glycine for 5 min at room temperature ( RT ) . Cells were permeabilized ( 0 . 1% sodium citrate , 0 . 1% Trition X-100 [pH 6] , in deionized distilled water [DDW] ) for 5 min and washed again . After 1 hr blocking ( 0 . 5% BSA , 0 . 1% Tween-20 in PBS ) , cells were incubated with primary antibody diluted in blocking buffer over night at 4°C . The next day , cells were washed three times with wash buffer ( 0 . 25% BSA , 0 . 1% Tween-20 in PBS ) , incubated for 1 hr with secondary antibody ( diluted in blocking buffer 1:200 ) at RT and washed three more times . Cells were then DAPI stained for three minutes at RT and washed with PBS twice before imaging . U2OS cells containing 256X LacO sequence repeats in their genome were transfected with plasmids of chimeric LacR-DDR enzyme-GFP/Cherry proteins . Cells were either co-transfected with a second plasmid of a fluorescent/Flag-tagged protein or immuno-stained ( see 'Immunofluorescence' ) for an endogenic protein . Cells expressing both proteins of interest and exhibiting visible foci of LacR-DDR-GFP/Cherry at LacO sites were located using an Olympus IX73 fluorescent microscope , whereas co-localization between both proteins was assessed visually using Olympus CellSens Software . Co-localization is defined as the common localization of large foci of the two proteins of interest at the LacO site . Co-localization was assessed as either positive ( 1 ) or negative ( 0 ) . From this analysis , the percentage of cells that exhibit co-localization ( positive cells ) was calculated , and defined as ‘percentage of co-localization between two proteins’ . The co-localization percentage for each protein of interest was compared to the co-localization percentage with LacR-GFP/Cherry as a control . Notes: the pQCXIP-Ku80-GFP-LacR plasmid used in this assay contains Ku80 that was acquired from Addgene ( cat . #46958 ) and contains the D158G mutation . The pQCXIP-SIRT1-GFP-LacR plasmid used in this assay contains SIRT1 that was obtained from the Mostoslavsky lab ( Zhong et al . , 2010 ) . This protein variant is lacking 79 amino acids in the N-terminus . Flag-tagged proteins were purified from transfected HEK293T cells . Cells were collected and washed with PBS . Cell disruption was performed in lysis buffer ( 0 . 5M KCl , 50 mM Tris-HCl [pH 7 . 5] , 1% NP40 , 0 . 5M DTT , 200 mM TSA and protease and phosphatase inhibitors in DDW ) by 10 min rotation at 4°C . Cell debris were sedimented by 15 min centrifugation at 21 , 000 g . Lysate was collected and added to ANTI-FLAG M2 Affinity Gel ( Sigma-Aldrich , A2220 ) beads for 2 hr rotation at 4°C . Beads were then washed three times with lysis buffer and once with SDAC buffer ( 50 mM Tris-HCl [pH 9] , 4 mM MgCl , 50 mM NaCl , 0 . 5 mM DTT , 200 mM TSA and protease and phosphatase inhibitors in DDW ) . Proteins were released by flag-peptide . Expression and purification of His-tagged and MBP-tagged proteins in E . coli were performed as previously described by Gertman et al . ( 2018 ) . FRAP experiments ( laser-induced damage ) were performed as previously described by Toiber et al . ( 2013 ) . In brief , cells were plated in Ibidi µ-Slide eight-well glass bottom plates ( Cat . No . : 80827 ) and transfected with the desired fluorescent plasmid . Pre-sensitization with Hoechst ( 1 mM ) was done for 10 min before the experiment . FRAP experiments were carried out using a Leica SP5 microscope ( German Cancer Research Center ( DKFZ ) and BioQuant , Heidelberg , Germany ) or using a LSM880 microscope ( Ben Gurion University , Be’er Sheva , Israel ) with a 63X oil immersion objective . Images were acquired in a 512 × 512 format with a scan speed of 1 , 400 Hz . Circular bleach spots of 2 µm diameter were positioned either at a damage site or at a distant reference site . Spots were bleached with an argon laser of 488 nm with a power of 1 mW in the back aperture of the objective . Images were taken at 3 s intervals , with three baseline images taken before bleaching . Acquisition before bleaching was used for normalization of each cell intensity ( average of the baseline intensity of the whole cell nucleus prior to DNA damage ) . Images analysis and fluorescence assessment were performed using ImageJ 1 . 52i software . To assess protein amounts and to compare between the different conditions , area under the curve was calculated using a MATLAB pipeline . Open-ended plasmids were prepared in advance by incubating DR-GFP plasmids with EcoRV for blunt ends , KpnI for 3' over hang or SalI for 5' over hang according to manufacture instructions . Circular plasmids were subjected to the same conditions with no restriction enzyme . To achieve protein–DNA binding , flag-tagged proteins that were previously immunoprecipitated were incubated at 37°C for 1 hr with same amount of circular or open-ended DNA , 1:5 of 5X deacetylation buffer ( 50 mM Tris HCl [pH 8] , 50 mM NaCl , 4 mM MgCl2 and 0 . 5 mM DTT in DDW ) , and 1:50 50X protease inhibitors in DDW . ANTI-FLAG M2 Affinity Gel ( Sigma-Aldrich , A2220 ) beads were blocked with 5% BSA supplemented with 1X deacetylation buffer ( with 1% phosphate inhibitors ) by rotation for 1 hr in 4°C . Beads were then centrifuged ( 1000 g , 3 min , 4°C ) and buffer was changed to clean deacetylation buffer 1X . Beads were then distributed equally between all samples . To achieve beads–protein binding , samples were rotated for 2 hr in 4°C . After rotation , samples were centrifuged ( 1000 g , 3 min , 4°C ) and washed 3 times with 1 ml of wash buffer ( 0 . 1% SDS , 0 . 5% Triton x-100 , 2 mM EDTA , 20 mM Tris-HCl [pH8] and 150 mM NaCl in DDW ) . Protein–DNA complexes were then released by two rounds of 20 min vortexing at room temperature with 100 μl elution buffer ( 0 . 1M NaHCO3 and 1% SDS in DDW ) . For His-tagged proteins ( acquired from PROSPEC ) , the assay was performed using HisPur Ni-NTA Resin ( ThermoFisher , 88221 ) under the same conditions with the appropriate buffers ( binding buffer — 20 mM Tris HCl [pH 8] , 150 mM NaCl , 10% PMSF , 1% phosphatase inhibitors; wash buffer — 20 mM Tris HCl [pH 8] , 150 mM NaCl , 20 mM imidazole; elution buffer — 20 mM Tris HCl [pH 8] , 150 mM NaCl , 500 mM imidazole ) . Notes: the SIRT1-Flag used in this assay was obtained from the Mostoslavsky lab ( PDMI: 20141841 ) . This protein variant is lacking 79 amino acids in the N-terminus . The SIRT1-His used in this assay was acquired from PROSPEC ( https://www . prospecbio . com/sirt1_human ) . This SIRT1 is a 280 aa poly-peptide ( aa 254–495 ) . 1:1 vol of phenol:chloroform:isoamyl alcohol ( 25:24:1 ) was added to the eluted DNA from the DNA binding assay , vortexed and centrifuged at RT for 5 min at 17 , 000 g . The top aqueous layer was then isolated and washed with 1 vol of chloroform: isoamyl alcohol ( 24:1 ) . Samples were then centrifuged under the same conditions , and the top aqueous layer was isolated . 1/10 vol 3M NaOAc , 30 μg glycogen and 2 . 5 volumes ice cold 100% EtOH were added to each sample , followed by incubation for at least 30 min at −80°C . After incubation , DNA was precipitated by centrifugation at max . speed for 30 min at 4°C , supernatant was discarded and the pellet was washed with 500 µl 70% ice-cold EtOH . Samples were then centrifuged at max . speed for 30 min at 4°C , before the supernatant was discarded and the DNA pellet was air dried before re-suspension with ultra-pure water . For relative quantification of the DNA isolated from all of the DNA-binding assays performed , qPCR was performed using SsoAdvanced Universal SYBER Green Supermix ( BIO-RAD , 172–5274 ) according to the manufacturer's instructions . Primers used for DR-GFP plasmid amplification: DR-GFP plasmid was cut with restriction enzymes generating linear DNA with blunt ( EcoRV ) or overhanging ssDNA ( SalI or KpnI ) . DNA cleavage was confirmed by agarose gel electrophoresis . 10 µg of the restricted DNA was incubated with BSA , NBS1 , MRE11 or SIRT6 purified proteins in NEB exonuclease buffer for 0 to 20 min . ExoI was then added to the samples . Samples of each reaction were taken at 0 , 10 and 20 min . DNA was purified using a Qiagen PCR purification kit . The purified DNA was run on 0 . 8% agarose gel , and the amount of DNA was assessed by image analysis using ImageJ 1 . 52i software and normalized to the amount of the DNA at the 0′ time point . Fluor de lys assay with SIRT6-point mutant-MBP proteins was performed as previously described by Gertman et al . ( 2018 ) . Purified SIRT6-Flag was incubated at 37 °C for 3 hr with either PstI digested pDR-GFP ( DSB ) , ssDNA or a H3K56 acetylated peptide with 2 . 5 mM NAD+ and HEPES buffer ( 50 mM HEPES [pH 7 . 5] , 100 mM KCl , 20 mM MgCl2 , and 10% glycerol ) . After incubation , samples were supplemented with 1 μM 1 , 3-propanediol dehydrogenase ( 1 , 3-PD ) and 170 mM 1 , 3-propanediol for an additional 3 hr incubation . NAD+ consumption by SIRT6 was assessed by NADH levels produced by 1 , 3-PDase activity , by measuring its absorption at 340 nm . To monitor spontaneous NAD+ consumption in the presence of PstI digested pDR-GFP , ssDNA or H3K56 acetylated peptide , the assay was conducted without SIRT6 , and each treatment was normalized to its control . SIRT6 in storage buffer ( 20 mM Tris-HCl [pH 7 . 4] , 150 mM NaCl and 50% glycerol ) was equilibrated with DNA ( or RNA ) for 20 min on ice . The buffer composition of EMSA was optimized to obtain the maximum resolution for resolving DNA/RNA . Reactions ( final volume 10 μL ) were resolved by electrophoresis at 4°C through native gel containing 5% ( for blunt-end and sticky-end DNA ) , 8% ( for ssDNA ) and 10% ( for RNA ) polyacrylamide ( 29:1 acrylamide: bisacrylamide ) in 1X TBE buffer . Autoradiographs of the dried gels were analyzed by densitometry using Fujifilm PhosphorImager . The signal was quantified by ImageQuant TL . GraphPad Prism 7 was used to estimate apparent Kd value for ssDNA ( one site , specific binding fit , y = Bmax[SIRT6]/ ( Kd + [SIRT6] ) and for blunt-end and sticky-end DNA ( specific binding with Hill slope , y = Bmax[SIRT6]h/ ( Kdh + [SIRT6]h ) . SAXS data were collected at BioSAXS beamline BM29 ( ESRF , Grenoble , France ) , possessing a Pilatus 1M detector . The scattering intensity was recorded in the interval 0 . 0035 < q < 0 . 49 Å−1 . The measurements were performed at 20°C . SIRT6 ( alone or in the presence of dsDNA ) was measured at a concentration of 0 . 5 mg/ml , as it tends to aggregate at higher concentrations . The scattering of the buffer was also measured and subtracted from the scattering of the samples by using Primus ( Konarev and Svergun , 2018 ) . Konarev and Svergun ( 2018 ) PyMOL ( https://pymol . org/ ) was used to extract the structures of the SIRT6 dimer and tetramer from the available crystal structure ( PDB ID code: 3pki ) . CRYSOL ( Svergun et al . , 1995 ) was then used to compute the artificial SAXS spectra of each protein species . These spectra served as a reference for the reconstitution of experimental SAXS data . Values for the radius of gyration ( Rg ) and the maximum particle dimension ( Dmax ) were derived from distance distribution function P ( r ) , using in-house script ( Akabayov et al . , 2010 ) . This script was designed to perform an automatic search for the best fitting parameters in GNOM ( Svergun , 1992 ) . In the end , DAMMIN ( Svergun , 1999 ) was used to reconstruct the molecular envelope on the basis of the best GNOM fit ( obtained from the script analysis and refined manually ) . E models were calculated and averaged using DAMMAVER ( Volkov and Svergun , 2003 ) . A miniDAWN TREOS multi-angle light scattering detector , with three detector angles ( 43 . 6° , 90° and 136 . 4° ) and a 658 . 9 nm laser beam ( Wyatt Technology , Santa Barbara , CA ) , with a Wyatt QELS dynamic light scattering module for determination of hydrodynamic radius and an Optilab T-rEX refractometer ( Wyatt Technology ) , were used in-line with a size exclusion chromatography analytical column , Superdex 200 Increase 10/300 GL ( GE , Life Science , Marlborough , MA ) equilibrated in buffer ( 50 mM tris , 150 mM NaCl and 4 mM MgCl2 [pH 8 . 0] ) . Experiments were performed using an AKTA explorer system with a UV-900 detector ( GE ) , at 0 . 8 ml/min . All experiments were performed at RT ( 25°C ) . Data collection and mass calculation by SEC-MALS analysis were performed with ASTRA 6 . 1 software ( Wyatt Technology ) . The refractive index of the solvent was defined as 1 . 331 and the viscosity was defined as 0 . 8945 cP ( common parameters for PBS buffer at 658 . 9 nm ) . dn/dc ( refractive index increment ) value for all samples was defined as 0 . 185 mL/g ( a standard value for proteins ) . For the SIRT6 experiment , 150 ul 4 . 5 mg/ml human-SIRT6-His was injected . For SIRT6+DNA , 200 μl human-SIRT6-His + 50 ul DNA was injected after 1 hr incubation at 37°C . Statistical analysis was done using GraphPad Prism 7 . Analysis included either one-way or two-way ANOVA followed by a post-hoc Dunnet test or a Tukey test , respectively . Significance was set at p<0 . 05 . For all DNA binding assay results , statistical analysis was preceded by logarithmic transformation to overcome large variance between the different experiments . Statistical analysis was performed on the transformed data as described . | DNA is a double-stranded molecule in which the two strands run in opposite directions , like the lanes on a two-lane road . Also like a road , DNA can be damaged by use and adverse conditions . Double-strand breaks – where both strands of DNA snap at once – are the most dangerous type of DNA damage , so cells have systems in place to rapidly detect and repair this kind of damage . There are three confirmed sensors for double-strand break in human cells . A fourth protein , known as SIRT6 , arrives within five seconds of DNA damage , and was known to make the DNA more accessible so that it can be repaired . However , it was unclear whether SIRT6 could detect the double-strand break itself , or whether it was recruited to the damage by another double-strand break sensor . To address this issue , Onn et al . blocked the three other sensors in human cells and watched the response to DNA damage . Even when all the other sensors were inactive , SIRT6 still arrived at damaged DNA and activated the DNA damage response . To find out how SIRT6 sensed DNA damage , Onn et al . examined how purified SIRT6 interacts with different kinds of DNA . This revealed that SIRT6 sticks to broken DNA ends , especially if the end of one strand slightly overhangs the other – a common feature of double-strand breaks . A closer look at the structure of the SIRT6 protein revealed that it contains a narrow tube , which fits over the end of one broken DNA strand . When both strands break at once , two SIRT6 molecules cap the broken ends , joining together to form a pair . This pair not only protects the open ends of the DNA from further damage , it also sends signals to initiating repairs . In this way , SIRT6 could be thought of acting like a paramedic who arrives first on the scene of an accident and works to treat the injured while waiting for more specialized help to arrive . Understanding the SIRT6 sensor could improve knowledge about how cells repair their DNA . SIRT6 arrives before the cell chooses how to fix its broken DNA , so studying it further could reveal how that critical decision happens . This is important for medical research because DNA damage builds up in age-related diseases like cancer and neurodegeneration . In the long term , these findings can help us develop new treatments that target different types of DNA damage sensors . | [
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] | 2020 | SIRT6 is a DNA double-strand break sensor |
Many insect species are host-obligate specialists . The evolutionary mechanism driving the adaptation of a species to a toxic host is , however , intriguing . We analyzed the tight association of Drosophila sechellia to its sole host , the fruit of Morinda citrifolia , which is toxic to other members of the melanogaster species group . Molecular polymorphisms in the dopamine regulatory protein Catsup cause infertility in D . sechellia due to maternal arrest of oogenesis . In its natural host , the fruit compensates for the impaired maternal dopamine metabolism with the precursor l-DOPA , resuming oogenesis and stimulating egg production . l-DOPA present in morinda additionally increases the size of D . sechellia eggs , what in turn enhances early fitness . We argue that the need of l-DOPA for successful reproduction has driven D . sechellia to become an M . citrifolia obligate specialist . This study illustrates how an insect's dopaminergic system can sustain ecological adaptations by modulating ontogenesis and development .
Morinda citrifolia fruit ( morinda ) is the sole host of Drosophila sechellia ( Tsacas and Baechli , 1981 ) , a close relative of Drosophila melanogaster and endemic to the Seychelles archipelago ( Louis and David , 1986 ) . A peculiar aspect of the specialization is that morinda fruits are toxic to all other drosophilids ( Legal et al . , 1992 ) . The toxicity stems from a high content of carboxylic acids ( primarily octanoic and hexanoic acid ) ( Legal et al . , 1994 ) , to which D . sechellia appears to be resistant ( Farine et al . , 1996 ) . The chemosensory system of D . sechellia is specialized in detecting and coding key volatiles produced by morinda ( Dekker et al . , 2006 ) while devoid of the repellence towards the acids ( Matsuo et al . , 2007 ) . On the other hand , D . sechellia females exhibit a low reproductive potential , partly because of a low ovariole number and partly because of fairly low egg production ( R'Kha et al . , 1991; R'kha et al . , 1997 ) , making it difficult to raise D . sechellia under laboratory conditions . In turn , morinda stimulates egg production ( R'kha et al . , 1997 ) , and D . sechellia clearly prefers to oviposit in medium containing morinda carboxylic acids ( Amlou et al . , 1998 ) . On its host , D . sechellia increases expression of genes involved with oogenesis and fatty acid metabolism ( Dworkin and Jones , 2009 ) . Thus , we here examined the dependence of Drosophila sechellia on morinda , for optimal reproduction . All animal embryos rely on maternally provided gene products for their initial development prior to zygotic genetic synthesis . Maternal effects can thus act as a form of cross-generational phenotypic plasticity , playing a role in an animal's adaptation to toxic environments . Embryonic survival in morinda is a maternally inherited trait and does not depend on the genotype of the embryo ( R'Kha et al . , 1991 ) . We therefore considered if maternal effects sustained the evolutionary process that has driven the specialization of D . sechellia . Results enhance our understanding of the reproductive behaviour of Drosophila and suggest an ontogenetic mechanism of insect adaptation to a toxic host .
The molecular traits underlying adaptations and endurance to new toxic-hosts remain unknown . We present a novel role for the Drosophila catecholamine regulatory protein Catsup in maternal secretory functions and suggest that the malfunction of CATSUP contributed to D . sechellia becoming an obligate specialist on its toxic host . We propose an evolutionary scenario in which an initial mutation in D . sechellia DA metabolism caused impaired female fertility and fecundity , but concomitantly provided eggs and adults with resistance to morinda toxic acids . Together with an early loss of repellence ( Matsuo et al . , 2007 ) , this initial maternally inherited tolerance allowed individuals to develop in morinda and feed the DA precursor , particularly enriched in this fruit , which strongly increased adult female fertility . In turn , the lack of repellence ( Matsuo et al . , 2007 ) , and instead the strong attraction to morinda volatiles ( Dekker et al . , 2006 ) , combined with the beneficial morinda effect on ovulation and egg laying ( Figure 2—figure supplement 1 ) , shaped D . sechellia's preference to oviposit in its natural host ( Amlou et al . , 1998 ) .
All used chemicals were of commercial origin and used without further purification . UHPLC-MS- grade water and methanol were used for chromatography . D . sechellia ( 14021–0248 . 03 , 14021–0248 . 07 , 14021–0248 . 08 , 14021–0248 . 25 , 14021–0248 . 27 , 14021–0248 . 28 , 14021–0248 . 31 ) were obtained from the Drosophila Species Stock Center ( DSSC , https://stockcenter . ucsd . edu ) . D . sechellia ( 14021–0248 . 25 ) , D . simulans ( 14021–0251 . 004 ) and D . mauritiana ( 14021–0241 . 01 ) were kindly provided by the Division of Chemical Ecology , Swedish University of Agricultural Sciences . D . melanogaster line wild-type Berlin was kindly provided by Silke Sachse . D . melanogaster line Oregon-R was kindly provided by Rafael Cantera . D . melanogaster pforta was captured in the Weingut Kloster Pforta ( Naumburg , Germany ) . D . melanogaster lines Canton-S ( BL1 ) , DGRP-357 ( BL25184 ) , DGRP-437 ( 25194 ) , DGRP-304 ( BL25177 ) and Catsup1/CyO ( BL5138 ) were obtained at the Bloomington Stock Center at Indiana University ( http://flystocks . bio . indiana . edu/ ) . The CatsupIn270De/Catsup1 flies were selected as not carrying CyO from the F1 of a corresponding crossing of the parental lines DGRP-357 and Catsup1/CyO . Flies were reared in vials ( 50 mm diameter × 95 mm high ) at 25°C , 70% RH in L:D 12:12 on standard cornmeal–yeast–agar medium ( standard diet ) , supplemented as specified in the text , or on fresh morinda pulp ( morinda diet ) collected from fruits of M . citrifolia plants kept in our greenhouse , or on banana . We observed no differences in survival to morinda fruit in our D . sechellia experimental stock along the successive generations ( 5 days survival in ripe morinda: 70 . 7 ± 5 . 6% and 83 . 2 ± 3 . 6% for new and old females [N = 3] , p = 0 . 147 using Student's t test to compare stocks , respectively; 83 . 3 ± 16 . 7 and 64 . 7 ± 18 . 2 for new and old males , [N = 3] , p = 0 . 493 using Student's t test to compare stocks , receptively ) , discarding an artificial adaptation to morinda acids . On the other hand , morinda toxicity ( Legal and Plawecki , 1995 ) hindered a permanent experimental stock of D . melanogaster in the fruit , for what flies were fed morinda for as long as the experiment lasted . Morinda carboxylic acids were added to the standard diet or agar plates as indicated in the text , using a range of natural concentrations ( 0 . 07% vol/V of 3:1 octanoic:hexanoic mix ) ( Legal et al . , 1994 ) . Triplicates of 10–20 flies were placed in vials ( 25 mm diameter × 95 mm high ) containing ripe morinda and maintained at 25°C , 70% RH in L:D 12:12 . Live adults were flipped daily to new vials , recording female and male survival . Data is expressed as percentage of females and males alive . Egg production was scored in groups of 10–20 females and males kept during 4 days in cages ( 37 . 5 mm diameter and 58 mm high ) holding agar plates containing 5% sucrose and devoid of yeast at 25°C , 70% RH in L:D 12:12 . Agar plates were changed daily and the total number of eggs was summed . Egg production is expressed as number of eggs per female per day , averaged over >5 independent experiments . Oviposition of the eggs retained by mated females fed a morinda diet supplemented with α-methyl-DOPA ( 0 . 4 mM , mDp ) was scored in groups of 20 females transferred to non-supplemented standard diet or morinda diet as oviposition substrates . The number of laid eggs was averaged over three independent experiments and expressed as number of eggs per female per day . Ovaries ( N > 8 ) were dissected and fixed in 500 μl of 4% paraformaldehyde in PT ( phosphate buffer saline [PBS] , 0 . 1% Triton X-100 [Sigma , St . Louis , MO] ) during 30 min at room temperature . After being rinsed three times during 10 min in 1 ml PT , ovaries were incubated in 500 μl PT + 0 . 5 μl TOTO-3 ( 1 mM , Invitrogen , Life Technologies GmbH , Germany ) + 0 . 5 μl SYTOX Orange ( 5 mM , Invitrogen , Life Technologies GmbH , Germany ) and protected from light , overnight ( ON ) at 4°C . Ovaries were rinsed three times during 10 min in 1 ml PT and mounted in Vectashield ( Sigma , St . Louis , MO ) . Oocyte-cyst stages were visually determined by inspection of stained ovaries under fluorescent microscope . Alternatively , fixed ovaries were blocked in PBST ( PT , 5% normal goat serum [Sigma , St . Louis , MO] ) , incubated 24 hr at 4°C with primary antibody anti-TH ( 1:100 , mouse , ImmunoStar , Acris Antibodies , GmbH , Germany ) or anti-SLC39A7 ( 1:1000 , rabbit , Sigma , St . Louis , MO ) , diluted in PBST , further rinsed three times in 1 ml PT during 10 min at room temperature ( RT ) and incubated respectively with fluorescently conjugated secondary antibodies anti-mouse ( AlexaFluor488 , Invitrogen , Life Technologies GmbH , Germany ) and anti-rabbit ( AlexaFluor546 , Invitrogen , Life Technologies GmbH , Germany ) , diluted 1:250 in PBST at RT , protected from light . Ovaries were rinsed three times in 1 ml PT during 10 min and mounted in Vectashield ( Sigma , St . Louis , MO ) . Confocal images were obtained at 1-μm intervals over 20 μm Z-stack using a LSM510 Meta confocal microscope ( Zeiss , Jena , Germany ) . Apoptosis in D . sechellia ovaries ( N > 6 ) was visualized following the protocol by Arama and Steller ( McCall , 2004 ) . Briefly , live ovaries were dissected in PBS and incubated in a freshly prepared solution of 0 . 6 μg/ml acridine orange ( Sigma , St . Louis , MO ) for 5 min , at RT . Ovaries were rinsed briefly in PBS and mounted in a drop of Halocarbon 700 oil ( Sigma , St . Louis , MO ) and observed immediately . Confocal images were obtained at 1-μm intervals over 20 μm Z-stack using a LSM510 Meta confocal microscope ( Zeiss , Jena , Germany ) . Feeding assay was performed as described previously ( Riemensperger et al . , 2011 ) . 10 D . sechellia 5-day-old flies ( five females and five males ) were starved for 2 hr at 25°C and transferred to vials with cornmeal-yeast food ( standard diet ) or morinda fresh pulp ( morinda diet ) , containing 10 mM sulforhodamine B ( Sigma , St . Louis , MO ) . Flies were allowed to feed for 1 hr and were immediately frozen at −20°C for 2 hr . Females and males were processed separately . Heads were removed to prevent contamination with eye pigments , and the bodies were homogenized in 250 μl of PBS . Samples were micro-centrifuged at 17 , 000×g ( Eppendorf Centrifuge 5415 R ) for 7 min at 4°C , after which the supernatant was collected , mixed with 60 μl of chloroform and micro-centrifuged for 6 min . The optical density of the supernatant was determined at 570 nm ( BioSpectrophotometer , Eppendorf ) . Results are the mean of three independent determinations in each food condition . Fresh morinda fruits ( 10 g ) were homogenized in 15 ml 0 . 1 M perchloric acid , incubated during 5 min at RT and micro-centrifuged at 17 , 000×g ( Eppendorf Centrifuge 5415 R ) for 5 min at 4°C . The resulting supernatant was measured in a RS-3000-LTQ-Orbitrap XL instrument ( Dionex and Thermo Fischer ) ( Docimo et al . , 2012 ) . l-3 , 4-dihydroxyphenylalanine ( l-DOPA ) values were estimated by calibration curve regression . Arithmetic mean and standard deviation were calculated over three independent samples and values expressed as nanogram of l-DOPA per gram of fruit . Other amines , DA , TA and OA , were not detected in morinda fruit under these conditions . Fly samples ( 5 whole flies , or 6 bodies or 10 ovaries ) were processed in 50 μl of 0 . 1 M perchloric acid with 0 . 3 mM mDp as an internal standard , using ceramic beads ( peqlab , Biotechnology GmbH ) in TissueLyser LT ( QIAGEN ) . Samples were micro-centrifuged twice at 17 , 000×g ( Eppendorf Centrifuge 5415 R ) for 5 min at 4°C and the supernatant measured as above . Tyrosine , l-DOPA and DA absolute values were calculated by calibration curve regression using XCMS/MZMatchR program ( Tautenhahn et al . , 2008 ) . Arithmetic mean and standard deviation were calculated over three independent samples . For flies fed a standard diet , values were expressed as picogram of l-DOPA or DA per fly ( fly weights showed no statistically significant difference [p = 0 . 85564545 , D . melanogaster vs D . sechellia , Student's t test] ) . To compare flies fed different diets , values were expressed as picogram of l-DOPA per milligram of body tissue . The fruit pulp of ripe or overripe morinda and banana was mashed lightly and the pH was measured with a glass electrode . Results are the mean of three independent determinations for each fruit . D . sechellia 10–20 females and males fed a standard diet were placed in oviposition cages ( 37 . 5 mm diameter and 58 mm high ) holding plates containing either a standard diet or fresh morinda pulp , at 25°C , 70% RH in L:D 12:12 . In parallel , D . sechellia 10–20 females and males fed a diet of morinda were placed in oviposition cages ( 37 . 5 mm diameter and 58 mm high ) holding plates containing fresh morinda pulp , at 25°C , 70% RH in L:D 12:12 . Plates were changed every 0 . 5 hr to get pools of synchronized eggs and further incubated at 25°C . Hatching rate was expressed as the relative number of hatched eggs ( larvae 1 ) to the total number of eggs produced . Egg size was measured within 1 hr of oviposition or on pre-fertilized eggs inside the ovary ( there was not a statistically significant difference between these groups ) . For each species , the lengths ( L ) and widths ( W ) of 15–30 eggs were measured and their volumes were determined according to the formula ( 1/6 ) πW2L ( Markow et al . , 2009 ) . Values were expressed as mm3 ( 10−3 ) . For each species , 20 adult flies were homogenized in 200 μl lysis buffer ( 50 mM Tris–HCl pH 7 . 5 , 0 . 1% [vol/V] Triton X-100 , 100 mM NaCl , 1 mM DTT , 10% glycerol , 15 mM EDTA ) freshly prepared with protease inhibitor cocktail ( Roche ) added immediately before use , using ceramic beads ( peqlab , Biotechnology GmbH , Germany ) in TissueLyser LT ( QIAGEN GmbH , Germany ) . Whole protein extracts of one same experiment were separated in parallel by 10% SDS-PAGE plus electronic transfer to PVDF membranes ( BioRad , Germany ) . After being blocked in 5% non-fat milk in TBS-tween ( TBS , 0 . 05% Tween-20 [Sigma , St . Louis , MO] ) for 2 hr , at RT , membranes were incubated with 1:1000 dilutions of primary antibodies ( anti-TH [mouse , ImmunoStar , Acris Antibodies , GmbH , Germany] , or anti-SLC39A7 [rabbit , Sigma , St . Louis , MO] or anti-α-tubulin [mouse , Sigma , St . Louis , MO] ) diluted in 2 . 5% non-fat milk in TBS tween ON at 4°C . Membranes were further washed in TBS-tween at RT and re-blocked in 10% non-fat milk TBS-tween for 10 min at RT before being incubated with 1:10000 dilution of corresponding secondary HRP-conjugated anti-mouse or anti-rabbit ( BioRad , Germany ) . Proteins were detected using an enhanced chemiluminescence detection kit ( Thermo scientific pierce , Germany ) . The densitometry of bands was performed using ImageJ package ( http://imagej . nih . gov/ij/ ) . Relative densities of TH-PLE and CATSUP were scaled using the relative densities of the loading-controls ( α-Tubulin ) . For each species , we prepared total RNA ( Trizol , Invitrogen , Life Technologies GmbH , Germany ) and synthesized cDNA ( SuperScript III First-Strand Synthesis System , Invitrogen , Life Technologies GmbH , Germany ) that was used to amplify Catsup transcript by PCR ( Advantage HD Polymerase , Clontech ) with specific primers ( forward 5′-ATGGCCAAACAAGTGGCTGA-3′ and reverse 5′-TTACTCGAACTTGGCGATAAC-3′ ) . Each PCR product was cloned in pCRII vector ( Invitrogen , Life Technologies GmbH , Germany ) and at least 10 colonies were picked for plasmid DNA purification and sequencing . These sequences were aligned using MegAlign ( DNASTAR ) and a consensus sequence was obtained for each species . The sequence of CatsupIn270Del was obtained from the Drosophila Polymorphism Database ( DPDB ) ( Casillas et al . , 2005 ) . Total ( macro and micro ) bristles of each sternopleural plaque were counted in females of D . melanogaster wild-type Berlin ( N = 34 ) , D . melanogaster Canton-S ( N = 54 ) , D . melanogaster DGRP-357 ( N = 61 ) , DGRP-437 ( N = 18 ) , DGRP-304 ( N = 16 ) —three independent lines carrying CatsupIn270Del allele—and D . sechellia lines 14021–0248 . 08 ( N = 98 ) , 14021–0248 . 25 ( N = 74 ) , 14021–0248 . 27 ( N = 25 ) , 14021–0248 . 28 ( N = 56 ) and 14021–0248 . 31 ( N = 50 ) . Data is expressed as frequency histograms and a bar-graph showing the mode of each population . 5-day-old flies were fed a standard diet either not- or supplemented with increasingly amounts of DA ( 5 mg/ml , 10 mg/ml and 100 mg/ml ) during 16 hr prior to their behavioural assessment , at 25°C , 70% RH in L:D 12:12 . The locomotion behaviour was scored in groups of 30 females and 30 males kept in cages ( 37 . 5 mm diameter and 58 mm high ) holding agar plates containing 5% sucrose and a mix of 0 . 07% vol/V 3:1 octanoic:hexanoic acids . M . citrifolia fruits contain highly volatile compounds that confer toxicity ( Legal et al . , 1994 ) , with the two most abundant being octanoic acid ( 58% ) and hexanoic acid ( 19 . 24% ) ( Farine et al . , 1996 ) . Octanoic acid is the most toxic compound in the ripe fruit ( Legal et al . , 1994 ) , which D . sechellia has been able to overcome by the development of both larval and adult tolerance mechanisms ( R'Kha et al . , 1991; Legal et al . , 1992; Amlou et al . , 1997 , 1998; Jones , 1998 , 2001 , 2005 ) . Given these observations , the ratio provided on par . A drop of fresh yeast was added to the plates forcing the attracted flies to enter in contact to the acids . Flies were carefully observed under a stereoscope and the number of immobilized flies was recorded every 5 min independently for females and males . Results are the mean of three to five independent determinations in each food condition . | Many insect species rely on another animal or plant species for their own reproduction . For example , a fruit fly called Drosophila sechellia—which is found in the Seychelles—will only feed and lay its eggs on the fruit of a species of tree called Morinda citrifolia . This pairing is particularly unusual because these fruits , commonly called morinda , are toxic to all other Drosophila species . Female Drosophila sechellia flies produce fewer eggs than other Drosophila species , which makes it difficult to raise this species in the laboratory . However providing these flies with morinda fruit , or chemicals from this fruit , was known to increase the expression of many genes involved in egg production and stimulate the flies to lay more eggs . Nevertheless , the reasons why this species of fruit fly depends on the toxic morinda fruit were unclear . Now Lavista-Llanos et al . have confirmed that feeding Drosophila sechellia flies a diet of morinda fruit—instead of a typical laboratory diet—causes these flies to produce six-times as many eggs . Furthermore , this morinda diet had effects that went beyond the previously reported stimulatory effects of acidic chemicals in the fruits triggering the flies to lay more eggs . Egg production in flies is controlled by dopamine , and a lack of this hormone is known to reduce the size of other fruit flies' ovaries and the number of eggs that they produce . Lavista-Llanos et al . went on to feed female Drosophila sechellia flies the chemical building blocks that make up the dopamine hormone , and one such chemical ( called l-DOPA ) caused the flies to produce more eggs . This did not occur when the flies were fed dopamine itself . Lavista-Llanos et al . discovered that Drosophila sechellia flies have very high levels of dopamine but much lower levels of l-DOPA than other Drosophila fly species; and revealed that this was because a gene called Catsup is mutated in Drosophila sechellia . When Lavista-Llanos et al . mutated the same gene in another Drosophila species , the mutant flies produced fewer eggs and abnormally accumulated an enzyme ( which makes l-DOPA ) inside their developing eggs—just like Drosophila sechellia . The presence of l-DOPA in morinda fruit partly compensates for the reduced fertility of Drosophila sechellia and the other flies with mutations in the Catsup gene . Lavista-Llanos et al . discovered that removing or replacing l-DOPA in the morinda fruit caused the flies to produce fewer eggs . Furthermore , the l-DOPA present in morinda increases the size of Drosophila sechellia eggs , which in turn helps them to survive their toxic environment . Lavista-Llanos et al . also discovered that feeding dopamine to vulnerable Drosophila species helps them to cope with the toxic effects of a morinda diet . One of the next challenges will be to uncover how chemicals from the morinda fruit affect the dopamine system of the flies . It is also unknown if the dopamine hormone also influences the strong attraction that Drosophila sechellia feels towards its only host , the morinda fruit . | [
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"ecology",
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] | 2014 | Dopamine drives Drosophila sechellia adaptation to its toxic host |
Correct orientation of the mitotic spindle in stem cells underlies organogenesis . Spindle abnormalities correlate with cancer progression in germ line-derived tumors . We discover a macromolecular complex between the scaffolding protein Gravin/AKAP12 and the mitotic kinases , Aurora A and Plk1 , that is down regulated in human seminoma . Depletion of Gravin correlates with an increased mitotic index and disorganization of seminiferous tubules . Biochemical , super-resolution imaging , and enzymology approaches establish that this Gravin scaffold accumulates at the mother spindle pole during metaphase . Manipulating elements of the Gravin-Aurora A-Plk1 axis prompts mitotic delay and prevents appropriate assembly of astral microtubules to promote spindle misorientation . These pathological responses are conserved in seminiferous tubules from Gravin−/− mice where an overabundance of Oct3/4 positive germ line stem cells displays randomized orientation of mitotic spindles . Thus , we propose that Gravin-mediated recruitment of Aurora A and Plk1 to the mother ( oldest ) spindle pole contributes to the fidelity of symmetric cell division .
Mitotic cell division is a process whereby genetic material is duplicated , separated , and packaged to yield two daughter cells ( Nigg and Raff , 2009 ) . This process relies heavily on the spatial and temporal synchronization of protein kinase activity at the mitotic spindle , a macromolecular machine that segregates the chromosomes and guides them towards the daughter cells ( Lowery et al . , 2004; Nigg and Stearns , 2011; Langeberg and Scott , 2015 ) . Correct orientation of the mitotic spindle during cell division combined with local kinase signaling is crucial for cell fate determination , tissue organization , and development ( Yamashita et al . , 2007; Lesage et al . , 2010; Gillies and Cabernard , 2011; Kiyomitsu and Cheeseman , 2012; Pelletier and Yamashita , 2012; Joukov et al . , 2014 ) . The mitotic spindle is constrained by two spindle poles that nucleate microtubules . The mother spindle pole contains the oldest centriole and remains anchored near the stem-cell niche , while the daughter spindle pole migrates to the opposite side of the cell to complete spindle formation ( Yamashita et al . , 2007; Izumi and Kaneko , 2012 ) . Recently , a spindle orientation complex has been identified at the mother spindle pole containing protein components that promote maturation ( Yamashita et al . , 2007; Izumi and Kaneko , 2012; Chen et al . , 2014 ) . Several disease-linked genes encode these proteins and their loss causes mitotic delays and spindle misorientation phenotypes ( Buchman et al . , 2010; Gruber et al . , 2011; Tan et al . , 2014; Chen et al . , 2014; Kim and Rhee , 2014 ) . Spindle orientation defects that promote an imbalance between symmetric and asymmetric cell divisions have been implicated in the progression of germ line-derived cancers such as teratomas , seminomas , and ovarian carcinomas ( Neumüller and Knoblich , 2009 ) . These cancers can be exacerbated by mislocalization or misregulation of mitogenic and mitotic protein kinase cascades ( Carnegie et al . , 2009; Scott and Pawson , 2009 ) . The A-kinase anchoring protein Gravin/AKAP12/SSeCKS has been implicated in the control of mitotic progression ( Xia et al . , 2001; Gelman , 2010; Canton et al . , 2012; Canton and Scott , 2013 ) . We now report that Gravin is depleted in proliferating germ line-derived tumors from several patients diagnosed with testicular seminoma . Mechanistic studies show that Gravin is required to spatially coordinate the activities of Aurora A and polo-like kinase 1 ( Plk1 ) , two kinases that act in concert to promote spindle orientation .
Mutation or amplification of Gravin has been linked to melanoma , prostate , and ovarian cancers , yet nothing is known about the role of this kinase-anchoring protein in solid tumors ( Xia et al . , 2001; Bateman et al . , 2015; Finger et al . , 2015 ) . Testicular germ line tumors are the most frequently diagnosed solid cancers in men aged 15–40 years . Currently , 200 , 000 men develop seminoma annually ( Fung et al . , 2007; Burum-Auensen et al . , 2010; Singh et al . , 2011 ) . Although seminoma screening and treatment is well understood , much less is known about the molecular events in germ line stem cells that underlie oncogenesis . Surprisingly , immunoblot analysis of clinical samples from three seminoma patients detected a 9 . 15-fold reduction in Gravin protein compared to adjacent tissue ( Figure 1A , B ) . Interestingly , the loss of Gravin was accompanied by a decrease in two essential cell cycle regulator kinases , Aurora A and Plk1 ( Figure 1A , mid panels , and Figure 1B ) . Similar trends were observed in four additional clinical samples from seminoma patients ( Figure 1—figure supplement 1A ) . 10 . 7554/eLife . 09384 . 003Figure 1 . Loss of Gravin correlates with perturbed mitosis in human seminomas and mouse seminiferous tubules . ( A ) Immunoblot analysis of tissue lysates from resected seminomas ( lanes 2 , 4 , and 6 ) and normal adjacent tissue ( lanes 1 , 3 , and 5 ) . Proteins were identified using antibodies against ( top ) Gravin , ( upper-mid ) Aurora A , ( lower-mid ) Plk1 , and ( bottom ) GAPDH loading control . ( B ) Quantification of immunoblot data ( A ) by densitometry ( n = 3 ± SEM ) . ( C , D ) Representative testis sections from ( C ) a 30-year-old individual and ( D ) a 26-year-old seminoma patient . Immunofluorescent staining shows Gravin ( green ) , p-H3B ( red ) , and DNA ( DAPI , blue ) . Scale bar , 40 μm . ( E , F ) Magnified insets from C and D are included . Scale bar , 40 μm . ( G ) Gravin signal intensity per mitotic cell was quantified from normal and seminoma sections of testis ( p-H3B positive , n-values are indicated , ***p < 0 . 001 ) . The number of cells used in each analysis is indicated . ( H ) The mitotic index was calculated for ( normal; n = 4 ) and ( seminoma; n = 6 ) tissue sections by determining the percentage of pH3B-positive cells . ( *p < 0 . 05 ) . ( I , J ) Related experiments were conducted on testis sections from 7-week-old wild-type ( I ) , and Gravin knockout ( J ) mice . Immunostaining with antibodies against Par3 ( green ) , p-H3B ( red ) , and DAPI ( blue ) is presented . Scale bar , 40 μm . ( K ) Calculation of the mitotic index in testis sections from wild-type ( gray ) and Gravin knockout ( orange ) mice . The number of tissue sections measured is indicated below each column ( *p < 0 . 05 ) . ( L ) TUNEL staining was used to monitor apoptosis in seminiferous tubule sections from wild-type ( gray ) and Gravin knockout ( orange ) mice . Data are presented as TUNEL-positive cells per seminiferous tubule . The number of sections is depicted below each column . ( **p = 0 . 01 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 00310 . 7554/eLife . 09384 . 004Figure 1—figure supplement 1 . Loss of Gravin in human summons and mouse tissues , and correlation with altered mitosis . ( A ) Immunoblot analysis of human testis lysates from normal ( lane 1 ) and seminoma samples ( lanes 2 , 3 , 4 , and 5 ) . Antibodies were used to detect ( top ) Gravin and ( bottom ) GAPDH loading control . ( B ) Immunoblot analysis of SV40 immortalized wild-type and Gravin null mouse embryonic fibroblasts ( MEFs ) . Analysis was conducted on asynchronous non-mitotic ( lanes 1 and 2 ) and synchronized mitotic ( lanes 3 and 4 ) cell lysates . Detection of ( top ) Gravin and ( bottom ) GAPDH loading control . ( C ) Testis sections from 7-week-old wild-type and Gravin knockout mice were stained for TUNEL ( blue ) and the stem cell marker Oct3/4 ( green ) . Scale bar , 40 μm . ( D ) Growth curves for wild-type ( gray ) and Gravin null ( orange ) MEFs from 13-day-old embryos ( n = 4 lines from each genotype ± SEM ) . Cell proliferation rates from day 1 to day 5 are depicted . ( E ) Wild-type ( gray ) and Gravin null ( orange ) MEFs were cultured for four days . Cells were scored for cellular senescence on the basis of a large flat cellular morphology ( n = 4 lines from each genotype , **p < 0 . 01 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 004 Immunofluorescent screening of seminoma tumor sections confirmed a reduction in Gravin ( Figure 1C–G ) . In normal testis , Gravin is uniformly distributed throughout all cell types of the seminiferous tubules ( Figure 1C , green ) . In contrast , the anchoring protein is regionally distributed in seminoma sections and the fluorescent intensity of the Gravin signal is markedly reduced ( Figure 1D , green ) . These differential protein expression patterns are clearly evident in magnified images of the indicated insets ( Figure 1E , F ) . Moreover , counterstaining with the mitotic marker phospho-Ser 10-Histone 3B ( p-H3B ) indicated that a 4 . 88-fold decrease in Gravin was observed in mitotic cells ( Figure 1E–G ) . The mitotic index was elevated in seminoma compared to normal tissue ( 3 . 78-fold , Figure 1H ) . Further evidence that Gravin loss alters mitotic progression was obtained from knockout mice ( Akakura et al . , 2008 ) . Seminiferous tubule sections were stained for a cytoplasmic marker , PAR6 ( green ) , a nuclear marker , DAPI ( blue ) , and the mitotic marker p-H3B ( Figure 1I , J ) . Under these conditions , Gravin knockout mice displayed a 3 . 39-fold increase in the mitotic index compared to wild-type seminiferous tubule sections ( Figure 1I–K ) . Gravin knockout mice also exhibited a 3 . 07-fold reduction in the number of cells undergoing apoptosis ( TUNEL-positive cells; Figure 1L , Figure 1—figure supplement 1B , C ) . In primary cultures of mouse embryonic fibroblasts ( MEFs ) , Gravin null cells displayed a slower rate of proliferation compared to wild type ( [Akakura et al . , 2010]; Figure 1—figure supplement 1D ) with a concomitant increase in senescent morphology ( Figure 1—figure supplement 1E ) . Collectively , the data in Figure 1 implicate reduced Gravin expression with changes in cell cycle progression that are observed in germ line-derived solid tumors . Our initial findings postulate that Gravin loss contributes to the mitotic abnormalities observed in seminoma . One plausible explanation is that loss of Gravin uncouples the location of protein kinases that drive the cell cycle . Phosphorylation of Gravin on Threonine 766 promotes recruitment of Plk1 , a kinase that prompts mitotic progression and appropriate spindle formation ( Canton et al . , 2012 ) . A key advance in our studies came with the discovery that Gravin also anchors an upstream mitotic kinase , Aurora A ( Figure 2A ) . 10 . 7554/eLife . 09384 . 005Figure 2 . Phospho766-Gravin interacts with Plk1 and Aurora A during mitosis . ( A ) Endogenous Gravin complexes were immunoprecipitated from non-mitotic HEK293 cell lysates ( lanes 3 and 4 ) and mitotic cell lysates ( lanes 5 and 6 ) . Samples were immunoblotted for ( top ) Gravin , ( upper-mid ) Aurora A , ( lower-mid ) Plk1 , and ( bottom ) RII subunit of protein kinase A ( PKA ) . Control immunoprecipitations ( mIgG ) are included ( lanes 3 and 5 ) . ( B ) Endogenous Gravin complexes were immunoprecipitated from mitotic lysate ( lane 4 ) and immunoblotted for ( top ) Gravin , ( mid ) Aurora B , and ( bottom ) Aurora A . Control immunoprecipitations ( mIgG ) are included ( lane 3 ) . ( C ) Metaphase cells were immunostained for Aurora A ( red ) , Plk1 ( green ) , and p766-Gravin ( blue ) . Confocal micrographs are presented as maximum projections . A composite image is included . Scale bar , 5 μm . ( D ) A structured illumination microscopy ( SIM ) maximum projection of a single mitotic spindle pole decorated with antibodies to Aurora A ( blue ) , p766-Gravin ( red ) , and Plk1 ( green ) . Scale bar , 1 μm . ( E–H ) A proximity ligation assay ( PLA ) was used to detect in situ interaction between ( E , F ) Aurora A/p766-Gravin and ( G , H ) Plk1/p766-Gravin during the cell cycle . ( F , H ) The integrated PLA signal intensity per cell was calculated for different stages of the cell cycle . Each value was normalized to the signal obtained in interphase cells ( n = 3 experiments ± SEM ) . Phospho-Gravin interaction with Aurora A ( F , **p < 0 . 001 ) and Plk1 ( H , ***p < 0 . 0005 ) was maximal during metaphase as compared to interphase or other phases of the cell cycle . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 00510 . 7554/eLife . 09384 . 006Figure 2—figure supplement 1 . Subcellular location of Gravin complex components during mitosis . ( A ) Spinning disc confocal micrograph of a metaphase cell immunostained for total Gravin ( red ) , centrioles ( Centrin , green ) , and kinetochores ( CREST , blue ) is presented as a maximum projection . Individual and composite images are included . Scale bar , 10 μm . An inset of a single spindle pole is shown on right at greater magnification . ( B ) Confocal micrographs of metaphase cells immunostained for Aurora B ( left ) and Aurora A ( right ) are presented as maximum projection images . Scale bar , 10 μm . ( C ) A comparison of widefield ( left ) and super-resolution structured illumination ( right ) maximum projection images of the same metaphase cell stained for CREST ( blue ) , p-Gravin ( red ) , and microtubules ( green ) . A composite image is included . Scale bar , 10 μm . ( D ) A structured illumination maximum projection of a single mitotic spindle pole immunostained for centrin ( centrioles , red ) and p766-Gravin ( green ) . Bar , 1 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 006 To test this concept , four complementary approaches were used . First , immunoblot analysis detected Aurora A and Plk1 in Gravin immune complexes isolated from mitotic cell lysates ( Figure 2A , lane 6 ) . Control experiments confirmed that the closely related Aurora B kinase does not interact with Gravin ( Figure 2B , lane 4 ) . Conversely , the RII subunit of protein kinase A ( PKA ) constitutively interacts with Gravin throughout the cell cycle ( Figure 2A , lanes 4 and 6 ) . Second , conventional immunofluorescent techniques demonstrate that Aurora A ( red ) , Plk1 ( green ) , and p766-Gravin ( blue ) are concentrated at mitotic spindle poles ( Figure 2C ) . Control experiments confirmed that total Gravin ( red ) organizes at mitotic spindle poles with a subpopulation dispersed throughout the cell ( Figure 2—figure supplement 1A ) , whereas Aurora B localizes to the metaphase plate ( Figure 2—figure supplement 1B ) . Third , structured illumination microscopy ( SIM , resolution ∼100 nm ) revealed that p-Gravin , Aurora A , and Plk1 decorated a higher-order lattice-like structure at mitotic spindle poles reminiscent of pericentriolar material ( PCM ( Lawo et al . , 2012 ) ; Figure 2D , Figure 2—figure supplement 1C ) . Lastly , a proximity ligation assay ( PLA ) was used to pinpoint p766-Gravin association with either kinase during the cell cycle ( Figure 2E–H ) . This approach combines antibody recognition with amplification of a DNA hetero-duplex to mark discrete protein–protein interaction pairs that reside within 40–60 nm of each other ( Samelson et al . , 2015 ) . Quantification of PLA puncta indicated that p766-Gravin/Aurora A sub-complexes was enhanced 3 . 86-fold during metaphase when compared to interphase cells ( Figure 2E , F ) . The p766-Gravin-Plk1 sub-complex was enriched 10 . 34-fold ( Figure 2G , H ) . Thus , we conclude that p766-Gravin scaffolds Aurora A with Plk1 at a PCM-like structure on mitotic spindle poles during metaphase . Since p766-Gravin , Aurora A , and Plk1 decorate mitotic spindle poles , we reasoned that the anchoring protein may actively constrain both enzymes at this location . To test this hypothesis , the subcellular distribution of both kinases was evaluated in MEFs from wild-type and Gravin knockout mice ( Figure 3A–H ) . Metaphase cells were identified by the presence of a bipolar microtubule spindle ( Figure 3A , top panel , green ) . Aurora A ( red ) and Plk1 ( blue ) were enriched at mitotic spindle poles ( Figure 3A , top panels ) ; however , both kinases were less evident at mitotic spindle poles of Gravin null MEFs ( Figure 3A , bottom panels ) . Signal intensity measurements at mitotic spindle poles provided a quantitative analysis of this phenomenon ( Figure 3B–E ) . Gravin null MEFs exhibited a loss of p766-Gravin ( Figure 3B ) , a 3 . 05-fold reduction in Aurora A ( Figure 3C ) , and a 1 . 87-fold reduction in Plk1 ( Figure 3D ) when compared to wild-type MEFs . Signal intensities for the spindle pole marker pericentrin were equivalent in cells from both genotypes ( Figure 3E ) . 10 . 7554/eLife . 09384 . 007Figure 3 . Gravin impacts the protrusion of astral microtubules . ( A ) Confocal micrographs of metaphase primary MEFs derived from wild-type ( top ) and Gravin knockout ( bottom ) mice are presented as maximum projections . MEFs from each genotype were immunostained for tubulin ( MTs , green ) , Aurora A ( red ) , and Plk1 ( blue ) . Composite images are included . Scale bar , 5 μm . ( B–E ) Quantification of immunofluorescent signal at mitotic spindle poles in wild-type ( gray ) and Gravin null ( orange ) MEFs is presented for ( B ) p766-Gravin , ( C ) Aurora A , ( D ) Plk1 , and ( E ) a spindle pole marker pericentrin . Total cell numbers used in calculation are indicated below each column . Data are from three independent experiments , **p-values <0 . 01 , ±SEM . ( F ) Astral microtubules are imaged at metaphase using SIM . Maximum projection images of wild-type ( top ) and Gravin null ( bottom ) MEFs immunostained with antibodies for tubulin ( MTs , green ) and pericentrin ( red ) show microtubules and spindle poles in these cells . Insets depict a magnified view of the astral microtubules protruding from the spindle pole in each genotype . ( G ) Quantitation of astral microtubule ( MT ) length in wild-type ( gray ) and Gravin null ( orange ) metaphase MEFs . Total cell numbers used in calculation are indicated below each column ( ***p < 0 . 0001 , amalgamated data from three independent experiments ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 007 Aurora A and Plk1 are required for spindle pole maturation ( Joukov et al . , 2014 ) . One aspect of this vital process is the formation of astral microtubules that project out from mitotic spindle poles to the cell cortex to regulate spindle orientation ( Kotak and Gönczy , 2013 ) . Super-resolution analysis by SIM revealed a near complete loss of astral microtubules protruding from the spindle poles of Gravin null MEFs when compared to wild-type controls ( Figure 3F , G ) . This phenomenon is perhaps most clearly demonstrated upon comparison of magnified sections of spindle pole regions from representative cells of both genotypes ( Figure 3F , right panels ) . Mitotic spindle poles are classified as either the mother spindle pole that contains material that formed the original centriole or the more recently assembled daughter spindle pole ( Bornens , 2012 ) . Upon closer inspection , we noted that p766-Gravin was unequally distributed between the spindle poles of metaphase cells ( Figure 4A ) . A technique known as ground state depletion microscopy followed by individual molecule return ( GSDIM ) was used to rigorously evaluate this phenomenon ( Fölling et al . , 2008 ) . On the basis of signal overlap with the mother centriole marker Cenexin , we were surprised to discover that components of the Gravin kinase scaffold were selectively enriched at this location ( Figure 4B , C , and Figure 4—figure supplement 1A–E ) . This observation was corroborated by proximity ligation as p766-Gravin/Aurora A PLA puncta were 4 . 16-fold more prevalent at mother spindle poles than at daughter spindle poles ( Figure 4—figure supplement 1F , G ) . Likewise , p766-Gravin/Plk1 PLA pairs were enriched 2 . 35-fold at the mother spindle pole ( Figure 4—figure supplement 1G ) . Thus , Gravin and both kinases are tethered within 20–40 nm of each other and form a locally anchored signaling complex at mother spindle poles . 10 . 7554/eLife . 09384 . 008Figure 4 . Gravin-Aurora A-Plk1 scaffold is preferentially sequestered at mother spindle poles . ( A ) Spinning disc confocal micrograph ( maximum projection ) of a metaphase wild-type MEF depicts asymmetric enrichment of p766-Gravin ( red ) at one spindle pole . Counterstaining with tubulin ( MTs , green ) and DAPI ( DNA , blue ) are shown . Composite image is shown . Bar , 5 μm . ( B ) Ground state depletion microscopy ( GSDIM ) was performed on prometaphase HEK293 cells ( top ) . Cells were immunostained for a mother spindle pole marker , Cenexin ( green ) and p-Gravin ( red ) . Quantification of these signals is shown below micrographs . Integrated intensity profiles for ( top ) cenexin and ( bottom ) p-Gravin at the mother spindle pole ( left ) . Intensity profiles for both proteins at the daughter spindle pole are also presented ( right ) . Scale bar , 1 μm . ( C ) Relative GSD signals for p766-Gravin , Aurora A , Plk1 , and Cenexin at the mother spindle pole ( red ) . Centrobin ( gray ) was used as a daughter spindle pole marker . Cell numbers used in each calculation are indicated on graph ( n = 3 experiments ± SEM ) . ( D ) GSDIM micrographs showing the distribution of Plk1 at spindle poles in ( top , left ) control and ( top , right ) Gravin-depleted HEK293 cells . Densitometric analyses depict the asymmetric distribution of Plk1 at mother and daughter spindle poles in ( bottom , left ) control and ( bottom , right ) Gravin knockdown cells . ( E ) Amalgamated data are shown in graph . Cell numbers used in each calculation are indicated on graph ( n = 3 experiments ± SEM ) . ( F ) SIM maximum projection of ( top ) wild-type and ( bottom ) Gravin null MEFs at metaphase . Immunostaining for tubulin ( green ) , centrobin ( red ) , and pericentrin ( blue ) are presented . The daughter spindle pole was decorated with centrobin ( red ) and marked on the micrograph with D , whereas the mother spindle pole is denoted with M . Composite images are included . Dashed line ( white ) depicts path of line-scan used to determine which pole contained the most centrobin ( see Figure 4—figure supplement 4I and J ) . Scale bar , 2 μm . ( G ) Comparison of astral microtubule ( MT ) length ( μm ) protruding from the mother ( red; n = 64 ) and daughter spindle poles ( gray , n = 62 ) in wildtype MEFs ( n = 5 cells , ±SEM , ***p < 0 . 0001 ) . ( H–I ) Quantitation of astral microtubule ( MT ) length protruding from the mother ( red; n = 34 ) and daughter spindle poles ( gray , n = 35 ) in Gravin null MEFs ( n = 5 cells , ±SEM , ns depicts not significant ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 00810 . 7554/eLife . 09384 . 009Figure 4—figure supplement 1 . Super-resolution microscopy identifies p-Gravin , Plk1 , and Aurora A location and codistribution in mitotic cells . ( A and B ) Improved resolution with GSD microscopy . ( A ) An HEK293 cell in metaphase stained for p-Gravin ( red ) and Cenexin ( green ) was imaged by widefield microscopy using the Leica oil-immersion HC PL APO 160×/1 . 43 NA super-resolution objective on a Leica GSD/TIRF microscope . White box outlines the area in which ground state depletion ( GSD ) was performed for part ( B ) . Bar , 20 μm . ( B ) Comparative analysis of the same cell by GSD microscopy . Mother and daughter spindle poles are indicated ( white boxes ) . Analyses of these regions are presented in Figure 4B , C . ( C ) GSDIM ( top ) shows cells immunostained for a mother spindle pole marker , Plk1 ( green ) and Aurora A ( red ) . Quantification of these signals is shown below micrographs . Integrated intensity profiles for ( top ) Plk1 and ( bottom ) Aurora A at the mother spindle pole ( left ) . Intensity profiles for both proteins at the daughter spindle pole are also presented ( right ) . Scale bar , 1 μm . ( D ) GSD microscopy showing the distribution of Centrobin ( green ) and pT766-Gravin ( red ) at both spindle poles of an HEK293 cell . Bar , 1 μm . An intensity profile for each signal is included . ( E ) GSD microscopy showing the distribution of Centrobin ( green ) and Aurora A ( red ) at both spindle poles of an HEK293 cell . Bar , 1 μm . Intensity profiles of each signal are included . ( F ) A PLA ( red ) was used in U2OS cells stably expressing a mother centriole marker ( myc-centriolin ) 1 to determine which spindle pole predominates for Aurora A/p766-Gravin interaction . The interaction sites are identified by PLA puncta ( red ) , and centriolin ( blue ) , and tubulin ( green ) are shown . Bar , 10 μm . ( G ) Quantification of the relative amount of PLA signal at the mother spindle pole compared to the daughter spindle pole is shown for ( light gray ) Aurora A/p766-Gravin interaction and ( dark gray ) Plk1/p766-Gravin interaction ( total number of cells are indicated , ±SEM , n = 3 experiments ) . ( H ) HEK293 cells that stably express control or Gravin shRNA were synchronized in mitosis . Lanes 1 and 2 were loaded with mitotic lysates , whereas lanes 3 and 4 were loaded with control interphase lysates . Immunoblots were probed for ( top ) Gravin , ( upper mid ) Aurora A , ( lower mid ) Plk1 , and ( bottom ) GAPDH as a loading control . ( I , J ) Line plot analysis of ( I , blue line ) pericentrin and ( J , red line ) centrobin showing the integrated fluorescent intensity of each protein ( points along a line ) across the mitotic spindle ( as depicted in Figure 4F ) . ( K ) The number of astral microtubule at the mother ( red bars ) and daughter ( gray bars ) spindle poles were counted in wild-type and Gravin null MEFs , and cells rescued with the murine Gravin T766A mutant that is unable to interact with Plk1 ( n = 5 cells ± SEM ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 009 GSDIM was also employed to test whether the asymmetric distribution of Plk1 to the mother spindle pole required Gravin ( Figure 4D , E ) . Surprisingly , the location of Plk1 changed upon shRNA-mediated gene silencing of Gravin ( Figure 4—figure supplement 1H ) . Plk1 now predominated at the daughter spindle pole ( Figure 4E , F ) . This unexpected finding demonstrates that Gravin governs the preferential recruitment of Aurora A and Plk1 to mother spindle poles , one of their appropriate sites of action during mitosis . Additional control experiments in HEK293 cells revealed that cellular levels of both kinases were unaltered in absence of Gravin ( Figure 4—figure supplement 1H ) . This latter observation argues that ablation of Gravin in tissue culture cell lines instigates the displacement of both kinases from the spindle pole but may not affect the cellular levels of each enzyme . However , on the basis of our analysis of clinical samples in Figure 1 , we propose that unidentified mitigating factors contribute to the mislocalization and depletion of both kinases in human tissue . We reasoned that one consequence of the aforementioned result could be that differential localization of the Gravin scaffold to the mother spindle pole favors more robust assembly of astral microtubules . In order to test this hypothesis , we evaluated astral microtubule abundance , length , and ultrastructure at super-resolution using SIM ( Figure 4F–I ) . Pericentrin served as a universal spindle pole marker ( Figure 4F and Figure 4—figure supplement 1I ) . Centrobin staining was selectively detected daughter spindle poles ( Figure 4F and Figure 4—figure supplement 1J ) . In wild-type MEFs , we observed protrusion of astral microtubules from both spindle poles ( Figure 4F ) . However , upon quantification of three-dimensional ( 3D ) reconstructed SIM images , it was evident that astral microtubules protruding from the mother spindle pool were longer than those emanating from the daughter spindle pole ( Figure 4G ) . Next , we investigated this process in Gravin null MEFs to ascertain whether the anchoring protein or its binding partners influence this phenomenon . Notably in Gravin null MEFs , the length of astral microtubules protruding from the mother spindle pool was reduced ( Figure 4H ) . Therefore , on the basis of these findings , we can propose that Gravin or elements of its signaling scaffold contribute to the assembly of astral microtubules . Additional rescue experiments with a Gravin mutant ( T766A ) that is unable to interact with Plk1 ( Canton et al . , 2012 ) were achieved at a low frequency but did not restore robust protrusion of astral microtubules ( Figure 4—figure supplement 1K ) . Nonetheless , we contend that Gravin-mediated anchoring of Plk1 to the mother spindle pole contributes to the assembly or maintenance of astral microtubules . We propose that Aurora A and Plk1 are assembled into a kinase cascade at the mother spindle pole through their simultaneous association with p766-Gravin . Cellular and molecular validation of this model was conducted in three phases . First , Aurora A/Plk1 PLA pairs were localized throughout the cell with a subpopulation accumulating at one mitotic spindle pole ( Figure 5A ) . Importantly , a 1 . 77-fold reduction in the PLA signal was measured upon shRNA depletion of Gravin ( Figure 5B ) . This implies that Gravin is necessary to co-localize both kinases . 10 . 7554/eLife . 09384 . 010Figure 5 . Gravin scaffolds an Aurora A and Plk1 kinase-network . ( A ) PLA ( red ) to identify the frequency and subcellular distribution of the in situ interaction between Aurora A and Plk1 in mitotic HEK293 cells . Staining of microtubules ( green ) is indicated . ( Top ) Cells treated with control shRNA , and ( bottom ) cells treated with Gravin shRNA . Bar , 5μm . ( B ) Quantitation of PLA signal intensity in ( light gray ) control and ( dark gray ) Gravin-depleted cells . One hundred fifty cells were analyzed for each condition from three independent experiments ( ±SEM , **p < 0 . 01 ) . ( C ) Gravin immune complexes isolated from mitotic lysates were assayed for protein kinase activity using Kemptide ( 300 μM ) as a substrate . Quantitation of 32P phosphate incorporation was measured by scintillation counting ( n = 3 ± SEM , *p < 0 . 05 ) . Total kinase activity ( gray ) is compared to enzyme activity in the presence of the Aurora A inhibitor alone ( VX-680 , red ) or VX-680 and a PKA inhibitor ( PKI , black ) . ( D ) Phospho-peptide antibodies were used as an index of Aurora A and Plk1 activity in Gravin immune complexes . Immunoblots show levels of ( top ) p766-Gravin , ( mid ) p288-Aurora A , and ( lower ) p210-Plk1 upon shRNA mediated depletion of Gravin from HEK293 cells . ( Bottom ) GAPDH loading controls are indicated . ( E ) Densitometric analysis of amalgamated data from three experiments as shown in ( D ) ( ±SEM ) . ( F ) A model depicting the proposed flow of phosphorylation signals through a Gravin associated Aurora A and Plk1 cascade . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 010 Second , we tested whether Gravin-associated Aurora A was active . Kinase activity toward a heptapeptide substrate ( Kemptide ) was measured in Gravin immune complexes isolated from mitotic lysates . Aurora A kinase activity was defined as the number of counts ( cpm x 103/IP ) blocked by the inhibitor drug VX-680 ( Tyler et al . , 2014 ) . Incubation with VX-680 reduced Kemptide phosphorylation by 33 . 5% ( Figure 5C , n = 3 ) . The remaining kinase activity was blocked by the PKA inhibitor peptide PKI and can be attributed to Gravin-associated PKA ( [Lester et al . , 1996]; Figure 5C ) . Third , phospho-peptide antibodies served as an independent index to detect active Aurora A ( p-288 ) and active Plk1 ( p-210 ) . Both active kinases were prominent in mitotic lysates from cells treated with control shRNA ( Figure 5D , lane 3 ) . Gravin-depletion resulted in a twofold reduction in the p288-Aurora A and p210-Plk1 signals as detected by immunoblot ( Figure 5D lane 4 , Figure 5E ) . The active kinases were absent from lysates prepared from interphase cells ( Figure 5D , lanes 1 and 2 ) . Collectively , these experiments indicate that Gravin constrains active Aurora A and Plk1 to facilitate signal relay from one kinase to the next ( Figure 5F ) . Mechanistic studies examined whether a Gravin-Aurora A-Plk1 scaffold manages mitotic progression . As a prelude to these studies , it was necessary to generate reagents that displace Aurora A and Plk1 from Gravin . In vitro binding studies using purified GST-Gravin fragments mapped a central region of the anchoring protein ( amino acids 451 to 900 ) that directly interacts with Aurora A ( Figure 6A top panel , lane 4 ) . More detailed mapping studies defined at least two extended Aurora A-binding sequences within this region , thus eliminating site-directed mutagenesis as the most direct approach to disrupt the Aurora A-Gravin interaction . As an alternate approach , we employed ectopic expression of a Gravin fragment as the primary means of displacing Aurora A from the scaffold . Mutation of Thr766 to Ala in the context of the Gravin 451-900 fragment ( Figure 6—figure supplement 1A ) created a cell-based ‘Aurora A disruptor’ that antagonizes Aurora A anchoring ( Figure 6B ) without impacting Plk1 ( Canton et al . , 2012 ) . Cells stably expressing histone 2B-Green Fluorescent Protein ( GFP ) and transfected with the ‘Aurora A disruptor’ were monitored by live-cell video microscopy ( Figure 6C–F ) . A completed cell cycle was defined as the time from chromatin condensation to the initiation of cytokinesis . Expression of the Aurora A disruptor caused a mitotic delay ( mean time 85 . 17 ± 3 . 77 min , n = 55 ) when compared to control cells ( 64 . 57 ± 4 . 09 min , n = 95; Figure 6C , D ) . Perhaps , the best depiction of this mitotic defect is upon comparison of time-lapse videos ( Video 1 ) . Thus , correct targeting of Aurora A facilitates efficient mitosis . 10 . 7554/eLife . 09384 . 011Figure 6 . Gravin-scaffolding of Aurora A and Plk1 facilitates metaphase progression . ( A ) Direct binding of purified Gravin GST-fusion proteins ( first and last amino acid number of each fragment is denoted above each lane ) with recombinant V5-tagged Aurora A kinase ( generated by in vitro transcription and translation ) . ( Top ) Immunoblot detection of Aurora A in complex with GST-Gravin fragments . ( Bottom ) Ponceau stained blot shows protein expression levels . ( B ) Spinning disc confocal image ( maximum projection ) of a metaphase cell ( control vector , left ) stained for ( green ) Aurora A and ( blue ) DNA . The subcellular rearrangement of Aurora A following over-expression of the Aurora A disruptor fragment ( right ) . DNA is shown in blue ( DAPI ) . ( C ) Live cell imaging of HeLa cells stably expressing H2B-GFP were monitored through mitosis from the time of DNA condensation until anaphase exit . Shown are ( top panels ) a representative control cell and ( bottom panels ) a cell expressing the Aurora A disruptor fragment . Bar , 5 μm . ( D ) Amalgamated data from multiple cells stably expressing H2B-GFP and monitored for time spent in mitosis . Control cells ( n = 95 cells ) and Aurora A disruptor expressing cells ( n = 55 cells ) were from three independent experiments ( **p < 0 . 001 ) . ( E–I ) Live cell imaging time courses ( 0–40 min ) of cells stably expressing H2B-GFP transfected with ( top ) control shRNA and ( second ) Gravin shRNA . Rescue experiments as indicated with ( third ) murine Gravin; ( fourth ) murine Gravin T766A; and ( fifth ) murine GravinΔPKA . Bar , 5 μm . ( F ) Amalgamated data from multiple cells treated with control or Gravin shRNA , and rescued with murine Gravin as shown in E . These cells were stably expressing H2B-GFP and monitored for time spent in mitosis . Total cell numbers are indicated on graph ( from three independent experiments , **p-values <0 . 001 ) . ( G–I ) Models depicting the kinase-binding properties of the Gravin mutants used in time course experiments E and F: rescue with intact Gravin ( G ) , Gravin T766A ( H ) , and GravinΔPKA ( I ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 01110 . 7554/eLife . 09384 . 012Figure 6—figure supplement 1 . Biochemical validation of reagents used in analysis of cell cycle progression . ( A ) Immunoblot analysis of ( lanes 1 , 2 , and 3 ) mitotic lysates expressing empty vector , the Aurora-binding fragment myc-Gravin ( 591–864 ) , or the Aurora-binding disruptor fragment myc-Gravin ( 591–864 ) T766A . Also ( lanes 4–9 ) the immunoprecipitated control , myc-Gravin ( 591–864 ) , or myc-Gravin ( 591–864 ) T766A fragments were incubated with purified Cdk1 ( lanes 8 and 9 ) . Immunoblots were probed for ( top ) p766-Gravin and ( bottom ) anti-myc . Empty vector controls are included ( lanes 1 , 4 , and 7 ) . Cdk1 phosphorylation of expressed myc-Gravin truncations is observed in cells expressing myc-Gravin ( 591–864 ) ( lane 8 ) and not in myc-Gravin ( 591–864 ) T766A ( lane 9 ) . ( B ) Immunoblot analysis of mitotic cells stably expressing a control shRNA ( lane 1 ) or Gravin shRNA ( lanes 2–4 ) and rescued with murine Gravin ( lane 3 ) or murine Gravin T766A ( lane 4 ) . Immunoblot detection with antibodies against ( top ) Gravin , ( upper mid ) Plk1 , ( lower mid ) Aurora A , and ( bottom ) GAPDH loading control . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 01210 . 7554/eLife . 09384 . 013Video 1 . Comparison of mitotic progression in control and Gravin-depleted cells . Time-lapse video of HeLa cells stably expressing Histone H2B-GFP . Frame by frame comparison of mitotic progression in cells transfected with ( left ) control shRNA and ( right ) Gravin shRNA . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 013 To define the Gravin-Plk1 interface during mitosis , we monitored cells depleted of Gravin and rescued with kinase-binding mutants of the anchoring protein . Metaphase was delayed in Gravin shRNA depleted cells ( 78 . 04 ± 3 . 06 min , n = 29 ) compared to control ( 45 . 86 ± 1 . 27 min , n = 28 , Figure 6E , F ) . This defect was corrected upon rescue with murine Gravin ( 57 . 41 ± 3 . 63 min n = 37; Figure 6E–G and Figure 6—figure supplement 1B ) . In contrast , rescue with Gravin T766A , a Plk1-binding defective mutant , was unable to restore normal mitotic progression ( 90 . 32 ± 6 . 33 min , n = 31; Figure 6E , F , H ) . Experiments with a GravinΔPKA mutant that cannot anchor PKA ( Nauert et al . , 1997 ) restored normal cell cycle progression , thereby excluding a role for Gravin-associated-PKA in this process ( 55 . 69 ± 3 . 41 min , n = 29; Figure 6E , F , I ) . Collectively , these studies propose a mechanism where Aurora A proximity to Plk1 facilitates efficient mitotic progression , as both kinases are constrained by Gravin . The orientation of the mitotic spindle during cell division determines whether a polarized epithelium will expand symmetrically ( Gillies and Cabernard , 2011; Williams et al . , 2014 ) . Orientation is influenced by the distribution of molecular components at mitotic spindle poles ( Yamashita et al . , 2007; Lesage et al . , 2010; Januschke et al . , 2011; Chen et al . , 2014 ) . Therefore , we reasoned that manipulation of the Gravin scaffold could affect spindle orientation during cell division . To test this hypothesis , we used a spindle tilt assay to measure the angle of each mitotic spindle relative to the substratum ( ( Delaval et al . , 2011; Hehnly and Doxsey , 2014 ) Figure 7A ) . Ectopic expression of the Aurora A disruptor perturbed spindle orientation . Mislocalization of Aurora A promoted a 20° misalignment of the mitotic spindle when compared to control cells transfected with empty vector ( Figure 7B , C ) . Most control spindles were parallel to the substratum ( averaging around 5–10°; Figure 7D , E ) . Yet in Gravin-depleted cells , the perturbation of spindle orientation was 40° ( Figure 7D , E ) . Notably , these more severe spindle angle defects were rescued upon expression of the mouse Gravin ortholog ( Figure 7D , E ) . In contrast , spindle angle defects were only partially rescued upon expression of the Plk1-binding defective Gravin T766A mutant ( angles averaging around 20°; Figure 7D , E ) . The magnitude of this partial rescue is reminiscent of the results obtained upon mislocalization of Aurora A ( Figure 7C ) . Further support was provided upon analysis of 3D reconstructed images collected from wild-type and Gravin null MEFs . In wild-type MEFs , the alignment of the mitotic spindle was parallel to the substratum as assessed by the positioning of the spindle poles ( Figure 7F and Video 2 , left ) . In contrast , abnormal orientation and positioning of mitotic spindles were observed in Gravin null MEFs ( Figure 7G and Video 2 , right ) . Thus , we propose that Gravin , through its interaction with Aurora A and Plk1 , ensures correct spindle orientation during mitosis . 10 . 7554/eLife . 09384 . 014Figure 7 . The Gravin-Aurora A-Plk1 scaffold regulates appropriate spindle orientation . ( A ) Diagram depicting how spindle angle was calculated for treatments in ( B–E ) . The z-axis of a metaphase cell with a defined spindle angle α [°] in relation to the cover glass . ( B ) A representative z-axis confocal projection for HeLa cells expressing an empty vector ( left ) or the Aurora A disruptor fragment . Cells were stained with tubulin ( green , MTs ) and centrosomes ( red , pericentrin ) . The dashed line connects the two spindle poles and is used to determine the spindle angle relative to the cover glass . Bar denotes 3 μm . ( C ) Spindle angles between cover glass and line bisecting spindle poles in z-axis projections were measured . The amalgamated data from three independent experiments show mean spindle angles ( [°]; total cell numbers are denoted above each column , ±SEM , **p-values <0 . 001 ) . ( D ) A representative z-axis confocal projection for HeLa cells treated with control shRNA and Gravin shRNA and rescue experiments with murine Gravin and the murine Gravin T766A . Cells were stained with tubulin ( green , MTs ) and centrosomes ( red , pericentrin ) . The dashed line connects the two spindle poles and is used to determine the spindle angle relative to the cover glass . Bar denotes 3 μm . ( E ) Spindle angle quantification for each treatment ( cell numbers depicted on graph ± SEM , **p-vales < 0 . 001 ) . ( F , G ) Single frames from z-axis confocal 3-dimensional videos of ( F ) wild-type and ( G ) Gravin null MEFs in metaphase . Staining with tubulin ( green ) and pericentrin ( red ) . Full videos are presented in Video 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 01410 . 7554/eLife . 09384 . 015Video 2 . Comparison of mitotic spindles formed in wild-type and Gravin null MEFs . Reconstructed z-axis confocal 3-dimensional movies mitotic spindles from ( left ) wild-type and ( right ) Gravin null MEFs . The mitotic spindles ( tubulin , green ) and spindle poles ( pericentrin , red ) are indicated . Related to Figure 7F , G . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 015 In vivo analyses of seminiferous tubules tested whether the Gravin scaffold determines spindle orientation of germ line stem cells during spermatogenesis . Initially , we examined the morphology of tissue sections from wild-type and Gravin knockout mice . Seminiferous tubules are organized into ascending cellular layers with germ line stem cells ( spermatogonia , magenta ) residing on the inner face of the basement membrane ( Figure 8A ) . Appropriate cellular layering was observed in wild-type sections stained for Gravin ( green ) and nuclei ( white ) ( Figure 8B , left ) . Parallel sections stained for acetylated tubulin established the correct lumenal organization of elongating spermatids ( Figure 8C , left ) . In contrast , the cellular layering of seminiferous tubules and lumenal organization of spermatids are disrupted in Gravin knockout mice ( Figure 8B , C; right ) . Of note , the diameter of the lumen is significantly decreased in seminiferous tubules from Gravin knockout mice ( Figure 8D ) . In addition , the elongating spermatids are haphazardly dispersed throughout the lumen ( Figure 8C , right ) . We postulate that both morphological changes are a consequence of abnormal spindle orientation within dividing germ line stem cells . 10 . 7554/eLife . 09384 . 016Figure 8 . Gravin contributes to spindle orientation of germ line stem cells during spermatogenesis . ( A ) Schematic depicting the cross-sectional topology of a seminiferous tubule . The organization of the basement membrane ( black ) , spermatogonia ( magenta ) , spermatocytes ( blue ) , round spermatids ( orange ) , and elongating spermatids ( yellow ) is indicated . ( B ) Testis sections from ( left ) wild-type and ( right ) Gravin knockout mice stained for Gravin ( green ) and DAPI ( white ) . ( C ) Parallel sections were stained for flagellum ( acetylated tubulin ) revealing a loss in polarized organization of seminiferous tubules in Gravin knockout mice . Bar denotes 10 μm . ( D ) Lumen diameter was measured within seminiferous tubules of wild-type and Gravin knockout mice ( total number of lumen measured are indicated on graph , and data are from three independent experiments , ***p < 0 . 001 , ±SEM ) . ( E ) Close-up of model in A showing germ line stem cells ( spermatogonia , magenta ) can undergo either self-renewing ( parallel spindle angle , left ) or differentiating divisions ( perpendicular spindle angle , right ) . ( F ) Representative cross-section of wild-type mouse seminiferous tubule . Germ line stem cells are stained for Oct3/4 ( magenta ) and microtubules ( green ) . Dashed line in the composite image that bisects both spindle poles was used to determine spindle angle orientation in relation to basement membrane . Two distinct spindle angle orientations are evident . Bar , 5 μm . ( G ) Representative images of mouse seminiferous tubule sections stained for p766-Gravin ( green ) and DNA ( DAPI , blue ) . ( Top and bottom left ) wild-type and ( bottom right ) Gravin knockout tissue . Dashed line bisects both spindle poles and was used to identify spindle angle orientation in relation to basement membrane . Bar , 5 μm . ( H ) Quantitation of spindle angle orientation in germ line stem cell sections from seminiferous tubule ( examples presented in F and G ) . Spindle angles relative to basement membrane were identified for wild-type and Gravin knockout mitotic cells . Wild-type mitotic cells fall into two spindle angle populations between 0 and 30° ( self-renewing ) and between 60 and 90° ( differentiating ) . However , Gravin null mitotic cells had a more randomized distribution of spindle angles . Total mitotic cell numbers in each genotype are indicated on graph . Experiments were conducted on tissue sections from 3 mice of each genotype . ( I ) Cross-sections of ( top ) wild-type and ( bottom ) Gravin knockout mouse seminiferous tubules were stained for acetylated tubulin ( red , left ) and Plk1 ( green , middle ) . Composite images ( right ) are shown . White boxes identify the regions that are magnified in J . Bar , 5 μm . ( J–K ) Insets from ( I ) are shown for ( top ) wild-type and ( bottom ) Gravin knockout sections at higher magnification . Dashed white line in composite image identifies line-scan measured to determine the distribution of Plk1 intensity at each spindle pole . ( K ) Line plot graphs show integrated intensity values for Plk1 for ( top ) wild-type and ( bottom ) Gravin knockout dividing cells . ( L ) Amalgamated data presented as area under the curve from line scans of wild-type ( n = 15 cells ) and Gravin knockout mitotic cells ( n = 25 cells ) . For Box and Whiskers plot , the box extends from the 25th to 75th percentiles using a standard method of computation via Prism software . The line in the middle of the box is plotted as the median . ( M ) Representative ( left ) wild-type and ( right ) Gravin knockout mouse seminiferous tubule sections stained for Oct3/4 ( magenta ) to identify germ line stem cells . ( N ) Relative abundance of Oct3/4-positive cells per seminiferous tubule section from ( gray ) wild-type and ( orange ) Gravin knockout mice . Number of seminiferous tubules analyzed per genotype is denoted on graph ( ±SEM , n = 3 mice each genotype , **p < 0 . 001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09384 . 016 During self-renewal , a mitotic cell chooses to orient its spindle either parallel or perpendicular to the basement membrane ( Lagos-Cabré and Moreno , 2008; Neumüller and Knoblich , 2009 ) . Self-renewing , parallel stem cell division produces two daughter stem cells , whereas perpendicular stem cell division yields one differentiating daughter cell and one stem cell ( [Siller and Doe , 2009] Figure 8E ) . Basement membrane sections of the seminiferous tubules were identified with the stem cell marker Oct3/4 to investigate the angle of spindle orientation in germ line stem cells ( Figure 8F , magenta ) . Mitotic spindle orientation was established by counterstaining with tubulin ( Figure 8F , green ) . Indeed , two populations of dividing stem cells were observed: those with a mitotic spindle perpendicular ( ∼90° ) to the basement membrane and those with a mitotic spindle parallel ( ∼10° ) to the basement membrane ( Figure 8F ) . Staining for p766-Gravin ( green ) and DNA ( blue ) confirmed that the phosphorylated anchoring protein was present in the few cells undergoing mitosis ( Figure 8G , top and lower left panels ) . In contrast , mitotic cells from Gravin knockout mice displayed a randomized mitotic spindle orientation ( Figure 8G , lower right panel ) . Importantly , this increased incidence of spindle angle randomization was evident in mitotic Gravin knockout germ line stem cells ( average angle of 47 . 67° , n = 98; Figure 8H ) . These findings indicate that interrupting spindle polarity may adversely affect the development within seminiferous tubules in Gravin knockout mice . On the basis of our cell-based and biochemical studies in Figures 3 , 4 , we postulated that the spindle misorientation phenotype observed in vivo is a consequence of displacing Plk1 from mitotic spindle poles . Therefore , we established the subcellular distribution of this kinase in seminiferous tubule sections from wild-type and Gravin knockout mice ( Figure 8I–L ) . Acetylated tubulin was used as a marker for mitotic spindle poles ( Figure 8I , left panels; red ) . In wild-type sections , the Plk1 signal was prominent at mitotic spindle poles ( Figure 8I , top mid panel; green ) . Conversely , in Gravin knockout sections , the Plk1 signal was more disperse ( Figure 8I , lower mid panel; green ) . Examination of representative cells from both genotypes at higher magnification best illustrates this phenomenon ( Figure 8J ) . Line scan analysis detected an asymmetric distribution of Plk1 at one spindle pole in wild-type cells ( Figure 8K , top panel ) . Conversely , a more uniform distribution of this kinase was detected in Gravin knockout cells ( Figure 8K , lower panel ) . Quantitative analysis of multiple cells confirmed these findings further suggesting that Gravin functions to preferentially anchor Plk1 at one spindle pole in vivo ( Figure 8L ) . Unfortunately , antibody compatibility issues precluded counterstaining with mother spindle pool markers . Two possible outcomes can arise from the loss of Gravin expression in stem cells . Existing stem cell populations that undergo misoriented division may expand exponentially . Alternatively , germ line stem cells may prematurely differentiate thereby diminishing the total stem cell population . In order to delineate between these two possibilities , we counted the number Oct3/4-positive cells in seminiferous tubule sections ( Figure 8M ) . A 3 . 82-fold increase in Oct3/4-positive cells was observed in Gravin knockout sections ( n = 4; Figure 8M , N ) . Thus , randomized spindle angles and the concomitant over-proliferation of germ line stem cells may underlie the pathological abnormalities observed in Gravin knockout seminiferous tubule organization as well as human seminoma ( Figures 1 , 8 ) .
Aurora A and Plk1 are two kinases generally considered to act together to promote spindle pole maturation ( Hannak et al . , 2001; Soung et al . , 2009; Lee and Rhee , 2011; Joukov et al . , 2014; Kong et al . , 2014 ) . We have discovered an additional role for both kinases in the establishment of spindle orientation during mitotic progression . A key element of our finding is that both active kinases must be associated with the scaffolding protein Gravin to fulfill this ancillary function . By combining biochemical approaches ( Figure 2 ) with quantitative super-resolution imaging and enzymology ( Figures 3 , 4 ) , we have uncovered a unique kinase complex that assembles at the mother spindle pole during metaphase . Formation of a Gravin-Aurora A-Plk1 scaffold at this location may facilitate signal relay as depicted in Figure 5F to drive the spatial and temporal phosphorylation pattern of as yet unknown mitotic substrates . Spindle maturation occurs during the transition from prophase to metaphase when dynein-mediated transport of PCM along the microtubules culminates in the formation of a mitotic spindle ( Purohit et al . , 1999; Mahen and Venkitaraman , 2012 ) . Surprisingly , we observed an asymmetric distribution of the Gravin kinase scaffold at the mother spindle pole ( Figure 4 and Figure 4—figure supplement 1 ) . Clustering of these enzymes at this location can either catalyze the efficient assembly of the PCM or alternatively coordinate the assembly of astral microtubules that orient the mitotic spindle . The super-resolution imaging analysis of Gravin null MEFs in Figure 3F argues strongly for the latter as these cells are deficient in astral microtubules but contain equal amounts of a canonical PCM protein , pericentrin ( ( Doxsey et al . , 1994 ) ; Figure 3E , G ) . This notion is also consistent with the anomalous spindle tilt angles that were measured in cultured cells ( Figure 7 ) . These observations were further validated in vivo upon analysis of murine seminiferous tubule sections lacking Gravin ( Figure 8 ) . One new concept that emerges from our studies is that Gravin-mediated clustering of Aurora A and Plk1 at the mother spindle pole provides a means to more precisely regulate symmetric cell division . We offer four lines of inquiry to support this new mechanism . First , data presented in Figure 3F , G indicate that loss of Gravin impacts the protrusion of astral microtubules . Second , data presented in Figure 8 show that symmetric cell division is lost in Gravin null MEFs . Third , we measure increased mitotic spindle angles ( >20° ) in Gravin shRNA-treated cells compared to controls ( Figure 7A–E ) . Fourth , a logical and important mechanistic extension of this latter observation is our finding that astral microtubules are lost from the mother spindle poles in Gravin null MEFs . Moreover , we postulate that the sequestering these enzymes at the mother spindle pole ensures that each kinase is optimally positioned to play a role in the regulation of astral microtubule protrusion , a process that influences the correct orientation of mitotic spindles during cell division . This mechanism is compatible with two recent reports indicating that signaling events downstream Plk1 modulate the correct orientation mitotic spindles ( Hanafusa et al . , 2015; Yan et al . , 2015 ) . However , a vital new piece of this puzzle , uncovered solely from our work , is that Gravin functions as the anchor for these enzymes . Another theme emerging from the data in Figure 8 is that the orientation of the mitotic spindle becomes increasingly important during stem cell division . These events influence cell fate determination and tissue organization ( Siller and Doe , 2009; Gillies and Cabernard , 2011 ) . In keeping with this notion , Gravin knockout mice exhibit an increase in Oct3/4 , a marker for stem cells within the seminiferous tubule ( Figure 8M , N ) . This is evocative of clinical studies that detect a marked increase in Oct3/4-positive cells in solid-state tumors including seminoma ( Singh et al . , 2011 ) . These clinical results are entirely consistent with the data presented in Figure 1 depicting a loss in tissue morphology observed in tissue sections from seminoma patients ( Figure 1C , D ) . At the molecular level , we also detect decreased Gravin , Aurora A , and Plk1 levels in several samples collected from seminoma patients ( Figure 1A , B ) . Therefore , we postulate that abnormalities in Gravin expression that promote mislocalization of Aurora A and Plk1 contributes to defects in orientation of mitotic spindles to potentiate seminoma progression . However , Gravin knockout mice do not develop seminomas , yet some of the aforementioned abnormalities are evident in these animals . For example , these animals have difficultly breeding and upon aging display a tendency to develop prostate hyperplasia ( Akakura et al . , 2008 ) . Both of these phenotypes may be indicative of developmental defects in polarized cell division . Therefore , we speculate that perturbation of Gravin-mediated signaling events during mitosis may promote a chronic decrease in organization of the seminiferous tubules with concomitant impairment of reproductive viability . Finally , when the mouse studies are considered in the context of our biochemical analysis and imaging of clinical samples , we can conclude that Gravin constrains Aurora A and Plk1 in an asymmetric manner to control spindle orientation during mitosis . Thus , we identify and define a new macromolecular signaling scaffold that drives stem cell maintenance and cell differentiation in seminiferous tubules .
The following antibodies were used: mouse α-tubulin , FITC-conjugated α-tubulin ( Sigma , St . Louis , MO , United States ) , rabbit anti-pericentrin [M8; ( Doxsey et al . , 1994 ) ] , goat anti-Aurora A ( Sigma ) , rabbit anti-p-Aurora A T288 ( Cell Signaling Technology , Danvers , MA , United States ) , mouse anti-Aurora B ( Abcam , Cambridge , MA , United States ) , human anti-CREST ( Antibodies Incorporated , 15–234 ) , rabbit anti-cenexin ( Protein Tech Group , Chicago , IL , United States ) , rabbit p-Gravin T766A ( Canton et al . , 2012 ) , mouse Gravin ( Sigma; clone JP74 ) , mouse anti-GAPDH ( Sigma; GAPDH71 . 1 ) , mouse anti-phospho-S10 Histone H3 ( abcam; ab14955 ) , rabbit anti-Plk1 ( Cell Signaling Technology ) , mouse anti-Plk1 ( Santa Cruz Biotechnology , Inc . , Santa Cruz , CA , United States ) , mouse anti-Plk1 ( Millipore; 35–206 , Billerica , MA , United States ) , rabbit anti p-Plk1T210 ( Cell Signaling Technology ) , mouse anti-Flag and Flag-HRP ( Sigma ) , mouse anti-Par3 ( Sigma ) , rabbit anti-Oct3/4 ( Santa Cruz Biotechnology , Inc . ) , mouse anti-centrin ( clone 20H5 , EMD Millipore ) , mouse anti-acetylated tubulin ( Sigma ) , mouse anti-centrobin ( Abcam ) . Anti-rabbit , anti-mouse , and anti-goat HRP-conjugated secondary antibodies were purchased from GE Healthcare . Anti-rabbit , anti-mouse , and anti-goat secondary antibodies were purchased from Life technologies conjugated to Alexa Fluor 488 , 647 , and 568 . DAPI ( prolong anti-fade diamond , Invitrogen , Carlsbad , CA , United States ) and phalloidin ( Alexa Fluor 568 , Invitrogen ) were purchased from Life Technologies . The Gravin knockout ( encoded by Akap12 locus ) mice were generated as described in ( Akakura et al . , 2008 ) and obtained from Irwin Gelman ( Roswell Park Cancer Institute ) . Hela cells , U2OS , and MEFs ( primary and immortalized ) were maintained in D ( Dulbecco's ) -minimal essential medium ( MEM ) and retinal pigment epithelial cells ( RPE ) were maintained in DMEM:F12 . All media was supplemented with 10% fetal bovine serum ( FBS ) , 100 U/ml penicillin/streptomycin , and 1% Glut-MAX ( Invitrogen ) . Infections for generation of stable knockdowns were performed with shRNA lentiviral particles ( Santa Cruz Biotech ) or retroviral particles ( for immortalization ) . Transient gene expression was performed by transfection using TransIT-LTI reagent ( Mirus ) for Hek293 cells , Hela monster ( Mirus ) for Hela cells , or by nucleofection using Ingenio ( mirus ) for RPE cells . MEFs were isolated following the protocol provided by ( Chen et al . , 2014 ) . Briefly , a timed pregnant female was sacrificed at embryonic day 12–13 . Under sterile conditions , embryos were dissected from their placenta and surrounding membranes , and their organs and head were removed . Fibroblasts were isolated by trypsinization of minced tissue ( 0 . 25% trypsin in DMEM ) . Cells were grown in DMEM , 10% FBS , and penicillin/streptomycin at 37°C and used for immunofluorescence analysis immediately at passage 0–2 . Immortalized MEF lines were established following standard protocols ( Chen et al . , 1997 ) . All human specimens were purchased from BioChain Institute , Inc . Reproductive age male mice ( ∼7 weeks of age ) were sacrificed , testes were removed , fixed in formalin for >24 hr at 4° , and embedded in paraffin . Samples were sectioned at 5 μm , mounted onto slides , and subjected to H&E or conventional antigen retrieval through deparaffination followed by immunostaining . Sections were deparaffinized , rehydrated , and incubated with antibodies as labeled . Staining procedures for cultured cells were performed as previously described ( Hehnly et al . , 2006; Hehnly and Doxsey , 2014 ) . Procedures were performed as described in ( Canton et al . , 2012 ) . Cells were cultured on 35-mm dishes containing a central 14 mm 1 . 5 glass coverslip ( MatTek ) . The cells were imaged in DMEM without phenol plus 20 mM HEPES ( 4- ( 2-hydroxyethyl ) -1-piperazineethanesulfonic acid ) and 10% FBS at 37°C . Spinning disk confocal microscopy was performed on the above system attached to a Hamamatsu ImagEM , EM-CCD Camera C9100-13 . For GFP imaging , frames were acquired every 2 to 5 min with an exposure time of 100 ms . Determination of spindle orientation relative to the horizontal plane or basement membrane in seminiferous tubules was performed as previously described ( Chen et al . , 2014; Hehnly and Doxsey , 2014 ) . Briefly , spindle angle was estimated using inverse trigonometric functions , specifically , arctan ( Kuo et al . , 2011 ) . Thus , if two spindle poles are in focus at the same z-plane , the estimated spindle orientation would be 0° . For cultured cells , at least 25 mitotic spindles were scored for each category in each experiment . For asymmetric divisions in the seminiferous tubules of the testis , a total of 5–10 tissue sections were analyzed per mouse . For each tissue sample , a z-series with depths of 5 μm ( 0 . 2 μm per step ) were collected . Procedures were done as described in Samelson et al . ( 2015 ) . Statistics were performed using paired Student's t-test or unpaired with Welch's correction or Mann–Whitney U-test , for two-group comparisons using Prism's Graph Pad . p-values less than 0 . 05 were considered statistically significant . | The genetic material inside our cells is contained within structures called chromosomes . When a cell divides , these chromosomes are copied and then must be correctly divided between the two daughter cells so that each cell has a complete set of genetic material . The correct separation of the chromosomes depends on a structure called the mitotic spindle whose location in the cell also determines where the point of division will be . Two structures called centrioles are associated with the mitotic spindle and help to organize and direct cell division . The cell carefully controls how these structures are inherited by the daughter cells . For example , when a stem cell divides to produce one stem cell and one cell of a different type , the older centriole can be inherited by the new stem cell . Incorrect placement of the spindle can disrupt this process and is linked to the progression of cancers that affect reproductive organs , such as testicular seminomas . Here , Hehnly , Canton et al . used biochemical and microscopy techniques to study how the spindle is positioned in cells from patients with testicular seminoma tumors . The experiments reveal that a protein called Gravin is found in lower amounts in the tumor cells than in normal cells . In mice that lack Gravin , the cells in a region of the testicle called the seminiferous tubule divide more rapidly , which is a hallmark of cancer . Gravin accumulates at the end of the spindle where the older centriole is present . This protein acts as a scaffold that holds two enzymes called kinases that regulate cell division in place at the end of the spindle . In the stem cells of the testicle , these kinases also appear to help to correctly position the spindle by organizing the proteins that anchor this end of the spindle to the membrane . Hehnly , Canton et al . 's findings suggest that Gravin helps to guard against errors occurring during cell division by recruiting two particular kinase enzymes to the mitotic spindle . A future challenge will be to identify the proteins that these kinases affect while anchored to the spindle . | [
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] | 2015 | A mitotic kinase scaffold depleted in testicular seminomas impacts spindle orientation in germ line stem cells |
New ages for flowstone , sediments and fossil bones from the Dinaledi Chamber are presented . We combined optically stimulated luminescence dating of sediments with U-Th and palaeomagnetic analyses of flowstones to establish that all sediments containing Homo naledi fossils can be allocated to a single stratigraphic entity ( sub-unit 3b ) , interpreted to be deposited between 236 ka and 414 ka . This result has been confirmed independently by dating three H . naledi teeth with combined U-series and electron spin resonance ( US-ESR ) dating . Two dating scenarios for the fossils were tested by varying the assumed levels of 222Rn loss in the encasing sediments: a maximum age scenario provides an average age for the two least altered fossil teeth of 253 +82/–70 ka , whilst a minimum age scenario yields an average age of 200 +70/–61 ka . We consider the maximum age scenario to more closely reflect conditions in the cave , and therefore , the true age of the fossils . By combining the US-ESR maximum age estimate obtained from the teeth , with the U-Th age for the oldest flowstone overlying Homo naledi fossils , we have constrained the depositional age of Homo naledi to a period between 236 ka and 335 ka . These age results demonstrate that a morphologically primitive hominin , Homo naledi , survived into the later parts of the Pleistocene in Africa , and indicate a much younger age for the Homo naledi fossils than have previously been hypothesized based on their morphology .
The caves in the Cradle of Humankind ( CoH ) , South Africa have yielded rich fossil assemblages of late Pliocene to early Pleistocene age , which include a range of hominin species ( A . africanus , A . prometheus , A . sediba , P . robustus , H . ergaster , H . naledi and early Homo ) and associated mammals , reptiles , and birds ( e . g . , Vrba , 1975 , 1995; Brain , 1993; Tobias , 2000; Berger et al . , 2010 , 2015 ) . For the past 3 million years , hominin-bearing deposits in caves formed in broadly similar settings , involving debris cone accumulations near cave openings ( Partridge , 1973; Wilkinson , 1985; Brain , 1993; Pickering et al . , 2007; de Ruiter et al . , 2009; Dirks and Berger , 2013; Herries and Adams , 2013; Dirks et al . , 2010 , 2016b; Bruxelles et al . , 2014; Stratford et al . , 2014 ) , with deposits cemented by carbonate-rich waters dripping from cave ceilings ( e . g . , Wilkinson , 1985; Pickering et al . , 2011b ) . In contrast to all other hominin deposits in the CoH , the deposits that host H . naledi in Rising Star Cave are composed of largely unconsolidated , mud-clast breccia in a mud matrix with no evidence of coarse clastic sediment being carried in by water flow . This suggests a different depositional regime and timing for the sediments and the fossils ( Dirks et al . , 2015 , Dirks et al . , 2016a ) . Rising Star Cave is situated in the Bloubank River valley , 2 . 2 km W of Sterkfontein Cave . The cave system comprises several kilometres of mapped passageways ( Figure 1a ) that are stratigraphically bound to a 20–30 m-thick , chert-poor dolomite horizon capped by a 1–1 . 3 m-thick chert unit that forms the roof to the cave system ( Dirks et al . , 2015 ) . Geological mapping and laser-theodolite surveys indicate that this roof is intact and not penetrated by significant shafts that open to surface ( Dirks et al . , 2015; Kruger et al . , 2016 ) . The broader geological setting of the cave is discussed in Dirks et al . ( 2015 ) , ( Dirks et al . , 2016a ) . 10 . 7554/eLife . 24231 . 003Figure 1 . Location of Rising Star Cave and the Dinaledi Chamber . ( a ) Simplified geological map showing the position of the Rising Star Cave ( in grey ) ; ( b ) close-up map of the Dinaledi Chamber showing the distribution of the dating samples , including: U-Th flowstone samples ( yellow dots , black text ) ; ESR samples ( purple dots , orange text ) ; and OSL samples ( red dots , blue text ) . Age estimates for the different samples are shown , with cross reference to Tables 1 , 7 and 8 . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 003 The Dinaledi Chamber , which contains most of the fossils of H . naledi , is ~30 m below surface and ~80 m in a straight line from the nearest present-day opening to the surface ( Figure 1a ) . The main cavity forming the Dinaledi Chamber is ~15 m long with variable widths not exceeding 2 . 5 meters ( Figure 1b ) , and expands near the intersection with a crosscutting passage , which is the location of the main excavation site to date ( Figure 1b ) . There is no evidence that the present entrance into the Dinaledi Chamber has significantly changed since the deposition of the fossil hominins , with sediment accumulating mostly near the current access point ( Dirks et al . , 2015 , Dirks et al . , 2016a; Figure 2 ) . Samples for dating were collected from the various flowstone horizons and stratigraphic units exposed in the Dinaledi Chamber ( Figures 1b , 2 , 3 , 4 and 5 ) as well as from fossil material itself ( Figures 4 , 6 and 7 ) . 10 . 7554/eLife . 24231 . 004Figure 2 . Geological face map and cross-sections through the sediment pile at different locations in the Dinaledi Chamber , illustrating the relationships between the flowstone groups and sedimentary units . The positions of the section lines are shown in ( a ) ; a face map of the entry zone of the Dinaledi Chamber ( looking NE ) is shown in ( b ) ; geological cross-sections through the central part of the Dinaledi Chamber near the excavation pit are shown in ( c ) and ( d ) . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 004 The Dinaledi Chamber contains deposits of fine-grained , muddy sediments intercalated with flowstone drapes . The sediments include various types of orange , laminated mudstone and mud clast breccia distributed across three broad lithostratigraphic units ( Units 1 , 2 and 3; Dirks et al . , 2015 ) that filled parts of the chamber over time . Based on variations in sediment composition , fossil content and/or stratigraphic position of each unit , we have divided Unit 1 into sub-units 1a , 1b and 1c , and Unit 3 into sub-units 3a and 3b , to more precisely define the stratigraphic packages targeted for dating ( Figure 2 ) . The units are separated by erosional unconformities or flowstone intercalations , but do not all necessarily occur in direct contact with one another due to the complex nature of caves as depositional systems ( e . g . , Brain , 1993; Martini et al . , 2003 ) . In addition , apart from sediments accumulating along the floor of the cave chamber , sediment in the form of orange mud deposits also accumulated inside fractures and along ledges higher up in the Dinaledi Chamber ( Figure 2b ) , where it formed as a result of the combined effect of in situ weathering and deposition from water flowing down fractures and side walls . All units and sub-units are time-transgressive , meaning that they are lithostratigraphic units and not chronostratigraphic units that occur in strict temporal order . Periods of sedimentation alternated with periods of erosion , during which sediments were either redeposited or removed from the chamber via floor drains , resulting in erosional remnants of all units occurring in a variety of stratigraphic positions ( Dirks et al . , 2015 ) . Stalactites have formed at drip points along the roof and associated stalagmites formed below these points . In one area below the entrance to the chamber , these drip points repeatedly formed flowstone aprons over cave sediments that dip towards the deeper part of the chamber . Flowstone also formed as cascades and curtains that developed where water seeped down fractures and ran along the walls to locally spread out , horizontally , across the sediments comprising the cave floor ( Dirks et al . , 2015 ) . The flowstones have preliminarily been sub-divided into three groups demarcating semi-contemporaneous generations of formation , which we named Flowstone Groups 1 , 2 and 3 based on their appearance and relationships with each other , and with the floor sediments and other litho-stratigraphic units in the chamber . In making this subdivision it was realised that each group of flowstones will probably comprise a range of ages representing separate flowstone forming events ( Dirks et al . , 2015 ) , a fact borne out by the ages presented below ( Table 1 ) . Flowstone Group 1 ( FS1 in Table 1; Figures 1b , 2 and 3 ) includes remnants of what are interpreted to be generally older flowstone units that were partly dissolved and resorbed to leave behind rims or aprons along the side walls of the cave chamber , some with sediment attached below them . Flowstone remnants interpreted as Flowstone Group 1 are mostly restricted to five staggered remnants ( Flowstones 1a-e ) , one above the other in reverse stratigraphic order ( oldest on top , youngest at the bottom ) , near the entry shaft into the Dinaledi Chamber ( Figure 2b ) . Flowstone Group 2 , the most extensive group of flowstones in the chamber ( FS2 in Table 1 , and Figures 1b , 2 and 3 ) , comprises wall aprons and sheets that have spread out across the floor of the Dinaledi Chamber together with drip pools , cascades , curtains , stalactites and stalagmites that connect to these sheets , and , therefore , formed in conjunction with them . Flowstone Group 3 ( FS3 in Table 1 and Figures 1b , 2 and 3 ) comprises the flowstone deposits that are actively forming below existing drip points , and include fresh growth of delicate crystals of aragonite and calcite in floor sediments and along cave walls . Sedimentary deposits within the Dinaledi Chamber can be organized into three primary stratigraphic units ( Dirks et al . , 2015 ) . Unit 1 consists of deposits of non-lithified , laminated , orange mud interpreted as suspension deposits in standing water ( Facies 1a of Dirks et al . , 2015 ) , and laminated mud with fine sand containing small-scale ripple cross laminations and rodent remains ( Facies 1b of Dirks et al . , 2015 ) , reflecting deposition by shallow , flowing water along the cave floor , with additional sandy material accumulating near local entry points , where fractures higher in the chamber act as sediment conduits ( Dirks et al . , 2015 , Dirks et al . , 2016a ) . Within the Dinaledi Chamber Unit 1 deposits can be divided into three sub-units provisionally called sub-units 1a , 1b and 1c . It is assumed that Unit 1 is time-transgressive and future work may reveal additional sub-units . Sub-unit 1a is composed of laminated orange mudstone with isolated lenses of sandy material , occurs as erosion remnants along the cave floor , and is possibly more extensive beneath younger deposits in the chamber . Sub-unit 1b is dominated by sandy orange mud deposits that are rich in micro-faunal remain , stratigraphically overlies deposits of sub-unit 1a ( Figure 2c and d ) , and may have formed through the partial erosion and re-deposition of sub-unit 1a . Deposits of sub-unit 1c are similar in appearance and composition to the laminated , muddy sediments of sub-unit 1a , but they occur along chert ledges , solution pockets and fractures in the chamber walls and along the entry shaft , higher up in the cave chamber ( Figure 2 ) . The orange mud is mostly the product of the cave formation process , representing the insoluble residue left over when cavities develop via dissolution of dolomite ( Dirks et al . , 2015 ) . Some of the mud-bearing waters seeping out of the fractures would have flowed as water films along the cave walls to deposit mud on ledges and in fractures to form sub-unit 1c , whilst elsewhere this water would have dripped to the floor to contribute to the deposition of sub-unit 1a and 1b . Unit 2 is composed of largely lithified mud clast breccia consisting of angular to sub-angular clasts of laminated orange mudstone ( similar to that found in Unit 1 ) , embedded in a brown mud matrix ( Facies 2 of Dirks et al . , 2015 ) . The mud clasts are interpreted to be derived locally due to wetting and drying of orange mud deposits , which led to auto-brecciation , and subsequent erosion and re-deposition of angular mud clasts ( Dirks et al . , 2015 ) . We hypothesize that the mud clasts forming Unit 2 are partly derived from erosion of deposits of sub-unit 1c , and partly from a yet unidentified unit that was likely deposited in fractures within and above the chamber entry zone . Two macro-fossils ( partial shafts of long bones ) that are non-specific , but not hominin , have been found in Unit 2 . Unit 2 sediments are only exposed as hanging remnants attached below the remains of a composite flowstone sheet ( Flowstone 1a ) near the entrance shaft into the chamber ( Figure 2b; Dirks et al . , 2015 ) . Note that in Dirks et al . ( 2015 ) Unit 2 was originally defined to also include sediments below Flowstones 1b-e; however , based on our new dating results , the revised definition of Unit 2 has been narrowed to only include the more indurated and distinctly darker coloured erosional remnants of mud clast breccia under Flowstone 1a , which are notable for their absence of hominin fossils . Unit 2 sediments accumulated as a sloping debris cone of mud clast breccia below a vertical fracture system before being covered by flowstone ( Flowstone 1a ) . The debris cone of mud clast breccia was subsequently eroded leaving behind hanging erosion remnants of Unit 2 below a flowstone apron ( Figures 2b and 3l ) . The processes that caused erosion of the Unit 2 debris cone led to the deposition of Unit 3 sediment along the floor of the Dinaledi Chamber as shown in Figure 8 . Unit 3 is composed of largely unlithified , clast-supported , mud clast breccia ( Facies 2 of Dirks et al . , 2015 ) , dominated by reworked angular to sub-angular mud clasts , which are interpreted as being locally derived from the reworking of Units 1 and 2 . Unit 3 accumulated below the hanging remnants of the Unit 2 debris cone near the entry shaft , and also extends along the current , sloping cave floor to the SW end of the chamber ( Figures 2c and 8 ) . Unit 3 sediments are dynamic in the sense that they are poorly lithified in most places and actively slump towards , and erode into , floor drains that occur in parts of the chamber where sediment is being washed down to deeper levels in the cave ( likely as a result of fluctuations in the ground water level ) . Remains of Unit 3 sediment are attached to apron-like erosional remnants of Flowstones 1b-e near the entrance shaft ( Figure 2a and 3l ) . Erosional remnants of Unit 3 under Flowstone 1c contain in situ long bones consistent with H . naledi , which are actively eroding out and accumulating along the present cave floor . Note that Dirks et al . ( 2015 ) originally included these erosional remnants as part of Unit 2 . Everywhere else , Unit 3 deposits are spread across the cave floor as loosely packed , semi-moist , orange mud clasts of varying sizes in which bone material of H . naledi is distributed . Unit 3 is partly covered by sheets of Flowstone Groups 1 , 2 and 3 . Unit 3 has been divided into a lower and an upper sub-unit , termed sub-unit 3a and 3b ( Figure 2 ) , based on the respective absence or presence of hominin fossils . Sediments belonging to sub-unit 3a are not directly exposed in the chamber , but their presence has been confirmed in the deepest part of the excavation area ( Figure 2d ) . In contrast sub-unit 3b is exposed within the talus cone near the entry shaft and along the cave floor , and contains all of the known H . naledi fossils in the chamber ( Figure 2c and d ) . The thickness of sub-unit 3b is thought to be no more than 20–30 cm ( see below ) . Most fossil deposits in the Cradle of Humankind that have been dated are between 0 . 5 and 3 . 7 Ma old and consist of bone material encased in well-cemented hard clastic rocks commonly referred to as cave breccia ( e . g . , Wilkinson , 1985; O'Regan and Reynolds , 2009; Herries et al . , 2009; Pickering et al . , 2011b; Granger et al . , 2015 ) . In the absence of volcanic deposits , it is generally difficult to obtain accurate ages for the fossils , not just because reliable techniques are few , but mostly because the stratigraphic sequences in the caves are complex , discontinuous and frequently reworked ( e . g . , Brain , 1993; Pickering et al . , 2011a; Bruxelles et al . , 2014; Stratford et al . , 2014 ) . Workers have relied on a combination of biochronology of faunal remains , palaeomagnetic work and a range of radiometric methods , including U-Pb , U-Th and ESR dating targeting flowstones and fossil teeth ( e . g . , Vrba , 1975; Partridge et al . , 1999; Berger et al . , 2002; Walker et al . , 2006; Herries et al . , 2006 , 2013 , 2014; Herries and Shaw , 2011; Dirks et al . , 2010; Pickering and Kramers , 2010; Pickering et al . , 2011a; Herries and Adams , 2013 ) , as well as limited cosmogenic ( 10Be , 16Al ) dating ( e . g . , Partridge et al . , 2003; Granger et al . , 2015; Dirks et al . , 2016b ) . Whilst some of these techniques are well established , others such as the application of cosmogenic isochrons ( e . g . , Granger et al . , 2015 ) are relatively new and not without significant analytical ( and interpretative ) challenges ( Kramers and Dirks , 2017 ) , and all efforts are strongly dependent on the stratigraphic interpretation of the fossils or units that are being dated . Unlike other fossil deposits in the Cradle of Humankind , the remains in the Dinaledi Chamber are largely restricted to hominins . This makes it impossible to use biochronology as a preliminary technique to assess the age of the fossils . In addition , the fossils are contained in mostly unconsolidated muddy sediment with clear evidence of a mixed taphonomic signature indicative of repeated cycles of reworking and more than one episode of primary deposition ( Dirks et al . , 2015 ) . This indicates that caution is required when interpreting the stratigraphy and the age of the fossils they contain . In preparation for this study , trial dating of the deposits in the Dinaledi Chamber was undertaken to obtain an indication of the age of the deposit and the best techniques to apply . Preliminary work was focussed on assessing the viability of U-series techniques for flowstone dating , using 14C for dating bone fragments , and using OSL to test samples of quartz-bearing Unit 1 ( Dirks et al . , 2015 ) . Initial tests were carried out at the University of Johannesburg ( UJ ) to assess suitability for U-Pb dating , which allows for the dating of older ( >500 ka ) flowstone material ( e . g . , Walker et al . , 2006; Pickering et al . , 2010; Pickering and Kramers , 2010 ) , on the assumption that the H . naledi material could be older than 1 Ma based on its primitive morphology ( Berger et al . , 2015; Dembo et al . , 2016; Hawks and Berger , 2016; Thackeray , 2016; Hawks et al . , 2017 ) . It was found that the older flowstones in the Dinaledi Chamber contained excessive common Pb caused by the inclusion of detrital material ( mainly clays ) making them unsuitable for U-Pb dating ( Dirks et al . , 2015 ) . In contrast , preliminary tests with U-Th disequilibrium dating at James Cook University ( JCU ) returned promising results . U-Th dating is more precise in the <500 ka range than U-Pb dating , and is much less critically affected by detrital material . The initial tests with U-Th disequilibrium dating revealed that the fossils may be much younger than originally anticipated ( e . g . , Dembo et al . , 2016; Thackeray , 2016 ) , and mostly well within the range of the U-Th technique . Therefore , U-Pb dating was not pursued further . Preliminary tests with OSL were conducted at the University of the Witwatersrand ( Wits ) on samples from Unit 1 , which were assumed to be older than the fossils of H . naledi . These preliminary studies , and the results contained in this paper , are the first OSL results for cave sediments from the CoH , and again indicated that the H . naledi fossils were probably relatively young ( i . e . , <500 ka ) . Tests with radiocarbon ( 14C ) dating were undertaken through a commercial facility ( Beta Analytic Inc . in Florida ) , to ensure a fast turn-around time for results . At the time these dating tests were done , it was already known from U-Th and OSL tests that the H . naledi fossils would be too old to be dated by 14C . Nevertheless , analyses were carried out as part of the due diligence process , and the results of these tests are presented here . Following this initial work , no further radiocarbon studies were carried out . The preliminary results have guided the subsequent dating strategy and sampling approach reported here . The dating strategy was designed to achieve three objectives: ( i ) establish a detailed stratigraphy for the cave sediments in the Dinaledi Chamber; ( ii ) date sedimentary units that potentially bracket the fossil-bearing deposits; and ( iii ) date the fossils directly . To obtain an upper age limit for the fossil-bearing deposits of Unit 3 ( i . e . sub-unit 3b ) , we conducted U-Th dating of flowstones that directly overlie Unit 3 . A large number of such flowstones were sampled with the aim of finding the oldest flowstone directly overlying H . naledi fossils . To obtain a lower age limit for sub-unit 3b , erosional remnants of Unit 1 sediments that were at least partially covered by fossil-bearing sub-unit 3b sediments , were sampled for OSL dating on the assumption that sub-units 1a and 1b in these areas are older than sub-unit 3b ( Dirks et al . , 2015 ) . This was done in the full knowledge that OSL dating of cave sediments is complex and difficult to interpret ( e . g . , Roberts et al . , 2009 ) , and probably imprecise . As an internal control , we also sampled flowstones that cover the outcrops of sub-units 1a and 1b from which OSL samples were taken . These flowstones were dated with U-Th with the expectation that they are younger than the underlying Unit 1 sediments . In addition to OSL , Flowstone 1a , which overlies Unit 2 sediments , was sampled for palaeomagnetic analyses . This flowstone was targeted , because it was expected to be the oldest flowstone in the chamber and possibly older than 780 ka , and hence could potentially record reverse magnetic polarity ( e . g . , Singer , 2014 ) . In this case , this would constrain the minimum age of Unit 2 . The best age estimates for H . naledi can be obtained by directly dating fossil material . It was clear from preliminary tests that this could not be achieved with 14C , and instead combined ESR and U-Th disequilibrium dating techniques ( US-ESR; Grün et al . , 1988 ) were applied to three H . naledi teeth that were freshly collected from near the site of the original excavation ( Figures 1 , 2 , 4 and 6 ) , as well as a single baboon tooth ( cf . Papio ) that had been recovered from sub-unit 3a below the hominin-bearing horizon ( Figures 2d and 7 ) . Once results were obtained for ESR and U-Th dating , it became apparent that OSL dating would only provide general age constraints that confirmed the ESR results , but in their own right did not return additional age constraints for the fossils . OSL results were also difficult to interpret in the complex cave environment that was strongly affected by Rn loss ( see Discussion ) . It was , therefore , decided not to pursue more detailed OSL studies at this stage , even though we did carry out preliminary tests for single grain and feldspar analyses at the University of Wollongong , to assess the suitability of these techniques . Pilot results are encouraging , and suggest that future , detailed OSL studies are worth pursuing .
U-Th dating of 17 flowstone samples ( Figure 3 ) has yielded minimum depositional age estimates for the sedimentary units they overlie , and has provided insights into the timing of flowstone formation events ( Tables 1 , 2 and 3 ) . Three separate checks were built into the U-Th dating strategy to ensure robust results would be obtained . Independent dates for the same samples were obtained by laboratories at JCU and at the University of Melbourne ( UoM ) , with results displaying a high degree of concordance . In instances where samples were obtained from the same flowstone layer , but at different stratigraphic levels ( e . g . , sample pairs RS13 and RS18 , RS22 and RS23 , RS16 and RS17 , and RS11 and RS21 ) all ages are consistent with stratigraphic order , and blind duplicates of the same sample ( RS1 and RS15 ) returned identical results within error , indicating that results are both accurate and precise . Double blind U-Th results from JCU and UoM are shown in Tables 2 and 3 , respectively . The distribution of flowstone ages across the Dinaledi Chamber is shown in Figures 1b and 2 . 10 . 7554/eLife . 24231 . 005Figure 3 . Field and close-up photographs of all flowstone samples collected for U-Th dating . The flowstone groups ( i . e . , Flowstone Groups 1 , 2 or 3 ) , sample numbers , and ages ( 2σ uncertainty ) , as listed in Table 1 , are shown below each sample . Ages reported here are from JCU , unless otherwise stated . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 00510 . 7554/eLife . 24231 . 006Table 1 . Summary table of U-Th disequilibrium ages obtained for samples from the Dinaledi Chamber by James Cook University ( JCU - 1 ) and the University of Melbourne ( UoM - 2 ) . The detailed analytical results are shown in Tables 2 and 3 . Sample locations are shown in Figure 1b . The data are ranked by increasing age of the oldest flowstone horizon within the sample , based on the JCU ages . The grey shading highlights the different age groupings observed within the flowstones: 24–32 ka , ~50 ka , 88–105 ka , ~242 ka , ~290 ka and >440 ka . Ages are reported relative to 1950 . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 006Sample IDFlowstone groupUnderlying unitAge1 ( ka ) 2σ1 ( ka ) Age2 ( ka ) 2σ2 ( ka ) RS19FS2sub-unit 3b24 . 70 . 224 . 530 . 43 RS11FS3 ( top to RS21 ) FS29 . 050 . 069 . 9460 . 063 RS21FS2 ( base to RS11 ) sub-unit 3b28 . 40 . 428 . 620 . 29 RS10FS2sub-unit 3b ( and bone ) 30 . 10 . 3–– RS20FS2sub-unit 1a ( Facies 1a; OSL5 ) 30 . 40 . 232 . 120 . 38 RS6FS2sub-unit 1a , sub-unit 3b49 . 80 . 350 . 820 . 43 RS15FS2 ( blind duplicate of RS1 ) sub-unit 3b92 . 61 . 091 . 400 . 65 RS1FS2 ( blind duplicate of RS15 ) sub-unit 3b––91 . 040 . 72 RS8FS2 ( below FS1a-e ) sub-unit 3b95 . 01 . 096 . 290 . 69 RS14FS2in drain , along dolostone wall100 . 11 . 296 . 200 . 36 RS17FS2 ( top to RS16 ) in drain , along dolostone wall102 . 60 . 898 . 61 . 4 RS16FS2 ( base to RS17 ) in drain , along dolostone wall104 . 01 . 999 . 11 . 4 RS13FS2 ( rim to RS18 ) sub-unit 3b––88 . 460 . 67 RS18FS1c ( core to RS13 ) sub-unit 3b2425242 . 96 . 6 RS5FS1sub-unit 1b ( Facies 1b; OSL4 ) 2906–– RS22FS1a ( top to RS23 ) Unit 2equilibrium–478+107/−41 RS23FS1a ( base to RS22 ) Unit 2equilibrium–502+181/−53 RS9n/a ( surface outcrop ) n/aequilibrium–equilibrium– 1James Cook University ( JCU ) , Advanced Analytical Centre . 2University of Melbourne ( UoM ) , paleochronology laboratory . 10 . 7554/eLife . 24231 . 007Table 2 . U-Th data table for James Cook University . Uncertainties include: analytical error , decay constant uncertainty , and uncertainty on initial 230Th/232Th . Ages are reported relative to 1950 and assume an initial 230Th/232Th activity of 0 . 83 ± 0 . 5 , and the equation given in Placzek et al . ( 2006 ) . Decay constants for 234U and 230Th are from Cheng et al . ( 2013 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 007Sample IDU ( ppm ) 234U/238U2σ230Th/238U2σ232Th/238U2σAge ( ka BP ) 2σ ( ka ) 234U/238Uinitial2σ ( activity ) ( activity ) ( corrected ) ( activity ) RS112 . 3141 . 7720 . 0500 . 1440 . 0010 . 00010880 . 00000059 . 050 . 061 . 81840 . 0003 RS190 . 6521 . 8550 . 0010 . 3870 . 0020 . 0021760 . 00000824 . 70 . 21 . 9890 . 001 RS210 . 4211 . 9460 . 0010 . 4600 . 0040 . 0019200 . 00001528 . 40 . 42 . 1090 . 002 RS100 . 8461 . 8850 . 0010 . 4660 . 0030 . 0007920 . 00000330 . 10 . 32 . 0530 . 001 RS200 . 7951 . 8550 . 0010 . 4630 . 0030 . 0013630 . 00000530 . 40 . 22 . 0220 . 001 RS60 . 5601 . 9660 . 0010 . 7470 . 0030 . 0009740 . 00000249 . 80 . 32 . 2630 . 002 RS150 . 4001 . 9120 . 0011 . 1640 . 0080 . 004720 . 0000192 . 61 . 02 . 4840 . 007 RS80 . 3281 . 8130 . 0031 . 1200 . 0080 . 003160 . 0000295 . 01 . 02 . 3730 . 007 RS140 . 7341 . 6390 . 0951 . 0390 . 0080 . 002980 . 00002100 . 11 . 22 . 1750 . 008 RS170 . 6801 . 6090 . 0011 . 0320 . 0050 . 0006790 . 000001102 . 60 . 82 . 1500 . 005 RS160 . 9731 . 5830 . 0001 . 0240 . 0110 . 0004030 . 000006104 . 01 . 92 . 120 . 01 RS180 . 1521 . 8480 . 0011 . 8560 . 0130 . 011750 . 0000524253 . 660 . 05 RS50 . 0901 . 7280 . 0011 . 8180 . 0090 . 017320 . 0000529063 . 920 . 07 RS230 . 3141 . 1870 . 0021 . 3150 . 0110 . 003460 . 00002>400–– RS220 . 3671 . 2090 . 0011 . 3220 . 0080 . 0001250 . 000001>400–– RS90 . 7371 . 0070 . 0021 . 0290 . 0040 . 0004620 . 000001>400––10 . 7554/eLife . 24231 . 008Table 3 . U-Th data table for the University of Melbourne . Activity ratios are determined after Hellstrom ( 2003 ) and Drysdale et al . ( 2012 ) . Ages are corrected for initial 230Th using Equation 1 of Hellstrom ( 2006 ) , the decay constants of Cheng et al . ( 2013 ) , and an initial 230Th/232Th activity of 1 . 5 ± 1 . 5 . The initial 234U/238U ratios are calculated using corrected ages , which are reported relative to 1950 . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 008Sample IDU ( ppm ) 234U/238U2σ230Th/238U2σ232Th/238U2σAge ( ka BP ) 2σ ( ka ) 234U/238Uinitial2σ ( activity ) ( activity ) ( corrected ) ( activity ) RS111 . 5181 . 8080 . 0030 . 15970 . 00090 . 00008750 . 00000049 . 9460 . 0631 . 8310 . 004 RS190 . 5011 . 8840 . 0110 . 39160 . 00260 . 0043220 . 00001024 . 530 . 431 . 9470 . 011 RS210 . 3611 . 9680 . 0110 . 46540 . 00300 . 00113420 . 000001928 . 620 . 292 . 0490 . 011 RS200 . 6261 . 8780 . 0110 . 49250 . 00320 . 00238370 . 000004032 . 120 . 381 . 9610 . 011 RS60 . 2762 . 0230 . 0040 . 78560 . 00210 . 004960 . 0001050 . 820 . 432 . 1810 . 004 RS130 . 0762 . 0060 . 0041 . 18370 . 00470 . 0047860 . 00005888 . 460 . 672 . 2910 . 005 RS150 . 3811 . 9340 . 0041 . 16610 . 00290 . 006390 . 0001291 . 370 . 652 . 2090 . 005 RS140 . 6651 . 6260 . 0031 . 00100 . 00150 . 0012620 . 00001496 . 240 . 361 . 8220 . 003 RS80 . 2571 . 8310 . 0041 . 13970 . 00340 . 0057460 . 00006096 . 290 . 692 . 0910 . 004 RS170 . 5171 . 6370 . 0091 . 02480 . 00660 . 00239630 . 000003798 . 61 . 41 . 8410 . 010 RS160 . 9051 . 5900 . 0100 . 99630 . 00670 . 00170990 . 000003799 . 11 . 41 . 7800 . 011 RS180 . 1042 . 0010 . 0112 . 03200 . 01400 . 0205570 . 000041242 . 96 . 62 . 9870 . 027 RS220 . 3241 . 2280 . 0071 . 30170 . 00830 . 00012010 . 0000008478+107/–41–– RS230 . 2061 . 2250 . 0071 . 30160 . 00930 . 0078180 . 000016502+181/–53–– RS90 . 8961 . 0100 . 0021 . 02040 . 00180 . 0009160 . 000012–––– The oldest dated flowstone in the assemblage is Flowstone 1a overlying Unit 2 , which yields age estimates of 502 +181/–53 ka ( RS 23 ) and 478 +107/–41 ka ( RS 22 ) ( Table 1 ) . The next oldest age comes from a flowstone interpreted as Flowstone Group 1 overlying sediment of sub-unit 1b to the W of the excavation pit ( Figure 1b ) with an age of 290 ± 6 ka ( RS5; Table 1 ) . This age is younger than the OSL age derived from sub-unit 1a ( OSL5 ) , but is slightly older than OSL ages derived from sub-unit 1b in this location ( OSL4; Figure 2c ) . This suggests that sub-unit 1a was deposited prior to precipitation of this flowstone and that sub-unit 1b formed out of stratigraphic order due to erosion and redeposition of the top of sub-unit 1a beneath this flowstone . However , the U-Th date for RS5 should be interpreted with caution as the flowstone has a porous texture ( Figure 3a ) , which probably indicates some degree of dissolution and/or recrystallization of the primary calcite , and may have affected the U-Th systematics ( see Discussion ) . Flowstone samples that overlie sub-unit 3b , which contains the H . naledi fossils , yield age estimates that fall within four distinct time periods: ~242 ka ( RS18 = 242 ± 5 ka [JCU] and 242 . 9 ± 6 . 6 ka [UoM] ) for Flowstone 1c; 88–106 ka ( RS1 , RS8 , RS13-17 ) ; 50 ka ( RS6 ) ; and 24–32 ka ( RS10 , RS19-21 ) for Flowstone Group 2 deposits . The results for RS18 provide a minimum age for the H . naledi fossils in this part of the cave . An actively forming ( i . e . , dripping ) stalagmite of Flowstone Group 3 ( RS11 ) on top of an older base of Flowstone Group 2 returned a younger age of 9–10 ka ( RS11 = 9 . 05 ± 0 . 06 [JCU] and 9 . 95 ± 0 . 06 ka [UoM] ) . The final flowstone sample that was dated in this study was collected on the land surface above the cave system . It was sampled from a ~14 cm wide vertical flowstone-filled fracture exposed on the surface above the southern end of the Dinaledi Chamber itself . This is the only possible alternative entry-way into the Dinaledi Chamber that we have observed . The replicate samples ( RS9 ) analysed at JCU and UoM both indicated secular equilibrium , which confirms that the flowstone sealed this fracture sometime before ~600 ka , eliminating this thin flowstone-filled fracture as a possible alternative entrance for H . naledi into the Dinaledi Chamber . It should also be noted that no evidence of a talus cone or any other evidence of sediment entry into the chamber below this point has been observed . The spatial distribution of the flowstones belonging to the different age groups ( Figure 1b ) indicates that the oldest flowstones ( Flowstone Group 1 ) occur near the entry zone into the chamber and as an erosional remnant ( RS 5 ) near the back of a WNW-trending fracture W of the excavation pit . The 88–106 ka flowstones formed in three separate parts of the chamber ( Figure 1b ) : ( a ) on top of older flowstones near the entry; ( b ) as wall drapes above a drain 6 m SW of the entry; and ( c ) deep within a N-trending fracture , 8 m N of the excavation pit . The ~ 50 ka flowstones originate from a W-trending fracture , 6 m W of the entry shaft where it overlies sub-unit 1a and sub-unit 3b sediments . The 24–32 ka flowstones all originate from around the area where the excavation pit is located at the intersection point of three fracture sets ( Figure 1b ) . The youngest flowstone sample comes from below an active drip point , 1 . 5 m E of the excavation pit , and similar actively forming flowstones can be seen in other parts of the chamber . The flowstone age groupings indicate that episodic wet periods in the Dinaledi Chamber alternated with periods during which no flowstone was deposited . U-Th disequilibrium analyses of four tooth samples were conducted to constrain U uptake models into dental tissues used in ESR dating . The analyses were also used to provide apparent U-Th age estimates ( Tables 4 and 5 ) . Analyses of all four teeth ( samples 1767 , 1788 and1810 from H . naledi , and sample 1841 from Papio sp . ) were performed at the University of Wollongong ( UoW ) , in collaboration with Southern Cross University ( SCU ) . Duplicate analyses of two of the tooth samples ( samples 1788 and 1810 ) were conducted at Griffith University ( GU ) in collaboration with the Australian National University ( ANU ) . 10 . 7554/eLife . 24231 . 009Table 4 . Summary table of U-Th disequilibrium ages obtained for the three H . naledi teeth ( samples 1767 , 1788 and 1810 ) and the baboon tooth ( sample 1841 ) from the Dinaledi Chamber obtained by SCU-UoW . No age calculations were carried out for U concentrations of ≤0 . 5 ppm or U/Th ≤250 ( indicated in red and underlined ) . Mean values in this table only incorporate values from which meaningful ages could be calculated ( indicated in black ) , however all values ( i . e . , red and black ) were averaged to obtain the relevant mean values reported in Table 4 . All uncertainties are given as 2σ . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 009Sample1767U ( ppm ) U/Th230Th/238U2σ234U/238U2σAge ( ka ) 2s ( ka ) ( 234U/238U ) i2σ1767-1 D7 . 226852 . 1670 . 0246 . 2590 . 00943 . 51 . 16 . 9490 . 0261767-2 D7 . 759962 . 2610 . 0236 . 2820 . 01045 . 51 . 17 . 0090 . 0301767-3 D8 . 031962 . 2250 . 8256 . 2760 . 012––––1767-4 D8 . 559512 . 2090 . 0316 . 3010 . 00944 . 11 . 47 . 0070 . 0301767-5* E3 . 6912382 . 2590 . 0316 . 1970 . 05546 . 21 . 86 . 9240 . 1261767-6* E1 . 761082 . 2391 . 1336 . 1650 . 038––––1767-7* E2 . 151092 . 3370 . 9476 . 2310 . 024––––1767-8* E2 . 465182 . 2760 . 0216 . 2530 . 01946 . 11 . 16 . 9860 . 048Mean:1767 D7 . 848772 . 2120 . 0266 . 2810 . 00944 . 51 . 26 . 9880 . 0291767 E3 . 088782 . 2680 . 0266 . 2250 . 03746 . 21 . 46 . 9550 . 087Sample1788U ( ppm ) U/Th230Th/238U2σ234U/238U2σAge ( ka ) 2s ( ka ) ( 234U/238U ) i2σ1788-1 D6 . 673902 . 9670 . 0266 . 4230 . 01161 . 41 . 57 . 4530 . 0541788-2 D7 . 081763 . 3700 . 8336 . 4410 . 010––––1788-3 D7 . 17603 . 2063 . 1266 . 3940 . 049––––1788-4 D7 . 4513913 . 3130 . 0236 . 4450 . 01070 . 31 . 47 . 6450 . 0561788-5 D5 . 5244233 . 2690 . 0236 . 3490 . 01070 . 41 . 47 . 5310 . 0521788-6 D5 . 0740903 . 4160 . 0146 . 3780 . 01474 . 11 . 17 . 6340 . 0541788-7 D5 . 3947293 . 3850 . 0206 . 4000 . 01472 . 91 . 47 . 6400 . 0541788-8 D5 . 9332093 . 4270 . 0156 . 3930 . 01374 . 21 . 17 . 6540 . 0541788-9 D5 . 2443293 . 4490 . 0146 . 4130 . 01474 . 51 . 07 . 6850 . 0521788-10 D4 . 8931613 . 3900 . 0106 . 4030 . 01173 . 00 . 97 . 6450 . 0521788-11 D4 . 825563 . 3940 . 0146 . 4160 . 01472 . 91 . 07 . 6590 . 0521788-12 D5 . 4816063 . 3560 . 0176 . 3840 . 01472 . 31 . 17 . 6090 . 0521788-13 D5 . 048383 . 3170 . 0256 . 4200 . 01470 . 71 . 57 . 6230 . 0581788-14 D5 . 69933 . 2812 . 4266 . 4080 . 013––––1788-15 D5 . 03723 . 3153 . 7316 . 4270 . 014––––1788-16 E0 . 1331 . 78618 . 1493 . 8340 . 267––––1788-17 E0 . 68250 . 7529 . 1496 . 2480 . 273––––1788-18 E0 . 4160 . 80113 . 0536 . 2360 . 050––––1788-19 E0 . 0831 . 78336 . 2314 . 3010 . 288––––1788-20 E1 . 023062 . 9900 . 1175 . 5410 . 15475 . 19 . 36 . 6170 . 3941788-21* E0 . 33502 . 04127 . 1355 . 7930 . 141––––1788-22* E0 . 12301 . 51324 . 8015 . 9750 . 098––––1788-23* E0 . 25341 . 36817 . 0715 . 9880 . 079––––1788-24* E0 . 36901 . 23713 . 5556 . 1670 . 055––––1788-25* E0 . 411071 . 0848 . 6726 . 2060 . 033––––1788-26* E0 . 481021 . 30211 . 3336 . 3840 . 081––––1788-27* E0 . 491650 . 6867 . 7336 . 3670 . 037––––1788-28* E0 . 311671 . 6156 . 9755 . 6020 . 246––––1788-29* E0 . 4462 . 31111 . 8985 . 5760 . 306––––1788-30 E0 . 44620 . 9885 . 3106 . 0890 . 075––––1788-31 E0 . 2981 . 06619 . 2566 . 1510 . 056––––1788-32 E0 . 23950 . 99417 . 4516 . 3520 . 064––––1788-33 E0 . 4161 . 10321 . 6516 . 3440 . 049––––1788-34 E0 . 28511 . 34011 . 4506 . 3820 . 061––––1788-35 E0 . 3541 . 28621 . 0886 . 3210 . 062––––1788-36 E0 . 41151 . 21612 . 8966 . 3720 . 041––––1788-37 E0 . 3611 . 10617 . 0596 . 3130 . 073––––1788-38 E0 . 542792 . 8100 . 2376 . 3000 . 06458 . 912 . 27 . 2620 . 270Mean:1788 D5 . 5927933 . 3350 . 0186 . 4020 . 01371 . 51 . 27 . 6160 . 0541788 E0 . 782932 . 9000 . 1775 . 9200 . 10967 . 010 . 86 . 9360 . 332Sample1810U ( ppm ) U/Th230Th/238U2σ234U/238U2σAge ( ka ) 2s ( ka ) ( 234U/238U ) i2σ1810-1 D7 . 073483 . 2310 . 0215 . 8140 . 01777 . 91 . 67 . 0030 . 0561810-2 D8 . 294113 . 1120 . 0305 . 8630 . 01073 . 42 . 16 . 9860 . 0621810-3 D8 . 889793 . 1060 . 0275 . 9290 . 01072 . 11 . 87 . 0460 . 0601810-4 D9 . 198333 . 0490 . 0445 . 9930 . 01169 . 42 . 67 . 0790 . 0661810-5 D9 . 175082 . 9370 . 0475 . 9900 . 00766 . 22 . 87 . 0200 . 0661810-6 D9 . 12553 . 1436 . 9195 . 9810 . 012––––1810-7 D7 . 954323 . 0990 . 0185 . 9770 . 01371 . 11 . 37 . 0890 . 0541810-8 D8 . 844892 . 9860 . 0746 . 0350 . 060674 . 17 . 0880 . 0841810-9 D9 . 39159053 . 1220 . 0135 . 8700 . 00673 . 61 . 16 . 9990 . 0521810-10 D9 . 7878393 . 1650 . 0175 . 8730 . 01174 . 81 . 37 . 0240 . 0541810-11 D9 . 0372423 . 1740 . 0305 . 8880 . 01574 . 82 . 07 . 0430 . 0581810-12 D9 . 5396263 . 1570 . 0195 . 8890 . 00974 . 31 . 47 . 0360 . 0541810-13 D10 . 19102403 . 0940 . 0185 . 9040 . 00872 . 21 . 37 . 0160 . 0521810-14 D10 . 64144633 . 1550 . 0305 . 9580 . 01073 . 11 . 97 . 0990 . 0581810-15 E0 . 0051−0 . 384146 . 0361 . 9650 . 186––––1810-16 E0 . 0022−1 . 06048 . 1681 . 0140 . 108––––1810-17 E0 . 00415 . 40317 . 5732 . 3570 . 194––––1810-18 E0 . 24553 . 1959 . 1094 . 0540 . 062––––1810-19 E0 . 544264 . 0090 . 1865 . 0540 . 109130 . 821 . 86 . 8720 . 4661810-20 E0 . 853283 . 6250 . 1194 . 2870 . 137146 . 822 . 75 . 9840 . 5241810-21 E0 . 41485 . 0094 . 4744 . 4940 . 186––––1810-22 E0 . 1577 . 6908 . 2254 . 3490 . 351––––1810-23 E0 . 0329 . 9121 . 8435 . 1530 . 599––––1810-24 E0 . 0100 . 661166 . 0561 . 8770 . 144––––1810-25 E0 . 0227 . 4089 . 3864 . 9640 . 557––––1810-26* E0 . 7333 . 7624 . 1004 . 7350 . 100––––1810-27* E0 . 1435 . 2714 . 5104 . 2550 . 112––––1810-28* E0 . 1853 . 5418 . 0794 . 5620 . 081––––1810-29* E0 . 2593 . 5117 . 0954 . 5620 . 058––––1810-30* E0 . 2134 . 0275 . 2914 . 0730 . 149––––1810-31* E0 . 0913 . 87539 . 1364 . 0290 . 072––––1810-32* E0 . 0522 . 4699 . 0994 . 1870 . 072––––1810-33* E0 . 0612 . 60224 . 1694 . 4260 . 141––––1810-34* E0 . 9120103 . 1310 . 0684 . 5610 . 032105 . 27 . 05 . 7980 . 1281810-35 E1 . 0134 . 29115 . 5144 . 0850 . 025––––1810-36 E0 . 04996 . 2978 . 8904 . 0600 . 242––––1810-37 E0 . 145285 . 7533 . 9324 . 3850 . 234––––1810-38 E0 . 02555 . 68728 . 4904 . 2110 . 437––––1810-39 E0 . 01174 . 20331 . 0484 . 4740 . 314––––1810-40 E2 . 0915863 . 9930 . 0494 . 9930 . 037132 . 56 . 26 . 8140 . 146Mean:1810 D9 . 0753323 . 1070 . 0305 . 9220 . 014572 . 31 . 97 . 0400 . 0601810 E1 . 1010883 . 6900 . 1054 . 7240 . 0788128 . 814 . 46 . 5950 . 316Sample1841U ( ppm ) U/Th230Th/238U2σ234U/238U2σAge ( ka ) 2s ( ka ) ( 234U/238U ) i2σ1841-1 E2 . 51784 . 4153 . 2525 . 8510 . 035––––1841-2 E1 . 96514 . 2687 . 6315 . 8420 . 044––––1841-3 E2 . 372184 . 3190 . 0415 . 8710 . 021115 . 53 . 57 . 7580 . 0901841-4 E1 . 883504 . 2610 . 0465 . 8910 . 016112 . 63 . 67 . 7300 . 0821841-5 E2 . 52144 . 3780 . 0455 . 8460 . 032118 . 74 . 37 . 7840 . 1241841-6 E2 . 5124 . 4282 . 7445 . 8810 . 044––––1841-7 E2 . 4634 . 4841 . 7445 . 9460 . 044––––1841-8 E2 . 14474 . 4992 . 4675 . 9620 . 037––––Mean:1841 E2 . 252614 . 3190 . 0445 . 8690 . 023115 . 63 . 87 . 7570 . 09910 . 7554/eLife . 24231 . 010Table 5 . Summary table of U-Th disequilibrium ages obtained for two H . naledi teeth ( samples 1788 and 1810 ) from the Dinaledi Chamber obtained by GU-ANU . No age calculations were carried out for U concentrations of ≤0 . 5 ppm or U/Th ≤250 ( indicated in red and underlined ) . Negative U/Th values are due to the Th background being higher than the measured values . Mean values in this table only incorporate values from which meaningful ages could be calculated ( indicated in black ) . All uncertainties are given as 2σ . CS = Closed System; Diff = diffusion ( i . e . , calculated ages are based on the assumption of continuous diffusion after Sambridge et al . ( 2012 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 010Sample 1810aU ( ppm ) U/Th230Th/238U2σ234U/238U2σAge – CS ( ka ) 2σ ( ka ) Age – Diff ( ka ) 2σ ( ka ) ( 234U/238U ) i*2σ 1 E0 . 03−273 . 71131 . 25084 . 43020 . 8881n/a––––– 2 E0 . 02−193 . 16480 . 93904 . 20830 . 4703n/a––––– 3 E0 . 04−333 . 02571 . 05315 . 12200 . 4988n/a––––– 4 E0 . 05−353 . 63521 . 38974 . 92240 . 4912n/a––––– 5 E0 . 19−2583 . 45040 . 19654 . 81060 . 1376n/a––––– 6 D6 . 07−29723 . 29090 . 06665 . 98010 . 055977 . 22 . 387 . 32 . 77 . 190 . 11 7 D6 . 10−53543 . 26180 . 08245 . 97680 . 031276 . 32 . 686 . 23 . 37 . 170 . 08 8 D6 . 39114363 . 31690 . 08005 . 98270 . 051477 . 92 . 688 . 33 . 37 . 210 . 11 9 D6 . 4761933 . 33180 . 08995 . 94700 . 087379 . 03 . 289 . 73 . 87 . 180 . 17 10 D6 . 65−50553 . 49850 . 10486 . 04620 . 040382 . 53 . 494 . 44 . 57 . 370 . 11 11 D6 . 9551493 . 54650 . 09106 . 05310 . 040683 . 83 . 096 . 34 . 07 . 400 . 11 12 D7 . 1532443 . 52380 . 09976 . 05010 . 042383 . 23 . 395 . 44 . 37 . 390 . 11Mean:1–5 E0 . 07 ± 0 . 063 . 43210 . 30034 . 79620 . 1504112 . 015 . 7137 . 125 . 5––6–12 D6 . 54 ± 0 . 313 . 40180 . 07496 . 00700 . 042880 . 12 . 591 . 13 . 27 . 270 . 11Sample 1810bU ( ppm ) U/Th230Th/238U2σ234U/238U2σAge – CS ( ka ) 2σ ( ka ) Age – Diff ( ka ) 2σ ( ka ) ( 234U/238U ) i*2σ 1 E0 . 01176 . 844214 . 04350 . 02285 . 5626n/a––––– 2 E0 . 0039 . 233016 . 2333−2 . 28384 . 6085n/a––––– 3 E0 . 00–216 . 168827 . 6564−0 . 10337 . 6336n/a––––– 4 E0 . 00–314 . 9980967 . 0421−0 . 7695259 . 2590n/a––––– 5 E0 . 02−1827 . 1338296 . 11363 . 625048 . 0742n/a––––– 6 E0 . 86−24934 . 51760 . 17864 . 65880 . 0795189 . 116 . 7381 . 3137 . 97 . 240 . 44 7 E0 . 98−6034 . 87970 . 14164 . 87370 . 0681201 . 414 . 00 . 00 . 07 . 840 . 40 8 D4 . 49204233 . 57780 . 06705 . 93270 . 077387 . 12 . 7100 . 83 . 17 . 310 . 15 9 D5 . 35−101283 . 30460 . 06595 . 91420 . 067778 . 72 . 489 . 22 . 87 . 140 . 13 10 D5 . 67−41973 . 40770 . 07775 . 94800 . 045981 . 32 . 692 . 83 . 37 . 230 . 10Mean:1–5 E0 . 01 ± 0 . 018 . 7750204 . 69881 . 654793 . 9630n/a-----6–7 E0 . 92 ± 0 . 124 . 71010 . 14654 . 77300 . 0602195 . 713 . 8471 . 0269 . 47 . 540 . 428–10 D5 . 17 ± 0 . 703 . 42140 . 07905 . 93190 . 052382 . 12 . 793 . 73 . 67 . 230 . 13Sample 1788aU ( ppm ) U/Th230th/238U2σ234U/238U2σAge – CS ( ka ) 2σ ( ka ) Age – Diff ( ka ) 2σ ( ka ) ( 234U/238U ) i*2σ 1E0 . 0348772 . 10953 . 30583 . 57401 . 6107n/a––––– 2E0 . 01−213 . 78455 . 52711 . 35252 . 7713n/a––––– 3E0 . 00–110 . 503027 . 8940−2 . 490910 . 1171n/a––––– 4E0 . 00–19 . 0249113 . 8912−0 . 712032 . 3636n/a––––– 5E0 . 00–26 . 679566 . 27500 . 776918 . 7506n/a––––– 6E0 . 01–63 . 02310 . 98442 . 19040 . 4875n/a––––– 7E0 . 24−1052 . 81390 . 20766 . 37910 . 1624n/a––––– 8E0 . 24−2041 . 64951 . 58116 . 01230 . 2961n/a––––– 9E0 . 195792 . 40754 . 26206 . 33411 . 3187n/a––––– 10E0 . 481893 . 17172 . 28626 . 10060 . 1341n/a––––– 11E1 . 34138333 . 87920 . 28646 . 35210 . 102488 . 59 . 2102 . 912 . 67 . 870 . 32 12E2 . 577554 . 17700 . 06096 . 32750 . 097298 . 63 . 0117 . 73 . 18 . 040 . 19Mean:1–6 E0 . 01 ± 0 . 013 . 51884 . 45312 . 34717 . 1463n/a---––7–10 E0 . 29 ± 0 . 132 . 64840 . 24696 . 18240 . 097856 . 26 . 561 . 07 . 7––11–12 E1 . 96 ± 1 . 234 . 07460 . 09416 . 33610 . 048295 . 13 . 2112 . 34 . 77 . 960 . 26Sample 1788bU ( ppm ) U/Th230Th/238U2σ234U/238U2σAge – CS ( ka ) 2σ ( ka ) Age – Diff ( ka ) 2σ ( ka ) ( 234U/238U ) i*2σ 1E0 . 0242 . 39451 . 87743 . 50401 . 4368n/a––––– 2E0 . 02141 . 96561 . 32993 . 30990 . 9022n/a––––– 3E0 . 01–82 . 81562 . 10342 . 53590 . 8082n/a––––– 4E0 . 021602 . 102458 . 18543 . 23427 . 4009n/a––––– 5E0 . 03−312 . 38591 . 40844 . 12851 . 5222n/a––––– 6E0 . 03−202 . 895111 . 29114 . 10463 . 9747n/a––––– 7E0 . 02−102 . 84863 . 53834 . 93622 . 3343n/a––––– 8E0 . 03−182 . 83251 . 61135 . 70520 . 7139n/a–––––Mean:1–8 E0 . 02 ± 0 . 012 . 55976 . 76184 . 13081 . 2209n/a–––––Sample 1788cU ( ppm ) U/Th230th/238U2σ234U/238U2σAge – CS ( ka ) 2σ ( ka ) Age – Diff ( ka ) 2σ ( ka ) ( 234U/238U ) i*2σ 1D5 . 44215783 . 92810 . 07076 . 42600 . 074088 . 62 . 6103 . 03 . 17 . 970 . 15 2D5 . 391550373 . 89080 . 05656 . 44160 . 052487 . 22 . 0101 . 02 . 47 . 960 . 11 3D4 . 9517083 . 89010 . 08286 . 40850 . 079287 . 83 . 0102 . 03 . 67 . 930 . 16 4D3 . 8716533 . 80330 . 08596 . 37860 . 106885 . 83 . 399 . 03 . 67 . 850 . 20 5D4 . 2511683 . 95690 . 08006 . 40510 . 095790 . 03 . 1105 . 03 . 67 . 970 . 19 6D5 . 1214933 . 94330 . 05796 . 49510 . 096187 . 82 . 5102 . 02 . 58 . 040 . 17 7D5 . 3426593 . 80200 . 05816 . 47130 . 058184 . 02 . 096 . 72 . 47 . 940 . 11 8D5 . 0610933 . 99480 . 06726 . 44790 . 063090 . 32 . 4105 . 53 . 08 . 030 . 13 9D4 . 7810184 . 04810 . 07186 . 44680 . 058692 . 02 . 5108 . 03 . 38 . 060 . 13 10D5 . 228173 . 90110 . 05826 . 51870 . 081386 . 12 . 399 . 62 . 48 . 040 . 15 11D5 . 254253 . 88720 . 08506 . 44150 . 062487 . 12 . 8101 . 03 . 67 . 960 . 14 12D5 . 463453 . 95610 . 05846 . 46580 . 073388 . 82 . 3103 . 32 . 58 . 030 . 14Mean:1–12 D5 . 01 ± 0 . 283 . 91750 . 07966 . 44790 . 046187 . 92 . 6102 . 03 . 67 . 980 . 06 Summaries of the U-Th analytical data and ages are reported in Table 4 ( SCU-UoW ) and Table 5 ( GU-ANU ) . In Table 4 only closed system dates are reported , while Table 5 also lists dates based on the continuous diffusion model of Sambridge et al . ( 2012 ) . In both datasets , the U content in enamel is much lower than in dentine . Note that apparent U-Th ages for the teeth are likely to provide apparent age estimates , which will approach the age for U uptake events that affected the teeth during wet periods in the chamber , typically after deposition . These ages should , therefore , be regarded as minimum age estimates for the teeth , and do not represent depositional ages for the fossils . 10 . 7554/eLife . 24231 . 011Figure 4 . Location of the three H . naledi tooth samples ( samples 1767 , 1788 and 1810 ) and one baboon ( cf . Papio ) tooth sample ( sample 1841 ) used for combined U-series and ESR dating . ( a ) Map of the Dinaledi Chamber showing the position of the excavation pit and the position of figures ( b ) and ( c ) ; ( b ) close-up of the SE corner of the excavation pit showing the sample site for sample 1810 and the 50 cm deep sondage from which sample 1841 was recovered; ( c ) the area to the W of the excavation pit from which samples 1767 and 1788 were collected . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 01110 . 7554/eLife . 24231 . 012Figure 5 . Samples of orange laminated mudstone of Unit 1 for OSL dating . ( a ) sample OSL3 with an estimated MAM age of 231 ± 41 ka taken from sub-unit 1b; ( b ) sample OSL4 with an estimated MAM age of 241 ± 37 ka , taken from sub-unit 1b and covered by a flowstone sheet dated at 290 ± 6 ka ( RS5 ) ; ( c ) sample OSL5 with an estimated MAM age of 353 ± 61 ka , taken from sub-unit 1a and covered by a flowstone sheet dated at 32 . 1 ± 0 . 4 ka ( RS20 ) . The scale bar in each of the photographs is 10 cm . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 01210 . 7554/eLife . 24231 . 013Figure 6 . Photographs of H . naledi teeth used for ESR dating . ( a ) U . W . 101–1767; ( b ) U . W . 101–1788; ( c ) U . W . 101–1810 . The order of images for each panel is: buccal , distal , lingual , mesial , and occlusal views . The scale bar in each panel is 1 cm . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 013 Sample 1767: This extremely worn upper premolar crown ( Figure 6a ) is heavily weathered , and only a small fragment of enamel was left attached to the dentine . It could , therefore , only be dated once ( at SCU-UoW ) . Both dentine and enamel yield consistent results with apparent U-Th ages of 44 . 5 ± 0 . 6 ka for dentine and 46 . 1 ± 0 . 7 ka for enamel , and initial 234U/238U activity ratios at 6 . 99 ± 0 . 01 and 6 . 99 ± 0 . 04 respectively . These results suggest that a single uptake event is dated . The tooth is characterised by an extremely high U content in the enamel when compared to the other teeth ( Table 4 ) . Uranium concentration gradients show the effects of diffusion into the enamel from all external surfaces , with enrichment at the Enamel Dentine Junction ( EDJ ) . Sample 1788: This lower right second molar was covered by a thin layer of sediment and is well-preserved ( Figure 6b ) . Uranium concentrations in enamel and dentine vary across the surface , with minor hotspots and leaching zones near enamel cracks and along the dentine canal . The EDJ is enriched in U , showing a diffusion gradient into the enamel , and resulting in elevated U concentrations in spots located close to the EDJ ( Figure 9 ) . The U uptake history appears complex and heterogeneous , and probably involved several episodes . Most of the U concentrations in the enamel are too low to provide a meaningful age . However , parts of the enamel and the dentine yielded consistent measurements for which SCU-UoW provide ages with mean values of 71 . 5 ± 0 . 6 ka for dentine and 67 . 0 ± 5 . 4 ka for enamel with initial 234U/238U activity ratios of 7 . 62 ± 0 . 03 and 6 . 94 ± 0 . 17 respectively . GU-ANU obtained a combined age of 95 . 1 ± 3 . 2 ka for two enamel spots with higher U concentrations , with parts of the enamel with lower U-enrichment yielding a combined age of 56 . 2 ± 6 . 5 ka . GU-ANU also provide a consistent mean apparent age of 87 . 9 ± 2 . 6 ka , associated with initial 234U/238U activity ratios of 7 . 98 ± 0 . 06 ( individual spots agreeing within error ) for dentine which is interpreted as the age of an U uptake event . Sample 1810: This lower left molar or premolar from the excavation pit ( Figures 4 and 6c ) , is near complete and only moderately weathered . Uranium diffusion patterns show U accumulating at the EDJ with slow diffusion into the enamel tissue . The U concentrations in most of the enamel are too low to calculate a meaningful age . Areas of enamel with higher U concentrations return older ages ( Tables 4 and 5 ) . SCU-UoW provide a mean age for high-U spots in enamel of 128 . 8 ± 7 . 2 ka and an associated initial 234U/238U activity ratio of 7 . 60 ± 0 . 16 . GU-ANU report a mean apparent U-Th age of 195 . 7 ± 13 . 8 ka , which is much higher than the adjacent dentine spots ( at 81 . 1 ± 2 . 7 ka ) , but is coupled with realistic initial 234U/238U activity ratios of 7 . 24 and 7 . 84 with overlapping error limits . This indicates that a secondary overprint of the dentine took place for which the U source had a similar 234U/238U composition as the source of the initial U uptake event . Dentine measurements are consistent along the measured sections with small regions affected by leaching and enrichment near cracks and the pulp cavity . The dentine analyses done by SCU-UoW ( Table 4 ) yield similar ages with consistent initial 234U/238U activity ratios of around 7 . 04 ± 0 . 03 , and a mean apparent age of 72 . 3 ± 1 . 0 ka . The combined analytical data for dentine from GU-ANU in samples 1810A and 1810B yield apparent U-Th ages of 80 . 1 ± 2 . 5 ka and 82 . 1 ± 2 . 7 ka respectively , coupled with consistent initial 234U/238U activity ratios ( Table 5 ) . Sample 1841 The baboon tooth consists of an enamel crown that is structurally intact , but the enamel is friable and weathered ( Figure 7 ) . The U distribution within the enamel appears homogenous , however , Th concentrations are low and the resolution of the elemental distribution is poor , which impairs the quality of the U-Th age estimates . A recent U uptake event may have occurred affecting enamel in contact with sediment , resulting in a mean apparent U-Th age estimate of 115 . 6 ± 1 . 9 ka with a mean initial 234U/238U activity ratio of 7 . 76 ± 0 . 05 ( Table 4 ) . 10 . 7554/eLife . 24231 . 014Figure 7 . Photographs of the baboon ( cf . Papio ) tooth ( sample 1841 ) , recovered from the sondage in the excavation pit , and used for ESR dating . Views are: ( a ) buccal , ( b ) occlusal , ( c ) lingual , and ( d ) internal . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 014 GU-ANU also provide age estimates in which the continuous diffusion assumptions of Sambridge et al . ( 2012 ) have been applied . The results obtained for samples 1788 and 1810 are about 20% older than the closed system ages ( Table 5 ) , but show much less consistency and are not further considered . Collectively , the results show that the teeth are older than 70 ka , and considering sample 1810 with a minimum age of around 200 ka , that the H . naledi fossils are probably older than 200 ka ( see Discussion ) . Combined U-series and ESR dating ( US-ESR; Grün et al . , 1988 ) of three hominin teeth ( samples 1767 , 1788 and 1810 ) and a baboon tooth ( sample 1841 ) was performed at SCU . Blind duplicate analyses of two of the hominin samples ( samples 1788 and 1810 ) were performed at the ‘Centro Nacional de Investigación sobre la Evolución Humana’ ( CENIEH ) , Spain in collaboration with GU ( CENIEH-GU ) . In obtaining the ages , each laboratory carried out independent sample preparation , and ESR and U-series analyses of the fossil teeth . Estimates for the environmental dose rates used in the age calculations were standardized for both laboratories ( Table 6 ) in order to produce comparable results ( see discussion and methodology sections for details ) . 10 . 7554/eLife . 24231 . 015Table 6 . Summary table of model parameters used in ESR dating separated by sample number and laboratory . See text for detailed discussion . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 015Sample:1767178818101841Laboratory:SCUSCUCenieh-guSCUCenieh-guSCUEnamel:De ( Gy ) 194 ± 4231 ± 8159 ± 10296 ± 14232 ± 301676 ± 127U ( ppm ) 2 . 52 ± 0 . 530 . 38 ± 0 . 170 . 07 ± 0 . 070 . 32 ± 0 . 120 . 16 ± 0 . 162 . 28 ± 0 . 48234U/238U6 . 21 ± 0 . 035 . 95 ± 0 . 326 . 258 ± 0 . 3494 . 04 ± 0 . 184 . 773 ± 0 . 0605 . 87 ± 0 . 03230Th/234U0 . 37 ± 0 . 050 . 55 ± 0 . 520 . 598 ± 0 . 0380 . 92 ± 0 . 050 . 950 ± 0 . 0340 . 785 ± 0 . 038Alpha efficiency*0 . 13 ± 0 . 020 . 13 ± 0 . 020 . 13 ± 0 . 020 . 13 ± 0 . 020 . 13 ± 0 . 020 . 13 ± 0 . 02Initial thickness ( μm ) 1027 ± 2101049 ± 2771486 ± 2481150 ± 2501527 ± 257650 ± 145Water ( % ) 000000Dentine:U ( ppm ) 7 . 88 ± 0 . 665 . 76 ± 0 . 864 . 71 ± 0 . 279 . 08 ± 0 . 445 . 81 ± 0 . 37–234U/238U6 . 28 ± 0 . 096 . 40 ± 0 . 036 . 448 ± 0 . 0465 . 93 ± 0 . 035 . 969 ± 0 . 035–230Th/234U0 . 35 ± 0 . 110 . 62 ± 0 . 020 . 608 ± 0 . 0120 . 52 ± 0 . 090 . 572 ± 0 . 010–Water ( % ) 10 ± 510 ± 510 ± 510 ± 510 ± 5–Sediment:U ( ppm ) 3 . 0 ± 0 . 32 . 9 ± 0 . 12 . 9 ± 0 . 13 . 2 ± 0 . 33 . 2 ± 0 . 30 . 64 ± 0 . 06†Th ( ppm ) 7 . 9 ± 0 . 48 . 3 ± 0 . 68 . 3 ± 0 . 68 . 6 ± 0 . 48 . 6 ± 0 . 44 . 72 ± 0 . 47†K ( % ) 1 . 17 ± 0 . 141 . 21 ± 0 . 141 . 21 ± 0 . 141 . 23 ± 0 . 141 . 23 ± 0 . 141 . 47 ± 0 . 15†Water ( % ) 25 ± 1025 ± 1025 ± 1025 ± 1025 ± 1025 ± 10Depth below ground surface ( cm ) 0225555Gamma Dose rate ( μGy a−1 ) 25 ± 10% Water , 80% Rn degassing 25 ± 10% Water , no Rn degassing534 ± 69 724 ± 116534 ± 69 724 ± 116534 ± 69 724 ± 116534 ± 69 724 ± 116534 ± 69 724 ± 116534 ± 69 724 ± 116Cosmic dose rate ( μGy a−1 ) 15 ± 115 ± 115 ± 115 ± 115 ± 115 ± 1*After Woodroffe et al . ( 1991 ) ;†A relative error of ± 10% was assumed . Analyses and results from both labs are presented in Table 7 and Figures 10 , 11 and 12 . Results are presented for two scenarios: scenario 1 in which the teeth are fully covered in sediment that contains 25 ± 10% water and experienced 80% Rn loss; and scenario 2 in which the teeth are fully covered in sediment that contains 25 ± 10% water and experienced no Rn loss . Scenario 1 reflects the measured present-day situation and is interpreted as a maximum age estimate . Scenario 2 provides a minimum age estimate ( Table 7 ) . Together these scenarios provide our best estimate for the age range of the fossil teeth . 10 . 7554/eLife . 24231 . 016Table 7 . Summary of ESR dating results ( 2σ uncertainties ) for two end-member scenarios: ( i ) complete burial of the samples , 80% Rn loss in the sediment and post Th-230 equilibrium in dental tissue ( i . e . , maximum age scenario ) ; ( ii ) complete burial of the samples and post-Rn equilibrium in sediment ( i . e . , minimum age scenario ) . See text for detailed discussion . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 016Sample:1767178818101841Laboratory:SCUSCUCenieh-guSCUCenieh-guSCU Scenario 1: 25 ± 10% Water , complete burial and 80% 222Rn degassing ( maximum age scenario ) internal dose rate ( μGy a−1 ) 1142 ± 515190 ± 12947 ± 47323 ± 175176 ± 1761411 ± 596alpha ( μGy a−1 ) *008 ± 208 ± 20†beta dose rate , dentine ( μGy a−1 ) 73 ± 3391 ± 6264 ± 1675 ± 4151 ± 14–‡beta dose rate , sediment ( μGy a−1 ) 101 ± 24105 ± 3186 ± 1795 ± 2486 ± 18358 ± 74gamma and cosmic ( μGy a−1 ) 549 ± 69549 ± 69549 ± 69549 ± 69549 ± 69549 ± 69total dose rate ( μGy a−1 ) 1865 ± 521935 ± 162754 ± 871042 ± 194870 ± 1902318 ± 606p enamel−0 . 030 . 49−0 . 02−0 . 70−0 . 770 . 91p dentine0 . 080 . 13−0 . 061 . 020 . 54–Age ( ka ) 104 ± 29247 ± 42211 ± 28284 ± 51267 ± 68723 ± 181Combined SCU/CENIEH-GU age ( ka ) 229 + 60/–46276 + 59/–77Average age for 1788 & 1810 ( ka ) 253 + 82/–70 Scenario 2: 25 ± 10% Water , complete burial and no 222Rn degassing ( minimum age scenario ) internal dose rate ( μGy a−1 ) 1277 ± 552216 ± 16551 ± 51335 ± 193184 ± 1841520 ± 630alpha ( μGy a−1 ) *008 ± 208 ± 20beta dose rate , dentine ( μGy a−1 ) 82 ± 35102 ± 7869 ± 1887 ± 5059 ± 16--beta dose rate , sediment ( μGy a−1 ) 132 ± 26134 ± 33111 ± 19126 ± 26112 ± 19380 ± 81gamma and cosmic ( μGy a−1 ) 739 ± 116739 ± 116739 ± 116739 ± 116739 ± 116739 ± 116total dose rate ( μGy a−1 ) 2230 ± 5861191 ± 219978 ± 1291287 ± 2321102 ± 2192639 ± 647p enamel−0 . 310 . 06−0 . 37−0 . 83−0 . 910 . 67p dentine−0 . 22−0 . 22−0 . 400 . 540 . 10–Age ( ka ) 87 ± 22194 ± 34163 ± 24230 ± 40210 ± 50635 ± 148Combined SCU/CENIEH-GU age ( ka ) 179 + 49/–40220 + 50/–60Average age for 1788 & 1810 ( ka ) 200 + 70/–61*using alpha attenuation values of Grün ( 1987 ) . †considered as negligible given the low radioelement concentrations in the sediment and the high total dose rate value . ‡for 1841 , the beta dose rate on both sides of the enamel layer is derived from the sediment . Combined US-ESR ages determined by SCU for samples 1810 , 1788 and 1767 under scenario 1 conditions ( i . e . , the maximum age scenario ) are 284 ± 51 ka , 247 ± 41 ka and 104 ± 29 ka ( 2σ uncertainty ) , respectively ( Table 7 ) . Combined US-ESR ages determined by CENIEH-GU for samples 1810 and 1788 under scenario 1 conditions are 267 ± 68 ka and 211 ± 28 ka ( 2σ uncertainty ) , respectively ( Table 7 ) . Combined US-ESR ages determined by SCU for samples 1810 , 1788 and 1767 under scenario 2 conditions ( i . e . , the minimum age scenario ) are 230 ± 40 ka , 194 ± 34 ka and 87 ± 22 ka ( 2σ uncertainty ) , respectively ( Table 7 ) . Combined US-ESR ages determined by CENIEH-GU for samples 1810 and 1788 under this scenario are 210 ± 50 ka and 163 ± 24 ka ( 2σ uncertainty ) , respectively ( Table 7 ) . Results for sample 1767 are based on anomalously high ( ~20 times ) U concentrations in enamel , and probably yield anomalously low age estimates ( Duval et al . , 2012 ) . This result is , therefore , considered to be unreliable and has been excluded from final age estimates ( see Discussion ) . The observed difference in age estimates obtained by SCU and CENIEH-GU for samples 1788 and 1810 , are most likely explained by natural dose variations within the tested enamel layers ( see Discussion and methodology sections ) , and we have no reason to prefer one age result over another . The optimal age estimate for the H . naledi fossils , therefore , combines the results from both laboratories with average maximum ( i . e . , scenario 1 ) age estimates for samples 1788 and 1810 of 229 + 60/–46 ka and 276 + 59/–77 ka ( 2 σ uncertainty ) respectively , and average minimum ( i . e . , scenario 2 ) age estimates of 179 + 49/–40 ka and 220 + 50/–60 ka ( 2 σ uncertainty ) respectively . Together these results provide an age range of 139–335 ka for the H . naledi remains , although dating of flowstone encasing H . naledi bones helps to better constrain this range ( see Discussion ) . Combined US-ESR ages for the baboon tooth ( sample 1841 ) determined by SCU using scenario 1 and 2 conditions are 723 ± 181 ka and 635 ± 148 ka respectively . The tooth contained no inner dentine ( Figure 7 ) , and was filled with sediment . In calculating the age it was , therefore , assumed that sediment occurred on both sides of the enamel layer . Sample 1841 was recovered from sub-unit 3a directly below the occurrence of articulated H . naledi remains in the excavation pit ( Dirks et al . , 2015 ) . The age results provide an upper age limit for the deposition of the H . naledi bearing layer , and mark an earlier stage of deposition of mud clast breccia in the cave assigned to sub-unit 3a , which predates the entry of H . naledi fossils into the cave . Optically stimulated luminescence ( OSL ) dating of three samples of sediment from Unit 1 in the Dinaledi Chamber ( samples OSL3 and OSL4 from sub-unit 1b , and sample OSL5 from sub-unit 1a; Figure 5 ) was performed at the University of the Witwatersrand ( Wits ) . The measurements were carried out on small aliquots containing ~30 grains . Summaries of the OSL analytical data and ages are reported in Table 8 . 10 . 7554/eLife . 24231 . 017Table 8 . Summary of OSL results obtained by the University of the Witwatersrand for samples of Unit 1 from the Dinaledi Chamber ( samples OSL3 , 4 and 5 ) . The ages were calculated using effective U concentration values ( taking disequilibrium into account; see text for details ) . CAM = Central Age Model; MAM = Minimum Age Model . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 017Sample IDH2O ( % ) Th ( ppm ) U ( ppm ) pre-RnU ( ppm ) post-RnK ( % ) Total dr ( Gy/ka ) 2σTotal de ( Gy ) CAM2σTotal de ( Gy ) MAM2σCAM Age ( ka ) CAM 2σMAM age ( ka ) MAM 2σOver dispersion ( % ) OSL318 . 9 ± 53 . 71 ± 1 . 600 . 75 ± 0 . 1770 . 193 ± 0 . 0440 . 45 ± 0 . 120 . 760 . 07428 . 5968 . 92176 . 427 . 75601032314163OSL425 . 8 ± 53 . 38 ± 1 . 600 . 485 ± 0 . 1770 . 097 ± 0 . 0440 . 47 ± 0 . 120 . 700 . 06379 . 8943 . 58168 . 020 . 7546792413755OSL522 . 7 ± 55 . 11 ± 1 . 600 . 692 ± 0 . 1770 . 138 ± 0 . 0440 . 56 ± 0 . 120 . 900 . 07759 . 54102 . 33315 . 6748 . 688491323536168 The reported dose rates for the samples range from 0 . 7 to 0 . 9 Gy ka−1 ( Table 8 ) , with significant within-sample scatter , resulting in uncertainties on age estimates of 15–18% . Overdispersion in De ranges from 50–70% , which is much higher than would be expected for a well-bleached sample , and indicates that it is most appropriate to apply a Minimum Age Model ( MAM ) to the dataset , in which the MAM age is likely to represent a maximum estimate for the age of the sediments ( see Discussion ) . As with ESR , significant Rn loss was detected in the samples of Unit 1 , and corrections to the measured U concentrations were applied ( Table 8 ) . The MAM calculations for the three samples yield maximum age estimates for the sediments of 231 ± 41 ka ( OSL3 ) , 241 ± 37 ka ( OSL4 ) , and 353 ± 61 ka ( OSL5 ) . The MAM apparent ages for OSL3 and OSL4 were obtained from the sandy facies of sub-unit 1b sediments and yield ages that are younger than the age of a Flowstone 1 sheet ( sample RS5 at 290 ± 4 ka; Table 1 ) that covers the outcrop of sub-unit 1a from which sample OSL4 was taken ( Figure 2c ) . This discrepancy can be attributed to inverted stratigraphy associated with erosion of the top of the older sub-unit 1a after the deposition of Flowstone 1 ( RS5 ) by running water and subsequent deposition of sub-unit 1b between sub-unit 1a and the flowstone ( Figure 2c ) . Sample OSL5 was obtained from muddy sediment of sub-unit 1a , and yields an older age than the covering flowstones ( RS5 and RS20 ) . Note that if a Central Age Model ( CAM ) is applied to the OSL data , results are significantly older at 560 ± 102 ka ( OSL3 ) , 546 ± 79 ka ( OSL4 ) , and 849 ± 132 ka ( OSL5 ) , however , this model is considered unrealistic within a cave environment ( Galbraith et al . , 1999 ) . Palaeomagnetic analysis of one composite sample of Flowstone 1a ( Figure 13 ) , covering erosional remnants of Unit 2 near the entry shaft into the Dinaledi Chamber , was performed at La trobe University , Melbourne ( LTU ) . The palaeomagnetic results for Flowstone 1a are presented in Figure 13 and Table 9 . The palaeomagnetic sample from Flowstone 1a comprises three distinct phases of flowstone formation ( from base to top: A , B and C; Figure 13a , b ) that have been analysed for their palaeomagnetic orientation ( Figure 13c ) . Note that the palaeomagnetic sample of Flowstone 1a was taken up-dip from the position where the U-Th sample of Flowstone 1a was taken ( i . e . , RS22 and RS23 ) . The basal , phase A flowstone observed in the palaeomagnetic sample tapers out down-dip ( Figure 13a ) and is not present in the U-Th sample; thus , RS23 at the base of the U-Th sample corresponds to phase B carbonate in the palaeomagnetic sample , and RS22 to phase C carbonate ( Figure 2b ) . 10 . 7554/eLife . 24231 . 018Table 9 . Final mean palaeomagnetic data for all subsamples analysed from each phase of Flowstone 1a as shown in Figure 13 . MAD = mean maximum angular deviation for individual samples; K = precision/sample dispersal parameter; Plat = palaeolatitude ) . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 018Flowstone 1aDeclination ( O ) Inclination ( O ) MADKPlat . Polarity Phase C15 . 5−39 . 7370 . 875 . 4N Phase B26−28 . 17 . 4156 . 263 . 3N Phase A156 . 415 . 95 . 730 . 2−60 . 0R Phase A flowstone at the base of the sample records a consistent reversed magnetic polarity , with a clear overprint that is anti-parallel to the characteristic , reversed polarity remanence ( ChRM ) and is removed by ~10 mT . The reversed polarity ChRM is then stable between 10 and 40 mT , although within this range there are two reversed polarity components that can be identified in some samples ( between 10 and 19 mT and then between 20 and 40 mT; Figure 13c ) . Both components have almost identical declination values , but the lower field component has a shallower inclination . This is not uncommon within speleothems ( Herries and Shaw , 2011 ) because a single subsample of 2 . 5 cm depth is measuring the remanence recorded in multiple layers of speleothem as well as multiple layers of detrital contamination . Phase B flowstone records a weak , normal polarity ChRM that is consistently isolated between 7 and 36mT ( Figure 13c ) . The results from this phase have the highest mean maximum angular deviation ( MAD ) values ( Table 9 ) due to the small and oddly shaped nature of the samples that provide less consistent measurements between each spin in the magnetometer . Phase C flowstone also records a normal ChRM that is similar to that seen within phase B ( Figure 13c ) , although with slightly steeper inclinations ( Figure 13c , Stereo Plot ) . The samples do not have strong secondary overprints as seen in many Plio-Pleistocene palaeo-cave deposits from the region ( Dirks et al . , 2010 ) , which may indicate that they are younger or have a different sediment source and , thus , mineralogy holding the remanence . The coercivity of phase B and C flowstones is similar , and distinct from phase A flowstone , although all the demagnetisation spectra suggest the dominant mineral holding the remanence is ferrimagnetic ( magnetite/maghaemite ) as at many other CoH sites ( Herries et al . , 2006; Herries and Shaw , 2011; Herries et al . , 2014 ) . Three weathered bone fragments of H . naledi were analysed via radiocarbon dating at Beta Analytic Inc . ( Florida , USA ) . Analyses indicated that no collagen was present in any of the samples and that the bone appeared possibly cremated . This was investigated with a bone carbonate extraction technique . Tests did not support cremation and indicated that extensive secondary CaCO3 replacement had occurred , providing ages of 33 . 0 ± 0 . 2 ka and 35 . 50 ± 0 . 16 ka for two of the fragments . We interpret these ages to relate to late calcite precipitation in the bones that may reflect a wet period in the cave .
The dating techniques applied in this study do not all work in the same way , and hence the results must be viewed differently . U-series analysis on carbonates , 14C analysis of bone and palaeomagnetic analyses of flowstones ( e . g . , Herries and Shaw , 2011; Taylor and Bar-Yosef , 2014; Hellstrom and Pickering , 2015 ) are well established dating techniques requiring few , if any , à priori assumptions . In contrast , ESR and OSL results are strongly dependent on model assumptions for the environmental conditions that affected the locations from which the samples were taken . The importance of a deep understanding of the stratigraphic position of the fossils and the geological processes that led to their deposition cannot be overstated considering the extremely complex nature of sedimentary cave fill in many cave systems ( e . g . , Wilkinson , 1985; Brain , 1993; Sasowsky , 1998; Stock et al . , 2005; Stratford et al . , 2014; Sutikna et al . , 2016 ) involving repeated cycles of deposition , erosion and reworking , leading to complex and sometimes contradictory age results ( e . g . , Granger et al . , 2015; Kramers and Dirks , 2017 ) . This problem is well illustrated with the on-going debate on the age of Stw 573 ( ‘little foot’ ) in the nearby Sterkfontein Cave , where after 20 years of dating efforts no definitive age is yet established ( see Partridge et al . , 1999 , 2003; Berger et al . , 2002; Walker et al . , 2006; Herries and Shaw , 2011; Pickering and Kramers , 2010; Granger et al . , 2015; Kramers and Dirks , 2017 ) . Another good example illustrating the difficulties of linking cave stratigraphy to a definitive age for the hominin fossils they contain is presented by the H . floresiensis remains in the Liang Bua cave , Indonesia ( Morwood et al . , 2004; Roberts et al . , 2009; Sutikna et al . , 2016 ) . The stratigraphy within the Dinaledi Chamber has been previously described by Dirks et al . ( 2015 ) . The ages presented here help to resolve outstanding questions about the stratigraphy in the Dinaledi Chamber , and allow us to more closely define the distribution of correlative stratigraphic units ( Figure 14 ) , and thus constrain the age of the H . naledi fossils . The oldest ages returned from the Dinaledi Chamber are from the baboon tooth that is embedded in sediment attributed to sub-unit 3a , followed by U-Th ages for Flowstone 1a , which directly covers erosional remnants of Unit 2 ( Figure 14 ) . Flowstone 1a consists of at least three generations of flowstone growth , named , from oldest to youngest , phase A , B and C ( Figures 2b , 13a , b and 14 ) . We dated phases B and C via U-Th , at 502 ka ( RS23; Table 1 ) and 478 ka ( RS22; Table 1 ) respectively , but the uncertainties are large because the ages are close to the upper dating limit of the U-Th technique . The oldest , phase A layer at the base of Flowstone 1a records reversed magnetic polarity indicating that it formed before 780 ka ( Singer , 2014 ) . The three age estimates are consistent with the stratigraphic position of the three phases in Flowstone 1a , and they indicate that the erosion remnant of Unit 2 encrusted by Flowstone 1a is also older than 780 ka . The fact that the erosion remnants of Flowstone 1a dip into the chamber suggests that at the time of formation of all three phases of Flowstone 1a , the debris cone of Unit 2 sediment was still in place; that is , erosion of Unit 2 sediment from below Flowstone 1a would have occurred sometime after 585 ka ( i . e . , the older age limit for phase C in Flowstone 1a ) and possibly as late as 437 ka ( i . e . , the younger age limit of phase C in Flowstone 1a ) . Unit 2 contains rare fossils of macrofauna , including a long bone in the erosion remnant , which must be older than 780 ka as well . We interpret the floor sediments of sub-unit 3a that contain the baboon tooth ( sample 1841 ) to represent , at last in part , the reworked remains of the debris cone that once existed below Flowstone 1a ( Figure 8 ) as material was removed from the chamber via floor drains , thereby undercutting the debris cone , which responded by slowly slumping into the chamber – a process ongoing today . The baboon tooth could be part of the original , Unit 2 debris cone and , therefore , older than 780 ka , although the US-ESR age indicates that it could also be younger ( with a mid point age of ~ 679 ka between a maximum age of 723 ± 181 ka and the minimum age of 635 ± 148 ka , with a possible age range of 487 ka to 904 ka ) . If the tooth is younger than 780 ka , it would suggest that it was derived from different sedimentary deposits not tested in this study , or that it may have entered the chamber separately during erosion of the Unit 2 debris cone , and its presence may reflect more direct entry points from surface into the chamber now sealed by flowstone . This possibility was tested with sample RS9 , a flowstone sample filling a thin ( <14 cm wide ) fracture in the dolomite on the surface that occurs above the Dinaledi Chamber . This flowstone yielded equilibrium U-Th results meaning that it formed before ~ 600 ka ( Table 1 ) , which is consistent with the interpretation that the Dinaledi Chamber was closed to direct entry of coarser-grained sediment from the surface prior to the entry of H . naledi into the cave system and remained closed until the present ( Dirks et al . , 2016a ) . Below Flowstone 1a are five other flowstones ( Flowstones 1b-e and Flowstone Group 2; Figure 2b ) , which each cover erosional remnants of sediments that we originally grouped as Unit 2 ( Dirks et al . , 2015 ) . The geochronology results presented here ( Table 1 ) now permit a better evaluation of the flowstone stratigraphy in the chamber , and it is evident that Unit 2 represents a significantly older stratigraphic unit that is restricted to deposits directly below Flowstone 1a , but not to the sediment deposits below Flowstones 1b-e . Flowstone 1c returns an age of ~243 ka ( Table 1 ) , suggesting that the sediments below Flowstones 1b-e are significantly younger than Unit 2 sediments below Flowstone 1a ( Figure 14 ) . It was noted before that the sediments below Flowstones 1b-e are less indurated and less weathered than the sediments below Flowstone 1a , and that they contain H . naledi material ( Dirks et al . , 2015 ) . Their distinct appearance and fossil content is now confirmed with the dating . Therefore , we reinterpret the H . naledi-bearing sediments below Flowstones 1b-e as part of Unit 3 ( sub-unit 3b ) , which means that all H . naledi-bearing sediments in the chamber are now part of sub-unit 3b ( Figure 8 ) . 10 . 7554/eLife . 24231 . 019Figure 8 . Cartoon illustrating the sedimentary history resulting in the deposition and redistribution of sediment of Units 2 and 3 , and Flowstone Groups 1 to 3 in the Dinaledi Chamber . Note that all hominin fossils are contained in sub-unit 3b , but that this sub-unit has been repeatedly reworked after its initial deposition . Fossil entry occurred during the initial stages of deposition of Unit 3 below the entry shaft and predated deposition of Flowstone 1c . H . naledi fossils may have continued to enter the Dinaledi Chamber as older parts of Unit 3 were eroded from below Flowstone 1c , and as remnants of all older units were reworked to be incorporated into Unit 3 sediments that accumulated along the floor of the Dinaledi Chamber . DOI: http://dx . doi . org/10 . 7554/eLife . 24231 . 019 Apart from the poorly consolidated erosional remnants below Flowstones 1b-e , hominin-bearing Unit 3 sediments also cover most of the floor of the Dinaledi Chamber . The layer of sub-unit 3b sediment has been interpreted as a relatively thin sheet ( ~20 cm ) of rubbly mud clast breccia material mixed with H . naledi fossils based on outcrops in the excavation pit and preliminary ground penetrating radar results ( Naidoo , 2016; Figures 2 and 8 ) . Age brackets for sub-unit 3b were obtained by dating underlying outcrops of sub-units 1a and 1b via OSL , and by dating overlying flowstone units . In this context sample OSL5 is the most relevant for obtaining a maximum age estimate for sub-unit 3b , because it was taken from an outcrop of Unit 1 that is overlain by Unit 3 . Here , a maximum age limit of 414 ka ( the upper error limit of OSL5 ) can be assigned to sub-unit 3b if the OSL ages are taken at face value . Note however , that the maximum age limits for sub-unit 1b as determined from samples OSL3 and OSL4 are significantly less at 272 ka and 278 ka ( upper error limits ) , respectively ( Table 8 ) , which supports the interpretation that sub-unit 1b formed due to erosion and redeposition on top of sub-unit 1a . The minimum age limit of sub-unit 3b can be more confidently constrained as it is overlain by flowstones with age ranges between 97 ka and 24 ka ( Table 1 ) . More importantly , a hanging remnant of sub-unit 3b with hominin material is covered by Flowstone 1c with a lower age limit of 236 ka obtained from the core of a stalactite overlying the rim of the flowstone remnant . This age provides the best minimum age estimate for sub-unit 3b , and by extension a minimum age for the H . naledi fossils . U-Th dating of an erosional remnant of Flowstone 1 that occurs directly above an outcrop of sub-unit 1b from which sample OSL4 was taken , and appears to cover it , provides an age of 290 ± 6 ka , suggesting that Unit 1 in this location must be older than 284 ka . The OSL results ( using the MAM model ) , however , suggest that the Unit 1 sediments in this location must be younger than 278 ka ( Table 8; using MAM age models ) . This apparent paradox may indicate that the OSL ages are unreliable , but could mean that sub-unit 1b was deposited in an inverse stratigraphic order in relation to the flowstone . This is supported by physical evidence in the chamber in which a gap between the flowstone drape and underlying sediment widens towards the back of the outcrop ( Figure 2c ) , implying that sub-unit 1b was deposited below an erosional remnant of Flowstone 1 , and is , therefore , younger than this flowstone . Recrystallization-dissolution textures , and anomalously high 234U/238U ratios ( >3 ) suggest that the age reported for this flowstone sample ( RS5 ) should be treated as a minimum age . Although the age constraints for sub-units 1a and 1b are imprecise they do suggest that Unit 1 in this part of the chamber is younger than Unit 2 , and that the red mud clasts forming Unit 2 sediment were derived from source material matching our description of sub-unit 1a , but positioned higher up in the cave . This source material was possibly part of sub-unit 1c , or it could represent part of an older and as yet undefined sub-unit of Unit 1 . No age assessment for sub-unit 1c deposits were done , because accumulations are too small to be tested with OSL or too difficult to access . The U-Th ages from flowstones and teeth place constraints on the changing physical environment experienced in the cave chamber over time . Flowstone deposition in the Dinaledi Chamber occurred during discrete periods including 24–32 ka , 50 ka , 88–105 ka , and during older events around 242 ka , 290 ka , between ~437 ka and 683 ka , and >780 ka . The flowstones are associated with relatively low initial 234U/238U ratios of 1 . 8–2 . 4 ( Tables 2 and 3 ) . Flowstones formed in different parts of the chamber as drip points shifted , but no clear pattern in age distribution is apparent ( Figure 1b ) other than that the flowstones deeper in the cave generally appear to be younger . The U-Th ages for the teeth also define a number of apparent U uptake events at around 43–48 ka , 70–75 ka , 80–90 ka , 100–120 ka and ~200 ka ( Tables 4 and 5 ) . The initial 234U/238U ratios associated with each of these events is similar and anomalously high ( 6 . 9–8 . 1 ) . The periods of time during which flowstones formed in the cave , by and large , do not overlap with the periods of time during which U-uptake appears to have occurred in the teeth , although both types of events were probably associated with wet periods in the chamber . The systematic difference in the initial 234U/238U ratios obtained from flowstones , as compared to teeth , indicates that an isotopically distinct water source led to U-uptake in the teeth , such as groundwater in the dolomitic aquifer of the Malmani Group with reported anomalously high initial 234U/238U ratios ( e . g . , Kronfeld et al . , 1994 ) . Working on the assumption that the high initial 234U/238U ratios are derived from the groundwater reservoir , the observed age groupings suggest that U uptake events in teeth represent ( partial ) inundation events of the Dinaledi Chamber , whilst the flowstone formation events reflect periods during which the groundwater table had dropped below floor level , but extensive drip still occurred within the Dinaledi Chamber caused by water derived from the surface with generally lower 234U excess . The age groupings also indicate that episodic wet periods in the Dinaledi Chamber alternated with periods during which no flowstone was deposited . Figures 1b and 14 summarize the results of all dating methods applied to the Dinaledi Chamber during the course of this study . It is clear from these results that the H . naledi assemblage in Unit 3 is of mid- to late-Middle Pleistocene age . The best age estimates for the H . naledi fossils come from the averaged US-ESR ages for samples 1788 and 1810: 229 +60/–46 ka ( maximum ) and 179 +49/–40 ka ( minimum ) for sample 1788 , and 276 +59/–77 ka ( maximum ) and 220 +50/–60 ka ( minimum ) for sample 1810 , with an age range of 139 ka to 335 ka . The maximum age scenario provides an average age for both teeth of 253 +82/–70 ka , and the minimum age scenario provides an average age of 200 +70/–61 ka . Considering the observed Rn loss in the cave sediments , the maximum age estimate is considered to be closer to the true age . The lower age limit of 139 ka must be disregarded and shifted to ~200 ka considering a U-Th minimum age estimate for parts of enamel on tooth sample 1810 ( Table 5 ) . This minimum age limit can be constrained further to 236 ka based on U-Th age estimates for Flowstone 1c ( 242 . 0 ± 5 . 0 ka; 242 . 9 ± 6 . 6 ka; Table 1 ) , which directly covers fossil material of H . naledi , noting that these ages may represent minimum age estimates for the flowstone as a result of possible U uptake during a period of raised groundwater levels ( Figure 14 ) . The maximum age limit of 335 ka for the H . naledi fossils relies on 80% Rn loss throughout the burial history of the fossils and is probably an over-estimate . A separate maximum age estimate for sub-unit 3b can be obtained from the baboon tooth ( sample 1841 ) in sub-unit 3a sediments devoid of hominin fossils , underlying the partly articulated remains of H . naledi , with US-ESR age estimates varying from 635 ± 148 ka ( minimum ) to 723 ± 181 ka ( maximum ) with an age range of 487 ka to 904 ka and a mid-point age of 679 ka . The older age estimate is considered to be more likely ( considering the measured Rn loss ) . These maximum age estimates are consistent with the direct age estimates for the H . naledi fossils , but do not further constrain the upper age limit ( Figure 14 ) . A further maximum age estimate for the H . naledi fossils can be obtained from age estimates for sub-unit 1a using sample OSL5 . This sample provides a maximum age limit for sub-unit 3b , of ~414 ka ( Table 8; using MAM ) . Although OSL ages are poorly constrained , and do not provide very precise age limits , this estimate is broadly consistent with the estimated age for reworking of Unit 2 sediments from below Flowstone 1a ( 437–585 ka ) and the US-ESR age estimates for the teeth . Considering all age results presented here the most parsimonious age estimate for the H . naledi fossils is sometime between 236 ka and 335 ka . More work will be needed in future to constrain these ages further ( Figure 14 ) . Until now , it has been generally assumed that morphologically primitive hominins like H . naledi ( Berger et al . , 2015 ) did not survive into the later parts of the Pleistocene in Africa . This general assumption has commonly guided the interpretation of fossil discoveries with poor geological or stratigraphic context , including initial estimates for the age of the H . naledi fossils ( Thackeray , 2016; Dembo et al . , 2016 ) . The new age estimates for H . naledi show that an approximate age for the hominin fossil fragments cannot be simply deduced from their morphology ( Thackeray , 2016; Dembo et al . , 2016 ) . Detailed geological investigations are critical before any attempt to ascribe an age to the fossils is made , and even then great care must be taken in interpreting results , which may not always be conclusive ( e . g . see famous examples such as Sterkfontein or Liang Bua; Partridge et al . , 2003; Wilkinson , 1985; Walker et al . , 2006; Roberts et al . , 2009; Pickering and Kramers , 2010; Granger et al . 2015; Sutikna et al . , 2016; Kramers and Dirks , 2017 ) . It is generally assumed that all African fossil hominins producing Middle Stone Age archaeological industries in the past 300 ka were part of a single variable species of early H . sapiens or an immediate precursor ( e . g . , Mcbrearty and Brooks , 2000; Lahr and Foley , 2001; Stringer , 2002 ) . The new ages now show that H . naledi existed at the same time as the first Middle Stone Age tools were produced in southern and eastern Africa , whilst skeletal evidence shows that H . naledi was probably capable of tool use ( Berger et al . , 2015; Hawks et al . , 2017 ) . This raises the possibility of H . naledi being responsible for some of the MSA traditions . The implications of the new ages for H . naledi are discussed in detail in Berger et al . , 2017 .
A total of seventeen flowstone samples ( RS1 , RS5-6 , RS8 , RS10-11 , and RS 13–23 ) from the Dinaledi Chamber were dated via U-Th geochronology , including one set of blind duplicates ( RS1 and RS15; Table 1 , Figures 1b and 3 ) . In addition , one flowstone sample ( RS9 ) was taken on the surface ( WGS84 571240–7121866 ) from a shallow pit about 11 m SW of the projected surface position of the excavation pit in the Dinaledi Chamber ( Figure 3d ) . For each sample a powder was prepared and then split , with one half being dated at JCU and the other half being dated at UoM; that is , for each sample both JCU and UoM dated the same material . In the Dinaledi Chamber samples of Flowstone Groups 1 , 2 and 3 were collected from a variety of stratigraphic positions ( Figures 1b , 2 and 3 ) . All the sampled flowstones formed as sheets , crusts , or drapes overlying older sediment units , with the exception of samples RS13 and RS18 , which were taken from a small stalactite that formed along the lip of an erosional remnant of Flowstone 1c . Additionally , samples RS14 , RS16 and RS17 were taken from a flowstone drape along a dolostone side-wall of the chamber . In all instances the flowstones have a free upper surface , that is , they are not overlain or covered by sediment , and all flowstones are interpreted to be younger than the sediment units they cover . The one exception to this rule could be RS5 , which comes from a partly resorbed erosion remnant of Flowstone 1 that appears to overlie Unit 1 , but is separated from the top surface of Unit 1 by a small opening that widens with depth ( Figure 2c ) . This leaves the possibility that this flowstone is an erosional remnant , and that Unit 1 ( sub-unit 1a ) sediments built up below it after it had been deposited . The location of each sample within the Dinaledi Chamber is shown in Figure 1b , and outcrop and close-up photos of the sampled flowstones are shown in Figure 3 . RS1 and RS15 are blind duplicate samples taken from a hanging remnant of Flowstone Group 2 that occurs in a N-trending fracture , ~10 m N of the excavation pit and ~3 m N of a major outcrop of sub-unit 1a sediment from which sample OSL3 was collected . The flowstone overlies largely unconsolidated floor sediments of Unit 3 , which in this locality have eroded from underneath the flowstone to leave a hanging remnant ~8 cm above the current floor level ( Figure 3i ) . The flowstone consists of a 15–18 mm-thick layer of calcite overlying an irregular surface of mud clast breccia , locally incorporating and growing around large mud clasts that were lying on the palaeo-surface . The flowstone is grey-white in colour and preserves 3–6 mm scale laminations visible due to subtle colour variations . The flowstone is recrystallized with elongated , acicular crystals of calcite growing from the base to the top of the layer across all internal laminations . The upper surface of the flowstone has a rough , pitted appearance as a result of partial resorption or dissolution of calcite along the grain boundaries of the needle-like crystals . Samples RS1 and RS15 were taken from a 3 mm-thick zone , 3 mm above the basal contact of the flowstone layer ( Figure 3i ) . RS5 was sampled from a thin sheet of Flowstone Group 1 in a WNW-trending fracture , ~4 m W of the excavation pit ( Figure 1b ) . The flowstone overlies orange sandy mudstone belonging to sub-unit 1b from which sample OSL4 was taken ( Figure 3a ) , but appears to be separated from this unit by a narrow opening that widens to the back of the outcrop ( Figure 2c ) . The flowstone consists of an 8–22 mm thick , cream white layer of carbonate with a sponge-like , porous , sugary texture that largely masks ( due to recrystallization of the primary calcite ) underlying mm-scale laminations . The flowstone layer appears to be partly dissolved along its stratigraphic top with the external surface of the layer truncating internal laminations . Sample RS5 was taken from a 5 mm-thick horizon , 3 mm below the top of the flowstone layer ( Figure 3a ) . RS6 was sampled from a thin sheet of Flowstone Group 2 in a WNW-trending fracture , ~6 m W of the entry zone ( Figure 1b ) . The flowstone overlies erosion remnants of Unit 1 and Unit 3 . At the sample site , sediments of Unit 3 have been partly eroded from underneath the flowstone leaving a hanging remnant , 5–10 cm above the current floor level ( Figure 3b ) . The flowstone consists of an 8–12 mm thick crust overlying an irregular sediment surface , incorporating fine ( <3 mm ) mud clasts in the base of the layer . It is white-grey in colour and semi-transparent , with limited evidence of internal layering except for a slightly lighter coloured basal layer that is several mm thick . The entire layer is recrystallized with fine , radiating acicular crystals of calcite growing upward from the basal contact . The upper surface has a rough pitted appearance as a result of partial resorption/dissolution along grain boundaries of the acicular crystals . Sample RS6 was taken from a 3 mm-thick horizon at the bottom of the flowstone ( Figure 3b ) . RS8 was sampled from a thin sheet of Flowstone 2 on the floor of the entry zone below the stack of hanging remnants of Flowstone 1a-e . The flowstone overlies sediments of Unit 3 that are partly eroded from underneath the flowstone leaving a 5–10 cm gap between the flowstone sheet and the current floor ( Figure 3c ) . The flowstone consists of a 5–22 mm thick layer overlying an irregular surface of mud clast breccia . The grey-white flowstone preserves 3–5 mm thick layering visible as subtle colour variations , despite partial recrystallization and replacement by elongated acicular crystals of calcite . The basal 10 mm is composed of a mesh of fine aragonite needles . Above this zone acicular calcite replaces the aragonite . The upper surface of the flowstone has a rough , pitted appearance as a result of partial resorption/dissolution along grain boundaries of the acicular crystals . Sample RS8 was taken from the bottom 3–5 mm of the flowstone layer ( Figure 3c ) . RS9 was sampled from a surface outcrop in a shallow mine pit that occurs about 11 m SW of the projected surface position of the excavation pit in the Dinaledi Chamber ( Figure 3d ) . The sample site occurs along strike of the same fracture along which the Dinaledi Chamber was formed at depth , and it is therefore possible that this surface outcrop in the past could have linked to the cave system below . The flowstone consists of a 12–25 mm thick layer of carbonate overlying an irregular surface of consolidated mud clast breccia similar to sediments of Unit 2 in the Dinaledi Chamber . The grey-white flowstone preserves 1–5 mm scale laminations visible due to colour variations and the presence of several thin brown marker surfaces . The laminations are locally recrystallized and overgrown by radiating , elongated crystals of calcite growing from the base to the top of the flowstone layer . Additionally , fibrous aragonite needles are widely distributed in sheaf-like patterns . The upper surface of the flowstone has a rough , pitted appearance as a result of partial resorption/dissolution along the grain boundaries of the acicular crystals . Sample RS9 was taken from a 4 mm-thick white horizon , 2 mm above the basal contact of the flowstone layer ( Figure 3d ) . RS10 was sampled from a thin crust of Flowstone Group 2 , about 2 m W of the excavation pit and 0 . 2 m W of the location where tooth sample 1810 was found ( Figure 3e ) . The flowstone directly overlies mud clast breccia of Unit 3 and hominin bone fragments . It consists of a 4–8 mm-thick laminated flowstone crust overlying an irregular sediment surface and incorporates fine ( <3 mm ) mudstone clasts within its basal laminae . This flowstone is white to grey in colour and is finely laminated and partly recrystallized , with recrystallization visible as white , fine , radiating needles of aragonite growing upward from the basal contact , along an irregular alteration front into grey-white , laminated calcite near the top of the layer . RS10 was taken from the laminated bottom 3 mm of the flowstone layer ( Figure 3e ) . RS11 and RS21 represent two samples taken from the top and bottom , respectively , of the same flowstone unit developed along the floor below an active drip point , 1 . 5 m E of the excavation pit . This flowstone consists of a basal layer of grey flowstone interpreted as Flowstone Group 2 overlying Unit 3 sediments , covered by a white flowstone layer containing small stalagmites interpreted as Flowstone Group 3 ( Figure 3f ) . The sample collected for dating consists of a 20–28 mm thick , finely laminated flowstone including the two distinct layers described above ( Figure 3f ) . The basal layer ( RS21; Figure 3f ) is 3–12 mm thick , and consists of brown-grey calcite resting on Unit 3 sediment . This layer is strongly recrystallized with radial , acicular crystals of calcite growing upward from the lower contact , which locally appears to have replaced an earlier generation of acicular aragonite . These crystals overgrow mm-scale laminations defined by white to brown colour variations . RS21 was sampled from the basal-to-central segment of this layer . The basal layer is overlain by an 8–16 mm thick layer of finely laminated ( sub-mm scale ) white speleothem . The basal 2–3 mm of this layer is recrystallized with fine aragonite needles radiating out from the base . Above that the flowstone consists of fine lamellae defined by subtle grey to white colour variations . Each lamina is composed of botryoidal aggregates of acicular aragonite . RS11 was taken from the top 3 mm of this white flowstone ( Figure 3f ) . RS13 and RS18 are two samples taken from the same stalactite developed along the lip of an erosional remnant of Flowstone 1c near the entrance into the Dinaledi Chamber ( Figure 1b ) . In this location , Flowstone 1c covers the erosional remains of a mud clast breccia containing a long bone consistent with the H . naledi assemblage ( Figure 3g ) , and interpreted as Unit 3 . The stalactite connects to part of the speleothem layers that cover the bone . The stalactite preserves well-developed internal layering , with layers asymmetrically developed around a core , in which layers thicken towards the outward facing side of the stalactite . From core to rim the stalactite reaches a maximum thickness of 53 mm and includes three separate zones that can be distinguished based on internal texture , layering , and colour ( Figure 3g ) . The innermost zone forms an 8 mm-thick , finely laminated ( sub-mm scale ) core centred on a small mud clast and is terminated by a thin brown , mud-rich rim . This core is surrounded by a ~23 mm-thick central zone that consists of more coarsely layered ( 3–10 mm-thick ) , cream to grey-white coloured calcite with layering preserved as subtle colour variations , which at its base shows replacement by radiating sheaves of aragonite needles . This zone is mantled by a ~22 mm-thick outer zone of white calcite preserving only remnants of internal layering . This outermost zone is characterized by extensive replacement of abundant , older aragonite needles by coarse-grained calcite , creating a patchy texture . The sample has been affected by recrystallization resulting in the formation of radiating acicular crystals of calcite that grow from the core outward . The white outer layer also shows evidence of further recrystallization , with the formation of coarse ( 2–4 mm ) , equant calcite grains , many with highly irregular grain boundaries that overgrow the acicular grains . The outer surface of the stalactite has a rough , pitted appearance as a result of partial resorption/dissolution along the surface with dissolution along the grain boundaries of the acicular crystals . RS18 was sampled from a 3 mm-thick horizon near the base of the central zone . RS13 was sampled from the outermost part of the outer zone ( Figure 3g ) . RS14 was sampled from an irregularly shaped , cascade-like crust of Flowstone Group 2 , along the side-wall of the main floor drain , between the entry shaft and excavation pit ( Figures 1b and 3h ) . The sample of flowstone crust consists of an 8–11 mm thick layer of cream coloured carbonate displaying mm-scale laminations and a sugary , recrystallized texture with numerous , fine pore spaces along laminar surfaces . Sample RS14 was taken from a 3 mm-thick zone encompassing several fine laminations , ~5 mm above the base of the flowstone layer ( Figure 3h ) . RS16 and RS17 are two separate samples from a cascade-like crust of Flowstone Group 2 that formed within the main floor drain between the entry shaft and excavation pit ( Figure 1b ) . This flowstone developed on top of the dolomite back-wall of the drain ( Figure 3h ) . The sample consists of a mostly cream coloured flowstone that is up to 73 mm thick , with a massive sugary , recrystallized texture , preserving mm-scale laminations visible due to subtle colour variations . Many of the equant calcite grains contain remnant aragonite needles , reflecting an earlier phase of aragonite growth . Laminations along the basal 3–4 mm of this flowstone are brown in colour , whereas the top 10–12 mm of this flowstone layer consists of what is possibly a separate , younger unit of laminated grey-brown carbonate with small pore spaces developed along some of the laminar surfaces . Sample RS16 was taken from a 3–4 mm-thick layer directly above the basal zone with brown laminations . Sample RS17 was taken from a 4 mm-thick zone at the top of the cream-coloured laminated flowstone , immediately below the darker coloured top unit ( Figure 3h ) . RS19 was taken from a wedge-shaped sample of Flowstone Group 2 that formed along the lip of a drip pool directly overlying Unit 3 sediments , 1 . 2 m S of the excavation pit ( Figure 1b ) . The flowstone is cream-coloured and up to 30 mm thick , and overlies a flat sediment surface . It preserves complex internal layering and a variety of textures ( Figure 3j ) . A basal 2–4 mm-thick layer characterised by fine calcite needles growing at right angles to layering is overlain by an irregular mass of darker cream coloured , unstructured calcite with a sugary texture and numerous fine voids . Towards its top this unstructured mass is interlayered with and covered by several 1–4 mm-thick lamellae characterised by fine calcite needles growing at right angles to layering similar to the basal layer , but containing pore spaces . These in turn are overlain by an 8 mm-thick zone of finely laminated flowstone with a fine-grained ( <0 . 5 mm ) sugary texture and abundant fine pore spaces . RS19 was taken from the basal flowstone layer with radiating calcite crystals ( Figure 3j ) . RS20 was sampled from a thin sheet of Flowstone Group 2 in a WNW-trending fracture , ~2 m W of the excavation pit ( Figure 1b ) . The flowstone forms a cascade-like drape that directly overlies orange mudstone of sub-unit 1a from which sample OSL5 was taken ( Figure 3k ) . RS20 occurs directly above sample RS10 , which was taken at the foot of the cascade ( Figure 1b ) . The sampled flowstone layer is irregular in shape as it covers sediments with topography . It consists of a finely laminated 15–22 mm thick crust overlying sub-unit 1a sediment and includes a ~6 mm thick , laminated basal unit of white flowstone that incorporates small ( 3–10 mm ) mud clasts , overlain by more coarsely laminated ( mm-scale ) grey brown carbonate free of inclusions . The flowstone is internally locally recrystallized with acicular aragonite crystals growing within ~3 mm thick zones at right angles to layering . RS20 was sampled from the basal layer of the sample ( Figure 3k ) . RS22 and RS23 are two samples from an eroded rim of Flowstone 1a near the entry shaft into the Dinaledi Chamber ( Figure 1b ) . This flowstone overlies erosional remnants of well-indurated mud clast breccia assigned to Unit 2 ( Figure 3l ) , and mostly consists of coarsely recrystallized white carbonate . The flowstone sample from which RS22 and RS23 were taken occurs down dip from the palaeo-magnetic sample taken from Flowstone 1a as described below ( Figures 2b and 13 ) . The flowstone layer is ~25 mm thick and comprises a 6 mm thick , basal unit of grey to brown calcite that is recrystallized into fine ( sub-mm scale ) , equigranular grains of calcite overgrowing an older acicular texture . The basal layer corresponds to phase B carbonate described for the palaeo-magnetic sample , and is overlain by a clean white calcite unit ( i . e . , phase C in the palaeo-magnetic sample ) that is coarsely recrystallized with 2–4 mm equidimensional calcite grains overgrowing ( and partly destroying ) an older texture formed by acicular grains . Small voids occur in the interstitial spaces between the coarse calcite grains . RS23 was taken from the grey basal unit ( i . e . , phase B ) of the flowstone layer and RS22 was sampled from the central part of the upper white recrystallized unit ( i . e . , phase C , Figures 3l and 13b ) . In this sample , flowstone belonging to the older phase A carbonate is no longer present , as this layer pinches out along dip between the point where the palaeomagnetic sample was taken and the point where the U-Pb sample was taken as can be seen in Figures 2b and 13 . Three H . naledi teeth ( samples 1810 , 1767 and 1788 ) and one baboon tooth ( sample 1841 ) were collected from Unit 3 , in the Dinaledi Chamber ( Figures 1b , 4 , 6 and 7 ) for ESR dating and U-Th analysis . Sample 1767 ( full catalogue number U . W . 101–1767 ) is an extremely worn upper premolar crown ( Figure 6a ) obtained from approximately 1 m SW of the excavation pit ( Figure 4 ) , and occurred on surface surrounded by mud clast fragments of Unit 3 . This tooth is deeply weathered , and preserved only a small rim of enamel on the buccal margin with a maximum height of 4 . 5 mm . Otherwise the crown is a concave dentine surface worn to the cervix , with bright white , highly bleached dentine . The tooth is brittle and appears strongly affected by water action . Sample 1788 ( full catalogue number U . W . 101–1788 ) is a lower right second molar ( Figure 6b ) obtained from approximately 2 m WSW of the excavation pit ( Figure 4 ) , embedded within loosely packed , mud clast breccia of Unit 3 , ~ 2 cm below the ground surface level . This tooth is partly broken at the root , but has an otherwise well preserved crown with thick , light-grey enamel . The distal root of the tooth is present and complete , but the lingual root is broken off just below the cervix . The tooth is morphologically consistent as an antimere of U . W . 101–284 . The dentine is highly bleached and brittle and appears affected by water action . Sample 1810 ( full catalogue number U . W . 101–1810 ) is a lower left third premolar or possibly lower left fourth premolar ( Figure 6c ) obtained from the SE corner of the excavation pit ( Figure 4 ) , embedded within sediments of Unit 3 , ~5 cm below the original ground surface level . This tooth is well-preserved with thick , light-blue-grey enamel , and shows little evidence of bleaching or weathering , with only a very slight polishing wear on the distal crest of the protoconid . The morphology of the crown is similar to other lower third premolars in the collection , however , it is slightly shorter than many of those . Sample 1841 ( full catalogue number U . W . 101–1841 ) is a well-preserved tooth crown , morphologically consistent with a lower left second molar of Papio sp . This is a complete enamel crown of an unerupted tooth with no wear facets or interproximal facets in evidence , and the roots had not formed ( Figure 7 ) . The specimen was recovered from a sediment sample taken at a depth of 55–60 cm below the original ground surface of the cave floor near the base of the sondage dug in the centre of the excavation pit ( Figures 1b , 2d and 4 ) . This tooth occurs in sub-unit 3a , ~40 cm below the stratigraphically lowest occurrence of partly articulated remains of H . naledi , and represents the only non-hominin macrofossil recovered from Unit 3 . Three OSL samples ( OSL3 , OSL4 and OSL5 ) were collected in the Dinaledi Chamber from erosion remnants of sub-unit 1a , in which sandy , laminated mudstones are exposed ( Figures 1b and 5 ) . These sandy intercalations were targeted because they contain fine-grained quartz and feldspar grains that can be extracted for analysis . For each sample a 30 cm length of aluminium piping with a diameter of 5 cm was hammered into the sediments in a horizontal direction or parallel to sedimentary laminations visible within the units . A core sample within the pipe was extracted for OSL analyses together with a sediment sample from the same unit to determine background radiation from measured values of U , Th and K . Sample OSL3 comes from an erosion remnant of sediments of sub-unit 1b collected near the intersection point of two fractures trending N and E respectively , ~6 m N of the excavation pit ( Figure 1b ) . Samples OSL4 and OSL5 come from an erosion remnant of Unit 1 sediment along an ENE-trending fracture , ~3 m W of the excavation pit . Sample OSL4 is obtained from sub-unit 1b directly below a thin , partly resorbed flowstone sheet attributed to Flowstone Group 1 ( Figure 3a ) . Sample OSL5 occurs as an erosional remnant of sub-unit 1a , 1 m E , and stratigraphically 10–20 cm below sample OSL4 . The Unit 1 sediments in this location are partly covered by Unit 3 sediments and a cascade of Flowstone Group 2 ( Figure 3k ) . The speleothem sampled for palaeomagnetic analysis comes from Flowstone 1a near the entry zone into the Dinaledi Chamber ( Figure 2b ) . The sample is layered and comprises three distinct phases ( from base to top: A-C ) separated by thin clastic horizons that mark disconformities ( Figure 13a , b ) . The lower phase ( phase A ) is interstratified with visible clastic laminations . The internal laminations are truncated along the lower surface of the sample indicating that phase A flowstone was partly dissolved during a phreatic event after the flowstone had been deposited . The middle phase ( phase B ) consists of intercalated flowstone speleothem and detrital sediment layers , with the detrital layering concentrated towards the downslope part of the sample . Detrital material is generally less than in phase A , indicating decreasing amounts of clastic contamination . Like phase A , the internal layering of phase B is truncated along the lower surface of the sample , indicating that the phreatic dissolution event occurred after deposition of phase B . There is no apparent truncation surface between phase A and B flowstone , indicating that phase B formed on top of phase A after a period of non-deposition during which detrital material accumulated on top of phase A . The upper surface of phase B truncates internal layering reflecting dissolution during a phreatic event . This was followed by a period of non-deposition of flowstone during which a thin layer of detrital clastic material accumulated , before deposition of the upper layer ( phase C ) of flowstone occurred . Phase C comprises younger , cleaner flowstone which displays extensive recrystallization along the base of the unit with the formation of elongated calcite crystals . This zone of recrystallization could potentially also indicate a younger infill of a cavity that had formed between phase B and C flowstone , and has been avoided during sampling . Phase C flowstone contains little to no detrital inclusions , and suggests that sediment influx into the chamber was not occurring during its deposition . Phase B and phase C flowstone correlate with samples RS23 and RS22 respectively , that were collected for U-Th dating . The top of the sample was oriented in the cave to magnetic N ( −18 . 2o degrees from true N at this location with a −62 . 9o inclination ) . The inclination was accounted for by marking the sample on a completely flat surface of the block . Three weathered bone fragments of H . naledi were collected for radiocarbon dating including: ( i ) a tibia shaft fragment , 53 mm in length ( U . W . 101–567 ) ; ( ii ) a femur shaft fragment comprising the whole circumference of the shaft , 79 mm in length ( U . W . 101–857 ) , and; ( iii ) a metatarsal or metacarpal shaft with no articular morphology , 48 mm in length ( U . W . 101–065 ) . All samples were collected from the surface of Unit 3 as isolated fragments near the excavation pit . | Species of ancient humans and the extinct relatives of our ancestors are typically described from a limited number of fossils . However , this was not the case with Homo naledi . More than 1500 fossils representing at least 15 individuals of this species were unearthed from the Rising Star cave system in South Africa between 2013 and 2014 . Found deep underground in the Dinaledi Chamber , the H . naledi fossils are the largest collection of a single species of an ancient human-relative discovered in Africa . After the discovery was reported , a number of questions still remained . Not least among these questions was: how old were the fossils ? The material was undated , and predictions ranged from anywhere between 2 million years old and 100 , 000 years old . H . naledi shared several traits with the most primitive of our ancient relatives , including its small brain . As a result , many scientists guessed that H . naledi was an old species in our family tree , and possibly one of the earliest species to evolve in the genus Homo . Now , Dirks et al . – who include many of the researchers who were involved in the discovery of H . naledi – report that the fossils are most likely between 236 , 000 and 335 , 000 years old . These dates are based on measuring the concentration of radioactive elements , and the damage caused by these elements ( which accumulates over time ) , in three fossilized teeth , plus surrounding rock and sediments from the cave chamber . Importantly , the most crucial tests were carried out at independent laboratories around the world , and the scientists conducted the tests without knowing the results of the other laboratories . Dirks et al . took these extra steps to make sure that the results obtained were reproducible and unbiased . The estimated dates are much more recent than many had predicted , and mean that H . naledi was alive at the same time as the earliest members of our own species – which most likely evolved between 300 , 000 and 200 , 000 years ago . These new findings demonstrate why it can be unwise to try to predict the age of a fossil based only on its appearance , and emphasize the importance of dating specimens via independent tests . Finally in two related reports , Berger et al . suggest how a primitive-looking species like H . naledi survived more recently than many would have predicted , while Hawks et al . describe the discovery of more H . naledi fossils from a separate chamber in the same cave system . | [
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] | 2017 | The age of Homo naledi and associated sediments in the Rising Star Cave, South Africa |
Histone H3 lysine 36 methylation ( H3K36me ) is thought to participate in a host of co-transcriptional regulatory events . To study the function of this residue independent from the enzymes that modify it , we used a ‘histone replacement’ system in Drosophila to generate a non-modifiable H3K36 lysine-to-arginine ( H3K36R ) mutant . We observed global dysregulation of mRNA levels in H3K36R animals that correlates with the incidence of H3K36me3 . Similar to previous studies , we found that mutation of H3K36 also resulted in H4 hyperacetylation . However , neither cryptic transcription initiation , nor alternative pre-mRNA splicing , contributed to the observed changes in expression , in contrast with previously reported roles for H3K36me . Interestingly , knockdown of the RNA surveillance nuclease , Xrn1 , and members of the CCR4-Not deadenylase complex , restored mRNA levels for a class of downregulated , H3K36me3-rich genes . We propose a post-transcriptional role for modification of replication-dependent H3K36 in the control of metazoan gene expression .
Eukaryotic genomes function within the context of chromatin fibers composed of nucleosome units , each of which contains roughly 147 bp of DNA wrapped around a single histone octamer composed of two pairs of heterodimers ( histone H2A-H2B , and H3-H4 ) ( Luger et al . , 1997 ) . Histones are decorated with an array of covalent post-translational modifications ( PTMs ) that have been proposed to demarcate distinct chromatin domains in the genome ( Kharchenko et al . , 2011; Rice et al . , 2003; Schneider et al . , 2004; Sullivan and Karpen , 2004 ) . The ‘histone code’ hypothesis posits that PTMs play crucial roles in controlling gene expression by adapting the local chromatin packaging environment and recruiting structural or catalytic binding partners to confer or deny access to transcriptional machinery ( Bannister and Kouzarides , 2011; Jenuwein and Allis , 2001; Rothbart and Strahl , 2014; Strahl and Allis , 2000; Taverna et al . , 2007 ) . Partly on the basis of this model , PTMs have been considered strong candidates for primary carriers of epigenetic information that contribute to cell fate specification during development ( Margueron and Reinberg , 2010 ) . This concept has been extended to suggest PTM dysregulation as a likely contributor to diseases characterized by altered gene expression and cell identity ( Chi et al . , 2010; Lewis et al . , 2013 ) . In multicellular eukaryotes , support for the histone code hypothesis is largely based on phenotypes observed from studies in which the ‘writer’ enzymes responsible for catalyzing histone PTMs were inhibited or ablated . However , such experiments cannot rule out the possibility that these enzymes have other non-histone substrates , or play other non-catalytic ( e . g . , structural ) roles , that confound analysis and assignment of observed phenotypes to the PTMs themselves . Several recent studies have employed a direct replacement of the endogenous , replication-dependent histone gene cluster in Drosophila melanogaster with transgenic clusters encoding non-modifiable mutant histones ( Graves et al . , 2016; Günesdogan et al . , 2010; Hödl and Basler , 2012; McKay et al . , 2015; Pengelly et al . , 2013; Penke et al . , 2016 ) . This approach has enabled the deconvolution of phenotypes specific to histone PTMs from those specific to their writers . These studies have elucidated the relationship between PTMs and their writers , both confirming ( Pengelly et al . , 2013 ) and refuting ( McKay et al . , 2015 ) previously reported roles for certain residues on the basis of their corresponding writer mutant phenotypes . The approach also affords an opportunity to directly interrogate the function of other well-characterized histone PTMs for which a variety of functional roles have been described . In contrast with many PTMs whose spatial distribution is skewed towards promoters and the 5’ regions of genes , H3K36 di- and tri-methylation ( H3K36me2/3 ) are enriched in coding regions and toward the 3’ end of actively transcribed genes ( Bannister et al . , 2005 ) . These marks are also preferentially enriched over exons as opposed to introns ( Kolasinska-Zwierz et al . , 2009 ) . This distribution pattern suggests that H3K36me interfaces with RNA polymerase and contributes to transcription elongation and/or RNA processing , rather than affecting gene expression via chromatin packaging at promoters . Indeed , H3K36me2/3 is known to suppress cryptic transcription initiation from coding regions in Saccharomyces cerevisiae by recruiting a repressive Rpd3 deacetylase complex to sites of active elongation ( Carrozza et al . , 2005; Keogh et al . , 2005 ) . It is also implicated in suppressing active incorporation of acetylated histones via histone exchange ( Venkatesh et al . , 2012 ) . In cultured cells , ablation of human SETD2 , which catalyzes H3K36 trimethylation , is suggested to alter a number of exon inclusion events by recruiting RNA binding proteins ( Luco et al . , 2010; Pradeepa et al . , 2012 ) . Conversely , H3K36me3 distribution across gene bodies is itself sensitive to perturbations in splicing ( de Almeida et al . , 2011; Kim et al . , 2011 ) . In addition to its role in transcription and RNA processing , a range of other activities have been attributed to H3K36me , including X-chromosome dosage compensation ( Larschan et al . , 2007 ) , DNA damage response ( Jha and Strahl , 2014; Li et al . , 2013; Pai et al . , 2014; Pfister et al . , 2014 ) , and three dimensional chromosome organization ( Evans et al . , 2016; Smith et al . , 2013; Ulianov et al . , 2016 ) . However , to date , none of these putative roles for H3K36me have been evaluated directly in an H3K36 mutant animal . Here , we report a comprehensive analysis of H3K36 function , focused on differential gene expression , transcription initiation , and chromatin accessibility phenotypes in transgenic Drosophila whose entire complement of replication-dependent H3 genes has been mutated to arginine at lysine 36 ( H3K36R ) . Arginine approximates the charge and steric conformation of lysine , but cannot be targeted by lysine methyltransferases , and therefore represents an appropriate mutation with which to study the PTM-specific functions of H3K36 . Although arginine is a conservative amino acid change , it also enables hydrogen bonding modalities that are distinct from those of lysine . In principle , in addition to phenotypes resulting from loss of H3K36 methylation , such a change could also result in other hypomorphic ( partial loss of function ) or neomorphic ( gain of function ) phenotypes . In H3K36R mutants , we observed a decrease in the steady-state levels of highly expressed RNAs concomitant with increased transcription and productive expression from a variety of low-usage promoters . Though mutants exhibited bulk increases in histone acetylation , chromatin accessibility did not appreciably change at promoters . Surprisingly , we found that previously reported roles for H3K36 methylation , including suppression of transcription initiation in coding regions and regulation of alternative splicing , are not supported in Drosophila by transcription start-site ( TSS ) and poly-A RNA-seq analyses , respectively . Intriguingly , we found that certain genes are downregulated in H3K36R mutants but are rescued to wild-type levels by depletion of the Xrn1 exonuclease pacman , or the CCR4-Not deadenylase subunits , twin and Pop2 . We posit a model whereby H3K36 methylation contributes to transcript fitness in order to maintain global transcriptome fidelity .
We utilized a bacterial artificial chromosome ( BAC ) -based histone gene replacement platform ( McKay et al . , 2015 ) to generate Drosophila bearing a K36R substitution mutation in each of its replication-dependent histone H3 genes . Using this system , the endogenous histone gene cluster was deleted and complemented by a transgenic array of 12 copies of the native 5 kb histone gene repeat ( Figure 1 ) . As previously reported , H3K36R ( K36R ) mutants pupate at significantly reduced frequency compared to histone wild type ( HWT ) control animals , and fail to eclose into adults with 100% penetrance , despite exhibiting no obvious cell proliferation defects ( McKay et al . , 2015 ) . Given the postulated role for H3K36 modification in co-transcriptional gene regulation , we sought to comprehensively compare the transcriptomic landscapes of HWT and K36R animals . We sequenced poly-A selected RNA , rRNA-depleted nuclear RNA , nucleosome depleted DNA ( via ATAC-seq [Buenrostro et al . , 2013] ) , and short , nascent , capped RNAs ( Henriques et al . , 2013; Nechaev et al . , 2010 ) from third instar larvae . Collectively these methods interrogate the major steps in mRNA biogenesis ( Figure 1 ) . 10 . 7554/eLife . 23249 . 003Figure 1 . Strategy for interrogating the transcriptomic life cycle of H3K36R animals . ( A ) Schematic of experimental high-throughput sequencing methods applied to H3K36R animals . Twelve tandem copies of the histone repeat unit were cloned into a custom BAC vector and site-specifically integrated into the D . melanogaster genome as described in McKay et al . ( 2015 ) . Poly-A-selected RNA was sequenced from whole third instar larvae , ATAC-seq and rRNA-depleted nuclear RNA-seq were carried out from nuclei isolated from third instar larvae , and short , nascent , capped RNAs were selected from nuclei and subjected to ‘Start-seq’ ( Henriques et al . , 2013 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 23249 . 003 We hypothesized that the K36R mutation would conform to a ‘cis-acting , ' direct model , wherein effects are confined primarily to genes containing high levels of H3K36me3 . However , when we analyzed genome-wide differential expression from poly-A RNA and stratified genes by the chromatin ‘states’ in which they reside ( as defined in Kharchenko et al . , 2011 ) , gene expression changes were not confined to states characterized by high levels of H3K36 methylation ( Figure 2—figure supplement 1A , states 1–4 ) . Instead , when we stratified genes by H3K36me3 density ( www . modencode . org ) , the mark was anticorrelated with gene expression change across the entire spectrum of H3K36me3 density , and largely uncorrelated with other methyl-states of H3K36 ( Figure 2A , Figure 2B ) . Genes with high H3K36me3 density tended to decrease expression in K36R animals , whereas genes with low H3K36me3 density tended to increase expression in K36R animals . This finding suggests a global role for H3K36me in regulating gene expression , but one that is not confined to H3K36me3-rich loci , and therefore argues against an exclusively direct , local effect . 10 . 7554/eLife . 23249 . 004Figure 2 . Transcriptome dystregulation in H3K36R mutants is correlated with H3K36me3 ChIP-seq . ( A ) Metagene plot describing the density of H3K36me3 ( top ) , H3K36me2 ( middle ) , and H3K36me1 ( bottom ) ChIP-seq across genes that are upregulated ( purple ) , unchanged ( blue ) , or downregulated ( yellow ) in H3K36R mutants as compared with HWT controls . ( B ) Boxplot of differential expression of gene cohorts stratified by density of H3K36me3 signal in the 3’ UTR ( 1=lowest density decile , 10=highest decile ) . ( C ) MA plot with accompanying LOESS regression line plotting log2 fold change ( y-axis ) vs . HWT FPKM ( x-axis ) interpreted from poly-A RNA-seq data . DOI: http://dx . doi . org/10 . 7554/eLife . 23249 . 00410 . 7554/eLife . 23249 . 005Figure 2—figure supplement 1 . Gene expression changes in H3K36R mutants . ( A ) Boxplot depicting log2 fold expression change for genes whose start sites reside in each of the nine different chromatin states defined in Kharchenko et al . , 2011 . ( B ) Histogram of mean FPKM for genes significantly upregulated ( blue ) or downregulated ( red ) in K36R animals ( p<0 . 05 ) . ( C ) Left: Boxplot of comparison of estimated cell content from five third instar larvae between HWT and K36R , based on normalizing by either quantitated RNA ( left ) or band intensity from histone H3 western blot signal ( right ) . Given that K36R larvae are expected to have higher cell content based on histone normalization , total RNA per cell is likely overestimated in K36R , and therefore log2 fold change gene expression values are not expected to be overestimated based on bias from higher RNA per cell in K36R . Right: This interpretation is confirmed by lower log2 fold expression change in histone- vs . RNA-normalized RT-qPCR for select differentially expressed genes . DOI: http://dx . doi . org/10 . 7554/eLife . 23249 . 005 Because H3K36me3 is catalyzed co-transcriptionally ( Kizer et al . , 2005 ) , and should therefore track roughly with gene expression , we also took the alternate approach of determining whether gene expression changes in K36R were correlated with the amount of expression normally observed in HWT . When we plotted differential expression against a specific transcript’s HWT level , we found that the effects of the K36R mutation were consistently anticorrelated with a gene’s HWT expression level . That is , genes that were normally silent or lowly-expressed in HWT larvae experienced the largest relative increases in expression in K36R mutants , and highly expressed genes were preferentially reduced in K36R ( Figure 2C , Figure 2—figure supplement 1B ) . RT-qPCR validation of select transcripts confirmed this observation , arguing against the likelihood of bias due to normalized RNA input ( Figure 2—figure supplement 1C ) . These results indicate that H3K36me-dependent expression changes could be caused by both direct ( locus-specific ) and indirect ( locus-independent ) effects . H3K36 methylation status has the potential to affect other histone PTMs , most notably H4 acetylation ( H4ac ) ( Carrozza et al . , 2005; Keogh et al . , 2005 ) and H3K27 trimethylation ( H3K27me3 ) ( Lu et al . , 2016; Yuan et al . , 2011 ) . This form of histone ‘crosstalk’ might contribute to the observed gene expression phenotypes . To formally evaluate this possibility , we assayed bulk levels of H4ac and H3K27me3 by western blotting . H3K27me3 levels were slightly reduced in H3K36 mutants ( Figure 3A , Figure 3—figure supplement 1A ) , however characteristic polycomb target genes were largely unaffected ( Figure 2—figure supplement 1A , Figure 3—figure supplement 1B ) . In contrast , H4ac levels were robustly increased ( Figure 3A , Figure 3—figure supplement 1A ) , confirming the previously identified link between H3K36me and H4ac ( Carrozza et al . , 2005; Keogh et al . , 2005 ) . 10 . 7554/eLife . 23249 . 006Figure 3 . H4 acetylation enrichment in mutants does not result in open-chromatin-dependent changes in gene expression . ( A ) Western blots measuring enrichment of histone H3 , H3K36me3 , H3K27me3 , and pan H4 acetylation ( H4ac ) in H3K36R mutants and HWT controls . Signal relative to first lane is denoted below each band . ( B ) Scatterplot of ATAC-seq signal mapping in a 200 nt window ( as denoted at top ) around obsTSSs , with R2 value indicated . ( C ) Scatterplot of log2 fold change of poly-A RNA-seq ( x-axis ) vs . that of ATAC-seq ( y-axis ) signal in a window around the corresponding gene’s transcription start site ( as identified by start-seq ) . Genes with codirectional , statistically significant changes in both RNA-seq and ATAC-seq are indicated in red . Example browser shot of a gene differentially expressed in mutants in the absence of changes in chromatin accessibility at its start site is shown at right . DOI: http://dx . doi . org/10 . 7554/eLife . 23249 . 00610 . 7554/eLife . 23249 . 007Figure 3—figure supplement 1 . Histone crosstalk and gene expression changes in H3K36R animals . ( A ) Barplot displaying the fold change in western blot signal intensity quantified from two biological replicates of whole larval nuclear lysate for the antibodies indicated . Asterisk indicates T-test p-value between H3 and indicated PTM is <0 . 05 . ( B ) Boxplot of normalized poly-A RNA-seq counts mapping to genes that co-occur with a strong polycomb regulatory region ( Schwartz et al . , 2006 ) in HWT and K36R . ( C ) Polytene chromosome salivary gland squash and immunofluorescent stain for H4K12ac from HWT and K36R third instar larvae . H4K12ac-bright ( yellow arrowheads ) , and transcriptionally silent DAPI bright ( orange arrowheads ) regions , are anticorrelated in both genotypes , suggesting H4K12ac accumulates to transcriptionally active regions in K36R mutants D ) Boxplot of differential expression of gene cohorts stratified by density of H4K16ac ChIP-seq signal in a 400 nt window surrounding the annotated gene start site ( 1=lowest density decile , 10=highest decile ) . DOI: http://dx . doi . org/10 . 7554/eLife . 23249 . 00710 . 7554/eLife . 23249 . 008Figure 3—figure supplement 2 . Metagene analysis of Start-seq reads at previously annotated ( observed ) transcription start sites , obsTSSs . ( A ) Metaplot of Start-seq signal aligned in a 100 nt window around all annotated TSSs in the dm3 reference gene model . ( B ) Metaplot of HWT and K36R nuclear RNA-seq signal aligned in a 1 kb window around all obsTSSs identified in Start-seq data . ( C ) Representative browser shot of Start-seq signal pileup at annotated gene promoter ( obsTSS ) . Direction of transcription denoted by arrow . ( D ) Metaplot of ATAC-seq signal in a 4 kb window surrounding obsTSSs identified by Start-seq . obsTSSs are binned by the average normalized signal across the window ( denoted in the legend ) . DOI: http://dx . doi . org/10 . 7554/eLife . 23249 . 008 To assay the spatial distribution of H4ac , we stained polytene chromosomes with an H4K12ac antibody . In both HWT and K36R mutants , we found that H4K12ac intensity was anticorrelated with DAPI bright bands ( Figure 3—figure supplement 1C ) . The DAPI bright regions are thought to correspond to more transcriptionally silent chromatin . Therefore , the observed hyperacetylation in K36R mutants occurs in the more actively transcribed ( DAPI dark ) regions , consistent with previous observations ( Carrozza et al . , 2005; Keogh et al . , 2005 ) . Given these findings , we initially hypothesized that H4 hyperacetylation might contribute positively to chromatin accessibility in promoter proximal regions of genes that are upregulated in H3K36 mutants . To investigate this possibility , we carried out open chromatin profiling ( ATAC-seq ) and correlated these data with our differential expression ( RNA-seq ) analysis . Wild-type H4 acetylation density was also calculated using H4K16ac ChIP-seq datasets obtained from the modEncode consortium . As shown in Figure 3—figure supplement 1D , genes with the lowest levels of H4K16ac at their predicted promoters increased their expression levels in K36R mutants . To localize open chromatin changes specifically to bona-fide sites of transcription initiation , we performed ‘Start-seq’ , which precisely determines transcription initiation events by capturing nascent RNAs associated with early elongation complexes ( Henriques et al . , 2013; Nechaev et al . , 2010 ) . We adapted the protocol to isolate short , nascent , capped RNA from nuclei purified from third instar larvae ( see Materials and methods ) . As shown in Figure 3—figure supplement 2A–C , Start-seq signal maps faithfully and robustly , with base-pair resolution , to annotated ( observed ) transcription start sites ( obsTSSs ) , and demarcates sites of high nuclear RNA-seq . ATAC-seq signal accumulates most robustly in a window spanning roughly 150 nt upstream , and 50 nt downstream , of obsTSSs ( Figure 3—figure supplement 2D ) . When we quantified HWT and K36R ATAC-seq signal from such a window surrounding all obsTSSs , we found that global changes in open chromatin were minimal between HWT and H3K36R animals ( Figure 3B ) . Furthermore , changes in ATAC-seq at obsTSSs and differential expression in their corresponding genes was largely uncorrelated , with a large proportion of genes exhibiting differential expression changes independent of increased chromatin accessibility ( Figure 3C ) . These results indicate that chromatin remodeling at promoters is not a major contributor to the observed global gene expression changes . Given that increases in H4 acetylation in response to loss of H3K36me were thought to promote cryptic transcription in S . cerevisiae ( Carrozza et al . , 2005; Keogh et al . , 2005 ) , we evaluated potential cryptic initiation phenotypes in Drosophila H3K36 mutants . The consistent accumulation of Start-seq signal at bona-fide transcription initiation sites ( Figure 3—figure supplement 2A ) shows that this method is particularly ideal for identifying novel initiation elsewhere in the genome . By quantifying Start-seq signal at loci outside of annotated start-sites ( obsTSSs ) , we identified thousands of novel unannotated TSSs ( nuTSSs ) spread throughout the HWT genome , including a large proportion located within H3K36me3-enriched exons ( Figure 4A–B ) . 10 . 7554/eLife . 23249 . 009Figure 4 . H3K36 modification does not suppress cryptic transcription initiation in coding regions . ( A ) Representative browser shot of gene containing novel unannotated transcription start sites ( nuTSSs , highlighted in red ) . Direction of transcription denoted by arrow , and read counts denoted on Y-axis . ( B ) Boxplot describing the fold change in Start-seq signal for nuTSSs classified by their genomic localization and strand of origin relative to the resident gene if applicable . Lower boxplot describes H3K36me3 ChIP-seq signal ( ChIP/input ) for the same gene cohorts . ( C ) Scatterplot of normalized nuclear RNA-seq reads mapping antisense to genes in the dm3 reference gene model in HWT ( x-axis ) or K36R ( y-axis ) . Genes containing or within 1 kb of a local H3K36me3-ChIP-seq peak are denoted by red dots . ( D ) Hex-plot heatmap plotting nuTSSs by their location relative to the gene boundaries of the nearest gene , and the absolute change in their Start-seq signal ( K36R – HWT ) . DOI: http://dx . doi . org/10 . 7554/eLife . 23249 . 00910 . 7554/eLife . 23249 . 010Figure 4—figure supplement 1 . Metagene analysis of Start-seq reads at novel , unannotated ( nu ) TSSs in comparison to open chromatin , nucleosome positioning and H3K36 trimethylation . ( A ) Metaplot of ATAC-seq signal mapping in a 1 kb window around nuTSSs , classified as in Figure 4B . ( B ) Metaplots of H3 ( left ) and H3K36me3 ( right ) ChIP-seq signal mapping to a 4 kb window around nuTSSs , separated by quartiles of absolute Start-seq signal change between K36R and HWT . ( C ) Metaplots of HWT ATAC-seq signal mapping to a 4 kb window around nuTSSs , separated by log2 fold change in Start-seq signal . ‘Up’ denoted increased by more than two fold in K36R , ‘Down’ denotes decreased by more than two fold in K36R , and ‘Unchanged’ denotes all other nuTSSs . ( D ) Heatmaps displaying H3 ( left ) and H3K36me3 ( right ) ChIP-seq signal mapping to a 4 kb window around nuTSSs representing the catergories listed in B . ( E ) Heatmaps displaying H3 ( left ) and H3K36me3 ( right ) ChIP-seq signal mapping to a 4 kb window around nuTSSs representing the catergories listed in C . DOI: http://dx . doi . org/10 . 7554/eLife . 23249 . 010 We examined whether the position of a nuTSS relative to its closest annotated gene had any bearing upon changes in nuTSS usage in K36R mutants . Because exons are characterized by higher overall H3K36me3 signal than introns , they might be more sensitive to pervasive initiation . Furthermore , antisense initiation might also be more prevalent in the absence of H3K36me , as has been observed in budding yeast ( Carrozza et al . , 2005; Keogh et al . , 2005 ) . To test these ideas , we sorted nuTSSs by their position ( exonic or intronic ) and orientation ( sense or antisense ) relative to the resident gene . Analysis of modEncode ChIP-seq read density in 400 bp windows around each nuTSS confirmed that exonic nuTSSs are enriched for H3K36me3 relative to intronic ones ( Figure 4B ) . Similarly , exonic nuTSSs are depleted of ATAC-seq open chromatin signal ( Figure 4—figure supplement 1A ) . Contrary to expectation , exonic and antisense nuTSS usage was not dramatically increased in K36R mutants ( Figure 4B ) . Across all nuTSSs , we found that H3K36me3 density was anticorrelated with change in nuTSS ‘usage , ' that is , nuTSSs with lower signal in K36R than in HWT tended to have high H3K36me3 density , and vice-versa ( Figure 4—figure supplement 1B–E ) . When we analyzed sense and antisense Start-seq reads mapping to annotated coding regions as a proxy for cryptic transcription in annotated genes , we found that antisense initiation did not globally accumulate in an H3K36me3-dependent manner ( Figure 4C ) . These results show that modification of replication-dependent H3K36 is not required to suppress cryptic transcription in gene bodies . Instead , we found that pervasive initiation in gene bodies is widespread throughout the Drosophila genome , even in the presence of H3K36me . We also studied the change in nuTSS usage relative to gene boundaries . When absolute change in Start-seq signal at each nuTSS is scaled to gene length , increased nuTSS usage occurs almost exclusively in intergenic regions ( Figure 4D ) . Decreased usage is most prominent in the gene body , proximal to the 3' end ( Figure 4D ) . Metagene analysis shows that these regions correlate with H3K36me3 ChIP-seq density ( Figure 2A ) . Importantly , these findings do not support a role in Drosophila for H3K36me in suppressing cryptic antisense transcription , as described in yeast . The H3K36me3 methyltransferase , SETD2 , is reported to play a role in regulating alternative splice site choice ( Luco et al . , 2010; Pradeepa et al . , 2012 ) . To determine whether changes in pre-mRNA splicing contribute to gene expression differences between HWT and K36R , we used the MISO analysis package ( Katz et al . , 2010 ) , which utilizes an annotated list of alternative splicing events , and quantitates changes between RNA-seq datasets . We found that very few annotated exon skipping events or retained intron events were significantly different between K36R and HWT , and there was no discernable bias toward inclusion or exclusion ( Figure 5A ) . Additionally , the majority of high-confidence differential splicing events we detected were mild changes at best ( ∆PSI < 0 . 25 ) , indicating that a lack of K36 modification had little effect on alternative splicing regulation in K36R mutants ( Figure 5B ) . 10 . 7554/eLife . 23249 . 011Figure 5 . H3K36 modification does not regulate alternative splicing . ( A ) Density plots reflecting the distributions of change in percent spliced in ( ∆PSI ) values for skipped exon ( red ) or retained intron ( blue ) alternative splicing events manually classified as significant based on MISO parameters ( see Materials and methods ) . ( B ) Volcano plots for skipped exon ( left ) and retained intron ( right ) events , with a local regression line ( blue line ) reflecting the skew in ∆PSI values ( x-axis ) based on Bayes factor ( y-axis ) . ( C ) Global analysis of splice junction usage , where R denotes the ‘retention ratio’ in one condition , and ∆R denotes the difference in R between K36R and HWT . DOI: http://dx . doi . org/10 . 7554/eLife . 23249 . 011 Inappropriate intron retention is another class of splicing defect observed in SETD2 mutants ( Simon et al . , 2014 ) . To examine intron retention events , we quantitated junction ( j ) and non-junction ( n ) reads mapping to every exon-exon boundary represented in our RNA-seq dataset . As shown in Figure 5C , we generated a retention ratio score ( R ) that measures the number of non-junction reads as a proportion of total reads ( j+n ) . For junctions meeting statistical power requirements ( >20 total reads ) , we observed no changes in the retention ratio , meaning that splice junction usage was unchanged in K36R ( Figure 5C ) . Taken together , these results support an H3K36me-dependent role for transcriptome regulation that is independent of alternative splicing . When comparing our poly-A and nuclear RNA-seq results , we identified a group of highly-expressed genes whose transcripts were reduced in the mutant poly-A RNA fraction but not in the corresponding nuclear RNA fraction ( Figure 6A , see full RNA-seq results in Supplementary file 1 ) . Transcripts identified in the nuclear RNA-seq data represent populations of newly transcribed as well as nuclear-retained RNAs , whereas poly-A selected RNA is thought to be comprised primarily of ‘mature’ mRNAs . We deduced that the observed differences between the two sequencing datasets could reflect a role for H3K36 in post-transcriptional , rather than co-transcriptional , mRNA maturation steps ( e . g . nuclear RNA surveillance and export ) . Therefore , we selected a handful of mRNAs with large discrepancies between their nuclear and poly-A RNA-seq expression values ( Figure 6B ) for validation and testing by RT-PCR . Fractionation of nuclear and cytoplasmic RNA from HWT and K36R larvae prior to reverse transcription revealed no significant changes in subcellular localization of the targets ( Figure 6—figure supplement 1A ) , suggesting that a global block to mRNA export due to H3K36R mutation is unlikely . 10 . 7554/eLife . 23249 . 012Figure 6 . A class of highly expressed genes is subject to exonuclease degradation and inefficient post-transcriptional processing in H3K36R mutants . ( A ) LOESS regression lines generated from MA plots of either nuclear or poly-A RNA-seq , plotting gene log2 fold change ( y-axis ) vs . normalized read counts in HWT ( x-axis ) . ( B ) Log2 fold change values between K36R and HWT in nuclear ( left ) and poly-A ( right ) RNA-seq , plotted for genes selected for further RT-PCR analysis . ( C ) RT-qPCR quantification of differential expression between HWT and K36R for select genes in a no RNAi , pacman RNAi , twin RNAi , or Pop2 RNAi background , using the -∆∆Ct method . ( D ) LM-PAT assay results for the YFP transcript in HWT and K36R , in a no RNAi , pcm RNAi , twin RNAi , or Pop2 RNAi background . Sanger sequencing trace confirming the poly-A site ( leftmost panel ) and differential tail lengths ( right two panels ) is shown below . DOI: http://dx . doi . org/10 . 7554/eLife . 23249 . 01210 . 7554/eLife . 23249 . 013Figure 6—figure supplement 1 . RT-PCR controls , alternative polyadenylation analysis and schematic of assay for gene-specific poly A tail length assay ( LM-PAT ) showing relative positions of primers . ( A ) RT-PCR for select genes from cytoplasmic ( lanes 1 and 2 ) or nuclear ( lanes 3 and 4 ) RNA from HWT ( lanes 1 and 3 ) or K36R ( lanes 2 and 4 ) animals . 7SK RNA is a control for nuclear enrichment . ( B ) RT-qPCR for select genes from Figure 6B measuring normalized Log2 fold change ( HWT/K36R ) in no RNAi or Dcp2 RNAi background . P-value obtained via t-test . ( C ) Schematic of modified LM-PAT assay , in which an adenylated oligonucleotide anchor is ligated to the 3’ end of total RNA , cDNA is generated using an anchor-specific RT primer , and genes of interest are amplified using a gene-specific forward primer and an anchor specific reverse primer that contains either an oligo-T sequence at its 3’ end ( tail-anchored ) so as to extend from the ends of poly-A tails , or an oligo-T-N sequence ( UTR-anchored ) in order to extend from the terminus of the 3’ UTR . ( D ) RT-qPCR for YFP measuring normalized Log2 fold change ( HWT/K36R ) in no RNAi , pcm RNAi , or twin RNAi backgrounds . P-value obtained via t-test . ( E ) Genome wide analysis of alternative polyadenylation using DaPars ( Xia et al . , 2014 ) , with percentage of distal poly-A site usage ( PDUI ) for each gene in HWT and K36R plotted on the x- and y-axes , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 23249 . 013 In the absence of a transport block , we surmised that mRNA surveillance and degradation pathways might contribute to the reduced transcript levels observed in the poly-A fraction . We therefore hypothesized that perturbation of RNA exonuclease activity might rescue target transcript levels by preserving immature mRNAs that would otherwise be degraded . We analyzed the effect on target mRNAs of depleting Rrp6 and Xrn1/pacman ( pcm in flies ) by RNA interference ( RNAi ) , utilizing the Gal4-UAS expression system ( Brand and Perrimon , 1993 ) . Flies sourced from the Transgenic RNAi Project ( Ni et al . , 2011 ) expressing short-hairpin ( sh ) RNA constructs and Gal4-drivers were crossed into the HWT and K36R genetic backgrounds . Unfortunately , RNAi for Rrp6 caused early larval lethality and animals of the appropriate genotype could not be obtained . However , we were able to introgress the Xrn1/pcm RNAi transgene into the HWT and K36R backgrounds and total RNA was prepared from whole third instar larvae . As shown in Figure 6C , the observed expression differences in poly-A RNA for a handful of highly expressed genes were restored to levels more similar to HWT in the K36R background by RNAi-mediated depletion of pcm . These results suggest that H3K36 contributes to post-transcriptional mRNA maturation in a manner that preserves target transcripts from exonuclease-mediated degradation . mRNA degradation by Xrn1/pcm is preceded by two major surveillance steps: deadenylation by the CCR4-NOT complex , and decapping of the 7-methylguanosine ( m7G ) cap , primarily by the Dcp2 decapping enzyme ( Sheth and Parker , 2003 ) . We therefore carried out RNAi against CCR4/twin , CNOT7/Pop2 , and Dcp2 in comparison with Xrn1/pcm . Across a panel of K36R downregulated genes , expression levels were rescued by depletion of pcm , twin , and Pop2 ( Figure 6C ) , but not by RNAi against Dcp2 ( Figure 6—figure supplement 1B ) . Given the known redundancies in decapping enzymes ( e . g . see Chang et al . , 2012 ) , the negative results for the Dcp2 RNAi are inconclusive . Indeed , previous studies in S2 cells showed that depletion of Dcp2 alone is insufficient to effectively inhibit decapping ( Eulalio et al . , 2007 ) . However , the positive results we obtained by depleting deadenylase factors led us to focus on polyadenylation . Changes in 3’ end formation and polyadenylation , which occur proximal to the H3K36me3-rich chromatin at the 3’ ends of genes , might render mRNAs more sensitive to surveillance and degradation . To investigate this possibility , we analyzed poly-A tail length in the CCR4/twin RNAi background for a YFP reporter transgene using a modified LM-PAT assay ( Sallés et al . , 1999 ) , as illustrated in Figure 6—figure supplement 1C . It is important to note that expression of the UAS:YFP transgene is directly tied to Gal4 expression and thus YFP is the only transcript that is guaranteed to be expressed in the same cells as the UAS:RNAi transgene . UAS:YFP is similarly sensitive to pcm and twin as our cohort of endogenous genes ( Figure 6—figure supplement 1D ) , making it an ideal reporter . As shown in Figure 6D , we found that the YFP transcript displayed reduced poly-A tail length in K36R mutants , indicative of a role for H3K36 methylation over terminal exons in recruitment or functioning of the polyadenylation machinery . Importantly , the shorter poly-A tail in K36R mutants was independent of deadenylation activity ( Figure 6D ) , demonstrating that the defect is in polyadenylation , not in the subsequent CCR4/twin- or CNOT7/Pop2-dependent deadenylation . Additional experiments will be needed to determine the prevalence of poly-A tail length changes in the K36R mutants transcriptome wide . Computational analysis of differential poly-A site usage demonstrated no change ( Figure 6—figure supplement 1E ) , indicating that poly-A site specification was largely unaffected by mutation of H3K36 . In summary , these data uncover a post-transcriptional role for H3K36 in the regulation of metazoan gene expression .
In this study , we focus on the role of H3K36 in transcriptome fidelity , assayed at the levels of transcription initiation , elongation , pre-mRNA splicing and maturation . Crucially , most of the studies on the roles of H3K36me3 in animal cells deplete SETD2 or its orthologue , making it difficult to discern the specific role of the histone residue itself . Enzymes that catalyze histone PTMs often have numerous non-histone substrates or non-catalytic structural roles that can confound analysis ( Biggar and Li , 2015; Huang and Berger , 2008; Sims and Reinberg , 2008; Zhang et al . , 2015 ) . Notably , alpha-tubulin was recently identified as a non-histone substrate of SETD2 ( Park et al . , 2016 ) . Perhaps more importantly , SETD2 catalyzes trimethylation of lysine 36 in both the ‘canonical’ replication-dependent H3 and in the replication-independent histone variant , H3 . 3 . H3 . 3 is thought to play a particularly important role in transcriptionally active regions where H3K36 methylation is enriched ( Ahmad and Henikoff , 2002 ) . Indeed , a protein with specific affinity for SETD2-catalyzed trimethylation of lysine 36 of the histone H3 . 3 variant was shown to serve as a regulator of RNA pol II elongation ( Wen et al . , 2014 ) and to associate with components of spliceosomal snRNPs to regulate co-transcriptional alternative mRNA splicing ( Guo et al . , 2014 ) . Beyond its other substrates , SETD2’s prominent association with the C-terminal domain of RNA pol II ( Kizer et al . , 2005 ) makes it likely that ablating this protein will result in transcriptional consequences that are unrelated to its catalytic activity . In view of these complications , the direct analysis of histone residue function enabled by our BAC-based gene replacement system is particularly well suited to the study of H3K36me in the context of transcriptional regulation . In budding yeast , H3K36me2/3 has been shown to negatively regulate histone acetylation within actively transcribed genes , both by recruiting a repressive Rpd3S deacetylase complex ( Carrozza et al . , 2005; Keogh et al . , 2005 ) and by suppressing incorporation of acetylated nucleosomes at sites of RNA polymerase II-initiated nucleosome displacement ( Venkatesh et al . , 2012 ) . However , a similar role has not yet been elucidated for H3K36me in animals , and studies that have correlated cryptic transcription with H3K36 methylation in metazoan systems have done so only through perturbation of the SETD2 writer enzyme ( Carvalho et al . , 2013; Xie et al . , 2011 ) . Furthermore , studies have implicated H3K36me3 in alternative splicing in human cell culture ( Luco et al . , 2010; Pradeepa et al . , 2012 ) and inefficient intron splicing in clear cell renal cell carcinomas ( Simon et al . , 2014 ) , again via SETD2 mutation . In this study , we used histone replacement to define whether modification of canonical H3K36 is responsible for these functions . We demonstrate that H3K36 is neither a significant contributor to the regulation of alternative splice site choice , nor the efficiency of canonical intron removal . We also present evidence that methylation of H3K36 does not suppress cryptic transcription in coding regions . Given the unprecedented depth of our Start-seq dataset ( >200 M reads per genotype ) , even very rare events would have been detected . To the contrary , we found evidence for pervasive initiation ( both sense and anti-sense ) events that largely fail to appear in the steady-state RNA population under wild type conditions . Interestingly , we confirm that H4 acetylation is strongly suppressed by H3K36 modification , despite the fact that cryptic transcripts do not appear . This finding argues for an uncoupling of H4ac levels from cryptic initiation in coding regions in metazoans , and suggests that the suppression of cryptic transcription initiation in multicellular organisms may be more complex than previously appreciated . One potential explanation for the discrepancy between our results and previous studies of SETD2 could be that modification of the aforementioned histone variant , H3 . 3 , is the primary functional contributor to the cryptic initiation or splicing phenotypes . Elucidating the effects of H3 . 3K36 methylation is outside the scope of this work , and thus phenotypes that have been reported in the literature as being sensitive to H3K36 methylation might plausibly respond specifically to H3 . 3K36 methylation . In fact , this serves as a useful feature of histone replacement in this context , since a functional separation of H3 and H3 . 3 lysine 36 methylation cannot be otherwise achieved . However , this possibility should be tempered by the fact that we observed very low levels of H3K36me3 signal in both western blots from H3K36R mutant larvae ( Figure 3A ) and immunofluorescent staining of salivary gland polytene chromosomes ( McKay et al . , 2015 ) . Thus H3 . 3 is , at best , a minor contributor to total H3K36me3 . Future experiments testing the transcriptional consequences of direct mutation of H3 . 3K36 , both on its own and in combination with mutation of replication-dependent H3K36 , will better define their contributions . Finally , we present evidence that H3K36 is required for proper mRNA maturation , providing a post-transcriptional benefit across a range of highly expressed genes . Additional studies will be required in order to elucidate a detailed molecular mechanism for this process . Our genetic suppression data suggest that this mRNA ‘fitness’ benefit is somehow linked to the efficiency of 3' end formation or polyadenylation ( Figure 6B–D ) . Interestingly , H3K36me3 depletion in SETD2-mutant renal cell carcinoma has been correlated with defects in transcriptional termination and readthrough into neighboring genes ( Grosso et al . , 2015 ) , suggesting that H3K36 methylation might influence termination and polyadenylation . Indeed , the enrichment of H3K36me3 at the 3' ends of genes makes it a likely candidate to interface with these activities . Another possibility is that H3K36 modification might recruit some type of RNA modifying enzyme . For example , Jaffrey and colleagues recently showed that dimethylation ( N6 , 2'-O-dimethyladenosine , or m6Am ) of the nucleotide adjacent to the m7G cap enhances transcript stability ( Mauer et al . , 2017 ) . Moreover , H3K36 might contribute to mRNA maturation across multiple processing steps , with the combined effect of protecting target mRNAs from surveillance and eventual degradation . The prevailing model for histone PTM modulation of gene expression , reinforced by recent direct evidence ( Hilton et al . , 2015 ) , suggests that it occurs directly proximal to the site of histone modification . However , the fact that genomic regions largely lacking H3K36me exhibit differential expression in K36R mutants argues against this idea . For that reason , a model for H3K36 control of gene expression should also consider indirect mechanisms . For example , it is possible that the rate of transcribing polymerase through nucleosomes that are modified at H3K36 might change , and therefore the capping , cleavage and polyadenylation machinery that associates with the C-terminal domain of RNA polymerase II ( Ho et al . , 1998; McCracken et al . , 1997 ) might become improperly distributed in K36R mutants . Alternatively , SETD2 could have additional ( unknown ) substrates that function in these processes . Finally , H3K36me’s previously reported role in three-dimensional genome organization ( Evans et al . , 2016; Smith et al . , 2013; Ulianov et al . , 2016 ) might extend to the concentration of factors related to mRNA maturation at sites of active transcription , which would be impaired upon H3K36 mutation . Future studies using alternative genetic approaches , including specific ablation of the catalytic activity of ‘writers’ to cross-reference our observations , should be instructive in this regard .
RNA-seq libraries were prepared using the Illumina TruSeq stranded library preparation kit from RNA prepared with TRIzol reagent ( Thermo Fisher ) from either whole third instar larvae ( poly-A ) or nuclei isolated from third instar larvae ( nuclear ) ( adapted from [Nechaev et al . , 2010] ) . Start-seq libraries were prepared as previously described ( Henriques et al . , 2013; Nechaev et al . , 2010 ) . Sequencing was carried out on a HiSeq2000 ( ATAC-seq , poly-A and nuclear RNA-seq ) or NextSeq500 ( Start-seq ) ( Illumina ) . For all assays , at least three biological replicates were prepared ( four in the case of Start-seq and nuclear RNA-seq ) . Total nuclear RNA from whole third instar larvae was used as input to each Start-seq library . For each RNA replicate used as input for a Start-seq library , 80 whole third instar larvae were collected . Five whole third instar larvae were selected for genomic DNA recovery via phenol chloroform extraction and ethanol precipitation in order to normalize Start-seq RNA spike-in controls to DNA content . The remaining ( 75 ) larvae were washed 3x with ice cold 1x ENIB buffer ( 15 mM Hepes pH7 . 6; 10 mM KCl; 3 mM CaCl2; 2 mM MgCl2; 0 . 1% Triton X-100; 1 mM DTT; 1 mM PMSF ) , and were then combined with 1 vol 0 . 3 M ENIB ( 1x ENIB +0 . 3 M Sucrose ) . Larvae were homogenized in a 1 mL dounce with 10 strokes with a type A pestle . Each replicate required douncing in three separate aliquots so as to avoid oversaturation of the dounce with larval cuticle , and homogenate was immediately transferred to ice once completed . Dounce was washed with 1 vol 0 . 3 M ENIB , combined with homogenate , and mixture was homogenized with 10 strokes with a type B pestle . Resulting homogenate was filtered through 40 µM Nitex mesh into a 50 mL conical tube on ice . For each 150 µL of filtered homogenate produced , a sucrose cushion was made by layering 400 µL 1 . 7 M ENIB followed by 400 µL 0 . 8 M ENIB in a 1 . 5 mL Eppendorf tube . 150 µL filtered homogenate was pipetted onto cushion , and spun at 20 , 000xg for 15 min at 4°C . After spinning , lipid residue was carefully removed from the walls of the tube with a micropippetor , and then the remainder of the supernatant was removed . The nuclear pellet was homogenized in 100 µL 0 . 3 M ENIB , and 10 µL was removed , stained with Trypan Blue , and observed under a microscope to confirm efficient nuclear isolation . Total RNA was extracted from the remaining homogenate with Trizol reagent using standard manufacturer’s protocols . Start-seq libraries were prepared from nuclear RNA as previously described ( Henriques et al . , 2013; Nechaev et al . , 2010 ) . Libraries were sequenced on a NextSeq500 generating paired-end , 26 nt reads . For each replicate , total RNA from 25 whole third instar larvae was isolated using Trizol reagent according to manufacturer’s protocols . RNA-seq libraries were generated with the Tru-seq Stranded Poly-A RNA-seq library preparation kit ( Illumina ) . Libraries were sequenced on a HiSeq2000 generating paired-end , 100 nt reads ( Illumina ) . Nuclei from whole third instar larvae were isolated as described above for Start-seq , and RNA was extracted using Trizol reagent . Total nuclear RNA was used as input to Ribo-zero Stranded RNA-seq library preparation ( Illumina ) . Libraries were sequenced on a HiSeq2000 generating paired-end , 50nt reads ( Illumina ) . For each replicate , nuclei from 10 whole third instar larvae were isolated as per Start-seq and nuclear pellets were gently homogenized with wide-bore pipette tips in 50 ∆uL ATAC-seq lysis buffer ( 10 mM Tris·Cl , pH 7 . 4 , 10 mM NaCl , 3 mM MgCl2 , 0 . 1% ( v/v ) Igepal CA-630 ) , and homogenate was directly used as input to the Nextera DNA library preparation kit ( NEB ) for tagmentation of chromatinized DNA , as described in Buenrostro et al . ( 2013 ) . Libraries were sequenced on a HiSeq2000 generating single-end , 100 nt reads ( Illumina ) . Sequencing reads were mapped to the dm3 reference genome using Bowtie2 ( Langmead and Salzberg , 2012 ) ( ATAC-seq , Start-seq ) or Tophat ( Trapnell et al . , 2012 ) ( RNA-seq ) default parameters . We used DESeq2 ( Love et al . , 2014 ) for differential expression analysis and Cufflinks ( Trapnell et al . , 2012 ) for novel transcript detection . We used the MISO package ( Katz et al . , 2010 ) to analyze annotated alternative splicing events , and custom scripts ( Source code 2 ) to analyze global splice junction usage . Start-seq and ATAC-seq reads were mapped using Bowtie2 ( Langmead and Salzberg , 2012 ) , and Poly-A and nuclear RNA-seq reads were mapped using the Tophat gapped read aligner ( Trapnell et al . , 2012 ) . Boxplots and Start-seq plots scaled to gene length were generated using ggplot2 in R ( www . r-project . org ) . For Start-seq , reads were quantified at base-pair resolution using a custom script ( Source code 1 ) , and nucleotide-specific raw read counts were normalized based on reads mapping to RNA spike-in controls . Exonic , intronic , and intergenic locations were determined using the dm3 gene model . For Poly-A and nuclear RNA-seq: to analyze annotated alternative splicing , we used MISO ( Katz et al . , 2010 ) , and considered splicing events with a ) a Bayes score greater than 10 with all replicates combined , b ) and consistent directionality of ∆PSI in each of the three individual replicates , as significant . To analyze global splice junction usage , we used a custom script ( Source code 2 ) to quantify reads spanning the junction location that either map to it ( ‘junction’ , i . e . containing an ‘N’ CIGAR designation that maps precisely to the junction in question ) or through it ( ‘non-junction’ ) . To analyze differential expression , we used DESeq2 ( Love et al . , 2014 ) to quantify log2 fold change in normalized read counts between K36R and HWT . To analyze alternative polyadenylation , we used DaPars ( Xia et al . , 2014 ) . All ChIP-seq data were downloaded from modEncode ( www . modencode . org ) . In all cases , data were derived from the third instar larval time point as determined by modEncode developmental staging procedures . For ChIP-seq and ATAC-seq , metagene plots were generated using the Deeptools package ( Ramírez et al . , 2014 ) . RNA was isolated with TRIzol reagent as described above , and reverse transcription was performed using random hexamers and Superscript III ( Invitrogen ) , according to the manufacturer’s protocols . For semi-quantitative PCR analysis , products were run on a 2% agarose gel , and bands were quantified using ImageJ . For qPCR , Maxima SYBR Green/ROX qPCR Master Mix ( Thermo Scientific ) was used . All qPCR analyses are based on three biological replicates , plotted with standard error . For semi-quantitative PCR , PCR reactions were prepared in biological triplicate using 2x Red Master Mix ( Apex Bioscience ) , and targets were amplified for 35 cycles of PCR with a 95°C denaturation step , a 60°C annealing step , and a 72°C elongation step . Reactions were run on a 2% agarose gel with EtBr for 30 min at 90 V , and bands were imaged on a UV transilluminator ( GE Healthcare ) and quantified using ImageJ . For RT-qPCR , reactions were prepared in biological triplicate using Maxima SYBR Green/ROX qPCR Master Mix ( Thermo Scientific ) , and fluorescence was monitored across 40 cycles in 96 well plate format . For LM-PAT , 1 µg total RNA was incubated with 5 pmol preadenylated lmPAT anchor primer ( ppApCAGCTGTAGCTATGCGCACCGAGTCAGATCAG ) ( adenylated using 5’ DNA Adenylation Kit , NEB ) , and ligated with T4 RNA Ligase 2 , truncated K227Q ( NEB ) using manufacturers protocol . Ligated RNA was reverse-transcribed with Superscript III ( Life Technologies ) using an lmPAT RT primer ( GACTCGGTGCGCATAGCTACAGCTG ) . Resulting first-strand cDNA was PCR-amplified using gene-specific forward primers ( see Supplementary file 2 ) paired with nested lmPAT RT primers that contain terminal thymidines ( GTGCGCATAGCTACAGCTGTTTT ) . PCR conditions were as follows: a preliminary round consisted of 12 cycles in which the annealing step was decreased by one degree Celsius in each cycle from 71°C to 60°C ( between 95°C and 72°C denaturation and elongation steps , respectively ) , followed by 18 additional cycles with an annealing temperature at 60°C . After completion of the first round , 2 µL PCR product was used as template for a second round of PCR amplification with 25 cycles and an annealing temperature at 60°C . For ‘tail’ measurement , template was amplified with a nested gene-specific forward primer and lmPAT nested RT reverse primer . For ‘UTR’ measurement , template was amplified with a nested gene-specific forward primer and a ‘TVN’ primer anchored at the 3’ UTR terminus . For each replicate , nuclei from 30 whole third instar larvae were isolated as per Start-seq and homogenized in 50 µL Extraction Buffer ( 320 mM ( NH4 ) 2SO4 , 200 mM Tris HCl ( pH 8 . 0 ) , 20 mM EDTA , 10 mM EGTA , 5 mM MgCl2 , 1 mM DTT , 1x Protease Inhibitor Cocktail ( Roche ) ) . Mixture was spun at 15 , 000xg for 5 min at 4°C and supernatant was recovered and immediately used in polyacrylamide gel electrophoresis . Gel was transferred to PVDF membrane and incubated with rabbit anti-H3 ( Abcam , ab1791 ) , rabbit anti-H3K36me3 ( Abcam , ab9050 ) , mouse anti-H3K27me3 ( Abcam , ab6002 ) , or rabbit anti-H4ac ( Active Motif , #39177 ) primary antibody overnight . We used ImageJ to quantify western blot band intensities , and calculated ratios of K36R/HWT intensity for each target across two independent biological replicates . Student’s t-test was used to obtain p-values for ratio comparisons between H3 and other targets . Salivary gland polytene chromosome squashes were performed on third instar larvae as previously described ( McKay et al . , 2015 ) , using rabbit anti-H4K12ac polyclonal primary antibody ( Active Motif , #39165 ) overnight , followed by AlexaFluor 594 goat anti-rabbit secondary antibody ( ThermoFisher ) for two hours , then DAPI for 10 min . | In a single human cell there is enough DNA to stretch over a meter if laid end to end . To fit this DNA inside the cell – which is less than 20 micrometers in diameter – the DNA is tightly wrapped around millions of proteins known as histones , which look like “beads” along a “string” of DNA . These histones can prevent other proteins from binding to DNA and activating specific genes . Therefore , cells use enzymes to chemically modify histones to allow particular stretches of DNA to be unwrapped at specific times . Proteins are made up of building blocks called amino acids . A specific amino acid on histones known as H3K36 is modified in certain sections of DNA that suggest it affects the activities of many genes . However , the precise role of this amino acid remains unclear . Previous studies have tried to investigate this by removing the enzymes that modify it , but these enzymes can also modify many other proteins , making it difficult to know what exactly causes the changes in gene activity . Fruit flies are often used in experiments as models of how genetic processes work in humans and other animals . Like us , fruit flies also package their DNA using histones . To investigate the role of H3K36 , Meers et al . generated a mutant fruit fly that has a version of the amino acid that cannot be chemically modified by the normal enzymes . Unexpectedly , the experiments suggest that some changes in gene activity that have been previously reported to be caused by modifying H3K36 might actually be due to other factors . Meers et al . found that H3K36 modifications may instead “mark” certain genes to be more active than they otherwise would be . These findings provide a starting point for understanding exactly how H3K36 regulates gene activity . The next challenge is to refine our understanding of how H3K36 modification affects genes in cancer and other diseases , which may aid the development of new therapies to treat these conditions . | [
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] | 2017 | Histone gene replacement reveals a post-transcriptional role for H3K36 in maintaining metazoan transcriptome fidelity |
Damage-associated molecular patterns ( DAMPs ) are molecules released by dead cells that trigger sterile inflammation and , in vertebrates , adaptive immunity . Actin is a DAMP detected in mammals by the receptor , DNGR-1 , expressed by dendritic cells ( DCs ) . DNGR-1 is phosphorylated by Src-family kinases and recruits the tyrosine kinase Syk to promote DC cross-presentation of dead cell-associated antigens . Here we report that actin is also a DAMP in invertebrates that lack DCs and adaptive immunity . Administration of actin to Drosophila melanogaster triggers a response characterised by selective induction of STAT target genes in the fat body through the cytokine Upd3 and its JAK/STAT-coupled receptor , Domeless . Notably , this response requires signalling via Shark , the Drosophila orthologue of Syk , and Src42A , a Drosophila Src-family kinase , and is dependent on Nox activity . Thus , extracellular actin detection via a Src-family kinase-dependent cascade is an ancient means of detecting cell injury that precedes the evolution of adaptive immunity .
Trauma , burns , ischemia , strenuous exercise , all induce a sterile inflammatory response . It is likely that this response evolved to clear cell debris , promote tissue repair and maintain tissue sterility ( Zelenay and Reis e Sousa , 2013; Eming et al . , 2014 ) but , if uncontrolled , it can lead to ( aseptic ) shock and , in some cases , death ( Rock et al . , 2010 ) . The prevailing notion is that sterile inflammation is initiated by pro-inflammatory signals that are released by damaged cells . These include intracellular components that are exposed when cells lose their membrane integrity , such as ATP , uric acid , RNA and DNA , collectively known as damage-associated molecular patterns ( DAMPs ) ( Rock et al . , 2010; Zelenay and Reis e Sousa , 2013 ) . The universe of DAMPs and their receptors , as well as the mechanisms regulating DAMP responses , remains underexplored . This is partly because early research in this area was tainted by issues of microbial contamination ( Beg , 2002 ) and because immunologists have often focussed on sterile inflammation from the narrow perspective of adaptive immunity ( Matzinger , 1994; Land et al . , 1994 ) . However , it is probable that responses to DAMPs , like responses to microbes , pre-date the vertebrate evolution of T and B cells and have an early metazoan origin , much like the clearance of dead cells ( Krysko et al . , 2011; Hochreiter-Hufford and Ravichandran , 2013; Eming et al . , 2014 ) . Therefore , the study of invertebrate responses to DAMPs could offer a different perspective into the induction of sterile inflammation , akin to how research into insect immunity to infection led to the identification of Toll signalling and paved the way to the discovery of an analogous pathway in vertebrates ( Lemaitre et al . , 1996 ) . The immune system of Drosophila melanogaster has been widely studied in the context of infection . It consists of a cellular and a humoural arm , in addition to cell-intrinsic antiviral RNAi responses ( Buchon et al . , 2014; Lemaitre and Hoffmann , 2007 ) . The cellular arm is made up of three macrophage-like types of cells , collectively termed haemocytes ( Buchon et al . , 2014; Lemaitre and Hoffmann , 2007 ) . The humoural immune response relies on antimicrobial peptides ( AMPs ) that are synthesised in the fat body ( the fly equivalent of the liver ) and then secreted into the haemolymph to provide systemic protection from bacteria and fungi ( Lemaitre and Hoffmann , 2007; Buchon et al . , 2014 ) . The production of AMPs is regulated by two different pathways . The Toll pathway is activated by peptidoglycan fragments of Gram-positive bacteria , fungal β-glucans , and pathogen-derived protease activity in the haemolymph ( Buchon et al . , 2014; Lemaitre and Hoffmann , 2007 ) . The Imd pathway is activated by peptidoglycan fragments from Gram-negative bacteria ( Buchon et al . , 2014; Lemaitre and Hoffmann 2007 ) . Activation of either pathway results in the translocation of distinct NF-κB family transcription factors into the nucleus and the subsequent synthesis of AMPs best suited to neutralise the type of microorganism detected ( Lemaitre and Hoffmann , 2007; Buchon et al . , 2014 ) . A third pathway contributing to Drosophila humoural immunity involves Janus Kinase/Signal Transducer and Activator of Transcription ( JAK/STAT ) signalling . In contrast to the Toll and Imd pathway , the JAK/STAT pathway has not yet been shown to be directly induced by sensors of invading microorganisms ( Myllymäki and Rämet , 2014 ) . However , it has been implicated in resistance to as well as tolerance to viral infections ( Dostert et al . , 2005; Lamiable et al . , 2016 ) . Notably , the JAK/STAT pathway is activated by different types of stresses ( e . g . heat , mechanical pressure , oxidative stress or UV irradiation ) ( Lemaitre et al . , 1996; Ekengren et al . , 2001; Ekengren and Hultmark , 2001 ) . All of these insults likely result in cell death suggesting the possibility that JAK/STAT pathway activation might be triggered by DAMPs rather than microbes . The JAK/STAT pathway is elicited by cytokines of the Unpaired ( Upd ) family – Upd1 ( Harrison et al . , 1998 ) , Upd2 ( Gilbert et al . , 2005; Hombría et al . , 2005 ) and Upd3 ( Agaisse et al . , 2003; Wright et al . , 2011 ) – all of which serve as ligands for the only JAK/STAT-coupled receptor in Drosophila , Domeless ( dome ) ( Brown et al . , 2001 ) . The binding of Upds induces Domeless dimerization and activation of a single JAK ( termed Hopscotch ) . Activated Hopscotch proteins phosphorylate one another allowing for recruitment of the single Drosophila STAT family transcription factor , STAT92E . The latter is then phosphorylated by Hopscotch , resulting in dimerisation and translocation into the nucleus . STAT92E dimers bind to the promoters of their target genes ( Kiu and Nicholson , 2012; Bina et al . , 2010; Müller et al . , 2005 ) including , amongst others , ones encoding proteins involved in viral resistance ( Lamiable et al . , 2016; Dostert et al . , 2005 ) , as well as proteins of the Turandot family such as Turandot M ( TotM ) . The exact function of Turandot family proteins is not known but they have been controversially argued to be linked to stress resistance ( Ekengren and Hultmark , 2001; Ekengren et al . , 2001; Mahapatra and Rand , 2012; Zhong et al . , 2013 ) . Besides a role in host defence , the JAK/STAT pathway has also been linked to energy metabolism ( Rajan and Perrimon , 2012 ) and regenerative processes , for example in the gut ( Jiang et al . , 2009 ) . The involvement of JAK/STAT signalling in regeneration is particularly interesting given the role of DAMPs in contributing to tissue repair ( Vénéreau et al . , 2015 ) . We have previously identified DNGR-1 ( also known as CLEC9A ) as a vertebrate-restricted innate immune receptor dedicated to DAMP recognition ( Sancho et al . , 2009 ) . DNGR-1 is phosphorylated by Src family kinases and then signals via Syk although it does not induce inflammation . Rather , DNGR-1 is expressed by dendritic cells ( DCs ) and signals to favour cross-presentation of antigens from dead cells , contributing to CD8+ T cell responses to cytopathic infections and , possibly , tumours ( Iborra et al . , 2012; Zelenay et al . , 2012; Sancho et al . , 2009 ) . We and others subsequently found that the DAMP recognised by DNGR-1 is F-actin , the polymer of G-actin that provides higher eukaryotic cells with structural integrity ( Ahrens et al . , 2012; Zhang et al . , 2012 ) . Actin is an ideal DAMP given that it is extremely conserved ( 90% identity between yeast and humans ) and highly abundant and ubiquitous within all eukaryotic cells but absent from extracellular fluids . We therefore hypothesised that released actin constitutes an evolutionarily-conserved DAMP whose detection might involve a signalling pathway conserved from flies to mammals . This would be analogous to the conservation of the Toll signalling pathway ( albeit not the upstream receptors ) in the Drosophila and vertebrate response to fungi and bacteria . Here , we show systemic administration of actin to Drosophila selectively triggers a JAK/STAT response and that this requires the fly homologues of Src and Syk . Our data therefore reveal an evolutionarily-conserved tryosine kinase-based pathway for recognising damage through sensing of released or exposed actin .
To test whether actin might act as a DAMP in Drosophila , we injected w1118 adult flies with actin or with buffer alone and carried out RNAseq gene expression profiling of total fly extracts ( Figure 1a ) . Actin injection led to the differential expression of a large number of genes as compared to injection of buffer control: 241 genes were induced or repressed at 3 hr , 1297 genes at 6 hr and 351 genes at 24 hr post-injection ( Figure 1b ) . Notably , among genes that were induced selectively in actin treated flies , we found the members of the Tot gene family including TotM , TotA and TotC , as well as Socs36E , Diedel and thioester-containing protein ( Tep ) 1 ( Figure 1b , c ) , all of which are STAT-dependent ( Boutros et al . , 2002; Müller et al . , 2005; Lagueux et al . , 2000 ) . Gene set enrichment analysis ( GSEA ) of published datasets confirmed that target genes of STAT92E were highly enriched in actin- compared to buffer-injected flies ( Figure 1d ) ( p<0 . 0001 ) . The presence of STAT binding sites in promoters of genes upregulated by actin but not buffer injection was validated by bioinformatics analyses ( STAT responsive elements: p=7 . 8×10–4 using Transfac database , p=1 . 63×10–8 using JASPAR database ) . 10 . 7554/eLife . 19662 . 003Figure 1 . Global gene expression profiling reveals strong enrichment for JAK/STAT regulated genes upon actin injection . ( a ) Groups of control ( w1118 ) flies were injected with either buffer or actin before euthanasia at 3 , 6 or 24 hr post injection . Flies within each group were pooled , RNA was isolated and processed for RNAseq analysis as described in the Methods . ( b ) Genes differentially expressed between buffer and actin-injected flies , including both up and down regulated transcripts , are represented for each time point in a Venn diagram . Only genes differentially expressed with a false discovery rate ( FDR ) < 0 . 05 were included in the analysis . The numbers within each set represent the numbers of genes differentially expressed . Select STAT target genes are indicated . ( c ) Differentially expressed genes between actin- and buffer-injected flies at 3 , 6 and 24 hr were used to draw a hierarchical heat map . Genes were clustered using a Euclidean distance matrix and average linkage clustering . Samples were ordered based on time and treatment . The heat map shows the average expression values from triplicate samples . Red indicates higher expression and blue indicates lower expression relative to the mean expression of probes across all samples . The black box highlights genes for which there was the biggest fold change increase in actin- relative to buffer-injected flies . ( d ) Enrichment plot from GSEA showing that targets of STAT92E are enriched within the upregulated gene set in actin-injected files relative to buffer injected flies after 24 hr . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 003 We confirmed by RT-qPCR that actin but not buffer injection potently triggers expression of Tot , Tep and Diedel genes peaking at 24 hr after injection ( Figure 2a-f and data not shown ) . Actin injection did not lead to the expression of the AMP genes Drs and Dpt , which were induced only upon experimental fungal or bacterial infection ( Figure 2g , h ) . This indicates the absence of microbial contaminants in actin preparations and demonstrates that the response to actin differs from that to septic injury , in which induction of STAT targets is invariably accompanied by that of genes downstream of Toll or Imd ( Agaisse et al . , 2003; Chakrabarti et al . , 2016; Brun et al . , 2006 ) . In this regard , TotM induction by actin was much greater than that elicited by injury ( clean or septic ) or by heat shock , which are classically regarded as inducers of the JAK-STAT signalling pathway ( Agaisse et al . , 2003; Chakrabarti et al . , 2016 ) ( Figure 2—figure supplement 1a and data not shown ) . 10 . 7554/eLife . 19662 . 004Figure 2 . G- and F-actin induce a unique sterile inflammatory response upon injection into Drosophila . Control ( w1118 ) flies were mock treated ( - ) or injected with actin buffer , actin , E . coli or C . albicans , as indicated . Flies were euthanised at 3 , 6 and 24 hr post injection , RNA was isolated and relative gene expression determined by quantitative RT-PCR . Depicted are expression levels of ( a–c ) Turandot ( Tot ) A , TotB and TotM , ( d–e ) Thioester-containing protein ( Tep ) 1 and Tep2 , ( f ) Diedel , ( g–h ) Diptericin ( Dpt ) and Drosomycin ( Drs ) . ( i ) Flies colonised with or free of Wolbachia ( Wol ) were reared under standard ( SPF ) or germ-free ( GF ) conditions . Relative TotM expression was assessed 24 hr post actin injection . ( j ) Dose-dependent TotM response to injected actin at 24 hr post injection . ( k ) Dose-response curve for latrunculin B-stabilised G-actin vs phalloidin-stabilised F-actin diluted in G-actin or F-actin buffer , respectively . Relative expression levels of TotM 24 hr post injection are shown . ( l ) A non-polymerisable G-actin mutant and F-actin were serially diluted in F-buffer at the indicated concentrations before injection into flies . Relative expression levels of TotM 24 hr after injection are depicted . Relative gene expression levels were calculated using the housekeeping gene Rp49 as a reference gene . Data are representative of at least two independent experiments with 10 flies/sample with duplicate samples . Bars represent mean ± SEM . Statistical analysis was performed using two-way ANOVA with Sidak’s multiple comparison test as post-test for pairwise comparisons . Results of Sidak’s multiple comparison test are shown ( ns , not significant; *p<0 . 05; **p<0 . 01; ***p<0 . 001; ****p<0 . 0001 ) . If not otherwise indicated , flies were injected with 36 . 8 ng of actin per fly . All data points are plotted even where no bars are visible . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 00410 . 7554/eLife . 19662 . 005Figure 2—figure supplement 1 . G-actin and F-actin induced TotM peaks after 24 hr . ( a ) Relative expression of TotM after injection with actin , buffer or after heat shock , 24 hr post treatment . Data are representative of three independent experiments with 10 flies/sample . TotM relative levels were calculated using the housekeeping gene Rp49 as a reference gene . Bars represent mean ± SEM . All data points are plotted even where no bars are visible . ( b ) G-actin mutant or human platelet F-actin was serially diluted in F-buffer as indicated and spotted onto nitrocellulose membranes , which were then probed with mDNGR-1 extracellular domain ( which specifically recognizes F-actin but not G-actin ) or mouse-anti-β-actin . Data are representative of two independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 00510 . 7554/eLife . 19662 . 006Figure 2—figure supplement 2 . TotM is selectively induced by injected actin . ( a ) Control ( w1118 ) flies were injected with either water or a selection of salts , sugars or lipids . Relative TotM levels 24 hr post injection are depicted . ( b ) Control ( w1118 ) flies were injected with either water or different peptides or polysaccharides . Relative TotM levels 24 hr post injection are depicted . ( c ) Control ( w1118 ) flies were injected with either water or amino acids . Flies injected with cysteine died in less than 24 hr . Relative TotM levels 24 hr post injection are depicted . The apparent modest increase in TotM expression after injection of threonine was not reproducible ( data not shown ) . ( d ) Control ( w1118 ) flies were injected with either buffer or a selection of vertebrate DAMPs . TotM relative levels were calculated 24 hr post injection . ( e ) Control ( w1118 ) flies were injected with buffer or denatured or native actin . Relative TotM levels were measured 24 hr post injection . TotM relative levels were calculated using the housekeeping gene Rp49 as a reference gene . Data are representative of at least two independent experiments with duplicate samples . Bars represent mean ± SEM . Statistical analysis was performed using one-way ANOVA with Sidak’s multiple comparison test as post-test for pairwise comparisons . Results of Sidak’s multiple comparison test are shown ( ns , not significant; *p<0 . 05; **p<0 . 01; ***p<0 . 001; ****p<0 . 0001 ) . All data points are plotted even where no bars are visible . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 006 The uniqueness of the response to actin was further patent in the fact that injection of a plethora of sugars , salts , lipids , polysaccharides , proteins , peptides , or amino acids did not induce TotM ( Figure 2—figure supplement 2a–c ) . Similarly , injection of ATP , DNA , uric acid ( monosodium urate – MSU ) or heat shock proteins ( HSPs ) – all of which act as DAMPs in mammals – failed to elicit TotM in flies ( Figure 2—figure supplement 2d ) . The finding that TotM is not induced by any of the above stimuli other than actin needs to be tempered by the fact that they were all tested at a single , somewhat arbitrary , dose but it also acts as a control for the effects of injection-mediated injury . Importantly , the globular tertiary structure of actin was required for the response as injection of denatured protein failed to elicit TotM ( Figure 2—figure supplement 2e ) . Finally , actin injection induced equivalent TotM levels in germ-free and SPF ( specific pathogen free ) flies , showing that microbiota or their products do not contribute to the response , despite the fact that they are inevitably introduced into the thorax of SPF flies upon piercing of the cuticle during injection ( Figure 2i ) . Similarly , the response to actin did not require Wolbachia , a genus of vertically-transmitted intracellular bacteria that infect many arthropods and is known to impact immune responses ( Teixeira et al . , 2008; Hedges et al . , 2008 ) . Altogether , these data indicate that the introduction of actin into the haemolymph of flies uniquely induces a sterile inflammatory response characterised by selective induction of STAT target genes . To ensure an experimental window for mechanistic dissection of the response ( see below ) , most of the above experiments employed injection of 36 . 8 ng of actin , which is roughly equivalent to the amount contained in 3000 HeLa cells ( data not shown ) . However , TotM induction could be observed upon injection of as little as 0 . 1 ng actin , which corresponds to the contents of 8–10 cells , thereby underscoring physiological relevance ( Figure 2j ) . Actin from all tested species ( human , rabbit and Drosophila ) induced TotM ( see Materials and Methods ) , consistent with the extreme evolutionary conservation of the protein . Importantly , TotM induction was not due to the cytopathic effects of actin filaments in vivo as the response was triggered equally by phalloidin-stabilised filamentous ( F ) - or latrunculin-stabilised globular ( G ) -actin ( Figure 2k ) . The ability of G-actin to stimulate the response in vivo in the absence of polymerisation could be formally demonstrated by injecting a Drosophila non-polymerisable G-actin mutant . The monomeric nature of the G-actin mutant used was first verified by probing with mDNGR-1 extracellular domain , which specifically binds F- but not G-actin ( Figure 2—figure supplement 1b ) ( Ahrens et al . , 2012; Hanč et al . , 2015 ) . Injection of the G-actin mutant induced a response equivalent to that triggered by an injection of polymeric F-actin ( Figure 2l ) . To further analyse the response , we focused on the tissues where STAT activation might occur . Analysis of physically-dissected body parts of w1118 flies injected with actin revealed that STAT target gene expression was enriched in regions containing the fat body ( Figure 3a ) . We confirmed that STAT activation takes place in the fat body by using reporter flies in which GFP is expressed under the control of a STAT response element and in which , additionally , fat body cells are labelled with Tomato fluorescent protein for ease of organ identification ( Stat92-dGFP+LPP-Gal4>UAS-Myr-td-Tom ) . Injection of actin , but not mock injection ( 0 hr ) or injection of buffer alone , resulted in a time-dependent increase in GFP fluorescence exclusively in Tomato+ fat body cells ( Figure 3b , c ) . We next used a genetic approach to understand how extracellular actin leads to STAT activity in the fat body and to assess the contribution of that organ to the global response measured in total fly extracts . First , we validated the requirement for the JAK/STAT pathway by expressing a dominant negative Domeless receptor ( dome ΔCyt2 . 3 ) . Overexpression of dome ΔCyt2 . 3 in the whole fly using a ubiquitous Actin-Gal4 driver resulted in significant attenuation of TotM expression in response to actin injection ( Figure 4a ) . The same result was obtained when overexpressing dome ΔCyt2 . 3 specifically in the fat body using a fat body-restricted , temperature-sensitive c564-Gal80ts driver ( c564-Gal4; Tub-Gal80ts ) ( Figure 4a ) . Concordant with those results , the response was also attenuated in extracts of flies in which STAT signalling was reduced by overexpressing the STAT inhibitor , dPIAS , in the fat body using two different fat body-specific drivers under the control or not of a Gal80ts temperature-sensitive repressor ( Figure 4b , c ) . Finally , RNAi-mediated knockdown of STAT92E specifically in fat body cells was sufficient to suppress TotM induction by injected actin ( Figure 4d ) . These results suggest that canonical STAT-dependent TotM induction in the fat body accounts for the global response seen in total fly extracts . 10 . 7554/eLife . 19662 . 007Figure 3 . Actin injection induces JAK/STAT activation in the fat body . ( a ) Control ( w1118 ) flies were injected with actin and euthanised at the indicated times , after which the fat bodies and intestines were dissected and RNA extracted . Relative TotM expression in the two organs compared to whole flies is depicted . At each time point , samples represent five whole flies , 15 intestines or 15 fat bodies . TotM relative levels were calculated using the housekeeping gene Rp49 as a reference gene . ( b ) STAT92E reporter activity in the fat bodies of Stat92-dGFP+Lpp-Gal4 > UAS-Myr-td-Tomato flies at 3 , 6 and 24 hr after injection with actin ( right panel ) of buffer ( left panel ) . Scale bar represents 20 μm . ( c ) Quantification of mean STAT fluorescence within the fat body ( n = 3–11 flies ) . Bars represent mean ± SEM . Statistical analysis was performed using one-way ANOVA with Sidak’s multiple comparison test as post-test for pairwise comparisons . Results of Sidak’s multiple comparison test are shown ( ns , not significant; *p<0 . 05; **p<0 . 01; ***p<0 . 001; ****p<0 . 0001 ) . All data points are plotted even where no bars are visible . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 00710 . 7554/eLife . 19662 . 008Figure 4 . Extracellular actin-driven TotM expression depends on canonical Domeless signalling in the fat body . ( a ) Relative TotM expression in flies overexpressing domeless ΔCyt2 . 3 dominant-negative isoform or no transgene ( w1118 control ) under the control of a ubiquitous driver , Act-Gal4 , or a fat body-inducible driver c564-Gal4; Tubulin-Gal80ts ( c564-Gal80ts ) , 24 hr after injection with actin . ( b ) Relative TotM expression in flies overexpressing dPIAS under the control of the fat body inducible driver c564-Gal4; Tubulin-Gal80ts ( c564-Gal80ts ) , 24 hr after injection with either buffer or actin . Data are pooled from two independent experiments with 5–10 flies/sample and duplicate samples . ( c ) Relative TotM expression in flies overexpressing dPIAS under the control of the fat body constitutive driver r4-Gal4 , 24 hr after injection with either buffer or actin . Data are representative of three independent experiments with 5–10 flies/sample with duplicate samples . ( d ) Relative TotM expression in flies overexpressing UAS-STAT92E IR under the control of the fat body inducible driver c564-Gal4; Tubulin-Gal80ts ( c564-Gal80ts ) , 24 hr after injection with actin . Data are pooled from two independent experiments with triplicate samples and 5–10 flies/sample . TotM relative levels were calculated using the housekeeping gene Rp49 as a reference gene . Bars represent mean ± SEM . Statistical analysis was performed using two-way ANOVA with Sidak’s multiple comparison test as post-test for pairwise comparisons ( a , b , c ) or unpaired t-test ( d ) . Significant differences with Sidak’s multiple comparison test or unpaired t-test are shown ( ns , not significant; *p<0 . 05; **p<0 . 01; ***p<0 . 001; ****p<0 . 0001 ) . All data points are plotted even where no bars are visible . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 00810 . 7554/eLife . 19662 . 009Figure 4—figure supplement 1 . STAT-dependence of selected actin-induced or -repressed genes . ( a ) Quantitation of select transcripts upregulated upon actin injection , as indicated by RNAseq analysis , in control flies ( c564-Gal80ts > w1118 ) or flies lacking fat body STAT expression ( c564-Gal80ts > UAS-STAT92E IR ) , 24 hr after injection with actin . ( b ) As in ( a ) , but for genes downregulated upon actin injection . TotM relative levels were calculated using the housekeeping gene Rp49 as a reference gene . Bars represent mean ± SEM . Statistical analysis was performed using two-way ANOVA with Sidak’s multiple comparison test as post-test for pairwise comparisons . Significant differences with Sidak’s multiple comparison test are shown ( ns , not significant; *p<0 . 05; **p<0 . 01; ***p<0 . 001; ****p<0 . 0001 ) . All data points are plotted even where no bars are visible . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 009 Although we focused on TotM , we also assessed whether some of the other most highly upregulated genes in actin-injected flies were similarly STAT-dependent . However , ablation of STAT92E in the fat body did not alter the inducibility of those we tested ( Figure 4—figure supplement 1a ) . In contrast , actin-induced downregulation of some tested transcripts was prevented by reduction of STAT92E in the fat body ( Figure 4—figure supplement 1b ) . These data , together with the GSEA analysis , underscore the notion that STAT activation is a major outcome of actin injection but suggest that extracellular actin likely triggers additional signalling pathways , which may or not involve the fat body and impact gene expression and/or transcript stability . Alternatively , the residual STAT92E protein in c564-Gal80ts>UAS-STAT92E IR flies might be sufficient for inducing expression of certain genes that require a lower threshold of STAT activity . Upd3 produced by haemocytes is essential to induce STAT responses in the fat body or in the intestine of flies subjected to septic injury or a high fat diet ( Agaisse et al . , 2003; Chakrabarti et al . , 2016; Woodcock et al . , 2015 ) . To determine if haemocytes were similarly required for the fat body STAT response to actin , we genetically ablated them in adult flies using two haemocyte-specific ( croquemort or Hemolectin ) temperature-sensitive Gal80ts-Gal4 drivers crossed to a UAS-rpr strain encoding Reaper , a protein that induces apoptosis . Ablation of haemocytes in flies containing UAS-rpr , but not in control flies , was confirmed by confocal microscopy taking advantage of a fluorescent reporter protein to mark the cells ( Figure 5a , b ) . Despite the near-complete elimination of haemocytes , TotM induction in response to actin in UAS-rpr flies was indistinguishable from that in controls ( Figure 5c ) , indicating that haemocytes are redundant . We further established that haemocyte-derived Upd3 is dispensable by using a upd3 RNAi line crossed to another line carrying a Hemolectin-Gal4 driver ( Figure 5d ) . We therefore tested the possibility that Upd3 might be essential but made by fat body cells themselves rather than haemocytes . Consistent with that notion , loss of the TotM response to actin injection was seen when the upd3 RNAi line was crossed to a line bearing a r4-Gal4 fat body driver ( Figure 5d ) . Similar results were obtained using a different fat body driver ( c564-Gal4 ) , this time under the control of Gal80ts ( Figure 5e ) . In contrast to Upd3 , knockdown of Upd1 and Upd2 in fat body cells had no effect on the response to actin ( Figure 5e ) , confirming Upd3 as the key cytokine . We conclude that extracellular actin leads to Upd3 production by fat body cells , which acts in an autocrine or paracrine fashion via Domeless to induce STAT activation and induction of STAT response genes . 10 . 7554/eLife . 19662 . 010Figure 5 . Extracellular actin-driven TotM expression requires fat body-derived Upd3 but not haemocytes . ( a ) Intravital confocal microscopy of the dorsum of Tub-Gal80ts , crq-Gal4 , UAS-mRFP or Hml△-Gal4 , UAS 2xeGFP; Tub-Gal80ts lines crossed to UAS-rpr or control ( w1118 ) flies after shifting to 29°C . ( b ) Quantification of the images shown in ( a ) . Haemocyte numbers in the lower dorsal thorax counted from two images ( Hml△-Gal4 ) or four images ( crq-Gal4 ) . Differences in overall haemocyte numbers between the two driver lines may be due to differing specificities of the crq and Hml markers . ( c ) Relative TotM expression in haemocyte-deficient ( Hml△-Gal4 > UAS-rpr or crq-Gal4 > UAS-rpr ) vs control flies ( Hml△Gal4 > w1118 or crq-Gal4 > w1118 ) , 24 hr after injection with actin . In order to increase statistical power , data were pooled from two independent experiments with 5–10 flies/sample and triplicate samples . ( d ) TotM expression levels in the constitutive fat body driver ( r4-Gal4 ) or constitutive haemocyte driver lines ( Hml△-Gal4 ) crossed to either control ( w1118 ) or UAS-upd3 IR lines , 24 hr after injection with actin . Data are representative of two independent experiments with 5–10 flies/sample and triplicate samples . ( e ) TotM expression levels in fat body driver line c564-Gal4; Tubulin-Gal80ts ( c564-Gal80ts ) crossed to control ( w1118 ) , UAS-upd1 IR , UAS-upd2 IR or UAS-upd3 IR lines shifted to the restrictive temperature , 24 hr after injection with actin . Data are representative of three independent experiments with 5–10 flies/sample and triplicate samples . TotM relative levels were calculated using the housekeeping gene Rp49 as a reference gene . Bars represent mean ± SEM . Statistical analysis was performed using one-way ANOVA with Sidak’s multiple comparison test as post-test for pairwise comparisons . Results of Sidak’s multiple comparison test are shown ( ns , not significant; *p<0 . 05; **p<0 . 01; ***p<0 . 001; ****p<0 . 0001 ) . All data points are plotted even where no bars are visible . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 010 There is no DNGR-1 orthologue in Drosophila . Furthermore , the ability of G-actin to trigger TotM suggested that a functionally-equivalent receptor is not involved . However , it was conceivable that signalling pathways downstream of DNGR-1 activation by F-actin might also be employed in the fruit fly response to extracellular G-actin . We therefore tested the requirement for Shark , a non-receptor tandem SH2 kinase and Drosophila Syk orthologue ( Fernandez et al . , 2000 ) . Indeed , conditional silencing of Shark in adult flies using a ubiquitous driver led to a significant reduction in TotM expression upon actin injection ( Figure 6a ) . Next , we asked whether the fat body itself might be the primary site of Shark signalling . Consistent with that notion , knockdown of Shark within the fat body using a c564-Gal80ts driver led to a large reduction in the levels of actin-induced TotM in total flies that had been shifted to the restrictive temperature ( Figure 6b ) . This result was confirmed using a different driver ( r4-Gal4 ) that led to constitutively reduced Shark levels in the fat body ( Figure 6c ) . Shark activation occurs downstream of Src family kinase activity ( Ziegenfuss et al . , 2008; Evans et al . , 2015 ) . Silencing of the Src family kinase Src42A using a ubiquitous temperature-sensitive driver also led to a striking reduction in actin-induced TotM levels ( Figure 6d ) . As for Shark , Src42A was specifically required in the fat body because silencing with fat body-specific drivers also led to complete loss of actin-induced TotM ( Figure 6d ) . In keeping with the knockdown data , overexpression of a dominant negative allele of Src42A ( Src42ADN ) using an ubiquitous or fat body-specific driver similarly led to a marked reduction in actin-driven TotM levels ( Figure 6e ) . Finally , the overexpression of a constitutively active form of Src42A within the fat body was sufficient to induce a TotM response that was comparable in magnitude to that induced by actin injection ( Figure 6f ) . 10 . 7554/eLife . 19662 . 011Figure 6 . Extracellular actin-induced TotM expression requires Shark and Src42A in the fat body . ( a ) TotM expression levels in flies that lack Shark ubiquitously ( Tub-Gal80ts; Tub-Gal4 > UAS-Shark IR R2/Fr ) or in control flies lacking a driver ( w1118 > UAS-Shark IR R2/Fr ) , 24 hr after injection with either buffer or actin . Data are representative of three independent experiments with 5–10 flies/sample with duplicate samples . ( b ) Flies lacking Shark selectively in the fat body ( c564-Gal80ts > UAS-Shark IR R2/Fr ) or control flies lacking a driver ( w1118 > UAS-Shark IR R2/Fr ) were injected with either buffer or actin . Relative TotM levels 24 hr post injection are depicted . Data are representative of two independent experiments with 5–10 flies/sample and triplicate samples . ( c ) The relative TotM expression in a constitutive fat body driver line ( r4-Gal4 ) crossed to control ( w1118 ) or UAS-Shark IR ( R2/R3 ) , 24 hr after injection with actin . Data are representative of three independent experiments with 5–10 flies/sample and duplicate samples . ( d ) Relative TotM levels in flies lacking Src42A either ubiquitously ( Tub-Gal80ts > UAS-Src42A IR ) or selectively in the fat body ( c564-Gal80ts > UAS-Src42A IR ) compared to control flies lacking a driver ( w1118 > UAS-Src42A IR ) , 24 hr after injection with either buffer or actin . Data are representative of three independent experiments with 5–10 flies/sample with duplicate samples . ( e ) Relative TotM levels in flies expressing a dominant negative version of Src42A ubiquitously ( Tub-Gal80ts > UAS-Src42ADN ) , within the fat body ( c564-Gal80ts > UAS-Src42ADN ) or in the absence of a driver ( w1118 > UAS-Src42ADN ) , 24 hr after injection with either buffer or actin . No driver control refers to Tub-Gal80ts; UAS-Src42ADN/TM6C . Sb1 . No UAS control refers to c564-Gal4; Tub-Gal80ts/TM6C . Sb1 . Data are representative of two independent experiments with 5–10 flies/sample with triplicate samples . ( f ) Relative TotM levels between untreated ( - ) fat body driver line crossed to constitutively active Src42A ( c564-Gal80ts > UAS-Src42ACA ) ; untreated control flies ( c564-Gal80ts > w1118 ) and actin-injected control flies ( c564-Gal80ts > w1118 ) . Data are representative of three independent experiments with 5–10 flies/sample with triplicate samples . TotM relative levels were calculated using the housekeeping gene Rp49 as a reference gene . Bars represent mean ± SEM . Statistical analysis was performed using one-way ( c , f ) or two-way ( a , b , d , e ) ANOVA with Sidak’s multiple comparison test as post-test for pairwise comparisons . Results of Sidak’s multiple comparison test are shown ( ns , not significant; *p<0 . 05; **p<0 . 01; ***p<0 . 001; ****p<0 . 0001 ) . All data points are plotted even where no bars are visible . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 01110 . 7554/eLife . 19662 . 012Figure 6—figure supplement 1 . Extracellular actin-induced TotM expression is independent of Draper . ( a ) Control ( w1118 ) fruit flies or Draper null mutants ( drprΔ5 ) were injected with buffer or actin . The relative expression of TotM was measured 24 hr post injection . The data are representative of two independent experiments with 5–10 flies/sample and duplicate samples . ( b , c ) Draper was ectopically expressed in a deficient background , either by use of a heat shock driver ( w; UAS-drpr/Hs-Gal4; drprΔ5 ) or two different fat body drivers ( w; UAS-drpr/c564-Gal4; drprΔ5or w; UAS-drpr/FB-Gal4; drprΔ5 ) . Relative TotM ( b ) or drpr ( c ) expression levels were measured 24 hr post actin injection and compared to a control line that lacked a driver ( w; UAS-drpr/+; drprΔ5 ) . The data are representative of three independent experiments with 5–10 flies/sample and duplicate samples . ( d ) The relative expression of TotM in flies in which draper was knocked down in the fat body ( Tub-Gal80ts; c564-Gal4 > UAS-drpr IR ) , measured 24 hr post actin injection and compared to control flies ( Tub-Gal80ts; c564-Gal4 > w1118 ) . Data are representative of two independent experiments with 5–10 flies/sample and triplicate samples . TotM relative levels were calculated using the housekeeping gene Rp49 as a reference gene . Bars represent mean ± SEM . Statistical analysis was performed using one-way ( b , c , d ) or two-way ( a ) ANOVA with Sidak’s multiple comparison test as post-test for pairwise comparisons . Results of Sidak’s multiple comparison test are shown ( ns , not significant; *p<0 . 05; **p<0 . 01; ***p<0 . 001; ****p<0 . 0001 ) . All data points are plotted even where no bars are visible . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 012 Uptake of axonal debris by glial cells and responses to wound healing in Drosophila both require Src42A phosphorylation of the ITAM-bearing receptor Draper ( drpr ) , which serves as a platform for Shark recruitment ( Ziegenfuss et al . , 2008; Evans et al . , 2015 ) . We found that drpr null mutants displayed a reduced TotM response to actin injection ( Figure 6—figure supplement 1a ) . However , this reduction was not specific to Draper loss as it could not be rescued by complementation ( Figure 6—figure supplement 1b , c ) . Consistent with that finding , conditional RNAi-mediated knockdown of drpr in the fat body had no effect ( Figure 6—figure supplement 1d ) . We conclude that expression of Src42A and Shark in the fat body , but not of Draper , are essential for the response to extracellular actin . Src family kinases are redox sensitive ( Giannoni et al . , 2005 ) and can be activated by wound-derived H2O2 in both zebrafish and Drosophila ( Yoo et al . , 2011; Niethammer et al . , 2009; Razzell et al . , 2013; Evans et al . , 2015 ) . Consistent with a requirement for superoxide in the response to extracellular actin , conditional expression of cytoplasmic superoxide dismutase ( Sod ) ( Missirlis et al . , 2003 ) ubiquitously or in the fat body diminished TotM induction by injected actin ( Figure 7a ) . H2O2 can be generated by either of two conserved NADPH oxidases , dual oxidase ( Duox ) and NADPH oxidase ( Nox ) , both of which are present as single family members in the Drosophila melanogaster genome ( Bae et al . , 2010 ) . Conditional knockdown of Duox in the fat body of adult flies had no effect on the TotM response to injected actin ( Figure 7b ) . In contrast , RNAi-mediated knockdown of Nox completely abrogated TotM induction , especially when using the c564 fat body-restricted driver ( Figure 7c , d ) . Together , these data suggest that extracellular actin leads to Nox activity in the fat body , which causes oxidation-dependent activation of Src42A and phosphorylation and activation of downstream targets , including Shark . 10 . 7554/eLife . 19662 . 013Figure 7 . Extracellular actin-induced TotM expression is dependent on the NADPH oxidase Nox . ( a ) Flies overexpressing superoxide dismutase ( Sod ) either ubiquitously ( Tub-Gal80ts; Tub-Gal4 > UAS-Sod ) or in the fat body ( Tub-Gal80ts; c564-Gal4 > UAS-Sod ) or control flies without transgene ( Tub-Gal80ts; c564-Gal4 > w1118 or Tub-Gal80ts; Tub-Gal4 > w1118 ) were injected with actin . TotM expression 24 hr post injection is shown . In order to increase statistical power , data were pooled from two independent experiments with 5–10 flies/sample and quadruplicate samples . ( b ) TotM expression levels 24 hr post actin injection in flies in which Duox was knocked down in the fat body ( Tub-Gal80ts; c564-Gal4 > UAS-Duox IR ) . Data are representative of two independent experiments with 5–10 flies/sample and triplicate samples . ( c–d ) TotM expression levels in flies in which Nox was knocked down either ubiquitously ( c ) ( Tub-Gal80ts; Tub-Gal4 > UAS-Nox IR ) or in the fat-body ( d ) ( Tub-Gal80ts; c564-Gal4 > UAS-Nox IR ) compared to control flies lacking RNAi ( Tub-Gal80ts; Tub-Gal4 > w1118 or Tub-Gal80ts; c564-Gal4 > w1118 ) , 24 hr after injection with actin . Data are representative of two independent experiments with 5–10 flies/sample and triplicate samples . TotM relative levels were calculated using the housekeeping gene Rp49 as a reference gene . Bars represent mean ± SEM . Statistical analysis was performed using one-way ANOVA with Sidak’s multiple comparison test as post-test for pairwise comparisons . Results of Sidak’s multiple comparison test are shown ( ns , not significant; *p<0 . 05; **p<0 . 01; ***p<0 . 001; ****p<0 . 0001 ) . All data points are plotted even where no bars are visible . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 013 Previous studies have shown that septic injury of adult flies leads to JAK/STAT activation and TotM induction in the fat body ( Brun et al . , 2006; Agaisse et al . , 2003 ) . Using a model of septic injury with either Escherichia coli or Micrococcus luteus , we found rapid accumulation of actin in the haemolymph of infected flies but not mock ( uninfected ) controls ( Figure 8a ) . As previously described , septic injury was accompanied by the induction of TotM ( Figure 8b ) . Strikingly , knockdown of Nox in the fat body led to roughly a 40% reduction in TotM while knockdown of Src42A completely abrogated the response ( Figure 8b ) . Importantly , expression of the Toll- or and Imd-regulated AMPs , Dpt and Drs , was unaltered by Nox or Src42A knockdown in the fat body ( Figure 8c , d ) . These data are consistent with the possibility that actin released into the haemolymph after septic injury can trigger JAK/STAT activation in the fat body via the Nox/Src42A-dependent pathway described here . 10 . 7554/eLife . 19662 . 014Figure 8 . Septic injury causes actin accumulation in hemolymph and TotM induction dependent on fat body expression of Nox and Src42A . ( a ) Immunoblotting for actin in the haemolymph ( Top ) and Ponceau stain ( bottom ) of mock ( uninfected ) , Escherichia coli or Micrococcus luteus infected flies at 3 or 6 hr post infection . Data are representative of two independent experiments with 10 flies per sample . ( b–d ) Expression levels of TotM ( b ) , Dpt ( c ) or Drs ( d ) in flies , in which Nox or Src42A was knocked down in the fat-body ( Tub-Gal80ts; c564-Gal4 > UAS-Nox IR or Tub-Gal80ts; c564-Gal4 >UAS-Src42A IR ) compared to control flies ( Tub-Gal80ts; c564-Gal4 > w1118 ) , 24 hr after septic injury with a mixture of Escherichia coli and Micrococcus luteus . In order to increase statistical power , data from two independent experiments with 5–10 flies/sample and duplicate samples were pooled . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 01410 . 7554/eLife . 19662 . 015Figure 8—figure supplement 1 . Model for extracellular actin-induced JAK/STAT pathway activation . Cell death induces release of actin that is detected by an unknown sensor , which then promotes ROS production by Nox in the fat body . Nox-derived ROS activate Src42A and phosphorylate either the sensor or another receptor , allowing Shark-dependent signalling . This leads to the production of Upd 3 , which engages Domeless and activates STAT , leading to induction of downstream genes such as TotM . Upd three is depicted as acting in a paracrine manner for ease of visualisation but is expected to also act in an autocrine fashion . Note that this model is speculative and will require further validation , for example to formally show that Src42A is upstream of Dome . DOI: http://dx . doi . org/10 . 7554/eLife . 19662 . 015
Inflammation is a response to microbial invasion or tissue damage designed to eliminate the offending stimulus , clear debris and stimulate tissue repair . While we have learned much about the pathways that trigger inflammation in response to pathogen invasion ( Medzhitov , 2010; Takeuchi and Akira , 2010 ) , we still understand relatively little about induction of sterile inflammation following tissue injury ( Rock et al . , 2010 ) . Importantly , dysregulated and/or chronic inflammation , often of sterile origin , is increasingly recognised as a contributing factor to a vast range of human diseases , from cancer to neurodegeneration ( Okin and Medzhitov , 2012; Rock and Kono , 2008; Heneka et al . , 2015; Zelenay and Reis e Sousa , 2013; Hanahan and Weinberg , 2011 ) . Furthermore , because injury and infection often overlap , our understanding of immunity necessitates a consideration of the interplay between the processes that detect pathogen invasion and those that sense tissue damage . The study of invertebrate responses to DAMPs might therefore lead to a new understanding of sterile inflammation and the identification of conserved elicitors , detectors and signaling pathways that are utilised across evolution to detect loss of cell integrity . We and others have previously reported that actin , one of the most abundant and conserved proteins in eukaryotic cells , acts as a DAMP in mouse and humans , binding to DNGR-1 , a Src and Syk-coupled dead cell receptor expressed on DCs ( Ahrens et al . , 2012; Sancho et al . , 2009; Zhang et al . , 2012; Hanč et al . , 2015 ) . Here , we provide evidence that actin is also a DAMP in Drosophila melanogaster , triggering a response that , like in vertebrates , requires Syk and Src family kinases . We show that the presence of extracellular actin in the haemolymph of Drosophila elicits a reaction in the fat body via Shark and Src42A , whose activation depends on reactive oxygen species ( ROS ) generated by the NADPH oxidase Nox . Consistent with our data , ROS generation by NADPH oxidases is a highly conserved response to wounding ( Nam et al . , 2012; Takeishi et al . , 2013 ) and has been shown to directly activate Lyn/Src42A in zebrafish and Drosophila through oxidation of a single redox-sensitive cysteine residue ( Yoo et al . , 2011; Niethammer et al . , 2009; Evans et al . , 2015; Razzell et al . , 2013 ) . In contrast to DNGR-1 dependent recognition , the fly response to extracellular actin is elicited equally by G- and F-actin , does not require phagocytes but the fat body and its function is not to prime adaptive immunity , which is absent in invertebrates . Rather , it is coupled to production of Upd3 cytokine , which acts in an autocrine and paracrine manner to induce Domeless signalling via STAT and to cause the induction of STAT-responsive genes , the products of which are released into the haemolymph . This systemic inflammatory-like response involving cytokine amplification and the fat body is reminiscent of the acute phase response in mammals , which can be triggered by infection or trauma and leads to the production of cytokines such as IL-6 that act on the liver ( mammalian equivalent of the fat body ) to cause production of acute phase proteins ( Medzhitov , 2010; Kopf et al . , 1994 ) . These are secreted into the plasma to regulate multiple processes such as host defence , coagulation , vascular permeability and metabolism ( Medzhitov , 2010 ) . Similarly , the Drosophila fat body response to actin results in secretion into the haemolymph of proteins that may regulate multiple aspects of fly physiology that coordinately impact resistance or tolerance to insult . However , it is important to note that while some components of the extracellular actin-sensing circuitry are conserved between flies and mammals ( Shark , Src42A and ROS ) , others are not ( DNGR1 , cross-presentation , dendritic cells ) . These differences suggest that DAMPs can be more conserved than their receptors or the responses they evoke . This is akin to pathogen-associated molecular patterns ( PAMPs ) such as , for example , lipopolysaccharide ( LPS ) , a hallmark of Gram-negative bacteria . The sensing of LPS is conserved in plants , protists and animals , but the relevant receptors and subsequent responses diverge depending on the host ( Neyen et al . , 2014 ) . Similarly , peptidoglycans and β-glucans are used in both flies and mammals to signify bacterial or fungal presence , yet are detected by different receptors that , nevertheless , can couple to conserved signalling pathways . The JAK/STAT pathway in Drosophila can be induced by mechanical pressure , heat shock , dehydration , cytopathic infection , septic wounds and other traumas ( Ekengren et al . , 2001; Agaisse et al . , 2003; Pastor-Pareja et al . , 2008; Dostert et al . , 2005 ) . How such seemingly disparate stimuli trigger a single pathway is puzzling . However , a common denominator in all these settings is cell death and it has been speculated that STAT activation might therefore occur in response to DAMP release ( Shaukat et al . , 2015; Buchon et al . , 2014 ) . Our data support that notion and suggest that actin is a potent DAMP for triggering the JAK/STAT pathway . Notably , pathogen infection in Anopheles gambiae and Drosophila melanogaster has been shown to lead to the release of actin into the haemolymph , where it can act as an antibacterial or antiparasitic agent ( Vierstraete et al . , 2004; Sandiford et al . , 2015 ) . Therefore , actin release may serve as a two-pronged defense mechanism , both directly as an antimicrobial and indirectly by activating a systemic JAK/STAT response . The role of the systemic JAK/STAT response is unclear at present . Despite being commonly used as a marker of STAT activation , the function of Tot and Tep proteins in Drosophila is unknown . Nevertheless , genetic loss-of-function studies have implicated JAK/STAT signaling in resistance and/or tolerance to viral , bacterial and parasitoid infections ( Yang et al . , 2015; Agaisse et al . , 2003; Agaisse and Perrimon , 2004; Brun et al . , 2006; Dostert et al . , 2005; Lamiable et al . , 2016; Chakrabarti et al . , 2016; Kemp et al . , 2013; Merkling et al . , 2015 ) . Furthermore , the JAK/STAT pathway has a well-established role in maintenance of fly intestinal homeostasis , both at steady state and following infection or injury ( Biteau et al . , 2011; Kohlmaier et al . , 2015; Jiang et al . , 2009; Micchelli and Perrimon , 2006; Zhou et al . , 2013; Osman et al . , 2012; Jiang et al . , 2011; Beebe et al . , 2010; Lin et al . , 2010; Zhai et al . , 2015 ) . Given these precedents , we attempted to investigate the role of the inducible actin-triggered JAK/STAT circuit by injecting actin into flies prior to challenge with viruses ( Flock house virus , Drosophila C virus , Sindbis virus and Cricket paralysis virus ) or bacteria ( Erwinia carotovora , Escherichia coli , Micrococcus luteus and Listeria monocytogenes ) but failed to find an effect on either resistance or tolerance to infection ( data not shown ) . Similarly , in models of stress or injury ( starvation , heat shock , irradiation , paraquat feeding and a recently-described model of concussion ( Katzenberger et al . , 2013 , 2015 ) ) , we found no evidence of protection or susceptibility afforded by actin pre-injection ( data not shown ) . Finally , we have also not found an effect of actin injection on fat body metabolism ( data not shown ) . The failure to find a system in which prior upregulation of STAT target genes by exogenous actin leads to a difference in outcome is a current experimental limitation . However , it might reflect the fact that STAT activation is already induced to sufficient levels in those models in response to actin released from dying cells . Consistent with this notion , we observed that septic injury led to a rapid increase in actin levels within the haemolymph . In such a situation , additional induction of the STAT pathway by actin pre-injection may not confer additional protection or tolerance . Reinforcing this notion is a recent study showing that loss of basal Diedel levels leads to reduced tolerance to Sindbis virus , yet the upregulation of Diedel levels that takes place during infection is itself dispensable ( Lamiable et al . , 2016 ) . Unfortunately , loss-of-function experiments to assess the effect of released actin on different challenges are not feasible because actin is essential for viability . Surrogate loss-of-function experiments , such as examining the role of Nox and Src42A or Shark in the fat body in the context of infection or injury , have not been reported and their interpretation is complicated by the pleiotropic effects of those proteins . Nevertheless , our finding that actin is released into the haemolymph upon septic injury and that this induces JAK/STAT activation dependent on fat body expression of Src42A and Nox may suggest that previous reports of septic injury-induced STAT activation can be partially ascribed to extracellular actin . The identity of the putative receptor that recognises extracellular actin in Drosophila remains unknown . The requirement for Upd3 rules out the possibility that actin serves as a direct ligand for Domeless , a conclusion further supported by the fact that actin does not induce TotM upregulation in various Drosophila cell lines that respond to Upd cytokines in vitro ( data not shown ) . Therefore , the simplest interpretation of our data is that Upd3 is synthesised by fat body cells that detect extracellular actin via a sensor ( s ) that couple ( s ) to a Nox-Src42A-Shark cascade ( Figure 8—figure supplement 1 ) . By analogy with other receptors that engage a Syk-dependent pathway , that sensor might be an ITAM- or hemITAM-bearing receptor or one that associates in trans with an ITAM-containing signalling chain ( Brubaker et al . , 2015; Robinson et al . , 2009 ) . Interestingly , in Drosophila responses to wounding and in the clearance of axonal debris and neuronal cell corpses , one such receptor is Draper , a member of the Nimrod family and orthologue of C . elegans Ced1 . Draper contains an ITAM that is phosphorylated by Src42A ( MacDonald et al . , 2006; Ziegenfuss et al . , 2008; Razzell et al . , 2013; Evans et al . , 2015 ) . However , we have found Draper to be dispensable for TotM induction in response to actin injection . Similarly , we have not found a role for Nimrod C1 , C4 and the scavenger receptor CD36 ( data not shown ) . Whether these data indicate the activity of an unknown receptor , multiple redundant receptors or an indirect sensing mechanism , akin to the activation of the vertebrate NLRP3 receptor ( Martinon et al . , 2009 ) , will need to be investigated . In sum , our data suggest that extracellular actin released by dead cells induces a response in Drosophila that requires signalling in the fat body via the non-receptor tyrosine kinase , Shark , and the Src family kinase , Src42A . This pathway leads to production of Upd cytokines that act in an autocrine and paracrine manner to induce Domeless signalling via STAT and cause induction of STAT-responsive genes ( Figure 8—figure supplement 1 ) . Thus , the presence of actin in the extracellular space triggers a response previously associated with wounding and dead cell clearance , indicating that actin exposure acts as an ancient sign of tissue damage and that actin constitutes an evolutionarily-conserved DAMP . The notion that actin exposure can act as a universal sign of cell damage might apply more generally to other cytoskeletal proteins .
Fly stocks were raised on standard cornmeal-agar medium at 25°C . Adult female flies 3–6 days of age were used in all experiments . For heat-shock induction of transgene expression , flies were incubated for 20 min at 37°C , followed by 30 min at 18°C and another 20 min at 37°C . After the treatment , flies were allowed to recover for 6 hr at 25°C before injection . For transgene induction using the Gal80ts system , flies were shifted to 29°C for three days prior to injection and were kept at that temperature until euthanasia . The following stocks were used: Fly stock Description w1118Control strain;;UAS-dome △Cyt 2 . 3 Overexpression of a dominant negative form of the receptor domeless . ;UAS-dPIAS/cyO;Overexpression of the JAK/STAT pathway negative regulator dPIAS . ;if/cyO;Hs-Gal4Heat shock-inducible , ubiquitous driver line . w1118;;Hml△-Gal4 , UAS-2xeGFP , Tub-Gal80ts/TM6C , Sb1;Temperature-sensitive , haemocyte-specific driver line . Haemocytes are labelled with GFP . w1118;;crq-Gal4 , UAS-2xmRFP , Tub-Gal80ts/TM6C . Sb1Temperature-sensitive , haemocyte-specific driver line . Haemocytes are labelled with RFP . w;;UAS-rpr/TM3 . Sb1Overexpression line for pro-apoptotic protein reaper . ;;r4-Gal4/TM6C . Sb1Constitutive fat body-specific driver line . ;;Stat92-dGFPDestabilised GFP expression under the control of a STAT response element . Bloomingtonstock centre number: 21699LPP-Gal4-UAS-Myr-td-TomFat body line , made from LPP-Gal4 ( FRT-LPPGal4-G-FRT/Tm3 , Zit ( Sb ) from Pierre Leopold;10XUAS-IVS-myr::tdTomato from Bloomington ( BL32221 ) . w1118;Tub-Gal80ts;Tub-Gal4/TM6C . Sb1Temperature-sensitive , ubiquitous driver line . ;UAS-Shark IR; ( Shark R2 RNAi ) National Institute of Genetics stock number: 18247 R-2 ;UAS-Shark IR; ( Shark R3 RNAi ) National Institute of Genetics stock number: 18247 R-3 ;UAS-Shark IR; ( Shark Fr RNAi ) Interfering RNA for knockdown of shark . Kindly donated by Marc Freeman . w;c564-Gal4;Tub-Gal80tsTemperature-sensitive fat body-specific driver line . ;UAS-Src42A IR V10708RNAiInterfering RNA for knockdown of Src42A . ;;UAS-Src42ADNOverexpression of a dominant negative form of Src42A . UAS-Src42ACABloomington stock centre number: 6410 . The DNA sequence encoding the constitutivelyactive form of Src42A ( Src42ACA ) has an amino acid substitution of Tyr511 to Phe . Tyr511corresponds to the inhibitory C-terminal Tyr of Src . UAS-Nox IR ( 753 ) VDRC ID: 100753UAS-Nox IR ( 559 ) VDRC ID: 102559UAS-Duox IR ( 692 ) Bloomington stock centre number: 32903UAS-Duox IR ( 934 ) Bloomington stock centre number: 33975;;drprΔ5Null mutant for the receptor Draper . Kindly donated by Marc Freeman . UAS-drpr IR ( 4833 ) VDRC ID: 4833UAS-drpr IR ( 4830 ) VDRC ID: 4830UAS-drpr IR ( 27084 ) VDRC ID: 27084UAS-drpr IR ( 27086 ) VDRC ID: 27086;FB-Gal4;Constitutive fat body-specific driver line . UAS-Sod Bloomington stock centre number: 24750 Unless stated otherwise , for actin injection , a 1 mg/ml purified rabbit muscle or human non-muscle G-actin ( Cytoskeleton Inc . , CO , USA ) solution was prepared in G-actin buffer ( 5 mM Tris HCl [pH 8 . 0]+0 . 2 mM CaCl2 ) as per manufacturer’s instructions and 36 . 8 nl was administered to flies by intrathoracic injection ( Nanoject II apparatus; Drummond Scientific , PA , USA ) . Injection of the same volume of G-actin buffer was used as a control . For experiments comparing G-actin and F-actin , human non-muscle G-actin was either stabilised with 100 μM latrunculin B or polymerised in F-actin buffer ( 10 mM Tris-HCl [pH 7 . 5]+50 mM KCl + 2 mM MgCl2 and 1 mM ATP ) in the presence of 5 μM phalloidin . For experiments with non-polymerisable G-actin , we used Drosophila 5C actin and created a Delta D-loop mutant that has residues 41–51 deleted ( HQGVMVGMGQK ) . This mutant was expressed and purified as described ( Zahm et al . , 2013 ) . For other test substances , in all cases a 36 . 8 nl volume of each sample was injected . They included 1 mg/ml purified human HSP70 ( Enzo Life Sciences , NY , USA ) , 1 mg/ml purified human HSP90 ( Abcam , UK ) , 1 mg/ml monosodium urate crystals ( Invivogen ) and 1 mM ATP ( Cytoskeleton Inc . ) , prepared as per manufacturer’s instructions . Genomic DNA was extracted from Drosophila . Briefly , flies were collected and mashed in extraction buffer ( 10 mM Tris-HCl pH 8 . 2 , 1 mM EDTA , 25 mM NaCl , 200 µg/ml Proteinase K ) . Subsequently , the sample was incubated at 37°C for 1 hr and the protease inactivated by heating to 95°C . DNA was purified using a column-based method ( Qiagen , Germany ) . Concentration of the injected DNA was 14 ng/µl in water . KCl , CsCl , NaCl , Sucrose ( Thermo Fisher Scientific , MA , USA ) and Dextrose ( Sigma , MO , USA ) were dissolved in water and used at 100 mM . Sunflower and olive oil were from Sainsbury’s and used neat . Trypsin inhibitor from bovine pancreas , heparin , chondroitin , glucagon , insulin ( Sigma ) and glutathione ( Merck-Millipore , Germany ) were prepared as 1 mg/ml solutions in PBS . Amino acids were used at a concentration of 34–500 mM in water and purchased from Sigma . For denaturation , actin was diluted in G-actin buffer supplemented with 100 mM DTT . An aliquot of that solution was denatured by heat treatment ( 10 min at 95°C ) . The heat-denatured and untreated actin aliquots were loaded into separate Dialysis Cassettes with a 3 . 5K molecular weight cut-off ( Slide-A-Lyzer; Thermo Fisher Scientific ) . Dialysis was performed overnight at 4°C against PBS . Following dialysis , protein concentration in the samples was measured by BCA ( Thermo Fisher Scientific ) and equalised before injection . For microbial infections , Escherichia coli and Micrococcus luteus were grown overnight in Lysogeny Broth at 37°C with shaking at 220 rpm . Candida albicans was grown overnight in yeast peptone dextrose media at 30°C with shaking at 220 rpm . Flies were pricked in their thorax with a needle dipped in concentrated microbial suspension ( optical density of 400 ) . Germ-free flies were generated by decontaminating fly embryos with bleach ( 2% sodium hypochlorite ) for 10 min . Embryos were subsequently washed in ethanol ( 70% ) for 5 min , followed by washing with sterile water . Finally the embryos were transferred using an autoclaved brush and reared on axenic food until use . Wolbachia-free flies were generated as previously described ( Chrostek et al . , 2013 ) . Unless otherwise indicated , each experimental point was obtained by injecting or infecting one or more pools of 5–10 individual flies . Following termination of the experiment , flies were euthanised and each pool was treated as a separate sample and stored at −20°C until further use . For RNA extraction , flies in each pool were mashed using a hand-held tissue homogenizer ( Kimble , TN , USA ) and a small pestle ( Sigma ) before clarification by Qiashredder columns ( Qiagen ) and RNA was extracted using a column-based method with DNAse treatment ( Qiagen ) . cDNA synthesis was performed using SuperScript II Reverse Transcriptase ( Thermo Fisher Scientific ) , and random hexamers ( Thermo Fischer Scientific ) . cDNA was then diluted five times in nuclease-free water and analysed for gene expression by qPCR using Express SYBR green universal master mix ( Thermo Fisher Scientific ) . Reactions were carried out using ABI 7500 Fast or QuantStudio 7 machines ( Thermo Fisher Scientific ) . Relative expression values were calculated from ΔCts using Rp49 house keeping gene as a reference gene . Note that such relative expression values are not a reflection of the actual relative transcript number as they are affected by PCR efficiency and by the fact that Rp49 is expressed ubiquitously in the fly whereas many of the targets measured here ( e . g . , TotM ) are only expressed in the fat body . The following primers were utilised: For each time point , three replicate pools of 10 flies each were injected with either buffer or actin . RNA was isolated from each pool before quality control checking using a Bioanalyzer and generation of independent libraries . Sequencing was performed on the Illumina HiSeq 2500 platform and generated ~53 million 100 bp paired end reads per sample . Sequenced reads were mapped to Flybase gene set ( version 6 . 01 ) from BDGP6 assembly [http://flybase . org/] , using RSEM ( version 1 . 2 . 11 ) ( Li and Dewey , 2011 ) . RSEM uses the bowtie2 alignment tool ( Langmead and Salzberg , 2012 ) . Gene counts were filtered to remove genes with 10 or fewer mapped reads per sample . TMM ( treated mean of M-values ) normalisation and differential expression analysis using the negative binomial model was carried out with the R-Bioconductor package 'EdgeR' ( Robinson et al . , 2010 ) ( www . bioconductor . org version 3 . 1 . 0 ) . Genes with logCPM > 1 and FDR < 0 . 05 were judged to be differentially expressed . Enrichment of fly pathways gene sets , downloaded from Fly Reactome ( http://fly . reactome . org/ ) , were assessed using GSEA ( Subramanian et al . , 2005 ) with logFC pre-ranked gene lists . Gene sets with an enrichment q value of less than 0 . 05 were judged to be statistically significant . Fastq data files are deposited in the NCBI Gene Expression Omnibus database ( GSE76150 ) . Genes induced by actin but not by buffer were identified and converted from Flybase IDs to Refseq IDs using ENSEMBL biomart . These genes were then interrogated using PScan ( Zambelli et al . , 2009 ) to identify overrepresented transcription factor binding motifs in the 500 bp region upstream of the start site with the TRANSFAC and JASPAR databases ( Mathelier et al . , 2014; Matys et al . , 2006 ) . Two haemocyte drivers ( HmlΔ-Gal4 , UAS-2xeGFP , Tub-Gal80ts/TM6C and crq-Gal4 , UAS-2xmRFP , Tub-Gal80ts/TM6C ) were crossed to UAS-rpr for temporally controlled induction of the pro-apoptotic gene reaper in haemocytes . w1118 flies were crossed to the same haemocyte driver lines as a control . Flies were grown at a permissive temperature of 18°C until adulthood and were then shifted to the non-permissive temperature of 29°C for three days to induce rpr expression and subsequent apoptosis of haemocytes . Flies were injected with actin or actin buffer and processed as described above . The lower dorsal thorax of at least two flies per cross was imaged using intravital confocal microscopy to assess haemocyte ablation . For this purpose , the dorsal side of the flies was affixed onto a coverslip using a drop of superglue , positioning the wings on the side . Flies were imaged within 15 min and flies that died during the procedure were excluded from the analysis . Images were acquired using an Invert LSM 710 laser scanning confocal microscope ( Zeiss , Germany ) equipped with a 40X Oil NA 1 . 25 objective and analysed with ImageJ software . The abdomens of 2–3 day old adult ( ;STAT92-dGFP/+ , LPP-Gal4 + 10XUAS-IVS-myr::tdTomato/+ ) females were dissected under a dissecting microscope on a silicone dish using forceps and microscissors ( Albert Heiss , Germany ) . The abdomen was first separated and the caudal end removed . The remainder was cut along one of the sides , placed in a drop of PBS and the gut was removed . Finally , the preparation was transferred onto a fresh drop of PBS on a glass bottom dish ( MatTek Corporation P35G-0–10-C ) with the exoskeleton side up and covered with a round coverglass ( VWR , PA , USA Cat . No 631–0150 ) . All imaging was carried out at room temperature . Z stacks of EGFP or RFP-expressing fat body cells in the abdomen with a 0 . 5 μm step size were taken using a 40×oil immersion objective lens on a Perkin Elmer UltraView spinning disc system . Images were captured using Volocity ( Perkin Elmer , Waltham , MA , USA ) and this set-up was also used to quantify STAT activation . The Z-stacks were cropped to the proximal half of fat body cells ( 4–6 slices with a 0 . 5 μm step size ) and were assembled into maximum projections with identical adjustments made to contrast across experimental groups . Regions of interest within the cytoplasm of fat body cells were selected and the mean intensity of the GFP channel was measured . Haemolymph was isolated by gently pricking flies in the thorax area using a 27G needle . 10 flies per group were then rapidly transferred into a pre-chilled 0 . 5 ml microfuge tube ( modified with a small hole at the nib created using a 25G needle ) . This was then placed into a 1 . 5 ml microfuge collection tube and spun for 5 min at 5000 rpm at 4°C . The resulting small drop of haemolymph was diluted in 10 μl RIPA buffer ( supplemented with protease inhibitors ( Roche , Switzerland ) ) . For Western blot , haemolymph samples were diluted into Laemmli buffer , resolved using reducing SDS-PAGE and transferred to nitrocellulose membranes ( Merck-Millipore ) . Equivalent protein levels across samples were confirmed by membrane staining with Ponceau Red dye . Actin levels were assessed by probing membranes with HRP-linked monoclonal rabbit anti β-actin ( clone 13E5 , Cell Signaling Technology , CA , USA ) . The signal was revealed with the SuperSignal West Pico Chemiluminescent substrate kit ( Thermo Fisher Scientific ) . Detection of F- and total actin by dot blot was carried out as described ( Ahrens et al . , 2012; Hanč et al . , 2015 ) . | All animals must be able to detect and repair injuries quickly . To do this , the body triggers a process called inflammation at the site of injury to remove dead and damaged cells , keep the area free from infection and trigger repair . However , if an area becomes excessively inflamed , or remains inflamed for a long period of time , it can contribute to diseases like cancer and Alzheimer’s disease . Inflammation starts when the body detects molecules that are released when cells die or are damaged to the extent that they become leaky . Actin , which is a protein that usually provides structural support to the cell , is one molecule that is sensed by the immune system in mammals when released from dying cells . However , it is not clear whether released actin can also trigger reactions in simpler animals like fruit flies . To address this question , Srinivasan , Gordon et al . injected fruit flies with actin . These animals developed a widespread reaction reminiscent of inflammation seen in mammals . Further experiments showed that actin switches on a signalling pathway called JAK/STAT , which is known to become active when flies experience other types of stress . The JAK/STAT proteins activate a signalling pathway that leads to changes in gene activity . Srinivasan , Gordon et al . also showed that part of a cascade of signals triggered by released actin in mammals is shared in fruit flies . These findings suggest that this important response to actin evolved a long time ago . A future challenge is to find out how the body detects and mops up released actin , which may help us to understand how actin can contribute to various inflammatory diseases . | [
"Abstract",
"Introduction",
"Results",
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"and",
"methods"
] | [
"immunology",
"and",
"inflammation"
] | 2016 | Actin is an evolutionarily-conserved damage-associated molecular pattern that signals tissue injury in Drosophila melanogaster |
Lasting changes in gene expression are critical for the formation of long-term memories ( LTMs ) , depending on the conserved CrebB transcriptional activator . While requirement of distinct neurons in defined circuits for different learning and memory phases have been studied in detail , only little is known regarding the gene regulatory changes that occur within these neurons . We here use the fruit fly as powerful model system to study the neural circuits of CrebB-dependent appetitive olfactory LTM . We edited the CrebB locus to create a GFP-tagged CrebB conditional knockout allele , allowing us to generate mutant , post-mitotic neurons with high spatial and temporal precision . Investigating CrebB-dependence within the mushroom body ( MB ) circuit we show that MB α/β and α’/β’ neurons as well as MBON α3 , but not in dopaminergic neurons require CrebB for LTM . Thus , transcriptional memory traces occur in different neurons within the same neural circuit .
Short-term memories ( STMs ) rely molecularly on rapidly acting biochemical processes altering the weight of defined synaptic connections . In contrast , long-lasting forms of memories require more permanent molecular changes including alterations of the transcriptional program . The cAMP response element binding protein ( CREB ) is an evolutionarily conserved basic leucine zipper transcription factor , which is involved in long-term memory ( LTM ) . Activated CREB binds to cAMP response element ( CRE ) sites of target gene regulatory regions promoting transcription . CREB is regulated by numerous cellular events , responding to a diverse set of physiological stimuli . It has been shown that the transcription factor CREB is implicated in many biological processes , including circadian rhythm , stress , metabolism , cell survival and drug adaptation ( Mayr and Montminy , 2001; Carlezon et al . , 2005 ) . However , CREB is best known for its role in memory formation . Studies in vertebrates and invertebrates showed that CREB-dependent gene transcription is a crucial component of long-term memory formation , but not of short-term memory ( Yin and Tully , 1996; Silva et al . , 1998; Kida and Serita , 2014 ) . The Drosophila genome contains two CREB-like genes: CrebA and CrebB ( Smolik et al . , 1992; Usui et al . , 1993 ) . CrebB , that produces multiple protein isoforms , has been shown to be essential for LTM in flies ( Yin et al . , 1994 ) . Fruit flies associate odors with different reinforcing stimuli . Sugar as reward paired with an odor , induces LTM that requires protein synthesis and lasts for several days . Flies display memory as a selective approach of the previously sugar-paired odor ( Krashes and Waddell , 2008; Colomb et al . , 2009 ) . The mushroom body ( MB ) is regarded as a main center of olfactory associative memory in the Drosophila brain ( Heisenberg et al . , 1985; de Belle and Heisenberg , 1994; Dubnau et al . , 2001 ) . MB intrinsic neurons forming the predominant lobe system are called Kenyon cells ( KCs ) . Olfactory information is transmitted by projection neurons ( PNs ) from the antennal lobe to the calyx of the MB , where they directly synapse onto KCs . KCs can be further subdivided into three major morphological and functional types , α/β , α′/β′ and γ neurons , forming the lobe system of the MB ( Crittenden et al . , 1998 ) . MB lobes are tiled by spatially separated input from Dopaminergic neurons ( DANs ) as well as dendritic arbors of MB output neurons ( MBONs ) . Dopaminergic innervation provides critical input to the MB circuit as conditioning signal . DANs of the protocerebral anterior medial ( PAM ) cluster respond to sugar and convey the reinforcing effect of sugar to defined MB lobe compartments , while other DANs are involved in other types of memories ( Burke et al . , 2012; Liu et al . , 2012 ) . Output of the KCs is transmitted to other brain regions by 34 MBONs that fall into 21 discrete anatomical classes ( Aso et al . , 2014 ) . While there is a wide consensus that CrebB is involved in LTM formation , it remains less clear in which neurons CrebB is required to induce LTM . Previous studies using overexpression of a CrebB repressor or RNAi to inhibit CrebB in the MB and MBONs resulted in conflicting interpretation of CrebB requirement ( Yin et al . , 1994; Yu et al . , 2006; Chen et al . , 2012; Hirano et al . , 2013 ) . To shed light onto this question we created a CrebB conditional knockout allele ( CrebBcKO ) allowing us to study CrebB function in defined cells . Using the CRISPR/Cas9 system , we replaced the endogenous CrebB gene with a GFP-tagged CrebB gene flanked by FRT sites . The generation of an in-frame CrebB::GFP fusion protein allowed us to precisely monitor CrebB expression and localization in the nervous system . Moreover , flippase ( FLP ) mediated FRT recombination allowed us to delete CrebB::GFP from the genome in genetically accessible cells hereby generating null mutant cells , within an otherwise wildtype animal . We used cell-type specific CrebB knockout in defined sets of neurons of the mushroom body circuit and assessed appetitive olfactory long-term memory . Our findings show that removing CrebB from the MB results in animals that are able to form STM , but no LTM , indeed highlighting the critical function of CrebB for LTM in the MB . Interestingly , removing CrebB in α/β and α’/β’ , but not in the γ lobe affected LTM further supporting the differential role of the three major MB lobe system in different phases of memory formation . We found that removing CrebB from the dopaminergic PAM neurons did not affect LTM , while genetic excision of the CrebB gene in MBON α3 severely reduced LTM formation . Thus , multiple neurons require CrebB within the same neural circuit to form an appetitive LTM trace .
To investigate the spatial requirement of CrebB for LTM formation within the mushroom body circuit we generated a CrebB conditional knockout allele ( CrebBcKO ) using the CRISPR/Cas9 system ( Bassett and Liu , 2014 ) . In short , two FRT sites and an N-terminal eGFP tag were introduced into the CrebB locus . The first CRISPR site was selected immediately upstream of the CrebB start codon and the second used CRISPR site was located before the last exon of CrebB thus replacing the coding region of the locus with a donor template sequence for homology-directed repair ( HDR ) containing two FRT sites flanking the coding sequence of eGFP and the CrebB genomic sequence ( Figure 1A , B ) . The successful generation of the CrebBcKO allele was confirmed by sequencing of the 3’ and 5’ introduced GFP and FRT sites . The FRT flanked CrebB::GFP locus allows visualization of CrebB protein localization and proof of flippase recombinase mediated removal of the fusion protein by the absence of GFP expression . Using different Gal4 driver lines and Gal80ts , a temperature sensitive inhibitor of Gal4 , spatially and temporally controlled UAS-FLP expression was performed ( McGuire et al . , 2003 ) . In FLP expressing cells , GFP-tagged CrebB is deleted from the genome , creating CrebB mutant cells ( Figure 1B ) . We first assessed the engineered FRT- GFP::CrebB -FRT locus for proper CrebB protein expression by generating a guinea-pig polyclonal anti-CrebB antibody raised against the CrebB full-length protein sequence of isoform F . CrebB::GFP is expressed in virtually all cells of the brains of CrebBcKO flies . Co-localization of anti-CrebB and anti-GFP antibody shows successful tagging of CrebB with GFP and further supports the pan-neuronal expression of CrebB ( Figure 1C and Figure 1—figure supplement 1 ) . UAS-FLP; nSyb-Gal4 was used to remove CrebB::GFP in all neurons . To prevent developmental defects , we temporally restricted FLP expression using a tubGal80ts transgene ( McGuire et al . , 2003 ) . Knockout of CrebB was induced after hatching by moving animals to 29°C for 6 days before antibody staining or conditioning experiments . Brains from flies with induced pan-neuronal CrebB deletion were dissected and antibody staining performed . Expression of CrebB::GFP in the brain was completely lost in neurons and only detectable in Repo-positive glia cells confirming the efficiency of the created CrebB conditional knockout ( Campbell et al . , 1994; Xiong et al . , 1994; Halter et al . , 1995 ) ( Figure 2A , B ) . To study the role of CrebB in memory formation , we used a classical olfactory conditioning paradigm , in which flies learned to approach an odor that was previously associated with sugar reward ( Krashes and Waddell , 2008; Colomb et al . , 2009 ) . We tested flies directly after conditioning to measure short-term memory ( STM ) , after 3 h to measure middle-term memory ( MTM ) or after 24 h to measure long-term memory ( LTM ) . We first tested flies with induced pan-neuronal CrebB knockout along with the corresponding parental lines for STM . Since CrebB is located on the X chromosome , we always calculated memory performance indices of male and female animals separately . Male offspring flies of CrebBcKO; tubGal80ts females crossed with +;UAS-FLP; nSyb-Gal4 males have only the CrebBcKO allele ( CrebBcKO/Y ) and therefore are CrebB null mutants after flippase induced excission . Female progeny of the cross have in addition to CrebBcKO a wild type allele of CrebB and served as control group together with male flies of the parental lines . The tested groups did not show significantly different memory scores measured immediately after training , confirming dispensability of CrebB for STM ( Figure 2C ) . We next assessed MTM and found that also for this memory phase CrebB is not required in neurons ( Figure 2D ) . To evaluate LTM , flies were tested 24 h after conditioning . Unflipped male CrebBcKO/Y flies showed intact LTM and did not perform significantly different from female CrebBcKO/+ , which further confirmed that conducted genome editing in the CrebB locus does not interfere with the function of CrebB in LTM formation . However , males with induced CrebB knockout in all neurons showed drastically reduced 24 h LTM performance ( Figure 2E ) . The memory performance of non-induced flies kept continuously at 18°C was similar to that of genetic controls ( Figure 2—figure supplement 1 ) . We next tested the requirement of CrebB in the mushroom body , a central structure for olfactory associative memory ( Heisenberg et al . , 1985; de Belle and Heisenberg , 1994; Dubnau et al . , 2001; Crittenden et al . , 1998 ) . To remove CrebB from the entire MB we used the OK107-Gal4 line , which is expressed in most of the KCs across the different lobes ( Aso et al . , 2009 ) . To confirm the CrebB removal we stained with the KC marker Eyeless ( Ey ) , which showed complete absence of CrebB::GFP from the entire MB six days after knockout induction ( Kurusu et al . , 2000 ) ( Figure 3B ) . Next , we tested MB-specific CrebB knockout flies for their memory performance . While control animals showed normal LTM , animals lacking CrebB in the MBs displayed impaired LTM , indicating that the MB is essential for CrebB mediated LTM formation ( Figure 3C ) . In contrast , STM was not affected by loss of CrebB in KCs ( Figure 3—figure supplement 1 ) and non-induced flies displayed normal LTM ( Figure 3—figure supplement 2A ) . We next examined the effect of CrebB knockout on LTM in each of the three major MB lobe subclasses using lobe specific Gal4 drivers ( c739-Gal4 for α/β , c305a-Gal4 for α′/β′ and 5-HTR1B-Gal4 for γ neurons ) ( Aso et al . , 2009; Yuan et al . , 2005; Xie et al . , 2013 ) . We observed a significant reduction in LTM of animals with CrebB knockout in MB α/β neurons compared to control groups ( Figure 3D ) . Similarly , flies with a CrebB knockout in MB α’/β’ neurons also exhibited impaired long-term memory performance ( Figure 3E ) . However , removing CrebB from MB γ neurons did not affect LTM formation . Performance indices did not differ between the tested groups ( Figure 3F ) . Effective knockout of CrebB from MB γ neurons could be confirmed by antibody staining ( Figure 3—figure supplement 3 ) . LTM performance was also tested in flies with non-induced CrebB knockout . Non-induced control flies of the KC subtype experiments did not show LTM defects ( Figure 3—figure supplement 2B–D ) . These findings show that appetitive LTM formation requires CrebB activity in α/β and α′/β′ KCs but not in γ KCs . For distinct forms of learning , different types of MB intrinsic and extrinsic neurons are required . Appetitive memories require dopaminergic input from the PAM cluster neurons ( Burke et al . , 2012; Liu et al . , 2012 ) . We used the GMR58E02-Gal4 line that strongly labels the PAM cluster neurons to mediate CrebB knockout in those DANs . However , flies with CrebB null mutant PAM neurons were able to form LTM indistinguishable of the concurrently tested control groups ( Figure 4A ) . To verify that CrebB was successfully removed from PAM neurons , antibody staining experiments were conducted . After induction of CrebB knockout in PAM neurons , CrebB::GFP could not be detected anymore in most TH-labeled dopaminergic neurons in the brain region where PAM neurons are located ( Figure 4—figure supplement 1 ) . We next assayed if CrebB is needed in MB efferent neurons . Two pairs of cholinergic mushroom body output neurons MBON α3 ( also called MB-V3 ) were reported to be necessary for appetitive LTM ( Plaçais et al . , 2013 ) . The G0239-Gal4 line is a highly specific driver that expresses Gal4 exclusively in MBON α3 in the adult brain ( Chiang et al . , 2011 ) . We used this line to express FLP and induce CrebB deletion from the genome of MBON α3 . Interestingly , memory measured 24 h after appetitive olfactory conditioning was impaired in these flies ( Figure 4B ) . Furthermore , LTM performance was not affected under low temperature conditions , in which FLP expression is not induced ( Figure 4—figure supplement 2 ) . Thus , while dopaminergic input neurons do not require CrebB , MBON α3 requires CrebB for appetitive LTM . This finding supports that LTM is not only encoded within the MB-lobe system , but also involves a memory trace in the MBON extrinsic element .
Formation of long-term memory requires de novo gene expression mediated by CrebB ( Yin et al . , 1994; Krashes and Waddell , 2008 ) . Previous studies made use of RNAi-lines to knock-down CrebB RNA levels or the expression of a repressor isoform to block CrebB , which resulted in conflicting results . It has been shown that temporal control of inhibiting transgene expression is critical , since expression throughout the development of the flies may produce neuroanatomical damage ( Chen et al . , 2012 ) . Moreover , it has been suggested that defects in LTM correlate with the amount of CrebB-RNAi or CrebB repressor expression . It seems that the usage of different inducible gene expression systems or the number of transgene copies can strongly influence the outcome ( Hirano et al . , 2013 ) . Experimental parameters , such as when and how strong inhibiting transgenes are expressed may bias the results and affect the study of the role of CrebB in LTM . We therefore performed experiments in which we genetically removed CrebBcKO specifically in adult flies after completion of brain development . Thus , the removal of the CrebB locus only occurred in post-mitotic , fully- differentiated neurons after eclosion . However , even with adult-specific knockout induction , neuroanatomical abnormalities cannot be completely ruled out . The cAMP signaling pathway mediates synaptic plasticity required for learning and memory ( Lee , 2015 ) . The transcription factor CrebB is downstream of the cAMP signaling pathway and its activity regulation during the memory formation process is crucial for LTM ( Yin et al . , 1994; Yin et al . , 1995; Fropf et al . , 2013 ) . Consistent with previous studies , we found that pan-neuronal CrebB knockout disrupted LTM , but left STM and MTM intact . While there is strong consensus that the MB plays a central role in LTM , if protein synthesis and CrebB activity are required in KCs remains debated . A study of Chen et al . , 2012 reported that CrebB mediated gene transcription is required for aversive spaced LTM in DAL neurons but not in MB neurons . In contrast , two groups suggested in recent articles CrebB necessity in the MB for aversive and appetitive LTM ( Hirano et al . , 2013; Hirano et al . , 2016; Musso et al . , 2017 ) . Our results , that were obtained using an alternative gene disruption strategy , also demonstrate CrebB necessity in the MBs for LTM formation . Thus , our findings , together with previous studies ( Yu et al . , 2006; Krashes and Waddell , 2008; Hirano et al . , 2013; Hirano et al . , 2016; Musso et al . , 2017 ) , convincingly show that CrebB activity is indeed required in KCs for LTM . It is conceivable that in the study of Chen et al . , 2012 CrebB inhibition was insufficient to disrupt LTM formation , leading to conflicting conclusions about the role of CrebB in KCs . MB α/β , α′/β′ and γ neurons , perform distinct roles in different memory types and phases . Functional studies that used UAS-shibirets to block synaptic transmission revealed different temporal requirements of the three major KC subtypes for olfactory associative memory ( Perisse et al . , 2013; Guven-Ozkan and Davis , 2014 ) . Output from MB α/β neurons is required for appetitive LTM ( Krashes and Waddell , 2008; Trannoy et al . , 2011; Cervantes-Sandoval et al . , 2013 ) . The α/β lobe neurons are particularly important for LTM , as suggested by multiple other lines of inquiry ( Blum et al . , 2009; Akalal et al . , 2011; Huang et al . , 2012; Ichinose et al . , 2015 ) . It has also been proposed that appetitive LTM depends on the activity of CrebB in MB α/β neurons . Flies expressing a CrebB repressor isoform constitutively in MB α/β neurons exhibited a reduced LTM performance ( Krashes and Waddell , 2008 ) . Coherent with previous work , our results indicate requirement of CrebB in MB α/β neurons for LTM . However , we observed intact LTM after induction of CrebB knockout in MB γ neurons , which have a main role in memory acquisition and expression of STM . It has been reported that expression of an adenylyl cyclase coding rutabaga transgene in MB γ neurons could restore STM in rutabaga mutant flies , but not LTM ( Schwaerzel et al . , 2003; Thum et al . , 2007; Trannoy et al . , 2011 ) . Moreover , output from MB γ neurons was shown to be necessary for STM , though appetitive LTM formation and retrieval did not require γ lobe neuron output ( Trannoy et al . , 2011; Cervantes-Sandoval et al . , 2013 ) . Nevertheless , a study observed an increased calcium response in MB γ neurons after aversive spaced conditioning that depends on CrebB activity . Expression of a CrebB repressor isoform throughout development in MB γ neurons blocked this LTM memory trace and impaired LTM measured 24 h after aversive spaced training ( Akalal et al . , 2010 ) . For appetitive memory , we found unaffected LTM in flies with adult-specific CrebB knockout in MB γ neurons . Thus , our results suggest that CrebB is not necessary in those neurons for appetitive olfactory LTM . MB α′/β′ neurons only contribute to around 18% of the total number of KCs and therefore were often neglected ( Aso et al . , 2009 ) . The importance of CrebB to drive LTM formation in α′/β′ lobe neurons has not been assessed before . Our findings argue that LTM formation requires CrebB activity in MB α′/β′ neurons . It has been suggested , that α′/β′ KCs play an essential role in LTM formation and consolidation . Disrupting synaptic activity from MB α′/β′ neurons after appetitive olfactory conditioning prevented LTM , but retrieval of LTM was independent of MB α′/β′ output ( Krashes and Waddell , 2008; Cervantes-Sandoval et al . , 2013 ) . MBONs , which are the downstream neurons of the KCs , can be classified into 21 cell types . Dendrites of different MBON types innervate specific regions of MB lobes and dopaminergic projections align with MBON dendrites , forming distinct compartmental units ( Aso et al . , 2014 ) . Dopamine is released during learning to stimulate plasticity of specific KC-MBON synapses ( Hige et al . , 2015; Owald et al . , 2015; Perisse et al . , 2016 ) . DANs from the PAM cluster were shown to be critical for learning the value of carbohydrates ( Burke et al . , 2012; Liu et al . , 2012 ) . Interestingly , it has been found that MBON α1 synapse onto a subset of PAM neurons that innervates the α1 compartment forming a recurrent network loop , which is necessary for the formation and consolidation of appetitive LTM ( Ichinose et al . , 2015 ) . This feedback circuit motif was also observed in other MB compartments ( Owald et al . , 2015; Felsenberg et al . , 2017 ) . Recently , plasticity in PAM neurons that lasted not less than 24 h was described . Caloric frustration memory , a new long-lasting memory form , reduced PAM neuron response to glucose ( Musso et al . , 2017 ) . It is possible that modifying efficacy of PAM neuron synapses is involved in appetitive LTM formation . However , we found that LTM does not depend on CrebB activity in MB afferent PAM neurons . This suggests that DANs induce synaptic modifications between KCs and MBONs , but CrebB mediated structural changes in PAM neurons are not required for olfactory appetitive LTM . Remarkably , we found that flies with CrebB knockout in MBON α3 showed impaired appetitive LTM . This is the first report that LTM requires CrebB activity in MBONs . MBON α3 are efferent from the MB and dendrites were found to project to the MB α lobe ( Pai et al . , 2013; Plaçais et al . , 2013 ) . It is likely that CrebB mediated transcription in MBON α3 leads to changed efficiency of the postsynaptic sites . The altered KC-MBON synapses would skew the MBON network towards approach behavior ( Owald and Waddell , 2015 ) . An alternative possibility is that plasticity is induced in MBON α3 presynapses . MBON α3 have presynaptic terminals in the superior medial , the superior intermediate and the superior lateral protocerebrum ( Aso et al . , 2014 ) . A recent report suggested that MBON α3 also connect to DAL neurons , which have axonal processes in the MB and are essential for aversive LTM ( Chen et al . , 2012; Wu et al . , 2017 ) . Thus , a recurrent loop back to the KCs would be possible . It has been shown that CrebB is required in DAL neurons for aversive spaced LTM , but not for appetitive LTM ( Chen et al . , 2012; Hirano et al . , 2013 ) . Future studies will be required to reveal the role of this anatomical network for olfactory LTM formation . While we here specifically focused on the neural circuits requiring CrebB for appetitive olfactory memory it will be interesting to extend the research to aversive memory , since the molecular and neuronal mechanisms are not identical between those two forms of memory . Prior to appetitive learning , it is necessary to deprive flies of food , since motivational drive is critical for memory formation . Furthermore , flies have to be hungry to efficiently express the learned association ( Krashes and Waddell , 2008; Colomb et al . , 2009 ) . This imposes limitations and can impede the interpretation of memory performance of distinct memory phases , because different feeding protocols are required . Our CrebBcKO allele may be useful to explore CrebB necessity for other processes , given that the transcription factor CrebB has diverse functions . A wealth of identified Gal4 driver lines in Drosophila provides the possibility to remove CrebB in a precise spatial and temporal manner , and investigate the consequences of CrebB knockout in a large number of cells and contexts .
Flies ( Drosophila melanogaster ) were generally kept at 25°C and subjected to a 12 h light – 12 h dark cycle . A cornmeal medium supplemented with yeast , fructose and molasses was used to rear the flies . Canton-S was used as wild-type ( courtesy of R . Stocker ) . c739-Gal4 was obtained from Hiromu Tanimoto ( Tohoku University ) and OK107-Gal4 ( 106098 ) was received from Kyoto stock center . nSyb-Gal4 ( 51635 ) , c305a-Gal4 ( 30829 ) , 5-HTR1B-Gal4 ( 27636 ) , GMR58E02-Gal4 ( 41347 ) , G0239-Gal4 ( 12639 ) , tubGal80ts ( 7019 ) , nos-Cas9 ( 54591 ) and UAS-FLP ( 55804 , 55806 ) were obtained from Bloomington stock center . Genomic DNA of nos-Cas9 flies was used as template for PCR of the CrebB genomic fragments . A 3 . 3 kb fragment of the CrebB encoding region including introns was amplified with primers ‘CrebB noStart R1 fw’ ( gcgaattcGACAACAGCATCGTCGAGGAGAACG ) and ‘CrebB intr RV re’ ( gagataTCCTGCCAAGTCGCAACTAAAGGC ) . The resulting sequence starts with the second codon ( Asp GAC ) of CrebB just after the EcoRI restriction site , and ends within the last intron 45 bp downstream of the facultative exon of isoforms PG , PI , PN and PQ followed by an EcoRV restriction site . For the upstream homology arm a 2 . 3 kb fragment upstream of the CrebB Start codon was amplified with primers ‘CrebB CR Not fw’ ( gggcggcCGCGGAGGTAATGCGGATTTGG ) and ‘CrebB 5’UTR Spe re’ ( ttactagtCCTGGCGATCTTCAGCAGCACC ) . This fragment starts 2312 bp upstream of the CrebB start codon within the sequence of the RNA expressing CR43686 gene , and ends 30 bp upstream of the CrebB Start codon within the 5’UTR . For the downstream homology arm a 2 . 1 kb fragment including the last exon of CrebB was amplified with primers ‘CrebB intr Xho fw’ ( ggaccactcgagAATCGAACTGGAATCGAGGGTCTATC ) and ‘CrebB 3’UTR Kpn re’ ( gtggtaCCGTCCCTTCGTCTCTTTTCTACC ) . This fragment starts within the last intron of CrebB 56 bp downstream of the facultative exon of isoforms PG , PI , PN and PQ , and ends within the 3’UTR of the long CrebB isoforms 1131 bp downstream of the Stop codon . All three genomic fragments were subcloned into pBluescript with the appropriate restriction enzymes . After verification of the sequences , the three fragments were assembled in a pBluescript derivative generated in our lab in which we have inserted two FRT sites and the coding sequence of GFP into the multiple cloning site allowing us to generate FRT-flanked and GFP-tagged CRISPR templates . The first coding exon of CrebB lacking the CrebB Start codon was fused in frame with GFP . One of the FRT sites is located immediately upstream of the GFP coding sequence between the 2 . 3 kb upstream homology arm and GFP , the other FRT site is located between the 3 . 3 kb CrebB coding region and the 2 . 1 kb downstream homology arm . Thus , upon FRT mediated recombination the GFP-tagged CrebB coding region from the first coding exon to the second last coding exon will be removed . For the first guide RNA oligos ‘CRISPR CrebB Start sense’ ( cttcGATCGCCAGGATCGGCAACA ) and ‘CRISPR CrebB Start antisense’ ( aaacTGTTGCCGATCCTGGCGATC ) were annealed and ligated into BbsI digested pU6-BbsI-chiRNA vector . This guide RNA will target Cas9 to cut 2 bp upstream of the CrebB Start codon . For the second guide RNA oligos ‘CRISPR CrebB intr sense’ ( cttcGGACCACTCGTAAATCGAAC ) and ‘CRISPR CrebB intr antisense’ ( aaacGTTCGATTTACGAGTGGTCC ) were annealed and ligated into BbsI digested pU6-BbsI-chiRNA vector . This guide RNA will target Cas9 to cut 61 bp downstream of the facultative exon of isoforms PG , PI , PN and PQ . The CRISPR sites are missing in the template DNA , where they were replaced with the FRT sites . A mix containing 0 . 4 µg/µl template and 0 . 2 µg/µl of each guide RNA plasmid was injected into nos-Cas9 expressing flies . The injected flies were crossed with w1118 mutants and their offspring screened for GFP expressing larvae . GFP positive flies were crossed with FM7 balancer flies to establish a stable line . The presence of the GFP-tag and the FRT sites was verified by PCR and sequencing of the genomic DNA of the homozygous CrebBcKO stock . CrebB cDNA was PCR amplified from EST clone RT01009 and cloned into the pGEX-6P-1 plasmid to produce a Gst-tagged version in bacteria . The resulting Gst-CrebB coding sequence had a small in-frame deletion removing some Glycine residues of the N-terminal Glycine stretch but the rest of the sequence was unaltered . Bacterially expressed Gst-CrebB was purified with Glutathione Sepharose beads ( GE healthcare ) and injected into guinea pigs for antibody production ( eurogentec ) . Flies , in which CrebB knockout was induced , were moved for 6 days to 29°C before antibody staining experiments . Male adult brains were dissected in phosphate-buffered-saline ( PBS ) and fixed at room temperature for 30 min with a 3 . 7% formaldehyde solution ( in PBS ) . Brains were washed at least five times with PBST ( PBS with 0 . 3% triton X-100 ) before primary antibodies were added for overnight incubation at 4°C . The following primary antibodies were used: rabbit anti-Eyeless ( 1:400 , courtesy of Uwe Waldorf ) , chicken anti-GFP ( 1:1000 , Abcam ab13970 ) , rabbit anti-GFP ( 1:1000 , Invitrogen A-6455 ) , guinea pig anti-CrebB ( 1:400 ) , rabbit anti-Tyrosine hydroxylase ( 1:100 , Merck AB152 ) , mouse anti-Repo ( 1: 20 , Developmental Studies Hybridoma Bank 8D12 ) , mouse anti-Discs large ( 1:50 , Developmental Studies Hybridoma Bank 4F3 ) . Brains were washed again before the overnight incubation at 4°C with secondary antibodies . The used secondary antibodies were conjugated with Alexa fluorescent proteins ( 488 , 567 or 647; Molecular Probes ) and diluted 1:200 . After a last washing step , brains were mounted in Vectashield H-1000 or Vectashield with DAPI H-1200 ( Vector Laboratories ) on a microscope slide . Samples were imaged with Leica SP5 confocal microscope and the images were processed with Imaris Bitplane 9 . 2 and Adobe Photoshop CS6 . Memory experiments were performed at 23–25°C and 70–75% relative humidity . Conditioning was carried out in dim red light and tests were done in darkness . The used conditioning apparatus is based on Tully and Quinn , 1985 and was modified to perform four experiments in parallel ( Schwaerzel et al . , 2002 ) ; CON-Elektronik , Greussenheim , Germany ) . The odors benzaldehyde ( Fluka , 12010 ) and limonene ( Sigma-Aldrich , 183164 ) were used . 60 μl of benzaldehyde was applied in plastic containers measuring 5 mm in diameter and 85 μl of limonene was applied in plastic containers measuring 7 mm in diameter . Odor delivery was effected with a vacuum pump adjusted to a flow rate of 7 l/min . Filter papers were soaked with distilled water or with a 1 . 5 M sucrose ( Sigma-Aldrich , 84100 ) solution the day before the experiment and left to dry at room temperature overnight . 19–21 h before conditioning groups of 60–100 flies were put into plastic vials with wet cotton wool on the bottom for starvation . For appetitive olfactory conditioning , flies were loaded in tubes lined with water filter papers . After an acclimatization period of 90 s , a first odor was presented for 2 min . Then , the odor was removed and animals were transferred within 60 s to tubes lined with sucrose filter papers . Subsequently , the second odor was presented for 2 min . For the memory tests , flies were moved to a two-arm choice point where they could choose between limonene and benzaldehyde for 2 min . After this period , the number of flies within each arm was counted and a preference index was calculated . PREF = ( ( Npaired odor – Ncontrol odor ) 100 ) /Ntotal One memory experiment consisted of two groups with reciprocal conditioning , in which the sucrose paired odor was exchanged . The preference indices from these two groups were averaged to calculate a performance index ( PI ) . To measure short-term memory , flies were tested immediately after conditioning . Middle-term memory was examined 3 h after conditioning . For those experiments , animals were put back into starvation vials after training and were starved until the test . Flies tested for 24 h memory were put in food vials after conditioning for 3–5 h and then transferred to starvation vials until the test . Flies used for memory experiments were reared at 18°C and collected after hatching ( 0–3 d old flies ) . To induce expression of flippase , flies were moved for 6 d to 29°C . For the starvation period before conditioning and until the test , those animals were kept at 25°C . Flies used for the non-induced FLP expression experiments were kept at 18°C after collection ( for 6 days ) and during the starvation time . Memory experiments with those flies were performed at 20–22°C . To compare PIs between two groups the Welch two sample t-test was used . Statistical analyses and graphical representation of the data were performed using R version 3 . 4 . 1 . | Our brains can store different types of memories . You may have forgotten what you had for lunch yesterday , but still be able to remember a song from your childhood . Short-term memories and long-term memories form via different mechanisms . To establish long-term memories , the brain must produce new proteins , many of which are common to all members of the animal kingdom . By studying these proteins in organisms such as fruit flies , we can learn more about their role in our own memories . Widmer et al . used this approach to explore how a protein called CrebB helps fruit flies to remember for several days that a specific odor is associated with a sugary reward . These odor-reward memories form in a brain region called the mushroom body , which has three lobes . Input neurons supply information about the odor and the reward to the region , while output neurons pass on information to other parts of the fly brain . CrebB regulates the production of new proteins required to form these long-term odor-reward memories: but where exactly does CrebB act during this process ? Using a gene editing technique called CRISPR , Widmer et al . generated mutant flies . In these insects CrebB could be easily deactivated ‘at will’ in either the entire brain , the whole mushroom body , each of the three lobes or in specific output neurons . The flies were then trained on the odor-reward task , and their memory tested 24 hours later . The results revealed that for the memories to form , CrebB is only required in two of the three lobes of the mushroom body , and in certain output neurons . Future studies can now focus on the cells shown to need CrebB to create long-term memories , and identify the other proteins involved in this process . In humans , defects in CrebB are associated with intellectual disability , addiction and depression . The mutant fly created by Widmer et al . could be a useful model in which to investigate how the protein may play a role in these conditions . | [
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] | 2018 | Multiple neurons encode CrebB dependent appetitive long-term memory in the mushroom body circuit |
Voltage-gated Ca2+ channels are involved in numerous physiological functions and various mechanisms finely tune their activity , including the Ca2+ ion itself . This is well exemplified by the Ca2+-dependent inactivation of L-type Ca2+ channels , whose alteration contributes to the dramatic disease Timothy Syndrome . For T-type Ca2+ channels , a long-held view is that they are not regulated by intracellular Ca2+ . Here we challenge this notion by using dedicated electrophysiological protocols on both native and expressed T-type Ca2+ channels . We demonstrate that a rise in submembrane Ca2+ induces a large decrease in T-type current amplitude due to a hyperpolarizing shift in the steady-state inactivation . Activation of most representative Ca2+-permeable ionotropic receptors similarly regulate T-type current properties . Altogether , our data clearly establish that Ca2+ entry exerts a feedback control on T-type channel activity , by modulating the channel availability , a mechanism that critically links cellular properties of T-type Ca2+ channels to their physiological roles .
Voltage-gated Ca2+ channels ( VGCCs ) are unique among voltage-gated ion channels because the permeant Ca2+ ion also acts as an intracellular second messenger , triggering diverse cellular functions ( Berridge et al . , 2003 ) . VGCCs are therefore involved in neuronal and cardiac excitability as well as in muscle contraction , neurotransmitter release , hormone secretion and gene expression ( Berridge et al . , 2003; Mangoni and Nargeot , 2008; Catterall , 2011; Simms and Zamponi , 2014; Zamponi et al . , 2015 ) . Consequently the modulation of VGCC activity plays a pivotal role in the regulation of cardiac and brain activities and this modulation is controlled by a variety of processes , including intracellular Ca2+ itself , which provides an important Ca2+-driven feedback control ( Eckert and Chad , 1984; Zühlke et al . , 1999; Peterson et al . , 1999; Liang et al . , 2003; Green et al . , 2007; Tsuruta et al . , 2009; Oliveria et al . , 2012; Hall et al . , 2013; Zamponi et al . , 2015 ) . VGCCs comprise three distinct subfamilies classified with respect to their biophysical and pharmacological ( type ) , and molecular ( Cav ) entities: the L-type / Cav1 , the N- , P/Q- , R-type / Cav2 and the T-type / Cav3 channels ( Ertel et al . , 2000 ) . It was well demonstrated that both Cav1 and Cav2 channels are modulated by intracellular Ca2+ ( Liang et al . , 2003; Dick et al . , 2008 ) . For the Cav1 / L-type VGCCs , this Ca2+ feedback mechanism has been extensively studied in a wide spectrum of biological contexts and a rise in submembrane Ca2+ concentration induces complex effects depending on both the Ca2+ concentration and the duration of the Ca2+ entry ( Eckert and Chad , 1984; Zühlke et al . , 1999; Peterson et al . , 1999; Liang et al . , 2003; Green et al . , 2007; Tsuruta et al . , 2009; Oliveria et al . , 2012; Hall et al . , 2013 ) . At the millisecond time scale , the Ca2+ entry via L-type channels induces a Ca2+-dependent inactivation ( CDI ) characterized by an acceleration of their inactivation kinetics ( Eckert and Chad , 1984; Zühlke et al . , 1999; Peterson et al . , 1999; Liang et al . , 2003; Hall et al . , 2013 ) . For several seconds to a few minutes of stimulation , the cumulative Ca2+ entry induces a decrease of the L-type current amplitude , which is reversible if stimulation ceases for several minutes ( Eckert and Chad , 1984; Oliveria et al . , 2007 , 2012; Hall et al . , 2013 ) . For longer period of stimulation , or activation of the ionotropic NMDA receptors , L-type channels are internalized , potentially degraded in lysosomes or recycled to the plasma membrane depending on the amount and the duration of the Ca2+ entry ( Green et al . , 2007; Tsuruta et al . , 2009; Hall et al . , 2013 ) . This precise Ca2+-dependent regulation of the L-type channel activity has a strong physiological role in avoiding cytotoxicity arising from Ca2+ overload ( Lee et al . , 1999; Berridge et al . , 2003; Green et al . , 2007; Tsuruta et al . , 2009; Hall et al . , 2013 ) . Consequently , alteration of the Ca2+-dependent regulation of the L-type channels is deleterious and has important pathophysiological consequences as observed in the Timothy syndrome ( Splawski et al . , 2004; Barrett and Tsien , 2008; Blaich et al . , 2012; Limpitikul et al . , 2014; Dick et al . , 2016 ) . Contrasting with this well-established Ca2+-dependent regulation of the L-type channels , it is presently unknown whether a change in intracellular Ca2+ concentration is involved in regulating T-type Ca2+ channel activity . The low-voltage-activated , T-type/Cav3 channels are specifically activated by small membrane depolarization below the threshold of classical sodium action potentials , producing a Ca2+ entry near the resting membrane potential and low-threshold Ca2+ spikes ( Huguenard , 1996 , 1998; Perez-Reyes , 2003; Zamponi , 2016 ) . Importantly , availability of Cav3 channels is critically regulated by the resting membrane potential to control T-type channel activity . Because of the negative range of their steady-state inactivation ( V0 . 5 near −70 mV ) , Cav3 channels are partially inactivated ( reduced availability ) in the range of the resting membrane potential of most neurons and a membrane hyperpolarization ( usually triggered by inhibitory postsynaptic events ) is needed to allow their recovery from inactivation ( de-inactivation ) and their subsequent opening ( Huguenard , 1996; Perez-Reyes , 2003; Zamponi , 2016 ) . This behavior is of particular importance in many types of neurons , in which Cav3 channels mediate rebound burst firing , especially in the thalamo-cortical circuit , where Cav3 channels control transition between awake and sleep states ( Huguenard , 1996; Perez-Reyes , 2003; Beenhakker and Huguenard , 2009; Zamponi , 2016; Tatsuki et al . , 2016 ) . Overall , Cav3 channels are involved in the control of the Ca2+ homeostasis ( Chemin et al . , 2000; Bijlenga et al . , 2000; Perez-Reyes , 2003 ) , in Ca2+-dependent differentiation of neuronal , muscular and neuroendocrine cells ( Bijlenga et al . , 2000; Mariot et al . , 2002; Chemin et al . , 2002b ) , as well as in Ca2+ overload toxicity in ischemia ( Nikonenko et al . , 2005; Bancila et al . , 2011; Gouriou et al . , 2013 ) . Importantly , an increase in the Cav3 channel activity has been implicated in several diseases including epilepsy , chronic pain , autism and primary aldosteronism ( Beenhakker and Huguenard , 2009; Zamponi , 2016 ) . Although it is evident that a tight control of Cav3 channel activity is necessary to maintain Ca2+ homeostasis , there is no evidence yet that Cav3 channels are regulated by intracellular Ca2+ ions and/or by Ca2+ entry . In this study , we have designed complementary electrophysiological experiments to explore whether the T-type/Cav3 channels are modulated by intracellular Ca2+ concentration . We document a feedback control mechanism that relies on Ca2+ entry via activated Cav3 channels or nearby Ca2+-permeable receptors . We provide evidence that dynamic changes and localized increase in the intracellular Ca2+ concentration at the vicinity Cav3 channels control availability of these channels , which underlies this novel regulation .
In order to evaluate whether T-type channels would be regulated by a rise in intracellular Ca2+ ( [Ca2+]INT ) , we first used the Ca2+ ionophore ionomycin . In D-hair mechanoreceptor sensory neurons , which specifically express a high density of T-type channels ( Shin et al . , 2003; Dubreuil et al . , 2004; Voisin et al . , 2016 ) , extracellular perfusion of 10 µM ionomycin induced a potent decrease of the T-type current ( Figure 1a ) . This current inhibition ( ~85% in average ) occurred in the minute range ( Figure 1b ) and was associated with a large hyperpolarizing shift in the steady-state inactivation curve ( ~22 mV , p<0 . 001 , Figure 1c ) . Similar findings were obtained with the three cloned Cav3 channels transiently expressed in tsA-201 cells . Ionomycin potently inhibited the Cav3 . 1 , Cav3 . 2 and Cav3 . 3 currents by about 80% ( Figure 1d ) . This effect was combined with an acceleration of the inactivation kinetics ( ~3 times , Figure 1e ) and a hyperpolarizing shift in the steady-state inactivation curve ( ~23 mV , p<0 . 001 , Figure 1f ) . 10 . 7554/eLife . 22331 . 003Figure 1 . Ionomycin induces inhibition of neuronal T-type and recombinant Cav3 currents . ( a–c ) Extracellular application of ionomycin induces inhibition of the native T-type current in D-hair sensory neurons . ( a ) Effect of 10 µM ionomycin ( iono ) on the T-type current recorded from a D-hair sensory neuron . ( b ) Time course and average effect ( inset ) of ionomycin ( n = 5 ) . ( c ) Steady-state inactivation of the native T-type current before ( ctrl ) and after ionomycin application ( n = 5 ) . ( d–f ) Ionomycin induces inhibition of the recombinant Cav3 . 1 , Cav3 . 2 and Cav3 . 3 currents . ( d ) Effect of ionomycin application ( 10 µM ) on the Cav3 . 3 current amplitude . Average effect of ionomycin on Cav3 . 1 , Cav3 . 2 and Cav3 . 3 currents ( inset , n = 6–7 per bar ) . ( e ) Inactivation kinetics of the Cav3 currents in the absence and in the presence of ionomycin ( n = 6–7 per bar ) . ( f ) Steady-state inactivation of Cav3 . 1 , Cav3 . 2 and Cav3 . 3 currents before and after ionomycin application ( n = 6–7 ) . In ( a ) , ( b ) , ( d ) and ( e ) , the currents were recorded at −30 mV from a holding potential ( HP ) of −80 mV . In ( c ) and ( f ) , the currents were elicited at −30 mV from HPs ranged from −130 to −40 mV ( 5 s duration ) and the data were fitted with the Boltzmann equation . DOI: http://dx . doi . org/10 . 7554/eLife . 22331 . 003 Considering that recombinant Cav3 channels , especially Cav3 . 3 channels , can generate large entry of Ca2+ during fast stimulation protocols ( Huguenard , 1998; Kozlov et al . , 1999; Chemin et al . , 2002a; Perez-Reyes , 2003 ) , we have investigated whether these channels might be modulated by their own activity . Cav3 currents were recorded in the presence of 2 mM extracellular Ca2+ using fast test-pulse ( TP ) stimulation ( 1 Hz ) , which allows a cumulative Ca2+ entry . Experiments were performed after dialyzing the cell with 10 mM EGTA , which delimits the change in Ca2+ concentration only at the vicinity of the channel ( Marty and Neher , 1985; Roberts , 1993; Deisseroth et al . , 1996 ) . The Cav3 . 3 current amplitude recorded using fast TP stimulation ( 1 Hz ) progressively decreased to ~50% of the control value ( Figure 2a ) . In average , the current decrease was maximal and stable after ~40 s with an average half-time of 18 s ( Figure 2a and f ) . The current decrease was also associated with a marked acceleration of the inactivation kinetics ( Figure 2a ) , ~3 times after 40 s stimulation ( Figure 2e ) . This effect was fully reversible and activity-dependent since the stopping of the TP stimulation for only 5 s already induced ~30% recovery of the current . Recovery was complete after ~2 min without TP stimulation with an average half-time of 20 s ( Figure 2f ) . 10 . 7554/eLife . 22331 . 004Figure 2 . Ca2+ induces a time-dependent inhibition of the Cav3 . 3 current at high frequency of stimulation . ( a ) Typical examples of Cav3 . 3 currents elicited using a 1 Hz test pulse ( TP ) stimulation of −30 mV ( 450 ms duration ) from a HP of −100 mV . The traces obtained at the beginning of the stimulation ( 1 s ) and after 40 s stimulation are indicated in blue and red , respectively . ( b–d ) Similar experiments for a TP of +100 mV ( b ) , in the absence extracellular Ca2+ ( c , TP −30 mV ) and in the presence of intracellular BAPTA ( d , TP −30 mV ) . ( e ) Inactivation kinetics of the Cav3 . 3 current measured at the beginning ( 1 s , blue bars ) and after 40 s stimulation ( red bars , n = 15–28 per bar ) . ( f ) Time-course of the Cav3 . 3 current inhibition during 1 Hz stimulation and time-course of the recovery of the Cav3 . 3 current as a function of time after stimulation ( n = 13–28 per point ) . The half-time of both inhibition and recovery of the Cav3 . 3 current are indicated in green . ( g ) Summary of the data obtained on the three Cav3 currents at different frequencies of TP stimulation ( n = 5–40 per bar ) . DOI: http://dx . doi . org/10 . 7554/eLife . 22331 . 00410 . 7554/eLife . 22331 . 005Figure 2—figure supplement 1 . The effect of free Ca2+ application on the Cav3 . 3 current recorded in cell-free inside-out patches . ( a ) Effect of 10 µM and 100 µM Ca2+-containing solution on the Cav3 . 3 current recorded during voltage-ramp protocols in the inside-out patch configuration . ( b ) Time course of the Cav3 . 3 current during the experiment presented in ( a ) . ( c ) Scatter plot and average effect of 1 µM , 10 µM and 100 µM Ca2+-containing solution ( n = 10–19 per bar ) . DOI: http://dx . doi . org/10 . 7554/eLife . 22331 . 00510 . 7554/eLife . 22331 . 006Figure 2—figure supplement 2 . Inhibition of the Cav3 . 1 current at a high frequency of TP stimulation but not of the Cav3 . 2 current . Typical examples of Cav3 . 1 and Cav3 . 2 currents elicited by a TP stimulation at a frequency of 1 Hz ( TP of −30 mV , 180 ms duration ) from a HP of −100 mV . ( a ) Cav3 . 1 current recorded at the beginning of the stimulation ( 1 s ) and after 80 s stimulation ( 80 s ) . ( b ) Time-course of the Cav3 . 1 current inhibition during this 1 Hz stimulation protocol ( n = 40 ) . ( c ) Inactivation kinetics of the Cav3 . 1 current measured at the beginning ( 1 s ) and after 80 s of stimulation ( n = 40 ) . ( d ) Cav3 . 2 current recorded at the beginning of the stimulation ( 1 s ) and after 80 s stimulation . ( e ) Time-course of the Cav3 . 2 current during a 1 Hz stimulation protocol ( n = 23 ) . ( f ) Inactivation kinetics of the Cav3 . 2 current measured at the beginning ( 1 s ) and after 80 s stimulation ( n = 23 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 22331 . 006 In contrast with the data obtained for a TP at −30 mV ( Figure 2a ) , the Cav3 . 3 current was unchanged when the TP was set at +100 mV ( a membrane potential value above the reversal potential of Cav3 current , leading to an outward current , Figure 2b ) . These experiments clearly indicate that the decrease in current amplitude observed for a fast TP stimulation at −30 mV does not involve a voltage-dependent inactivation process that would occur at high frequency of stimulation , but is rather related to the Ca2+ entry via Cav3 . 3 channels . Also , in the absence of extracellular Ca2+ , the sodium inward current through Cav3 . 3 channels remained unchanged during the time of the fast TP protocol at −30 mV ( Figure 2c ) . In addition , no change in Cav3 . 3 current properties were obtained in the presence of 2 mM extracellular Ca2 when cells were dialyzed with an intracellular medium containing BAPTA instead of EGTA ( Figure 2d and e ) . This difference in susceptibility to BAPTA and EGTA is characteristic of a process driven by a localized rise in submembrane Ca2+ , without the need for a global Ca2+ increase ( Marty and Neher , 1985; Roberts , 1993; Deisseroth et al . , 1996 ) . We further investigated whether Ca2+ ions could affect the Cav3 . 3 current in cell-free inside-out patches ( Figure 2—figure supplement 1 ) . Cav3 . 3 currents were recorded by voltage-ramps in the presence of 100 mM external Ba2+ whereas Ca2+-containing solutions were applied to the internal side of the membrane in the inside-out patch configuration ( Figure 2—figure supplement 1a ) . Because of the surface charge effect due to the use of 100 mM Ba2+ , voltage ramps were applied from a HP −50 mV to match whole-cell experiments ( see also Figure 4 ) . In this configuration , the application of 1 , 10 or 100 µM Ca2+ during more than 60 s ( Figure 2—figure supplement 1b ) did not induce a significant inhibition of the Cav3 . 3 current as compared to a control solution containing 1 mM EGTA/0 mM Ca2+ ( Figure 2—figure supplement 1c ) , suggesting that Ca2+-induced Cav3 . 3 inhibition requires some additional components preserved in the whole-cell configuration . However , our data do not exclude a possible direct effect of Ca2+ ions at higher ( mM ) concentrations . The decrease in Cav3 . 3 current gradually developed with the increase in TP frequency . While no decrease was observed at low frequency of TP ( 0 . 033 Hz ) , the decrease in Cav3 . 3 current became significant at 0 . 2 Hz and further increased at 0 . 5 and 1 Hz ( Figure 2g ) . Similar experiments were conducted with the Cav3 . 1 and Cav3 . 2 T-type channels . The amplitude of Cav3 . 1 current decreased modestly and only at the TP frequency of 1 Hz ( Figure 2g and Figure 2—figure supplement 2a–b ) . Contrasting with the results described above , the Cav3 . 2 current showed no inhibition but rather a small increase in amplitude at fast stimulation ( Figure 2g and Figure 2—figure supplement 2d–e ) . For Cav3 . 1 channels , the decrease in current amplitude was associated with faster inactivation kinetics ( Figure 2—figure supplement 2c ) , while inactivation kinetics of the Cav3 . 2 current was unchanged ( Figure 2—figure supplement 2f ) . Similar to that described for the Cav3 . 3 current , Cav3 . 1 current amplitude and inactivation kinetics were unchanged after dialyzing the cells with BAPTA ( Figure 2g ) . We next investigated the biophysical mechanism underlying the Cav3 . 3 current decrease . We first hypothesized that the recovery from inactivation of the Cav3 . 3 current might be affected during a fast stimulation protocol in a Ca2+-sensitive manner . A paired-pulse protocol with increasing inter-pulse durations ( 100 , 400 or 1000 ms ) was designed to analyze the kinetics of recovery from the first ( Stim 1 ) to the fortieth ( Stim 40 ) paired-pulse stimulation , as exemplified for an inter-pulse duration of 100 ms in Figure 3a . These experiments revealed that the recovery kinetics of the Cav3 . 3 current was unaffected at the three inter-pulse durations tested ( Figure 3b ) . In contrast , we found that the steady-state inactivation of Cav3 . 3 current was strongly modified using fast stimulation protocols . This was evidenced using a paired-pulse protocol with variable inter-pulse potentials ranging from −40 to −110 mV ( as exemplified for an inter-pulse potential of −70 mV in Figure 3c ) . This fast stimulation protocol produced a ~10 mV hyperpolarizing shift in the V0 . 5 value of the steady-state inactivation curve ( from −72 . 4 mV for Stim 1 to −81 . 1 mV for the Stim 40 , p<0 . 001 ) , without any change in the slope of the inactivation curve ( Figure 3d ) . Importantly , this effect was lost in the presence of intracellular BAPTA ( Figure 3d ) . In addition , we found that the fast stimulation of Cav3 . 3 current also induced a small but significant leftward shift in the steady-state activation ( from −52 . 6 to −55 . 9 mV , p<0 . 01 ) as well as an increase in the slope of the activation curve ( from 4 . 7 to 5 . 6 mV , p<0 . 05 , Figure 3e–f ) . 10 . 7554/eLife . 22331 . 007Figure 3 . High frequency stimulation induces a Ca2+-dependent negative shift in the Cav3 . 3 steady-state inactivation properties . ( a–b ) Recovery from short-term inactivation of the Cav3 . 3 current measured by a paired-pulse stimulation ( inter-stimulation 1 s ) applied 40 times ( Stim . 1 to Stim . 40 ) . The interval between the first pulse ( P1 ) and the second pulse ( P2 ) , i . e . interpulse , is 100 , 400 or 1000 ms , as presented in ( a ) for an interpulse interval of 100 ms . The recovery from short-term inactivation ( P2/P1 ) , as a function of the interpulse duration is quantified for the first stimulation ( Stim . 1 ) and the 40th stimulation ( Stim . 40 ) ( b , n = 5–7 per point ) . ( c–d ) Steady-state inactivation of the Cav3 . 3 current measured using a paired-pulse stimulation applied 40 times . The Vm between the two pulses ranged from −110 to −40 mV , as illustrated in ( c ) for a Vm of −70 mV . Steady-state inactivation ( measured at P2 ) as a function of the Vm is determined for the first stimulation ( Stim . 1 ) and for the 40th stimulation ( Stim . 40 ) ( d , n = 5–9 per point ) . ( e–f ) Current-voltage ( I–V ) protocol ( e ) and activation curve ( f ) of the Cav3 . 3 current during 1 Hz or 0 . 05 Hz stimulation . The Cav3 . 3 current was stimulated at 1 Hz or 0 . 05 Hz until reaching the steady-state just before I-V protocols , which were performed by a double-pulse protocol to maintain the 1 Hz stimulation effect ( n = 17 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 22331 . 00710 . 7554/eLife . 22331 . 008Figure 3—figure supplement 1 . Paired-pulse stimulation at high frequency induces a negative shift in the Cav3 . 1 steady-state inactivation properties . ( a–b ) Steady-state inactivation of the Cav3 . 1 current measured using a paired-pulse stimulation ( inter-stimulation 1 s ) applied 80 times ( Stim 1 to Stim 80 ) . The membrane potential ( Vm ) between the two pulses ranged from −110 to −50 mV , as illustrated in ( a ) for a Vm of −70 mV . Steady-state inactivation ( measured at P2 ) as a function of the Vm is determined for the first stimulation ( Stim 1 ) and for the 80th stimulation ( Stim 80 , ( b ) , n = 5–13 per point ) . ( c ) Recovery from short-term inactivation of the Cav3 . 1 current measured by a paired-pulse stimulation ( inter-stimulation 1 s ) applied 80 times ( Stim 1 to Stim 80 ) . The recovery from short-term inactivation , as a function of the interpulse duration ( 100 , 400 or 1000 ms ) is quantified for the first stimulation ( Stim 1 ) and for the 80th stimulation ( n = 8–9 per point ) . ( d ) Activation curve of the Cav3 . 1 current measured during 1 Hz or 0 . 05 Hz stimulation . The Cav3 . 1 current was stimulated at 1 Hz or 0 . 05 Hz until reaching the steady-state just before I-V protocols , which were performed by a double-pulse protocol to maintain the 1 Hz stimulation effect ( n = 6 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 22331 . 00810 . 7554/eLife . 22331 . 009Figure 4 . Inhibition of Cav3 . 3 current as a function of the HP . ( a ) Simulation of the Cav3 . 3 current at −30 mV from an HP of −70 mV . The Cav3 . 3 current was modeled from experiments presented in Figure 3 . The blue trace corresponds to the Cav3 . 3 current properties obtained before the 1 Hz stimulation whereas the red trace corresponds to the Cav3 . 3 current properties obtained after 40 s stimulation at 1 Hz . ( b ) Cav3 . 3 current elicited at a frequency of 0 . 2 Hz from a HP of −110 mV ( left panel ) and a HP of −80 mV ( right panel ) . ( c ) Inhibition of Cav3 . 3 current after 150 s stimulation as a function of the HP ( n = 5–8 per point ) . DOI: http://dx . doi . org/10 . 7554/eLife . 22331 . 009 Similar findings were obtained with Cav3 . 1 channels since these paired-pulse protocols revealed a ~5 mV hyperpolarizing shift of the steady-state inactivation curve ( p<0 . 001 , Figure 3—figure supplement 1a–b ) with no significant effect on the recovery kinetics ( Figure 3—figure supplement 1c ) and the steady-state activation curve of the Cav3 . 1 current ( Figure 3—figure supplement 1d ) . These latter findings strongly suggest that the decrease in Cav3 . 3 current observed in fast stimulation protocols might be more important in the range of physiological resting membrane potentials ~−70 / −80 mV , for which Cav3 . 3 channels are partially inactivated . In order to test this possibility , the Cav3 . 3 current properties before and after fast stimulation were modeled using the Hodgkin-Huxley formalism as previously described for the native T-type current in thalamic neuron ( Huguenard and Prince , 1992; Destexhe et al . , 1996 ) . We used the NEURON simulation environment ( Hines and Carnevale , 1997 ) as modified previously in order to perform voltage-clamp experiments ( Destexhe et al . , 1996 ) . In this simulation , the model indicated that the Cav3 . 3 current elicited from HP −70 mV would decrease ~75% ( Figure 4a ) according to the shift in the steady-state inactivation observed in Figure 3d . To validate experimentally these data , we performed voltage-clamp experiments at different HPs . Using 0 . 2 Hz frequency of TP stimulation ( to allow current recovery at more depolarized HPs ) , the decrease in Cav3 . 3 current was below 20% at HP −110 mV whereas the current decrease was more prominent ( ~40% , p<0 . 01 ) at HP −80 mV ( Figure 4b ) . Interestingly , the decrease in Cav3 . 3 current was also reduced at HPs above −70 mV and was less than 10% at HP −55 mV ( Figure 4c ) . This U-shaped relationship ( Figure 4c ) could be explained by two interlinked mechanisms: ( i ) at negative membrane potentials ( below −90 mV ) the shift in the steady-state inactivation curve has little impact on the decrease in current amplitude ( see Figure 3d ) ; ( ii ) the small decrease observed at HPs above −70 mV would be related to the reduced Ca2+ entry at depolarized membrane potentials . To directly test this latter hypothesis , the Cav3 . 3 current was recorded using fast TP stimulation ( 1 Hz ) at HP −100 mV and then immediately at HP −70 mV ( Figure 5a ) . At HP −100 mV during 40 s , the fast TP stimulation induced a large Ca2+ entry as evidenced by the decrease in Cav3 . 3 current amplitude ( Figure 5b , left panel ) . Then , when the HP was immediately switched to −70 mV ( Figure 5b , right panel ) , we observed a significant increase in the current amplitude , i . e . recovery , that reached a steady-state after ~100 s ( Figure 5b ) . The Cav3 . 3 current increased furthermore after the stopping of the simulation for 2 min ( Figure 5b ) . The average current amplitude increase during these experiments was ~400% when using a 0 . 2 Hz TP stimulation , and up to 600% at 1 Hz ( Figure 5d ) . Similarly , large effects on the Cav3 . 1 current were also found in these experiments ( Figure 5c ) . Whereas the Cav3 . 1 current decreased only modestly at HP −100 mV ( ~10% , see also Figure 2g ) , the increase in the Cav3 . 1 current following the switch to HP −70 mV reached ~600% ( Figure 5c and d ) as described for the Cav3 . 3 current . This strong recovery of Cav3 . 3 and Cav3 . 1 currents at depolarized HPs clearly indicate that the shift in the steady-state inactivation is a dynamic and reversible mechanism . Importantly , no increase in Cav3 . 3 and Cav3 . 1 current was obtained in cells dialyzed with BAPTA ( Figure 5d ) , further confirming a local Ca2+-dependent feedback mechanism . 10 . 7554/eLife . 22331 . 010Figure 5 . Potent T-type current recovery at physiological resting potentials . ( a–c ) Fast TP stimulation ( TP −30 mV ) using HP of −100 mV to induce inhibition , switched to a HP of −70 mV to induce recovery ( see protocol in ( a ) ) . The inhibition of Cav3 . 3 and Cav3 . 1 currents was measured using fast TP stimulation at a HP of −100 mV ( b–c , left panels ) whereas the recovery of the Cav3 . 3 and Cav3 . 1 currents was measured on a HP of −70 mV ( b–c , right panels ) . ( d ) Quantification of the increase ( recovery ) in Cav3 . 3 , Cav3 . 1 and Cav3 . 2 currents ( at HP −70 mV ) as a function of the TP stimulation frequency ( n = 5–40 per bar ) . The normalized current corresponds to the ratio of the current obtained after 2 min without stimulation ( blue trace ) to the initial current ( 1 s , red trace ) recorded at a HP −70 mV . DOI: http://dx . doi . org/10 . 7554/eLife . 22331 . 01010 . 7554/eLife . 22331 . 011Figure 5—figure supplement 1 . Modulation of the Cav3 . 2 Met1549Ile mutant channel at a high frequency of stimulation . Similar to that described for Figure 5 , the Cav3 . 2 current recovery was measured at a HP of −80 mV ( a–b , right panels ) after their inhibition by 1 Hz stimulation at a HP of −100 mV ( a-b , left panels ) for wild-type ( WT ) Cav3 . 2 channels ( b ) and for the Cav3 . 2 Met1549Ile channels ( c ) . The average current decrease at HP −100 mV and the average current increase at HP −80 mV are shown in ( c ) as insets in the left and the right panels , respectively ( n = 23–31 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 22331 . 011 Interestingly , we did not observe any variation in the Cav3 . 2 current in these experiments ( Figure 5d and Figure 5—figure supplement 1b ) . This finding suggests that because of its biophysical properties , i . e . rapid inactivation kinetics ( as compared to Cav3 . 3 ) and its slow recovery from inactivation ( as compared to Cav3 . 1 and Cav3 . 3 ) ( Klöckner et al . , 1999; Kozlov et al . , 1999; Satin and Cribbs , 2000; Chemin et al . , 2002a; Perez-Reyes , 2003 ) , the Cav3 . 2 current generated in fast TP stimulation does not allow sufficient Ca2+ entry to induce the Ca2+-dependent regulation observed for Cav3 . 1 and Cav3 . 3 channels . To directly test this hypothesis , we have studied a Cav3 . 2 gain of function mutant at Met1549 , recently identified in patients with hypertension due to primary aldosteronism ( Scholl et al . , 2015; Daniil et al . , 2016 ) . The Met1549Ile Cav3 . 2 mutant presents slower inactivation and deactivation kinetics and is expected to induce much larger Ca2+ entry than the wild-type channel ( Daniil et al . , 2016 ) . We found that the Met1549Ile Cav3 . 2 current decreased by ~15% during 1 Hz stimulation at HP −100 mV , whereas the current progressively increased when switched at HP −80 mV to reach a ~300% increase ( Figure 5—figure supplement 1c ) . Altogether , these results demonstrate a common Ca2+-dependent modulation mechanism for the three Cav3 currents , which depends mainly on the amount of the Ca2+ entry and on Cav3 biophysical properties . In order to investigate whether the Ca2+-dependent modulation of the T-type current occurred during more physiological paradigms , we recorded Cav3 . 3 current during a voltage-clamp protocol mimicking thalamic neuronal activities , which was previously described in details ( Chemin et al . , 2002a ) . In these experiments , we found that the Cav3 . 3 current progressively increased during the first burst of spikes and then progressively decreased during the time course of the stimulation ( Figure 6a ) . In addition , a ‘rebound’ in the Cav3 . 3 current was clearly associated with the depolarization after potential ( DAP ) transition , as previously described ( Chemin et al . , 2002a ) . We estimated the current increase as the ratio of the Cav3 . 3 current obtained at the fourth spike to the first one ( Figure 6b ) , whereas the decrease of the current was estimated as the ratio of the Cav3 . 3 current obtained at the thirteenth spike to the first one ( Figure 6c ) . Interestingly , the Cav3 . 3 current increase was similar when cells were dialyzed with an intracellular medium containing either EGTA or BAPTA and reached in average ~300% , suggesting that the current increase is not dependent of intracellular Ca2+ ( p>0 . 05 , Figure 6b ) . In contrast , the decrease in the Cav3 . 3 current was bigger in cells dialyzed with EGTA as compared to BAPTA-dialyzed cells ( p<0 . 001 , Figure 6c ) . Importantly , these results were obtained during the first stimulation of the Cav3 . 3 current suggesting that the Ca2+-dependent modulation of the Cav3 . 3 current could have a strong neuronal impact . To further investigate the behavior of the Cav3 . 3 current during AP clamp experiments , we performed this stimulation several times at a frequency of 1 Hz whereas the cells were clamped at HP −100 mV between each stimulation to allow Cav3 . 3 current recovery ( Figure 6a ) . We found that the Cav3 . 3 current recorded during an AP as well as the ‘rebound’ in the Cav3 . 3 current associated with the DAP progressively decreased when the protocol was repeated 40 times in cells dialyzed with EGTA ( Figure 1a ) . To account for the total Cav3 . 3 current variation , we calculated the integral of the Cav3 . 3 current at each stimulation ( Figure 6d ) . This analysis revealed that the total Cav3 . 3 current decrease was ~80% in cells dialyzed with EGTA whereas the current decrease was less than 15% in cells dialyzed with BAPTA ( p<0 . 001 , Figure 6d ) , indicating further the robust Ca2+-dependent modulation of Cav3 . 3 current during AP-clamp stimulation . 10 . 7554/eLife . 22331 . 012Figure 6 . Ca2+-dependent inhibition of the Cav3 . 3 current during action potential-clamp experiments . ( a ) The top trace represents a burst activity of a thalamic neuron which was used as a waveform . Typical Cav3 . 3 current recorded after dialyzing the cell with EGTA ( middle panel ) or with BAPTA ( lower panel ) . ( b ) Average increase of the Cav3 . 3 current during the first spikes recorded at the first stimulation . The current increase is quantified as the ratio of the current recorded at the fourth to the first spike ( n = 12–14 ) . ( c ) Average decrease of the Cav3 . 3 current during the first stimulation quantified as the ratio of the thirteenth to the first spike ( n = 12–14 ) . ( d ) Time course of the total Cav3 . 3 current during 40 s stimulation . The total Cav3 . 3 current is quantified as the area under the curve ( pA * ms ) ( n = 12–14 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 22331 . 012 We next investigated whether activation of a Ca2+-permeable ionotropic receptor might also induce Cav3 current inhibition , including Cav3 . 2 . In cells co-expressing the purinergic P2X4 receptor and Cav3 . 1 , the Cav3 . 1 current was strongly decreased after extracellular perfusion of a solution containing the purinergic agonist ATP , which generated an inward current of several seconds ( Figure 7a ) . Similar results were obtained for Cav3 . 2 and Cav3 . 3 currents , and ATP caused in average ~80% inhibition of the three Cav3 current ( Figure 7b ) . The decrease in Cav3 current induced by P2X4 activation was coupled to an acceleration of the current inactivation kinetics ( Figure 7c ) and a negative shift in the steady-state inactivation ( Figure 7d ) . Importantly , these effects were absent when similar experiments were performed in the absence of extracellular Ca2+ ( Figure 7b ) . Because the current decrease could involve a change in the cell surface density of the channels , we have investigated whether the membrane expression of Cav3 . 3 channels would be modulated by P2X4 activation ( Figure 7—figure supplement 1 ) . To this end , we used a Cav3 . 3 channel construct containing an extracellular HA tag ( Baumgart et al . , 2008 ) , which allows the measure of its cell surface expression by enzyme-linked immunosorbent assay/luminometry ( Figure 7—figure supplement 1a ) . We found that the ATP treatment did not induce significant change in membrane expression of Cav3 . 3-HA channels in cells co-transfected with either the P2X4 receptor or the pcDNA3 plasmid ( Figure 7—figure supplement 1b ) , suggesting that the current decrease did not involve a change in the cell surface density of the channels . To extend these findings to other classes of physiologically relevant ionotropic receptors , electrophysiological experiments were performed with the Ca2+-permeable NMDA , 5-HT3 , TRPA1 and TRPV1 receptors ( Figure 7e ) . According to the results obtained with the P2X4 receptors , activation of all these receptors produced a 60% to 90% decrease of Cav3 . 1 , Cav3 . 2 and Cav3 . 3 currents and this effect was only observed in the presence of extracellular Ca2+ ( p<0 . 001 , Figure 7e ) . This set of experiments further demonstrate that Ca2+ entry into cells controls a Ca2+-dependent modulation of Cav3 availability . 10 . 7554/eLife . 22331 . 013Figure 7 . Ca2+ entry via Ca2+-permeable ionotropic receptors inhibits Cav3 currents . ( a ) Effect of 10 µM ATP application on the Cav3 . 1 current recorded in a tsA-201 cell expressing the P2X4 receptor . The Cav3 . 1 current recorded just before ( blue trace ) and just after ( red trace ) the application of an ATP solution ( black trace , P2X4 current ) . ( b ) Summary of ATP effect on the Cav3 . 1 , the Cav3 . 2 and the Cav3 . 3 current recorded in tsA-201 cells expressing or not ( pcDNA3 ) the P2X4 receptor in the presence or in the absence of 2 mM Ca2+ in the extracellular solution ( n = 5–23 per bar ) . ( c ) Inactivation kinetics of the Cav3 currents in the absence and in the presence of ATP ( 2 mM Ca2+ , n = 11–18 per bar ) . ( d ) Steady-state inactivation properties of the Cav3 currents the presence and in the absence of ATP ( 2 mM Ca2+ , n = 10–17 per point ) . ( e ) Summary of the effect of NMDA , 5-HT3 , TRPA1 and TRPV1 receptor activation on the Cav3 current amplitude recorded in the presence or in the absence of 2 mM Ca2+ in the extracellular saline ( n = 5–30 per bar ) . In all these experiments the HP is −80 mV . DOI: http://dx . doi . org/10 . 7554/eLife . 22331 . 01310 . 7554/eLife . 22331 . 014Figure 7—figure supplement 1 . Effect of P2X4 activation on membrane density of Cav3 . 3 channels . ( a ) Schematic representation of a Cav3 . 3 channel construct containing an extracellular HA tag ( located in the IS5-pore loop ) used to measure its membrane expression in tsA-201 cells . The cells were co-transfected with the Cav3 . 3 channel and either the P2X4 receptor or the pcDNA3 plasmid . ( b ) Quantification by ELISA/luminometry of the membrane expression levels of HA-tagged Cav3 . 3 channels after treatment with 10 µM ATP . The histogram represents relative light unit ( RLU ) normalized toward the values obtained in the control condition ( P2X4 without ATP treatment ) . DOI: http://dx . doi . org/10 . 7554/eLife . 22331 . 014
This study reveals that T-type / Cav3 channels are dynamically regulated by changes in intracellular Ca2+ concentration . This novel regulation involves a Ca2+-dependent modulation of Cav3 availability . It was unraveled by demonstrating that a rise in submembrane Ca2+ at the vicinity of the Cav3 channels can cause a hyperpolarizing shift in the steady-state inactivation , leading to a strong Cav3 current decrease at physiological resting membrane potentials . This localized increase in intracellular Ca2+ can be generated by the Cav3 channel activity itself , especially for Cav3 . 3 channels , or by other routes of Ca2+ entry through the plasma membrane as obtained following activation of various Ca2+-permeable ionotropic receptors , all inducing a potent inhibition of Cav3 . 1 , Cav3 . 2 and Cav3 . 3 currents . Importantly , all the effects described here were observed in 2 mM Ca2+-containing saline , which corresponds to the physiological range of extracellular Ca2+ concentration in native tissues ( Jones and Keep , 1988 ) , and were lost in the presence of intracellular BAPTA . The Ca2+-dependent modulation revealed here was best evidenced with Cav3 . 3 , which allows the largest Ca2+ entry among Cav3 channel family ( Klöckner et al . , 1999; Kozlov et al . , 1999; Chemin et al . , 2002a; Perez-Reyes , 2003 ) . The decrease in Cav3 . 3 current amplitude was well correlated with the increase in the frequency of the TP stimulation . The reduction in Cav3 . 3 current amplitude was ~50% at 1 Hz frequency of stimulation and reached ~80% during neuronal activities in action potential clamp , suggesting that intracellular Ca2+ could be an important physiological regulator of Cav3 current . Importantly , this effect was very dynamic both in its induction ( T0 . 5~18 s ) and in its recovery ( T0 . 5~20 s ) . Altogether , our results demonstrate that the Cav3 current decrease is linked to the cumulative Ca2+ entry through Cav3 channels . This Ca2+-dependent inhibition of Cav3 channels was observed only in the presence of external Ca2+ indicating that a voltage-dependent component in the mechanism is unlikely to contribute to the reported effect . Importantly , this Cav3 current inhibition induced by the Ca2+ entry is observed using intracellular EGTA but not anymore after the cell dialysis with BAPTA . These data reveal that this Ca2+-dependent modulation of Cav3 channels involves a localized increase in submembrane Ca2+ at the vicinity of the Cav3 channels without the need for a global Ca2+ increase ( Marty and Neher , 1985; Roberts , 1993; Deisseroth et al . , 1996 ) . Interestingly , in the inside-out patch configuration , the direct application of Ca2+-containing solutions up to 100 µM to the internal side of the membrane did not produce the current inhibition , suggesting that this phenomenon requires some additional components preserved in the whole-cell configuration . A key finding of this study is that the current inhibition is closely linked to the resting membrane potential ( Vm; see the schematic representation in Figure 7 ) . On one hand , an increase in submembrane Ca2+ promotes a strong negative shift in the steady-state inactivation properties of T-type channels , leading to a more prominent inhibition of the Cav3 current at physiological Vm ( ~ −65/–85 mV; Figure 7 ) . On the other hand , the Ca2+ entry via T-type channels is maximal at negative Vm ( ~ −100 mV ) , but the consequence of the shift in the steady-state inactivation is minimized at negative Vm ( Figure 7 ) . Consequently , the inhibition of the Cav3 current is maximal at physiological resting membrane potentials after stimulation at negative HP . Using this paradigm our data revealed an important finding: a wide range of modulation ( ~600% ) of both Cav3 . 3 and Cav3 . 1 currents , but not of Cav3 . 2 current which was resistant to this modulation . The lack of Ca2+-dependent modulation of Cav3 . 2 channels using fast TP stimulation protocols could be explained by the specific electrophysiological properties of this Cav3 isoform . It is expected that the Cav3 . 2 current would generate little cumulative Ca2+entry because of its rapid inactivation kinetics combined with its slow recovery from inactivation ( Klöckner et al . , 1999; Kozlov et al . , 1999; Satin and Cribbs , 2000; Chemin et al . , 2002a; Perez-Reyes , 2003 ) . Interestingly , the Met1549Ile gain-of-function mutant of the Cav3 . 2 channel that displays very slow inactivation and deactivation kinetics ( Scholl et al . , 2015; Daniil et al . , 2016 ) , exhibits a significant Ca2+-dependent inhibition and a wide range of modulation ( ~300% ) . Collectively , our data indicate that the Ca2+-dependent mechanism described here can modulate all three Cav3 isoforms . Overall , the amplitude of the modulation ( Cav3 . 3 > Cav3 . 1 >> Cav3 . 2 ~ 0 ) critically depends on the amount of the Ca2+ entry and therefore relies on the biophysical properties of each Cav3 isoform . The large Ca2+ entry generated by the activation of ionotropic receptors induces a strong inhibition ( ~85% ) of all three Cav3 currents , including wild-type Cav3 . 2 . We observed this effect with a variety of Ca2+-permeable ionotropic receptors , including the purinergic P2X4 , the glutamatergic NMDA , the serotoninergic 5-HT3 and the TRP ( TRPV1 and TRPA1 ) receptors . Notably , no inhibition of the T-type current is observed when these experiments are performed in the absence of extracellular Ca2+ , demonstrating the critical role played by the Ca2+ entry in this mechanism of Cav3 inhibition . It is noteworthy that the tsA-201 cells , which derived from HEK-293 cells , endogenously express another class of purinergic receptors , P2Y , which are Gq-coupled receptors and induce a global increase in intracellular Ca2+ in response to ATP ( Chemin et al . , 2000; Fischer et al . , 2005 ) . Interestingly , we observed no change Cav3 current properties in pcDNA3 transfected cells ( Figure 6b ) indicating that Cav3 currents are not inhibited in P2X untransfected cells following ATP application . These data suggest that activation of P2Y receptors is unable to mediate a Ca2+-dependent modulation of Cav3 currents further supporting a membrane-delimited mechanism for the Ca2+-dependent modulation of Cav3 channels . Since the initial discovery of T-type currents , it was admitted that these channels were not regulated by intracellular Ca2+ ions or by changes in intracellular Ca2+ concentration because they do not present CDI ( Carbone and Lux , 1984; Fedulova et al . , 1985; Bean , 1985; Bossu et al . , 1985; Bossu and Feltz , 1986; Dupont et al . , 1986 ) . More recently , these findings were confirmed using cloned T-type channels ( Staes et al . , 2001; McRory et al . , 2001; Perez-Reyes , 2003 ) but see ( Lacinová et al . , 2006 ) . Indeed , our data showing that the inactivation kinetics are similar at the beginning of the stimulation obtained in external Ca2+ and in external sodium ( Figure 2e ) also support the lack of CDI for Cav3 channels . Accordingly , the important structural motifs for CDI present on L-type channels , as the Ca2+ ( EF Hand ) and the calmodulin ( IQ motif ) binding sites are absent on the C-terminus of Cav3 channels ( Staes et al . , 2001; McRory et al . , 2001; Perez-Reyes , 2003 ) . Therefore , CDI was considered in pioneer studies as a hallmark to distinguish between L-type and T-type Ca2+ currents ( Carbone and Lux , 1984; Fedulova et al . , 1985; Bean , 1985; Bossu et al . , 1985; Bossu and Feltz , 1986; Dupont et al . , 1986 ) . Increasing the intracellular Ca2+ concentration [Ca2+]INT from 10−10 to 10−7 M or even to 10−6 M was classically used to isolate a ‘pure’ T-type current , which presented no change in its inactivation kinetics , whereas the L-type current disappeared because of the acceleration of its ‘run-down’ ( Bossu et al . , 1985; Bossu and Feltz , 1986; Dupont et al . , 1986 ) . Interestingly , in these seminal studies , the native T-type current was mostly related to Cav3 . 2 ( nickel-sensitive ) channels ( Carbone and Lux , 1984; Fedulova et al . , 1985; Bean , 1985; Bossu et al . , 1985; Bossu and Feltz , 1986; Dupont et al . , 1986 ) , for which we show that the intrinsic electrophysiological properties do not allow the triggering of the Ca2+-dependent current inhibition , contrary to Cav3 . 1 and Cav3 . 3 channels . However , and consistent with our present findings , these early studies performed in the presence of an increasing amount of [Ca2+]INT have reported important features of the T-type current: ( 1 ) at 10−8 M , the T-type current was stable during 60 min whereas at 10−7 M the T-type current decreased and was suppressed after 10–15 min ( Bossu et al . , 1985 ) ; ( 2 ) at 10−7 M , an hyperpolarizing shift ( ~10 mV ) of the steady-state inactivation occurred ( Bossu and Feltz , 1986 ) ; and ( 3 ) at 10−6 M no T-type current was recorded at HPs above −100 mV ( Dupont et al . , 1986 ) . Altogether , these historical results and our present data are in favor of a ‘minute scale’ Ca2+-dependent modulation of the T-type current , which induces a hyperpolarizing shift in the steady-state inactivation and consequently a decrease in T-type current amplitude ( see Figure 7 ) . It is also important to depict our results in the light of recent findings obtained on L-type channels using high frequency of stimulation ( Oliveria et al . , 2007 , 2012 ) . In 15 mM external Ca2+ , the Cav1 . 2 current dropped to 40% of the control amplitude during 1 Hz stimulation and this inhibition was abolished in the presence of intracellular BAPTA ( Oliveria et al . , 2007 , 2012 ) . The Cav1 . 2 current decrease was stable after 3–5 min , similar to that obtained with 40–50 s stimulation for Cav3 . 3 in the presence of the physiological 2 mM Ca2+ concentration , and the recovery was almost total after 4–5 min . However , contrary to Cav3 currents , the decrease in the Cav1 . 2 current stimulated at 1 Hz did not involve a shift in its steady-state inactivation ( Oliveria et al . , 2007 , 2012 ) , suggesting distinct mechanisms for the L-type and the T-type current modulation . In addition , a prolonged stimulation of Cav1 . 2 current ( in 10 mM external Ca2+ saline ) induced channel endocytosis ( Green et al . , 2007; Tsuruta et al . , 2009; Hall et al . , 2013 ) and , in this case , the recovery of the L-type current took approximately 30 min ( Green et al . , 2007 ) . This Ca2+-dependent modulation of L-type channels appears distinct of that described here for Cav3 channels , both regarding the time of the recovery and the shift in the steady-state inactivation . In addition , we found that ATP treatment in cells expressing both the P2X4 receptors and the Cav3 . 3 channels did not induce significant changes in membrane expression of Cav3 . 3 , suggesting that the current decrease did not involve an endocytosis mechanism . Therefore , although the L- and the T-type channel regulation share apparent similar properties , modulation of the T-type current by cumulative Ca2+ entry has unique features , depending mostly on the shift of the steady-state inactivation , i . e . the modulation of Cav3 availability . The discovery of a Ca2+-driven feedback regulation of T-type channels may have important physiological and pathophysiological implications . Indeed , an increase in the activity of T-type channels have been implicated in several diseases linked to altered Ca2+ signaling ( Orestes et al . , 2013; Jagodic et al . , 2007; Scholl et al . , 2015; Zamponi , 2016; Daniil et al . , 2016 ) and T-type channel activity is also linked to Ca2+ overload toxicity occurring in ischemia ( Nikonenko et al . , 2005; Bancila et al . , 2011; Gouriou et al . , 2013 ) . Also , our study reveals that activation of Ca2+-permeable ionotropic receptors could also markedly inhibit T-type channel activity , and interestingly , cross-talk between these receptors and T-type channels have been recently observed ( Comunanza et al . , 2011; Kerckhove et al . , 2014; Wang et al . , 2015; Tatsuki et al . , 2016 ) . In summary , we have identified a novel regulation pathway for T-type Ca2+ channels . By demonstrating that Ca2+ entry exerts a feedback control on T-type channel activity , our study opens up new horizons towards deciphering how this local and dynamic Ca2+-dependent modulation of Cav3 channels can impact the cellular and physiological roles of T-type channels in normal and disease states .
tsA-201 cells ( RRID:CVCL_2737 ) were obtained from the European Collection of Authenticated Cell Cultures ( ECACC 96121229 ) . The identity of tsA201 has been confirmed by STR profiling and the cells have been eradicated from mycoplasma at ECACC . We routinely tested the cells for the absence of the mycoplasma contamination . Cells were cultivated in DMEM supplemented with GlutaMax , 10% fetal bovine serum and 1% penicillin / streptomycin ( Invitrogen , Fisher Scientific , France ) . Transfections were performed using jet-PEI ( Ozyme , France ) with a DNA mix ( 1 . 5 µg total ) containing 0 . 5% of a GFP encoding plasmid and 99 . 5% of either of the plasmids ( pcDNA3 . 1 ) that code for the human Cav3 . 1a , Cav3 . 2 , Cav3 . 3 and Met1549Ile Cav3 . 2 constructs . In experiments with ionotropic receptors , 1 µg of either of the plasmid constructs that code for human P2X4 , mouse 5-HT3 , human TRPV1 , mouse TRPA1 and rat NMDA receptor ( NR1A and NR2A ( 0 . 5 µg each ) ) were added to the DNA mix . Two days after transfection , tsA-201 cells were dissociated with Versene ( Invitrogen , Fisher Scientific , France ) and plated at a density of ~35 × 103 cells per 35 mm Petri dish for electrophysiological recordings , which were performed the following day . All animal use procedures were done in accordance with the directives of the French Ministry of Agriculture ( A 34-172-41 ) . Dorsal root ganglion ( DRG ) neurons were prepared as described earlier ( Voisin et al . , 2016 ) . Briefly , adult male C57BL/6J mice were anaesthetized with pentobarbital injection and transcardially perfused with HBSS ( pH 7 . 4 , 4°C ) . Lumbar DRGs with attached roots were dissected and collected in cold HBSS supplemented with 5 mM HEPES , 10 mM D-glucose and 1% penicillin/streptomycin . DRGs were treated with 2 mg/ml collagenase II and 5 mg/ml dispase for 40 min at 37°C , washed in HBSS and resuspended in 1 ml of neurobasal A medium supplemented with B27 , 2 mM L-glutamine and 1% penicillin/streptomycin ( Invitrogen , Fisher Scientific , France ) . Single-cell suspensions were obtained by 5 passages through three needle tips of decreasing diameter ( gauge 18 , 21 , and 26 ) . Cells were plated onto polyornithine/laminin-coated dishes . After 2 hr , the medium was removed and replaced with neurobasal B27 supplemented with 10 ng/ml neurotrophin 4 ( NT4 ) and 2 ng/ml glial derived neurotrophic factor ( GDNF ) . Patch clamp recordings were performed 6–24 hr after plating on neurons with a ‘rosette’ morphology corresponding to D-hair neurons that express alarge density of T-type current ( Dubreuil et al . , 2004; Voisin et al . , 2016 ) . Macroscopic currents were recorded at room temperature using an Axopatch 200B amplifier ( Molecular Devices , Sunnyvale CA ) . Borosilicate glass pipettes had a resistance of 1 . 5–2 . 5 MOhm when filled with an internal solution containing ( in mM ) : 140 CsCl , 10 EGTA , 10 HEPES , 3 Mg-ATP , 0 . 6 GTPNa , and 3 CaCl2 ( pH adjusted to 7 . 25 with KOH , ~315 mOsm , ~100 nM free Ca2+ using the MaxChelator software , http://maxchelator . stanford . edu/ ) . Similar results were obtained using either 10 mM or 20 mM EGTA . In some experiments , BAPTA ( 20 mM ) was substituted with EGTA . The extracellular solution contained ( in mM ) : 135 NaCl , 20 TEACl , 2 CaCl2 , 1 MgCl2 , and 10 HEPES ( pH adjusted to 7 . 25 with KOH , ~330 mOsm ) . To avoid inhibition of 5-HT3 and NMDA receptors , NaCl was substituted with TEACl and MgCl2 was also omitted in NMDA experiments . In the cell-free inside-out patch experiments the intrapipette solution contained 100 mM BaCl2 and 10 mM HEPES ( pH adjusted to 7 . 25 with NaOH , ~310 mOsm ) and the bath solution contained ( in mM ) : 145 KCl , 10 HEPES and 1 MgCl2 ( pH adjusted to 7 . 25 with KOH , ~305 mOsm ) . In the inside-out configuration the patch was perfused with the bath solution supplemented with either 1 mM EGTA or increasing the concentration of CaCl2 ( 1 , 10 and 100 µM ) . For D-hair neuron recordings , the bath solution contained ( in mM ) : 140 NaCl , 10 HEPES , 5 KCl , 2 CaCl2 , 1 MgCl2 and 10 glucose ( pH adjusted to 7 . 25 with NaOH , ~330 mOsm ) and cells were perfused with an extracellular solution containing ( in mM ) : 140 TEACl , 10 HEPES , 5 KCl , 2 NaCl , 2 CaCl2 , 1 MgCl2 and 10 glucose ( pH adjusted to 7 . 25 with TEAOH , ~330 mOsm ) . Recordings were filtered at 2 kHz . Steady-state inactivation curves were fitted using the Boltzmann equation where I/I max = 1/ ( 1+exp ( ( Vm−V0 . 5 ) /slope factor ) ) . Data were analyzed using pCLAMP9 ( Molecular Devices ) and GraphPad Prism ( GraphPad ) softwares . Results are presented as the mean ± SEM , and n is the number of cells . Statistical analysis was performed with the Student t-test or with one-way ANOVA combined with a Tukey post-test for multiple comparisons ( *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 ) . Modelling was performed using the NEURON simulation environment ( Hines and Carnevale , 1997 ) . The model was modified to simulate voltage-clamp experiments in thalamic reticular neurons ( Destexhe et al . , 1996 ) ( as available from the model database at Yale University ( https://senselab . med . yale . edu/modeldb/ ) . The electrophysiological properties of the Cav3 . 3 channels were modelled using Hodgkin-Huxley equations as described previously ( Huguenard and Prince , 1992; Destexhe et al . , 1996 ) . The values obtained for Cav3 . 3 were substituted for the corresponding values of native T-channels in thalamic reticular neurons ( Huguenard and Prince , 1992 ) , as previously described ( Chemin et al . , 2002a ) . To match the voltage clamp data , the modelling experiments were performed at 24°C . The equations to model the Cav3 . 3 current properties at rest were:m∞=1/ ( 1+exp ( − ( v+52 . 6 ) /4 . 7 ) ) h∞=1/ ( 1+exp ( ( v+72 . 4 ) /5 . 7 ) ) taum= ( 1 . 377+1 . 512/ ( exp ( ( v+12 . 52 ) /14 . 38 ) +exp ( − ( v+81 . 59 ) /5 ) ) ) tauh= ( 65 . 34+1/ ( exp ( ( v+41 . 04 ) /4 . 01 ) +exp ( − ( v+333 . 1 ) /46 . 86 ) ) ) The equations to model the Cav3 . 3 current properties after 40 s stimulation at 1 Hz frequency were:m∞=1/ ( 1+exp ( − ( v+55 . 9 ) /5 . 6 ) ) h∞=1/ ( 1+exp ( ( v+81 . 1 ) /5 ) ) taum= ( 1 . 141+0 . 9592/ ( exp ( ( v+14 . 95 ) /13 . 97 ) +exp ( − ( v+81 . 53 ) /5 ) ) ) tauh= ( 26 . 55+0 . 66/ ( exp ( ( v+32 . 42 ) /6 . 4 ) +exp ( − ( v+225 ) /22 . 21 ) ) ) The membrane expression of the Cav3 . 3 channel was quantified as previously described ( Chemin et al . , 2007 ) . The tsA-201 cells were cultured in 24-well plates and co-transfected with a Cav3 . 3-HA construct ( Baumgart et al . , 2008 ) and either the P2X4 receptor or the pcDNA3 plasmid ( ratio 1:1 ) . Two days after transfection , ATP treatments were performed as in the electrophysiological experiments . The cells were washed with the electrophysiological extracellular solution containing 3 µM ivermectin and afterward 10 µM ATP was applied for 30–45 s at room temperature . Then the cells were directly fixed for 5 min in PBS containing 4% paraformaldehyde . After three PBS washes , the cells were incubated for 30 min in blocking solution ( PBS plus 1% fetal bovine serum ) . The Cav3 . 3-HA protein was detected using a rat anti-HA monoclonal antibody ( 1:1000 dilution; clone 3F10 , Roche Diagnostics , France ) after incubation for 1 hr at room temperature . After four washes with PBS plus 1% fetal bovine serum for 10 min , cells were incubated for 30 min with horseradish peroxidase-conjugated goat anti-rat secondary antibody ( 1:1000 dilution; Jackson ImmunoResearch Laboratories , West Grove , PA ) . Cells were rinsed four times with PBS for 10 min before addition of SuperSignal enzyme-linked immunosorbent assay Femto maximum sensitivity substrate ( Pierce , Fisher Scientific , France ) . Luminescence was measured using a VICTOR2 luminometer ( PerkinElmer Life Sciences , Waltham , MA ) , and the protein amount in each well was then measured using the BCA assay ( Pierce , Fisher Scientific , France ) to normalize the measurements . All data were normalized to the level of signal obtained in P2X4 transfected cells without the ATP treatment . Each experiment was performed in quadruplicate and three independent sets of transfection experiments were performed under each condition . The results are presented as the mean ± SEM . Compounds were purchased from Sigma ( France ) . To activate ionotropic receptors , we used 10 µM ATP in the presence of 3 µM ivermectin for P2X4 , 100 µM glutamate in the presence of 100 µM glycine for NMDA , 10 µM serotonin for 5-HT3 , 0 . 5 µM capsaicin for TRPV1 and 100 µM allyl isothiocyanate for TRPA1 . Drugs were applied using a gravity-driven homemade perfusion device and control experiments were performed similarly using the vehicle alone . | Neurons , muscle cells and many other types of cells use electrical signals to exchange information and coordinate their behavior . Proteins known as calcium channels sit in the membrane that surrounds the cell and can generate electrical signals by allowing calcium ions to cross the membrane and enter the cell during electrical activities . Although calcium ions are needed to generate these electrical signals , and for many other processes in cells , if the levels of calcium ions inside cells become too high they can be harmful and cause disease . Cells have a “feedback” mechanism that prevents calcium ion levels from becoming too high . This mechanism relies on the calcium ions that are already in the cell being able to close the calcium channels . This feedback mechanism has been extensively studied in two types of calcium channel , but it is not known whether a third group of channels – known as Cav3 channels – are also regulated in this way . Cav3 channels are important in electrical signaling in neurons and have been linked with epilepsy , chronic pain and various other conditions in humans . Cazade et al . investigated whether calcium ions can regulate the activity of human Cav3 channels . The experiments show that these channels are indeed regulated by calcium ions , but using a distinct mechanism to other types of calcium channels . For the Cav3 channels , calcium ions alter the gating properties of the channels so that they are less easily activated . As a result , fewer Cav3 channels are “available” to provide calcium ions with a route into the cell . The next steps following on from this work will be to identify the molecular mechanisms underlying this new feedback mechanism . Another challenge will be to find out what role this calcium ion-driven feedback plays in neurological disorders that are linked with altered Cav3 channel activity . | [
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] | 2017 | Activity-dependent regulation of T-type calcium channels by submembrane calcium ions |
Texture discrimination is a fundamental function of somatosensory systems , yet the manner by which texture is coded and spatially represented in the barrel cortex are largely unknown . Using in vivo two-photon calcium imaging in the rat barrel cortex during artificial whisking against different surface coarseness or controlled passive whisker vibrations simulating different coarseness , we show that layer 2–3 neurons within barrel boundaries differentially respond to specific texture coarsenesses , while only a minority of neurons responded monotonically with increased or decreased surface coarseness . Neurons with similar preferred texture coarseness were spatially clustered . Multi-contact single unit recordings showed a vertical columnar organization of texture coarseness preference in layer 2–3 . These findings indicate that layer 2–3 neurons perform high hierarchical processing of tactile information , with surface coarseness embodied by distinct neuronal subpopulations that are spatially mapped onto the barrel cortex .
The somatosensory system and especially the highly developed mystacial whisker system is one of the most important senses used by rats to sense the external world . The rat mystacial pad contains an array of vibrissae ( whiskers ) that rhythmically move back and forth ( whisk ) to palpate the environment ( Woolsey and Van der Loos , 1970; Carvell and Simons , 1990 ) . Using their whiskers , rodents can locate and distinguish objects in their immediate sensory environment ( Carvell and Simons , 1990; von Heimendahl et al . , 2007; Diamond et al . , 2008b ) . Texture discrimination is one of the major sensory tasks performed by the barrel whisker system , and just a few whisker palpations are sufficient for discriminating between different texture coarseness with a high degree of sensitivity and reliability ( Guic-Robles et al . , 1989; Carvell and Simons , 1990 ) . Previous studies have shown that activation of the primary barrel cortex is crucial for texture coarseness discrimination ( Guic-Robles et al . , 1989; Carvell and Simons , 1990; Guic-Robles et al . , 1992 ) , yet despite its importance the functional organization and texture coarseness coding in the S1 barrel cortex are largely unknown . Previous studies of texture coding in anaesthetized rats and texture discrimination in awake behaving rats revealed that the degree of texture coarseness was correlated with the average firing rate of the granular and infragranular neurons ( Arabzadeh et al . , 2005 , 2006; von Heimendahl et al . , 2007; Diamond et al . , 2008a; Jadhav et al . , 2009 ) . However , a simple averaged firing coding scheme was not sufficient to explain the psychophysical discrimination curve of the rats ( Arabzadeh et al . , 2006 ) . Thus , other or additional coding schemes are required to achieve the high performances of the rat whisker system ( Jadhav et al . , 2009; Morita et al . , 2011 ) . One such additional coding scheme that has been suggested previously is a temporal code scheme of texture coarseness ( Arabzadeh et al . , 2006; Wolfe et al . , 2008; Jadhav et al . , 2009 ) . In different sensory modalities , sensory features are coded selectively by specialized neurons that respond best to certain parameters of the stimulus which in turn can be spatially mapped across the cortex . This spatio-functional representation is one of the fundamental principles governing organization of different sensory cortexes . For example , in the visual system , neurons that selectively respond to certain bar orientations or movement directions in the visual field are organized in columns ( Hubel and Wiesel , 1977; Ohki et al . , 2005 ) . In the somatosensory whisker system the most conspicuous functional spatial map is the somatotopical organization of the vibrissae array onto the S1 cortex ( Woolsey and Van der Loos , 1970; Andermann and Moore , 2006 ) . Information about the organization within the barrel column is lacking . It has been shown that neurons in the barrel cortex selectively respond to certain angular directions of whisker deflection ( Bruno et al . , 2003; Andermann and Moore , 2006; Lavzin et al . , 2012 ) , which tend to be arranged in maps ( Hubel and Wiesel , 1977; Andermann and Moore , 2006; Lottem and Azouz , 2009; Kremer et al . , 2011 ) . However , aside from angular tuning ( Bruno et al . , 2003; Andermann and Moore , 2006 ) , mapping of other fundamental sensory features including texture coarseness onto the somatosensory barrel cortex is largely unknown . The present study sets out to use two-photon calcium imaging to further understand texture coarseness coding and explore the spatial representation of texture coarseness in layer 2–3 of the rat barrel cortex in vivo .
We recorded the responses of layer 2–3 neurons during artificial whisking ( Semba and Egger , 1986; Szwed et al . , 2003 ) ( ‘Materials and methods’ ) across sandpapers with different degrees of coarseness ( P120 , P320 , P600 , and P1000 ) and whisking in air ( free whisk–FW ) while simultaneously recording calcium transients from multiple neurons bulk loaded with Fluo-4 using two-photon laser scanning microscopy ( TPLSM ) ( Figure 1A–D ) . Sandpapers are traditionally used as a substrate of choice to investigate coarseness ( Guic-Robles et al . , 1989; Diamond et al . , 2008b ) as different sandpapers mainly differ by the average size of their particles ( grit size ) , which are uniformly distributed across a flat surface . In experiments that included FW only three sandpapers were examined ( P320 , P600 , and P1000 ) due to time limitation imposed by the dye washout observed in the AM bulk loading technique ( Sato et al . , 2007 ) . To increase the temporal resolution during acquisition of calcium signals from large number of neurons , we developed a new free hand line scan routine that allowed us to retain high temporal resolution ( 50–100 Hz ) while simultaneously recording from dozens of neurons ( 50 . 3 ± 3 . 4 neurons per experiment , range of 24–89 neurons per experiment , 23 rats ) ( ‘Materials and methods’; Figure 1A ) . The stability of the line scan path was checked continuously during the experiment ( Figure 1—figure supplements 1 and 2 ) . Responses were recorded from the principle barrel , as determined by intrinsic imaging during whisker deflection ( Figure 1—figure supplement 3 ) , and whisker displacements were measured by an infrared photo-sensor and a high speed camera ( Flare , IO industries at 1000 fps ) ( Figure 1B , Figure 1—figure supplement 4; Video 1 ) . Our imaging measurements showed that the calcium transients were locked to the whisking envelopes ( Figure 1B ) . However , the temporal resolution of our calcium measurements ( up to 100 Hz ) was not sufficient to detect changes resulting from individual micro movements of the whisker . To characterize the variability and reliability of the calcium and whisking responses between repeated trails , we calculated the mean and SEM of the responses in individual neurons ( individual traces are shown for four neurons in Figure 1D and the average values are shown for all neurons in a single experiment in Figure 1E ) . Furthermore , we tracked whisker movements during artificial whisking using the fast camera ( 1000 Hz ) ( Video 1 ) . We found little variability in the whisker angle between consecutive trails and between different trial blocks ( Figure 1—figure supplement 4A ) , indicating robust whisker movements during artificial whisking . As expected , we did observe differences in the whisker curvature ( Figure 1—figure supplement 4B ) and differences in the stick and slip events between different texture coarseness ( Figure 1—figure supplement 5 ) . 10 . 7554/eLife . 03405 . 003Figure 1 . Two-photon calcium imaging of layer 2–3 neurons evoked by artificial whisking against textures in the barrel cortex in vivo . ( A ) Experimental set-up . A small ( 2 × 2 mm ) craniotomy was performed above the barrel cortex , and the dura was carefully removed . Nerve stimulation was performed with a silver hook attached electrode to the buccal nerve . Only the principle whisker was left intact while other whiskers were trimmed . Artificial whisking ( 10 trains at 5 . 5 Hz ) was performed either in free air ( FW ) or against different sandpapers . The calcium indicator Fluo-4 AM and the astrocytic dye SR101 were injected to layer 2–3 via a glass pipette under visual control using the two-photon microscope . The right upper panel shows an image of all cells filled with Fluo-4 and the right lower panel shows the astrocytes filled with SR101 in the same field of view ( 260 µm from pia ) . Left lower panel , calcium dependent fluorescent signals were collected from cells ( horizontal aspect ) using the free hand line scan mode ( cyan line in right upper panel ) and presented as a function of time ( vertical aspect ) . Red lines mark the stimulus time . ( B ) Simultaneous recordings of the fluorescent calcium signal ( red ) and whisker trajectory ( gray and blue ) recorded using an optic sensor . The blue trace shows the unfiltered optical signal , and the gray trace shows the same optical data filtered ( band pass of 50–500 Hz ) . 10% scale bar denotes the ΔF/F calcium transient signal . ( C ) Simultaneous voltage recording ( gray ) and calcium imaging ( red ) from a single neuron . The cell was bulk loaded with Fluo-4 and electrophysiological recordings were performed in the cell-attached recording mode from visually targeted neurons . Action potentials were partially truncated in the electrophysiological traces . ( D ) Example of the averaged calcium transient ( red ) and the corresponding single traces ( 14 repetitions , gray ) from 4 single cells are presented together with the whisker stimulation ( blue , 10 whisking cycles at 5 . 5 Hz ) . The traces in the different neurons were obtained simultaneously in the free hand line-scan imaging mode . ( E ) Average peak amplitude ( ±SEM ) ( red ) and area ( gray ) of the calcium transients evoked by artificial whisking for all the cells ( n = 51 ) recorded in one experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 00310 . 7554/eLife . 03405 . 004Figure 1—figure supplement 1 . Free hand line scan position stability . Example of control images acquired every 5 min to verify the location of the free hand line scan path relative to the neurons . If necessary the line scan path was corrected so the same neurons can be compared throughout the length of the experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 00410 . 7554/eLife . 03405 . 005Figure 1—figure supplement 2 . Quantification of the free hand line scan position stability . ( A ) Example of a reference image of Fluo-4-labeled neurons with the scanned line passing through the neurons ( yellow , scale bar 50 µm ) . ( B ) Measurements of X and Y image shifts compared to the reference image during all trails in a single experiment . ( C ) Average shift per trial ( +SD ) of the image in the X and Y directions ( n = 15 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 00510 . 7554/eLife . 03405 . 006Figure 1—figure supplement 3 . Intrinsic optical imaging mapping of the principle barrel . ( A ) The bone above the barrel cortex was thinned and the surface blood vessels were imaged . ( B ) Intrinsic optical imaging ( 610-nm LED ) shows a decrease in the reflectance during stimulation of whisker D2 in this example ( 6 Hz deflections over 2 s duration ) . Red circle marks the region where decrease in reflectance was observed . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 00610 . 7554/eLife . 03405 . 007Figure 1—figure supplement 4 . Kinematic variables of a whisker movement in artificially whisking rat . ( A ) The angle at base of the whisker ( mean ± SEM ) during artificial whisking against free air ( FW ) and two different sandpapers , the finest ( P1000 ) and the coarseset we used ( P120 ) . Whisker was photographed at 1000 Hz . Three individual artificial whisking trains were performed in two separate stimulation blocks , each composed of five consecutive repetitions . The average ( ±SEM ) of the two whisking blocks is presented separately ( red and black traces ) . The SEM is shown for every fifth point . ( B ) Average curvature calculated from the 10 repetitions for the three different conditions recorded ( P120-blue , P1000-black , and FW-red ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 00710 . 7554/eLife . 03405 . 008Figure 1—figure supplement 5 . Slip-stick events characteristic to different coarseness . ( A ) Mean number of acceleration events per second during whisker movement on four sandpapers ( P120-blue , P320-red , P600-green , P1000-yellow ) , calculated on C3 whisker . Number of events per second is plotted on a log scale . Each point shows the cumulative number of events with acceleration greater than the threshold indicated on the x-axis . ( B ) Number of low-acceleration events ( events with acceleration in the range 4–9 µm/ms2 ) measured on four textures . ( C ) Number of high-acceleration events ( ≥28 mm/ms2 ) across four textures . ( D ) Ratio of the number of high- to low-acceleration events per second , as a function of texture roughness . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 00810 . 7554/eLife . 03405 . 009Figure 1—figure supplement 6 . The relative fluorescence contributed from neuropil and out of focus signals . Left panel shows the average transients of traces with and without action potential firing in the neurons in which we recorded simultaneously both electrophysiological and imaging data ( n = 6 ) . Traces without action potential firing represented the contribution of the neighboring out of focus neurons and neuropil signals , gray line represents stimulus time . Right panel shows the net fluorescence value ( after background subtraction ) in cells and in neuropil . Background was measured in neighboring blood vessels . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 00910 . 7554/eLife . 03405 . 010Video 1 . Whisker movement against textures induced by facial nerve stimulation ( artificial whisking ) . Whisking was induced by facial nerve stimulation ( trains of 10 stimuli at 100 Hz repeated at 5 . 5 Hz ) . Whisker movement was captured with a fast camera ( 1000 Hz acquisition rate ) while swiping 4 different textures ( upper left P1000 , upper right P600 , lower left P320 , lower right P120 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 010 In our experiments , besides using intrinsic imaging to determine the principle barrel , the barrel boundaries and their relation to the bulk loaded neurons were more accurately determined using two additional methods . First , in all experiments , we identified the vertical blood vessels at the TPLSM plane and histological images , and vertically reconstructed down to the layer 4 barrel which was identified by cytochrome oxidase staining . Second , in some experiments ( n = 7 ) , we validated our conclusions by performing biocytin electroporation of selected bulk loaded neurons and later performed post hoc histology of cytochrome oxidase barrel staining aligned to the neuronal biocytin staining ( Figure 2 , Figure 2—figure supplement 1 ) . For these experiments at the end of the imaging session several bulk loaded neurons were electroporated with biocytin using a patch electrode ( ‘Materials and methods’ ) . The biocytin-labeled neurons served as second clear anatomical landmark for alignment with the barrel boundaries ( Figure 2 , Figure 2—figure supplement 1 ) . We found a good agreement between the two alignment methods in the seven experiments where both blood vessels and electroporated neurons were used . 10 . 7554/eLife . 03405 . 011Figure 2 . Alignment of the bulk loaded neurons in the TPLSM with the cytochrome oxidase barrel staining using biocytin electroporation of single neurons . ( A ) TPLSM Fluo-4 bulk loaded image of the imaged field . At the end of the recording session single cell electroporation of biocytin was performed on the bulk loaded neurons ( typically , 1–4 calcium loaded neurons ) . Yellow arrow point to the one of the electroporated cells ( right panel ) . These biocytin electroporated neurons served as a definite marker to the location of the calcium bulk staining . ( B ) Brains were fixed and sliced ( 100 µm thick ) at the tangential plane , and barrels were visualized according to the cytochrome oxidase dense regions typical to layer 4 barrels . Alignment of the neurons labeled with biocytin ( in cortical layer 2–3 ) to the layer 4 cytochrome oxidase stained barrel according to identified ascending blood vessels ( designated by numbers 1–7 ) in three consecutive slices from layer 2–3 to layer 4 ( slices: 100–200 µm; 200–300 µm; 400–500 µm ) . ( C ) Superposition of the biocytin-labeled neurons onto the identified barrel in layer 4 ( C2 in this example ) . ( D ) The biocytin-labeled neurons are shown in higher magnification ( arrow point to the identified cell in A ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 01110 . 7554/eLife . 03405 . 012Figure 2—figure supplement 1 . Alignment of the bulk loaded neurons onto the barrel using single elecrtoporated cells . Top left panel ( A ) shows electroporated cells superimposed on a ‘normalized barrel’ in seven experiments ( scale bare: 100 µm ) . Remaining seven panels ( B ) show the location of biocytine electroporated neurons ( closed colorful circles ) relative to the bulk loaded neurons ( open circles ) in seven individual experiments . Subset figures: the biocytin-labeled neurons are shown in higher magnification . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 012 Combined TPLSM and cell-attached patch clamp recordings from the same neurons ( Figure 1C ) indicated that TPLSM calcium imaging effectively detected most single action potentials ( 82% in all neurons and 91% in neurons with superior signal to noise ratio , see ‘Materials and methods’ ) and essentially all trains of two or more action potentials . In principle , the calcium responses could be presented either as the average ΔF/F or as peri-stimulus time histograms ( PSTH ) of spikes after transforming the calcium responses of individual traces to spike trains . We chose to present the data as averaged ΔF/F rather than spike trains for two main reasons: first , while our algorithm of transforming individual calcium responses to spike trains robustly detected firing events , determining the exact number of action potentials in each event was less reliable especially when firing is more intense as in our case . Second , reliable spike train transformation can only be performed in ∼50% ( 56 . 4 ± 3 . 5% ) of our recorded neurons in accordance with previous studies thus reducing the number of simultaneously analyzed cells ( Kerr et al . , 2007; Sato et al . , 2007; Rothschild et al . , 2010 ) ( ‘Materials and methods’ ) . We compared the response of neurons to the different textures ( sandpapers P120 , P320 , P600 , P1000 ) and found that only a small minority of neurons showed either monotonically increasing or decreasing responses with surface coarseness ( Romo and Salinas , 2003; Arabzadeh et al . , 2005; von Heimendahl et al . , 2007; Diamond et al . , 2008b ) . Instead , many neurons responded preferentially to one of the four textures ( Figure 3A , B ) . To quantify the response to different textures , we calculated the selectivity index ( SI ) for each neuron ( see Figure 3A , B for examples; for definition of SI see ‘Materials and methods’ ) . We further defined neurons with S . I . ≥ 0 . 35 as texture coarseness preferring neurons ( for more details see ‘Materials and methods’ ) . The SI calculations were performed independently using both the peak and area of the averaged calcium transients , and in 90 . 6 ± 3 . 2% of neurons , the preferred texture determined by the peak amplitudes and response areas corresponded . 10 . 7554/eLife . 03405 . 013Figure 3 . Texture preferring neurons in layer 2–3 barrel cortex evoked by artificial whisking: barrel vs septa regions . ( A ) Examples of the average calcium transient responses ( 30 repetitions ) of six neurons to different textures ( upper panels ) and their corresponding tuning curves ( lower panels; mean ± SEM ) as calculated from the peak of the averaged calcium response evoked by each texture . For each cell the selectivity index ( SI ) is calculated and presented in the upper left corner . P120-blue , P320-red , P600-green , P1000-yellow . ( B ) Examples of the average calcium transient responses ( 30 repetitions ) of three neurons to different textures and free whisking ( FW ) and their corresponding tuning curves ( lower panels; mean ± SEM ) as calculated from the peak of the averaged calcium response evoked by each texture or FW . For each cell the selectivity index ( SI ) is calculated and presented in the upper left corner . P320-red , P600-green , P1000-yellow , and FW-cyan . ( C ) The percentage of neurons ( out of 1158 neurons within barrel boundaries in 23 experiments; 301 neurons in septa in five experiments ) that were either coarseness preferring , non-preferring ( NP ) or non-responsive ( NR ) . Neurons were divided to those within the barrel boundaries ( red ) and those located in the septa regions ( blue ) . ( D ) The coarseness preferring neuronal population ( S . I . ≥ 0 . 35 ) was further subdivided according to their texture coarseness preference ( either P1000 , P600 , P320 , P120 , or FW ) . ( E ) Cumulative histogram of S . I . ( in percent values ) calculated from the calcium transient signals of all responsive neurons . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 01310 . 7554/eLife . 03405 . 014Figure 3—figure supplement 1 . Repeated stimulation sessions of same textures resulted in reliable calcium responses in layer 2–3 neurons . ( A–D ) Box plots of the peak amplitude ( gray ) and area ( red ) of the average calcium transients ( mean ± SEM ) in individual traces presented for different experiments ( n = 4 ) . The 30 traces were divided into two repetition blocks separated in time . In addition for each experiment the grand average calcium transient of all the neurons is presented for each repetition block ( first black , second red ) below the box plots . ( E ) The average ( mean ± SEM; n = 5 ) peak amplitude ( left panel ) and area ( right panel ) of the grand averaged calcium responses during the first and second repetition blocks . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 01410 . 7554/eLife . 03405 . 015Figure 3—figure supplement 2 . Stability of single neuron selectivity in repeated stimulation sessions . ( A and B ) Examples from three neurons showing the tuning curves in the first ( A , left ) and second stimulation blocks ( B , right ) . ( C ) Summary plot of the percent change ( mean ± SD; n = 453 neurons ) in the amplitude ( left panel ) and area ( right panel ) of the overall average calcium response between the two stimulation blocks . The data are shown for the response evoked by the four different textures . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 015 Overall , texture coarseness preferring neurons were almost exclusively located within the principle barrel boundaries as determined with the post hoc histology with single cell resolution ( Figures 2 and 3C ) . Aside from rare exceptions , neurons residing outside the barrel boundaries were either non-responsive or showed low selectivity ( S . I . < 0 . 35 ) ( Figure 3C ) . The average calcium transients of responsive cells in the septa regions was approximately 10% smaller than that recorded within barrel boundaries ( 3 . 7 ± 0 . 3% compared to 4 . 0 ± 0 . 2% , p < 0 . 01 ) . Thus , it is unlikely that the lack of coarseness preferring neurons in the septa regions resulted solely from the small amplitude of the responses in the septa . On average 54 . 4 ± 3 . 5% of all recorded neurons within the barrel boundaries showed either texture coarseness or FW preference ( Figure 3C , D ) . We further divided these neurons according to their preferred texture and found neurons that preferentially responded to each of the textures examined ( Figure 3D ) . Figure 3E presents a cumulative histogram of the SI values of all responsive neurons . To further confirm the TPLSM results , we performed electrophysiological extracellular single unit recordings from layer 2–3 neurons ( 293 neurons in 19 rats ) . In these experiments , we recorded the response to active whisking against five textures ( P120 , P320 , P600 , P1000 , and compact disc [CD] ) . Similar to the TPLSM data , we found texture coarseness preferring neurons which maximally responded to each of the textures we examined ( Figure 4 ) . The percent of non-responsive neurons was lower in the electrophysiological recordings ( 2 ± 1 . 3% ) ( Figure 4B ) , probably due to the larger number of repetitions in the electrophysiological experiments . Taken together the electrophysiological findings further support our TPLSM findings with regard to the existence of texture coarseness preferring neurons in layer 2–3 of the barrel cortex . 10 . 7554/eLife . 03405 . 016Figure 4 . Coarseness preferring neurons as determined by extracellular single unit recordings . Multi electrode single unit recordings were performed using multi-contact extracellular electrodes from the S1 barrel cortex . ( A ) Example of PSTHs from two neurons in response to different texture coarseness ( CD , P320 , P600 P1000 ) . Each PSTH was acquired during artificial whisking ( 10 whisks applied at 5 . 5 Hz ) against CD and three textures ( P320 , P600 , and P1000 ) . The responses of 170 consecutive artificial whisk trains are summed and presented in 10-ms time bins . The underlying gray line designates the time of whisking train . Note that the two neurons prefer different coarsenesses . The neuron presented on the right preferred the P1000 texture , while the neuron shown on the left prefer the P600 texture . ( B ) A summary of the responses acquired from single unit experiments ( altogether 293 neurons from 19 rats ) . Responses were divided according to the percentage of neurons that were texture coarseness preferring ( S . I . ≥ 0 . 35 ) , non-preferring ( NP; S . I . < 0 . 35 ) or non-responsive ( NR ) to all textures examined . ( C ) The coarseness preferring neurons were further subdivided to neurons that preferred either P1000 , P600 , P320 , P120 textures or CD . ( D ) Cumulative histogram of S . I . calculated from the single unit recordings of all responsive neurons neurons . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 016 An essential requirement for reliable comparison between the responses of layer 2–3 neurons to different textures is the stability of the responses evoked by the different textures during the experiments . To assess the stability of responses during our calcium imaging recordings , we divided the repetitions of each texture stimulus ( typically ∼30 repetitions ) into two blocks segregated in time and interleaved the different texture blocks ( ‘Materials and methods’ ) . In each experiment , we compared the peak and area of the averaged calcium transient responses of all recorded neurons ( Figure 3—figure supplement 1 ) and of individual neurons ( Figure 3—figure supplement 2 ) to each texture between the two blocks . Experiments were included in the analysis only if we did not find a significant difference between the responses of the two blocks of all textures ( Figure 3—figure supplements 1 and 2 ) . We next investigated the spatial organization of neurons with respect to their preferred texture coarseness ( Figures 5 and 6 ) . The preferred texture coarseness is mapped for all responsive cells and for cells with selectivity indexes ≥0 . 35 ( Figure 5 ) . For both cases , we found spatial clustering of layer 2–3 neurons according to their preferred texture coarseness ( Figure 5 ) . 10 . 7554/eLife . 03405 . 017Figure 5 . Spatial organization of texture preferring neurons during artificial whisking . ( A ) Texture coarseness preferring neurons from two experiments ( upper and lower panels ) were color coded according to their preferred texture ( P120-blue , P320-red , P600-green , P1000-yellow , non-preferring-white and non-responsive-black ) . Left panels: only cells with S . I . ≥ 0 . 35 were included ( S . I . thresholded maps ) . Right panels: all responsive neurons ( unthresholded maps ) from the same two experiments were color coded according to their preferred texture and the selectivity strength ( value of the S . I . is scaled for each color and presented on the right ranging from 0 to 1 ) . ( B and C ) The probability of having 1 , 2 , 3 , or 4 next neighbors with similar preferred textures was calculated for the thresholded maps ( B ) and unthresholded maps ( C ) and compared with the probabilities expected from random spatial distribution ( n = 12 rats ) ( **p < 0 . 01 ) . ( D ) Histograms of the Euclidian distance for 1000 runs for the two maps presented in A . To obtain these histograms the number of neurons that preferred each texture coarseness , as well as the number of non-responsive and non-preferring neurons was determined for each experiment . Later these neurons were randomly distributed in the recorded field 1000 times , and the Euclidian distance between neurons with the same preferred texture coarseness was measured . The broken line represents the lowest 5% of the histogram . The arrows represent the average experimental intra group distances for each texture coarseness ( blue: P120-P120; red: P320-P320; green: P600-P600; yellow: P1000-P1000 ) . Note that all intra-group distances in the two example experiments were below the 5% threshold value . The same result was obtained for all experiments tested ( n = 20 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 01710 . 7554/eLife . 03405 . 018Figure 6 . Spatial mapping of the preferred texture of neurons aligned to a normalized barrel . ( A ) Examples of four spatial texture preference maps taken from four different brains ( 3 inside the barrel boundaries and 1 in the septa ) are presented . We carefully aligned the neurons with the barrel field ( Scale bar: 50 µm ) ( see Figure 2; ‘Materials and methods’ for details ) . ( B ) Left: superimposition of the spatial maps from 12 experiments onto a ‘normalized’ barrel ( Scale bar: 100 µm ) , middle panel smoothened map . In 7 out of the 12 maps imaging location was determined using the electroporation method as described in Figure 2; in the remaining maps we used vertical blood vessels alignment . ( The preferred textures of neurons were color coded as P120-blue originated from 6 rats , P320-red originated from 10 rats , P600-green originated from 8 rats , P1000-yellow originated from 10 rats , non-preferring-white and non-responsive-black . ) ( C ) Distance from the MC-LR diagonal ( upper ) and radial distance from the center of the barrel ( lower ) of each cell in the different experiments is presented for each texture separately and for the combined coarser textures ( black , P120 and P320 ) and finer textures ( gray , P600 and P1000 ) ( **p < 0 . 01 for comparison of coarser and finer textures ) . Comparison of the four textures with ANOVA was not significant for the radial distance and yielded a p < 0 . 01 for the distance from the MC-LR diagonal . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 01810 . 7554/eLife . 03405 . 019Figure 6—figure supplement 1 . Mapping of texture coarseness , non-selective and non-responsive cells relative to the barrel borders . ( A ) Superimposition of the spatial maps of all neurons obtained from 15 experiments onto a ‘normalized’ barrel . These experiments include 12 maps in which cells were predominantly within the barrel boundaries and three additional maps in which neurons were mostly out of the barrel boundaries ( blue P120 , red P320 , green P600 , yellow P1000 , white non-preferring neurons and black non-responsive neurons ) ( Scale bar: 100 µm ) . ( B ) Three examples of individuals maps showing the non-responding/non-selective cells located in septal regions while selective cells were mostly confined to the barrel ( blue P120 , red P320 , green P600 , yellow P1000 , white non-preferring neurons and black non-responsive neurons ) ( Scale bar: 100 µm ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 01910 . 7554/eLife . 03405 . 020Figure 6—figure supplement 2 . The relationship between barrel location and dominant texture preference . Each barrel was assigned a dominant texture , which was the texture preferred by the largest number of neurons recorded in the barrel . The number of barrels with the different dominant textures was plotted as a function of the row ( upper panel ) and arc ( lower panel ) of the barrel ( blue P120 , red P320 , green P600 , and yellow P1000 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 020 To quantify the degree of spatial clustering , we used two methods . First , we examined the probability of having the same preferred texture coarseness in the closest 1–4 neighboring neurons ( Figure 5B , C ) . The preferred texture coarseness in the closest neighboring cell was 49 . 0 ± 3 . 9% compared to only 23 . 6 ± 3 . 4% expected from chance occurrence for cells with S . I ≥ 0 . 35 and 47 . 9 ± 13 . 2% compared to 36 . 6 ± 3 . 4% for all neurons ( Figure 5B , C; p < 0 . 01 ) . Significant differences between the measured and expected probabilities were also observed for 2–4 closest neighbors with the same preferred texture coarseness ( Figure 5B , C ) . The second method we used to assess the clustering significance of neurons according to their preferred texture coarseness was the Monte Carlo analysis . Using this method , we compared the average distance between neurons with the same preferred texture coarseness in our experimental data with that of randomly distributed neurons ( calculated for 1000 runs ) ( for details see ‘Materials and methods’ section ) . We found that in all our maps the average distance between neurons sharing the same texture coarseness preference was significantly smaller than that expected by chance occurrence ( 20 maps , at the p < 0 . 05 level , examples are shown in Figure 5D ) . Aligning the TPLSM image with the histologically confirmed barrel boundaries ( Figure 2 , Figure 2—figure supplement 1 ) revealed that in all cases clusters of texture selective neurons were observed within the barrel boundaries , while septal neurons were mostly non-preferring and non-responsive neurons ( Figure 6 , Figure 6—figure supplement 1 ) . When we aligned multiple maps from different experiments and projected them onto a single ‘normalized’ barrel , we observed a tendency for texture coarseness to be arranged along the rostro/medial-caudo/lateral diagonal of the barrel with a tendency of the coarser texture ( P120 ) to be represented in the rostro-medial region and the finer texture ( P1000 ) at the caudo-lateral region ( Figure 6C; p < 0 . 01 4 way ANOVA ) . In addition , we found that the coarser textures tended to be preferentially represented at the perimeters of the barrel . Although the four-way ANOVA did not reach statistical significance , this was evident by the fact that neurons which preferred coarser textures ( P120 and P320 ) had a significantly larger average radial distance compared with neurons that preferred the finer textures ( P600 and P1000 ) ( p < 0 . 01; Figure 6C ) . The statistical difference in the ANOVA test indicated that the arrangement of coarseness across the diagonal of the barrel is stronger compared to the radial arrangement . To find out whether coarseness might be mapped according to the anterior-posterior positioning of the barrel within the barrel field as well , we plotted the dominant frequency preference of barrels as a function of their location along the row and arc of the barrel field . We did not observe any spatially consistent trend according to the arc or row barrel position ( Figure 6—figure supplement 2 ) . To investigate whether texture coarseness preference is retained along the vertical depths of layer 2–3 ( z-axis ) , we performed single unit recordings with multi-contact , single shaft silicon probe electrodes . With these electrodes we were able to simultaneously record neurons from six different vertical depths across layer 2–3 ( 250–500 µm , inter-contact distance of 50 µm ) . We found that the probability of having the same texture coarseness preference in neurons recorded within all contacts of the same electrode was significantly higher than that expected from chance occurrence ( 3 . 5-fold for cells with S . I . > 0 . 34 and 2 . 2-fold for all cells; Figure 7A–C ) . We next calculated the probability of retaining the same texture coarseness preference across the z-axis depth of layer 2–3 . For this analysis , we initially calculated the dominant preferred texture coarseness of neurons in the first contact ( 250 µm from the pia ) and next calculated the probability that neurons in consecutive contacts will have the same preferred texture coarseness . The findings show that neurons tended to retain the same texture coarseness preference across the different contacts along the depth of layer 2–3 ( Figure 7C , D ) . 10 . 7554/eLife . 03405 . 021Figure 7 . Columnar organization of preferred texture coarseness across layer 2–3 of the barrel cortex . ( A and B ) Comparison of the measured ( solid ) and expected ( striped ) probabilities for having the same preferred texture coarseness across all neurons recorded in different vertical depth of layer 2–3 using single shaft multi-contact electrodes ( inter-contact distance of 50 µm ) . The data are presented for each individual electrode ( A ) and for the average of all electrodes ( B ) . The data is also shown for all neurons with S . I . > 0 . 34 ( red ) and for all responsive neurons ( average ± SEM ) ( blue ) . The expected probabilities were calculated from all 133 neurons in 12 electrodes from 12 rats ( single multi-contact electrode per rat ) . ( C ) The preferred texture coarseness of all units across the six contacts of two example individual single shaft multi-contact electrodes from two different rats ( electrode 5 and 9 in panel A ) Circle with black perimeter mark units with S . I < 0 . 35 . ( D ) The probability of retaining the same preferred texture along the different vertical contacts of a single electrode in layer 2–3 . We initially determined the dominant preferred texture coarseness in the first contact ( 250 µm from the pia ) . Later , we presented the probability neurons will have the same preferred texture coarseness in consecutive contacts . For this analysis , we only used electrodes in which the first recording contact showed a clear dominant preferred texture ( at least 66% of neurons with the same preferred texture , 9 out of 12 electrodes with 93 neurons ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 021 To better control for whisker movements and texture placement , we repeated the experiments using a fully controlled passive vibration protocol ( Figure 8 ) . With this protocol , we initially recorded the micro vibrations of the whiskers when contacting a rotating cylinder placed orthogonally to the whisker and covered with different sand paper textures ( P100 , P320 , P600 , and P1000 ) . The actual micro vibrations of the whiskers when contacting the different sandpapers were measured by an infrared photo-sensor and converted to voltage signals . These voltage signals showed typical power spectrum characteristics for the different texture coarseness used ( Figure 8—figure supplement 1 ) . Later we used galvanometers to replay the typical micro vibrations evoked by the different texture coarsenesses to vibrate the principle whisker in anaesthetized rats . 10 . 7554/eLife . 03405 . 022Figure 8 . Preferred responses of neurons in layer 2–3 barrel cortex during passive replay of texture-like vibrations . ( A ) Experimental set up . The principle whisker was passively stimulated using a galvanometer with four texture-like micro vibrations at amplitudes of 15–30 µm . ( B ) Examples of the average calcium transient responses ( 30 repetitions ) of four neurons to different passive coarseness-like vibrations simulating the four different textures ( upper panels ) and their corresponding tuning curves ( lower panels; mean ± SEM ) as calculated from the peak of the averaged calcium response . For each cell the selectivity index ( S . I . ) is calculated and presented in the upper left corner ( P100-blue , P320-red , P600-green , P1000-yellow ) . ( C ) The percentage of neurons ( out of 324 neurons in five experiments ) that were either texture-like preferring , non-preferring or non-responsive . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 02210 . 7554/eLife . 03405 . 023Figure 8—figure supplement 1 . Passive whisker stimulation characteristics . Power spectral densities ( left panel ) and power centroids ( right panel ) of the simulated whisker vibrations of the four textures presented to the whiskers during the experiments using galvanometers . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 023 Similar to the artificial whisking experiments , passively replaying the four different texture-like vibrational signals ( corresponding to different sandpapers P100 , P320 , P600 , and P1000 ) to the principle whisker resulted in a significant increase of firing to at least one of the simulated texture stimuli in 84 . 1 ± 13 . 4% of neurons within the barrel boundaries ( Figure 8 ) . Moreover , similar to the artificial whisking experiments , we have seen different response curves to the different texture-like vibrations ( Figure 8B ) . 53% ( 52 . 7 ± 8 . 12% ) of neurons within the barrel boundaries selectively preferred one of the texture-like vibrations ( S . I . > 0 . 35; Figure 8C ) . We next examined the spatial organization of texture-like preferring neurons . We found that similar to artificial whisking , neurons tended to spatially cluster across the S1 barrel cortex according to their preferred texture-like vibration ( Figure 9A ) . We quantified the spatial clustering using the close neighbor analysis ( Figure 9B ) and found that on average the probability of having the same texture-like preference in the closest neighbor neuron was 40 . 9 ± 6 . 1% compared to 24 . 2 ± 4 . 0% as expected from random distribution ( p < 0 . 01 ) . Similarly , the probability of sharing the same texture-like preference with 2–4 of the closest neighbors was significantly higher than expected from random spatial distribution of neurons ( Figure 9B ) . 10 . 7554/eLife . 03405 . 024Figure 9 . Spatial organization of responding neurons during passive replay of coarseness-like vibrational signals . ( A ) ( B ) Left: texture-like preferring neurons ( S . I . ≥ 0 . 35 ) from two experiments ( upper and lower panels ) were color coded according to their preferred simulated coarseness . Right: all responsive neurons ( with no S . I . thresholding ) were color coded according to their preferred texture coarseness ( blue P120 , red P320 , green P600 , yellow P1000 , white non-preferring neurons and black non-responsive neurons ) , and the selectivity strength ( S . I . value ) which is depicted by the color scale ( scale for each color is presented on the right , ranging from 0 to 1 ) . ( B ) The probability of having 1 , 2 , 3 , or 4 next neighbors with similar preferred simulated coarseness was calculated for the unthresholded maps and compared with the probabilities expected from random spatial distribution for all experiments performed ( n = 6 rats ) ( **p < 0 . 01 ) . ( C ) Superimposition of the passive maps from six experiments onto a ‘normalized’ barrel before ( left ) , and after smoothing with a spatial filter ( middle panel ) . In all six maps imaging location was determined using the electroporation method as described in Figure 2 . ( D ) Distance from the MC-LR diagonal ( upper ) and radial distance from the center of the barrel ( lower ) of each cell in the different experiments is presented for each texture separately and for the combined coarser textures ( black , P120 and P320 ) and finer textures ( gray , P600 and P1000 ) ( **p < 0 . 01 and *p < 0 . 05 . Comparison of the four textures with ANOVA was not significant for the radial distance and yielded a p < 0 . 01 for the distance from the MC-LR diagonal . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 02410 . 7554/eLife . 03405 . 025Figure 9—figure supplement 1 . Spatial organization of responding neurons-combined artificial whisking and passive replay of coarseness-like vibrational signals . ( A ) Superimposition of maps from all active and passive experiments onto a ‘normalized’ barrel ( left panel , scale bar: 100 µm ) . Right panel shows a smoothened map of the data presented in the left panel . The radial distance from the center of the barrel ( B ) and distance from the MC-LR diagonal ( C ) of each cell in the different active and passive experiments is presented for each texture separately ( left ) , and for the combined coarser ( black , P120 and P320 ) and finer textures ( gray , P600 and P1000 ) ( right , **p < 0 . 01 ) . ANOVA testing of the four textures were not significant for the radial distance and significant ( p < 0 . 01 ) for the distance from the MC-LR diagonal . DOI: http://dx . doi . org/10 . 7554/eLife . 03405 . 025 Similar to the artificial whisking results , superposition of the passive maps onto a normalized barrel shows a strong arrangement along the rostro/medial-caudo/lateral diagonal of the barrel ( Figure 9D ) . We also observed a tendency for the coarser-like textures to be preferentially represented at the perimeters of the barrel although this tendency was weaker compared to the active maps ( Figure 9D ) . Superposition of the combined passive and artificial whisking maps onto one normalized barrel further strengthen our conclusion with regard to spatial arrangement of texture coarseness along the rostro/medial-caudo/lateral diagonal of the barrel and to a lesser extent the radial arrangement of coarseness as well ( Figure 9—figure supplement 1 ) . Taken together , the well-controlled passive replay of texture-like vibration confirm our findings with the artificial whisking paradigm and rule out the possibility that the spatial clustering of texture coarseness preference resulted from experimental variability in presenting the textures or other unknowns associated with artificial whisking .
Using their whiskers , rats can reliably detect small differences in surface coarseness ( Guic-Robles et al . , 1989; Carvell and Simons , 1990 ) . Psychophysical tests show that using their macro vibrissae , rats can discriminate between grain sizes as small as 10–20 µm ( Morita et al . , 2011 ) . The underlying cortical coding mechanisms responsible for the formidable capability of rodents to distinguish texture coarseness have been studied extensively , mainly in granular and infra-granular cortical layers using multi- and single-unit recordings ( Guic-Robles et al . , 1992; Arabzadeh et al . , 2003 , 2006; von Heimendahl et al . , 2007; Diamond et al . , 2008a; Morita et al . , 2011 ) . In the granular and infra-granular cortical layers of the barrel cortex neurons mostly respond to stick and slip events , and surface coarseness has been shown to be coded for the most part by the average spike count in the neuronal population . However , a rate coding spike count scheme could only explain discrimination between rough vs smooth textures ( von Heimendahl et al . , 2007 ) , but could not explain discrimination between finer textures well within the psychophysical discrimination curve of the rat ( Arabzadeh et al . , 2003 , 2005; Diamond et al . , 2008a ) . Thus , additional coding schemes based on the timing of action potential firing or on sparse synchronous firing have been suggested ( Arabzadeh et al . , 2006; Diamond et al . , 2008b; Jadhav et al . , 2009 ) . In addition to the ‘temporally based coding schemes’ , a ‘resonance’ coding theory has also been suggested . The resonance hypothesis suggests that texture identity is represented spatially across the whisker pad . This representation stems from the gradient of whiskers' lengths across the pad , denoting each whisker with a distinct resonance frequency ( Neimark et al . , 2003; Andermann et al . , 2004; Sato et al . , 2007; Diamond et al . , 2008a ) . Our results suggest a novel complementary strategy for coding texture coarseness in layer 2–3 of the barrel cortex . According to our results , coarseness is coded by neurons which prefer different coarsenesses , including the intermediate texture coarseness P600 . We corroborated the existence of these neurons using electrophysiological single unit recordings . Interestingly , we found that neurons with similar coarseness preference are spatially clustered . Though certainly differences in coarseness induce differences in multiple parameters in whisker kinematics , the spatial clustering by itself may hint that coarseness is a fundamental quality that is represented in the rodent cortex . A coding scheme based on coarseness selective neurons can increase the reliability of texture coarseness coding as coarseness can be extracted by comparing the activity of neurons with different coarseness tuning . The alternative coding scheme where coarseness is extracted from the average firing of the neuronal population maybe more sensitive to the activity state of the animal and the context of the stimulus . This coding scheme is highly reminiscent of the coding suggested in primary visual and auditory cortexes in which subsets of neurons respond with increased firing selectively to a specific feature of the physical stimulus such as orientation or tone frequency ( Hubel and Wiesel , 1968; Nelken et al . , 2004 ) . As to the ‘temporal coding’ theories , the low acquisition rate of our calcium imaging data precluded us from critically testing these hypotheses , yet our findings do not contradict the use of temporal coding in the barrel cortex ( Arabzadeh et al . , 2006; Diamond et al . , 2008a; Jadhav et al . , 2009 ) . Most of the previous studies including this present study preferred to treat texture coarseness as a gestalt quality due to its complex nature and psychophysical importance . The downside of such an approach is that it does not address the question of what features are extracted by neurons during different coarseness representation . Keeping in mind that in sensory research one can only find tuning within the parameter space examined , designing the appropriate parameter space of stimuli that will encompass in unbiased way what features are extracted by neurons is a highly complicated issue and awaits further investigation . It is important to stress that this problem is generic to all sensory systems . Just as in the visual system , for example , both simple , artificial stimuli ( e . g . , drifting gratings ) , and more naturalistic stimuli ( e . g . , natural image patches ) revealed complementary aspects of sensory coding ( Felsen et al . , 2005; Gollisch and Meister , 2008 ) , also in the somatosensory system both simple passive and more complex natural stimuli are needed to reveal the full coding scheme of the system . The observation that functional information is mapped onto the cortical layers is one of the fundamental organizational principles of the central nervous system . In different sensory modalities , neurons which process closely related sensory information are contiguous . Examples of this basic organizational principle include orientation selectivity maps in the visual cortex and sound frequencies that are mapped in a tonotopic manner onto the auditory cortex of cats and monkeys ( Hubel and Wiesel , 1968; Stiebler et al . , 1997 ) . Interestingly , in rats visual object orientation and auditory tonotopic cortical mapping were significantly less organized than in primates ( Ohki et al . , 2005; Rothschild et al . , 2010 ) . In layer 2–3 of the rodent visual cortex the neurons did not show the typical columnar organization rather a ‘salt and pepper’ organization in which orientation selectivity was not spatially organized ( Ohki et al . , 2005 ) . Similarly , in the auditory cortex of rodents the tonotopic organization was only present on large-scale mapping , but broke down at finer scales ( Bandyopadhyay et al . , 2010; Rothschild et al . , 2010 ) . In our study , using TPLSM calcium imaging , we could map coarseness preference in a population of S1 layer 2–3 neurons . In contrast to the primary visual and auditory cortexes in rats , we found a new spatio-functional organization in which similar texture selective neuron cluster together . These clusters were arranged along the rostro/medial-caudo/lateral diagonal of the barrel and to a lesser extent showed a tendency for radial arrangement of coarseness as well . This coarseness map is the second map described in S1 barrels in addition to the whisker angular direction map ( Bruno et al . , 2003; Andermann and Moore , 2006; Kremer et al . , 2011 ) . It is not known whether these two spatial maps ( angular tuning and coarseness ) are independent from one another or represent different aspects of a mutual physical property . Using biocytine electroporation , we overlaid the calcium maps with the anatomical coordinates of the barrels with single cell accuracy . From this analysis , we found that selective neurons to texture coarseness were almost exclusively confined to the barrel boundaries , whereas neurons in the septa area were non-responsive or showed low preference to texture coarseness . Previous anatomical and functional studies have shown that barrel and septa neurons belong to different sub-networks associated with different thalamocortical and intra-cortical circuits ( Brecht and Sakmann , 2002; Shepherd and Svoboda , 2005; Bureau et al . , 2006; Kerr et al . , 2007 ) . The large differences in responses of barrel and septa neurons in our experiments are consistent with these findings . Our findings further suggest that texture coding is performed mostly by barrel neurons , yet these findings need to be confirmed in awake behaving animals . Taken together our findings in cortical layer 2–3 combined with previous findings from the lower cortical layers of the barrel cortex suggest that layer 2–3 neurons may represent a higher processing station compared with the granular layer 4 neurons ( Diamond et al . , 2008a ) . Our data show that layer 2–3 neurons , rather than using a simple rate coding scheme of the physical properties like layer 4 neurons ( Romo et al . , 2003 ) , are organized in subpopulations of coarseness selective neurons , which are spatially mapped in the cortex . This coding scheme is consistent with the general strategy to extract fundamental features from the tactile stimuli and code them in spatially organized neuronal sub networks . In this study , we did not investigate the mechanisms underlying the formation of coarseness clusters . We hypothesize that the emergence of coarseness clusters in layer 2–3 results from the feed forward connectivity patterns from lower cortical layers , possibly combined with higher connection probabilities between neurons belonging to the same coarseness cluster and dendritic amplification mechanisms ( Lavzin et al . , 2012 ) .
Wistar rats ( male P27–35 ) were anesthetized with intra-peritoneal urethane ( 1 . 5 gr/kg body weight ) . After exposing the skull a stainless steel frame was attached to the bone using dental acrylic cement . A 2 × 2 mm wide craniotomy was opened above the barrel cortex and the dura was carefully removed . Brain pulsations were reduced by filling the craniotomy with 1 . 2% agarose in ACSF solution and covering with immobilized coverslip . The exposed cortex was super fused with normal rat ringer's solution . Body temperature was maintained at 36–37°C using a heating blanket ( FHC ) . For the artificial whisking stimulation , the buccal branch of the facial nerve was exposed and cut at its proximal end ( Semba and Egger , 1986 ) . The principle whisker was identified using intrinsic optical imaging ( Grinvald et al . , 1986 ) . Functional imaging was performed using a Qcam CCD camera ( Q-imaging , Canada ) equipped with a tandem lens system ( DO-2595; F/0 . 95; and DO-5095; F/0 . 95; Navitar Inc . , NY ) and 610-nm LEDs ( Telux VLWTG 9900; Vishay Electronic GmbH , Germany ) , while stimulating a single whisker by a galvanometer ( Model 6210H , Cambridge instruments; 6 Hz deflections over 2 s duration ) controlled via an isolated pulse stimulator ( model 2100; A-M systems ) . The surface blood vessel pattern was imaged for reference . Image acquisition of the reflectance changes in the hemodynamic signal and analysis were made using a frame grabber board ( PCI-2110; National Instruments ) and custom software written in our lab in the Matlab software . Images were acquired at 10 Hz frame rate ( total of 200 frames per trial ) with a 2 × 2 binning ( ∼300 × 300 pixels , 7 . 4 μm pixel size ) . Bolus loading of the calcium indicator Fluo-4 AM ( Invitrogen ) to layer 2–3 neurons ( 250–350 μm below the pia ) in the barrel cortex was performed as previously described ( Stosiek et al . , 2003; Sato et al . , 2007 ) . Briefly , the dye was first dissolved in DMSO ( Sigma ) and 20% ( wt/vol ) Pluronic acid F-127 to yield a concentration of 10 mM . This solution was further diluted to a final concentration of 1 mM in a solution composed of 125 mM NaCl , 5 mM KCl , 10 mM glucose , 10 mM HEPES , 2 mM CaCl2 , 2 mM MgSO4 , and 0 . 2 mM sulforhodamine 101 ( SR101 ) . The dye was injected into the cortex under visual control using a broken glass electrode ( ∼10 μm tip diameter ) . Two-photon imaging was performed using a Prairie two photon laser scanning microscope ( TPLSM ) platform ( Prairie Technologies , Wisconsin , USA ) equipped with a Ti: Saphire laser excitation ( Spectra Physics ) and a 40X water immersion objective lens ( 0 . 8 NA , Olympus ) . For calcium imaging , 870 nm excitation was used . Emission light from the Fluo-4 and the SR101 was collected simultaneously by two external photomultiplier ( Hamamatsu; 570 nm dichroic mirror for separation of the emission to the two photomultipliers ) and displayed using the software package Prairieview 3 . 1 . 0 . 0 . A ‘free hand line scan routine’ was written in the lab ( by Yoav Rubin ) to allow faster control of the galvanometers and is now added to the Prairie View software . This enabled us to define a line that passed through large numbers of neurons with relatively high temporal resolution ( acquisition rate of 50–100 Hz ) . To assure the stability of the line scan a reference image was captured every 3–5 scans and if needed corrections were made ( Figure 1—figure supplement 1 ) . Methods that allow for scanning along a user defined path were described in the past for a two-dimensional plane ( Lillis et al . , 2008 ) and for three-dimensional volume ( Gobel et al . , 2007 ) . To obtain a free hand line scan routine , we created a scan path on a reference image which is built of a list of scanned pixel locations on that image . Later , we constructed a matrix of voltage values where Vx ( i ) and Vy ( j ) are the voltages that are needed by the X–Y galvanometer to drive the scanning beam to the ith and jth positions . Using this matrix , we can back-map the voltage values from a reference location on a scanned area . A scan line is a vector or positions on the reference image defined as follows: Line = { ( Xi , Yi ) |Xi is the X position of the ( i ) th pixel of the line , Yi is the Y position of ( i ) th pixel of the line , 1 ≤ i ≤ n , n is the number of pixels along the scan line} . With this scan mode the time resolution ( scan time and the scan frequency ) is calculated as: Single-line-scan-period = n*dwellTime + MAX ( X-return-time , Y-return-time ) , measured in seconds . Scan frequency = 1/Single-line-scan-period , measured in Hz . The return path is the straight line between the points ( Xn , Yn ) and ( X1 , Y1 ) . X-return-time = X-delta/X-galvo-speed ( measured in seconds ) ; Y-return-time = Y-delta/Y-galvo-speed ( measured in seconds ) . Where X-delta = |Xn−X1| ( this is the distance between the scanline endpoints , along the X axis , measured in pixels ) . Y-delta = |Yn−Y1| ( this is the distance between the scanline endpoints , along the Y axis , measured in pixels ) . dwellTime is the time it takes for the beam to scan a single pixel ( this is configurable by the user ) . Y-galvo-speed: the speed of the Y-galvo when not doing a scan ( during the returning to the initial point movement ) , measured in pixel/second . X-galvo-speed: the speed of the X-galvo when not doing a scan ( during the returning to the initial point movement ) , measured in pixel/second . This scan routine enabled us to define a line that passed through large numbers of neurons with relatively high temporal resolution , typically 50–100 Hz for more than 50 neurons . A multi-contact silicone electrode ( NeuroNexus , Ann Arbor , Michigan ) was inserted into the barrel cortex . The electrode was lowered using a precision stereotactic micromanipulator ( TSE-systems , Germany ) . During the recordings the signals were amplified ( X1000 ) , filtered ( 0 . 1–10 , 000 KHz ) , and stored in a computer using the ME-16 amplifier and MC-Rack software ( MEA , Germany ) . After completion of the experiments , the recorded data were replayed with a band-pass filter of 1–5 KHz to obtain spikes . Single units were sorted with an offline spike sorter ( OFS version 3; Plexon , Dallas Texas ) . The spike train data obtained after spike sorting were later analyzed using the NeuroExplorer ( Nex Technologies , Littleton , Massachusetts ) and homemade software using the MATLAB ( MathWorks , NA ) platform . The single unit data are presented as peri stimulus time histograms ( PSTH ) with a 10-ms time bin . For determining selectivity and temporal dynamics of firing , spike counts were calculated along 100 ms time bins . The prolonged time bin was used for better comparison with the calcium imaging data which has prolonged time course . Selectivity calculations were performed on the spike count during the stimulation period . Selectivity to a texture or to CD was defined as showing both a p < 0 . 01 significance level in the ANOVA test when all textures were compared and a selectivity index of ≥0 . 35 ( see data analysis for definition of selectivity index ) . Aside from rare cases , the preferred textures determined by the peak or area measurements coincided . These rare cases were excluded from the analysis . Targeted patch-clamp recordings were performed from neurons bulk loaded with Fluo-4 ( n = 7 ) using patch pipettes electrodes ( 4–6 MΏ ) ( Margrie et al . , 2003 ) . Electrodes were filled with extracellular solution consisting of 135 mM NaCl , 5 . 4 Mm KCl , 1 mM MgCl2 , 1 . 8 mM CaCl2 , 5 mM HEPES , and 0 . 04 mM Alexa 594 , pH 7 . 4 . Gigaohm seal cell attached recordings were acquired at 10 KHz using a MultiClamp 700B amplifier , Digidata 1322A ( Axon Instruments ) , and the acquisition software Clampex ( Axon Instruments ) . Artificial whisking was induced by stimulating the buccolabialis motor branch of the facial nerve ( Brown and Waite , 1974; Semba and Egger , 1986; Szwed et al . , 2003 ) . The nerve was cut , and its distal end mounted on a bipolar tungsten electrodes . Bipolar , rectangular electrical pulses ( 0 . 5–4 . 0 V , 40 µs duration ) were applied through an isolated pulse stimulator ( A360 , WPI ) at 100 Hz to produce whisker protraction , followed by a passive whisker retraction . The stimulation magnitude was adjusted to the minimal value that reliably generated the maximal possible movement amplitude . Typically , we evoked 10 consecutive trains ( 5 . 5 Hz , 50% duty cycle , 2 s ) with 30 s between runs . All other whiskers except the principle whisker were cut off , the principle whisker was presented with different sandpapers ( 2 cm2 ) with the tip of the vibrissa just touching the sand paper . The textures were manually placed to be coplanar with the trajectory of the whisker motion , and the distance from the whisker pad was set such that the whisker contacted the texture and made a full move on the texture without being stuck . Whisker displacements were measured by an infrared photo-sensor ( resolution: 1 µm; Panasonic: CNZ1120 ) placed 2 mm from the whisker pad . The voltage signals were digitized at 10 KHz and amplified or photographed using a high-speed camera ( see next section ) . To control for stability of the responses for each texture stimulus , the repetitions ( typically 30 repetitions ) were divided to two separate blocks that were segregated in time and appeared in a random order during the experiment . Thus during the experiment , the different textures were randomly interleaved , and each texture was presented twice in two separate blocks ( see Figure 3—figure supplements 1 and 2 ) . Cases in which the same textures evoked significantly different responses ( p < 0 . 01 ) in the two separate blocks were excluded from analysis . Artificial whisking was performed against sandpapers of four different coarseness grades ( the numbers in the parentheses indicate the average grain diameter ) : P120 ( 127 µm ) , P320 ( 46 µm ) , P600 ( 25 µm ) , and P1000 ( 18 µm ) . These coarsenesses were chosen both in accordance with previous studies ( Arabzadeh et al . , 2005; Hipp et al . , 2006; Lottem and Azouz , 2008 ) and based on the findings that rats can discriminate between sandpapers varied by as little as 10–20 µm mean grain size ( Guic-Robles et al . , 1989; Morita et al . , 2011 ) . The whisking movement was photographed with a high speed camera ( Flare , 4M180MCL , 4 Megapixel , Dalsa Xcelera-x4-CL , IO industries at 1000 fps ) and software ( Streams 6; IO industries ) with resolutions 600 × 350 pixels . Movement of full-length whisker was tracked semi-manually , and the angle and curvature of the whisker were calculated as described in Knutsen et al . ( 2005 ) using a homemade software written in Matlab ( MathWorks , NA ) . The mechanical parameters of the whisker were calculated from a spline function using cubic interpolation ( Matlab spline function ) that was fit to the whisker tracking points in each frame . After interpolation , the 2D curve with coordinates {x ( s ) , y ( s ) } that matches whisker geometry is approximated by set of points {xn , yn} , where n = 1…N . We compute first and second order discrete derivatives asx˙n=xn−xn−1 , y˙n=yn−yn−1 , n=2…N , x¨n=x˙n−x˙n−1 , y¨n=y˙n−y˙n−1 , n=3…N . The whisker angle at its base was computed from the gradient of the whisker–spline curve as:θ ( s ) |s=0=arctan ( y˙ ( s ) /x˙ ( s ) ) . In the discrete case , the angle at the base is given by:θbase=arctan ( y˙2x˙2 ) . We approximate the standard formula of the 2D line curvatureκn=|x˙y¨−y˙x¨| ( x˙2+y˙2 ) 3/2 , using discrete curvatureκn=|x˙ny¨n−y˙nx¨n| ( x˙n2+y˙n2 ) 3/2 . We computed global curvature κmax as the maximal value over all κn . We designate θbase[i] , κmax[i] as base angle and max curvature at each image frame i . The curvature and angle were found to be robust between the different repetitions ( Figure 1—figure supplement 4; p < 0 . 01 ) . To passively move the whiskers , we placed a rotating cylinder covered with textures orthogonal to the whiskers . The cylinder ( 30 mm diameter ) was driven by a DC motor ( Farnell , Leeds , UK ) . We employed surfaces of four different grades ( the numbers in the parentheses indicate the average grain diameter ) : P100 ( 162 µm ) , P320 ( 46 µm ) , P600 ( 26 µm ) , P1000 ( 18 µm ) . The cylinder surface was oriented so that the whisker rested on it and remained in contact during the entire session . For each texture , we recorded 50 revolutions ( trials ) per texture of the rotating cylinder , each lasting approximately 1 s . Whisker displacements transmitted to the receptors in the follicle were measured by an infrared photo-sensor ( resolution: 1 µm; Panasonic: CNZ1120 ) placed 2 mm from the pad . The voltage signals were digitized at 10 KHz and amplified . The principle whisker was stimulated using a galvanometer ( Model 6210H; Cambridge instruments ) . The voltages were delivered to evoke movements with amplitudes 15–30 µm ( calibration of the galvanometer movement was performed using optical displacement measuring system resolution , µm; LD1605-2; Micro-Epsilon ) ( Lottem and Azouz , 2009 ) . The whisker movements during passive whisker stimulation were characterized by their power spectrum ( Figure 8—figure supplement 1; Lottem and Azouz , 2009 ) . In order to measure spectral characteristics of this signal , we applied short time Fourier transform using pwelch in Matlab . Let Sk ( fm ) be Power Spectral Density ( PSD ) at frequency fm for texture k . Using power spectrum results , we estimated the center of mass ( Power Centroid ) for each textureCk=∑m=1128Sk ( fm ) ·fm∑m=1128Sk ( fm ) . Thus , Ck indicates frequency position of the PSD centroid for texture k . At the end of each experiment , a wide-field ( 500 µm × 500 µm ) image stack of Fluo-4 cellular labeling was collected from the brain surface down to ∼350 µm deep . Large radial vessels could be identified within these stacks . Then the neurons that were imaged with Fluo-4 were labeled with 5% biocytin using targeted electroporation via a glass pipette ( 10 V , square-wave pulses of 200 ms duration at 2 Hz for 5 . 5 s; Pinault , 1996 ) . Thirty minutes later the animals were perfused with 0 . 1 PBS followed by 4% paraformaldehyde . The brains were removed and stored at the fixative for at least 24 hr . After fixation tangential slices ( 100 µm thick ) were cut . Slices from cortical layer 4 ( 400–900 µm ) were processed with the Cytochrome oxidase staining to reveal the barrel pattern . Slices from cortical layers 2–3 ( 0–400 µm ) were processed with the 3 , 3-diaminobezidine ( DAB ) using avidin-biotin-peroxidase method for staining neurons filled with biocytin ( Horikawa and Armstrong , 1988 ) . All processed slices were mounted on slides embedded in Immu-mount . Imaging of the stained sections was conducted using a confocal microscope ( Olympus FV 1000 ) . Control of the microscope was done using Flouview10-ASW 2 . 1 software . The biocytin and Fluo-4 images were superimposed one upon the other with the neurons mutually stained in both images as anatomical landmarks . The relationship between the neurons ( found in layers 2–3 ) and the barrels ( stained in layer 4 ) was determined by the blood vasculature ascending through the cortex ( Figure 2 ) . For our analysis , we used custom-made analysis tools in Matlab ( Source code 1; Mathworks , NA ) and in Igor ( Wavemetrics , USA ) softwares . Relative florescence change Rj[tn] ( ΔF/F ) was calculated for each cell j and each time sample tn . Acquisition rate Ts varied for individual line scans from 10 ms to 17 ms . Base line value Bj was computed by averaging 10% of the pixels with minimal fluorescence Fj[tn] . Rj[tn] was calculated using the following formula:Rj[tn]=Fj[tn]−BjBj . The response of each neuron to the whisking stimulus was calculated by averaging the fluorescence signals of consecutive individual traces ( typically 30 traces ) obtained in response to the same texture . To correct for baseline firing the baseline pre-stimulus value ( 1 s time window preceding the whisker stimulus ) was fitted with a linear line and subtracted from the trace . Responding neurons were defined as neurons with a peak average response that was significantly different than the pre-stimulus baseline value at the p < 0 . 01 significance level using the ANOVA test . To determine texture selectivity , we calculated the selectivity index ( SI ) for each cell j . Texture selectivity was calculated in the following manner:SIj=Max ( Pjk ) − Min ( Pjk ) Max ( Pjk ) . When Max ( Pjk ) is the peak amplitude of the response to the best ( preferred ) texture ( k ) and Min ( Pjk ) is the peak amplitude to the texture with the smallest response . The peak response for each texture was determined by averaging10 sampling points around the highest value of the response . In each cell , we also calculated the SI using the area of responses ( calculated during the whisking train ) . We applied this algorithm only to responding neurons . Neurons were considered selective if the SI was larger than 0 . 35 , and there was a significant difference ( at the p < 0 . 01 significance level ) between the responses to the different textures using the ANOVA ( for equal variance , as determined by the Levene's test ) or the Kruskal–Wallis statistical tests ( unequal variance , for cases that failed the Levene's test ) . With respect to the out of focus signal , this is an inherent problem of all imaging signals . Two-photon imaging minimizes this problem , but does not eliminate it all together . To partially deal with this problem we: ( 1 ) excluded all neurons in which the decay time constant was smaller than 650 ms ( indicating a dominant neuropil component ) . ( 2 ) In the neurons in which we recorded simultaneously both electrophysiological and imaging data , we calculated the average transients with and without action potential firing . Traces without action potential firing represented the contribution of the neighboring out of focus neurons and neuropil signals . We found only very small transients ( <10% ) during non-responsive traces ( Figure 1—figure supplement 6 ) . ( 3 ) We compared the fluorescent values of three structures in our images , cells , non-cell regions ( neuropil and out of focus neighboring neurons ) , and blood vessels ( corresponding to background fluorescence ) . The net fluorescent value in cells ( after background subtraction , as measured in blood vessels ) was 1845 ± 546 fluorescence units compared to 574 ± 376 fluorescence units and in non-cell structures ( Figure 1—figure supplement 6 ) , indicating a small out of focus and neuropil contribution in our recordings . We used two methods to perform clustering analysis . First , we calculated expected vs measured closest neighbor probabilities . The expected closest neighbor probability for each experiment was calculated using the following equation where N depicts the number of cells:P=N320 ( N120+N320+N600+N1000 ) ×N320Ntotal+N600 ( N120+N320+N600+N1000 ) ×N600Ntotal+N1000 ( N120+N320+N600+N1000 ) ×N1000Ntotal+N120 ( N120+N320+N600+N1000 ) ×N120Ntotal . The measured probability was calculated usingP=N same selectivity in closest neighbor ( N120+N320+N600+N1000 ) . Second , we have performed Monte Carlo analysis to identify the significance of the spatial clustering . For each pair of cells j , k the Euclidian distance d ( Xj , Xk ) between their position vectors Xj , Xk is computed using d ( Xj , Xk ) = ( Xj−Xk ) T ( Xj−Xk ) . 2-D vectors Xj , Xk describe cell position in image coordinates . The corresponding NxN distance matrix Djk = {d ( Xj , Xk ) } is used to compute intra group ( neurons with the same preferred coarseness ) and extra group ( neurons with different preferred coarseness ) cell distances . For example , if a subset of cells IA = {k: cells k prefer texture A} . then between group A and B the distance is given by:DAB=1|A||B|∑j∈IA∑k∈IBd ( Xj , Xk ) , |A| and |B|—are the number of cells in groups A and B . The intra group distance is given by:DAA=1 ( |A|−1 ) |A|∑j∈IA∑k∈IAd ( Xj , Xk ) , where the term ( |A| − 1 ) |A| describes the number of elements without main diagonal . In order to determine the significance of the clustering information , we compare the resulting distances DAB with randomly selected positions . Using the same number of cells , we randomly and uniformly selected the position of each cell in 2D image plane . Then the same distance criterion was applied . We computed a histogram of the between-cell distances for 1000 runs , normalized it , and selected the 5% population threshold value . This value indicates how the distances below this threshold differ from the uniform distribution . We found that the intra-group distances are significantly below the selected 5% threshold . To generate smoothed texture coding maps of the normalized barrel each cell was assigned set of point coordinates ck ( xi , yi ) with i = 1…Nk , Nk—number of cells found for texture k , and ck—color assigned to a texture k and xi , yi are the spatial coordinates of cell i . For each texture , we generated a map of the normalized barrel . In order to fill the regions where there are no cells found we smooth each map by performing a convolution with 2-D Gaussian kernel which standard deviation of σ = 15 pixels . We get four maps Mk ( x , y ) defined for all x , y coordinates/pixels:Mk ( x , y ) =∑i=1Nke− ( ( x−xi ) 2+ ( y−yi ) 2 ) /2σ2 . We normalize each map for each pixel ( x , y ) as followsMnormk ( x , y ) =Mk ( x , y ) ε+∑t=14Mt ( x , y ) , where ε a small number that prevents division by zero . The physical meaning of this number is some cell response to a texture that we haven't measured . Please note that Mnormk is scaled to range ( 0:1 ) and could be associated to the probability function . We next generated an integrated colored map C ( x , y ) for all four textures , which represents a weighted mixture of the four individual color maps: C ( x , y ) =∑k=14ckMnormk ( x , y ) . For a very small value of ∑k=14Mnormk ( x , y ) , we set C ( x , y ) to be white . Measurment of inter-trial spatial movement: We performed FFT-based correlation between two images , one is a target/template reference and the other is the current image data . Maximal value position in correlation image gives the relative shift between the two images in X and Y direction . The template image was defined as a time average of the images in the selected trial:Itmp ( x , y ) =1N∑t=1 . . NI ( x , y , t ) . where I ( x , y , t ) image for frame t from total N . | As nocturnal tunnel-dwelling animals , rats rely on their whiskers to enable them to navigate in the dark . By moving their whiskers back and forth in movements called whisking , rats can obtain detailed information about the shape , texture , and size of objects in their path and also work out whether they can fit through narrow gaps . Like all hairs , whiskers are made of dead cells . However , the follicles from which whiskers grow are densely packed with the ends of sensory nerves . When a whisker bends , information is transmitted along these nerves to a region of the brain called the barrel cortex , which takes its name from the barrel-shaped structures that represent the individual whiskers . These structures are arranged in an orderly grid with adjacent barrels corresponding to neighboring whiskers . One of the key functions of whiskers is to help animals to distinguish between textures . By recording nerve impulses from the barrel cortex of anesthetized rats as the animals' whiskers were moved back and forth across four sandpapers of differing coarseness , Garion et al . have obtained key insights into how this process works . Only a small minority of barrel cortex neurons were more active or less active in response to an increase or a decrease in coarseness . Instead , most neurons showed a preference for one particular coarseness and fired much more when the rats' whiskers encountered the corresponding sandpaper . Neurons with the same preferred coarseness were usually located close to one another . This gives rise to a gradient across the barrel cortex in which successive columns of neurons fired when the whiskers detected surfaces that were increasingly coarse , while neurons at different depths within each column fired at the same coarseness . This pattern was also seen using a different experimental method—in which whisker vibrations that were typical for different textures were converted to electrical signals and then played back to the rat's whiskers . By revealing that the barrel cortex essentially contains a texture map , Garion et al . have identified a fundamental difference in how tactile ( or touch ) information is represented in the rat brain compared to visual and auditory information . Further work is now required to determine how the neurons identify different textures—whether they use particle size or sharpness , for example—and to confirm that the same results can be seen in awake animals . | [
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"neuroscience"
] | 2014 | Texture coarseness responsive neurons and their mapping in layer 2–3 of the rat barrel cortex in vivo |
The advent of a new generation of electron microscopes and direct electron detectors has realized the potential of single particle cryo-electron microscopy ( cryo-EM ) as a technique to generate high-resolution structures . Calculating these structures requires high performance computing clusters , a resource that may be limiting to many likely cryo-EM users . To address this limitation and facilitate the spread of cryo-EM , we developed a publicly available ‘off-the-shelf’ computing environment on Amazon's elastic cloud computing infrastructure . This environment provides users with single particle cryo-EM software packages and the ability to create computing clusters with 16–480+ CPUs . We tested our computing environment using a publicly available 80S yeast ribosome dataset and estimate that laboratories could determine high-resolution cryo-EM structures for $50 to $1500 per structure within a timeframe comparable to local clusters . Our analysis shows that Amazon's cloud computing environment may offer a viable computing environment for cryo-EM .
AWS is a division of Amazon that offers a variety of cloud-based solutions for website hosting and high-performance computing , amongst other services . Many different types of privately held companies take advantage of Amazon's computing infrastructure because of its affordability , flexibility , and security . Of note , global biotechnology companies such as Novartis ( AWS , 2014a ) , Bristol-Myers-Squibb ( AWS , 2013 ) , and Pfizer ( AWS , 2014b ) have utilized the computing power of Amazon for scientific data processing . Many academic researchers have also begun to use Amazon's EC2 resources for analyzing datasets from super-resolution light microscopy ( Hu et al . , 2013 ) , genomics ( Krampis et al . , 2012; Yazar et al . , 2014 ) , and proteomics ( Mohammed et al . , 2012; Trudgian and Mirzaei , 2012 ) . The overall workflow starts with users logging into a virtual machine ( ‘instance’ ) on AWS ( Figure 1 ) . AWS offers a variety of instance types that have been configured for different computing tasks . For example , instances have been optimized for computing performance , GPU-based calculations , or memory-intensive calculations . After logging onto an instance , storage drives are mounted onto it , allowing data , which can be encrypted for security , to be transferred onto the storage drives ( Figure 1 ) . 10 . 7554/eLife . 06664 . 003Figure 1 . Workflow for analyzing cryo-EM data on Amazon's cloud computing infrastructure . After collecting cryo-EM data ( Step 1 ) , particles are extracted from the micrographs and prepared for further analysis ( Step 2 ) . After logging into an ‘instance’ ( Step 3 ) , data are uploaded to a storage server ( elastic block storage ) ( Step 4 ) . At this point , STARcluster can be configured to launch a cluster of 2–30 instances that is mounted with the data from the storage volume ( Step 5 ) . A detailed protocol can be found at an accompanying Google site: http://goo . gl/AIwZJz . DOI: http://dx . doi . org/10 . 7554/eLife . 06664 . 003 While users can utilize a single instance for calculations , the maximum number of CPU cores per instance is 18 . Therefore , creating a computing cluster with a larger number of CPUs on AWS requires additional steps . The Software Tools for Academics and Researchers ( STAR ) group at the Massachusetts Institute of Technology developed a straightforward package that allows users to group individual AWS instances into a cluster . The STARcluster program is a python-based , open source package that automatically creates a cluster preconfigured with the necessary software to manage a computer cluster ( Ivica et al . , 2009 ) . This package allows users to specify the number of instances to be included in the clusters as well as the instance type . By taking advantage of this tool , private clusters can be built with sizes ranging from 16 to 480 CPUs ( Figure 1 ) . While Amazon provides dedicated access to instances through ‘on-demand’ reservations , there are ‘spot instances’ that are 80–90% cheaper than the on-demand price . Spot instances are unused instances within Amazon EC2 that are open for competitive bidding , where users gain access to them by making offers above the current minimum bid . This means that while the on-demand rate for high-memory , 16-CPU instances ( called ‘r3 . 8xlarge’ ) is $2 . 80/hr , spot instance prices can be as low as $0 . 25–$0 . 35/hr . In order to determine if spot instances offer a consistent reduction in price , we analyzed the global availability of r3 . 8xlarge spot instances . Currently , Amazon has 9 regions worldwide within 7 countries: US-East-1 ( United States ) , US-West-1 ( United States ) , US-West-2 ( United States ) , SA-East-1 ( Brazil ) , EU-Central-1 ( Germany ) , EU-West-1 ( Ireland ) , AP-Northeast-1 ( Japan ) , AP-Southeast-1 ( Singapore ) , and AP-Southeast-2 ( Australia ) . For each region , we retrieved spot instance prices for r3 . 8xlarge instances over the past 3 months and analyzed the time they spent at prices below $0 . 35–$0 . 65/hr ( corresponding to discounts of 87 . 5–76 . 8% over the full on-demand rate of $2 . 80/hr ) ( Figure 2 and Figure 2—figure supplement 1 ) . This analysis revealed that , globally , 49 . 8% of r3 . 8xlarge instances were below $0 . 35/hr , 12 . 5% the on-demand price ( Figure 2 ) . For $0 . 65/hr , 76 . 5% below full price , one could access 82 . 2% of the global r3 . 8xlarge spot instances . These data indicate that spot instances provide dependable , cost-effective access to Amazon's computing resources . 10 . 7554/eLife . 06664 . 004Figure 2 . Global availability of Amazon r3 . 8xlarge spot instances . Shown is the average percentage time spent by the r3 . 8xlarge type of instance when the current spot instance price was less than the queried price . The data are averaged over all Amazon's regions worldwide ( except for SA-East-1 , which does not offer r3 . 8xlarge instances ) . Spot instance prices were calculated over a 90-day period from 1 January 2015—1 April 2015 , where the average is shown ± the s . e . Source data: Figure 2—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 06664 . 00410 . 7554/eLife . 06664 . 005Figure 2—source data 1 . Global spot instance price data from 1 January 2015 to 1 April 2015 . DOI: http://dx . doi . org/10 . 7554/eLife . 06664 . 00510 . 7554/eLife . 06664 . 006Figure 2—figure supplement 1 . Availability of virtual machines within regions at specified spot instance prices . For each Amazon region ( excluding SA-East-1 , which does not offer r3 . 8xlarge instances ) , r3 . 8xlarge spot instance prices were retrieved for each availability zone , where separate availability zones are shown as separate data points for a given spot instance price . ( Note: each region can have different number of availability zones ) . From the spot instance prices , the percentage time of the spot instances that were spent below the specified spot instance price were calculated . The average value is shown as a solid black line . Source data: Figure 2—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 06664 . 006 To test the performance of Amazon's EC2 environment , we analyzed a previously published 80S Saccharomyces cerevisiae ribosome dataset ( Bai et al . , 2013 ) ( EMPIAR 10002 ) on a 128 CPU cluster ( 8 × 16 CPUs; using the r3 . 8xlarge instance ) . After extracting 62 , 022 particles , we performed 2D classification within Relion . Subsequent 3D classification of the particles into four classes revealed that two classes adopted a similar structural state . We merged those two classes and used the associated particles to carry out a 3D refinement in Relion—we were able to obtain a structure with an overall resolution of 4 . 6 Å ( Figure 3A–C ) . 10 . 7554/eLife . 06664 . 007Figure 3 . Cryo-EM structure of 80S ribosome at an overall resolution of 4 . 6 Å . ( A ) Overall view of 80S reconstruction filtered to 4 . 6 Å while applying a negative B-factor of −116 Å2 . ( B ) Gold standard FSC curve . ( C ) Selected regions from the 60S subunit . Cryo-EM maps were visualized with UCSF Chimera ( Pettersen et al . , 2004 ) . Source data: Dryad Digital Repository dataset ( http://datadryad . org/review ? doi=doi:10 . 5061/dryad . 9mb54 ) ( Cianfrocco and Leschziner ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06664 . 007 This structure , whose generation included particle picking , CTF estimation , 2D and 3D classification , and refinement , cost us $99 . 64 on Amazon's EC2 environment . This cost was achieved by bidding on spot instances for particle picking ( m1 . small at $0 . 02/hr ) , 2D classification ( STARcluster of r3 . 8xlarge instances at $0 . 65/hr ) , and 3D classification and refinement ( STARcluster of r3 . 8xlarge instances at $0 . 65/hr ) . Thus , even though obtaining this structure required 1266 total CPU-hours , Amazon's EC2 computing infrastructure provided the necessary resources to calculate it to near-atomic resolution at a reasonable price . To further test the performance of Amazon instances , we carried out 3D classification and refinement on a variety of STARcluster configurations using Relion . As before , we ran our tests on clusters of r3 . 8xlarge high-memory instances ( 256 GiB RAM and 16 CPUs per instance ) . Comparing performance across cluster sizes showed that 256 CPUs had the fastest overall time and the highest speedup relative to a single CPU for both 3D classification and refinement ( Figure 4A , B ) . However , cluster sizes of 128 and 64 CPUs were the most cost effective for 3D classification and refinement , respectively , as these were the cluster configurations where the speedup per dollar reached a maximum ( Figure 4C ) . Importantly , the average time required to boot up these STARclusters was ≤ 10 min for all cluster sizes ( Figure 4D ) and , once booted up , the clusters do not have any associated job wait times . Therefore , these tests showed that Amazon's EC2 infrastructure was amenable to the analysis of single particle cryo-EM data using Relion over a range of STARcluster sizes . 10 . 7554/eLife . 06664 . 008Figure 4 . Relion performance on STARcluster configurations of Amazon instances . ( A ) Processing times ( minutes ) for Relion to perform 3D Classification or 3D refinement on 80S ribosome dataset . ( B ) Speedup for each cluster size relative to a single CPU ( black line ) shown alongside performance estimate for a perfectly parallel cluster using Amdahl's Law ( curve labeled ‘Theoretical limit’ ) . For cluster sizes ≤ 64 CPUs , Relion exhibits near-perfect performance on STARcluster configurations , while cluster sizes > 64 show that Relion's performance reaches a maximum at 256 CPUs for both 3D classification and 3D refinement . ( C ) Speedup/Cost is plotted against cluster size , where Speedup/Cost is defined as the speedup observed divided by the cost associated with Amazon's pricing at $0 . 35/hr/16 CPUs . ( D ) Average STARcluster boot up time ( ± s . d . ) was measured for clusters of increasing size ( n = 5 ) . Source data: Figure 4—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 06664 . 00810 . 7554/eLife . 06664 . 009Figure 4—source data 1 . Performance analysis statistics for Relion 3D classification and 3D refinement on STARcluster configurations . DOI: http://dx . doi . org/10 . 7554/eLife . 06664 . 009 From our analysis of the 80S yeast ribosome , we extrapolated the processing times and combined them with previously published 3D refinement times to estimate typical costs on Amazon's EC2 . First , we estimated the cost for 3D refinement in Relion for previously published structures ( Supplementary file 2A ) —these calculated costs ranged from $12 . 65 to $379 . 03 per structure , depending on the spot instance price and required CPU-hours . We then combined these data with conservative estimates for particle picking , CTF estimation , particle extraction , 2D and 3D classification to predict the overall cost of structure determination on Amazon's EC2 ( Supplementary file 2B ) . From these considerations , we estimated that published structures could be determined using Amazon's EC2 environment at costs of $50–$1500 per structure ( Supplementary file 2B ) . Given the success we had in analyzing cryo-EM data on Amazon's EC2 at an affordable price and within a reasonable timeframe , we have made our software environment publicly available as an ‘Amazon Machine Image’ ( AMI ) , under the name ‘EM-packages-in-the-Cloud-v3 . 93 . ’ The EM-packages-in-the-Cloud-v3 . 93 AMI provides the software environment necessary for analyzing data on a single instance , and is preconfigured with STARcluster software . The EM-packages-in-the-Cloud-v3 . 93 AMI has the following cryo-EM software packages installed: Relion ( Scheres , 2012 , 2014 ) , FREALIGN ( Grigorieff , 2007 ) , EMAN2 ( Tang et al . , 2007 ) , Sparx ( Hohn et al . , 2007 ) , Spider ( Frank et al . , 1996 ) , EMAN ( Ludtke et al . , 1999 ) , and XMIPP ( Sorzano et al . , 2004 ) . In addition to this AMI that is capable of running on a single instance , we have also made available a second AMI—EM-packages-in-the-Cloud-Node-v3 . 1—that provides users with the same software packages as described above , but can set up and run within a cluster of multiple EC2 instances . These two publicly available AMIs allow users to boot up a cluster to analyze cryo-EM data in a few short steps . The protocols describing this can be found as a PDF ( Supplementary file 1 ) or on a Google site that is being launched in conjunction with this article: http://goo . gl/AIwZJz . In addition to detailed instructions , the site includes a help forum to facilitate a conversation on cloud computing for single particle cryo-EM . Recent advances in single particle cryo-EM have drawn the interest of the broader scientific community . In addition to technical advances in electron optics , the new direct electron detectors and data analysis software have dramatically improved the resolutions that can be achieved for a variety of structural targets . In contrast to the other high-resolution techniques ( X-ray crystallography , NMR ) , structure determination by cryo-EM is extremely computationally intensive . The publicly available ‘EM-packages-in-the-Cloud’ environment we have presented and characterized here will help remove some of the limitations imposed by these computational requirements . We believe that cloud-based approaches have the potential to impact the future of cryo-EM image processing in two fronts: ( 1 ) new cryo-EM users or laboratories will have immediate access to a high performance cluster , and ( 2 ) existing labs may use this resource to increase their productivity . As the number of laboratories using cryo-EM increases , and as existing laboratories begin to pursue high-resolution cryo-EM , gaining immediate access to a high performance cluster may become difficult . For instance , while there are government-funded high performance clusters in the United States ( e . g . , XSEDE STAMPEDE ) , it may take up to a month for a user application to be reviewed ( Rogelio Hernandez-Lopez , personal communication ) . Assuming that the application is approved , these clusters may not have appropriate software installed , which further delays data processing . Finally , the user will have a set limit for the number of CPU hours available per project , requiring a new application to be submitted to access the cluster again . All of these problems can be circumvented by using Amazon's EC2 infrastructure , which provides immediate , cost-effective access to hundreds of CPUs with no geographic restrictions . The power of cloud-based solutions to alleviate the computational burden associated with cryo-EM data processing stems from its high-degree of scalability and reasonable cost . By minimizing computational time and increasing global accessibility , high-performance cloud computing may help usher in the era when high-resolution cryo-EM becomes a routine structural biology tool .
Global spot instance prices were retrieved from the 90-day period from 1 January 2015 to 1 April 2015 using the Amazon Command Line Tools command ec2-describe-spot-price-history . Retrieval of spot instance prices for all regions was implemented automatically in a custom python program get_spot_histories_all_regions_all_zones . py . From these spot instance prices , the percentage time spent below given prices was calculated using measure_time_at_spotPrice . py , where the cumulative time of spot instances below a given price divided by the total time ( 90 days ) . Both programs can be found in the Github repository mcianfrocco/Cianfrocco-and-Leschziner-EMCloudProcessing . In order to minimize costs , STARclusters were assembled from ‘spot instances , ’ which are unused instances that can be reserved through a bidding process . The spot instances are different from ‘on-demand’ instances: on-demand instances provide users with guaranteed access while spot instances are reserved until there is a higher bid , at which point the user is logged out of the spot instance . When this happens , the MPI-threaded Relion calculation will abort , requiring the user to resubmit the job to the STARcluster and start Relion from the previous iteration . Even if the user is logged out of all instances within a STARcluster , the data is automatically saved within the EBS-backed volumes on Amazon EC2 . In selecting an instance type , new users should be aware of the differences between CPUs and vCPUs on Amazon's EC2 network . Namely , that there are two vCPUs per physical CPU on Amazon . This means that while r3 . 8xlarge instances have 32 vCPUs , there are actually only 16 physical CPU cores in each instance , with each CPU having two hyperthreads . Practically , this means that Amazon's instances have higher performance than a 16 CPU machine and less performance than a 32 CPU machine . To account for this difference , all numbers reported here were CPU numbers that were converted from vCPUs: 1 CPU = 2 vCPUs . Micrographs from the 80S S . cerevisiae ribosome dataset ( Bai et al . , 2013 ) were downloaded from the EMPIAR database for electron microscopy data ( EMPIAR 10002 ) . The SWARM feature of EMAN2 ( Tang et al . , 2007 ) was used to pick particles semi-automatically . Micrograph defocus was estimated using CTFFIND3 ( Mindell and Grigorieff , 2003 ) . The resulting particle coordinates and defocus information were used for particle extraction by Relion-v1 . 3 ( Scheres , 2012 , 2014 ) . The particle stacks and associated data files were then uploaded to an elastic block storage volume on Amazon's EC2 processing environment at a speed of 10 MB/s ( 24 min total upload time ) . After 2D classification in Relion , 3D classification was performed on 62 , 022 80S Ribosome particles ( 1 . 77 Å/pixel ) , also in Relion . These were classified into 4 groups ( T = 4 ) for 13 iterations using a ribosome map downloaded from the Electron Microscopy Data Bank ( EMDB-1780 ) that was low pass filtered to 60 Å . Further 3D classification using a local search of 10° and an angular sampling of 1 . 8° continued for 13 iterations . At this point , two classes were identified as belonging to the same structural state and were selected for high-resolution refinement ( 32 , 533 particles ) . Refinement of these selected particles continued for 31 iterations using 3D auto-refine in Relion . The final resolution was determined to be 4 . 6 Å using Post process in Relion , applying a mask to the merged half volumes and a negative B-factor of −116 Å2 . 80S ribosome data were reanalyzed on clusters of increasing size using both 3D classification and 3D refinement . The time points collected involved running 3D classification for 2 rounds and 3D refinement for 6 rounds , using the same number of particles and box sizes listed above: 62 , 022 particles for classification and 32 , 533 particles for refinement with box sizes of 240 × 240 pixels . The Relion commands were identical to the commands used above and the calculations were terminated after the specified iteration . From these time points , the speedup of each cluster size was calculated relative to a single CPU . Speedup ( S ) was calculated as:S=Calculation time for 1 CPUCalculation time for x CPUs . The measured speedup values were then compared to the speedup expected for a perfectly parallel algorithm ( P = 1 ) using Amdahl's law ( Amdahl , 1967 ) :S=1 ( 1−P ) +1n ( P ) =1 ( 1−1 ) +1n ( 1 ) =n , Where P is the fraction of an algorithm that is parallel and n is the number of processors . The calculation times for 3D classification on a single CPU were obtained by using 1 CPU on a 16 CPU r3 . 8xlarge instance . For calculating a 3D refinement on a single CPU , ( or two vCPUs ) , the refinement was run on 4 vCPUs and then converted to a single CPU ( or two vCPUs ) by multiplying the calculation time by 2 . For cost analysis , the measured speedup was divided by the cost to run the job on spot instances of r3 . 8xlarge at a price of $0 . 35/hr . Cluster boot up times were calculated from the elapsed time between submitting the STARcluster command and the STARcluster fully booting up . Further information regarding ‘EM-Packages-in-the-Cloud’ can be found in Supplementary file 1 and at an associated Google Site: http://goo . gl/AIwZJz . The final 80S yeast ribosome structure at 4 . 6 Å has been submitted to the EM Databank as EMDB 2858 . A detailed description of global spot instance price analyses and image processing is available at https://github . com/mcianfrocco/Cianfrocco-and-Leschziner-EMCloudProcessing/wiki . Associated computing scripts and data files have been uploaded to Github ( https://github . com/mcianfrocco/Cianfrocco-and-Leschziner-EMCloudProcessing ) and Dryad Digital Repository ( http://dx . doi . org/10 . 5061/dryad . 9mb54 ) ( Cianfrocco and Leschziner , 2015 ) , respectively . | Microscopes can be used to view objects or structural details that are not visible with the naked eye . A type of microscope called an electron microscope—which uses beams of particles called electrons—is particularly useful for examining tiny objects or structures because it can produce images with a higher level of detail than microscopes that use light . There are several ways to prepare biological samples for electron microscopy . One technique is called cryo-electron microscopy , or cryo-EM for short , where the sample is rapidly frozen and then viewed under the electron microscope . Using this technique it is possible to produce highly detailed images of viruses , individual compartments within cells and even single proteins . To convert the images of proteins into three-dimensional models , high-performing clusters of computers are required . It can be difficult and expensive for many scientists to access these resources , which may limit the wider use of cryo-EM in research . To address this problem and aid the spread of cryo-EM , Cianfrocco and Leschziner developed a publicly available ‘off the shelf’ system on Amazon's elastic cloud computing infrastructure . This provides users with software packages and the ability to create a cluster containing up to around 480 computers to analyze cryo-EM data . Cianfrocco and Leschziner tested the system using a publicly available cryo-EM dataset of a structure in yeast cells called the 80S ribosome , which contains proteins and molecules of ribonucleic acid . This revealed that a highly detailed model of the 80S ribosome could be developed in a time frame similar to what it would have taken on a local high-performing computing cluster within a university . The cost of using this system was also competitive in price with that of maintaining a local computing cluster , with the added flexibility of its ‘pay-as-you-go’ structure . These findings show that Amazon's cloud computing infrastructure may be a useful alternative to using clusters of computers based within a research institute or university . This will help the spread of cryo-EM as a general tool to reveal the three-dimensional structures of large molecules . Further work is required to make this cloud-based computing tool easily accessible to researchers who may have limited experience with using Linux software and computing clusters . | [
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] | 2015 | Low cost, high performance processing of single particle cryo-electron microscopy data in the cloud |
Gustatory pheromones play an essential role in shaping the behavior of many organisms . However , little is known about the processing of taste pheromones in higher order brain centers . Here , we describe a male-specific gustatory circuit in Drosophila that underlies the detection of the anti-aphrodisiac pheromone ( 3R , 11Z , 19Z ) -3-acetoxy-11 , 19-octacosadien-1-ol ( CH503 ) . Using behavioral analysis , genetic manipulation , and live calcium imaging , we show that Gr68a-expressing neurons on the forelegs of male flies exhibit a sexually dimorphic physiological response to the pheromone and relay information to the central brain via peptidergic neurons . The release of tachykinin from 8 to 10 cells within the subesophageal zone is required for the pheromone-triggered courtship suppression . Taken together , this work describes a neuropeptide-modulated central brain circuit that underlies the programmed behavioral response to a gustatory sex pheromone . These results will allow further examination of the molecular basis by which innate behaviors are modulated by gustatory cues and physiological state .
For many animals , exogenously released chemical cues known as pheromones heavily influence social behaviors that are crucial to survival and reproduction ( Karlson and Luscher , 1959 ) . Elucidating the neural basis of pheromone detection provides a means for understanding how information from sensory stimuli is encoded and used to modulate complex behaviors such as mating ( Pavlou and Goodwin , 2013; Haga-Yamanaka et al . , 2014 ) and aggression ( Chamero et al . , 2007; Wang et al . , 2011; Fernández and Kravitz , 2013 ) . The neural circuits underlying olfactory pheromone detection are well described in the silkmoth Bombyx mori ( Sakurai et al . , 2014 ) and honey bee ( Roussel et al . , 2014 ) . In Drosophila , the pathways mediating detection of the sex pheromone 11-cis-vaccenyl acetate ( cVA ) have been refined down to 4 neurons connected by 3 synapses ( Ruta et al . , 2010 ) . In addition , the projection patterns for several other olfactory receptor neurons that likely detect sex pheromones have been mapped from the antennal lobe to the ventral lateral horn , implicating this region in the central brain as a specialized site for processing pheromone odors ( Jefferis et al . , 2007 ) . In contrast to the olfactory system , the higher order pathways for the processing of gustatory pheromones are largely unknown despite their importance in behavior ( Wang et al . , 2011; Fernández and Kravitz , 2013 ) . Several pheromone receptors located in the primary gustatory organs ( proboscis , labellum , forelegs ) have been identified . Gr32a ( Miyamoto and Amrein , 2008 ) is thought to respond to the male pheromone ( 7Z ) -tricosene ( Wang et al . , 2011; Fan et al . , 2013; Andrews et al . , 2014 ) . Furthermore , Gr33a ( Moon et al . , 2009 ) , Gr39a ( Watanabe et al . , 2011 ) , and a member of the class of ionotropic receptors ( IRs ) , Ir20a ( Koh et al . , 2014 ) , contribute to courtship behavior , though the ligands remain unidentified . Lastly , a recently discovered class of ion channels belonging to the pickpocket family of proteins ( ppk23 , ppk25 , and ppk29 ) responds to both female pheromones and male anti-aphrodisiacs ( Thistle et al . , 2012; Toda et al . , 2012; Mast et al . , 2014; Vijayan et al . , 2014 ) . Without exception , processes from all Gr-expressing neurons map to the ventral cord and the subesophageal zone ( SEZ ) ( Wang et al . , 2004; Kwon et al . , 2014 ) . However , little is known about the post-synaptic targets of the SEZ as well as the neurotransmitter systems used to mediate pheromone-related behaviors in the central brain . In this work , we describe a central neural circuit that mediates the detection and behavioral response to a gustatory sex pheromone , ( 3R , 11Z , 19Z ) -3-acetoxy-11 , 19-octacosadien-1-ol ( CH503 ) . CH503 is transferred from males to females during mating and inhibits courtship from other males ( Yew et al . , 2009 ) . The pheromone also functions as a potent suppressor of male courtship behavior in other drosophilids ( Ng et al . , 2014 ) . We show that CH503 is detected by Gr68a-expressing gustatory neurons on the male foreleg and that information is transduced via peptidergic cells to the central brain . Specifically , a cluster of 8–10 neurons within the SEZ mediates the pheromone-triggered courtship-avoidance response through the release of the neuropeptide tachykinin .
Courtship in Drosophila consists of a stereotyped sequence of behaviors including orientation , wing vibration , tapping with the forelegs , abdomen curling , and copulation ( Spieth , 1974 ) ( Figure 1A ) . To determine the sensitivity of males to CH503 and to examine the courtship features that are suppressed by the pheromone , wild-type CantonS male flies were placed with virgin females perfumed with doses of synthetic CH503 ranging from 0 to 2667 ng . Males exhibited a dose-dependent response to the pheromone . A dose of 167 ng/fly was sufficient to suppress courtship in approximately 50% of the behavioral trials ( Figure 1B ) . The latency to courtship initiation ( as measured by the first instance of wing vibration ) was similar across all doses , ranging from 350 to 400 s ( Figure 1C ) . However , the overall courtship vigor was significantly suppressed by the pheromone ( Figure 1D ) . Taken together , these findings indicate that CH503 inhibits later stages of the courtship sequence and sustained courtship behavior . 10 . 7554/eLife . 06914 . 003Figure 1 . Functional properties of the male sex pheromone CH503 . ( A ) The typical courtship sequence of D . melanogaster is comprised of wing vibration performed by the male towards the female , tapping and tasting of the female abdomen with the forelegs , and abdomen curling followed by copulation . ( B ) Courtship behavior exhibited by wild-type Drosophila males decreases in a dose-dependent manner with increasing amounts of CH503 on the surface of virgin females . N = 25–30 , Fisher's exact probability test , *p < 0 . 05 , **p < 0 . 01 , ***p < 0 . 001 . ( C ) CH503 does not change the latency to courtship initiation , as measured by the latency to wing vibration . N = 16–24 , ANOVA with Tukey's multiple comparison test . Error bars represent SEM . ( D ) CH503 suppresses the amount of time the fly actively spends courting . Courtship vigor is defined as the total time the male spends courting , calculated from the first instance of orientation and wing vibration . N = 8–24 , ANOVA with Tukey's multiple comparison test , **p < 0 . 01 . Error bars represent SEM . ( E ) CH503 has low volatility and inhibits courtship only when detected on female cuticles . The absence of female cuticular hydrocarbons in oenocyte-less ( oe- ) flies also did not affect CH503-induced courtship suppression . N = 8 , Fisher's exact probability test , ***p < 0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 003 Wing extension is one of the first steps of the courtship ritual and can be triggered in the absence of tactile contact through visual cues ( Ejima and Griffith , 2008; Pan et al . , 2012; Agrawal et al . , 2014 ) and volatile pheromones ( Tompkins et al . , 1980; Venard and Jallon , 1980 ) . Since the latency to wing extension did not appear to be affected by CH503 , we hypothesized that the pheromone is detected only at close proximity . To determine whether tactile contact with CH503 is necessary for courtship suppression , we positioned a pheromone source separated from the female target at various distances . To prevent contact with the pheromone , a mesh barrier was placed between the courtship chamber and a second chamber containing a piece of filter paper soaked with 64 μg of CH503 . Despite the high dose of pheromone , male courtship was uninhibited when the filter paper was placed 6 or 3 mm away , or on the floor of the courtship chamber ( allowing for direct contact ) . In each case , 100% of male flies initiated courtship towards females ( Figure 1E ) . Potentially , female pheromones act in synergy with CH503 and both cues are needed to inhibit courtship . However , a mixture of female cuticular extract and CH503 applied to filter paper that was placed on the floor of the chamber was ineffective ( Figure 1E ) . We further tested for synergistic effects from female pheromones by using transgenic female flies in which oenocytes , the pheromone-producing cells in Drosophila , were genetically ablated . In the presence of oenocyte-less flies perfumed with CH503 , male courtship was still significantly inhibited ( Figure 1E ) . In sum , these results indicate that female-specific pheromones synthesized in the oenocytes do not mediate the detection of CH503 . Furthermore , CH503 is effective as an anti-aphrodisiac only when placed on the cuticular surface of females , indicating that sensory cues other than cuticular lipids are required . Based on the relatively high molecular weight of CH503 and its low volatility , we hypothesized that CH503 is likely to be perceived as a tastant . We performed a proboscis extension reflex ( PER ) assay to test whether CH503 is detected by foreleg gustatory receptors . Stimulation of the foreleg with rewarding stimulants such as a sugar solution induces extension of the proboscis ( Kimura et al . , 1986 ) while aversive , bitter substances suppress the PER ( Lacaille et al . , 2007 ) . Exposure of the forelegs to 0 . 5–2 . 0 µg of CH503 together with 4% sucrose reduced the frequency of the PER ( Figure 2A ) . Similarly , caffeine , a bitter stimulus , also suppressed the PER . In contrast , females did not exhibit a change in the PER when exposed to CH503 , indicating that either the pheromone was not detected or that it does not carry a negative valence for females ( Figure 2—figure supplement 1 ) . To further examine whether CH503 is detected as an odorant or tastant , we tested the responses of transgenic males defective in smell or taste perception . Flies lacking the olfactory co-receptor Or83b ( Orco ) exhibit significant defects in olfactory pheromone detection ( Larsson et al . , 2004 ) . However , Or83b-defective flies still suppressed courtship in the presence of CH503 ( Figure 2B ) . In contrast , Voila1 mutants , which exhibit gustatory defects ( Balakireva et al . , 1998 ) , continued to court CH503-perfumed females ( Figure 2B ) . Taken together , these results indicate that CH503 is perceived as an aversive tastant by males and not females and is detected by gustatory receptors on the foreleg . 10 . 7554/eLife . 06914 . 004Figure 2 . Gr68a expression in the male foreleg is required for CH503 detection . ( A ) Simultaneous stimulation of the male foreleg with 4% sucrose and CH503 or caffeine significantly inhibits the proboscis extension reflex ( PER; shown in pictures ) in CantonS males ( white ) . The PER suppression was not observed in ΔGr68a mutant flies ( gray ) but was restored upon re-introduction of the Gr68a gene ( Gr68aRes; blue ) . For each genotype , the response to each test compound was compared to the response to sucrose alone . N = 18 , Fisher's exact probability test , ***p < 0 . 001 , ****p < 0 . 0001 , ns: not significant . ( B ) A behavioral screen targeting foreleg-specific gustatory receptor neurons , pheromone receptors , and a pheromone binding protein reveals that Gr68a is a candidate receptor for detecting ( S , Z , Z ) -CH503 . The number of flies exhibiting courtship in response to the pheromone ( purple ) was compared to the response to a solvent-perfumed female ( white ) . For some genotypes ( far right of graph ) , the basal courtship level was too low to observe a courtship suppression effect . N = 12–73 , Fisher's exact probability test , *p < 0 . 05 , **p < 0 . 01; ***p < 0 . 001 . ( C ) Silencing Gr68a expression with RNAi or genetic deletion resulted in a loss of sensitivity to CH503 . The courtship suppression response was unaltered in parental control lines and restored upon re-introduction of the gene into the mutant . Hyperactivation of Gr68a-expressing neurons using dTrpA1 at the activation temperature ( 29°C ) resulted in a slight but non-significant courtship suppression compared to the inactive temperature ( 19°C ) . Parental control lines exhibited no difference in courtship behavior at 29°C or 19°C . N = 12–37 , Fisher's exact probability test , *p < 0 . 05 , ns: not significant . ( D ) ( Top ) A schematic of the Gr68a gene locus shows that the single coding exon ( blue ) resides within the intronic region ( black ) of another gene , CG6024 . ( middle ) The homologous recombination strategy for deletion and rescue of Gr68a involves replacement of the endogenous gene with the mini-white marker using recombinase-mediated cassette exchange ( RMCE ) . Genomic rescue of Gr68a is accomplished by exchanging mini-white via RMCE with the Gr68a sequence . ( Bottom ) Analysis by semi-quantitative PCR of genomic DNA shows the complete absence of Gr68a expression in two homozygous mutant alleles and successful rescue in the respective Gr68aRes lines . Rp49 expression is used as a loading control . CG6024 expression is not changed in mutant or rescue lines ( Figure 2—figure supplement 3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 00410 . 7554/eLife . 06914 . 005Figure 2—figure supplement 1 . Sexually dimorphic PER response to CH503 . The PER response to ( S , Z , Z ) -CH503 is sexually dimorphic . Females do not respond to the more potent stereoisomer ( S , Z , Z ) -CH503 ( green ) while males respond across a range of doses ( black ) . Both males and females suppress PER when presented with other aversive substances , ( 7Z ) -tricosene and caffeine . N = 18 , Fisher's exact probability test , ns: not significant; ***p < 0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 00510 . 7554/eLife . 06914 . 006Figure 2—figure supplement 2 . Chemical structures of ( R , Z , Z ) -CH503 , ( S , Z , Z ) -CH503 , and CH503 analogs . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 00610 . 7554/eLife . 06914 . 007Figure 2—figure supplement 3 . Characterization of ΔGr68a mutant alleles by quantitative PCR . Gr68a levels are effectively reduced in homologous recombinant mutant ( ΔGr68a ) and restored in rescue ( Gr68Res ) alleles . Gene expression levels are shown normalized to wild-type CantonS levels . The expression of CG6024 is unaffected by manipulation of the Gr68a locus . Data show normalized average ± standard deviation ( SD ) , N = 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 007 To identify the subset of neurons on the male foreleg that detect CH503 , we performed a behavioral screen by using the Gal4-UAS transgene system to ablate or functionally suppress each of the 19 known foreleg-specific gustatory neurons ( Ling et al . , 2014 ) . The courtship behavior of transgenic or mutant males was then tested in the presence of CH503-perfumed females . The synthetic stereoisomer ( S , Z , Z ) -CH503 ( Figure 2—figure supplement 2 ) was used for preliminary screening since it is more potent than naturally occurring ( R , Z , Z ) -CH503 both in suppressing courtship ( Shikichi et al . , 2012; Ng et al . , 2014 ) and the PER ( Figure 2—figure supplement 1 ) and thus , would lessen the likelihood of false positives . Males from 15 of the gustatory Gal4 lines displayed normal courtship suppression behavior in the presence of CH503-perfumed females ( Figure 2B ) . Four lines exhibited significantly reduced levels of courtship behavior with control females , thus confounding our ability to detect CH503-related courtship suppression . However , males in which the Gr68a-encoding gene was transcriptionally silenced continued to court females in the presence of ( S , Z , Z ) -CH503 ( Figure 2B ) . A similar response was found when males were tested with the natural pheromone at a 333 ng/fly dose using two independent Gal4 lines ( Figure 2C ) . To ensure complete loss of Gr68a expression , we generated a ΔGr68a mutant using targeted ends-out homologous recombination and confirmed the loss of expression with PCR ( Figure 2D; Figure 2—figure supplement 3 ) . ΔGr68 mutants displayed robust courtship behavior in the presence of CH503-perfumed females ( Figure 2C ) . Importantly , the sensitivity to CH503 was restored upon re-introduction of the Gr68a-encoding gene ( Gr68aRes; Figure 2C ) . The coding region for Gr68a resides within the intronic region of another gene , CG6024 ( Figure 2D ) . However , CG6024 expression was not significantly changed in either mutant or rescue lines ( Figure 2—figure supplement 3 ) . To determine whether activation of Gr68a neurons was sufficient to induce the courtship avoidance response , we expressed the conditionally activated cation channel Drosophila TrpA1 ( dTrpA1 ) . In the presence of unperfumed female targets , a slight but non-significant suppression in courtship behavior was observed at the activation temperature of 29°C compared with the inactive condition at 19°C ( Figure 2C ) . Thus , activation of Gr68a neurons is not sufficient to suppress courtship , possibly due to conflicting signals resulting from mutual activation of mechanosensory and chemosensory neurons ( see ‘Discussion’ ) . To visualize Gr68a neurons and their processes , we drove expression of a membrane-tethered green fluorescent protein molecule ( UAS-mCD8::GFP ) using Gr68a-Gal4 . Consistent with previous reports ( Bray and Amrein , 2003; Ejima and Griffith , 2008; Ling et al . , 2014 ) , a sexually dimorphic GFP expression pattern was observed in the chemosensory neurons of gustatory bristles found in male forelegs ( Figure 3A ) . Each of the male tarsal segments exhibited more labeled neurons than females ( Table 1 ) . Labeled non-neuronal cells ( distinguished by larger , irregularly shaped membranes lacking projections ) were also observed but only in male legs ( Figure 3A; Table 1 ) . Notably , knockdown of Gr68a expression using the pan-neuronal elav-Gal4 driver resulted in a loss of sensitivity to CH503 ( Figure 2B ) . The results recapitulate the phenotype observed with Gr68a-Gal4 and indicate that neuronally expressed receptors are important for CH503 detection . 10 . 7554/eLife . 06914 . 008Figure 3 . Gr68a is essential for CH503-evoked neuronal responses in the male foreleg . ( A ) Visualization of GFP-labeled Gr68a-expressing neurons reveals neuronal and non-neuronal cells ( arrowheads ) in tarsal segments T2-5 from the male foreleg . Scale bar: 35 μm . ( B ) Gr68a-expressing neurons in the male foreleg show changes in Ca2+ activity in response to two doses of CH503 ( pink , red ) . The behaviorally inert analog ( R ) -3-Acetoxy-11 , 19-octacosadiyn-1-ol fails to elicit a significant response ( gray ) . No increase in ΔF/F is observed from the forelegs of ΔGr68a-mutant flies ( red stripes ) . Cells are designated according to the schematic ( left ) showing sensory neurons ( green ) and non-neural cells ( yellow ) . For each cell type , the averaged response ± SEM and sample size is shown; Student's t-test with unequal variance , *p < 0 . 05 , **p < 0 . 01 . Unless otherwise indicated , statistical power is at least 0 . 8 for a significance level of 0 . 05 for the 50 and 500 ng CH503 doses . ‡N = 47 required for power of 0 . 8; †N = 201 required for 0 . 8 power . ( C ) A color-coded time course from 0–96 s showing the response in T2 Gr68a neurons evoked by 500 ng of CH503 . The positions of the neurons on the foreleg are shown in the raw fluorescent image ( bottom right corner , square ) . See also Figure 3—figure supplement 1 and Video 1 . Scale bar: 10 μm . ( D ) Gr68a-expressing neurons on the female foreleg do not show a statistically significant response to ( R , Z , Z ) -CH503 . Student's t-test with unequal variance , p > 0 . 05 for all cells tested . Error bars indicate SEM; sample sizes are shown below each cell type . Unless otherwise indicated , statistical power is at least 0 . 8 for a significance level of 0 . 05 for the 50 and 500 ng CH503 doses . †N ∼ 100 needed to achieve 0 . 8 power . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 00810 . 7554/eLife . 06914 . 009Figure 3—figure supplement 1 . Line graph representation showing the tonic response of a T2 Gr68a neuron upon stimulation with 500 ng of CH503 . Red arrow indicates the time at which the stimulus was added . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 00910 . 7554/eLife . 06914 . 010Figure 3—figure supplement 2 . Physiological responses of male Gr68a neurons to ( S , Z , Z ) -CH503 . An increase in intracellular Ca2+ levels was observed in Gr68a-expressing neurons upon application of ( S , Z , Z ) -CH503 . Two cells ( o , x ) showed a higher ΔF/F increase in response to 500 ng of ( S , Z , Z ) -CH503 ( dark blue ) compared with the natural pheromone applied at the same dose ( Figure 3B , red ) . Maximum ΔF/F values plateau at 50 ng of ( S , Z , Z ) -CH503 in 5 cells ( x , y , a , c , d ) . The averaged response for each cell type ± SEM and sample size is shown; Student's t-test with unequal variance , *p < 0 . 05 , **p < 0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 01010 . 7554/eLife . 06914 . 011Figure 3—figure supplement 3 . Physiological responses of Gr68a neurons upon RNAi-mediated silencing of Gr68a expression . RNAi-mediated suppression of Gr68a severely reduces neural responses induced by ( R , Z , Z ) -CH503 ( red ) . In most cells , the response to the pheromone was not distinguishable from the response to buffer ( white ) , with the exception of cell f . The averaged response for each cell type ± SEM and sample size is shown; Student's t-test with unequal variance , *p = 0 . 016 . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 01110 . 7554/eLife . 06914 . 012Figure 3—figure supplement 4 . Physiological responses of ppk23 proboscis neurons to ( R , Z , Z ) -CH503 . ( A ) Application of 500 ng of CH503 elicited a significant ΔF/F increase in ppk23-expressing neurons on the male proboscis ( red ) . In contrast , ppk23-expressing neurons from the female proboscis did not show a significant change in ΔF/F . The averaged response for each cell type ±SEM and sample size is shown; Student's t-test , *p = 0 . 012 . ( B ) A color-coded time course from 0 to 96 s showing the response of ppk23 neurons on the proboscis to 500 ng of CH503 . Heterogeneity in response times is apparent amongst the different cell types with some cells responding intensely at 6 . 4 s and others only ∼88 s after CH503 application . See also Video 2 . The positions of the neurons on the proboscis are shown in the raw fluorescent image ( bottom right corner ) . Scale bar: 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 01210 . 7554/eLife . 06914 . 013Figure 3—figure supplement 5 . Physiological responses of ppk23 leg neurons to ( R , Z , Z ) -CH503 . ( A ) Visualization of GFP-labeled ppk23-expressing neurons in the male foreleg in tarsal segments T2-5 . Scale bar: 35 μm . ( B ) Ppk23-expressing neurons on tarsal segments T2-5 were stimulated with 50 ng of ( S , Z , Z ) -CH503 , an equivalent dose of the behaviorally inactive analog ( S ) -3-acetoxy-11-octacosen-1-ol or 500 ng of ( R , Z , Z ) -CH503 . A robust increase in ΔF/F specific to ( S , Z , Z ) -CH503 was elicited from 2 cells on T2 and 1 cell on T3 . Several cells in each segment displayed a robust response to the analog . The averaged response for each cell type ± SEM and sample size is shown , Student's t-test , *p < 0 . 05 , **p < 0 . 01 , ***p < 0 . 001 . ( C ) A color-coded time course from 0 to 96 s showing the response in ppk23-expressing neurons on the male foreleg to 500 ng of CH503 . The cells exhibit a bursting response consisting of fluorescence intensity increasing at periodic intervals for the duration of the recording . See also Video 3 . The positions of the neurons on the foreleg are shown in the raw fluorescent image ( bottom right corner ) . Scale bar: 10 μm . ( D ) Line graph representation showing the oscillatory response of a T2 ppk23 neuron upon stimulation with 500 ng of CH503 . Red arrow indicates the time point at which the stimulus was added . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 01310 . 7554/eLife . 06914 . 014Figure 3—figure supplement 6 . Gr68a-Gal4 and fruitless ( fru ) -expression in the foreleg do not co-localize . Pearson's coefficients: 0 . 06 ( left ) and 0 . 02 ( right ) ; scale bar: 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 01410 . 7554/eLife . 06914 . 015Table 1 . Average number of GFP-positive cells in male and female foreleg segments labeled using Gr68a-Gal4DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 015T1T2T3T4T5Total*♂ neurons2 ± 12 ± 13 ± 12 ± 10 ( 9/12 flies ) 9 ± 1♀ neurons2 ± 11 ± 12 ± 11 ± 10 ( 11/12 flies ) 6 ± 2♂ non-neural cells2 ± 13 ± 02 ± 01 ± 008 ± 1♀ non-neural cells000000*Averaged count ( ±SD ) from 12 flies . To measure directly the response of Gr68a-expressing neurons to CH503 , we performed in vivo calcium imaging of the forelegs of live flies by expressing the calcium sensor GCaMP5 under control of Gr68a-Gal4 . The maximum fluorescent change ( ΔF/F ) was elicited with a bath-applied dose of 500 ng of ( R , Z , Z ) -CH503 , close to the minimum dosage required to elicit a behavioral response in courtship assays ( Figure 3B ) . At this dose , 6 of 9 cells in segments T2–4 showed significant responses compared to the control solvent , ranging from ΔF/F 0 . 34 ± 0 . 05 to 1 . 39 ± 0 . 46 ( mean ± SEM ) . At a dose of 50 ng , only 1 cell ( T4 , Cell a ) showed a significant signal increase , with an average ΔF/F of 1 . 52 ± 0 . 37 . Gr68-neurons exhibited two general patterns of responses: more commonly , a phasic response is observed where maximum fluorescent intensity occurred immediately after the addition of the pheromone and was followed by a gradual decline to baseline levels after 6 s . A tonic response is also seen where fluorescence gradually increased and peaked after approximately 120 s ( Figure 3C; Figure 3—figure supplement 1; Video 1 ) . Gr68a-expressing neurons also responded to the synthetic stereoisomer ( S , Z , Z ) -CH503 over the same range of doses though they differed in terms of dynamic range and response pattern ( Figure 3—figure supplement 2 ) . 10 . 7554/eLife . 06914 . 016Video 1 . Physiological response from Gr68a neurons on the male foreleg expressing GCaMP . Cell bodies in T3 exhibit a tonic response upon stimulation with 500 ng of CH503 . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 016 Stimulation of Gr68a-expressing neurons with a synthetic CH503 analog containing two triple bonds did not elicit a change in fluorescent signal ( Figure 3B ) , consistent with previous observations that the analog is behaviorally inert ( Shikichi et al . , 2013 ) . Additionally , CH503 failed to evoke a physiological response when Gr68a expression was either reduced using RNAi or eliminated in ΔGr68a mutants ( Figure 3B; Figure 3—figure supplement 3 ) . In females , the responses to the pheromone from the forelegs were indistinguishable from that of the solvent control ( Figure 3D ) . In the case of cells j and s , it may be that the responses are too subtle to be reliably measured without a sample size ∼100 . The functional imaging results are consistent with observations from the appetitive PER assay where females also failed to exhibit a measurable response to CH503 ( Figure 2—figure supplement 1 ) . Taken together , direct measurement of neural response in Gr68a-expressing neurons reveals robust functional activation by CH503 in males only . Moreover , the double-bonds in the carbon backbone of the CH503 molecule are an essential structural feature for pheromone activity . Recently , male and female leg neurons expressing the ion channel ppk23 were shown to respond physiologically to non-volatile gustatory pheromones in D . melanogaster ( Lu et al . , 2012; Thistle et al . , 2012; Toda et al . , 2012 ) . We measured responses from GCaMP-expressing ppk23-Gal4-labeled cells in the labella and forelegs of both male and female flies . In the male proboscis , CH503 activated approximately 8 of 14 neurons while none of the female ppk23 neurons responded ( Figure 3—figure supplement 4; Video 2 ) . The response of ppk23 neurons in the male foreleg was more heterogeneous compared to Gr68a-expressing neurons . Depending on the cell , neural responses could be elicited by the natural pheromone ( R , Z , Z ) -CH503 , the artificial stereoisomer ( S , Z , Z ) -CH503 , a synthetic CH503 analog or solvent controls ( Figure 3—figure supplement 5 ) . While most ppk23-expressing neurons typically responded in a phasic manner with a maximum change in ΔF/F shortly after pheromone addition , some cells exhibited bursting responses with large increases in ΔF/F that recurred frequently during the time course of the experiment ( Figure 3—figure supplement 5; Video 3 ) . 10 . 7554/eLife . 06914 . 017Video 2 . Physiological response from ppk23 neurons on the male proboscis expressing GCaMP . Following stimulation with 500 ng of CH503 , the projections of ppk23 neurons exhibit a bursting response . The cell bodies respond in a tonic manner , displaying a gradual increase in fluorescence intensity . The tonic response could be due to persistent stimulation from the pheromone . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 01710 . 7554/eLife . 06914 . 018Video 3 . Physiological response from ppk23 neurons on the male foreleg expressing GCaMP . Following stimulation with 500 ng of CH503 , cell bodies and projections in T3 display a bursting response . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 018 To test whether ppk23 plays a role in mediating the behavioral response to CH503 , we examined the behavior of male flies in which ppk23 expression was silenced using RNAi . Males continued to respond to CH503 though the courtship suppression effect is partially mitigated compared to other gustatory receptors ( Figure 2B ) . It was not possible to assess the courtship behavior of Δppk23 mutants due to low basal courtship levels . Knockdown of a second member of the ppk family , ppk25 , also resulted in a partially reduced sensitivity to CH503 ( Figure 2B ) . While these results do not eliminate the possibility that ppk-23/25-expressing neurons contribute to CH503 detection , both the physiological and behavioral data indicate that the responses of the cells by themselves are not the primary sensory pathway and do not appear to be necessary for courtship suppression to occur . The Gr68a-Gal4-labeled neurons send axonal projections to each of the six thoracico-abdominal ( TAG ) neuromeres and extend into the SEZ and the antennal mechanosensory and motor center ( AMMC ) ( Figure 4A ) . To identify upstream pathways that relay information from the SEZ , we screened 22 Gal4 drivers and 9 UAS-RNAi lines targeting central brain regions , neuropeptide systems , and neurotransmitter circuits for their contribution to CH503-induced courtship suppression ( Figure 4—figure supplement 1; Table 2 ) . Interestingly , suppression of neural activity using the c929-Gal4 line resulted in a marked decrease in sensitivity to CH503 ( Figure 4B ) . The peptide driver labels both neuroendocrine and peptidergic neurons in the central nervous system and processes in the TAG ( [Taghert et al . , 2001; Hewes et al . , 2003]; Figure 4—figure supplement 2 ) . We partially limited c929-Gal4 expression by suppressing Gal4 activity in the ventral nerve cord with the repressor tsh-Gal80 and observed that sensitivity to CH503 was not restored ( Figure 4B; Figure 4—figure supplement 2 ) . Hence , c929-labeled processes in the thoracic ganglia do not appear to contribute to the behavioral response to CH503 . 10 . 7554/eLife . 06914 . 019Figure 4 . Higher order neural circuits essential for processing CH503 . ( A ) Gr68a-Gal4-labeled afferent projections extend to the thoracico-abdominal ganglia ( TAG ) , subesophageal zone ( SEZ ) , and antennal mechanosensory and motor center ( AMMC ) . Image represents a maximum intensity Z-series projection . Scale bar A: 25 μm; A′: 50 μm . ( B ) Inhibition of electrical activity in c929-Gal4-labeled neurons with UAS-dORKΔC , an inwardly rectifying K+ channel , resulted in high courtship levels in the presence of CH503 . Suppressing Gal4 expression in the ventral cord with a tsh-Gal80 transgene ( hence , limiting dORKΔC expression primarily to the central brain ) failed to restore sensitivity to CH503 . No change in CH503 response was observed in the absence of the c929-Gal4 driver . N = 23–25 , Fisher's exact probability test , ns: not significant , ****p < 0 . 0001 . ( C ) Ablation of central brain neural circuits associated with NPF abolished the courtship suppression response to CH503 . The courtship behavior of genetic controls was unaffected . Silencing NPF expression in all neural cells ( using elav-Gal4 ) or in peptidergic neurons ( using c929-Gal4 ) did not alter flies' sensitivity to CH503 . N = 14–33 , Fisher's exact probability test , ns: not significant , **p < 0 . 01 , ****p < 0 . 0001 . ( D ) NPF-expressing processes are closely apposed to Gr68a-Gal4 synaptic terminals labeled with synaptobrevin-GFP ( syb-GFP ) in the AMMC ( D′ ) and SEZ ( D′′ ) . No co-localization is observed ( Pearson's coefficient: 0 . 01 ) . Image represents a maximum intensity Z-series projection . Scale bar D: 50 μm; D′ , D′′: 20 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 01910 . 7554/eLife . 06914 . 020Figure 4—figure supplement 1 . Central brain screen to identify CH503-processing circuits . Inactivation of neural activity by expression of the temperature-sensitive Shibire transgene ( UAS-Shits1 ) or UAS-dORKΔC within NPF- and c929-Gal4 circuits resulted in a loss of sensitivity to CH503 ( purple vs white ) . It was not possible to assess some Gal4 lines ( far right ) due to low activity or lethality upon suppression of neural activity . N = 8–24 for each line , Fisher's exact probability test , ns: not significant; *p < 0 . 05; **p < 0 . 01; ***p < 0 . 001; ****p < 0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 02010 . 7554/eLife . 06914 . 021Figure 4—figure supplement 2 . Co-expression of anti-NPF immunostaining with c929-Gal4-directed GFP expression . ( A ) Co-expression is observed in cell bodies housed in the thoracico-abdominal ganglia ( TAG ) , subesophageal zone ( SEZ ) , superior medial protocerebrum ( SMP ) , and processes along the median bundle ( MBDL ) . ( A′ ) Magnification of square in A . ( A′′ ) Z-section from another depth . Images represent maximum intensity Z-series projections . Scale bar A: 25 μm; A′ and A′′: 50 μm . ( B ) The tsh-Gal80 transgene restricts c929-Gal4 expression to the brain Scale bar: 25 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 02110 . 7554/eLife . 06914 . 022Figure 4—figure supplement 3 . Screen of tachykinin and small transmitter systems within the c929-Gal4 circuit . RNAi-mediated silencing of tachykinin ( TK ) expression within the c929-Gal4 circuit inhibits sensitivity to ( S , Z , Z ) -CH503 ( purple vs white ) . RNAi manipulation of other neurotransmitter systems did not have a significant effect on CH503-detection ( black vs white ) . N = 21–35 , Fisher's exact probability test , ns: not significant; **p < 0 . 01; ***p < 0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 02210 . 7554/eLife . 06914 . 023Figure 4—figure supplement 4 . Characterization of NPF transcript levels . Quantitative PCR analysis of NPF transcript levels from heads of elav>NPF-RNAi and elav/+ control flies . RNAi induces a 5 . 3-fold reduction of NPF transcript levels . Gene expression levels are shown normalized to wildtype CantonS levels . Data show normalized average ± standard deviation ( SD ) , N = 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 02310 . 7554/eLife . 06914 . 024Table 2 . Gal4 and RNAi lines used to screen for CH503-related defectsDOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 024StockExpression pattern or gene targeted*Sourcec386a-Gal4EBwww . fly-trap . org2-72-Gal4EBgift from U Heberlein ( Janelia Farm , VA , USA ) 4-67-Gal4EBGift from U Heberlein9-161-Gal4PC , FSB , MBGift from U Heberlein2-13-Gal4interneurons , PCGift from U Heberleinc819-Gal4 ( Pan et al . , 2012 ) ( Pan et al . , 2012 ) c061-Gal4PC , FSB , MB ( Pan et al . , 2012 ) R71GO1-Gal4PI , VNC ( Pan et al . , 2012 ) OK107-Gal4MBDGRC #106098TrH-Gal45HT cellsBloomington #10531TH-Gal4DA cellsBloomington #8848Tdc2-Gal4TA and OCT cellsBloomington #9313MB247-Gal4MB , EBBloomington #50742JO-15-Gal4Johnston's organBloomington #6753DDC-Gal4DA and 5HT cellsBloomington #7010c309-Gal4MB , SEZ , CX , AL , PI , TGBloomington #6906c305-Gal4MB , EB , AL , gliaBloomington #30829c305-Gal4MBBloomington #30829c205-Gal4FBBloomington #30827c161y-Gal4EB , FSB , PC , chordotonal organBloomington #27893c107-Gal4EB , FSB , PC , chordotonal organBloomington #3082330Y-Gal4MBBloomington #30818CheB42aΔ5–68CheB42a ( Park et al . , 2006 ) UAS-TbH-RNAi #1 , 2Tyrosine β-hydroxylaseVDRC #107070 , 51667UAS-OAMB-RNAi #1 , 2 , 3Mushroom body OA receptorBloomington #31233 , 31711; VDRC #106511UAS-Cha-RNAiCholine acetyltransferaseVDRC #20183UAS-VAChT-RNAiVesicular acetylcholine transporterVDRC #40918UAS-DAT-RNAiDA transporterVDRC #12082UAS-TH-RNAiTyrosine hydroxylaseVDRC #3308UAS-Dop1R1-RNAiDA 1-like receptor 1VDRC #107058UAS-Dop1R2-RNAiDA 1-like receptor 2VDRC #105324UAS-VGAT-RNAi #1 , 2Vesicular GABA transporterVDRC #103586 , 45916UAS-Tdc2-RNAiTyrosine decarboxylaseBloomington #25871UAS-SERT-RNAi5HT transporterVDRC #11346UAS-GABA B R1-RNAiMetabotropic GABA-B receptor subtype1VDRC #101440UAS-GABA B R2-RNAiMetabotropic GABA-B receptor subtype2VDRC #1785UAS-GABA B R3-RNAi #1 , 2Metabotropic GABA-B receptor subtype3VDRC #108036 , 50176UAS-GAD1-RNAiGlutamic acid decarboxylaseVDRC #32344*AL: antennal lobe; CX: central complex; EB: ellipsoid body; FSB: fan-shaped body; MB: mushroom body; PC: protocerebrum; PI: pars intercerebralis; SEZ: subesophageal zone; TG: thoracic ganglion; VNC: ventral nerve cord; 5HT: serotonin; DA: dopamine; OCT: octopamine; TA: tyramine . We next attempted to identify other neurotransmitters within the c929-Gal4 circuit that could mediate pheromone processing . RNAi-mediated silencing of genes related to aminergic , cholinergic , glutamatergic , and GABAergic synthesis , transport , and receptor systems did not have an effect ( Figure 4—figure supplement 3 ) . However , genetic ablation or suppression of neural activity in two neuropeptidergic circuits , neuropeptide F ( NPF ) and tachykinin ( TK ) , reduced sensitivity to CH503 ( Figure 4C; Figure 5A ) . 10 . 7554/eLife . 06914 . 025Figure 5 . Tachykinin-expressing cells in the SEZ are a second order circuit for Gr68a neurons . ( A ) Ablation of TK-expressing circuits using two independent Gal4 drivers ( TK2 and TK3 ) removed sensitivity to CH503 . Homozygous or trans-heterozygous ΔTK deletion mutants also exhibit a loss of sensitivity to CH503 . Rescuing TK expression in two different mutant backgrounds restored the behavioral response to CH503 . See Figure 5—figure supplement 2 for parental controls . N = 15–31 , Fisher's exact probability test , ns: not significant , ****p < 0 . 0001 . ( B , C , E ) TK-expressing cells are closely apposed to Gr68a-Gal4 synaptic terminals labeled with synaptobrevin-GFP ( syb-GFP ) in the SEZ ( C ) and AMMC ( E ) . No co-localization is observed ( Pearson's coefficient: 0 ) . Image represents a maximum intensity Z-series projection . ( D , D′ ) Positive GRASP-GFP signal in the SEZ indicates synaptic connectivity between Gr68a neurons and TK processes . The GFP signal is overlaid on a phase-contrast image of the tissue ( arrows ) . ( F , F′ ) Positive GRASP-GFP signal in the AMMC indicates synaptic connectivity between Gr68a neurons and TK processes . The GFP signal is overlaid on a phase-contrast image of the tissue ( rectangle ) . Scale bar B: 50 μm; all other scale bars: 20 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 02510 . 7554/eLife . 06914 . 026Figure 5—figure supplement 1 . Tachykinin is essential for CH503 detection . Three different TK-Gal4 drivers were tested for their contribution to CH503 processing . Cell ablation or inactivation of neural activity using TK2-Gal4 and TK3-Gal4 ( purple vs white ) but not TK1-Gal4 ( black vs white ) removed sensitivity to ( S , Z , Z ) -CH503 . N = 28–36 , Fisher's exact probability test , ns: not significant; **p < 0 . 01; ***p < 0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 02610 . 7554/eLife . 06914 . 027Figure 5—figure supplement 2 . Parental control lines for tachykinin mutant rescue experiments . The presence of the Gal4 or UAS transgene is not sufficient to rescue the response to CH503 . N = 23–30 , Fisher's exact probability test , all results are not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 02710 . 7554/eLife . 06914 . 028Figure 5—figure supplement 3 . Non-specific diffuse staining is observed in tissue from GRASP negative controls lacking the Gr68a-Gal4 driver . Left: the brightness of the confocal image is exaggerated in order to visualize the tissue . Right: the fluorescent image is overlaid on a phase-contrast image of the tissue . Scale bar: 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 028 The peptide NPF co-localizes with c929-Gal4 throughout the TAG , SEZ , and protocerebrum ( Figure 4—figure supplement 2 ) . Anatomical analysis revealed that Gr68a synaptic terminals and NPF-positive processes are closely apposed to each other in the SEZ , implicating NPF-expressing circuits as a putative second order relay to the central brain ( Figure 4D ) . Notably , silencing NPF expression using c929-Gal4 or the pan-neural driver elav-Gal4 ( which caused a 5 . 3-fold decrease in transcript level , Figure 4—figure supplement 4 ) was ineffective at changing the male inhibitory courtship response ( Figure 4C ) . These findings indicate that the NPF peptide itself is not likely to be essential for mediating CH503-related behavior . Consistent with this observation , silencing of the neuronal circuits associated with the NPF receptor , NPFR , also did not change the response to CH503 ( Figure 4—figure supplement 1 ) . TK-expressing cells represent a second higher order neural circuit that mediates CH503-induced courtship suppression . We observed a significantly altered response in males both to the natural pheromone ( Figure 5A ) and the more potent ( S , Z , Z ) -CH503 stereoisomer ( Figure 5—figure supplement 1 ) following genetic cell ablation or functional suppression of TK-expressing cells . The results were consistent using two independent TK-Gal4 drivers which label overlapping but not identical cell populations . Genetic excision of the TK gene also caused a striking loss of sensitivity to CH503 ( Figure 5A ) . The phenotype was rescued upon ectopic expression of TK in the mutant background ( Figure 5A ) . Thus , in contrast to our findings with the NPF peptide , the product of the TK gene is necessary for mediating the behavioral actions induced by CH503 . To examine TK expression relative to Gr68a processes , we used an antibody to TK to label brain tissue from Gr68a-Gal4>UAS-sybGFP flies . Positive TK staining is evident in the SEZ and AMMC and is positioned closely to Gr68a-Gal4-labeled terminals ( Figure 5B , C , E ) . To determine if there is synaptic connectivity , we used the GFP reconstitution across synaptic partners ( GRASP ) method ( Feinberg et al . , 2008; Gordon and Scott , 2009 ) . Complementary fragments of membrane-tethered GFP are expressed using Gr68a-Gal4 and TK-LexA drivers . By themselves , the fragments are non-fluorescent . However , close apposition of the fragments reconstitutes a functional GFP molecule and in this way , labels sites of synaptic contacts . Positive GRASP-GFP staining was apparent in the SEZ ( Figure 5D; 6 out of 10 brains ) and the AMMC ( Figure 5F; 9 out of 10 brains ) , indicating connectivity . In tissue from negative controls lacking the Gr68a-Gal4 driver , only diffuse non-specific staining is seen ( 4 out of 4 brains; Figure 5—figure supplement 3 ) . We next asked whether the TK-positive cells involved in the processing of CH503 overlap with the NPF-Gal4 or c929-Gal4 circuit . Indeed , silencing TK expression only within either of these Gal4-defined populations resulted in a reduced response to CH503 , with male courtship levels indistinguishable from those of the solvent-perfumed fly ( Figure 6A , C ) . Conditional suppression of TK only from late pupal stage onwards ( using temperature-sensitive Gal80 ) also produced the same phenotype , indicating that loss of CH503 sensitivity from TK knockdown is not due to non-specific developmental effects ( Figure 6A ) . 10 . 7554/eLife . 06914 . 029Figure 6 . Tachykinin release within the NPF- and c929-defined circuits is required for the processing of CH503 . ( A ) RNAi-mediated knockdown of TK only in central NPF-Gal4 circuits abrogates the CH503-induced courtship suppression response . Conditional knockdown only from late pupal stage onwards ( 29°C permissive temperature , TK-RNAi expressed ) elicits the same phenotype . At the 25°C restrictive temperature ( TK-RNAi not expressed ) , flies continue to respond to the pheromone . Restoring TK expression only in the NPF-Gal4 circuit is not sufficient to restore sensitivity to CH503 . See Figure 6—figure supplement 2 for parental controls . N = 14–23 , Fisher's exact probability test , ns: not significant , ***p < 0 . 001 . ( B ) Co-expression and co-localization of anti-NPF immunostaining with TK2-Gal4 processes is observed only in two pairs of bilateral cells in the ventrolateral protocerebrum ( indicated by arrowheads; Pearson's coefficient: 0 . 7 ) . No co-expression is observed in midline cells—the apparent co-localization observed in some cells ( yellow signal ) is due to overlapping signals from stacking different optical layers . Image represents a maximum intensity Z-series projection . Scale bar: 50 μm . ( C ) Silencing TK expression only in the c929-Gal4 circuit removes CH503 sensitivity . Rescuing TK expression only in the c929-Gal4 circuit restores sensitivity . N = 16–24 , Fisher's exact probability test , ns: not significant , *p < 0 . 05 . ( D ) Co-expression of anti-TK immunostaining with c929-Gal4 GFP expression is observed in 10 cell bodies housed in the SEZ ( arrowheads; D′ ) . Images represent maximum intensity Z-series projections . Scale bar D: 50 μm; D“: 35 μm . ( E ) Histogram showing frequency of cells in the SEZ that are triple-labeled with anti-NPF antibody , anti-TK antibody , and c929-Gal4 GFP expression . ( F , G ) Seven or three triple-labeled cells ( arrows ) in the SEZ . Images represent maximum intensity Z-series projections . Scale bar: 20 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 02910 . 7554/eLife . 06914 . 030Figure 6—figure supplement 1 . The TK3-Gal4 circuit does not co-localize with NPF . ( A ) Inactivation of TK neurons using a third TK-Gal4 line results in a loss of sensitivity to CH503 . The sensitivity is restored upon rescue of TK expression . ( B ) No co-localization or co-expression is evident ( Pearson's coefficient: 0 . 02 ) . Image represents a maximum intensity Z-series projection . Scale bar: 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 03010 . 7554/eLife . 06914 . 031Figure 6—figure supplement 2 . Parental control lines for tachykinin mutant rescue experiments . N = 24–32 , Fisher's exact probability test , all results are not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 031 To examine the intersection of TK- and NPF-defined circuits , TK2-Gal4-labeled brains were stained with an antibody to NPF . Co-expression in 7 cells ( designated NPF-TK cells ) was evident in the anterior ventrolateral protocerebrum ( Figure 6B ) . However , two lines of evidence indicate that these 4 cells are not sufficient for mediating courtship suppression . First , on a TK mutant background , rescuing TK expression only within the NPF circuit did not restore the courtship suppression response ( Figure 6A ) . Second , the four NPF-TK cells are not labeled by TK3-Gal4 , a second population of cells that was also found to contribute to CH503 detection ( Figure 6—figure supplement 1 ) . We next attempted to rescue pheromone sensitivity by replacing TK expression only within the c929-Gal4 circuit . In wild-type flies , co-expression is evident in 10 cells in the SEZ , designated as c929-TK cells ( Figure 6D , D′ ) . Rescue of TK expression only in these 10 cells restored the pheromone-response behavior in the ΔTK2 and ΔTK1/2 transheterozygote mutant background ( Figure 6C ) . Failure to rescue in the ΔTK1 background is likely due to a stronger mutant allele . Triple labeling of NPF and TK within the c929 circuit revealed positive but variable co-expression between animals ( Figure 6E–G ) . The majority of tissue samples contained between 1 and 3 triple labeled cells ( Figure 6F ) , with one brain showing as many as 7 cells ( Figure 6G ) . Taken together , these results indicate that TK release from a cluster of 8–10 cells within the SEZ is necessary to mediate the response to the gustatory pheromone CH503 . In addition , NPF-Gal4 and TK expression overlap in a small population of cells in the SEZ and protocerebrum .
Previous characterization of Gr68a indicated that female pheromones are likely to serve as ligands and that Gr68a-impaired males are defective in their courtship of females ( Bray and Amrein , 2003 ) . In contrast , our study shows that Gr68a-impaired males courted females at similar levels to control males and that one ligand for the receptor is the male sex pheromone CH503 . Several factors can account for these discrepancies . First , the courtship defect of Gr68a-impaired males is only evident in larger-sized behavioral chambers ( Ejima and Griffith , 2008 ) . Our study used smaller chambers ( ∅ = 10 mm ) compared to the previous study ( 30 mm; Bray and Amrein , 2003 ) hence masking the courtship defect . Second , Ejima and Griffith previously showed that Gr68a is needed to relay motion-related cues that stimulate courtship initiation . The dual sensory role of Gr68a is consistent with its expression pattern in males in separate mechanosensory and gustatory neuron populations ( Ejima and Griffith , 2008 ) and afferent projections to the SEZ and AMMC , a relay center for auditory information . Thus , males' inability to detect motion rather than female pheromones may underlie the previously reported Gr68a-related courtship initiation defects . Taken together with the results of our current study , we propose that excitatory mechanosensory information is integrated with inhibitory CH503 detection via Gr68a neurons and both types of sensory cues shape the decision to initiate and sustain courtship . This model is consistent with our findings that activation of Gr68a-neurons using TrpA1 did not promote courtship suppression . Simultaneous activation of both courtship-promoting and suppressing channels likely resulted in an overall null response . Similar dual functioning neurons integrating chemical information with touch have been described in nematodes ( Kaplan and Horvitz , 1993 ) , mammalian olfactory receptors ( Grosmaitre et al . , 2007 ) , and vertebrate nociceptor systems ( Besson and Chaouch , 1987; Woolf and Walters , 1991 ) . The nature of the ligand–receptor interaction is an open question in the absence of data from heterologous expression studies or structure–function analysis . Our observation that CH503 induces courtship inhibition only when detected on female cuticles has several implications for the underlying mechanisms . Potentially , activation of Gr68a receptors found on other parts of the male body ( e . g . , other regions of the legs or wings ) is needed and occurs only when males assume courtship postures . Alternatively , concurrent detection of a co-factor residing on female cuticles might be required . Though our results show that hexane-soluble molecules on the cuticle are not needed , the possibility remains that cuticular peptides or proteins may help transduce pheromone detection . Previous work indicated that a pheromone binding protein , CheB42a , is found in the lumen surrounding Gr68a-neurons and is needed for detection of female pheromones ( Park et al . , 2006 ) . While CheB42a mutants are still sensitive to CH503 , other families of binding proteins may be used to detect female-specific signals that facilitate the activity of CH503 . Lastly , tactile contact might be needed together with chemosensory stimulation . Previously , Kohatsu et al . showed that in Drosophila , contact between the male foreleg and female abdomen was necessary to initiate courtship ( Kohatsu et al . , 2011 ) . Abdomen contact induced transient activity in the transmidline interneurons of the P1 neuron cluster , a designated ‘command center’ for courtship behavior that integrates input from multiple sensory modalities ( Kimura et al . , 2008; Kohatsu et al . , 2011; von Philipsborn et al . , 2011 ) . Information relayed from the upstream targets of Gr68a-expressing neurons could contribute to the silencing of P1 neurons and in this way , lower the probability of sustained courtship behavior . Whether both chemical and tactile cues are mediated by the same receptor or the same cell remains to be determined . The majority of Gr68a neurons responded to CH503 at a 500 ng dose , an amount consistent with the behaviorally active dose . However , the quantity that is transferred to the female during courtship is approximately 60–100 ng based on semi-quantitative mass spectral analysis of recently mated females . Why is there a discrepancy between the amount that is needed for biological activity and the amount that is transferred ? It could be that the presence of cVA , another anti-aphrodisiac , reduces the necessity for a large amount of CH503 or that each molecule synergizes the efficacy of the other . Additionally , recent findings indicate that male D . melanogaster have adapted to become less sensitive to CH503 to circumvent the courtship inhibitory response ( Ng et al . , 2014 ) . While ∼80 ng is sufficient to inhibit male courtship in other non-CH503 producing drosophilid species , D . melanogaster males ( and males of other species that express CH503 ) have become less sensitive to the molecule in order to avoid chemical coercion from other males ( Ng et al . , 2014 ) . Thus , it is not surprising that males are not sensitive to the amount of CH503 transferred to females . The ion channels ppk23 and ppk25 are expressed in a specialized class of gustatory neurons that were recently shown to be involved in the detection of non-volatile , male- and female-specific cuticular hydrocarbons ( Lu et al . , 2012; Thistle et al . , 2012; Toda et al . , 2012; Vijayan et al . , 2014 ) . Though we were able to measure CH503-induced physiological activity from ppk23-expressing neurons on the proboscis and the male foreleg , the responses in the foreleg are not likely to be specific to the natural pheromone since behaviorally inert chemical analogs also caused a change in Ca2+ flux . It is unclear as well whether the activity of ppk23 and ppk25-expressing neurons contributes to CH503-mediated courtship avoidance since males continue to respond to the pheromone ( albeit more weakly ) when expression of either channel is suppressed . Since ppk23 neurons co-localize with fruitless ( Thistle et al . , 2012; Toda et al . , 2012 ) , a master gene that regulates many aspects of courtship behavior ( Yamamoto and Koganezawa , 2013 ) , whereas Gr68a neurons do not ( Figure 3—figure supplement 6 ) , it is clear that Gr68a and ppk23 expression will also be in distinct populations of neurons . Based on previous observations that ppk23 neurons respond to both attractive and aversive sex pheromones , we speculate that they function as general detectors for a subset of chemosensory cues rather than encode information about valence . Afferent inputs from Gr-expressing sensory neurons extend to the neuromeres of the TAG and the SEZ in distinct projection patterns ( Kwon et al . , 2014 ) . Are there distinct loci within the SEZ that differentially process food and pheromonal cues ? In the olfactory system of the fly , information from fruit and pheromones appears to segregate into distinct regions of the lateral horn of the protocerebrum ( Jefferis et al . , 2007 ) ( though some exceptions have been found , see Ronderos et al . , 2014 and Grosjean et al . , 2011 ) . In the SEZ , the distinctions are less clear . The Gr32a , Gr33a , and Gr39a receptors , which are predicted to detect courtship-inhibitory pheromones , overlap with bitter-sensing neurons in the labellum ( Wang et al . , 2004; Weiss et al . , 2011 ) and have very similar SEZ projection patterns ( Kwon et al . , 2014 ) . In addition , no obvious differences in receptor expression at the periphery have been found between males and females . In contrast , Gr68a expression is specialized for male forelegs , appearing in both gustatory and mechanosensory neuron populations . Expression in females is largely restricted to cells located next to chordotonal organs ( Ejima and Griffith , 2008 ) . Moreover , the Gr68a neuron projection pattern to the TAG appears unique amongst the gustatory receptors , perhaps reflecting its dual role in mechanosensation and chemical detection ( Kwon et al . , 2014 ) . Refined analysis of individual afferent processes in the SEZ will allow us to better understand whether the quality and valence of a tastant is encoded at the level of the sensory receptor or transformed within the SEZ and higher order regions . Intriguingly , the AMMC was recently identified as a higher-order processing target for gustatory projection neurons from the SEZ which convey sweet information ( Kain and Dahanukar , 2015 ) . Perhaps some AMMC-projecting neurons may exist for bitter or pheromone-specific information . The decision to mate involves considerable investment of resources and risk of predation ( Daly , 1978 ) . In this regard , the TK circuitry serves as a point of convergence for multiple , opposing physiological drives which underlie an animal's decision to mate ( Figure 7 ) . TK has been implicated in numerous systemic functions including lipid metabolism ( Song et al . , 2014 ) , stress resistance ( Kahsai et al . , 2010 ) , and modulation of aggression levels ( Asahina et al . , 2014 ) . In addition , the mammalian homolog Substance P controls sexual behavior , stress responses , appetite , and aggression ( Argiolas and Melis , 2013 ) . The convergence of aggression and sex within a common neural pathway alludes to the possibility that the choice to fight or court is modulated by TK release which is , in part , regulated by external stimuli such as pheromones . Interestingly , the c929-TK cells identified in this study were shown previously to be involved in water conservation in response to desiccation ( named ipc-1 and 2a; [Kahsai et al . , 2010] ) , indicating that physiological stress could inform the decision to initiate and sustain courtship via TK signaling . 10 . 7554/eLife . 06914 . 032Figure 7 . A model for gustatory pheromone perception in peripheral and central neural circuits . Gr68a neurons on the foreleg relay chemosensory and mechanosensory signals to the subesophageal zone ( SEZ ) and AMMC , respectively . Movement detection via Gr68a neurons contributes to the decision to court , possibly through TK signaling . The c929-TK cell cluster within the SEZ transduces information via TK release to higher order centers , potentially including the P1 courtship ‘command center’ . Metabolic stress responses and pheromone detection converge on the c929-TK cluster . Overall levels of TK release from this group of 8–10 cells could modulate the behavioral switch between aggression and courtship . DOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 032 The NPF peptide itself does not appear to play a direct role . It is likely that the circuit defined by NPF-Gal4 line effectively inhibits the response to CH503 due to expression in non-NPF expressing cells , some of which express TK . It is intriguing to consider that NPF-related circuits were shown recently to be important in transducing pheromone perception from ppk23-positive cells to the central brain ( Gendron et al . , 2014 ) . Perhaps overlapping TK circuits may function as a second order circuit for ppk23 neurons . In summary , the circuit described in this work provides a tractable model in which to study how gustatory pheromone information converges with physiological state to modulate complex social behavior . Identifying the neural substrates that respond to TK release and determining how third order neurons integrate with components for processing olfactory pheromone information will be essential in piecing together a connectome for mating behavior .
The following lines were used: Or83b-Gal4 ( Larsson et al . , 2004 ) ; Gr-Gal4 collection including Gr68a-Gal42 ( Weiss et al . , 2011 ) ; Gr68a-Gal41 ( Bray and Amrein , 2003 ) ; ppk23-Gal4 , Δppk23 , and Δppk29 ( Thistle et al . , 2012 ) ; Voila1 ( Balakireva et al . , 1998 ) ; NPF-Gal4 and NPFR1-Gal4 ( Wu et al . , 2003 ) ; c929-Gal4 ( Hewes et al . , 2003 ) ; oeno-Gal4 and UAS-hid , stinger ( Billeter et al . , 2009 ) ; tsh-Gal80 ( kind gift of Julie Simpson ) ; UAS-GCaMP5G ( Akerboom et al . , 2012 ) ; ΔTK1 , ΔTK2 , and UAS-TK ( Asahina et al . , 2014 ) ; UAS-spGFP and LexAop-spGFP ( Gordon and Scott , 2009 ) ; TK-Gal41 ( #51975 ) , TK-Gal42 ( #51974 ) , TK-Gal43 ( #51973 ) , TK-LexA ( #54080 ) , UAS-mCD8:GFP , UAS-stinger , UAS-syt . eGFP , UAS-reaper , UAS-dORKΔC , UAS-DTI , UAS-Shibirets1 , UAS-dTrpA1 ( Bloomington Stock Center , Indiana , USA ) ; UAS-Gr68a-RNAi ( 13380 , 13381 from VDRC , Vienna , AU ) and UAS-TK-RNAi ( 103662 from VDRC ) . All other stocks used for screening are described in Table 2 . ΔGr68a and ΔGr68a-rescue ( Gr68aRes ) flies were generated by ends-out homologous recombination as previously described ( Chen et al . , 2011 ) using the pw25-RMCE-targeting vectors and verified by PCR using primers to the vector sequence ( Weng et al . , 2009 ) . Loss of the Gr68a sequence was verified by quantitative PCR . To generate Gr68a-Gal4 and UAS-GCaMP5-expressing alleles in the mutant and rescue backgrounds , Gr68a-Gal42 and UAS-GCaMP5 transgenes were re-combined onto flies with the ΔGr68a or Gr68aRes background and verified by labeling with UAS-mCD8::GFP . The chemical syntheses of ( 3S , 11Z , 19Z ) -CH503 , ( 3R , 11Z , 19Z ) -CH503 , ( S ) -3-Acetoxy-19-octacosen-1-ol and ( R ) -3-Acetoxy-11 , 19-octacosadiyn-1-ol have previously been described ( Mori et al . , 2010; Shikichi et al . , 2013 ) . All other solvents and reagents were obtained from Sigma–Aldrich ( St . Louis , MO , USA ) . Males ( 5–10 days old ) were isolated at the pupal stage and raised at 23°C with 60% humidity in 10 ml polypropylene vials containing 2 ml of standard cornmeal media . A decapitated virgin female target and a 5–10 days old socially naïve experimental male were placed in a courtship chamber ( ∅: 10 mm , height: 3 mm ) and digitally recorded for 30 min . The female courtship targets were perfumed with CH503 or evaporated solvent ( control ) as previously described ( Yew et al . , 2009 ) . Briefly , six female flies were placed in 1 . 5-ml glass vials containing 0 . 25 , 0 . 5 , 1 , 2 , or 4 μg of ( R , Z , Z ) -CH503 and vortexed three times with 20 s rest intervals . Approximately 25% of the vial contents are transferred to the flies using this method ( Billeter et al . , 2009 ) . A single fly from each vial was tested using direct analysis in real time mass spectrometry ( DART MS; [Yew et al . , 2008 , 2009] ) to check the abundance of the CH503 signal relative to other cuticular hydrocarbons . For the tests of CH503 volatility , a 2-layer courtship chamber was constructed with the top layer containing a male and an unperfumed decapitated virgin female and the bottom layer containing filter paper overlaid with 64 μg of ( R , Z , Z ) -CH503 . To prepare female fly extract , 1 or 2 flies were submerged in hexane for 10 min at room temperature , after which the solvent was removed and added to the filter paper . The courting pairs % refers to the number of trials in which courtship was observed for longer than one minute divided by the total number of trials . Behavioral assays for perfumed and solvent-perfumed animals were performed in parallel . The courting pairs % was compared between pheromone and solvent-perfumed flies bearing the identical genetic background . Statistical analysis was performed using a Fisher's exact probability test with Yates correction ( VassarStats , www . vassarstats . net ) . Courtship vigor and latency were calculated for the 30 min observation period and compared using a one-way ANOVA with a Tukey's post-hoc test ( SPSS Statistics , IBM , USA ) . Behavioral screens were performed using female targets perfumed with 83 ng/fly of ( S , Z , Z ) -CH503 or 333 ng/fly dose of ( R , Z , Z ) -CH503 . Both doses were previously established as the minimum necessary for each stereoisomer to elicit significant courtship suppression ( Mori et al . , 2010 ) . Transgenic flies were generated using the Gal4–UAS system to drive expression of toxin or pro-apoptotic transgenes ( UAS-Reaper , UAS-DTI , and UAS-hid , stinger ) or transgenes that interfere with synaptic transmission ( UAS-Shibirets1 and UAS–dORKΔC ) . For Gr-Gal4 lines , UAS-Reaper was used since UAS-DTI expression led to low basal courtship activity . For central circuit screening , UAS-Shibirets1 was used to avoid developmental lethality . For Gal4 lines in which UAS-Shibirets1 expression led to paralysis , seizures , or low courtship activity , UAS–dORKΔC was used as a milder form of neural inhibition . Knockdown of Gr68a expression was performed using an RNAi line since the use of other transgenes resulted in larval lethality . Manipulation of neurotransmitter levels was performed using RNAi to target the respective transporters , receptors , or biosynthetic enzymes . See Table 2 for a complete list of Gal4 and UAS-RNAi lines used for screening . For experiments involving temperature-sensitive transgenes ( UAS-Shibirets1 and UAS-dTrpA1 ) , flies were placed in a humidity-controlled incubator at 29°C for 2 hr prior to the assay to activate the transgene . Courtship chambers were placed on a hotplate pre-warmed to 29°C , during which time the flies were introduced into each chamber . The chamber was placed in an incubator at 29°C and the temperature was monitored throughout the assay . Control experiments were carried out in parallel at 23°C ( for assays using UAS-Shibirets1 ) or 19°C ( for assays using UAS-dTrpA1 ) . The proboscis extension reflex ( PER ) assay was performed as previously described ( Lacaille et al . , 2007; Shiraiwa and Carlson , 2007 ) . Virgin 1-day-old male and female flies were starved for 36 hr in a vial containing tissue soaked with water . Flies were mounted with nail polish on the dorsal side onto glass slides and placed in a humidified Petri dish for at least 2 hr prior to the assay . Paper wicks coated in 20 μl of a test solution were used for bilateral stimulation of the tarsi of mounted flies . One leg was touched with a paper wick soaked in 4% sucrose ( in distilled H2O , wt/vol ) while the second leg was simultaneously stimulated with one of the following solutions: ( i ) 4% sucrose , ( ii ) 25–100 μg/ml ( R , Z , Z ) -CH503 in hexane , ( iii ) 0 . 25–25 μg/ml ( S , Z , Z ) -CH503 in hexane , and ( iv ) 100 mM caffeine in dH2O . Each substance was tested three times with a 2 min rest between stimulations . A response was counted as positive when the fly extended its proboscis for at least 2 of 3 stimulations . The response to each substance was compared to the response induced by 4% sucrose alone using a Fisher's exact probability test . The assays were carried out at the same time each day , and the experimenter was blind to the identity and concentration of stimulants tested in the assay . Adult Drosophila brains and thoracic ganglia from 6- to 10-day-old virgin flies were dissected in phosphate buffered-saline with 0 . 3% Triton X-100 , pH 7 . 2 ( PBST ) and fixed in ice-cold 4% paraformaldehyde for 25 min . Samples were washed three times , for 15 min each in PBST , treated in a blocking solution containing PBST and 10% normal goal serum for 30 min at room temperature , and incubated in primary antibody solution . After three washes in PBST , the tissues were incubated overnight at 4°C in secondary antibody solution . Following three washes in PBST , the brains were mounted on glass slides with Vectashield mounting medium ( Vector Laboratories , Burlingame , CA ) . Images were acquired on a Zeiss LSM 510 Meta inverted microscope equipped with 488 , 543 , and 633 nm lasers . For all tissues , 132 frames with a z step size of 0 . 46 μm were acquired . The following secondary antibodies used were anti-chicken 488 ( 1:500; Jackson ImmunoResearch Laboratories , West Grove , USA ) , anti-rabbit Cy3 ( 1:500; Jackson ImmunoResearch Laboratories ) , anti-guinea pig Cy3 ( 1:500; Jackson ImmunoResearch Laboratories ) , anti-mouse 633 ( 1:500; Jackson ImmunoResearch Laboratories ) . Image analysis was done using ImageJ software ( NIH ) . The following primary antibodies and dilutions were used: chicken anti-GFP ( 1:1000; Abcam , Cambridge , UK ) , mouse anti-nc82a ( 1:50; Developmental Studies Hybridoma Bank , Iowa City , USA ) , mouse anti-GFP ( 1:1000 , for GRASP; A11120 , Life Technologies , NY , USA ) , mouse anti-GFP ( 1:100 , for GRASP; G6539 , Sigma–Aldrich ) , rabbit anti-NPF ( 1:2000; kind gift from P Shen; [Wu et al . , 2003] ) , and guinea pig anti-TK ( 1:2000; kind gift from D Anderson; [Asahina et al . , 2014] ) . The Pearson's coefficient , a measure of co-localization , was calculated using Imaris software ( Bitplane AG , Zurich , Switzerland ) . For each biological replicate , RNA from 200 flash-frozen fly forelegs ( for Gr68a experiments ) or 20 flash-frozen heads ( for NPF experiments ) was extracted using TRIzol Reagent ( Ambion , Austin , TX , USA ) according to manufacturer's instructions . DNAse treatment was performed using TURBO DNA-free kit ( Ambion ) and cDNA was synthesized with SuperScript III ( Invitrogen , Waltham , MA , USA ) with an oligo-dT primer . Quantitative PCR was performed using CFX Connect Real-Time PCR System ( Bio-Rad , Hercules , CA , USA ) and SYBR Fast ABI Prism qPCR kit ( Kapa Biosystems , Wilmington , MA , USA ) . See Table 3 , for primer sequences and annealing temperatures . 10 . 7554/eLife . 06914 . 033Table 3 . Primers used for quantitative and semi-quantitative ( semi-Q ) PCR experimentsDOI: http://dx . doi . org/10 . 7554/eLife . 06914 . 033Forward primer ( 5′–3′ ) Reverse primer ( 5′–3′ ) Annealing temperature ( °C ) Gr68a ( qPCR ) CCAAGGTGATACCGAGGAGGAGATCGTGAAGAGTGCGAAAGTG60Gr68a ( semi-Q PCR ) CCAAGGTGATACCGAGGAGACATTGGCCAGCAGATACTCA55CG6024CCAAGGTGATACCGAGGAGATCATGAAGAGTGCGAAAGTG60NPFGCGAAAGAACGATGTCAACACTGTTGTCCATCTCGTGATTCC60rp49CCAAGGACTTCATCCGCCACCGCGGGTGCGCTTGTTCGATCC55RMCE vectorGTACTGACGGACACACCGAAGGGATCAACTACCGCCACCT52 In vivo GCaMP imaging experiments were performed on 14- to 28-day-old adults . A live fly was immobilized on a 0 . 17-mm coverslip with nail polish ( Sally Hansen , USA ) . 10 µl of PBST were placed onto the tarsal segments using a pipette , after which three pre-stimulation images were acquired for a total duration of 2 . 4 s . For each measurement , 117 post stimulation images were acquired for a total duration of 93 . 6 s immediately after the addition of 10 µl of the natural stereoisomer of CH503 , ( R , Z , Z ) -CH503 ( final concentrations of 50 or 500 ng in PBST ) . Identical conditions were used for measurements using ( S , Z , Z ) -CH503 . No reflux flow is used in the sample preparation . Control stimulants consisted of 10 µl of solvent or an analog of CH503: ( S ) -3-Acetoxy-19-octacosen-1-ol ( 50 ng , final concentration ) or ( R ) -3-Acetoxy-11 , 19-octacosadiyn-1-ol ( 500 ng , final concentration ) . The analogs were previously established to be behaviorally inert ( Shikichi et al . , 2013 ) . Images were acquired on a spinning disk confocal microscope ( Ti-E; Nikon Instruments , Melville , USA ) equipped with a CSU-X1 scan head ( Yokogawa Electric , Tokyo , JA ) and either a digital sCMOS camera ( ORCA-Flash4 . 0; Hamamatsu Photonics , Shizuoka , JA ) or a cooled CCD camera ( CoolSNAP HQ2; Photometrics , Tucson , USA ) using a 60×/1 . 4 N . A . oil objective lens . A 491 nm laser was used to excite the GCaMP5 reporter . Four Z-slices with a thickness of 0 . 5 µm each were acquired every 800 ms , for a total of 120 frames . To calculate the maximum change in fluorescence signal ( ∆F/F ) , the signal density over the whole cell body was divided by the signal from an equivalent volume of an adjacent region ( background ) . Confocal Z-stacks were analyzed using ImageJ ( Schneider et al . , 2012 ) . For Gr68a-Gal4-labeled neurons , ∆F/F was calculated from single neurons . For ppk23-Gal4-labeled neurons on the foreleg , ∆F/F was calculated from the total signal from either two adjacent cell bodies or the base of the axon projections . Due to their close proximity to each other , some individual cell bodies could not be differentiated . In some experiments , the maximum ∆F/F occurred in projections though it could not be discerned from which cell body the projection originated . For proboscis measurements , ∆F/F represents the averaged values from 14 cells ( for CH503 stimulation ) or 20 cells ( for PBST stimulation ) , measured from 5–6 flies . For all measurements , the averaged , normalized response to the stimulant vs the averaged , normalized response to control solvent was compared using a Student's t-test for equal or unequal variances ( Vassar Stats ) . Comparison of variance was determined with an F-Test ( Vassar Stats ) . Statistical power analysis was performed using G*Power 3 ( Faul et al . , 2007 ) . | In many species of animals , the male decides to pursue a potential female mate based on how she smells and tastes . Powerful chemical signals known as pheromones control this decision . When a male fruit fly mates with a female fruit fly , he often leaves behind an anti-aphrodisiac pheromone that , when males taste it , deters them from mating with the female . Until recently , however , little was known about how the brain processes information from such taste pheromones . Now , Shankar et al . have investigated this problem in a series of experiments with normal and genetically modified flies . In the first experiment normal male flies were exposed to the chemical on its own , to the chemical on a sample of female skin , and to the chemical on actual female flies . The male flies did not respond to the pheromone on its own , but they did respond to it in the other two scenarios . Next , Shankar et al . used genetic techniques to eliminate individual neurons in the male flies and then observed how the loss of specific neurons influenced the response of the fly to the pheromone . These experiments showed that male flies have a special group of sensory neurons in their legs that detect the chemical and then send an electrical signal to the brain . Shankar et al . then went on to identify a brain circuit consisting of 8–10 neurons that responds to this signal and to show that the release of a neurochemical called Tachykinin is essential in communicating the signal . In a final set of experiments , Shankar et al . introduced two sensors—one in the sensory neurons in the legs , the other in the 8–10 neurons in the brain—that light up when the neurons in that region are close enough to each other to form connections . The results suggest that the sensory neurons in the legs form connections with the 8–10 neurons in the brain . A challenge for the future is to understand how the nervous system combines different social cues and information about the physiological state of the animal , and how this influences the decision to mate . | [
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] | 2015 | The neuropeptide tachykinin is essential for pheromone detection in a gustatory neural circuit |
Drug treatment of 3D cancer spheroids more accurately reflects in vivo therapeutic responses compared to adherent culture studies . In EGFR-mutated lung adenocarcinoma , EGFR-TKIs show enhanced efficacy in spheroid cultures . Simultaneous inhibition of multiple parallel RTKs further enhances EGFR-TKI effectiveness . We show that the common RTK signaling intermediate SOS1 was required for 3D spheroid growth of EGFR-mutated NSCLC cells . Using two distinct measures of pharmacologic synergy , we demonstrated that SOS1 inhibition strongly synergized with EGFR-TKI treatment only in 3D spheroid cultures . Combined EGFR- and SOS1-inhibition markedly inhibited Raf/MEK/ERK and PI3K/AKT signaling . Finally , broad assessment of the pharmacologic landscape of drug-drug interactions downstream of mutated EGFR revealed synergy when combining an EGFR-TKI with inhibitors of proximal signaling intermediates SOS1 and SHP2 , but not inhibitors of downstream RAS effector pathways . These data indicate that vertical inhibition of proximal EGFR signaling should be pursued as a potential therapy to treat EGFR-mutated tumors .
Lung cancer is the leading cause of cancer-related death worldwide; adenocarcinomas are the most common subtype of lung cancer . Oncogenic driver mutations in the RTK/RAS pathway are found in over 75% of lung adenocarcinomas ( Cancer Genome Atlas Research Network , 2014 ) . Activating EGFR mutations occur in 10–30% of lung adenocarcinomas and are the major cause of lung cancer in never-smokers . In patients whose tumors harbor either an L858R mutation or an exon 19 deletion ( 85% of EGFR mutated tumors ) , first-generation EGFR-tyrosine kinase inhibitors ( TKIs ) erlotinib and gefitinib enhance progression-free survival ( Mok et al . , 2009; Yang et al . , 2017; Eberhard et al . , 2005 ) . However , resistance to first generation EGFR-TKIs invariably occurs . In most cases , acquired resistance to first generation EGFR-TKIs occurs via either a secondary EGFR ‘gatekeeper mutation’ ( T790M , 50–60% of cases ) that renders the receptor insensitive to first generation EGFR-TKIs or oncogenic shift to alternative RTKs ( 15–30% ) . To treat patients with T790M-mutated resistant tumors , the third generation EGFR-TKI osimertinib , which selectively targets activating EGFR mutant proteins including T790M but spares wild-type EGFR , was developed ( Jänne et al . , 2015; Cross et al . , 2014 ) . However , despite further enhancing survival of patients with EGFR-mutant tumors , resistance again emerges . Unlike first-generation EGFR-TKIs , mechanisms driving osimertinib resistance are more variable , including both EGFR-dependent ( 10–30% ) and EGFR-independent mechanisms ( Mancini et al . , 2018; Romaniello et al . , 2018; La Monica et al . , 2017; Eberlein et al . , 2015 ) . The most common EGFR-independent resistance mechanisms involve reactivation of the RTK/RAS/effector pathway ( Eberlein et al . , 2015 ) , often via enhanced signaling through parallel RTKs ( Mancini et al . , 2018; Romaniello et al . , 2018; La Monica et al . , 2017; Shi et al . , 2016; Park et al . , 2016; Kim et al . , 2019; Taniguchi et al . , 2019; Jimbo et al . , 2019; Namba et al . , 2019 ) . Here , combining osimertinib with individual RTK inhibitors can both inhibit the development of resistance through the inhibited RTK and kill cancer cells with resistance driven by the specific RTK being inhibited . However , simultaneous inhibition of multiple RTKs with osimertinib may be required to eliminate oncogenic shift to alternative RTKs ( Romaniello et al . , 2018 ) . Downstream of RAS , co-targeting intermediates of the RAF/MEK/ERK and PI3K/AKT pathways enhances of osimertinib effectiveness , however , signaling through the uninhibited effector pathway may drive resistance ( Tricker et al . , 2015; Jacobsen et al . , 2017; Ku et al . , 2018; Ichihara et al . , 2017 ) . Thus , it may be important for therapeutic combinations including osimertinib to stifle all downstream RTK/RAS signaling to be effective . Recent studies suggest that pharmacologic assessments of targeted therapeutics should be performed under 3D culture conditions rather than in 2D adherent cultures ( Nunes et al . , 2019; Langhans , 2018 ) . 3D spheroids show altered growth characteristics , changes in cell surface proteins , altered metabolism , changes in activation of signaling pathways or altered responses to targeted pathway inhibitors , and are more resistant to drug-induced apoptosis compared to 2D adherent cultures signaling ( Hao et al . , 2019; Kim et al . , 2011; Riedl et al . , 2017; Jones et al . , 2019 ) . These differences may be particularly relevant in EGFR-mutated NSCLC . EGFR-mutated cells show differential RTK expression and phosphorylation in 3D versus 2D conditions ( Ekert et al . , 2014 ) . Further , EGFR-mutated cells respond more robustly to first-generation EGFR-TKIs in 3D cultures , and these responses more closely resemble responses seen in vivo ( Jacobi et al . , 2017 ) . These data highlight the need for pharmacologic assessment of therapeutics designed to treat EGFR-mutated NSCLC under 3D culture conditions . The ubiquitously expressed RasGEFs ( guanine nucleotide exchange factors ) SOS1 and SOS2 ( son of sevenless 1 and 2 ) are common signaling intermediates of RTK-mediated RAS activation . Although not initially considered as drug targets because of the low oncogenic potential of SOS ( Vigil et al . , 2010 ) , there has been renewed interest in SOS proteins as therapeutic targets for cancer treatment . We and others have shown that SOS1 and SOS2 may be important therapeutic targets in KRAS-mutated cancer cells ( Jeng et al . , 2012; Sheffels et al . , 2018; Sheffels et al . , 2019 ) , and a specific SOS1 inhibitor ( BAY-293 ) has recently been identified ( Hillig et al . , 2019 ) . Here , we investigate SOS1 and SOS2 as potential therapeutic targets in EGFR-mutated lung adenocarcinoma cells . Using two distinct measures of pharmacologic synergy , we demonstrate that SOS1 inhibition using BAY-293 synergizes with osimertinib only under 3D spheroid culture conditions , and in doing so add to the growing evidence that pharmacologic assessment of novel therapeutics designed to treat cancer must be performed under 3D culture conditions ( Ekert et al . , 2014; Sheffels et al . , 2018; Nunes et al . , 2019; Janes et al . , 2018; Jacobi et al . , 2017 ) . By assessing the pharmacologic landscape of EGFR/RAS pathway inhibitors , we demonstrate that inhibition of proximal signaling is required to synergize with osimertinib , and that combined EGFR and SOS1 inhibition synergizes to inhibit RAS effector signaling in 3D culture . These findings have significant therapeutic implications for the development of combination therapies to treat EGFR-mutated lung adenocarcinoma .
Previous studies showed that EGFR-mutated NSCLC cell lines show much more robust responsiveness to first-generation EGFR-TKIs in 3D culture ( monoculture cancer cell line spheroids or monoculture or mixed culture organoids in ECM/Matrigel ) compared to 2D adherent culture , and further that 3D conditions more readily mirror EGFR-TKI responses seen in vivo ( Jacobi et al . , 2017 ) . To confirm these findings and extend them to third-generation EGFR-TKIs , we assessed dose-dependent survival of both first-generation EGFR-TKI sensitive ( HCC827 , exon 19 deletion [Δex19] ) or resistant ( NCI-H1975 , L858R/T790M ) NSCLC cell lines to either gefitinib or osimertinib treatment under both adherent ( 2D ) or spheroid ( 3D ) culture conditions ( Figure 1A ) . HCC827 and H1975 cells were plated in either adherent or spheroid cultures , allowed to rest for 48 hr , and then treated with increasing doses of either the first-generation EGFR-TKI gefitinib or the third-generation EGFR-TKI osimertinib for 4 days . HCC827 cells showed responsiveness to both EGFR-TKIs under 2D and 3D culture conditions , however in both cases 3D spheroid cultures showed a > 1 log enhancement in EGFR-TKI efficacy and enhanced overall growth inhibition . While NCI-H1975 cells were not sensitive to gefitinib , osimertinib treatment of H1975 cells showed enhanced efficacy and increased overall growth inhibition in 3D spheroids over 2D adherent cultures . SOS1 and SOS2 are ubiquitously expressed RasGEFs responsible for transmitting EGFR signaling to downstream effector pathways . To determine whether SOS1 or SOS2 were required for 2D anchorage-dependent proliferation or 3D spheroid growth in EGFR-mutated NSCLC cells , SOS1 ( Figure 1—figure supplement 1 and Munoz et al . , 2016 ) or SOS2 ( 31 ) were deleted in pooled populations of HCC827 and H1975 cells to avoid clonal effects , and both proliferation and spheroid growth were assessed versus NT controls ( Figure 1B and C ) . In adherent culture , neither SOS1 nor SOS2 deletion altered proliferation ( Figure 1B ) . In contrast , SOS1 deletion completely inhibited spheroid growth in both HCC827 and H1975 cells , indicating that SOS1 was required to maintain the transformed phenotype in both cell lines . To determine whether SOS1 was generally required for mutant EGFR-driven transformation , we further deleted SOS1 or SOS2 in both first-generation sensitive NCI-H3255 ( L858R ) and PC9 ( Δex19 ) cells and in subcultures of these cell lines that had acquired T790M mutations after continuous EGFR-TKI treatment ( PC9-TM [de Bruin et al . , 2014] and H3255-TM [Engelman et al . , 2006] ) . In all cases , SOS1 deletion significantly diminished oncogenic transformation , whereas SOS2 deletion had variable effects on transformation depending on the EGFR mutated cell line examined ( Figure 1D ) . These data indicate that SOS1 is the major RasGEF responsible for oncogenesis downstream of mutated EGFR . BAY-293 was recently described as a specific inhibitor for SOS1 ( Hillig et al . , 2019 ) . To determine whether SOS1 inhibition was similarly more effective in 3D spheroids over 2D adherent culture , we assessed dose-dependent survival of H1975 cells after BAY-293 treatment under both 2D and 3D culture conditions ( Figure 1E ) . Similar to what we observed after either EGFR-TKI treatment ( Figure 1A ) or SOS1 deletion ( Figure 1C and D ) , BAY-293 showed enhanced efficacy and increased overall growth inhibition in 3D spheroids over 2D adherent cultures . To confirm the specificity of BAY-293 for SOS1 , we further treated 3D spheroid cultured H1975 , PC9-TM , and H3255-TM cells where either SOS1 or SOS2 had been deleted versus NT controls with increasing doses of BAY-293 for four days , and assessed cell viability within the spheroids using Cell Titre Glo ( Figure 1F and Figure 1—figure supplement 2 ) . BAY-293 treatment did not inhibit survival of spheroids where SOS1 had been deleted , indicating the specificity of BAY-293 for SOS1 . Further , cells where SOS2 had been deleted showed an approximately 1-log enhancement in BAY-293 efficacy and enhanced overall growth inhibition compared to NT controls , indicating that SOS1 and SOS2 have some overlapping functions in supporting survival of spheroid cultured EGFR-mutated NSCLC cells . For these experiments , the untreated sample cell number at day four of treatment for each cell line ( NT , SOS1 KO , SOS2 KO ) was set to 100% , so differences in transformation ( see Figure 1B–D ) will not be appreciated . Further , for NCI-H1975 and NCI-H3255-TM cells , SOS1 deletion does not show transformation differences after four days . Overall , these data suggest that EGFR-mutated NSCLC cells are more sensitive to either mutant EGFR or SOS1 inhibition in 3D spheroid culture compared to traditional 2D adherent conditions . Previous studies reported that combining osimertinib with an alternative RTK inhibitor may inhibit or treat the development of resistance driven by that specific RTK ( Mancini et al . , 2018; Romaniello et al . , 2018; La Monica et al . , 2017 ) , whereas simultaneous inhibition of multiple parallel RTKs with osimertinib may be required to effectively potentiate osimertinib action ( Romaniello et al . , 2018 ) . Further , while many studies show enhanced drug activity in combination therapies versus osimertinib treatment alone , they do not assess whether the effects of the two-drug combinations are truly synergistic; synergistic interactions between therapeutics allow for maximization of the therapeutic effect while minimizing adverse events and may be required for effective therapeutic combinations with targeted agents ( Roell et al . , 2017 ) . SOS1 is a common downstream mediator of RTK signaling . We hypothesized that SOS1 could be an effective drug target to synergize with EGFR-TKI inhibition to treat EGFR-mutated lung adenocarcinoma . To directly assess synergy between osimertinib and SOS1 inhibition , we use two distinct methods based on the most widely established reference models of drug additivity . The first method , isobologram analysis , assesses changes in the dose-response curves for mixtures of two drugs compared to sham mixtures of each individual drug with itself . The second method , Bliss independence analysis , assesses whether a mixture of two individual drug doses has a greater effect than would be expected if the two drugs acted independently . We will first describe and then use each method in turn to determine the whether SOS1 inhibition using BAY-293 could synergize with the EGFR-TKI osimertinib in EGFR-mutated lung adenocarcinoma cells . Isobologram analysis is a dose-effect analysis based on the principle of Loewe additivity , which states that a drug mixed with itself , and by extension a mixture of two or more similar drugs , will show additive effects . For two drugs ( Drug A and Drug B ) that have parallel dose-response curves so that a constant potency ratio is maintained at all doses of A and B ( Figure 2A ) , treatment using any dose-equivalent ( DEQ ) mixture of Drugs A and B will show a similar effect to treatment with either Drug A or Drug B alone if the effects of the two drugs are additive . In contrast , if the two drugs show synergism , then the effect seen by treatment with DEQ mixtures of A and B will be greater than the effect for either drug alone . By generating dose-response curves for different DEQ mixtures of Drugs A and B ( Figure 2B ) , one can compare the EC50 of each DEQ mixture to the EC50 of Drug A or Drug B alone on an isobologram plot ( Figure 2C ) . The EC50 of each individual drug is plotted as the x- or y-intercept , and the calculated contribution of each drug to the overall EC50 for each DEQ mix is plotted as a single point ( EC50 , A , EC50 , B ) on the graph . If the EC50 values for each DEQ mix fall along the straight line ( isobole ) that connects the individual drug EC50 values , then the drug-drug interaction is additive . In contrast , points that fall above or below the isobole indicate antagonism or synergy . The extent to which two drugs interact can be further quantified from the EC50 data as a combination index ( CI ) ( Figure 2D ) . A CI between 0 . 8 and 1 . 2 indicates the two drugs have additive effects when combined , a CI <0 . 8 indicates synergy , and a CI >1 . 2 indicates antagonism . To assess drug-drug synergy between osimertinib and BAY-293 via isobologram analysis , NCI-H1975 cells were cultured under 2D adherent or 3D spheroid conditions for 48 hr , and were treated with varying DEQ combinations of osimertinib:BAY-293 ( see Figure 2B ) for four days . Cell viability data was assessed using CellTiter-Glo and EC50 values from each DEQ mixture were used to generate isobologram plots and calculate combination indices ( Figure 2E ) . When cells were cultured under 2D conditions , osimertinib and BAY-293 showed additive effects , as DEQ EC50 values fell on the isobole and CI values were between 0 . 8 and 1 . 2 . In contrast , when cells were cultured as 3D spheroids , osimertinib and BAY-293 showed significant synergy , as DEQ EC50 values were well below the isobole and CI <0 . 8 . Bliss independence analysis is an effect-based analysis based on the principle of Bliss additivity , which assumes that two drugs will act independently of each other so that their combined effect can be assessed by assessing the effect of each drug sequentially ( Figure 2F ) . Unlike isobologram analysis , this method does not require that two drugs being assessed have parallel dose-response curves and can be calculated based as few as three drug treatments , the effect each drug has on its own on the cell population , and the effect of combining the two drug treatments together . By representing the effect of each drug treatment as a probabilistic outcome between 0 ( no effect ) and 1 ( 100% effect ) , we can compare the observed effect of the drug-drug combination to the expected effect if each drug acted independently ( Figure 2E ) . The ratio of the expected effect to the observed effect is the Bliss Index ( BI ) , where a BI <1 indicates synergy ( Figure 2G ) . Alternatively , the magnitude of the difference between the observed and expected result can be reported as the excess over Bliss ( Figure 2H ) . While excess over Bliss is the most widely reported synergy metric , the Bliss Index can be directly compared with the combination index in isobologram experiments and should be used when both synergy methods are used to assess a given drug-drug interaction . To assess drug-drug synergy between osimertinib and BAY-293 via Bliss Independence analysis , NCI-H1975 cells were cultured under 2D adherent or 3D spheroid conditions for 48 hr and were treated with increasing doses of BAY-293 , osimertinib , or combinations of the two drugs over a 3-log scale for four days . Cell viability was determined using CellTiter-Glo and overall viability ( Figure 2I ) , Bliss index ( Figure 2J ) , and excess over Bliss ( Figure 2K ) were represented as heat-maps . Similar to what we observed for isobologram analysis , osimertinib and BAY-293 did not show significant synergy in cells cultured under 2D adherent conditions . In contrast , we observed significant synergy between osimertinib and BAY-293 , mostly at dose combinations of osimertinib and BAY-293 falling just below the individual drug EC50 values . Overall , the data presented in Figure 2 indicate that osimertinib and BAY-293 show significant drug-drug synergy in EGFR-mutated H1975 cells , but only in 3D spheroid culture conditions . To determine whether the SOS1 inhibitor BAY-293 could generally synergize with EGFR-TKIs in EGFR-mutated lung adenocarcinoma cells , we extended our assessment of drug-drug synergy to isobologram analysis ( Figure 3 ) and Bliss independence analysis ( Figure 4 ) in six different EGFR-mutated lung adenocarcinoma cell lines . In cells that were sensitive to first-generation EGFR-TKIs ( HCC827 , PC9 , H3255; T790 wild-type ) , we assess drug-drug synergy between BAY-293 and either a first-generation ( gefitinib ) or third-generation ( osimertinib ) EGFR-TKI . In cells that were resistant to first-generation EGFR-TKIs ( H1975; PC9-TM , H3255-TM; T790M ) we limited our assessment to synergy between BAY-293 and osimertinib . To first determine the individual EC50 values for gefitinib , osimertinib , and BAY-293 in each cell line , cells were cultured as 3D spheroids for 48–72 hr , and then treated with increasing doses of drug for four days followed by assessment of cell viability by CellTiter-Glo ( Figure 3—figure supplement 1 ) . In five of six cell lines , the individual dose-response curves for BAY-293 , osimertinib , and gefitinib ( where appropriate ) showed similar maximal effects and Hill coefficients , and were thus appropriate for linear isobologram analysis for each two-drug combination of BAY-293 , osimertinib , and gefitinib ( Tallarida , 2011 ) . In contrast , H3255-TM cells were only moderately sensitive to osimertinib , showing at most a 50% reduction in viability at high doses . Therefore , we limited our assessment of drug-drug synergy in H3255-TM cells to Bliss independence analysis . Further , to simplify our assessment of Bliss independence across multiple drugs and cell lines , we limited our drug treatments to 1:2 , 1:1 , and 2:1 mixtures of each drug combination based on dose equivalence ( see Figure 4A ) . For each first-generation EGFR-TKI sensitive cell line ( HCC827 , PC9 , H3255 ) , gefitinib and osimertinib did not show any synergy with each other by either isobologram analysis ( Figure 3 ) or Bliss Independence analysis ( Figure 4 ) , instead showing additive effects ( CI and BI ~1 ) as would be expected for two drugs with the same molecular target . In contrast , BAY-293 showed significant synergy with gefitinib and osimertinib by both isobologram analysis ( Figure 3 ) and Bliss Independence analysis ( Figure 4 ) , suggesting that SOS1 inhibition can act as a secondary treatment for all EGFR-TKIs . Further , in all three T790M mutated cell lines ( H1975 , PC9-TM , H3255-TM ) , BAY-293 again showed synergy with osimertinib . These data suggest that combined SOS1 and EGFR inhibition is a robust therapeutic combination that synergize to inhibit EGFR-mutated lung adenocarcinoma cell growth . We showed that SOS2 deletion sensitized NCI-H1975 cells to the SOS1 inhibitor BAY-293 ( Figure 1F ) . We wanted to determine whether the synergy we observed between EGFR- and SOS1-inhibition ( Figures 3 and 4 ) was enhanced by SOS2 deletion in EGFR-mutated NSCLC cell lines . To examine whether SOS2 deletion alters the synergy between osimertinib and BAY-293 in EGFR ( T790M ) mutated cells , SOS2 was deleted in H1975 , PC9-TM , and H3255-TM cells . For H1975 and PC9-TM cells , SOS2 KO cells vs NT controls were cultured under 3D spheroid conditions for 48–72 hr , and were then treated with varying DEQ combinations of osimertinib:BAY-293 for 4 days . Cell viability data was assessed using CellTiter-Glo and EC50 values from each DEQ mixture were used to generate Isobologram plots and calculate confidence intervals ( Figure 5A and B ) . For both cell lines , SOS2 deletion sensitized cells to BAY-293 , decreasing EC50 by 5–10-fold compared to NT controls without altering the EC50 to osimertinib treatment alone . However , unlike what we observed in the NT control cells , osimertinib and BAY-293 showed only mild synergy in EGFR-mutated cells where SOS2 was deleted as assessed by the distance of the interaction points to the isobole and the increased combination index vs . NT controls . Further , when we overlaid the NT and SOS2 KO isobologram plots at two different scales of BAY-293 , the drug combination data points were overlapping between NT and SOS2 KO cells , suggesting that SOS2 deletion did not enhance synergy between osimertinib and BAY-293 . Since H3255-TM cells are not appropriate for linear isobologram analysis between BAY-293 and osimertinib , we instead performed Bliss independence analysis to assess potential synergy between osimertinib and BAY-293 in the presence or absence of SOS2 . H3255-TM cells where SOS2 had been deleted vs NT controls were cultured under 3D spheroid conditions for 48–72 hr , and were then treated with increasing doses of osimertinib alone , BAY-293 alone , or mixtures of each drug dose at 1:2 , 1:1 , and 2:1 mixtures of osimertinib and BAY-293 based on dose equivalence for four days . Cell viability data was assessed using CellTiter-Glo , and the Bliss index was calculated for each drug mixture as shown in Figure 2C and Figure 4 . As was the case in H1975 and PC9-TM cells , while the SOS2 deletion sensitized H3255-TM cells to BAY-293 we observed less overall synergy between osimertinib and BAY-293 H3255-TM cells where we had deleted SOS2 vs NT controls . These data suggest that although osimertinib and BAY-293 synergize to limit viability of EGFR-mutated lung adenocarcinoma cells , the synergy between osimertinib and BAY-293 is independent of SOS2 . Mutated EGFR signals through downstream RAF/MEK/ERK and PI3K/AKT effector pathways to promote proliferation , transformation , and survival . Since SOS2 deletion did not further enhance synergy between BAY-293 and osimertinib , we hypothesized that SOS1 inhibition specifically enhanced EGFR-TKI-dependent inhibition of downstream signaling in 3D culture . To perform signaling experiments on 3D cultured spheroids , cells were seeded in 24-well micropatterned low-attachment culture plates ( Aggrewell , StemCell ) containing ~1200 individual spheroids per condition . To determine the extent to which SOS1 inhibition and/or SOS2 deletion altered osimertinib-dependent inhibition of downstream effector signaling in 3D culture , H1975 or PC9-TM cells where SOS2 was deleted vs . NT controls were cultured as spheroids for 48–72 hr and then treated with increasing doses of osimertinib +/- BAY-293 prior to spheroid collection , lysis , and western blotting for phosphorylated ERK and AKT ( Figure 6 ) . In both NT and SOS2 knockout cells , BAY-293 reduced the dose of osimertinib required to inhibit both ERK and AKT phosphorylation ( Figure 6 ) . For Raf/MEK/ERK signaling , Bliss Independence analysis of pERK quantitation revealed that either SOS1 inhibition or SOS2 deletion independently synergized with osimertinib to inhibit Raf/MEK/ERK signaling , and the combination of inhibiting SOS1/2 signaling further enhanced this synergy . In contrast , for PI3K/AKT signaling SOS2 deletion did not enhance the synergy between osimertinib and BAY-293 . While either osimertinib treatment or SOS2 deletion independently synergized with BAY-293 to inhibit AKT phosphorylation , SOS2 deletion did not further enhance the ability osimertinib to inhibit PI3K/AKT signaling in the presence or absence of BAY-293 . These data strongly suggest that vertical inhibition of EGFR and SOS1 limits call viability by inhibiting activation of both RAF/MEK/ERK and PI3K/AKT effector pathways . Since the most common EGFR-independent resistance mechanisms involve reactivation of RTK/RAS/effector pathways ( Mancini et al . , 2018; Romaniello et al . , 2018; La Monica et al . , 2017; Eberlein et al . , 2015 ) , we wanted to assess whether inhibition of different proteins within the EGFR/RAS signaling pathway could synergize to inhibit 3D survival of EGFR ( T790M ) mutated cancer cells . To determine drug-drug synergies after inhibition of EGFR-RAS pathway signaling at different levels , we assessed synergy between osimertinib , inhibitors of EGFR signaling intermediates upstream of RAS ( BAY-293 for SOS1 and RMC-4450 for SHP2 ) , and inhibitors of the Raf/MEK/ERK ( trametinib ) and PI3K/AKT ( buparlisib ) pathways ( Figure 7A ) . H1975 and PC9-TM cells were treated with each individual inhibitor or 1:1 DEQ mixtures of every drug-drug combination , and the combination index was calculated to assess drug-drug synergy . Since H3255-TM cells are not suitable for isobologram analysis , these cells were treated with full-dose mixtures based on dose equivalence and the Bliss Index was calculated for each drug-drug combination ( Figure 7B ) . Intriguingly , all three cell lines showed drug-drug synergy with any combination of EGFR , SOS1 , and SHP2 inhibition . In contrast , inhibition of downstream Raf/MEK/ERK or PI3K/AKT pathways failed to consistently synergize with either osimertinib or any other inhibitor ( Figure 7B , top ) . These data support the premise that combined vertical inhibition of proximal EGFR signaling may constitute an effective strategy to treat EGFR-mutated lung adenocarcinomas . SHP2 is important for the stabilization of the GRB2:SOS1/2 complexes on EGFR ( Dance et al . , 2008 ) , and the mechanism of allosteric SHP2 inhibitors depends on SOS1 ( Nichols et al . , 2018 ) , although the contribution of SOS2 to SHP2 inhibitors was not assessed . To determine whether SOS2 deletion altered the spectrum of drug-drug synergies in EGFR-mutated cells , parallel studies were performed in EGFR-mutated cells where SOS2 was deleted ( Figure 7B , bottom ) . Unlike what we observed for synergy between EGFR- and SOS1 inhibition , synergy between SOS1 and SHP2 inhibition was enhanced by SOS2 deletion . These data suggest that SOS2 plays a role in SHP2-dependent signaling . SOS1 inhibition also synergized with MEK inhibition in SOS2 KO cells . Given the strong synergy between SOS1 inhibition and SOS2 deletion in inhibiting Raf/MEK/ERK signaling ( Figure 6 ) , these data suggest that deep inhibition of MEK signaling is sufficient to inhibit survival in EGFR-mutated cells . To further evaluate synergy between inhibitors of proximal EGFR signaling , we examined combinations of EGFR- SOS1- and SHP2 inhibition both by expanded evaluation of each two-drug combination and by assessing whether combined inhibition of EGFR , SOS1 , and SHP2 would be more effective than two drug combinations of these inhibitors . To assess each two-drug combination , H1975 cells cultured under 3D spheroid conditions were treated with dose-equivalent combinations of osimertinib , BAY-293 , and RMC-4550 , assessed for cell viability , and subjected to isobologram analysis to assess drug-drug synergy . Each two-drug combination showed synergy at three different DEQ ratios ( Figure 7C ) , suggesting that inhibition of any two proximal signaling proteins may be an effective therapeutic regimen to treat EGFR-mutated cancer . To assess whether adding a third proximal inhibitor to each two-drug combination would further enhance synergistic inhibition of spheroid survival , each two-drug combination was mixed at 1:1 ratio , and then a third proximal pathway inhibitor was added to give the indicated three-drug mixtures ( Figure 7D ) . Isobologram analysis of these three drug mixtures revealed that addition of a third proximal pathway inhibitor to any two-drug combination of osimertinib , BAY-293 , and RMC-4550 further enhanced synergy above what was observed for each two-drug combination ( Figure 7D ) . Finally , comparing the combination index for the three-drug combination at a 1:1:1 ratio when each drug is treated independently versus the two-drug combinations showed marked synergy for the three drug combination , but that this synergy was not significantly enhanced compared to the combination of osimertinib and BAY-293 ( Figure 7E ) . These data indicate that vertical inhibition of proximal EGFR signaling with the combination of osimertinib and a SOS1 inhibitor may be the most the most effective therapeutic combination to treat EGFR-mutated NSCLC .
Activating EGFR mutations are found in 10–30% of lung adenocarcinomas and are the major cause of lung cancer in never smokers . The third-generation EGFR-TKI osimertinib enhances both progression-free ( Soria et al . , 2018 ) and overall survival ( Ramalingam et al . , 2020 ) compared to first generation EGFR-TKIs and is now considered first-line treatment in EGFR-mutated NSCLC . Osimertinib resistance often develops via activation of parallel RTK pathways ( Mancini et al . , 2018; Romaniello et al . , 2018; La Monica et al . , 2017 ) , and broad inhibition RTK signaling may enhance osimertinib efficacy and delay therapeutic resistance . Here , we demonstrate that inhibition of the common RTK signaling intermediate SOS1 using BAY-293 showed marked synergy with osimertinib in 3D spheroid-cultured EGFR-mutated NSCLC cells . Our observations that ( i ) osimertinib–BAY-293 synergy was only observed in 3D spheroids but not in adherent ( 2D ) cultures and ( ii ) synergy between RTK-signaling intermediates and osimertinib was not broadly applicable to EGFR downstream signaling components but was limited to proteins upstream of RAS reveal novel insights into pharmacologic studies assessing therapeutics designed to treat NSCLC . While most studies designed to identify or test therapeutic targets to treat cancer are done in 2D adherent culture , a growing body of evidence suggests that pharmacologic assessment of novel therapeutics must be performed in 3D culture systems ( Nunes et al . , 2019 ) . Here , there are many different 3D model systems available that vary in both ease-of-use and complexity of the system . The simplest systems employ non-scaffold-dependent monoculture of cancer cell lines where spheroids are either generated using hanging-drop methodology , magnetic levitation , or using ultra-low attachment plates . More complex systems include embedding spheroids in an extracellular matrix ( Matrigel , collagen , gelatin , or a synthetic hydrogel ) either as a cancer cell line monoculture or in combination with cancer-derived fibroblasts , or using specialized microfluidics or culturing cancer-derived organoids . These methods are have been thoroughly reviewed elsewhere ( Langhans , 2018 ) . In the current study , we use ultra-low attachment plates of monoculture NSCLC cell lines as these have the advantage of recapitulating in vivo findings while allowing for dose-response studies done at scale ( Mittler et al . , 2017 ) . In NSCLC , multiple studies have now revealed the importance of 3D culture systems in order to recapitulate in vivo findings . EGFR-mutated cells show differential RTK expression and phosphorylation in 3D versus 2D conditions ( Ekert et al . , 2014 ) and respond more robustly to EGFR-TKIs in 3D cultures compared to 2D settings ( Figure 1 and Jacobi et al . , 2017 ) ; KRAS-mutated cell lines deemed ‘KRAS-independent’ in 2D culture ( Balbin et al . , 2013; Singh et al . , 2009; Singh et al . , 2012; Scholl et al . , 2009; Lamba et al . , 2014 ) still require KRAS for anchorage-independent growth ( Fujita-Sato et al . , 2015; Rotem et al . , 2015; Zhang et al . , 2006; McCormick , 2015 ) , and some KRASG12C-mutated NSCLC cell lines respond to KRAS ( G12C ) inhibitors in 3D culture and in vivo but not in 2D adherent culture ( Janes et al . , 2018 ) . The relevance of 3D culture systems extends to the identification of novel therapeutic targets and therapeutic combinations . We recently showed that SOS2 is specifically required for PI3K-dependent protection from anoikis in KRAS-mutated NSCLC cells ( Sheffels et al . , 2019 ) and SOS2 deletion synergizes with MEK inhibition to kill KRAS mutated cells only under 3D culture conditions ( Sheffels et al . , 2018 ) . Here , we show marked synergy between vertical inhibition of EGFR and SOS1 in EGFR mutated cancer cells , but only under 3D culture conditions ( Figure 2 ) . CRISPR screens performed in spheroid cultures of KRAS- and EGFR-mutated NSCLC cell lines more accurately reproduce in vivo findings and identify drivers of oncogenic growth compared to screens performed in 2D cultures ( Han et al . , 2020 ) . Intriguingly , in this study SOS1 was essential for 3D spheroid survival but not 2D spheroid growth of both EGFR- and KRAS-mutated cells , and a recently accepted publication assessing a novel SOS1 inhibitor showed that it was more effective in 3D compared to 2D culture ( Hofmann et al . , 2020 ) . These data are in complete agreement with our data from Figure 1 showing the requirement for SOS1 in 3D transformation but not 2D proliferation , and support our conclusion that SOS1 is an important therapeutic target in EGFR-mutated NSCLC . We hypothesize the requirement for SOS1 ( and SOS2 ) to promote oncogenic growth in 3D versus proliferation in 2D culture are due to the requirement for PI3K signaling to promote cell survival in 3D but not 2D . Downstream of EGFR activation , the threshold for Raf/MEK/ERK versus PI3K/AKT pathway activation are drastically different , so small amounts of EFGR signaling ( in the presence of either SOS1 or SOS2 ) promote Raf/MEK/ERK signaling , whereas high levels of EGFR signaling are required to activate the PI3K/AKT pathway ( Fortian and Sorkin , 2014 ) . While this hypothesis remains to be tested , we speculate that depending on the specific oncogenic contexts , either SOS1 or SOS2 inhibition will be sufficient to modulate RTK signaling and change the threshold for PI3K signaling , thereby affecting oncogenic growth . These data suggest that future studies assessing novel therapeutics to treat lung adenocarcinomas must be performed in a 3D setting , and that SOS1 and SOS2 might be ubiquitous therapeutic targets in RTK-driven tumors . Osimertinib resistant can occur via oncogenic shift to alternative RTKs including c-MET ( Shi et al . , 2016 ) , HER2 and/or HER3 ( Mancini et al . , 2018; Romaniello et al . , 2018; La Monica et al . , 2017 ) , IGF1R ( Park et al . , 2016 ) , and AXL ( Kim et al . , 2019; Taniguchi et al . , 2019; Jimbo et al . , 2019; Namba et al . , 2019 ) . The variety of RTK bypass pathways that can lead to osimertinib resistance suggests that broad inhibition of RTK signaling may be a more effective therapeutic strategy than any individual RTK inhibitor to limit osimertinib resistance , whereas once resistance via oncogenic shift to an alternative RTK occurs then inhibition of the upregulated RTK would have therapeutic benefit . Toward this end , Phase I and II clinical trials are currently examining whether combining osimertinib with inhibitors of AXL ( DS-1205c , NCT03255083 ) or c-MET ( teponitib , NCT03940703; savolitinib , NCT03778229 ) are effective in patients who have progressed on osimertinib treatment . Combining osimertinib with a MEK inhibitor can enhance osimertinib efficacy ( Eberlein et al . , 2015; Tricker et al . , 2015; Ichihara et al . , 2017; Shi et al . , 2017; Della Corte et al . , 2018 ) and Phase II clinical trials are currently underway to assess combining osimertinib with the MEK inhibitor selumetinib in EGFR-mutated NSCLC ( NCT03392246 ) , although resistance to combined osimertinib and MEK inhibition still occurs ( Tricker et al . , 2015 ) . In a recent study designed to understand resistance to combined osimertinib and MEK inhibition , Kurppa et al . , 2020 show that combining osimertinib with the MEK inhibitor trametinib results in EGFR-mutated cells entering a senescent state that is dependent on the activation of the Hippo pathway effector YAP and its transcription-factor-binding partner TEAD ( Kurppa et al . , 2020 ) . Inhibition of YAP/TEAD signaling overcame this senescence and enhanced killing of EGFR-mutated cells ( Kurppa et al . , 2020 ) . EGFR-signaling drives YAP nuclear translocation and transcriptional regulation through PI3K-PDK1 signaling ( Fan et al . , 2013; Xia et al . , 2018; Tumaneng et al . , 2012 ) . This suggest that therapeutic combinations able to synergistically inhibit both Raf/MEK/ERK and PI3K/AKT effector signaling should overcome YAP-dependent senescence and treat EGFR-mutated NSCLC . Here , we show that osimertinib does not broadly synergize with inhibitors of downstream EGFR/RAS/RAS effector signaling . Instead , we found that synergy was limited to combinations of osimertinib with inhibitors of proximal EGFR signaling intermediates SOS1 and SHP2 ( Figure 7 ) . Further , SOS1 inhibition significantly enhanced osimertinib-dependent inhibition of both Raf/MEK/ERK and PI3K/AKT signaling ( Figure 6 ) , whereas inhibition of individual downstream Raf/MEK/ERK or PI3K/AKT effector pathways did not synergize with osimertinib ( Figure 7 ) to inhibit 3D spheroid growth . We hypothesize that these two findings are inexorably linked , so that any potential therapeutic must synergize with osimertinib to inhibit all downstream RAS effector signaling to show drug-drug synergy in 3D culture . In support of this idea , previous studies showed inhibition of SRC family kinases ( SFK ) potentiated osimertinib to a much greater extent than either MEK or PI3K inhibition ( Ichihara et al . , 2017 ) , and that SFK inhibition synergized with osimertinib to inhibit both Raf/MEK/ERK and PI3K/AKT signaling ( Ichihara et al . , 2017; Watanabe et al . , 2017 ) . There remain several open questions regarding SOS1 inhibition as a therapeutic strategy to limit osimertinib resistance . First , does SOS1 inhibition enhance osimertinib efficacy in vivo using xenograft studies ? While BAY-293 shows tremendous specificity toward SOS1 ( Figure 1—figure supplement 1 and 2 , and Hillig et al . , 2019 ) and is a very useful tool compound for in vitro studies , it has limited bioavailability making it unsuitable for in vivo use . Thus , new SOS1 inhibitors that can be used in vivo are needed to move SOS1 forward as a therapeutic target . Intriguingly , while this paper was under review Boehringer Ingelheim reported two orally available SOS1 inhibitors suitable for in vivo studies ( Hofmann et al . , 2020 ) . They found that SOS1 inhibition could overcome MEK inhibitor resistance in KRAS-mutated cell lines and that the combination of SOS1 and MEK inhibition showed marked show efficacy in KRAS-mutated cell lines and xenograft models . They are now moving one of these compounds into Phase I safety trials for KRAS mutated solid tumors ( BI-1701963 , NCT04111458 ) . It will be exciting to assess whether these new SOS1 inhibitors work in combination with osimertinib to limit the growth EGFR-mutated tumors . Further , these studies will be necessary to translate SOS1-targeted therapies for use in EGFR-mutated lung adenocarcinoma . Second , does SOS1 inhibition actually limit the development of osimertinib resistance ? While outside the scope of the current paper , it will be intriguing to use in vitro models of EGFR-TKI resistance ( Tricker et al . , 2015 ) to assess whether SOS1 inhibition can block the development of osimertinib resistance . Third , while we have focused on the RAF/MEK/ERK and PI3K/AKT effector pathways as the major contributors to mutant EGFR-driven NSCLC , there are many different effector pathways downstream of RAS that may be SOS1-dependent and contribute to the oncogenic phenotype . Here , and unbiased approach at understanding the individual and combined effects of osimertinib and SOS1 inhibition on RAS activation ( to validate relatively new SOS1 inhibitors ) and RAS effector signaling would provide valuable insight into how these therapies alter EGFR-driven signaling in NSCLC . Overall , our data suggest that inhibitors of proximal signaling may be the most efficacious therapeutics to combine with osimertinib to treat EGFR-mutated tumors . Toward this end , Phase I trials are currently underway assessing the combination of osimertinib and the SRC inhibitor dasatinib ( NCT02954523 ) in EGFR-mutated NSCLC , and recently developed SOS1 ( BI-1701963 , NCT04111458 ) and SHP2 ( JAB-3068 , NCT03565003; RMC-4630 , NCT03634982 ) inhibitors have entered Phase I safety trials . Our study provides a framework for the systematic , preclinical assessment of therapeutic combinations designed to treat EGFR-mutated cancer cells . We show both how to use basic pharmacologic principles to assess drug-drug synergy and that these combinations must be assessed under 3D culture conditions . Using this framework , we show that the combination of osimertinib and the SOS1 inhibitor BAY-293 shows marked efficacy in 3D spheroid culture and should be pursued as a therapeutic option to treat EGFR-mutated lung adenocarcinoma .
Cell lines were cultured at 37°C and 5% CO2 . HCC827 , NCI-H1975 , PC9 , and PC9-TM cells were maintained in Roswell Park Memorial Institute medium ( RPMI ) , each supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin . Cell lines were authenticated by STR profiling and confirmed as mycoplasma negative . EGFR mutations were confirmed by Sanger sequencing . H3255 and H3255-TM were maintained in ACL4 medium formulated in DMEM:F-12 including: Bovine Serum Albumin 0 . 5% ( w/v ) ( Sigma cat no . A8022 ) , apo-Transferrin ( human ) ( Sigma cat no . T5391 ) 0 . 01 mg/mL , Sodium Selenite ( Sigma cat no . S9133 ) 25 nM , Hydrocortisone ( Sigma cat no . H0135 ) 50 nM , Ethanolamine ( Sigma cat no . E0135 ) 0 . 01 mM , O-Phosphorylethanolamine ( Sigma cat no . P0503 ) 0 . 01 mM , 3 , 3’ , 5-Triiodo-L-thyronine [T3] ( Sigma cat no . T5516 ) 100pM , Sodium Pyruvate ( Sigma cat no . P4562 ) , HEPES ( Invitrogen cat no 15630–080 ) 10 mM , Epidermal Growth Factor [EGF] 1 ng/mL , Recombinant Human Insulin ( Sigma cat no . I9278 ) 0 . 02 mg/mL , and 1% penicillin-streptomycin . For signaling experiments , cells were seeded in 24-well micropatterned AggreWell 400 low-attachment culture plates ( Stem Cell # 34415 ) at 1 . 2 × 106 cells/well in 2 mL of medium . 24 hr post-plating , half of the media was carefully replaced with fresh media to not disturb the spheroids . At 48 hr , 1 mL media was removed and replaced with 2 x inhibitor . Cells were treated with inhibitor for 6 hr and then collected for cell lysis and western blot analysis . Cells were lysed in RIPA buffer ( 1% NP-40 , 0 . 1% SDS , 0 . 1% Na-deoxycholate , 10% glycerol , 0 . 137 M NaCl , 20 mM Tris pH [8 . 0] , protease ( Biotool #B14002 ) and phosphatase ( Biotool #B15002 ) inhibitor cocktails ) for 20 min at 4°C and spun at 10 , 000 RPM for 10 min . Clarified lysates were boiled in SDS sample buffer containing 100 mM DTT for 10 min prior to western blotting . Proteins were resolved by sodium dodecyl sulfate-polyacrylamide ( Criterion TGX precast ) gel electrophoresis and transferred to nitrocellulose membranes . Western blots were developed by multiplex Western blotting using anti-SOS1 ( Santa Cruz sc-256; 1:500 ) , anti-SOS2 ( Santa Cruz sc-258; 1:500 ) , anti-β-actin ( Sigma AC-15; 1:5 , 000 ) , anti-pEGFR ( Cell Signaling 3777; 1:1000 ) , anti-EGFR ( Cell Signaling 4267; 1:1000 ) , anti-pERK1/2 ( Cell Signaling 4370; 1:1 , 000 ) , anti-ERK1/2 ( Cell Signaling 4696; 1:1000 ) , anti-pAKT Ser473 ( Cell Signaling 4060; 1:1000 ) , anti-AKT ( Cell Signaling 2920; 1:1000 ) , anti-HSP90 ( Santa Crux sc-7947 , 1:1000 ) , primary antibodies . Anti-mouse and anti-rabbit secondary antibodies conjugated to IRDye680 or IRDye800 ( LI-COR; 1:10 , 000 ) were used to probe primary antibodies . Western blot protein bands were detected and quantified using the Odyssey system ( LI-COR ) . For quantification of SOS1 and SOS2 abundance , samples were normalized to either β-actin or HSP90 . For quantification of pERK and pAKT , samples were normalized to a weighted average of HSP90 , β-actin , total ERK1/2 , total AKT , and total EGFR ( Janes , 2015 ) . For 2D proliferation assays , 5 × 102 cells were seeded on cell culture-coated 96-well white-walled CulturePlates ( Perkin Elmer #6005688 ) . Cells were lysed with CellTiter-Glo 2 . 0 Reagent ( Promega ) , and luminescence was read using a Bio-Tek Cytation five multi-mode plate reader . Cell number was assessed 24 hr after plating to account for any discrepancies in plating ( Day 1 ) , and then on days 3 , 5 , and 7 . Data were analyzed as an increase in luminescence over Day 1 . H3255 and H3255-TM cells were seeded in 0 . 32% Nobel agar at 2 × 104 cells per 35 mm dish to assess anchorage-independent . Soft agar colonies were counted 28 days after seeding . For all other cell lines spheroid growth assessed in ultra-low attachment 96-well round bottomed plates ( Corning Costar #7007 ) , cells were seeded at 500 cells per well . Images were taken 24 hr after plating to assess initial spheroid size , and then 7 , 14 , and 21 days later to assess transformation . Cell number was assessed in parallel plates at 0 , 7 , 14 , and 21 days using CellTiter-Glo 2 . 0 reagent . A non-targeting ( NT ) single guide RNA ( sgRNA ) , a SOS2-targeted sgRNA ( Sheffels et al . , 2018 ) , and eight potential SOS1-targeted sgRNAs previously used to target SOS1 in a genome-wide CRISPR screen ( Munoz et al . , 2016 ) were each cloned into pLentiCRISPRv2 as previously described ( Sanjana et al . , 2014 ) . SOS1-2 was chosen as the SOS1 sgRNA for the study , and SOS2-9 was chosen as previously described ( Sheffels et al . , 2018 ) . For studies in Figure 1 , cells were infected lentivirus to express the given sgRNA with Cas9 , and cells were selected for 10 days with puromycin prior to Western blotting . Cell lysates were probed for SOS1 or SOS2 , and only cell populations showing grater that 80% SOS deletion within the overall population were used . Importantly , cell clones were not used , rather cell populations where > 80% of cells showed SOS deletion were used to minimize clonal effects . Independent infections were used for each experiment . ConstructsgRNANTCCATATCGGGGCGAGACATGSOS2-9GAGAACAGTCCGAAATGGCGSOS1-1GGGCAGCTGCTGCGCCTGCASOS1-2GCATCCTTTCCAGTGTACTCSOS1-3TATTCTGCATTGCTAGCACCSOS1-4AGTGGCATATAAGCAGACCTSOS1-5ATTGCAAGAGACAATGGACCSOS1-6GCTTATATGCCACTCAACTGSOS1-7GAAGGAACTCTTACACGTGTSOS1-8CTATTGGGTGTAAGGTGAGC Lentiviruses were produced by co-transfecting MISSION lentiviral packaging mix ( Sigma ) into 293 T cells using Mirus TransIT-Lenti transfection reagent ( Mirus Bio # MIR6605 ) in Opti-MEM ( Thermo Scientific #31-985-062 ) . At 48 hr post-transfection , viral supernatants were collected and filtered . Viral supernatants were then either stored at −80°C or used immediately to infect cells in combination with polybrene at 8 μg/mL . 48 hr post-infection , cells were selected in 4 μg/mL Puromycin ( Invitrogen ) . Twelve days after selection , cells were analyzed for SOS1 and SOS2 expression and plated for proliferation and transformation assays . For all studies , outer wells ( rows A and H , columns 1 and 12 ) were filled with 200 μL of PBS to buffer inner cells from temperature and humidity fluctuations . Triplicate wells of cells were then treated with increasing concentrations 100 μL of 2 × inhibitor at either a semilog ( single drug dose response curves to determine EC50 ) or a 1/3-log scale ( isobologram and Bliss independence experiments ) for 72 ( adherent cultures ) or 96 ( spheroids ) hr . Cell viability was assessed using CellTiter-Glo 2 . 0 ( 30 μL/well ) . Luminescence was assessed using a Bio-Tek Cytation five multi-mode plate reader . Data were normalized to the maximum luminescence reading of untreated cells , and individual drug EC50 values were calculated using Prism eight by non-linear regression using log ( inhibitor ) vs . response with a variable slope ( four parameters ) to assess for differences in the Hill Coefficient between different drug treatments . For all drug-treatment studies , the untreated sample for each cell line was set to 100% . This would mask any differences in 3D cell proliferation seen between cell lines . Dose equivalence was first determined by assessing individual-drug EC50 values; individual-drug Hill Coefficients were determined to assure that the two drugs could be assessed for synergy by Lowe additivity . To generate dose-equivalent dose-response curves , the dose for each drug closest to the EC50 on a 1/3-log scale was set as equivalent , and 10-point dose response curves were generated for each individual drug on either side of the equivalent dose to ensure the top ( no drug effect ) and bottom ( maximal drug effect ) were represented on the dose-response curve . 100 μL of drug each drug dose was added as outlined above . To generate dose-equivalent mixtures for isobologram analysis , equivalent doses of the two drugs were mixed at different ratios so that the total dose ( 100 μL ) would be expected to have an equivalent effect on the cells if the two drugs were additive . Drugs were mixed at either five ( 4:1 , 2:1 , 1:1 , 1:2 , and 1:4 ) or three ( 2:1 , 1:1 , and 1:2 ) different drug mixtures depending on the experiment . Cells were treated and EC50 values for each individual drug or drug mixture based on each drug’s dosing were determined for as outlined above . To generate an isobologram plot , the EC50 of each individual drug was plotted as the x- or y-intercept , and the calculated contribution of each drug to the overall EC50 for each DEQ mix is plotted as a single point ( EC50 , A , EC50 , B ) on the graph . CombinationIndex=EC50AmixEC50Aalone+EC50BmixEC50Balone To calculate the combination index for each dose equivalent mixture , the calculated contribution of each drug to the overall EC50 were used in the equation: As an example , we will show data for one trial analyzing the combination of osimertinib and BAY-293 in 3D spheroid cultured H1975 cells in Figure 2B . The EC50 values for each individual drug were first determined: −8 . 57 for osimertinib and −5 . 73 for BAY-293 . Based on these EC50 values , the dose equivalence was set at −8 . 67 for osimertinib −5 . 67 for BAY-293 ( approximated EC50 for each drug in bold ) , and the following 10-point dose response curves were generated: Osimertinib−11−10 . 67−10 . 33−10−9 . 67−9 . 33-9−8 . 67−8 . 33-8BAY-293-8−7 . 67−7 . 33-7−6 . 67−6 . 33-6−5 . 67−5 . 33-5 Cells were then treated with the following volumes of each drug to generate seven dose-equivalent dose response curves: 4:1 mixture2:1 mixture1:1 mixture1:2 mixture1:4 mixtureosimertinib100 μL80 μL66 μL50 μL34 μL20 μL0 μLBAY-2930 μL20 μL34 μL50 μL66 μL80 μL100 μL EC50 values for each dose-response curve were then determined based on each drug’s dosing: OSM alone4:1 mixture2:1 mixture1:1 mixture1:2 mixture1:4 mixtureBAY aloneosimertinib EC50 ( nM ) 2 . 620 . 840 . 700 . 921 . 491 . 192 . 40BAY-293 EC50 ( μM ) 2 . 141 . 010 . 831 . 091 . 491 . 041 . 82 EC50 values were then adjusted based on the amount of each drug that was put in the mixture to determine the contribution of each drug in the mixture to the overall EC50 . For example , the 4:1 mixture was 80% osimertinib , so the osimertinib EC50 for that mixture is multiplied by 0 . 8 . The corresponding corrected EC50 values and combination indices were: OSM alone4:1 mixture2:1 mixture1:1 mixture1:2 mixture1:4 mixtureBAY aloneosimertinib EC50 ( nM ) 2 . 620 . 670 . 450 . 460 . 520 . 240BAY-293 EC50 ( μM ) 00 . 200 . 290 . 540 . 970 . 841 . 82Combination Index0 . 400 . 340 . 440 . 650 . 46 Unlike Isobologram analysis , individual drug doses are not reduced for drug-drug combinations when performing Bliss independence analysis . For data in Figure 2 , wells were treated with a full dose of each individual drug or drug combination in a 10 × 10 matrix of dose combinations for osimertinib and BAY-293 on a 1/3-log scale . Data were normalized to the maximum luminescence reading of untreated cells , and a heat-map depicting cell viability was generated using Prism 8 . The Bliss index was calculated by first converting viability ( on a scale of 0 to 1 ) for each treatment to the effect of each drug or drug combination , where 0 represents no effect and 1 represents 100% effect ( no viable cells ) . effect=1−viability From the effect data , the expected effect for each drug combination is calculated:Expectedeffect=EA+EB∗ ( 1−EA ) Expectedeffect=EA+EB−EA∗EB The Bliss Index is the ratio of the expected effect/actual effect:BlissIndex= ( expectedeffect ) / ( actualeffect ) BlissIndex= ( EA+EB–EA∗EB ) / ( EA+BMIX ) A Bliss Index of 1 indicates that the actual and expected effects are equivalent , and the effects of the two drugs are additive . Bliss Index < 1 indicates increasing synergy , whereas Bliss Index > 1 indicates antagonism . Excess over Bliss is calculated by determining how much greater the actual effect of the drug combination is versus the expected effect , and is calculated as:ExcessoverBliss=100∗[actualeffect–expectedeffect]ExcessoverBliss=100∗[EA+BMIX− ( EA+EB−EA∗EB ) ] An excess over Bliss of 0 indicates that the actual and expected effects are equivalent , and the effects of the two drugs are additive; values > 0 indicate increasing synergy , whereas values < 0 indicate antagonism . Since synergy occurred at drug combinations at or just below the EC50 values for each individual drug , Bliss experiments in Figures 4 and 5 , drug mixtures were limited to 3 × 10 drug mixtures based on dose equivalence with mixtures at approximately 2:1 , 1:1 , and 1:2 mixes of the two drugs based on dose equivalence . Here , the doses used for one drug were held constant , and the second drug dose wash shifted by 1/3 log up or down to generate 2:1 and 1:2 mixtures . For example , for the combination of osimertinib and BAY-293 in H1975 cells , the following drug doses were used: Osimertinib ( 1:2 ratio of OSM:BAY ) −11 . 33−11−10 . 67−10 . 33−10−9 . 67−9 . 33-9−8 . 67−8 . 33Osimertinib ( 1:1 ratio of OSM:BAY ) −11−10 . 67−10 . 33−10−9 . 67−9 . 33-9−8 . 67−8 . 33-8Osimertinib ( 2:1 ratio of OSM:BAY ) −10 . 67−10 . 33−10−9 . 67−9 . 33-9−8 . 67−8 . 33-8−7 . 67BAY-293 ( constant ) -8−7 . 67−7 . 33-7−6 . 67−6 . 33-6−5 . 67−5 . 33-5 For three-drug isobologram studies with osimertinib ( EC50 = −8 . 57 ) , BAY-293 ( EC50 = −5 . 74 ) , and RCM-4550 ( EC50 = −6 . 84 ) , drugs were again mixed based on dose equivalency . The dose-equivalent 10-point dose-response curves for these drugs in 3D cultured H1975 cells were ( approximated EC50 for each drug in bold ) : Osimertinib−11−10 . 67−10 . 33−10−9 . 67−9 . 33-9−8 . 67−8 . 33-8BAY-293-8−7 . 67−7 . 33-7−6 . 67−6 . 33-6−5 . 67−5 . 33-5RMC-4550-9−8 . 67−8 . 33-8−7 . 67−7 . 33-7−6 . 67−6 . 33-6 Each two-drug combination was set as a single ‘drug mixture’ at a 1:1 ratio , and the third drug was combined with this drug mixture at 2:1 , 1:1 , and 1:2 drug ratios . To generate the proper two and three-drug mixtures for analysis , 21 total dose response curves were generated . The five dose-response curves on the right represent the mixtures used to generate the isobologram plots in Figure 7D . The other two two-drug mixtures in bold ( two-drug 2:1 and 1:2 mixtures ) were used to generate the isobologram plots in Figure 7CCombination indices were calculated based on whether addition of the third drug to each two-drug 1:1 mixture further enhanced synergy when added to the two-drug mixture . [osimertinib:BAY-293] mixture vs . RCM-4550: CombinationIndex=EC50OSM+BAY3−drugmixEC50OSM+BAY50:50+EC50RCM3−drugmixEC50RCMaloneOSM:BAY 2:1OSM:BAY 1:2Osm:BAY 1:1 ( 1+1 ) :1 2:1 mixture ( 1+1 ) :2 1:1 mixture ( 1+1 ) :4 1:2 mixtureRCM aloneosimertinib66 μL34 μL50 μL33 μL25 μL17 μL0 μLBAY-29334 μL66 μL50 μL33 μL25 μL17 μL0 μLRMC-45500 μL0 μL0 μL34 μL50 μL66 μL100 μL [osimertinib:RCM-4550] mixture vs . BAY-293: CombinationIndex=EC50OSM+RCM3−drugmixEC50OSM+RCM50:50+EC50BAY3−drugmixEC50BAYaloneOSM:RCM 2:1OSM:RCM 1:2Osm:RCM 1:1 ( 1+1 ) :1 2:1 mixture ( 1+1 ) :2 1:1 mixture ( 1+1 ) :4 1:2 mixtureRCM aloneosimertinib66 μL34 μL50 μL33 μL25 μL17 μL0 μLBAY-2930 μL0 μL0 μL34 μL50 μL66 μL100 μLRMC-455034 μL66 μL50 μL33 μL25 μL17 μL0 μL [BAY-293:RCM-4550] mixture vs . osimertinib: CombinationIndex=EC50BAY+RCM3−drugmixEC50BAY+RCM50:50+EC50OSM3−drugmixEC50OSMaloneBAY:RCM 2:1BAY:RCM 1:2Bay:RCM 1:1 ( 1+1 ) :1 2:1 mixture ( 1+1 ) :2 1:1 mixture ( 1+1 ) :4 1:2 mixtureRCM aloneosimertinib0 μL0 μL0 μL34 μL50 μL66 μL100 μLBAY-29366 μL34 μL50 μL33 μL25 μL17 μL0 μLRMC-455034 μL66 μL50 μL33 μL25 μL17 μL0 μL To calculate the three-drug combination index where each drug was considered independently ( Figure 7E ) , the following equation was used:CombinationIndex=EC50OSM3−drugmixEC50OSM50:50+EC50BAY3−drugmixEC50BAYalone+EC50RCM3−drugmixEC50RCMalone | Lung cancer is the leading cause of cancer-related deaths worldwide . In non-smokers , this disease is usually caused by a mutation in a protein found on the surface of a cell , called EGFR . In healthy lung cells , these proteins trigger a chain of chemical signals that tell the cells to multiply . However , faulty forms of EFGR make the cells grow uncontrollably , leading to the formation of tumors . Current treatments use EGFR inhibitors that block the activity of these proteins . But cancer cells often become resistant to these treatments by activating other types of growth proteins . One way to overcome this resistance has been by targeting the signaling pathways within individual tumors . But since those pathways differ between tumors , it has been challenging to find a single therapy that can treat all drug-resistant cancer cells . Now , Theard et al . assessed the therapeutic effects of blocking a specific protein inside lung cells , called SOS1 , which is involved in growth signaling in all tumor cells . Six different types of human lung cancer cells were used , all of which had faulty forms of EGFR , with three of the cell types showing drug resistance to current therapies . The cancer cells were either exposed to EGFR inhibitors only or to a combination of EGFR and SOS1 inhibitors . The most effective treatment was found to be through combinational therapy , with enhanced killing of drug-resistant cells . Theard et al . further assessed the effect of combinational therapy using cells kept in two different ways . Cancer cells were either grown in a two-dimensional format , with cells forming a single cell layer , or in a three-dimensional format , where cells were multi-layered and grew on top of each other as self-aggregating spheroids . Combinational therapy treatment was only successful when the cells where grown in a three-dimensional format . These findings highlight that future drug development studies should give consideration to the way cells are grown , as it can impact the results . They also provide a steppingstone towards tackling drug resistance in lung cancers that arise from EGFR mutations . | [
"Abstract",
"Introduction",
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"cancer",
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] | 2020 | Marked synergy by vertical inhibition of EGFR signaling in NSCLC spheroids shows SOS1 is a therapeutic target in EGFR-mutated cancer |
The best characterized signaling pathway downstream of transforming growth factor β ( TGF-β ) is through SMAD2 and SMAD3 . However , TGF-β also induces phosphorylation of SMAD1 and SMAD5 , but the mechanism of this phosphorylation and its functional relevance is not known . Here , we show that TGF-β-induced SMAD1/5 phosphorylation requires members of two classes of type I receptor , TGFBR1 and ACVR1 , and establish a new paradigm for receptor activation where TGFBR1 phosphorylates and activates ACVR1 , which phosphorylates SMAD1/5 . We demonstrate the biological significance of this pathway by showing that approximately a quarter of the TGF-β-induced transcriptome depends on SMAD1/5 signaling , with major early transcriptional targets being the ID genes . Finally , we show that TGF-β-induced epithelial-to-mesenchymal transition requires signaling via both the SMAD3 and SMAD1/5 pathways , with SMAD1/5 signaling being essential to induce ID1 . Therefore , combinatorial signaling via both SMAD pathways is essential for the full TGF-β-induced transcriptional program and physiological responses .
Members of the transforming growth factor β ( TGF-β ) family of ligands , which includes the TGF-βs , Activins , NODAL , BMPs and GDFs , have pleiotropic effects on cell behavior ranging from germ layer specification and patterning in embryonic development , to tissue homeostasis and regeneration in adults ( Massagué , 2012; Morikawa et al . , 2016; Wu and Hill , 2009 ) . TGF-β family signaling is also deregulated in a number of human diseases through mutation or altered expression of either the ligands or downstream signaling pathway components ( Miller and Hill , 2016 ) . In this context , the most widely studied pathology is cancer ( Bellomo et al . , 2016; Massagué , 2008; Meulmeester and ten Dijke , 2011; Wakefield and Hill , 2013 ) , where TGF-β itself has both tumor suppressive and tumor promoting effects ( Massagué , 2008 ) . At early stages of cancer TGF-β’s tumor suppressive effects dominate , such as its cytostatic and pro-apoptotic functions ( Padua and Massagué , 2009 ) . As tumors develop , however , mutations in key components of the pathway or downstream target genes allow the tumor to evade TGF-β’s tumor suppressive effects , whilst remaining sensitive to its tumor-promoting activities . TGF-β directly promotes the oncogenic potential of tumor cells , for example by driving epithelial-to-mesenchymal transition ( EMT ) , a hallmark of cancer that enhances cell invasion and migration , and also increases the frequency of tumor-initating cancer stem cells ( Massagué , 2008; Ye and Weinberg , 2015 ) . TGF-β’s dual role in cancer thus provides an excellent example of how diverse responses can be elicited by a single ligand . The TGF-β family ligands all signal via a common mechanism , initiated by ligand binding to two cell surface serine/threonine kinase receptors , the type II and type I receptors . In the receptor complex , the type II receptors phosphorylate and activate the type I receptors ( Wrana et al . , 1994 ) . These in turn phosphorylate the downstream effectors of the pathway , the receptor-regulated SMADs ( R-SMADs ) on two serines in an SXS motif at their extreme C-termini . Phosphorylated R-SMADs form complexes with the common SMAD , SMAD4 , which accumulate in the nucleus and directly regulate the transcription of target genes , leading to new programs of gene expression ( Shi and Massagué , 2003 ) . In the classic view of TGF-β family signaling , there are two branches , characterized by distinct combinations of type II and type I receptors , and the recruitment of specific R-SMADs to particular type I receptors ( Wakefield and Hill , 2013; Shi and Massagué , 2003 ) . One branch is activated by TGF-β , Activins and NODAL and is mediated via the type I receptors TGFBR1 , ACVR1B and ACVR1C ( also known as ALK5 , ALK4 and ALK7 respectively ) , which phosphorylate SMAD2 and 3 . The other is activated by BMPs and GDFs and is mediated via ACVRL1 , ACVR1 , BMPR1A and BMPR1B ( also known as ALK1 , ALK2 , ALK3 and ALK6 respectively ) , which phosphorylate SMAD1 , 5 and 9 ( Miller and Hill , 2016 ) . In general , while this pairing between type I receptors and R-SMADs broadly fits the assignment of specific ligands to the different branches of TGF-β family signaling , it is an oversimplification . For example , ACVR1 is now described as a BMP receptor , but early work indicated that it could bind Activin and TGF-β ( Massagué , 1996; Miettinen et al . , 1994 ) , and very recently it has been shown to signal downstream of Activin in the context of the disease , fibrodysplasia ossificans progressiva ( Hatsell et al . , 2015; Hino et al . , 2015 ) . Furthermore , ACVRL1 , a type I receptor that recognizes BMP9 and 10 , also transduces TGF-β signals in endothelial cells ( Pardali et al . , 2010 ) by phosphorylating SMAD1/5 in parallel with the canonical TGF-β-induced phosphorylation of SMAD2/3 ( Goumans et al . , 2002; Goumans et al . , 2003 ) . This SMAD1/5 arm of TGF-β signaling has also been shown to occur in a wide range of other cell types , including epithelial cells , fibroblasts and cancer cell lines , which do not express ACVRL1 ( Liu et al . , 1998; Daly et al . , 2008; Liu et al . , 2009; Wrighton et al . , 2009 ) . Important questions concerning this noncanonical TGF-β-induced SMAD1/5 phosphorylation remain unanswered . First , the mechanism by which TGF-β induces SMAD1/5 phosphorylation , in particular , the type I receptors involved , is not known . Some studies have concluded that the canonical TGF-β receptors TGFBR1 and TGFBR2 are sufficient for phosphorylation of both SMAD2/3 and SMAD1/5 ( Liu et al . , 2009; Wrighton et al . , 2009 ) . In contrast , others demonstrated that one of the classic BMP type I receptors ( ACVR1 or BMPR1A ) , or in endothelial cells , ACVRL1 , is additionally required ( Daly et al . , 2008; Goumans et al . , 2002; Goumans et al . , 2003 ) . The second crucial issue concerns the biological relevance of TGF-β-induced SMAD1/5 signaling . Nothing is known about the transcriptional program activated by this arm of TGF-β signaling , or indeed , the specific SMAD complexes involved . It is also not known to what extent this pathway is required for the physiological responses to TGF-β . Here , we dissect the SMAD1/5 arm of TGF-β signaling and define the underlying mechanism and its biological function . We show that TGF-β-induced SMAD1/5 phosphorylation requires both TGFBR1 and ACVR1 and using biosensors , and an optogenetic approach , we establish a new paradigm for TGF-β receptor activation . We have mapped the binding sites on chromatin of nuclear phosphorylated SMAD1/5 ( pSMAD1/5 ) genome-wide , which led us to define the target genes regulated by this arm of TGF-β signaling . We go on to show that this arm of signaling is required for TGF-β-induced EMT . Our data reveal that the full transcriptional programme activated in response to TGF-β requires integrated combinatorial signaling via both the SMAD2/3 and SMAD1/5 pathways .
To begin to dissect which receptors are required for TGF-β-induced SMAD1/5 phosphorylation , we compared the kinetics of SMAD1/5 and SMAD2 phosphorylation in response to TGF-β . Using the human breast cancer cell line MDA-MB-231 and the mouse mammary epithelial cell line NMuMG as model systems , we found that TGF-β induced only transient phosphorylation of SMAD1/5 that peaked at 1 hr and then returned to baseline ( Figure 1A ) . This was in contrast to a more sustained TGF-β-induced SMAD2 phosphorylation , or SMAD1/5 phosphorylation in response to BMP4 . However , transient SMAD1/5 phosphorylation is not a defining characteristic of this arm of TGF-β signaling , as another human breast cancer line , BT-549 , exhibited sustained SMAD1/5 phosphorylation that is still readily detectable 8 hr after TGF-β stimulation ( Figure 1—figure supplement 1A ) . Furthermore , when BT-549 cells were grown as non-adherent spheres , TGF-β-induced SMAD1/5 phosphorylation did not attenuate at all in the first 8 hr of signaling ( Figure 1—figure supplement 1A ) . Thus , the kinetics of TGF-β-induced SMAD1/5 phosphorylation are cell-type-specific and dependent on the culture conditions and are independent of the kinetics of TGF-β-induced SMAD2/3 phosphorylation , suggesting a distinct receptor complex may be involved . To address whether new protein synthesis was required for the transient nature of TGF-β-induced SMAD1/5 phosphorylation , cells were induced with TGF-β in the presence of either a translation inhibitor , cycloheximide or a transcription inhibitor , actinomycin D . Inhibition of translation was uninformative because it also led to a very rapid loss of TGFBR2 and TGFBR1 , due to their short half-lives ( Vizán et al . , 2013 ) . Use of actinomycin D , however , circumvented this problem , as TGFBR2 and TGFBR1 mRNAs are relatively stable ( Figure 1—figure supplement 1B ) and their translation was unimpeded . In these conditions , SMAD1/5 phosphorylation was sustained ( Figure 1B; Figure 1—figure supplement 1C ) . Thus , the rapid loss of pSMAD1/5 at later time points after TGF-β stimulation requires new transcription , suggesting that it is mediated by a component whose expression is induced by TGF-β . Acute TGF-β stimulation results in the rapid internalization of the receptors , which is sufficient to deplete almost all of the type II receptor TGFBR2 from the cell surface ( Vizán et al . , 2013 ) . As a result , cells are refractory to further acute TGF-β stimulation , read out by SMAD2 phosphorylation ( Vizán et al . , 2013 ) . Cells in this refractory state were also unable to induce SMAD1/5 phosphorylation in response to TGF-β , although they remained responsive to BMP4 ( Figure 1C , Figure 1—figure supplement 1D ) . This suggested that TGFBR2 is required for TGF-β-induced SMAD1/5 activation . The distinct kinetics of TGF-β-induced SMAD1/5 phosphorylation compared with SMAD2/3 phosphorylation suggested that different receptor complexes are likely involved . To explore this further , we used combinations of well-characterized small molecule inhibitors of the type I receptor kinases in MDA-MB-231 cells . SB-431542 , a selective TGFBR1/ACVR1B/ACVR1C inhibitor ( Inman et al . , 2002 ) , completely inhibited the phosphorylation of both SMAD1/5 and SMAD2 in response to TGF-β when used at 10 µM ( Figure 1D ) , indicating that the kinase activity of TGFBR1 is essential for TGF-β-induced SMAD1/5 phosphorylation . Interestingly , a 40-fold lower dose also substantially inhibited SMAD1/5 phosphorylation , whilst having less effect on SMAD2 phosphorylation ( Figure 1D ) . TGF-β-induced SMAD1/5 phosphorylation was also partially inhibited by the BMP type I receptor inhibitor LDN-193189 ( Cuny et al . , 2008 ) ( Figure 1D ) , establishing a requirement for a member of this class of type I receptors . Strikingly , the combination of the low-dose SB-431542 and LDN-193189 completely inhibited TGF-β-dependent SMAD1/5 phosphorylation , without affecting phosphorylation of SMAD2 ( Figure 1D ) . Analogous results were obtained in NMuMG cells ( Figure 1—figure supplement 1E ) . We conclude that the kinase activity of both classes of type I receptor is required for maximal SMAD1/5 phosphorylation downstream of TGF-β . Taking these results together with the receptor expression profiles of these cells and receptor knockdown experiments ( Daly et al . , 2008 ) , we deduce that the receptors involved are TGFBR1 , a canonical BMP type I receptor ( ACVR1 and/or BMPR1A ) and TGFBR2 . We next used an in vitro approach to explore why TGF-β-induced phosphorylation of SMAD1 requires two different type I receptors . We focused on ACVR1 as a representative of the BMP type I receptor class , as it is the most homologous to ACVRL1 that responds to TGF-β in endothelial cells ( Chen and Massagué , 1999 ) . Moreover , in some cell types , knockdown of ACVR1 was sufficient to block TGF-β-induced pSMAD1/5 ( Daly et al . , 2008 ) . SMAD1 is known to be a poor substrate for TGFBR1 in vivo ( Kretzschmar et al . , 1997; Hoodless et al . , 1996 ) . We demonstrated that SMAD1 is also a poor substrate for TGFBR1 in vitro , although it is efficiently phosphorylated by both ACVR1 and BMPR1A as expected ( Figure 1—figure supplement 2A , B ) . As a control , we showed that TGFBR1 could potently phosphorylate SMAD2 , and surprisingly , ACVR1 was also able to phosphorylate SMAD2 ( Figure 1—figure supplement 2A , B ) . Given that SMAD1 is a poor substrate for TGFBR1 , it is intriguing that the kinase activity of TGFBR1 is essential for TGF-β-induced SMAD1 phosphorylation . We hypothesized that TGFBR1 might catalyze a priming phosphorylation on SMAD1 , which then serves as a substrate for ACVR1 , or vice versa . To address this , we mapped the sites phosphorylated by ACVR1 on full length SMAD1 . We identified three species of C-terminal SMAD1 phosphorylation by ACVR1 – a dually phosphorylated S[pS]V[pS] and the singly phosphorylated [pS]SVS and S[pS]VS ( Figure 1—figure supplement 2C ) . From this it was clear that ACVR1 could phosphorylate both serines in the critical SVS motif and we deduced that the order of phosphorylation is the penultimate serine of the motif , followed by the terminal one . Moreover , if the preceding serine was phosphorylated , it prevented the phosphorylation of the other sites . Taking all these results together , we conclude that in response to TGF-β , the receptor kinase that phosphorylates SMAD1 is ACVR1 and not TGFBR1 , and it does so on both serines in the SVS motif in a defined order . The absence of a role for the TGFBR1 kinase activity in phosphorylating SMAD1 left open the question of why it is required in vivo for TGF-β-induced SMAD1/5 phosphorylation . We postulated that it might be necessary for ACVR1 activation , and therefore investigated whether TGFBR1 could directly phosphorylate ACVR1 . Both TGFBR1 and ACVR1 exhibit significant autophosphorylation activity in vitro , which was inhibited by SB-505124 ( another more potent TGFBR1 inhibitor; DaCosta Byfield et al . , 2004 ) and LDN-193189 , respectively ( Figure 2A ) . Crucially , when TGFBR1 and ACVR1 were co-incubated , ACVR1 was phosphorylated , even in the presence of LDN-193189 , indicating that ACVR1 is a substrate of TGFBR1 ( Figure 2A ) . To determine whether TGFBR1 could activate ACVR1 in vivo , we used an optogenetic approach . To this end , we fused the light-oxygen-voltage ( LOV ) domain of aureochrome1 from Vaucheria frigida , which dimerizes upon blue light stimulation ( Sako et al . , 2016 ) , to the C-terminal ends of the intracellular domains of a constitutively-activated TGFBR1 ( mutation T204D ) ( Wieser et al . , 1995 ) and of wild-type ACVR1 , along with an N-terminal myristoylation motif to anchor them to the plasma membrane ( Figure 2B; Supplementary file 1 and 2 ) . We refer to these constructs as Opto-TGFBR1* and Opto-ACVR1 , respectively . We tested their ability , alone or in combination , to induce phosphorylation of SMAD1/5 in NIH-3T3 cells co-transfected with FLAG-SMAD1 to increase the range of the assay . Transfection of the Opto-ACVR1 alone resulted in no phosphorylation of co-transfected FLAG-SMAD1 , either in the absence or presence of blue light . However , when Opto-ACVR1 and Opto-TGFBR1* were co-transfected , a robust light-inducible phosphorylation of FLAG-SMAD1 was observed ( Figure 2C ) . Importantly , this was inhibited by both SB-505124 and LDN-193189 , confirming the involvement of both receptors ( Figure 2D ) . This directly demonstrates that TGFBR1 can activate ACVR1 in vivo . As a control , we showed that Opto-TGFBR1* phosphorylated co-expressed GFP-SMAD3 in the presence of light , which was inhibited by SB-505124 , but to a much lesser extent by LDN-193189 ( Figure 2E ) . As a further control to ensure that the activation of ACVR1 by TGFBR1 required the kinase activity of the latter , we made a kinase-dead version of Opto-TGFBR1 . This construct was unable to induce the activity of ACVR1 in a light-inducible manner and was also unable to induce phosphorylation of GFP-SMAD3 ( Figure 2—figure supplement 1 ) . To confirm that the light-inducible phosphorylation of FLAG-SMAD1 observed with the combination of Opto-ACVR1 and Opto-TGFBR1* genuinely resulted from activation of Opto-ACVR1 by Opto-TGFBR1* , we generated a mutant version of Opto-ACVR1 , in which the serines and threonines of the GS domain were mutated to alanine and valine , respectively . Since phosphorylation of these serines and threonines is required for type I receptor activation , we would expect this mutant to be uninducible ( Wieser et al . , 1995 ) . Indeed , we found that light-inducible phosphorylation of FLAG-SMAD1 was inhibited when this GS domain mutant of Opto-ACVR1 was used instead of the wild-type Opto-ACVR1 ( Figure 2F , G ) . We therefore conclude that the requirement of the kinase activity of both TGFBR1 and ACVR1 for TGF-β-induced phosphorylation of SMAD1/5 reflects a requirement for activation of ACVR1 by TGFBR1 through phosphorylation of the ACVR1 GS domain . Having shown that both type I receptors are required , we next tested whether they were components of the same tetrameric receptor complex , or whether they resided in separate receptor complexes that clustered at the cell membrane in response to ligand stimulation ( compare model I and model II , Figure 3A ) . To distinguish between these possibilities , we used previously published recombinant versions of TGF-β3 , designated WW and WD ( Huang et al . , 2011 ) . TGF-β3WW , the wild-type TGF-β3 dimer , is composed of two identical monomeric TGF-β3 subunits , whereas TGF-β3WD contains one wild-type subunit of TGF-β3 and one mutated subunit that cannot bind to either TGFBR2 or TGFBR1 ( Huang et al . , 2011 ) . Thus , while the TGF-β3WW ligand engages two type II:type I pairs in the tetrameric complex , the TGF-β3WD ligand can only engage one pair . In addition , TGF-β3WW does not bind ACVR1 , and by inference , neither does TGF-β3WD ( data not shown ) . It was previously demonstrated that TGF-β3WD binding to a single type II:type I receptor pair is sufficient to induce phosphorylation of SMAD3 ( Huang et al . , 2011 ) . We therefore reasoned that if model I was correct then only TGF-β3WW would induce phosphorylation of SMAD1/5 , as the heterotetrameric complex would not be able to be assembled by TGF-β3WD . If model II was correct , however , then both TGF-β3WW and TGF-β3WD would be competent to induce pSMAD1/5 . Treatment of MDA-MB-231 or NMuMG cells with either TGF-β3WW or TGF-β3WD led to a dose-dependent increase in both SMAD1 and SMAD2 phosphorylation ( Figure 3B; Figure 3—figure supplement 1 ) . Thus , TGF-β stimulation is unlikely to lead to formation of a heterotetrameric complex comprising TGFBR2/TGFBR1/ACVR1 , but instead , leads to the formation of a higher order receptor cluster at the cell surface that includes TGFBR2/TGFBR1 complexes and ACVR1 . To obtain direct evidence that TGF-β activates ACVR1 , we generated an ACVR1 biosensor that fluoresces when activated . In this construct , ACVR1 is fused to the conformation-sensitive circularly permutated yellow fluorescent protein ( cpYFP ) core of the InversePericam Ca2+ sensor and FKBP1A ( formerly FKBP12 ) to make ACVR1-InversePericam-FKBP1A ( ACVR1-IPF ) ( Michel et al . , 2011 ) . When the receptor is inactive , the FKBP1A moiety binds to the GS domain of the receptor , which suppresses cpYFP fluorescence . Upon ligand induction , phosphorylation of the GS domain releases FKBP1A , allowing the cpYFP to adopt a fluorescent conformation ( Michel et al . , 2011 ) . We first showed that ACVR1-IPF is functional in that it is able to induce phosphorylation of SMAD1/5 when overexpressed ( Figure 4—figure supplement 1A ) . We then stably expressed this biosensor in a number of cell lines ( Figure 4—figure supplement 1B ) . In the polarized epithelial cell line , MDCKII and in NIH-3T3 fibroblasts , ACVR1-IPF is readily detectable at the cell membrane , as well as in internal structures , and had no adverse effect on the inducibility of these cells in response to TGF-β or BMP4 ( Figure 4—figure supplement 1B–D ) . As a control , we showed that ACVR1-IPF was activated in response to FK506 which binds FKBP1A and releases it from the GS domain of ACVR1 ( Wang et al . , 1994 ) ( Figure 4—figure supplement 1E ) . Treatment of the MDCKII ACVR1-IPF cells with TGF-β resulted in a significant increase in fluorescence that was inhibited by SB-431542 ( Figure 4A and B; Videos 1–3 ) . Furthermore , using flow cytometry for a more quantitative approach we demonstrated that the TGF-β-induced increase in fluorescence was blocked by both SB-431542 and a TGF-β neutralizing antibody and was independent of BMP signaling , as it was unaffected by the BMP antagonist Noggin ( Figure 4C ) . Similarly , TGF-β also activated ACVR1 in NIH-3T3 ACVR1-IPF cells in a TGFBR1-dependent manner ( Figure 4—figure supplement 1F , G; Videos 4–6 ) . Although the existence of TGF-β-induced pSMAD1/5 has been known for some time , its transcriptional role has never been addressed . Earlier experiments had suggested that TGF-β-induced pSMAD1/5 could only be found in complex with pSMAD2/3 ( Daly et al . , 2008 ) , but using optimized immunoprecipitation conditions it was clear that TGF-β-induced pSMAD1/5 can also be part of pSMAD1/5–SMAD4 complexes ( Figure 5A ) . We therefore used chromatin immunoprecipitation sequencing ( ChIP-seq ) for pSMAD1/5 to explore where in the genome pSMAD1/5 binds in response to TGF-β . We also wanted to determine which SMAD complexes were primarily responsible for regulating transcription in addition to the canonical pSMAD2/3–SMAD4 complexes ( Figure 5A ) . ChIP-seq in MDA-MB-231 cells for pSMAD1/5 and SMAD3 ( as a control ) resulted in 2378 pSMAD1/5 peaks and 2440 SMAD3 peaks identified in response to TGF-β after filtering ( Figure 5—source data 1 , sheet 1 ) . The majority of the pSMAD1/5 peaks ( 2287 ) were also bound by SMAD3 . To identify binding sites preferentially bound by pSMAD1/5 versus SMAD3 we calculated the ratio of the number of tags in the pSMAD1/5 peaks versus the SMAD3 peaks , and focused on the 100 peaks with the highest pSMAD1/5:SMAD3 tag ratio ( Figure 5—source data 1 , sheet 2 ) . Interrogating the nearest genes to these peaks we found a significant enrichment of both TGF-β and BMP target genes ( Figure 5—source data 1 , sheets 2 and 3 ) . Strikingly , 8 of the top 10 peaks flanked known BMP target genes ( ID1 , ID3 , ID4 , ATOH8 , BIRC3 ) ( Figure 5B; Figure 5—figure supplement 1A; Figure 5—source data 1 , sheet 2 ) ( Grönroos et al . , 2012 ) . In contrast , classical TGF-β target genes like JUNB , BHLHE40 , PMEPA1 , SERPINE1 ( Levy and Hill , 2005 ) were not in this top 100 list , but were amongst those with the highest enrichment for SMAD3 ( Figure 5B; Figure 5—figure supplement 1A; Figure 5—source data 1 , sheet 1 ) . Using ChIP-qPCR , we validated these different binding patterns ( Figure 5C; Figure 5—figure supplement 1A ) . For pSMAD1/5 , the binding in response to TGF-β was transient , peaking at 1 hr and thereafter decreasing , whilst SMAD3 binding at JUNB and PMEPA1 was sustained . A subset of the peaks were also validated in BT-549 cells ( Figure 5—figure supplement 1B ) . We performed motif enrichment analyses on the top 50 and 100 peaks with the highest pSMAD1/5:SMAD3 tag ratio . In both cases , a SMAD1/5 binding motif GGCGCC was found ( Figure 5D and E; Figure 5—figure supplement 1C ) ( Gaarenstroom and Hill , 2014 ) . In addition , in the top 50 peaks the composite SMAD1/5–SMAD4 site was clearly identified ( GGCGCC ( N5 ) GTCT ) ( Gaarenstroom and Hill , 2014; Morikawa et al . , 2011 ) ( Figure 5—figure supplement 1C ) , with a slightly more degenerate version being present in the top 100 peaks ( Figure 5D ) . This strongly suggests that TGF-β-induced SMAD1/5–SMAD4 complexes are responsible for regulating the genes with the highest enrichment of pSMAD1/5 . The enrichment of pSMAD1/5 on the ID genes in response to TGF-β suggests that they are bona fide target genes of this arm of TGF-β signaling . We confirmed this using siRNAs to deplete specific SMADs . TGF-β induction of ID1 and ID3 in MDA-MB-231 cells depended on SMAD1/5 and SMAD4 , but not SMAD3 ( Figure 5—figure supplement 2A and B ) . In contrast , the induction of JUNB required SMAD3 and SMAD4 , but was independent of SMAD1/5 ( Figure 5—figure supplement 2A and B ) . We further corroborated these observations using the drug dosing strategy that selectively inhibits SMAD1/5 phosphorylation in response to TGF-β ( Figure 1D ) . The combination of low-dose SB-431542 and LDN-193189 greatly decreased ID gene induction without impacting on the induction of JUNB in both MDA-MB-231 and NMuMG cells ( Figure 5—figure supplement 2C and D ) . The induction of target gene expression was also examined after treatment of cells with TGF-β3WW or TGF-β3WD . As expected both TGF-β ligands induced the expression of the IDs and JUNB ( Figure 5—figure supplement 2E ) . The results in this section reveal that pSMAD1/5–SMAD4 complexes formed in response to TGF-β are responsible for regulating the genes with the highest enrichment of pSMAD1/5 , and that the IDs are major early downstream targets . The ID proteins have been implicated in many processes involved in oncogenesis ( Lasorella et al . , 2014 ) , and importantly , ID1 was shown to be upregulated by TGF-β in tumor cells isolated from pathological pleural fluids from patients with ER− and ER+ metastatic breast cancer , and also in patient-derived glioblastomas ( Anido et al . , 2010; Padua et al . , 2008 ) . Since we have now shown that the pSMAD1/5 arm of TGF-β signaling is responsible for TGF-β-induced ID1 induction , this prompted us to explore further the biological relevance of the pSMAD1/5 arm of TGF-β signaling in oncogenic processes , and to gain a comprehensive view on the relative contribution of this arm of signaling to longer term TGF-β responses . We decided to focus on the process of EMT , as this is a key step in tumorigenesis that confers a migratory phenotype , acquisition of stem cell properties and resistance to chemotherapeutic agents ( Ye and Weinberg , 2015 ) . For these studies , we primarily used the NMuMG cell model , as we have shown above that these cells show a robust phosphorylation of SMAD1/5 in response to TGF-β and are well known to undergo a TGF-β-induced EMT within 48 hr ( Piek et al . , 1999 ) . CRISPR/Cas9 was used to generate clones of NMuMG cells deleted for SMAD1 and SMAD5 ( Figure 6A; Figure 6—figure supplement 1A–C ) . We compared the TGF-β-induced transcriptome at 48 hr of the parental clone with one deleted for SMAD1/5 using RNA-sequencing ( RNA-seq ) . Of the 5798 genes that are significantly up- or down-regulated by TGF-β in this time frame we found that approximately a quarter ( 1398 ) were dependent on the SMAD1/5 branch of signaling ( see Materials and methods for the cut-offs used ) ( Figure 6—source data 1 , sheets 1 and 2 ) . This demonstrates that this arm of TGF-β signaling plays a crucial role in long term downstream transcription responses . To corroborate the RNA-seq results , we validated a subset of them by qPCR , measuring levels of mRNA over time in response to TGF-β ( Figure 6—figure supplement 2 ) . Gene set enrichment analysis revealed that the TGF-β target genes that depend on this arm of signaling were involved in processes such as regulation of the cytoskeleton , focal adhesions , adherens and tight junctions , as well as EMT ( Figure 6—source data 1 , sheet 3 ) . We therefore next investigated whether TGF-β-induced EMT required SMAD1/5 signaling . Using delocalization of the adherens junction marker CDH1 ( also called E-Cadherin ) together with loss of the tight junction marker TJP1 ( also called ZO-1 ) as a measure of EMT , we could readily demonstrate that signaling through SMAD1/5 was crucial for this process in two separate ΔSMAD1/5 clones ( Figure 6B; Figure 6—figure supplement 1D ) . In addition , we observed that two mesenchymal markers , Acta2 ( also called smooth muscle actin ) and Fn1 were more weakly induced in the ΔSMAD1/5 clone compared with the wild-type ( Figure 6—figure supplement 2 ) . We also used an siRNA knockdown approach , and showed that EMT was dependent on SMAD1/5 , SMAD4 and SMAD3 , but independent of SMAD2 ( Figure 6—figure supplement 3A and B ) . Furthermore , treatment of the cells with the BMP type I receptor inhibitor , LDN-193189 also inhibited EMT either alone or when combined with low-dose SB-431542 which we have shown is sufficient to inhibit TGF-β-induced SMAD1/5 signaling , but not signaling through SMAD2/3 ( Figure 6C and D; Figure 6—figure supplement 3C ) . Moreover , DMH1 , another BMP type I receptor inhibitor , had a similar effect ( Figure 6—figure supplement 3C and D ) . Finally , to confirm that the dependence of TGF-β-induced EMT on SMAD1/5 signaling was not unique to NMuMG cells , we used another mouse mammary cell line , EpRas that also undergoes a TGF-β-induced EMT ( Daly et al . , 2010; Grünert et al . , 2003 ) . SMAD1/5 signaling in this line was also essential for EMT ( Figure 6E and F ) . Thus , we conclude that TGF-β-induced EMT requires the SMAD1/5 arm of the signaling pathway , as well as the canonical pathway through SMAD3 . Taking our ChIP-seq and RNA-seq analyses together , we found that the ID genes are major early transcriptional targets of the SMAD1/5 arm of the TGF-β pathway . Of these , ID1 was the prominent family member up-regulated by TGF-β in NMuMGs ( Figure 7—figure supplement 1A ) . We hypothesized that the dependency on the SMAD1/5 arm of the TGF-β pathway could reflect a requirement of ID1 for EMT . We tested this by knocking down Id1 with siRNAs , both as a pool and as individual siRNAs and found that cells depleted of ID1 were indeed unable to undergo TGF-β-induced EMT ( Figure 7A and B; Figure 7—figure supplement 1B and C ) . Thus , we conclude that TGF-β-induced up-regulation of ID1 is essential for EMT .
Here , we have defined both the mechanism whereby TGF-β induces the phosphorylation of SMAD1/5 , and its functional role . We have shown that two type I receptors are required , the canonical TGF-β receptor TGFBR1 , and additionally , one of the classical BMP type I receptors , ACVR1 . Using in vitro kinase assays , an optogenetic approach and an ACVR1 receptor fluorescent biosensor , we have uncovered a new mechanism for receptor activation whereby one type I receptor activates another . We show that in response to TGF-β , TGFBRI phosphorylates and activates ACVR1 , which phosphorylates SMAD1/5 . To address the functional significance of this arm of TGF-β signaling , we used genome-wide ChIP-seq and RNA-seq and show that approximately a quarter of the TGF-β-regulated transcriptome is dependent on SMAD1/5 , with major early targets being the ID transcriptional regulators . Finally , we have also demonstrated that the SMAD1/5 pathway is essential for TGF-β-induced EMT , and this reflects a requirement for ID1 . Taking these results together with previous work ( Liu et al . , 2009; Daly et al . , 2008 ) , we propose a model of combinatorial signaling that is essential for the TGF-β cellular program ( Figure 7C ) . In most cells tested the induction of pSMAD1/5 is more transient than the pSMAD2/3 induction ( Liu et al . , 2009; Daly et al . , 2008 ) . Thus , the initial transcriptional program is regulated by both SMAD pathways and is refined at later time points by the SMAD2/3 pathway . Therefore , the full TGF-β-induced transcriptional program requires combinatorial signaling via both SMAD pathways . With respect to the functional relevance of TGF-β-induced SMAD1/5 phosphorylation , we have now shown that complete EMT requires both SMAD pathways . TGF-β-induced anchorage-independent growth , migration and invasion have also been shown to require SMAD1/5 signaling , whilst TGF-β-induced growth arrest is only dependent on SMAD2/3 signaling ( Liu et al . , 2009; Daly et al . , 2008 ) ( Figure 7C ) . Since we have now demonstrated that TGF-β induces the formation of SMAD1/5–SMAD4 complexes that regulate canonical BMP target genes , it is important to ask what discriminates TGF-β signaling from BMP signaling as it is well known that BMP and TGF-β functional responses are distinct ( Itoh et al . , 2014; Miyazono et al . , 2010 ) . The answer lies in the combinatorial signaling , and likely also in the signaling dynamics . In contrast to TGF-β , BMP stimulation leads to a sustained phosphorylation of SMAD1/5 in the absence of SMAD2/3 activation ( Grönroos et al . , 2012; Daly et al . , 2008 ) . As a result , although the gene expression program downstream of BMP shares some common targets with that downstream of TGF-β at early time points , it will be completely distinct at later time points as a result of the sustained SMAD1/5 signaling and the absence of SMAD2/3-driven transcription ( Figure 7C ) . We have shown that two classes of type I receptors are necessary for TGF-β-induced SMAD1/5 phosphorylation , the canonical TGF-β receptor , TGFBR1 and one of the BMP type I receptors , of which we have focused on ACVR1 . Our results demonstrate that the kinase activity of TGFBR1 is essential for activation of ACVR1 , whereas the kinase activity of ACVR1 is necessary to phosphorylate SMAD1/5 . Surprisingly , we found that inhibition of TGF-β-induced SMAD1/5 phosphorylation by LDN-193189 , which inhibits the BMP type I receptors , is incomplete , even though the same LDN-193189 concentration is sufficient to inhibit BMP-induced SMAD1/5 phosphorylation . This same result was also previously seen when the BMP type I receptor inhibitor dorsomorphin was used ( Daly et al . , 2008 ) . A complete inhibition of TGF-β-induced pSMAD1/5 is achieved by combining LDN-193189 with a sub-optimal dose of SB-431542 . This is likely explained by the fact that LDN-193189-inhibited ACVR1 is still able to efficiently recruit SMAD1/5 , where it may be inefficiently phosphorylated by TGFBR1 , which is sensitive to the sub-optimal dose of SB-431542 . The requirement for two distinct type I receptors fits well with what was shown for TGF-β responses in endothelial cells , where ACVLR1 and TGFBR1 were both required ( Goumans et al . , 2003; Goumans et al . , 2002 ) . Our optogenetic experiments revealed that activated TGFBR1 phosphorylates and activates ACVR1 in vivo . We previously hypothesized that TGFBR1 and ACVR1 could be in the same receptor complex ( Daly et al . , 2008 ) , but our use of the mutant TGF-β3 ligands here indicated that these two type I receptors are not part of an obligate heterotetrameric receptor , but rather that TGFBR1 , activated by TGFBR2 as a result of TGF-β stimulation can phosphorylate and activate ACVR1 in the membrane as a result of receptor clustering ( Figure 7C ) . We were surprised to see in our optogenetic experiments that light-induced dimers of the activated kinase domain of TGFBR1 were much more active than the monomeric domains , as this suggests that TGFBR1 is able to autophosphorylate and auto-activate in the absence of type II receptors , if brought into close proximity . In fact , a similar observation was made in early studies using chimeric receptors with the extracellular domain of the erythropoietin receptor and the intracellular domain of constitutively active TGFBR1 ( Luo and Lodish , 1996 ) . This chimeric receptor could only mediate a growth arrest after stimulation with erythropoietin , indicating that clustering was important for receptor activity in vivo . We and others have observed that in most cell types TGF-β-induced SMAD1/5 phosphorylation is transient compared with SMAD2/3 phosphorylation ( Daly et al . , 2008; Liu et al . , 2009; Wrighton et al . , 2009 ) . Using pSMAD2 as a readout , we previously showed that pSMAD2 levels attenuate over time , and remain at a low steady state level that depends on receptors replenishing the cell surface , for as long as ligand is available ( Vizán et al . , 2013 ) . Our demonstration that levels of fluorescence of the ACVR1-IPF biosensor steadily increase over a number of hours indicates that ACVR1 can also be continuously activated for as long as ligand is present . We have shown that the transience of SMAD1/5 phosphorylation requires new protein synthesis , indicating that SMAD1/5 phosphorylation is likely to be actively terminated by an inhibitor induced by the pathway . Given the prolonged activation of ACVR1-IPF in response to ligand , we hypothesize that such an inhibitor is unlikely to target the receptors , but might be a TGF-β-induced phosphatase that targets phosphorylated SMAD1/5 directly . The transience of SMAD1/5 phosphorylation is not a defining characteristic of this arm of TGF-β signaling as BT-549 breast cancer cells exhibit a more sustained response , which is even more pronounced when the cells are grown as spheres . Comparing TGF-β target genes in BT-549s versus MDA-MB-231s where the response is transient , might shed light on the identity of the putative inhibitor . Our ChIP-seq analysis demonstrates for the first time that TGF-β-induced pSMAD1/5 accumulates in the nucleus and binds to chromatin . These experiments revealed that the peaks with the highest pSMAD1/5 enrichment flanked classical BMP target genes , such as ID1 , ID3 and ATOH8 . Analysis of the binding sites led us to the discovery that the SMAD complexes responsible for inducing these target genes downstream of TGF-β were pSMAD1/5–SMAD4 complexes . The ChIP-seq analysis also revealed widespread co-binding of pSMAD1/5 and SMAD3 , which was surprising . For the classical BMP targets , the ratio of pSMAD1/5:SMAD3 in the peaks was high , whereas at classical TGF-β targets like JUNB , PMEPA1 , SERPINE1 and BHLHE40 ( Kang et al . , 2003; Levy and Hill , 2005 ) , this ratio was less than 1 . We do not fully understand the functional significance of the pSMAD1/5 and SMAD3 co-binding . We previously demonstrated that at least in some contexts , pSMAD3–pSMAD1/5 complexes are inhibitory ( Grönroos et al . , 2012 ) , and this is evident in the work presented here for ID3 induction . However , for JUNB we found that knockdown of SMAD1/5 had no effect on TGF-β-induced transcription , suggesting that pSMAD1/5 is not contributing to its transcriptional regulation . This may also be true of other genes with a similar pattern of SMAD3/pSMAD1/5 binding . We have now shown that SMAD1/5 signaling in response to TGF-β is required for a complete TGF-β-induced EMT in NMuMG cells and in EpRas cells . This accounts for a previously unexplained observation that overexpression of dominant negative ACVR1 in NMuMGs caused a partial loss of EMT in response to TGF-β ( Miettinen et al . , 1994 ) . In an earlier study using siRNAs we had concluded that the SMAD1/5 arm of the TGF-β pathway was not required for EMT in EpRas cells ( Daly et al . , 2008 ) . The likely explanation for this discepency is the poor SMAD1/5 knockdown we achieved in those cells compared with the very effective strategy of inhibiting this arm of TGF-β signaling using the combined small molecule inhibitors that we have employed here . We have gone on to show that TGF-β-induced ID1 is required for EMT . Importantly , although ID1 is necessary for EMT , it is clearly not sufficient , as BMPs cannot induce EMT in NMuMGs ( Kowanetz et al . , 2004 ) . Consistent with this we have also shown that the SMAD3 pathway is essential for EMT . This arm of the pathway is likely required for the induction of some or all of the so-called EMT-associated transcription factors , most notably SNAI1 , SNAI2 , ZEB1 , ZEB2 and BHLH proteins such as TWIST1 and E47 ( now called TCF3 ) , some of which are known direct TGF-β targets ( Peinado et al . , 2007; Diepenbruck and Christofori , 2016 ) . Our finding that EMT depends on TGF-β-induced ID1 expression has implications for the role of SMAD1/5 and the IDs in cancer . The prevailing view is that ID1 is downregulated by TGF-β in non-tumorigenic human epithelial lines , but upregulated by TGF-β in established tumor cell lines , as we have observed here in MDA-MB-231 and BT-549s , and also in patient-derived tumor cells ( Anido et al . , 2010; Padua et al . , 2008; Lasorella et al . , 2014 ) . Furthermore , ID proteins are overexpressed in many different tumor types and are implicated in the maintenance of tumor stem cells and for some cancer-related phenotypes ( Lasorella et al . , 2014 ) . ID1 was also found in a lung metastatic gene signature of breast cancer ( Minn et al . , 2005 ) . The role of ID1 in EMT is context dependent . In a recent study of breast cancer , ID1 was shown to be expressed in tumor cells that had already undergone an EMT , and it contributed to the growth of the primary tumor by inducing a stem cell-like phenotype . At the metastatic site , however , TGF-β-induced ID1 was proposed to induce an mesenchymal-to-epithelial transition ( MET ) by interferring with the activity of TWIST ( Stankic et al . , 2013 ) . In light of our current data it will be important to investigate in what tumor contexts ID1 is required for EMT , and more broadly how the TGF-β–SMAD1/5 pathway contributes to different aspects of tumorigenesis .
MDA-MB-231 cells were obtained from the ECACC/HPA culture collection , BT-549 cells were obtained from the Francis Crick Institute Cell Services , NMuMG cells were obtained from ATCC , MDCKII cells were obtained from Sigma ( UK ) , NIH-3T3 cells were obtained from Richard Treisman ( Francis Crick Institute ) and EpRas cells were obtained from Martin Oft and Hartmut Beug ( IMP , Vienna ) . All cell lines have been banked by the Francis Crick Institute Cell Services , and certified negative for mycoplasma . In addition , MDA-MB-231 and BT-549 cells were authenticated using the short tandem repeat profiling , while MDCKII , NIH-3T3 and EpRas cells had species confirmation at the Francis Crick Institute Cell Services . Their identity was also authenticated by confirming that their responses to ligands and their phenotype were consistent with published history . MDA-MB-231 , BT-549 , EpRas , NIH-3T3 and MDCKII cells were maintained in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% fetal calf serum ( FCS ) and 1% penicillin/streptomycin . NMuMG cells were grown in the same medium , but supplemented with 10 µg/ml insulin . MDA-MB-231 and MDCKII cells were starved overnight in OptiMEM prior to ligand stimulation; NMuMG cells were starved overnight in OptiMEM with 10 µg/ml insulin; NIH-3T3 cells were starved in DMEM with 0 . 5% FCS . For ligand stimulation experiments , BT-549 cells were plated in the mammosphere culture media ( Dontu et al . , 2003 ) ( MEBM ( PromoCell , Germany ) with B27 ( Thermo Fisher , UK ) , 20 ng/ml EGF ( PeproTech , UK ) , 20 ng/ml bFGF ( PeproTech ) and 4 μg/ml heparin ( Sigma ) . All recombinant ligands were reconstituted in 4 . 4 mM HCl supplemented with 0 . 1% BSA . Cells were treated with recombinant TGF-β1 ( PeproTech , 100–21C; 2 ng/ml ) , BMP4 ( PeproTech , 120-05ET; 20 ng/ml ) and Noggin ( PeproTech , 250–38; 300 ng/ml ) . TGF-β3WW and TGF-β3WD were as described ( Huang et al . , 2011 ) . SB-431542 ( Tocris , UK ) was used at the concentrations indicated , SB-505124 ( Tocris ) at 10 or 50 µM , LDN-193189 ( a gift from Paul Yu ) at 1 or 0 . 5 µM , DMH1 ( Selleck Chemicals , Germany ) at 1 µM , cyclohexamide ( Sigma ) at 20 µg/ml and actinomycin D ( Sigma ) at 1 µg/ml . For TGF-β blocking experiments , the pan-TGF-β blocking antibody ( 1D11 ) and the control antibody ( 13C4 ) were used at 30 µg/ml ( Nam et al . , 2008 ) . From the wild-type NMuMG cells , a parental clone was selected that expressed robust junctional markers ( TJP1 and CDH1 ) and underwent an efficient EMT in response to TGF-β . Two guide RNAs ( see Key Resources Table ) targeting the MH1 domain ( SMAD1 ) and MH2 domain ( SMAD5 ) were expressed from the plasmid pSpCas9 ( BB ) −2A-GFP ( PX458 ) ( Ran et al . , 2013 ) and used to knock out SMAD1 and SMAD5 . NMuMG parental clone cells were simultaneously transfected with both plasmids , sorted for GFP expression , plated as single cells in 96-well plates and screened by sequencing to verify mutations in SMAD1 and SMAD5 . Two knockout clones , ΔSMAD1/5 clone 1 and 2 , were used in these studies . The same parental clone of NMuMG cells was also used to generate a line knocked out for ACVR1 and BMPR1A . The strategy was as described for the SMAD1/5 knockout and the guides are given in the Key Resources Table . The InversePericam FKBP1A ( IPF ) fusion protein was amplified by PCR from the pCS2+zALK3 IPF ( Michel et al . , 2011 ) and cloned in-frame downstream of the human ACVR1 cDNA sequence in the pcDNA3 . 1 Hygro ( + ) vector ( Thermo Fisher ) . MDCKII and NIH-3T3 cells were transfected with the ACVR1-IPF construct and selected with 400 µg/ml hygromycin or 40 µg/ml hygromycin , respectively . After selection , cells were FACS sorted for GFP expression . MDCKII ACVR1-IPF cells were maintained as a pool , while a single clone was isolated for NIH-3T3 cells . To test the functionality of ACVR1-IPF , NMuMG cells knocked out for ACVR1 and BMPR1A were transfected with empty pcDNA3 . 1 Hygro ( + ) , ACVR1-IPF or FLAG-ACVR1 ( Daly et al . , 2008 ) as a positive control , and activity was monitored by their ability to induce phosphorylation of SMAD1/5 . The general design of the Opto receptors was as previously described ( Sako et al . , 2016 ) . Opto-TGFBR1* and Opto-ACVR1 were generated by overlapping PCR ( Horton et al . , 1990 ) to include a N-terminal myristyolation domain , the intracellular domain of either human TGFBR1 ( residues 149–503 ) or human ACVR1 ( residues 147–509 ) , a light-oxygen-voltage ( LOV ) domain from Vaucheria frigida ( Takahashi et al . , 2007 ) and a C-terminal HA-tag and cloned into the pCS2 expression plasmid ( see Supplementary file 1 and 2 ) . In the case of TGFBR1 , the T204D point mutation was introduced that renders the kinase constitutively active ( Wieser et al . , 1995 ) , thus generating the construct Opto-TGFBR1* . A kinase dead version of Opto-TGFBR1 was also generated in which K232 was mutated to R ( Wrana et al . , 1994 ) . Furthermore , the GS-domain of ACVR1 ( 189TSGSGS194 ) was mutated to VAGAGA to generate Opto-ACVR1 GS-mut . NIH-3T3 cells were transfected with a total of 2 μg of plasmid DNA that included either 5 ng of GFP-SMAD3 ( Nicolás et al . , 2004 ) or 25 ng of Flag-SMAD1 ( Lechleider et al . , 2001 ) alone or in combination with 25 ng of Opto-TGFBR1* and/or 50 ng of Opto-ACVR1 ( WT or GS-mut ) . We co-transfected the SMADs with the Opto-receptors to increase the range of the assay . Twenty-four hours post-transfection , cells were starved overnight in DMEM with 0 . 5% FCS . Cells were then left untreated or pre-treated with 0 . 5 μM LDN-193189 or 50 μM SB-505124 and then exposed to blue light from an LED array for 1 hr at 37°C in a humidified incubator . Control cells ( i . e . in the dark ) were wrapped in aluminium foil and placed in the same incubator . All siRNAs were purchased from Dharmacon/GE Health Care Life Sciences ( UK ) and are listed in Supplementary file 3 . MDA-MB-231 and NMuMG cells were transfected with siRNAs at a final concentration of 20 nM with Interferin ( Polyplus , France ) . Twenty four hours post-transfection , cells were starved overnight , and the following day cells were treated with TGF-β or BMP4 for 1 hr and RNA and/or protein extracted . NMuMG cells were also treated with TGF-β for a further 24–48 hr to assess the effects of target gene knockdown on EMT . NMuMG or EpRas cells were plated on glass coverslips in six-well plates ( 200 , 000 or 75 , 000 cells , respectively ) . For NMuMG cells treated with small molecule inhibitors , the media was changed the day after plating to OptiMEM with 10 µg/ml insulin and the cells treated with 2 ng/ml TGF-β alone or in combination with 0 . 125 µM SB-431542 , 1 µM LDN-193189 or DMH1 for the durations indicated . For knockdown experiments , NMuMG cells were transfected the day after plating with the indicated siRNAs . Twenty-four hours after transfection , the media was changed to OptiMEM with 10 µg/ml insulin and the following day , cells were treated with TGF-β for the durations indicated . For EpRas , cells were treated with 2 ng/ml TGF-β alone or in combination with 0 . 125 µM SB-431542 and 1 µM LDN-193189 the day after plating . EpRas cells were then split and re-plated at the initial splitting density in the presence of 2 ng/ml TGF-β alone or in combination with SB-431542 and LDN-193189 every 3 days . All primary and secondary antibodies used are listed in the Key Resources Table . Western blots using whole cell extracts and immunoprecipitations followed by western blotting were as previously described ( Germain et al . , 2000; Daly et al . , 2008 ) . Indirect immunofluorescence of the ACVR1-IPF was performed after fixing cells in 4% formaldehyde for 5 min . Indirect immunofluorescence for CDH1 and TJP1 was performed after fixation in methanol:acetone ( 1:1 ) as previously described ( Nicolás and Hill , 2003 ) . Nuclei were counter stained with DAPI ( 0 . 1 µg/ml ) . Imaging was performed on a Zeiss Upright 780 confocal microscope . Z-stacks were acquired for all channels and maximum intensity projection images are shown . Live cell imaging was performed for MDCKII ACVR1-IPF and NIH-3T3 ACVR1-IPF cells on a Zeiss Invert 780 confocal microscope . Cells were plated on 35 mm MatTek dishes ( MatTek , Ashland , MA , USA ) and starved overnight in phenol-free , HEPES-buffered DMEM with 0 . 5% FCS . During imaging , the temperature was maintained at 37°C . Data were acquired every 15 min over a time course . At each time point , a z-stack was acquired , and maximum intensity z-projections were quantified with ImageJ . MDCKII ACVR1-IPF and NIH-3T3 ACVR1-IPF cells were treated with ligand ± inhibitors . Twenty four hours post treatment , cells were trypsinized , washed and analyzed for GFP/YPF fluorescence on a LSRII flow cytometer ( BD Biosciences , San Jose , CA , USA ) , gated for viable , single cells . Treatment with FK506 ( Sigma ) was performed for 4 hr prior to analysis . Recombinant SMAD proteins were expressed in E . coli and purified as previously described ( Ross et al . , 2006 ) . Recombinant intracellular domains of ACVR1 , BMPR1A and TGFBR1 which were expressed in insect cells were purchased from Carna Biosciences Inc ( Japan; see Key Resources Table ) . Radioactive kinase reactions were performed with varying amounts of receptor ( 25–200 ng ) at 37°C for 1 hr in a 20 µl reaction volume with 50 mM Tris-Cl ( pH 7 . 5 ) , 50 mM NaCl , 5 mM MnCl2 ( ACVR1 and TGFBR1 ) or MgCl2 ( BMPR1A ) , 16 . 5 nM 32P-γ-ATP ( Perkin Elmer , UK; NEG502A500UC ) and either 200 µM or 50 µM cold ATP . Substrates were either the receptors themselves ( autophosphorylation ) or 2 µg of recombinant SMAD proteins . Reactions were stopped by adding Laemmli sample buffer and heating to 95°C for 5 min . Proteins were resolved on a NuPAGE Novex 4–12% Bis-Tris gradient gel ( Thermo Fisher ) and stained with Colloidal Blue ( Thermo Fisher ) . Gels were destained , dried and radioactivity measured by autoradiography . To map phosphorylated residues on SMAD1 , radioactive kinase reactions were performed in triplicate with 200 ng ACVRI , 2 µg recombinant SMAD1 , 200 µM cold ATP , 0 . 33 µM 32P-γ-ATP . For phospho-residue mapping , 32P-labeled SMAD1 was digested with trypsin , the peptides were resolved by HPLC with an acetonitrile gradient and the 32P-labeled peptides eluted . Edman sequencing and mass-spectrometry ( Orbitrap Classic , Thermo Fisher ) were then used to confirm phospho-residues , as described previously ( Campbell and Morrice , 2002 ) , with the addition of multi-stage activation during the MS2 analysis . Four million MDA-MB-231 or BT-549 cells were plated; 24 hr later , cells were starved overnight and the following day treated with TGF-β or BMP4 . One 15 cm plate was used per immunoprecipitation . Chromatin immunoprecipitations , ChIP-seq library preparation , next generation sequencing and data analysis were performed in biological duplicate essentially as previously described ( Coda et al . , 2017 ) . In brief , ChIP-seq was performed on an Illumina HiSeq2500 ( Illumina , San Diego , CA , USA ) generating 50 bp single end reads . Reads were aligned to the human GRCh37/hg19 genome assembly using BWA version 0 . 6 ( Li and Durbin , 2009 ) with a maximum mismatch of 2 bases . Picard tools version 1 . 81 ( http://sourceforge . net/projects/picard/ ) was used to sort , mark duplicates and index the resulting alignment bam files . Normalized tdf files for visualization purposes were created using IGVtools software ( Robinson et al . , 2011 ) ( http://software . broadinstitute . org/software/igv/ ) by extending reads by 50 bp and normalizing to 10 million mapped reads per sample . Peaks were called by comparing stimulated samples to the respective untreated samples using MACS version 1 . 4 . 2 ( Zhang et al . , 2008 ) , using mfold change parameters of between 5 and 30 . Peaks called by MACS were annotated using the annotatepeaks command in the Homer software ( Heinz et al . , 2010 ) ( http://homer . salk . edu/homer/ ) . Peaks with less than 20 tags in the pSMAD1/5 IP after TGF-β treatment or less than 30 tags in the SMAD3 IP after TGF-β treatment were excluded from the analysis . In addition , peaks that had less than 1 tag per 10 bp in either of the above conditions were also excluded . Finally a ratio was taken between the number of tags in the pSMAD1/5 IP and the number of tags in the SMAD3 IP after TGF-β treatment to determine the top 100 peaks with preferential SMAD1/5 binding . Of these , the top 50 peaks with the highest density of tags per 10 bases in the pSMAD1/5 IP after TGF-β treatment were used for more refined motif enrichment analysis and gene annotation . Motif enrichment was performed using MEME ( http://meme-suite . org/ ) with default parameters ( zero or one occurrence per sequence , motifs between 6 and 50 bases in width ) . NMuMG parental and ΔSMAD1/5 clone 1 were plated , starved the next day in OptiMEM with 10 µg/ml insulin and treated for a further 48 hr with 2 ng/ml TGF-β . Total RNA was extracted as previously described ( Grönroos et al . , 2012 ) , DNase I ( Qiagen , Germany ) treated and cleaned up with RNeasy columns ( Qiagen ) . Biological triplicate libraries were prepared using the TruSeq RNA Library Prep Kit ( Illumina ) and were single-end sequenced on an Illumina HiSeq 2500 platform . Sequencing yield was typically ~80 million strand-specific reads per sample . The RSEM package ( version 1 . 2 . 31 ) ( Li and Dewey , 2011 ) in conjunction with the STAR alignment algorithm ( version 2 . 5 . 2a ) ( Dobin et al . , 2013 ) was used for the mapping and subsequent gene-level counting of the sequenced reads with respect to Ensembl mouse GRCm . 38 . 86 version genes . Normalization of raw count data and differential expression analysis was performed with the DESeq2 package ( version 1 . 10 . 1 ) ( Love et al . , 2014 ) within the R programming environment ( version 3 . 2 . 3 ) ( R Development Core Team , 2009 ) . Genes were first identified as differentially expressed in the parental clone if they had more than 10 reads in either the untreated or TGF-β treated samples and a fold change between untreated and TGF-β induced of > 1 . 5 or < 0 . 75 and FDR < 0 . 05 . An interaction contrast was then used to determine differentially regulated genes after TGF-β treatment in the parental clone versus ΔSMAD1/5 clone 1 . The resulting gene lists ranked by the Wald statistic were used to look for pathway and biological process enrichment using the Broad’s GSEA Tool ( Subramanian et al . , 2005 ) . Genes with a fold difference between the two clones after TGF-β treatment of > 1 . 5 or < 0 . 75 and an FDR < 0 . 05 were judged to be dependent on SMAD1/5 . The ChIP-seq data have been submitted to the NCBI Gene Expression Omnibus ( GEO ) under the accession number GSE92443 . The RNA-seq data has been submitted to GEO under the accession number GSE103372 . Oligonucleotides used are listed in Supplementary file 3 . Total RNA extraction and reverse transcription were performed as previously described ( Grönroos et al . , 2012 ) . The cDNA was diluted 10-fold and then used for quantitative PCR ( qPCR ) . All qPCRs were performed with Express Sybr Greener ( Thermo Fisher ) with 300 nM of each primer and 2 µl of diluted cDNA or eluted immunoprecipitated chromatin . Fluorescence acquisition was performed on a 7500 FAST machine ( Thermo Fisher ) . Quantification for relative gene expression was done using the comparative Ct method with target gene expression normalized to GAPDH . Quantification for ChIPs was performed using a standard curve and presented normalized to input . Western blots , immunofluorescence experiments and ChIP-PCRs are representative of at least two biological replicate experiments . All qPCRs are the mean and SEM of three independent biological experiments except gene expression after actinomycin D treatment and stimulation with TGF-β3WW and TGF-β3WD and validation of RNA-sequencing results that are a representative of two independent experiments . Statistical analyses were performed with the unpaired Students T-Test , *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 , ns , non significant . | Cells communicate with other cells via signaling molecules to coordinate their activities . Signals released from one cell can influence the behavior of neighboring cells . Signaling molecules belonging to the TGF-β family play crucial roles in animals . For example , these molecules guide the formation of tissues and organs and help maintain them throughout the animal’s adult life . Abnormal regulation of TGF-β family signaling can fuel the growth of cancer cells and also contribute to other diseases in humans . Molecules in the TGF-β family bind to and bring together specific receptors on the surface of the receiving cell . This allows the receptors to activate so-called SMAD proteins within that cell . Activated SMADs move to the cell’s nucleus , where they regulate the activity of target genes . This in turn changes how the cell behaves . The best-studied member of the TGF-β family is TGF-β itself . It is well known to activate two particular SMAD proteins called SMAD2 and SMAD3 . Recent research showed that TGF-β could also activate two different SMAD proteins , SMAD1 and SMAD5 . However , it was not understood how this was achieved , or what its biological consequences were . Ramachandran et al . set out to address these questions in mouse and human cells grown in the laboratory . The experiments showed that , in addition to its known dedicated receptors , TGF-β also requires a third receptor to activate SMAD1 and SMAD5 . Also , TGF-β signaling leads to changes in the activity of several thousand genes , and approximately a quarter of them require signaling via SMAD1 and SMAD5 . Further work showed that SMAD1 and SMAD5 are needed for a process called epithelial-to-mesenchymal transition . This is a normal part of animal development , and is also a common feature of cancer cells , allowing them to spread to distant parts of the body . Understanding of how TGF-β signaling works in more detail may reveal new ways to target this pathway to treat diseases like cancer . The next step is to see how the signaling via SMAD1 and SMAD5 contributes to different aspects of cancer development . | [
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] | 2018 | TGF-β uses a novel mode of receptor activation to phosphorylate SMAD1/5 and induce epithelial-to-mesenchymal transition |
During vertebrate heart development , two progenitor populations , first and second heart fields ( FHF , SHF ) , sequentially contribute to longitudinal subdivisions of the heart tube ( HT ) , with the FHF contributing the left ventricle and part of the atria , and the SHF the rest of the heart . Here , we study the dynamics of cardiac differentiation and morphogenesis by tracking individual cells in live analysis of mouse embryos . We report that during an initial phase , FHF precursors differentiate rapidly to form a cardiac crescent , while limited morphogenesis takes place . In a second phase , no differentiation occurs while extensive morphogenesis , including splanchnic mesoderm sliding over the endoderm , results in HT formation . In a third phase , cardiac precursor differentiation resumes and contributes to SHF-derived regions and the dorsal closure of the HT . These results reveal tissue-level coordination between morphogenesis and differentiation during HT formation and provide a new framework to understand heart development .
The heart is the first organ to form and function during embryonic development . At embryonic day ( E ) 7 . 5 , cardiac precursors in the splanchnic mesoderm ( mesoderm apposed to the endoderm ) differentiate into cardiomyocytes by assembling the contractile sarcomere machinery ( Tyser et al . , 2016 ) and form a bilateral structure known as the cardiac crescent ( cc ) in the mouse . Concomitant with foregut invagination , the cc swings inwards to become placed underneath the developing head folds . By a complex morphogenetic process , the cc subsequently transforms into an early heart tube ( HT ) initially opened dorsally , which by E8 . 25 has transformed into a closed and beating linear HT , also known as the primitive HT ( Evans et al . , 2010; Kelly et al . , 2014 ) . The cc and early HT mainly give rise to the left ventricle ( Zaffran et al . , 2004 ) . The right ventricle ( RV ) , the outflow tract and most of the atria derive instead from cardiac progenitors located dorso-medially to the cc in the splanchnic mesoderm , that are progressively recruited at the poles of the HT at subsequent developmental stages ( Cai et al . , 2003; Kelly et al . , 2001; Mjaatvedt et al . , 2001; Waldo et al . , 2001; Galli et al . , 2008 ) . These findings led to the concept that cardiac mesodermal progenitors contain two populations of cells: the first heart field ( FHF ) precursors , recruited early in development to form the initial HT and mostly containing the LV primordium , and the second heart field ( SHF ) , recruited later and elongating the HT ( Buckingham et al . , 2005 ) . Clonal analysis ( Devine et al . , 2014; Lescroart et al . , 2014; Meilhac et al . , 2004a ) supports the idea that FHF and SHF precursors are two independent developmental fields with dedicated molecular pathways . Clonal analysis also showed that the SHF shares a common origin with the skeletal muscles of the head and neck within the pharyngeal mesoderm ( Lescroart et al . , 2010; Lescroart et al . , 2015 ) , further supporting differences between FHF and SHF populations . However , the existence of a common precursor between FHF and SHF was also reported in the early mouse embryo ( Meilhac et al . , 2004a ) and other views suggest that the heart would form by a continuous differentiation process from a single population of cardiac precursors and only timing of recruitment would distinguish cells of the FHF and SHF ( Abu-Issa et al . , 2004; Moorman et al . , 2007 ) . In support of the latter , the typical marker of the SHF , Islet1 ( Cai et al . , 2003 ) , is also transiently expressed in FHF precursors and must therefore be considered as widespread cardiac progenitor marker instead ( Cai et al . , 2003; Prall et al . , 2007; Yuan and Schoenwolf , 2000 ) . Whether the recruitment of cardiomyocytes from progenitors is a continuous process and how this coordinates with morphogenesis , however , has not been directly studied . This is partly because the spatial arrangement of progenitors and differentiated cardiomyocytes has so far been analyzed on fixed embryos ( Cai et al . , 2003; Später et al . , 2013 ) and the expression dynamics of genes reporting differentiation together with cell movements during HT morphogenesis have not been captured so far ( Abu-Issa , 2014 ) . Here , we report the live-imaging and 3D+t cell tracking of HT formation in whole mouse embryos . Using this method , in conjunction with an Nkx2 . 5eGFP reporter line , which provides high level of GFP in differentiated cardiomyocytes , we studied the dynamics of cardiac field differentiation . During an initial phase , FHF cardiac precursors differentiate rapidly to form a cardiac crescent , while limited morphogenesis takes place . During a second phase , no differentiation events are detected and extensive morphogenesis , including splanchnic mesoderm sliding over the endoderm , results in HT formation . Finally , using an Isl1-Cre lineage-tracing assay combined with live-imaging , we show that during a third phase , cardiac precursor differentiation resumes and contributes not only to the known SHF-derived regions but also to the dorsal aspect of the HT . These results show essential properties of FHF and SHF contribution to heart development and reveal tissue-level coordination between alternating phases of differentiation and morphogenesis during HT formation .
To assess how the initial cardiogenic region transforms into a HT and differentiates , we first analyzed Nkx2 . 5cre/+; Rosa26Rtdtomato+/-embryos , in which both cardiac precursors and cardiomyocytes are labeled ( Stanley et al . , 2002 ) . Before cc differentiation , at early head fold stage ( EHF , ~E7 . 5 ) , the cardiogenic region is visualized as a flat horse shoe-shaped tdtomato+ mesodermal layer at the rostral border of the embryo ( Figure 1A , A’ , and Video 1 ) . Figure 1—figure supplement 1 shows the criteria for embryo staging ( Downs and Davies , 1993; Lawson and Wilson , 2016 ) . In the Nkx2 . 5cre/+; Rosa26Rtdtomato+/- embryos , tdtomato labeling is also observed in the endocardium and endothelial cells ( Stanley et al . , 2002 ) but not in the endoderm ( Figure 1—figure supplement 2A , A’ ) . We next studied the distribution of Cardiac troponin T ( cTnnT ) , one of the first evident sarcomeric proteins to appear in the cardiac crescent ( Tyser et al . , 2016 ) . At EHF stage ( Figure 1B ) , while most embryos are negative for cTnnT expression , some embryos show weak cTnnT localization in subsets of cells ( Figure 1—figure supplement 3A , A’ ) . At a subsequent embryonic stage ( ~E7 . 7 ) , cTnnT signal reveals the cc , which is folding inwards . During folding , the cTnnT signal increases . cTnnT+ cells are initially columnar epithelial cells and show apical localization of the tight junction component zona-occluden-1 ( ZO-1 ) ( Figure 1—figure supplement 3B , B’ ) . During differentiation , cardiac precursors switch to a rounded shape ( Linask et al . , 1997 ) ( Figure 1C , D ) and separate from the endoderm , while maintaining a basal lamina at the endocardial side ( inset in Figure 1D and Figure 2D ) . Morphogenetic changes starting at ~E8 subsequently lead to the formation of a hemi-tube whose major axis is transversal to the embryo A-P axis . We will refer to this stage as transversal HT ( Figure 1E ) . Later , the tube adopts a more spherical shape , very similar to the linear HT but still open dorsally . We will refer to this stage as open HT ( Figure 1F ) . The HT eventually closes dorsally ( Figure 1G , red arrows in Figure 1G’’ ) and a prominent arterial pole ( prospective RV ) ( Zaffran et al . , 2004 ) becomes visible , completing linear HT formation by ~E8 . 25 ( yellow arrows in Figure 1G’’ , Figure 1H , see also Video 2 ) . To assess the overall growth of the forming HT , we measured cTnnT+ tissue volume in segmented z-stacks , at the stages described above ( Figure 1I and Figure 1—source data 1 ) . During the first phase of cardiomyocyte differentiation , the cTnnT signal expands resulting in a cardiac crescent rapidly doubling in volume ( Figure 1C’ , D’ , I ) . During the subsequent phase of morphogenesis , from cc to open HT stage , growth is less pronounced despite extensive morphological changes ( Figure 1E’ , F’ , I ) . The volume of the HT appears to increase again upon addition of the RV primordium to the arterial pole and dorsal HT closure ( Figure 1G’ , I ) . HT growth likely reflects an increase in cell number occurring during the formation of the heart tube . Cardiomyocytes are proliferative at this stage ( de Boer et al . , 2012 ) , and we can indeed observe mitotic figures in the HT ( Figure 1J ) . From this analysis , however , it is unclear how much of the growth observed is due to cardiomyocyte proliferation versus addition of new cardiomyocytes from cardiac progenitor cells located in the splanchnic mesoderm . To visualize the boundary where cardiac progenitors abut differentiating cardiomyocytes during HT morphogenesis , we immunostained Nkx2 . 5cre/+Rosa26Rtdtomato+/- embryos at the transversal-HT stage with the differentiation marker cTnnT and acquired whole-mount images ( Figure 2A ) . Cre activation of tdtomato was detected in both the FHF and SHF , as well as in the endoderm and endocardium ( see optical sections in Figure 2A’ , A’’ , F , F’ and Figure 2—figure supplement 1A ) ( Stanley et al . , 2002 ) . Cardiomyocytes are separated from the endoderm by the endocardium . In contrast , undifferentiated cardiac precursors lie medio-dorsally in direct contact with the endoderm in areas where endocardial cells are not detected ( Figure 2A’ , A’’ ) . This is confirmed by the absence of the endothelial marker CD31 ( Figure 2—figure supplement 2 ) . We then rendered in 3D the Nkx2 . 5cre-labeled lineages , including both FHF and SHF , and the cTnnT +tissues , including only the FHF/cc , which allowed visualizing the boundary between SHF and cc at the tissue level ( Figure 2A , B , B’ , Figure 2—figure supplement 1 , Video 3 and see Materials and methods – non-cardiac tdtomato signal was removed manually ) . To identify the changes associated to cardiomyocyte differentiation at the cellular level , we labeled single cells with membrane-GFP ( see Materials and methods ) . cTnnT- progenitors have an epithelial-like columnar cell shape , while the differentiated cTnnT+ cardiomyocytes are rounder and have lost the columnar epithelial organization ( Figure 2C–E and Figure 2—source data 1 ) . This is reminiscent of the cell shape transition observed in the distal outflow tract ( OFT ) at later stages of heart development , when SHF progenitor-to-cardiomyocyte differentiation takes place ( Francou et al . , 2014; Ramsbottom et al . , 2014; Sinha et al . , 2012 ) . Interestingly , some cells at the boundary zone exhibit weak cTnnT localization and yet show columnar shapes typical of mesodermal cardiac precursors ( Figure 2F–F’’’’ and red arrows in Figure 2F’’’ , F’’’’ ) . Unlike differentiated cardiomyocytes , these cells do not show rounded shapes , and therefore they may represent a transient state between progenitors and differentiated cardiomyocytes; however , the nature of such state cannot be addressed by static analysis . Differentiation of cardiac progenitors is thus accompanied by changes in cell shape and detachment from the endoderm . We next assessed the expression pattern of the Nkx2 . 5eGFP enhancer reporter line , in which GFP expression is restricted to cardiomyocytes ( Lien et al . , 1999; Prall et al . , 2007; Wu et al . , 2006 ) . To characterize this reporter line in detail , we immunostained Nkx2 . 5eGFP embryos against cTnnT , ( ) ( Figure 3A , B and Figure 3—figure supplement 1A , B ) and compared the relative intensities of cTnnT and GFP in manually segmented single cells ( Figure 3C , D Figure 3—figure supplement 2A and Figure 3—source data 1 ) . We found that the GFP level varied linearly with cTnnT level ( Figure 3D ) , although considerable variability of GPF levels was observed within each cTnnT+ and cTnnT- cell populations . Scoring of a large number of cells allowed to reproducibly identify the top 50% GFP-expressing cells as positive for cTnnT+ ( Figure 3—source data 1 ) . Genetic tracing experiments using the Nkx2 . 5cre/+;Rosa26Rtdtomato+/- line , instead show strong tdtomato level in both the FHF and SHF ( Figure 3—figure supplement 3A ) . We next characterized the boundary between cardiomyocytes and cardiac precursors in transversal HT stage embryos of the Nkx2 . 5eGFP reporter line . We measured mean fluorescent intensity in manually segmented cells at the boundary zone and found that GFP level and cTnnT signals correlate at the individual-cell level ( Figure 3B’ , E , Figure 3—figure supplement 2B , C and Figure 3—source data 1 ) . Altogether , these results indicate that the Nkx2 . 5-eGFP reporter is suitable for tracking cardiomyocytes in live-imaging and reliably identifies the top 50% GFP-expressing cells as cTnnT-positive . We next established a live-imaging method to dynamically characterize the formation of the HT in the mouse embryo ( Figure 4A ) ( Chen et al . , 2014 ) . We adapted a previously reported culture system ( Nonaka , 2009; Nonaka et al . , 2002 ) in which the whole mouse embryo is immobilized by inserting the extraembryonic region in a holder ( Figure 4C ) . After culture , embryos showed normal morphology , their hearts were beating and circulation was initiated ( Figure 4D , E and Video 4 ) . This culture system in combination with two-photon microscopy enabled the generation of high-resolution 3D+t videos ( Figure 4B and see Materials and methods ) . Maximum culture time and imaging achieved was 24 hr ( Figure 4—figure supplement 1A , n = 3 ) . However , typical acquisition times varied in most cases between 3 and 13 hr . The rate of acquisition varied in most cases between 4 and 9 min , with some exceptions depending on the specific aim of the recording ( see video legends ) . Imaging Mesp1cre/+; Rosa26Rtdtomato+/- embryos allowed tracking the anterior mesoderm ( Saga et al . , 1996; Saga et al . , 1999 ) , including cardiac lineages , from cc stage up to HT stage ( Figure 4F , F’ and Videos 5–6 ) . The time-lapse analysis provided insight on the formation of the endocardial lumen ( Figure 4F’ , F’’ ) . The endocardium is initially observed as a bilayer of cells and eventually splits into dorsal and ventral layers , which move apart from each other allowing the lumen to become visible between the layers ( see Videos 6 and 7 for confocal views and Video 8 for a bright field view ) . Thin cytoplasmic bridges between the endocardial layers persist and extend as the endocardial layers separate from each other ( see white arrows in Figure 4F’ and Video 6 ) . Heartbeat becomes detectable around this stage and circulation in the embryo is then initiated . Imaging Nkx2 . 5cre/+; Rosa26RmT/mG+/- embryos , in which Cre-recombined cells activate membrane-bound GFP , we could track cells during differentiation , as they transit from a columnar to a round shape and start contracting ( Figure 4G–G’’ , Video 7 and bright-field Video 8 ) . We found that the endocardial lumen started to appear while cc cells still remained columnar ( compare time points 2 hr 13 m and 3 hr 48 m in Figure 4G’ ) . Cell rounding is therefore unlikely to initiate the formation of the cardiovascular lumen . Interestingly , this analysis also showed that the transformation of the cc into the HT involves the antero-medial displacement of the splanchnic mesoderm ( white arrows in Figure 4F , F’ ) . This displacement promotes the folding of the cardiac crescent into a hemi-tube by bringing closer the future borders between the HT and the mesocardium ( see yellow lines in Figure 4F’ indicating the distance between the borders at different times ) . These borders coincide with the frontiers between cardiomyocytes and undifferentiated splanchnic mesoderm ( see details in Videos 7 , 17 and Figure 7A’ , A’’ ) . Interestingly , the movement of the splanchnic mesoderm is not coherent with the endoderm but a relative displacement is detected between the two layers ( Video 9 ) . Estimation of splanchnic mesoderm displacement speed toward the midline from time-lapse analyses ( Figure 4F’ , and Video 6 ) indicates a range of average speeds from 12 to 20 µm/hr ( 15 . 8 ± 2 . 4 µm/hr , mean ±SD , n = 3 , Figure 4—figure supplement 2 and Figure 4—source data 1 ) . These measurements estimate that midline convergence of the splanchnic mesoderm takes approximately 5–7 hr from the late cc stage until the open HT stage ( Figure 4—figure supplement 2 ) . Our results show the feasibility of live time-lapse analysis of mouse HT formation and reveal that splanchnic mesoderm displacement , at least in part by sliding over the endoderm , is an essential aspect of HT morphogenesis . Next , to specifically track cardiac differentiation , we used the Nkx2 . 5eGFP live reporter . We first studied the general activation pattern of this reporter in live analysis . At E7/bud stage , a faint and scattered GFP signal is detected in proximity to the yolk sack , at the anterior border of the embryo ( Figure 5—figure supplement 1A ) . At neural plate stage , just prior to the ventral folding of the embryo , the GFP signal remains weak but spreads to delineate a crescent in the anterior region of the embryo ( Figure 5—figure supplement 1A and Video 10 ) . During about 5–6 hr starting at the EHF stage , the GFP signal increases in intensity , which correlates with the previous observations on cTnnT activation ( Figure 4H , Figure 1C and Video 11 ) . From transversal to open HT ( 5–7 hr ) and from the open HT to HT ( 2–3 hr ) the GFP signal remains stable ( Figure 5—figure supplement 1B , B’ , C , C’ and Video 12 ) . We conclude that an increase in GFP level in Nkx2 . 5eGFP embryos reports cardiomyocyte differentiation . In addition , these results reveal the timing of the main phases of linear HT development; cc differentiation , formation of the open HT and dorsal closure ( Figure 4I ) . We next sought to track the trajectories and differentiation of individual cardiac precursors within the entire cardiogenic region by 3D+t live imaging . To this end , we used the Polr2aCreERT2 ( RERT ) allele ( Guerra et al . , 2003 ) , which provides ubiquitous tamoxifen-inducible Cre activity in combination with a Rosa26Rtdtomato reporter . We then titrated the tamoxifen dose for a labeling density that would allow single cell tracking during prolonged time-lapse analysis and combined this with the Nkx2 . 5eGFP reporter ( see materials and methods ) . Typically , for each video , we acquired z-slices every 3–5 μm achieving a total z-depth of 200 μm – with some variations depending on the stage considered- and manually tracked in 3D+t for several hours an initial population of ~50 to ~100 cells per video , which represents around 5–10% of the total number of cells present in the cc ( de Boer et al . , 2012 ) . We first tracked cells of the cardiac forming region starting at EHF stage – when cardiac precursors are undifferentiated – up to stages in which cardiomyocytes have differentiated in the cc but the transversal HT stage has not been reached yet ( Figure 5A and Figure 5—source data 1 ) . At the onset of cardiac differentiation , the cardiac crescent swings ventrally concomitant with foregut pocket formation . During this movement , we found that the relative positions of the cardiac progenitors are maintained from the initial stage through the differentiated cc ( Video 13 ) . Relative cell positions therefore remain mostly coherent as the embryonic tissues undergo this initial global movement . Differentiation events are detected in some of the tracked cells by cell shape change from columnar to rounded and by the increase in GFP signal ( see example in Figure 5B , D and Figure 5—source data 1 ) . In contrast , other tracked cells located in the splanchnic mesoderm remain in contact with the endoderm , retain a columnar shape and show low GFP level throughout the videos ( Figure 5C ) . These cells are likely to be SHF cardiac progenitors and not endocardial cells , which are not present in this area ( Figure 2—figure supplement 2A , B , B’ ) . Endocardial cells are instead present in the cardiac crescent and have typical elongated spindle-like shapes ( Figure 4F and Figure 5—figure supplement 2A ) . Next , in order to establish a fate map of the cardiac forming region at the EHF stage , we tracked back in time the population of cells that showed high GFP intensity ( top 50% ) at the end of the video ( Figure 5F , H and Figure 5—source data 1 ) . According to our previous analysis ( Figure 3D ) , these cells can reliably be assigned to cardiomyocytes . The initial location of this cell population fated to become cc cardiomyocytes delineates a crescent-shaped domain at EHF stage ( Figure 5H ) . Cells retaining lower GFP intensity level throughout the video initially localize posteriorly and medially to this crescent . Most of the cells that have high GFP levels at the final time point show low GFP levels at the initial time point and increase their GFP level over time ( Figure 5E–G and Figure 5—source data 1 ) . These results suggest that cardiomyocytes of the cc differentiate during 5–6 hr starting at the EHF stage , which is consistent with the onset of detectable cTnnT at that stage ( Figure 1B and Figure 1—figure supplement 3 ) . Cells in the cardiac mesoderm do divide during the observation time , so we identified cell division events and tracked the descendant cells . 43% of the tracked cells underwent one division during the 4–5 hr videos . To determine whether cell fate ( differentiation versus progenitor ) is allocated in the cardiogenic mesoderm at the EHF stage , we tracked GFP levels in dividing cells and their descendants . We found that most sister cell pairs show matched high or low GFP intensity levels at the end of the observation period ( 38 out of 39 , Figure 5I , Figure 5—figure supplement 3A–D and Figure 5—source data 1 ) . This observation suggests that commitment of cardiac precursors to differentiation is already established by the EHF stage and largely transmitted by lineage . We next studied cardiac differentiation dynamics during subsequent stages when the cc transforms into the HT by extensive morphogenesis . To do so , we tracked cells located in the splanchnic mesoderm and cc in Nkx2 . 5-eGFP embryos at successive periods of around 3 hr covering the 5–7 hr during which the transversal HT transforms into the open HT ( Figure 6A , B , E , Video 14 and Figure 4—source data 1 ) . This analysis revealed that the trajectories of cc cells move apart from each other over time as the tissue expands during the transition from the transverse HT to the more spherical open HT . The HT starts to beat during the observation period , especially at the later stages , and therefore , in some cases , cardiomyocyte cell shape appears distorted in single optical sections ( Figure 6F ) ; however , the GFP level could be determined . As mentioned above , antero-medial movement of the splanchnic mesoderm can be observed concomitant with the transformation of the transversal HT into the open HT ( visible also in Videos 5 and 12 ) . We found that cells with high GFP level at the initial time points –differentiated cardiomyocytes – retain rather stable GFP levels ( green tracks in Figure 6C , D , G and Figure 6—source data 1 ) . In addition , all cells that initially showed low GFP levels did not increase GFP intensity during time-lapse; thus new events of cardiac differentiation were not detectable by this approach ( red tracks in Figure 6C , D ) . Tracking cells for longer periods of time , covering from cc differentiation continuously up to the open HT stage , confirmed that cc differentiation is followed by a period of time in which no differentiation events occur ( Figure 6—figure supplement 1 , n = 19 cells tracked in one embryo ) . To confirm the absence of detectable cardiac differentiation events during this period , we next focused on the live analysis of cells located at the boundary between cardiomyocytes and undifferentiated splanchnic mesoderm . As expected , we observed GFP-low cells located adjacent to GFP-high cells in the boundary zone ( Figure 6H and Video 14 ) . Those cells retain stable GFP levels throughout the tracking time and did not increase their GFP level ( Figure 6I , boundary imaged 20 times in different locations and in six independent embryos ) . Importantly , they retain a columnar shape typical of weak cTnnT+ and cTnnT- cells located at the boundary zone ( see Figure 2F’’ and Figure 3B’ ) . Although these cells migrate antero-medially relative to the underlying more static endodermal cells ( see endodermal cell highlighted by the red arrow from t = 69 m in Video 9 ) , they do not contribute to the HT during the observation period . We confirmed this observation in longer time-lapse videos spanning 7 hr that covered the whole transition from transversal to open HT stage . Again , progenitors strictly respected the boundary with the HT throughout the entire time-lapse video ( Figure 7A–A’’’ and Video 15 , boundary imaged five times in different locations and in two independent embryos ) . All together , these data suggest that during the transformation of the cc into the dorsally open HT no cardiomyocytes are added to the HT from the SHF . These observations suggest two distinct phases of early HT formation: a first phase of differentiation of the FHF into the cc , lasting around 5 hr , and a second phase of HT morphogenesis in which the SHF progenitors remain undifferentiated , lasting around 5–7 hr . During this second phase , extensive remodeling of the cardiac crescent is concomitant with the antero-medial splanchnic mesoderm displacement . The LIM domain transcription factor Islet1 ( Isl1 ) is a cardiac progenitor marker . Its expression is transient in the precursors of the cc , while it remains expressed in SHF progenitors for an extended period ( Brade et al . , 2007; Prall et al . , 2007; Yuan and Schoenwolf , 2000 ) . Cells of the Isl1-expressing lineage detected with Cre reporters therefore contribute only scarcely to the cc , while extensively to the SHF and its derivatives ( Cai et al . , 2003; Ma et al . , 2008 ) . To test these observations in live imaging , we combined Nkx2 . 5eGFP with tracing of the Isl1 cell lineage using the Isl1cre driver and the Rosa26Rtdtomato reporter . We found that tdtomato labeling is first detectable in scarce isolated cells of the GFP+ cc during the period when the cc swings ventrally and differentiates ( from t = 2 hr 36 m to t = 4 hr in Figure 8A , B ) . In contrast , a dense tdtomato labeling appears in the GFP-low cells of the splanchnic mesoderm as the cardiac crescent fully differentiates ( from t = 2h24 m to t = 4 hr in Figure 8A and C and Video 16 ) . tdtomato is as well detected in the endoderm and endocardium ( not shown ) . Consistently with previous reports ( Cai et al . , 2003 ) , Isl1cre-induced recombination detected in live analysis is thus low in the cc and complete in the SHF . Once the cc is formed , if cells of the SHF would continuously differentiate , then regions of the forming heart tube contributed by the SHF precursors should appear densely co-labeled with both GFP and tdtomato . Live imaging shows instead that tdtomato+ cells establish a boundary with GFP+ cells , confirming no signs of differentiation of SHF precursors during the observation period ( Figure 8A , C and Video 17 ) . The live imaging , however , did not allow to unambiguously identify all cells located deep inside the live tissue at the final stages recorded . The arterial pole in particular is located deep in the embryo . It is therefore challenging to accurately track cardiac differentiation by the increase of GFP levels there ( Figure 8—figure supplement 1A–A’’ , from around 200 μm depth , see next section ) . To overcome these limitations , we fixed and immunostained embryos against cTnnT after completion of the live-imaging experiments , and imaged them by 3D confocal microscopy . No solid domains containing double-labeled cells were detected , indicating that progenitors located in the SHF did not undergo differentiation in the boundary zone from cc to open HT stage ( Figure 8D ) . These results are consistent with the single-cell tracking analysis and confirm that the SHF does not differentiate during linear HT morphogenesis . We next wanted to determine when cardiac progenitors located in the SHF start to differentiate . In order to address the timing of SHF contribution to the arterial pole , we next fixed and optically cleared Nkx2 . 5eGFP; Ils1cre/+; Rosa26Rtdtomato+/- embryos at different stages from cc up to heart looping ( n = 10 ) and assessed the appearance of GFP and tdtomato double-positive domains in the HT . In agreement with our previous observations , we found that SHF cells do not differentiate up to the open HT stage , when the dorsal seam of the heart is still open . In contrast , massive appearance of solid domains of double positive cells is observed subsequently in the fully closed HT , reinforcing our previous interpretation ( Figure 9A , B and Videos 18 , 19 ) . At this stage , the primordium of the RV has been added at the arterial pole ( Zaffran et al . , 2004; Laugwitz et al . , 2005; Moretti et al . , 2006 ) and is fully composed of double-positive cells . The dorsal seam of the HT is also densely populated by double-positive cells , indicating a contribution of precursors from the splanchnic mesoderm to the cardiomyocyte population that finalizes the dorsal closure of the linear HT . In order to capture the initiation of SHF contribution to the HT by live imaging , we first focused on the venous pole , as this region is more directly exposed than the rest of the HT and therefore more suitable for live-imaging . In videos that captured the transition from the open HT to the closed linear HT , some cells at the border between the FHF and SHF maintain low GFP levels during the first part of the recording and upregulate GFP to the level of the cardiomyocyte population as the HT forms ( Figure 8—figure supplement 2 ) . We next aimed to live-track the activation of SHF differentiation at the arterial pole . Because of the imaging limitations in this area , quantitative analysis of the GFP signal was not possible and we instead used the qualitative detection of SHF cells addition to the HT . To achieve this , we imaged an Nkx2 . 5eGFP; Ils1cre/+; Rosa26Rtdtomato+/- embryo at successive time points throughout the transition from open to linear HT and tracked tdtomato+ cells incorporation to the HT at the arterial pole . This study further confirmed that SHF differentiation at the arterial pole is initiated when the HT is about to close dorsally but not before ( Figure 9C ) . Thus , SHF cells do not differentiate during the 5–7 hr period when morphogenesis of the open HT takes place , but coordinately start differentiation at different regions of the HT during dorsal closure . Interestingly , these regions do not only include arterial and venous poles but also the dorsal seam of the HT .
Here , we established a whole-embryo live-imaging method based on two-photon microscopy that allows whole-tissue tracking at cellular resolution . By combining various genetic tracing tools , we labeled progenitor and differentiated cardiomyocytes and performed 3D cell tracking over time combined with 3D reconstruction of the HT at multiple stages . We report three distinct temporal phases of HT formation ( Figure 10 ) . During the first phase , the cc differentiates rapidly and morphogenesis , in terms of changes in the relative position of cells , is minimal . During the second phase , differentiation is not detected and morphogenetic remodeling gives rise to a dorsally open HT . During the third phase , cardiac precursor recruitment and differentiation resumes , contributing to the formation of the RV and the dorsal closure of the HT . Our results support the early establishment of distinct FHF and SHF cell populations and show that the morphogenetic changes that transform the cc into a HT largely take place in the absence of cardiac precursor differentiation . These observations indicate tissue-level coordination of differentiation and morphogenesis during early cardiogenesis in the mouse . The series of 3D reconstructions from fixed embryos was important to establish a reference staging of HT formation . This allowed us to accurately stage embryos in live experiments based on morphology and it will also be useful in the future for gene expression mapping and accurate phenotypic analysis of mutant embryos . The tissue growth pattern observed in static 3D reconstructions was insightful to suggest variability in growth rates during different phases of HT formation . Growth of the differentiated cardiac tissue is relatively paused when the cc undergoes morphogenesis to form the open HT during the second phase . This is consistent with previous studies in mouse , chick and human models showing that proliferation drops in the differentiated myocardium of the forming HT , while proliferation remains high in the splanchnic mesoderm ( van den Berg et al . , 2009; de Boer et al . , 2012; Sizarov et al . , 2011 ) . Our live analysis further showed that SHF cells do not contribute to the forming HT during the differentiation pause , which correlates with the growth rate reduction during this phase . This period coincides as well with the onset of cardiac contractility in the embryo ( Tyser et al . , 2016 ) . Following the phase of differentiation pause , growth of the HT is reinitiated by incorporation of new cells as the HT closes dorsally and the RV precursors are added at the arterial pole during the third phase . During this third phase , similarities were found in the differentiation dynamics of SHF precursors and splanchnic precursors contributing to the dorsal regions of the linear HT . The dorsal aspect of the linear HT gives rise to the inner curvature of the looped heart , which has an important contribution to non-chamber myocardium , including atrio-ventricular canal and parts of the conduction system ( Christoffels et al . , 2000 ) . Our results suggest that the late recruitment of progenitors to the dorsal HT could contribute to differences between inner curvature cardiomyocytes and the rest of the heart tube . While the live-imaging experiments were essential for the identification of the 5–7 hr hiatus between FHF and SHF differentiation , live imaging of the arterial pole during the SHF differentiation phase was challenging and was complemented by 3D reconstructions based on fixed and optically cleared embryos . These experiments confirmed the pause in differentiation during open HT formation and its reactivation during linear HT closure . In the future , it will be interesting to explore whether novel non-toxic index-matching media compatible with embryo viability ( Boothe et al . , 2017 ) may alleviate the limitations for deep cardiac imaging during late HT formation . Regarding the specification of FHF and SHF populations , previous prospective clonal analyses showed that these lineages diverge around gastrulation ( Devine et al . , 2014; Lescroart et al . , 2014 ) . In agreement with this , our tracking of cell lineages in the cardiac forming region shows that sister cells share fates to either the cardiac crescent or the SHF . In addition , the fact that cells contributing to the SHF do not differentiate during the period when the cardiac crescent transforms into the primitive heart tube may contribute to the establishment of the sharp boundary observed between left and right ventricles later in development ( Devine et al . , 2014 ) . Further studies will be required to assess how this temporal pause of cardiac differentiation is regulated . The molecular analyses of early FHF and SHF precursors suggest that intrinsic molecular differences between the two lineages appear around or shortly after gastrulation ( Lescroart et al . , 2014 ) . These intrinsic differences may contribute to the regulation of the two distinct differentiation schedules described here . These studies and our observations , however , cannot discriminate whether this lineage allocation results from intrinsic differences between these lineages or it is due to their exposure to position-specific environments , especially as in our studies sister cells remain close neighbors . Environmental cues thus could also control the sequential differentiation of FHF and SHF precursors . The Wnt and BMP pathways are well known regulators of cardiac differentiation ( Ai et al . , 2007; Jain et al . , 2015; Klaus et al . , 2007; Kwon et al . , 2007; Marvin et al . , 2001; Qyang et al . , 2007; Tirosh-Finkel et al . , 2010; Ueno et al . , 2007 ) and specific mechanisms affecting these pathways could be operating during the formation the HT , whereby the differentiation pathways could be temporally restrained . Finally , the endoderm may also play a key role in mediating FHF differentiation . Indeed cardiac mesoderm differentiation is affected in the absence of Sox17 -a transcription factor required for maintenance of the definitive endoderm- and this is accompanied by the formation of a morphologically abnormal HT ( Pfister et al . , 2011 ) . It remains , however , unclear whether this effect is secondary to an initial defect in foregut development that is observed in this mutant . A recent study reported spontaneous calcium transients propagating laterally thought the cardiac crescent ( Tyser et al . , 2016 ) . Left/Right ( L/R ) asymmetry therefore exists within the cardiac crescent , prior to any detectable cardiac contraction , and BMP/SMAD1 signaling in the lateral plate mesoderm may be involved in this asymmetry ( Furtado et al . , 2008 ) . In our studies , however , we did not detect any L/R difference in cTnnT expression by whole-mount immunodetection or Nkx2 . 5GFP activation by live analysis in the cc . Regarding the possible conservation of the temporal differentiation sequence described here , elegant experiments in zebrafish using a cardiac myosin light chain reporter line and a Kaede photo-conversion assay , addressed the temporal order of cardiac differentiation in live embryos ( de Pater et al . , 2009; Liu and Stainier , 2012 ) . Two distinct phases of cardiomyocyte differentiation were also observed . During a first phase , cardiomyocytes were recruited first into the ventricle and atria at the venous pole . During a second phase , cardiomyocyte differentiation was observed at the arterial pole of the HT . These pulse-chase experiments , however , do not address whether cardiac differentiation is continuous or includes a differentiation-paused phase , so further studies will be required to establish the conservation of the observations made here for the mouse embryo . Finally , an important question to address is the functional relevance of the three distinct phases described here; more specifically , what is the role of the observed differentiation pause . An interesting possibility is that this pause would be functionally related to the extensive morphogenetic events that transform the cardiac crescent into the HT . Our study demonstrates coordinated splanchnic mesoderm movements during this phase . These movements involve a very active antero-medial displacement of the splanchnic mesoderm surrounding the cc -mostly fated to the SHF- . This behavior of the splanchnic mesoderm appears essential for transforming the cc into a dorsally closed HT . Importantly , this displacement involves the sliding of the splanchnic layer over the endoderm , suggesting an active role of SHF precursors in this morphogenetic movement . Such displacement of the splanchnic mesoderm over the endoderm during cardiogenesis had been suggested by classical time-lapse studies in the chick embryo ( Dehaan , 1963 ) , and it is tempting to speculate that a similar phenomenon contributes to the incorporation of SHF cells to both poles of the HT at later stages of HT development ( van den Berg et al . , 2009; Kelly et al . , 2001; Zaffran et al . , 2004 ) . These observations suggest that SHF cells do not represent just a reservoir of cardiac precursors but play a morphogenetic role essential for heart tube formation . The main consequence of the displacement of the splanchnic mesoderm layer over the endoderm is the medial convergence of the left and right frontiers between the cc and SHF to form the dorsal mesocardium and close the HT . An important consequence of the differentiation pause described here is the stability of the cc/SHF frontiers during these morphogenetic movements . This stability prevents further spreading of the differentiation wave from the cc into the SHF/splanchnic mesoderm , which could interfere the ability of the latter to efficiently displace over the endoderm . We therefore hypothesize that the stability of the cc/SHF frontiers – and thus the differentiation pause – would be essential to allow the effective displacement of SHF/splanchnic mesoderm and elicit HT formation . The temporal allocation of the morphogenetic phase after cc differentiation allows the formation of the HT while simultaneously providing an incipient cardiac function essential for the organization of embryonic circulation . This hypothesis poses a functional basis for the alternation of differentiation and morphogenesis phases during HT formation . Our study applies whole-embryo live analysis of cardiac development at tissue level and with cellular resolution . We expect that extending this experimental approach will allow to further uncover unexpected and novel mechanisms of organogenesis . While limited attention had been paid so far to the temporal dynamics of differentiation during embryonic development , this is an essential aspect of organogenesis ( Gogendeau et al . , 2015; Parchem et al . , 2015; Yang et al . , 2015 ) . Here , we show the relevance of differentiation timing regulation during heart tube formation and its coordination with morphogenesis at the tissue level . Further understanding of the molecular and cellular mechanisms underlying these phenomena will help us expanding pools of cardiac progenitors in vitro or directing them towards differentiation .
Mouse alleles used in the manuscript are listed including bibliographic references and allele identities at the ‘Mouse Genome Informatics’ data base ( MGI , http://www . informatics . jax . org/ ) . Mesp1cre ( Saga et al . , 1999 , MGI:2176467 ) , Isl1cre ( Cai et al . , 2003 , MGI:3623159 ) , Nkx2 . 5cre ( Stanley et al . , 2002 , MGI:2448972 ) , Rosa26Rtdtomato ( Madisen et al . , 2010 , MGI:3809524 ) , Rosa26RmTmG ( Muzumdar et al . , 2007 , MGI:3716464 ) , Nkx2 . 5eGFP ( Wu et al . , 2006 , MGI:5788423 ) , Polr2aCreERT2 ( RERT ) ( Guerra et al . , 2003 , MGI:3772332 ) and C57BL/6 ( Charles River ) . Mice were genotyped as previously described . All animal procedures were approved by the CNIC Animal Experimentation Ethics Committee , by the Community of Madrid ( Ref . PROEX 220/15 ) and conformed to EU Directive 2010/63EU and Recommendation 2007/526/EC regarding the protection of animals used for experimental and other scientific purposes , enforced in Spanish law under Real Decreto 1201/2005 . Embryos dissected in Dulbecco’s modified Eagle’s medium ( DMEM , Invitrogen ) were fixed overnight in 2% PFA at 4C , then permeabilized in PBST ( PBS containing 0 . 1% Triton X-100 ) and blocked ( 5% goat serum ) . Embryos were incubated overnight at 4°C with antibodies diluted in PBST: mouse anti-cTnnT ( 1:250 , MS-295 Thermo Scientific ) , rabbit anti-PH3 ( 1:250 , 06–570 Millipore ) , CD31 ( 553370 BD Pharmingen clone MEC 13 . 3 ) , SMA ( C6198 Sigma ) and rabbit anti-Laminin1 ( 1:500 , Sigma , L9393 ) . After washing in freshly prepared PBST at 4°C , embryos were incubated with secondary antibodies ( Molecular Probes , A21121 , A21141 , A11035 ) coupled to 488 , 549 or 649 fluorophores as required at 1:250 and DAPI at 1:500 ( Molecular Probes , D3571 ) overnight at 4°C . Before imaging , embryos were washed in PBST at room temperature and cleared with focus clear ( Cell Explorer , FC-101 ) to enhance the transparency of the embryo . Confocal images were obtained on a SP8 Leica confocal microscope with a 20X oil objective ( 0 . 7 NA ) at a 1024 × 1024 pixels dimension with a z-step of 2–4 μm . Embryos were systematically imaged throughout the entire heart tube from top to bottom . For 3D rendering , fluorescent signal in confocal z-stacks was first segmented by setting intensity thresholds using the trainable Weka segmentation tool plugin available in Fiji ( Arganda-Carreras et al . , 2017; Schindelin et al . , 2012 ) . The resulting z-stacks were then corrected manually on a slide-by-slide basis to eliminate segmentation mistakes . In case of the cTnnT immunofluorescence images ( Figure 1 ) , background signal from the yolk sack was manually masked . The volume of the cTnnT positive myocardium was then computed by multiplying the total segmented area by the z-stack interval using a custom Fiji macro . In the Nkx2 . 5cre/+; Rosa26tdtomato+/- and Nkx2 . 5eGFP embryos , fluorophore signal present in the endothelium , endocardium and endoderm cells was manually masked prior to segmentation ( Figure 1A , Figure 1—figure supplement 1 , Figure 2B , Figure 2—figure supplement 2A , A’ and , Figure 6A’’ ) . For 3D visualization of the 3D segmented image stacks , Imaris software ( Bitplane ) was used . Embryos were dissected at E7 . 5 in pre-equilibrated DMEM supplemented with 10% foetal bovine serum , 25 mM HEPES-NaOH ( pH 7 . 2 ) , penicillin ( 50μml21 ) and streptomycin ( 50mgml21 ) . Embryos were staged on the basis of morphological criteria ( supplementary Figure 1 ) ( Downs and Davies , 1993; Lawson and Wilson , 2016 ) , and those between the bud and early somitogenesis stages were used for culture and time-lapse imaging . To track the early phase of cardiac differentiation and subsequent phases of morphogenesis , we used embryos at EHF to transversal HT stage . Embryos were cultured in 50% fresh rat serum , 48% DMEM without phenol red , 1% N-2 neuronal growth supplement ( 100X , Invitrogen 17502–048 ) and 1% B-27 supplement ( 50X Thermo Fisher Scientist 17504044 ) filter sterilised through a 0 . 2 mm filter . To hold embryos in position during time-lapse acquisition , we made special plastic holders with holes of different diameters ( 0 . 5–3 mm ) to ensure a good fit of embryos similarly to the traps developed by Nonaka , 2009 , Nonaka et al . ( 2002 ) . Embryos were mounted with their anterior side facing up . To avoid evaporation , the medium was covered with mineral oil ( Sigma-Aldrich; M8410 ) . Before starting the time-lapse acquisition , embryos were first pre-cultured for at least 2 hr in the microscopy culture set up . The morphology of the embryo was then carefully monitored and if the embryos appeared unhealthy or rotate and move , they were discarded , otherwise , time-lapse acquisition was performed . For the acquisition , we used the Zeiss LSM780 equipped with a 5% CO2 incubator and a heating chamber maintaining 37°C . The objective lens used was a 20X ( NA = 1 ) dipping objective , which allowed a long working distance for imaging mouse embryos and tissues . A MaiTai laser line at 1000 nm was used for two-channel two-photon imaging . Acquisition was done using Zen software ( Zeiss ) . Typical image settings were: output power: 250 mW , pixel dwell time: 7μs , line averaging: two and image dimension: 610 × 610 μm ( 1024 × 1024 pixels ) . To maximize the chance of covering the entire heart tube during long-term time lapse videos , we allowed 150–200 μm of free space between the objective and the embryo at the beginning of the recording . For labeling single cells , tamoxifen was administered by oral gavage ( 2–4 mg/mL ) in RERT;Rosa26R-tdtomato ( cell tracking ) or RERT;Rosa26RmTmG mice ( cell shape study ) at E7 . Cell shape measurements were done on single cells , imaged in mosaic-labeled , fixed and immunostained embryos and analyzed with Fiji software ( Figure 2D , E ) . Tracking of tdtomato-labeled cells was done on single cells located within the cardiogenic mesoderm -excluding endothelial , pericardial and endodermal cells- and their GFP intensity was measured over time . To track cells manually in 4D stacks , the MTrackJ Fiji plugin ( Meijering et al . , 2012 ) was used . A local square cursor ( 25 × 25 pixels ) on the cell of interest snaps according to a bright centroid feature on a slice-by-slice basis . Only tracks lasting for the entire length of the video were kept . When an ambiguity arises in the tracking between consecutive time points , the track was discarded . Tracks split at cell divisions . A cell division event is normally clearly distinguishable over at least two time points . In case one of the two daughter cells is not tractable , the other daughter cell is still tracked . Each track is assigned an ID number and excel files with all the tracks coordinates in x , y , z and t was generated . Coordinates of each track were converted into 8-bit 4D images using a custom Fiji macro in which each cell was represented by a sphere of specific pixel intensity , from 1 to 255 , while pixels corresponding to background were set to zero . The 4D images were then opened with Imaris to perform visualization of the 3D trajectories of each cell using the ‘spots’ tool , where each object were identified according to pixel intensity . GFP intensity measurement is performed by segmentation of cell shape . A Gaussian filter whose radius is adjusted to the typical size of a cell was first applied , followed by a Laplacian filter . The resulting 32 bits image was next converted to a mask by thresholding . When objects touched each other , a watershed on the binary mask and manual corrections was applied . Each segmented cell was checked and tracked manually for accuracy . In Figure 3B’ nuclei segmentation was performed manually . The mean GFP signal intensity of the segmented objects was then measured using the ‘analyze particle’ tool in Fiji . To quantify the GFP level of tracked cells through time , four to five successive time points were arbitrarily chosen in each video ( Figure 5F , Figure 6C , D and Figure 5—figure supplement 1C ) except in Figure 5D and Figure 6I , where GFP intensity level was measured in every time point . Background intensities were measured in neural tube cells , which are known to be negative for GFP and cTnnT . Tables containing ID number of tracked cells and GFP intensities were generated and plotted using Prism statistical software . For comparisons of two groups , a Mann–Whitney U-test was used using Prism statistical software . To find a correlation between GFP and cTnnT levels of 0 . 8 with an alpha-level f 0 . 05 and a power of 0 . 2 at least 10 cells per embryo were required ( Figure 3D and Figure 3—figure supplement 2C ) . Many more cells were computed for each experiment . The linear fit was done using ‘lm’ function from R statistical software ( https://www . r-project . org/ ) . To calculate the average speed of splanchnic mesoderm displacement , the shortest distance between the left and right splanchnic mesoderm was calculated in two z-level , using the measure tool in Fiji , and the variation in this distance by time unit was divided by 2 , to determine the speed of movement of each sliding side of the splanchnic mesoderm . | We all start life as a single cell , which – over the course of nine months – multiplies to generate the billions of cells that can be found in a newborn . As an embryo develops , the cells need to achieve two major tasks: they need to diversify into different types of cells , such as blood cells or muscle cells , and they need to organize themselves in space to form tissues and organs . The heart of an embryo , for example , first forms a simple structure called the heart tube that can pump blood and later develops into the four chambers that we see in adults . The tube is made up of cells from two different origins , known as the first and second heart fields . Unlike other organs , the heart has to start beating while it is still developing , and until now , it was unclear how the heart manages this difficult task . Here , Ivanovich et al . studied mouse embryos grown outside the womb by using a combination of advanced microscopy and genetic labeling to track how single cells turn into beating cells and move while the heart forms . The results showed that specializing into beating cells and forming the heart tube shape happened during alternating phases . The first heart-field cells turned into beating cells and began to contract at an early stage before the heart tube was formed . Next , the cells of the second heart field did not instantly develop into beating cells , but instead , helped the first heart-field cells to acquire the shape of a heart tube . Once this was completed , the second heart-field cells started to specialize into beating cells and created the additional parts of the more complex adult heart . This research shows that the second heart field plays an active role in helping the heart tube form . The alternating phases of cell specialization and tissue formation allow the heart to become active whilst it is still developing . A better insight into how the heart forms may help us to create new treatments for various genetic heart conditions . The methods used here could also help to study how cells build other organs . | [
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] | 2017 | Live imaging of heart tube development in mouse reveals alternating phases of cardiac differentiation and morphogenesis |
Histone tails harbor a plethora of post-translational modifications that direct the function of chromatin regulators , which recognize them through effector domains . Effector domain/histone interactions have been broadly studied , but largely using peptide fragments of histone tails . Here , we extend these studies into the nucleosome context and find that the conformation adopted by the histone H3 tails is inhibitory to BPTF PHD finger binding . Using NMR spectroscopy and MD simulations , we show that the H3 tails interact robustly but dynamically with nucleosomal DNA , substantially reducing PHD finger association . Altering the electrostatics of the H3 tail via modification or mutation increases accessibility to the PHD finger , indicating that PTM crosstalk can regulate effector domain binding by altering nucleosome conformation . Together , our results demonstrate that the nucleosome context has a dramatic impact on signaling events at the histone tails , and highlights the importance of studying histone binding in the context of the nucleosome .
Eukaryotic DNA is packaged into the cell nucleus in the form of chromatin . This DNA/histone complex contributes to DNA compaction and restricts its accessibility , also providing a mechanism for regulating the genome . Dynamic modulation of chromatin structure is vital in all DNA-templated processes . The basic subunit of chromatin is the nucleosome , comprised of an octamer containing two copies each of histones H2A , H2B , H3 , and H4 , around which ~ 147 bp of DNA is wrapped . The N-termini of all four histones , as well as the C-terminus of H2A , protrude to the exterior of the nucleosome core and are commonly referred to as histone tails . These tails can be extensively post-translationally modified , which is thought to be critical in regulation of chromatin structure . Genome-wide studies have revealed that particular post-translational modifications ( PTMs ) are correlated with specific genomic states and/or elements , and notably , strong correlations have led to the suggestion that it is patterns of PTMs that are functionally important ( Zentner and Henikoff , 2013 ) . Though some histone tail PTMs have been found to directly affect chromatin array compaction ( Kan et al . , 2009; Wang and Hayes , 2008; Zhou et al . , 2012; Dhall et al . , 2014; Mishra et al . , 2016 ) , most are thought to act through indirect mechanisms , by recruiting regulatory complexes to modified nucleosomes or modulating their activity once there . Modified histone tails are recognized by effector domains , often referred to as histone readers . A large number of families of histone effector domains have been identified over the past two decades including bromodomains , chromodomains , and PHD fingers , and these often exist in multiples within chromatin regulators ( Musselman et al . , 2012; Andrews et al . , 2016 ) . A wealth of studies have examined the structural determinants of effector domain specificity ( Musselman et al . , 2012; Andrews et al . , 2016 ) . However , due to difficulties in crystalizing these domains in complex with modified nucleosomes , the mechanism of histone binding has largely been studied with peptides corresponding to segments of the histone tails . Biochemical studies suggest that the histone tails are highly solvent accessible , and the large majority of crystal structures of the nucleosome do not resolve the tails , suggesting a high degree of conformational heterogeneity ( Böhm and Crane-Robinson , 1984; Rosenberg et al . , 1986; Luger et al . , 1997 ) . This is further supported by nuclear magnetic resonance ( NMR ) spectroscopy studies , in which the histone tails are found to have a high degree of conformational flexibility ( Zhou et al . , 2012; Gao et al . , 2013 ) . Together , this has led to a model of the nucleosome where the tails are extended into solution and fully accessible . If correct , this would suggest that effector domain binding to a nucleosome versus a histone tail peptide should be largely similar . However , there is also ample evidence that the histone tails stabilize nucleosome structure and alter DNA binding . In particular , histone tails have been shown to alter transcription factor accessibility ( Lee et al . , 1993; Polach et al . , 2000; Yang et al . , 2005 ) , thermal stability of the nucleosome ( Ausio et al . , 1989; Iwasaki et al . , 2013 ) , and DNA wrapping/unwrapping rates ( Andresen et al . , 2013 ) . There has also long been evidence that the tails can interact with DNA ( Cutter and Hayes , 2015 ) ( in some studies quite robustly ) , including a recent study in which it was found that the tails transiently interact with linker DNA , inhibiting the activity of histone modifying enzymes ( Stützer et al . , 2016 ) . Notably , in the one crystal structure of the nucleosome where the tails are resolved , several of them are associated with the DNA of crystallographic symmetry mates ( Davey et al . , 2002 ) . In addition , in silico all-atom studies of the nucleosome have repeatedly suggested that the histone tails collapse onto core DNA ( Biswas et al . , 2013; Li and Kono , 2016; Shaytan et al . , 2016; Ikebe et al . , 2016; Chakraborty and Loverde , 2017 ) , and tail dynamics have been correlated with the DNA unwinding process through free energy calculations ( Kenzaki and Takada , 2015 ) . Thus , our current understanding of the histone tail conformation within the nucleosome is incomplete , and we know very little about how effector domains recognize histone tails in the context of the nucleosome . Here , we utilize NMR spectroscopy to investigate the interaction of a PHD finger with the nucleosome , specifically , the BPTF ( bromodomain PHD finger transcription factor ) PHD finger . The BPTF PHD finger is a well-characterized histone effector domain that recognizes the first 6 residues of the histone H3 tail , with specificity for tri-methylated lysine 4 ( H3K4me3 ) ( Li et al . , 2006 ) . We find that in the context of the nucleosome , the PHD finger association with the methylated H3 tail is inhibited . Using NMR and molecular dynamics ( MD ) simulations , we demonstrate that this inhibition is due to the conformation of the H3 tail within the nucleosome . Our data support a model where the H3 tails are collapsed onto the nucleosome core through robust interaction with DNA . However , they adopt an ensemble of heterogeneous DNA-bound conformations that are in fast exchange between one another , reconciling that they can be both DNA bound and have a high level of conformational flexibility . Furthermore , we find that modification or mutation of H3 tail residues outside the PHD finger binding region weakens tail association with DNA and increases accessibility to the PHD finger . This suggests that PTM cross-talk may be mediated by histone tail conformation within the nucleosome . Altogether , our data reveal a far more complex interface for effector domain binding as compared to histone peptides , and demonstrates that it is critical to characterize these associations in the proper context .
In order to determine how the context of the nucleosome may affect the association of PHD fingers with the histone H3 tail , we utilized NMR spectroscopy to compare binding of the BPTF PHD finger to histone tail peptide and the nucleosome core particle ( NCP ) . Specifically , we collected sequential 1H-15N heteronuclear single quantum coherence ( 1H-15N HSQC ) spectra on 15N-PHD upon titration of increasing concentrations of a methylated histone tail peptide or a methylated nucleosome . Addition of a peptide corresponding to H3 residues 1–10 methylated at lysine 4 ( H3 ( 1–10 ) K4me3 ) ( Figure 1A , left , and Figure 1—figure supplement 1A and 2C ) to 15N-PHD resulted in extensive chemical shift perturbations ( CSPs ) in resonances for residues in the binding pocket ( Figure 1C ) as previously determined by NMR and crystallographic studies ( Li et al . , 2006 ) . The pattern of CSPs observed as a function of peptide concentration denotes low μM affinity and is consistent with previously reported affinities ( Li et al . , 2006 ) . The methylated nucleosome was generated using a methyl lysine analogue ( MLA ) at histone H3 lysine 4 ( H3KC4me3-NCP ) using human histones and the 147 base pair ( bp ) Widom 601 DNA sequence . Importantly , mass spec analysis of the H3KC4me3 protein demonstrated that alkylation at position four was complete , with no evidence of over-alkylation , or carbamylation due to refolding in urea ( Figure 1—figure supplement 3A , B ) . Addition of H3KC4me3-NCP to 15N-PHD led to CSPs indicating binding ( Figure 1A , center ) . Compared to the peptide , the same set of resonances is perturbed , and the majority of resonances track along the same trajectory ( Figure 1A–C ) . This demonstrates that the mode of interaction between the PHD finger and H3 tail in the context of the NCP is the same as observed for free histone tail peptide and that the PHD finger does not make any significant contacts with other histone tails or the NCP core . The latter is further confirmed by demonstration that binding is dependent on methylation , as seen by titration of unmodified NCP into 15N-PHD , which does not result in any significant CSPs ( Figure 1—figure supplement 4 ) . Surprisingly , though the binding pocket was the same , the magnitude of CSPs upon titration of H3KC4me3-NCP was significantly smaller as compared to an equivalent addition of histone peptide ( Figure 1A , C ) , and the titration with NCP does not reach saturation ( i . e . a fully bound state ) at the same molar ratios as does the peptide ( Figure 1—figure supplements 1C and 2C ) . If the mode of interaction between the PHD finger and NCP is identical to the PHD-H3 ( 1–10 ) K4me3 complex , as is suggested by the equivalent CSP trajectories , then the chemical shift values of the fully bound state should be identical . Under this assumption , the small shift towards the bound state seen for the NCP indicates that binding in the context of the NCP is dramatically weaker . In general , the MLAs are robust mimetics of methylated lysines , but in some cases , they bind substantially weaker ( Seeliger et al . , 2012 ) . A recent study extensively compared the interaction of the BPTF PHD finger with a histone peptide containing a true methylated lysine versus one containing the MLA . Though a crystal structure revealed the mechanism of association is largely the same , the authors reported a loss in binding affinity of about an order of magnitude for the MLA ( Chen et al . , 2018 ) . Thus , we first sought to ensure that the weaker binding observed for the H3KC4me3-NCP was not simply due to the analogue itself . To do this , we produced an MLA version of the histone tail alone , H3 ( 1–44 ) KC4me3 ( which we will refer to as H3KC4me3-Tail ) . Titration of the analogue-containing H3KC4me3-Tail into 15N-PHD resulted in large CSPs ( Figure 1A , right ) , immediately suggesting that the analogue is not the sole cause of the minor CSPs observed for H3KC4me3-NCP . Consistent with Chen et al . , binding to the MLA-containing peptide was moderately weaker as denoted by the pattern of CSPs for a subset of residues changing from intermediate ( seen with the true methylated lysine ) to fast exchange on the NMR time-scale ( Figure 1A , and Figure 1—figure supplement 1B ) . Importantly , the residues perturbed are identical between the two peptides ( Figure 1B , C ) , though comparison of spectra of the PHD finger saturated with either H3 ( 1–10 ) K4me3 or H3KC4me3-Tail reveals that the bound states are not quite identical ( Figure 1A , and Figure 1—figure supplement 2A ) . Though for the majority of residues the resonances progress along the same trajectory from apo to bound , the difference in chemical shift ( Δδ ) between the free and fully bound states of the PHD is generally greater for H3 ( 1–10 ) K4me3 as compared to H3KC4me3-Tail ( Figure 1A , C , and Figure 1—figure supplement 2B ) . Fitting CSPs as a function of ligand concentration indicates that the PHD finger binds H3KC4me3-Tail with a low micromolar Kd of 12 ± 1 μM ( Figure 1—figure supplement 2C , center ) . As saturation is not reached during the NMR titration with the H3KC4me3-NCP , a robust binding affinity cannot be determined . However , the CSPs as a function of H3KC4me3 mark suggest a dissociation constant in the low millimolar range ( Figure 1—figure supplement 2C , right ) . Thus , the NMR data indicate that binding to the NCP is substantially weaker than binding to the free histone tail peptide . To confirm this , we utilized biolayer interferometry ( BLI ) . A biotin-PHD construct was immobilized on streptavidin-coated sensors , and experiments were carried out with H3KC4me3-Tail or H3KC4me3-NCP under similar solution conditions as the NMR experiments . Clear association and dissociation curves were observed for binding to the H3KC4me3-Tail with strong response signal ( Figure 1—figure supplement 5A ) . Fitting the equilibrium response at the end of the association phase , the binding affinity was determined to be 7 . 0 ± 0 . 1 μM ( Figure 1—figure supplement 5B ) . This is slightly higher affinity than that determined via NMR , likely due to the fact that the PHD finger is immobilized for BLI . In contrast , no association was detected with the H3KC4me3-NCP at the highest concentrations used for the H3KC4me3-Tail , even though it is expected to elicit a larger BLI response signal due to its significantly larger size . Even at a ten-fold higher concentration , H3KC4me3-NCP produces minimal response signal ( Figure 1—figure supplement 5C ) . Together , these data demonstrate that the BPTF PHD finger adopts the same bound state with the H3 tail in the context of the nucleosome as with the free histone tail peptide , making no direct interaction with the nucleosome core . However , association is substantially inhibited in the context of the nucleosome . Though moderate effects are seen from the use of the MLA versus true methylated lysine , these do not account for the dramatically weaker association observed for the NCP . Thus , it is possible that the conformation of the nucleosome itself is causing a reduction in the observed binding affinity . To further investigate the conformation of the H3 tail ( Figure 2A ) and its potential effect on PHD finger binding , 15N-H3-NCPs were prepared by refolding octamer with 15N-labeled H3 , and unlabeled H2A , H2B , and H4 , and reconstituting this with the 147 bp 601 Widom DNA . Similar to previous NMR spectra on the Drosophila nucleosome reconstituted with 167 bp DNA and X . laevis nucleosome reconstituted with 187 bp DNA ( Zhou et al . , 2012; Stützer et al . , 2016 ) , the 1H-15N HSQC spectrum of the 15N-H3-NCP shows only 32 of the possible 129 non-proline peaks for H3 . Assignments were performed using traditional triple resonance experiments and confirm that these correspond to residues 3–36 of the N-terminal tail . The majority of these resonances have 1H chemical shifts in the range of 8 . 0–8 . 5 ppm ( Figure 2B , left ) , consistent with an unstructured region , and chemical shift analysis using CSI3 . 0 ( Hafsa et al . , 2015 ) ( Figure 2—figure supplement 1A ) confirms a random coil conformation , consistent with previous studies ( Zhou et al . , 2012; Stützer et al . , 2016 ) . The single set of peaks observed implies that the two H3 tails within the NCP are identical . Due to the large size ( ~200 kDa ) and resultant slow tumbling of the NCP , it is expected that the residues in the core would not be observable using this isotope labeling scheme . Thus , the observation of the H3 tail suggests that this region experiences greater intrinsic conformational dynamics than do the residues in the core . This is consistent with previous structural and biochemical studies that suggest that the histone tails are largely solvent exposed . The ability to detect NMR resonances for the H3 tail within the nucleosome has been interpreted to reflect a disordered and predominantly accessible conformation ( Zhou et al . , 2012; Gao et al . , 2013 ) . However , a recent NMR study on the X . laevis nucleosome reconstituted with 187 bp DNA revealed differences in the nucleosomal H3 tail as compared to a histone tail peptide , and ultimately concluded that this was due to transient interactions with the linker DNA ( Stützer et al . , 2016 ) . Here , there is no linker DNA present , thus we sought to investigate the conformation of the H3 tail in the context of the minimal NCP . Consistent with a solvent exposed conformation , changes in solution conditions , specifically temperature , KCl , and MgCl2 , result in global perturbations in the 1H-15N HSQC spectra ( Figure 2—figure supplement 1B–G ) , though notably , none of the conditions tested structure the H3 tail . However , similar to studies with the 187bp-nucleosome ( Stützer et al . , 2016 ) , comparison of the 1H-15N HMQC/HSQC spectra for an isolated peptide corresponding to residues 1–44 of the H3 tail ( 15N-H3-Tail ) and for the 15N-H3-NCP reveals significant chemical shift differences between the free peptide and NCP states along the full length of the H3 tail ( Figure 2B right , 2C ) . Together these data are consistent with nucleosomal H3 tails that are unstructured and highly mobile . However , chemical shift data indicate that the entire H3 tail ( not just residues near the core ) has a distinct conformation in the context of the NCP , consistent with an interaction within the NCP itself . To determine if the H3 tail can interact with the NCP under physiological conditions , we titrated NCPs into the isolated tail ( Figure 3A , and Figure 3—figure supplement 1A ) . Upon an initial addition of a 0 . 2:1 molar ratio of unlabeled NCP into 15N-H3-Tail , the sample severely aggregated , and signal was lost within the 1H-15N HSQC spectrum . However , subsequent addition of NCP to a ratio of 0 . 5:1 led to a partial re-appearance of signal , with significant CSPs as compared to apo . Further addition of NCP resulted in an increase in peak intensity without perturbation in chemical shift ( Figure 3—figure supplement 1C , D ) . This pattern is characteristic of a slow exchange process on the NMR timescale and indicates a robust association . Note that for slow exchange , generally a decrease in the intensity of the apo state is observed in conjunction with an increase in intensity for the bound state . However , due to the initial aggregation , only the bound species is observed . The origin of the aggregated or phase-separated state is uncertain at this point . This titration was repeated using tailless NCPs ( tlNCPs ) generated through treatment of reconstituted NCPs with trypsin ( Figure 3B , and Figure 3—figure supplements 1B and 2 ) . Upon addition of tlNCP into 15N-H3-Tail , the same initial loss of signal was observed as for the NCP . Subsequent addition of tlNCPs led to re-appearance of signals with identical chemical shifts to those observed upon binding NCP ( containing tails ) . This reveals that the tlNCP-bound state of H3-Tail is identical to the NCP-bound state ( Figure 3A vs . B , and Figure 3—figure supplement 1A–C ) , and indicates that the H3-Tail-NCP interaction is not mediated through tail-tail interactions but instead through components within the core . Plotting the intensity of H3 tail bound-state resonances as a function of NCP/tlNCP concentration shows greater intensity in binding to tlNCP than to NCP throughout the titration . In addition , peak intensities plateau ( indicating saturation ) with tlNCP but not NCP over the concentration range tested ( Figure 3—figure supplement 1C , D ) . Together , this indicates that the H3-Tail binds tlNCP tighter than NCP . This is consistent with a mechanism in which free H3 tail peptide must compete with tethered tails to associate with the core , providing additional evidence that nucleosomal H3 tails are associating with the NCP core . Plotting the CSPs as a function of residue for association with the NCP/tlNCP reveals that residues along the entire H3 tail are affected by binding ( Figure 3C ) . The structure of the isolated H3 tail peptide , the H3 tail peptide bound to the NCP in-trans , and the native H3 tail in the context of the NCP can be compared through overlay of the corresponding 1H-15N HSQC/HMQC spectra ( Figure 3A , B ) . As noted previously , there are large differences in the spectra of H3-Tail and H3-NCP ( Figure 3A , compare black vs . gold ) . In contrast , the spectra for H3-Tail bound in-trans to the NCP or tlNCP much more closely resembles H3-NCP ( Figure 3A , B ) . In fact , the majority of resonances for H3-Tail bound in-trans to the NCP/tlNCP lie along a linear or nearly linear trajectory between the corresponding resonances for apo H3-Tail and H3-NCP . This suggests that the in-trans NCP-bound state of the H3 tail peptide closely reproduces the environment of the native H3 tail , consistent with the native tail associating with the NCP core . Differences between the in-trans bound and native histone tails likely arise because the H3 tail is uniquely tethered to the NCP in the native state , and when binding in-trans , the tail is likely to associate with regions inaccessible to the restricted tail binding in-cis . Indeed , when comparing the chemical shift of the H3 tail peptide bound to the NCP in-trans and the native H3 tail , the greatest differences are in the C-terminal half of the H3 tail ( Figure 3D ) . One of the largest differences is seen for lysine 36 , which is immediately adjacent to where the H3 tail connects to the core histone fold and protrudes from between the DNA gyres . Additionally , there are significant differences observed for residues 24–28 and Thr32 . Notably , these data indicate that the interaction between the tails and core is quite robust , yet the fact that the resonances are detectable indicates that the tails are also conformationally dynamic . To investigate this further , MD simulations of the complete NCP were carried out . Simulations were conducted on NCPs containing unmodified H3 tails ( un-NCP ) . Each NCP was initiated from three different H3 tail conformations: one based on the 1KX5 crystal structure ( the only crystal structure that resolves all H3 histone tail residues ) , another in which the H3 tails were extended linearly from residues 1 to 40 , and a third based on de novo modeling of the tails . Consistent with the robust interaction between the H3 tail and the NCP core seen by NMR , simulations show that the H3 tail consistently collapses onto the NCP regardless of the initial conformation ( Figure 3E , Video 1 ) . This is measured quantitatively through calculation of the radius of gyration ( Rg ) of the full histone H3 protein , which reduced from a maximum of 45 Å to ~26 Å upon tail collapse ( Figure 3—figure supplement 3 , Table 1 ) . Notably , the average root mean square deviation ( RMSD ) of H3 tail conformations calculated between the final frames of all simulations , after aligning NCP core conformations , is 50 . 0 Å , suggesting that there exists a wide range of potential conformations available to the H3 tail . If only simulations of the same initial tail conformation are considered , then this value is reduced by varying degrees to values between 30 . 0 Å and 41 . 2 Å . This reduction of 10–20 Å suggests that the final conformation of the H3 tail is influenced somewhat by the choice of initial tail structure , but that the ensemble of final states is still extensive for a single initial conformation . In addition , since the tail compacts onto the outer surface of the NCP core , a significant amount of surface area of the tails is still exposed to solvent ( >2600 Å2 ) , which is in agreement with data suggesting high solvent-accessibility . Together , these data show that the H3 tail binds robustly to the nucleosome core . The MD simulations suggest a heterogenous ‘bound state’ comprised of an ensemble of conformations of the H3 tail bound to the core . The fact that resonances are visible in the HSQC spectrum , and that there is only one resonance per residue , suggests that there is a fast , dynamic transition between these core-associated states . Several previous studies have identified an interaction between histone tails and DNA ( Cutter and Hayes , 2015 ) , including a recent NMR study that demonstrated interaction of the H3 tail with linker DNA in the nucleosomal context ( Stützer et al . , 2016 ) . To test if the interaction observed here between the H3 tail and the nucleosome core is driven by DNA , we compared spectra of the H3 tail bound to a 21 bp double-stranded DNA fragment to those of the H3 tail bound to the nucleosome in-trans and also in its native state within the nucleosome . Comparison of the corresponding 1H-15N HMQC/HSQC spectra for the DNA-bound H3-Tail with the H3-NCP reveals marked similarities ( Figure 4—figure supplement 1 ) . As was seen for NCP/tlNCP-bound H3-Tail , the majority of resonances for the DNA-bound H3-Tail lie along a linear or near-linear trajectory between the chemical shift values for the apo H3-Tail and the H3-NCP ( Figure 4A ) . This strongly suggests that the interaction of the H3 tail with the nucleosome core is driven through contacts with DNA . This is very similar to previous observations for the X . laevis 187bp-nucleosome containing linker DNA ( Stützer et al . , 2016 ) and reveals that , even in the absence of accessible linker DNA , the native histone H3 tails are DNA bound . Notably , the chemical shift values for the tlNCP-bound state are more similar to the native H3 tail than those for the 21bp-DNA-bound state ( Figure 4B ) . This is most notable for arginine residues and , to a lesser extent , their neighboring residues ( see residues 5–9 , 17–18 , and 24–27 ) , which still deviate considerably in chemical shift between the DNA-bound and native H3 tails whereas binding to tlNCP much better reproduces the native NCP chemical shift . It is possible that the arginines , which fit into the minor groove of DNA , are more sensitive to DNA shape ( linear vs . bent ) . Consistent with the experimental data , the three-dimensional distribution of tail atoms in the MD simulation of the un-NCP shows that tails package primarily onto the core DNA surface ( Figure 5A ) . MM-GBSA analysis on a per residue basis suggests that association is driven by arginines and , to a lesser extent , lysines , which is aligned with the NMR CSP data ( Figure 5C ) . By calculating the sum of contributions from H3 tail residues for the Gibbs free energy ( ΔG ) of DNA binding , it is observed that the tails bind to DNA at a ΔG of −70 . 8 ± 4 . 0 kcal/mol . In contrast , the interaction energy between H3 tail residues and other histone components is a disfavorable 5 . 2 ± 0 . 9 kcal/mol . We note that the MM/GBSA analysis method presented here involves several approximations , including a mean-field solvent model and the lack of the solute configurational entropy change , therefore the free energy change values presented should only be interpreted qualitatively ( Hou et al . , 2011; Rastelli et al . , 2010; Genheden et al . , 2011 ) . Despite these limitations , our results demonstrate that interactions between the H3 tails and the NCP core are robust and favorable , and driven by preference for the DNA surface . While some previous simulations , secondary structure predictions , and circular dichroism studies have suggested that the H3 tail adopts distinct α-helical portions ( Banères et al . , 1997; Wang et al . , 2000; Li and Kono , 2016; Ikebe et al . , 2016; Potoyan and Papoian , 2011 ) , others present a tail that is largely unstructured ( Shaytan et al . , 2016; Erler et al . , 2014; Roccatano et al . , 2007 ) . Here , consistent with the NMR analysis ( see above ) , simulation results also suggest that the H3 tails are largely unstructured when bound to the nucleosome core , as determined by a DSSP characterization . This reveals that , on average , tail residues in the un-NCP are only 5 . 5% α-helical and 2 . 9% β-strand ( Table 1 ) . Inspecting on a per-residue basis shows that random coil is the preferred state of the entire tail ( Figure 5—figure supplement 1 ) . Identical calculations were also performed for NCPs containing H3K4me3 ( H3K4me3-NCP ) . Notably , a nearly identical three-dimensional distribution is observed for tails containing H3K4me3 ( Figure 5B , Figure 5—figure supplement 2 and Video 2 ) . The ΔG of DNA binding of the H3 tails was also not significantly altered by the presence of the tri-methyl modification ( ΔG = −72 . 0 ± 4 . 0 kcal/mol ) , and there were no effects on secondary structure , with 6 . 0% α-helical and 1 . 6% β-strand configurations . In agreement with this , an 1H-15N HSQC spectrum of the 15N-H3KC4me3-NCP only demonstrates perturbations in residues around lysine 4 , whereas the remainder of the tail is not perturbed , confirming that methylation of lysine four does not substantially alter the interaction of the H3 tail with the nucleosome core ( Figure 5D ) . Together , these data reveal that the H3 tail is binding the DNA component within the context of the NCP . NMR and MD simulations demonstrate that the H3 tail is robustly associated with the nucleosomal DNA . The interaction is driven largely by basic residues suggesting an electrostatic interaction . Consistent with this , spectra recorded on 15N-H3-NCP upon increasing concentrations of mono-valent ions results in CSPs that follow a trajectory roughly towards the free peptide ( Figure 2—figure supplement 1G ) . Notably , methylation of lysine four does not release the H3 tail from the DNA . To test if the interaction between the H3 tail and DNA is the cause of the decreased association between the BPTF PHD finger and H3KC4me3-NCP , we probed the interaction between the PHD finger and H3 tail peptide in the presence of DNA . To this end , 1H-15N HSQC spectra were collected on 15N-labeled PHD finger upon titration of unlabeled H3 tail peptide ( H3 ( 1–10 ) K4me3 or H3KC4me3-Tail ) that was pre-bound to a 21 bp DNA at a 1:2 molar ratio . The titration of DNA-bound H3 ( 1–10 ) K4me3 into 15N-PHD yielded very similar CSPs as were observed upon titration of H3 ( 1–10 ) K4me3 alone ( Figure 6A ) . CSPs are observed along the same trajectories from apo to bound between the two titrations , indicating that the presence of DNA does not alter the binding mode . Comparison of the CSP as a function of molar ratio of ligand between histone tail alone and histone tail pre-bound to DNA reveals only a slightly lower population in the bound state when DNA is present ( Figure 6A ) . In contrast , a large effect was observed for the full-length tail peptide , H3KC4me3-Tail . Titration of H3KC4me3-Tail pre-bound to DNA into 15N-PHD leads to CSPs that , similar to the shorter peptide , progress along the same trajectories from apo to bound as compared to H3KC4me3-Tail alone . However , with the longer peptide , comparison of the CSPs as a function of ligand indicates a much smaller population of PHD in the bound state for each PHD:peptide ratio when the peptide is pre-bound to DNA than for the peptide alone ( Figure 6B ) . Fitting the CSPs as a function of peptide concentration yields an apparent Kd of 180 ± 30 μM for H3KC4me3-Tail , which is 15-fold weaker than peptide alone ( 12 ± 1 μM ) , revealing that the interaction between the H3 tail and PHD is decreased due to competitive binding of the H3 tail to DNA ( Figure 6C ) . Peak broadening suggests the possibility of a ternary complex , where the PHD finger and DNA could be simultaneously binding to different regions of H3KC4me3-Tail , but this would require further investigation to confirm . Together these data demonstrate that association of the H3 tail with DNA does indeed inhibit association of the PHD finger . A significant effect is only seen with the full length H3 tail , indicating that multiple contacts along the length of the tail are required for a high avidity interaction with DNA . Although the effect is not as strong as that observed for the H3KC4me3-NCP ( Figure 1A , center ) , it is expected that in the context of the nucleosome , where the tail is tethered close to the DNA , the greatly increased effective local concentration would increase the apparent affinity between the histone tail and DNA and thus its competition for PHD finger binding . Our results show that the H3 tail is stabilized on the nucleosomal DNA through multiple contacts along the entire length of the tail . Given this , it is possible that a variety of PTMs may alter this interaction and thus the H3 tail conformation and accessibility . Notably , our results reveal that H3K4me3 does not significantly alter the H3 tail-DNA interactions ( discussed above , Figure 5 ) , which is likely due to the fact that methylation of lysine does not alter the side-chain charge . However , there are a number of PTMs , such as lysine acetylation and other acylations , arginine citrullination , and serine/threonine/tyrosine phosphorylation , that alter the side-chain charge of histone residues and thus might perturb the electrostatic interaction between the histone tails and nucleosomal DNA . Acetylation neutralizes the positive charge on the lysine side-chain . To investigate how acetylation along the H3 tail alters the tail-DNA interaction we conducted MD simulations of NCPs containing acetylation on lysines 14 , 18 , 23 , and 27 along the H3 tail ( quadAc-NCP ) ( Figure 7—figure supplement 1 ) . Interestingly , the H3 tails in the quadAc-NCP compact to a similar extent as in the un-NCP and H3K4me3-NCP , with an Rg of 26 . 5 ± 0 . 4 Å for H3 ( Table 1 ) . Also , similar to the un-NCP and H3K4me3-NCP the quadAc-H3 tails compact primarily onto the nucleosomal DNA ( Figure 7A , Video 3 ) . The quadAc-H3 tail shows no change in secondary structure , with an average helicity of 5 . 9% and an average β-strand content of 3 . 3% ( Figure 5—figure supplement 1 , Table 1 ) . However , the interaction energies between the DNA and H3 tail are significantly weakened ( ΔG = −52 . 1 + /- 2 . 7 kcal/mol , p-value of 0 . 0003 ) in the quadAc-NCP ( Figure 7B , Table 1 ) . This is in-line with previous studies using UV-induced laser-crosslinking , in which it was proposed that the histone tail-DNA interaction persists even upon acetylation , but is weakened ( Mutskov et al . , 1998 ) . We note that the acetylated lysines only account for 33% of the total change in free energy ( 6 . 2 kcal/mol of the total 18 . 9 kcal/mol disfavorable shift ) , while the immediate neighbors of the acetylated lysines combine to contribute an additional 37% of the difference ( 6 . 9 kcal/mol ) . Other residues that experience significant drops in binding strength include A1 ( ΔΔG = 1 . 7 kcal/mol , 9% of total ) , K4 ( ΔΔG = 1 . 8 kcal/mol , 9% of total ) , and S10 ( ΔΔG = 0 . 9 kcal/mol , 5% of total ) . Additionally , the average amount of solvent-exposed surface area of the H3 tails is increased in the quadAc-NCP to 2928 ± 42 Å2 ( Table 1 ) , in part due to the increased size of the acetylated side chains . This increase in solvent-accessibility may provide a larger site for interaction with binding partners . Together , these results show that acetylation of lysines 14 , 18 , 23 , and 27 reduce the strength of H3 tail interactions with DNA by removing not only direct interactions formed by these residues , but also by consequentially weakening the interactions of neighboring cationic and polar residues with the DNA . While results show that neutralization of lysines substantially weakens tail-DNA binding , simulations also reveal that interactions between arginines and DNA provide the largest individual residue contributions . To probe the effects of arginine neutralization , we conducted simulations of modified NCPs where the arginine residues that are not implicated in PHD binding ( R8 , R17 , and R26 ) were mutated to alanine in the presence of K4me3 ( H3K4me3/3xR-A-NCP ) . As was seen with acetylation of lysine , the H3 tails in these simulations compacted to a similar extent as was seen for the unmodified NCP ( H3 Rg = 25 . 4 + /- 0 . 3 Å ) , though notably , they collapsed at a slower rate than the other NCPs ( Figure 7—figure supplement 2 ) . The H3K4me3/3xR-A tails occupy a similar volume of the DNA surface as compared to K4me3 alone ( Figure 7A , right vs . Figure 5B , Video 4 ) . There was a small increase in the average helical content ( Table 1 ) , however each residue was still disordered in over 50% of the simulations ( Figure 7—figure supplement 3 ) . In comparison to the unmodified NCP , the mutated tails have a decreased level of solvent exposed surface area ( 2534 ± 39 Å2 ) , largely due to the decreased size of the alanine side-chain . Most notably , however , is that the strength of DNA binding by the mutated tail is the lowest of all NCPs tested ( ΔG = −45 . 1 + /- 2 . 7 kcal/mol ) , and this decrease in interaction strength can be almost entirely attributed directly to the alanine mutations ( Figure 7C ) , with contributions from neighboring lysines only modestly affected by the mutations . This is in stark contrast to the quadAc-NCP , which displayed a correlation between PTMs at lysine sites and a reduced interaction strength of neighboring residues with the nucleosomal DNA . Together this reveals that mutation or modification along the H3 tail weakens , but does not abolish , the H3 tail interaction with DNA , which is mediated by a number of contacts along the tail . This suggests that modifications outside of an effector domain binding site might alter accessibility to interaction with an effector domain , mediating cross-talk between PTMs . To determine if these mutations and modifications increase accessibility for PHD finger binding , we used NMR . Specifically , we tested PHD finger binding to three distinct H3KC4me3-NCPs: one in which R8 , R17 , and R26 were mutated to alanine ( H3KC4me3/3xR-A-NCP ) , one in which K14 , K18 , K23 , and K27 were mutated to glutamine ( H3KC4me3/4xK-Q-NCP ) , and one in which S10 and S28 were phosphorylated using Aurora B kinase ( H3KC4me3/phos-NCP ) . Note that glutamine is a commonly used acetyl-lysine mimetic because the polar side chain of glutamine mimics that of the neutralized lysine . To confirm the validity of this mimic , we compared the simulations of NCPs containing acetylated lysine with those containing glutamine at the same positions ( with and without K4me3 ) and found that they were in good agreement with one another for the Rg value for H3 , secondary structure , and interaction energy with DNA ( Figure 7—figure supplements 4–6 , Videos 5 and 6 ) . The only difference was in the solvent exposed surface area , but this is entirely accounted for by the acetylated lysine being physically larger than its glutamine counterpart ( Figure 7—figure supplement 7 ) . Sequential 1H-15N HSQC spectra were recorded on 15N-PHD upon titration of H3KC4me3/4xK-Q- , H3KC4me3/3xR-A- , or H3KC4me3/phos-NCP ( Figure 8A , and Figure 8—figure supplement 1–3 ) . Compared to titration of H3KC4me3-NCP containing no additional modifications , the same set of residues is perturbed , and the resonances track along the same trajectories . However , at equivalent concentrations of NCP , PHD finger resonances have progressed farther towards the H3KC4me3-Tail-bound state in binding to H3KC4me3/4xK-Q- , H3KC4me3/3xR-A- , and H3KC4me3/phos-NCP as compared to binding to H3KC4me3-NCP . This indicates that the PHD finger is binding the H3 tail with the same molecular mechanism between all NCP samples , but that when additional tail residues are modified or mutated , the observed binding affinity is higher . Failure to reach saturation precludes the ability to determine exact Kd values; however , plotting the CSPs as a function of concentration of the H3KC4me3 mark indicates that the binding trends in the order of the weakest to tightest binding to H3KC4me3-NCP , followed by H3KC4me3/4xK-Q- and H3KC4me3/phos-NCP , with the tightest binding to H3KC4me3/3xR-A-NCP ( Figure 8B , Figure 8—figure supplement 4 ) . Thus , neutralizing the charge of basic residues or introducing negative charges results in an increase in accessibility to PHD finger binding . This is consistent with the smaller computed binding energy observed between the H3 tail and nucleosomal DNA in the MD simulations of H3K4me3/quadAc- , H3K4me3/4xK-Q- and H3K4me3/3xR-A-NCP ( see above ) , and the computed binding energies trend in the same order as the experimental data ( see Table 1 ) . Neutralization of arginine appears to have a greater effect than neutralization of lysine , especially when taking into account that only 3 arginine but four lysine residues were mutated . This is consistent with the computed greater favorable energetic contribution of the arginine than lysine residues ( Figure 7C ) . It is clear that altering the charge of several H3 tail residues is not sufficient to fully release the H3 tail from the nucleosomal DNA and recover the full binding potential of the PHD finger-H3KC4me3 interaction . This further supports that many contacts along the length of the tail , albeit dominated by the basic residues , are involved in a high avidity interaction with the nucleosomal DNA .
In this study , we find that the nucleosome inhibits binding of the BPTF PHD finger to the methylated histone H3 tail . This adds to a growing body of evidence that the nucleosome context can have a significant effect on chromatin signaling events , including histone tail binding and modification ( Stützer et al . , 2016; Wang and Hayes , 2007; Munari et al . , 2012; Gatchalian et al . , 2017 ) . We show that inhibition of PHD finger binding is due to the conformation of the tail in the context of the nucleosome core particle . Our data indicate a robust , but conformationally heterogeneous interaction of the H3 tail with the nucleosome core , driven by contacts with DNA ( Figure 9A ) . The interaction with DNA is competitive with respect to PHD finger binding . As this collapsed conformation is favored under physiological conditions , this inhibits association of the PHD finger with the H3 tail in the context of the nucleosome ( Figure 9B ) . The classical model of the nucleosome depicts the tails as extended and accessible to binding . Indeed , the tails are highly susceptible to protease degradation and generally not resolved in crystal structures , and NMR data indicates substantial conformational dynamics within the tails ( Zhou et al . , 2012; Böhm and Crane-Robinson , 1984; Rosenberg et al . , 1986; Luger et al . , 1997; Gao et al . , 2013 ) . However , many biochemical and biophysical studies have also suggested that the histone tails interact with DNA and have an effect on nucleosome stability . Molecular dynamics studies also widely show that the histone tails collapse onto DNA during the course of a simulation ( Li and Kono , 2016; Shaytan et al . , 2016; Ikebe et al . , 2016; Erler et al . , 2014; Bowerman and Wereszczynski , 2016 ) . Moreover , a CHIP-exo study suggests that the H3 tails associate with linker DNA in vivo ( Rhee et al . , 2014 ) . Here we develop a model that is consistent with both ( Figure 9B ) . We find that the H3 tails associate robustly with DNA in the context of the nucleosome , but with substantial conformational flexibility . Notably , DNA binding is independent of the presence of available linker DNA , as we find here that it also robustly associates with the core DNA . Our simulations reveal that there exists a plethora of energetically similar but structurally heterogeneous DNA-bound states of the H3 tail . Though simulations show that these binding modes remain highly stable up to the 100ns timescale , the fact that there is only a single peak observed for each residue by NMR suggests that movement between these collapsed conformations is fast on the NMR timescale . Fast exchange on the NMR timescale indicates dynamic transitions on the order of sub-microseconds . Movement between collapsed conformations could involve partially or fully released/extended conformations as intermediates or could take the form of a ‘tethered diffusion’ of the H3 tail along the surface of the core DNA . Given current computing power , it is highly unlikely that individual MD simulations can rigorously sample a representative region of the H3 tail ensemble when bound to the NCP . These findings support previous studies , which showed that multiple trajectories must be collected and/or enhanced sampling approaches implemented in order to robustly simulate the bound state of the H3 tails ( Li and Kono , 2016 ) , and that caution should be exercised when drawing conclusions regarding the role of the tails in NCP stability and dynamics . The equilibrium of states is likely determined by a balance of the extended and collapsed modes , the former of which is driven by conformational entropy and the latter of which is driven by the enthalpy of tail-DNA interactions . At first glance , it may seem that the extended state should dominate due to its high flexibility and inherently large entropy . However , our results suggest that the collapsed tail state is actually more favorable . Specifically , our MM-GBSA calculations indicate that there is a significant enthalpic advantage to compacted configurations due to strong Coulomb interactions that overwhelm the loss of configurational entropy upon tail collapse . Furthermore , the large variability in compacted tail configurations observed in our simulation trajectories suggests that the collapsed state likely has a significant configurational entropy of its own , which may also alleviate some of the entropic cost of compaction . Our results align with a recent study by Stützer et al . , 2016 . on the X . laevis 187bp-nucleosome . Similar to what we show here with inhibition of PHD finger binding , they found that acetylation and methylation of the H3 tail by a subset of histone acetyltransferases ( Gcn5 , p300 , CBP ) and methyltransferases ( G9a , PRC2 , PRMT5 , Set7/9 ) , respectively , is inhibited by the nucleosome . Using NMR , they determined this to be due to a transient interaction of the tail with linker DNA . We suggest a robust yet dynamic interaction not only with linker DNA but also with DNA in the nucleosome core . It is likely that all histone tails are associated in some manner with nucleosomal DNA . An NMR analysis of the histone H4 tail found that the small basic patch within the tail ( residues 16–20 ) is associated with the nucleosome core , as evidenced by the complete lack of signal in an 1H-15N HSQC ( Zhou et al . , 2012 ) . The contrast to observable signals for H3 could represent either a different exchange regime between states or even a single stable state for the H4 tail , as suggested by a recent computational study ( Erler et al . , 2014 ) . In addition , a number of biochemical studies have shown through chemical reactivity and cross-linking that the N- and C-terminal tails are associated with the nucleosome core ( for a review see [Pepenella et al . , 2014] ) . This collapsed conformation presents a much more complex binding interface to histone effector domains than would an extended tail , and many effectors will likely be inhibited as seen here with the BPTF PHD finger . In fact , studies of fluorescein 5-maleimide reactivity towards single cysteine mutants of the H2B tail in the context of the nucleosome suggest that effector domain binding should be 10- to 50-fold weaker than binding to an H2B tail peptide ( Wang and Hayes , 2007 ) . A modest decrease in binding to the H3K9me3-NCP as compared to peptide was also observed for the chromodomain containing HP1β ( Munari et al . , 2012 ) . This nucleosomal inhibition is less than that observed here for the BPTF PHD finger , but regions flanking the chromodomain are thought to interact with DNA , which could somewhat counter the inhibition . Another recent study reported on binding of the tandem CHD4 PHD fingers to the histone H3 tail unmodified at lysine 443 . Similar to our results , the individual CHD4 PHD fingers bound too weakly to the NCP to accurately measure binding affinity . Though the linked PHD fingers also bound more weakly to the nucleosome than to histone peptides , there was only a ~ 6 × difference in affinity ( Gatchalian et al . , 2017 ) . The increase in affinity for the dual domain as compared to the individual PHD fingers is purportedly due to the multivalent activity of the tandem PHD fingers as well as contributions to histone binding by the region linking the two PHD fingers ( Gatchalian et al . , 2017 ) . There are many mechanisms by which the inhibitory conformations of the histone tails could be regulated . We show here that neutralization of lysine or arginine along the H3 tail , as occurs with several types of lysine acylation and arginine citrullination , weakens the association with DNA and promotes PHD finger binding . We also observe an increase of binding upon phosphorylation of serine . This is in agreement with recent studies showing that acetylation or phosphorylation of H3 tails promotes subsequent additional modification of the H3 tail ( Stützer et al . , 2016 ) . Notably , there are many additional modifications including ADP-ribosylation and certain acylations that not only neutralize lysine , but add negative charge to the side-chain . In addition , modification by the small proteins ubiquitin and SUMO would likely lead to large steric hindrance . This suggests that cross-talk between histone PTMs is not only mediated through effector domains themselves ( i . e . recognition of multiple PTMs by multiple domains , or antagonism between neighboring PTMs by direct inhibition of binding ) but can also be mediated by the integrated effects of PTM recognition and nucleosome structure ( Figure 9C ) . It is also possible that DNA-binding domains could displace the histone tail and increase accessibility . There are now several examples of histone effector domains that also harbor DNA-binding ability and have been found to bind tighter to nucleosomes than to histone peptides alone ( Charier et al . , 2004; Kim et al . , 2010; Qiu et al . , 2012; Eidahl et al . , 2013; Musselman et al . , 2013; van Nuland et al . , 2013; Savitsky et al . , 2016; Miller et al . , 2016; Morrison et al . , 2017 ) . This could result from a combination of additional contacts with the nucleosome and displacement of the histone tail . A similar effect could be seen with adjacent DNA binding domains as suggested previously ( Pilotto et al . , 2015 ) . Altogether our data not only supports a model of the nucleosome where the tails are collapsed rather than extended , but also suggests many higher order regulatory mechanisms within chromatin signaling cascades , and highlights the importance of understanding the molecular mechanism of effector complexes at the nucleosome .
A codon optimized BPTF PHD finger gene fragment ( residues 2865–2924 of UniProt entry Q12830 ) was obtained from Integrated DNA Technologies ( IDT , Coralville , IA ) and cloned into the pDEST15 vector using Invitrogen Gateway recombination cloning technology ( ThermoFisher Scientific , Waltham , MA ) with an engineered N-terminal PreScission Protease cleavage site . BL21 ( DE3 ) Chemically Competent E . coli ( ThermoFisher Scientific or New England Biolabs , Ipswich , MA ) were used for expression . E . coli was grown in LB media or M9 minimal media supplemented with vitamin ( Centrum , New York City , NY ) , 1 g L-1 15NH4Cl , and 5 g L−1 D-glucose to produce unlabelled or 15N-isotopically enriched protein , respectively . Media was supplemented with 100–200 μM ZnCl2 . Bacteria was grown to an OD600 ~1 . 0 and induced with 0 . 3 mM IPTG at 18°C for 16–20 hr . For purification of the GST-fusion PHD finger , cells were lysed in 20 mM Tris pH 7 . 5 , 500 mM NaCl , 3 mM DTT , 0 . 5% Triton X-100 , 0 . 5 mg mL−1 lysozyme with DNaseI and Pierce EDTA-free Protease Inhibitor Tablets ( ThermoFisher Scientific ) using an Avestin ( Canada ) EmulsiFlex or sonication . The soluble portion of the lysate was incubated with glutathione agarose resin ( ThermoFisher Scientific ) and washed extensively with buffer ( 20 mM MOPS pH 7 . 0 , 150 mM KCl , 1 mM DTT ) . Samples were cleaved from the GST tag overnight with PreScission Protease and further purified using anion exchange ( Source 15Q , GE Healthcare Life Sciences , Pittsburgh , PA ) and size exclusion chromatography ( Superdex 75 10/300 , GE Healthcare Life Sciences ) . The final buffer for all samples was 20 mM MOPS pH 7 . 0 , 150 mM KCl , and 1 mM DTT . Note that 20 mM MOPS buffers were adjusted to pH 7 by adding 7 mM NaOH . The concentrations of PHD finger samples were determined via UV-vis spectroscopy using the absorbance ( calculated ε280 = 12 , 950 M−1cm−1 ) . Unmodified human histones ( H2A . 1 uniprot accession P0C0S8 , H2B . 1C uniprot accession P62807 , H4 uniprot accession P62805 with T71C ) were expressed out of pET3a vectors . A codon-optimized version of H3 . 2 ( uniprot accession Q71DI3 ) was obtained from IDT and cloned into pET3a . Codon and non codon-optimized versions of H3 were made with a cysteine-free background ( C110A ) , and the non codon-optimized version also carries the common G102A mutation . The Q5 mutagenesis kit ( New England Biolabs ) was used to introduce additional mutations . The H3 mutants generated were: 1 ) K4C , 2 ) K4C/K14Q/K18Q/K23Q/K27Q ( referred to as H3K4C/4xK-Q ) , and 3 ) K4C/R8A/R17A/R26A ( referred to as H3K4C/3xR-A ) . The K4C mutation was made to the non codon-optimized H3 and the K4C/K14Q/K18Q/K23Q/K27Q and K4C/R8A/R17A/R26A mutations were made to the codon-optimized H3 . Rosetta 2 ( DE3 ) pLysS ( Novagen , Burlington , MA ) or BL21 ( DE3 ) ( New England Biolabs ) chemically competent E . coli were used for expression . Unlabelled growths were carried out in either LB or M9 media and were induced at OD600 ~ 0 . 4 with 0 . 2 mM ( for H4 ) or 0 . 4 mM ( for H2A , H2B , and H3 ) IPTG for 3–4 hr . 15N- and 13C/15N-isotopically enriched H3 was grown in M9 minimal media supplemented with vitamin ( Centrum ) , 1 g L-1 15NH4Cl , and 5 g L−1 unlabelled or 3 g L-1 13C D-glucose . Histones were extracted from inclusion bodies following ( Qiu et al . , 2012 ) and purified via ion exchange chromatography . The trimethyl lysine analogue was installed using alkylation of H3K4C following ( Simon , 2010 ) . Briefly , H3K4C was dissolved at 10 mg/mL in alkylation buffer ( 4M guanidine hydrochloride , 1M HEPES pH 7 . 8 , 10 mM DL-methionine ) with 20 mM fresh DTT by incubating at 37°C for 1 hr . ( 2-Bromoethyl ) trimethyl ammonium bromide was added as the alkylating agent at 100 mg/mL and incubated at 50°C for 2 . 5 hr . An additional 10 mM of fresh DTT was added , followed by another 2 . 5 hr incubation . Each 1 mL reaction was quenched with 50 μL β-mercaptoethanol , and H3KC4me3 was de-salted using a PD10 column ( GE Healthcare Life Sciences ) and dialyzed against 2 mM β-mercaptoethanol in H2O . Unmodified , H3KC4me3 , H3KC4me3/4xK-Q , and H3KC4me3/3xR-A octamers were prepared as described in ( Qiu et al . , 2012 ) . Briefly , equimolar ratios of histones were mixed in 20 mM Tris pH 7 . 5 , 6M Guanidine HCl , 10 mM DTT , dialyzed into 20 mM Tris pH 7 . 5 , 2M KCl , 1 mM EDTA , 5 mM β-ME , and purified over a sephacryl S-200 column ( GE Healthcare Life Sciences ) via FPLC . A plasmid containing 32 repeats of the 147 bp Widom 601 sequence ( ATCGAGAATCCCGGTGCCGAGGCCGCTCAATTGGTCGTAGACAGCTCTAGCACCGCTTAAACGCACGTACGCGCTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCACGTGTCAGATATATACATCCGAT ) was amplified in E . coli and purified via alkyline lysis methods , largely as outlined in ( Qiu et al . , 2012 ) . The 601 repeats were released by cleavage with EcoRV and purified from parent plasmid by polyethylene glycol precipitation , and further purified over a source 15Q column ( GE Healthcare Life Sciences ) via FPLC . Unmodified , H3KC4me3 , H3KC4me3/4xK-Q , and H3KC4me3/3xR-A NCPs were reconstituted with the 147 bp Widom 601 sequence via desalting methods ( Qiu et al . , 2012 ) . Briefly , octamer and 601 DNA were mixed at a 1:1 molar ratio and desalted using a linear gradient from 2M to 150 mM KCl over ~48 hr . NCPs were heat-shocked at 37°C for 30 min to obtain uniform positioning and then purified using a 10–40% sucrose gradient . Proper nucleosome formation was confirmed via native polyacrylamide gel electrophoresis and by the sucrose gradient profile . Nucleosome concentrations were determined via UV-vis spectroscopy using the absorbance from the 601 DNA ( calculated ε260 = 2 , 312 , 300 . 9 M−1cm−1 ) . Before measuring the concentration , samples were diluted into 2M KCl in order to promote nucleosome disassembly for more accurate concentration determination . In order to produce tailless NCPs ( tlNCPs ) , reconstituted NCPs were treated with TPCK Trypsin immobilized on magnetic beads ( Takara Bio , Japan ) . NCPs were incubated with the beads at room temperature for 30 min in 0 . 5xTE ( which resulted in little digestion ) and then for 70 min with 75 mM KCl ( see Figure 3—figure supplement 2 for time points ) . The digestion and final sample resembled ( Ausio et al . , 1989 ) . The H3KC4me3-NCP sample was treated with Aurora B kinase in order to phosphorylate serine 10 and 28 on H3 ( H3KC4me3/S10ph/S28ph-NCP , referred to as H3KC4me3/phos-NCP ) . After titrating the H3KC4me3-NCP sample into 15N-BPTF PHD , the H3KC4me3-NCP sample was recovered and ~1 mM EDTA was added to unfold the PHD finger . This recovered NCP was treated with recombinant human Aurora B ( AurB ) protein ( abcam product ab51435 , Cambridge , MA ) . The NCP was treated with 3 μg of AurB for 4 . 2 hr at 30°C ( 4 . 4 mM MgCl2 , 0 . 6 mM EGTA , 0 . 7 mM EDTA , 1 . 5 mM β-glycerophosphate , 2 mM ATP , 0 . 6 mM benzamidine , 20 mM MOPS pH 7 , 150 mM KCl , 0 . 7 mM DTT ) . Following the incubation , an additional 4 mM EDTA was added to chelate the free Mg2+ . The treated NCP was separated from PHD finger and kinase reaction buffer components by purifying it over a Superdex 75 10/300 column ( GE Healthcare Life Sciences ) . Phosphorylation of H3KC4me3 was confirmed via an 18% SDS-PAGE gel prepared using Zn2+-Phos-tag acrylamide AAL-107 ( Wako Pure Chemical Industries , Japan , see Figure 8—figure supplement 3 ) . The gel was prepared and run according to recommendation as outlined in the Wako Pure Chemical Industries manual for Zn2+-Phos-tag for SDS-PAGE using a neutral-pH buffer system , using 50 μM Zn2+-Phos-tag acrylamide AAL-107 , including ZnCl2 at a final concentration of 0 . 3 mM in all gel samples ( including the ladder and empty wells ) , and running the gel on ice . The histone peptide H3 ( 1–10 ) K4me3 was obtained from Anaspec ( Fremont , CA ) . Concentrated peptide stocks were prepared in H2O based on the weight provided by the company . The pH of stock solutions was adjusted to pH ~7 with NaOH . The H3 ( 1–44 ) construct ( ARTKQTARKS TGGKAPRKQL ATKAARKSAP ATGGVKKPHR YRPG ) was expressed out of pET3a after inserting two stop codons ( TAATAA ) between codons for residues 44 and 45 into the pET3a plasmid containing codon-optimized H3 using the Q5 mutagenesis kit ( New England Biolabs ) . This kit was also used to generate the K4C mutant . Unlabeled , 15N- or 13C/15N-isotopically enriched H3 ( 1–44 ) or H3K4C ( 1-44 ) were expressed in BL21 ( DE3 ) chemically competent E . coli ( New England Biolabs ) in the same manner as for the full length H3 ( see above ) , except that cells were grown to OD600 ~1 . 0 prior to induction . To purify H3 ( 1–44 ) constructs , cells were lysed in 50 mM Tris pH 7 . 5 , 100 mM NaCl , 1 mM benzamidine , 2 mM EDTA , 0 . 5% Triton X-100 , 0 . 5 mg mL−1 lysozyme with DnaseI and Pierce Protease Inhibitor Tablets ( ThermoFisher Scientific ) using an Avestin EmulsiFlex . The soluble portion of the lysate was purified using cation exchange resin and size exclusion chromatography ( Superdex 30 , GE Healthcare Life Sciences ) in 50 mM KPi pH 7 , 50 mM KCl , 0 . 5 mM EDTA . Samples were then desalted by purifying over a Superdex 75 column ( GE Healthcare Life Sciences ) and lyophilized . Final samples were prepared in 20 mM MOPS pH 7 . 0 , 150 mM KCl , and 1 mM DTT ( and with 1 mM EDTA for samples that would not be used with the BPTF PHD finger ) . Note that 20 mM MOPS buffers were adjusted to pH 7 by adding 7 mM NaOH . The H3 ( 1–44 ) K4C sample was alkylated following the same protocol elaborated above for the full length H3K4C , and the proper alkylation ( trimethyl lysine analogue ) was confirmed via ESI mass spectrometry ( see Figure 1—figure supplement 3 ) . The concentrations of H3 ( 1–44 ) KC4me3 stocks were determined via UV-vis spectroscopy using the absorbance from the native tyrosine Y41 ( using the calculated ε280 = 1490 M−1cm−1 ) . ESI mass spectrometry was used to analyze the full length H3 histones and histone tails , to confirm the removal of the N-terminal methionine , check that there was no carbamylation , and confirm proper alkylation of the MLAs . A Waters Q-Tof Premier instrument ( Milford , MA ) was used with positive electrospray ionization ( ESI ) . Samples were diluted 1:2 or 1:4 in water/acetonitrile ( 1:1 ) with 0 . 1% formic acid . The acquisition and deconvolution software used during data collection and analysis were MassLynx and MaxEnt , respectively . Oligos for use in NMR studies were obtained from IDT with the following sequences: 5′-CTCAATTGGTCGTAGACAGCT-3’ and 5′-AGCTGTCTACGACCAATTGAG-3′ . Double stranded oligos were annealed at a concentration of 350 μM by heating to 94°C for 10 min followed by a slow cooling to room temperature . The annealed duplexes were purified by size exclusion chromatography ( Superdex 75 10/300 , GE Healthcare Life Sciences ) in NMR buffer ( 20 mM MOPS pH 7 . 0 , 150 mM KCl , 1 mM DTT , and 1 mM EDTA in samples not used with the PHD finger ) and concentrated . Concentration of DNA stocks was determined via UV-vis spectroscopy using the extinction coefficient predicted by the IDT Biophysics UV spectrum tool for duplex DNA at 260 nm ( http://biophysics . idtdna . com/cgi-bin/uvCalculator . cgi ) , which is 333 , 804 . 5 M−1cm−1 for this DNA . Assignments for the BPTF PHD finger were transferred from ( Li et al . , 2006 ) . Data was compared between buffer conditions and temperature titrations were performed to ensure proper transfer of assignments . Titrations of H3 tail peptides and NCPs into 15N-PHD were carried out by collecting 1H-15N HSQC spectra on 15N-PHD in the apo state and with increasing concentrations of substrate , using TROSY with NCP substrates . 15N-PHD samples were at 50 μM in 20 mM MOPS pH 7 , 150 mM KCl , 1 mM DTT , and 7% D2O . Titrations were collected at PHD:peptide ratios of 1:0 , 1:0 . 1 , 1:0 . 25 , 1:0 . 5 , 1:1 , 1:2 , 1:5 , and additionally 1:10 with H3 ( 1–44 ) KC4me3 . For titrations with H3 tail peptides pre-bound to DNA the peptide and DNA were mixed at a ratio of 1:2 , and this stock was titrated into the 15N-PHD at the same PHD:peptide ratios ( without the 1:0 . 1 point for H3 ( 1–44 ) KC4me3 ) . Titrations with H3KC4me3- , H3KC4me3/4xK-Q- , H3KC4me3/3xR-A- , and H3KC4me3/phos-NCP were collected at PHD:NCP ratios of 1:0 , 1:0 . 1 , 1:0 . 25 , 1:0 . 5 , 1:1 , and 1:2 and additionally a final point at 1:3 , 1:2 . 8 , and 1:2 . 7 for H3KC4me3- , H3KC4me3/4xK-Q- , and H3KC4me3/3xR-A-NCP . The NCPs were stable over the course of all titrations , as supported by native gels ( see Figure 8—figure supplement 3 for examples of gels ) . Data was collected at 37°C on an 800MHz Bruker ( Billerica , MA ) spectrometer with a cryogenic probe , collecting 24 scans for titrations with the H3 tail peptides and 32 scans for titrations with the NCPs . Titration data were processed in NMRPipe ( Delaglio et al . , 1995 ) and analyzed using CcpNmr Analysis ( Vranken et al . , 2005 ) . Binding curves were fit using a nonlinear least-squares analysis in Igor ( Wavemetrics , Portland , OR ) to a single-site binding model under ligand-depleted conditions:Δδ=Δδmax ( ( [L]+[P]+Kd ) − ( [L]+[P]+Kd ) 2−4[P][L] ) / ( 2[P] ) where [P] is the concentration of protein , [L] is the concentration of histone peptide or DNA , Δδmax is the chemical shift difference at saturation . The combined chemical shift difference ( Δδ ) at each point in the titration is calculated by:Δδ= ( ΔδH ) 2+ ( 0 . 154ΔδN ) 2where ΔδH and ΔδN are the changes in the 1H and 15N chemical shift , respectively , at each titration point with respect to the apo chemical shifts . Binding curves were fit using two independent variables ( ligand and protein concentrations , to account for dilution ) for residues that were significantly perturbed upon binding . Residues were determined to be significantly perturbed if the Δδ was larger than the average plus one-half standard deviation of the Δδ values for all residues . ( The relatively low cutoff of one-half standard deviation was chosen for the PHD finger because the binding interface of such a small domain represents a relatively large portion of the domain . ) Reported Kd values were determined by fitting Kd values for the significantly perturbed individual residues , calculating the average and standard deviation of the Kd values for these residues , and removing residues with fit Kd values not within the average ±two standard deviations ( which was never more than a single residue ) . This resulted in using 14 residues for H3 ( 1–44 ) KC4me3 and 15 residues for H3 ( 1–44 ) KC4me3 pre-bound to DNA . Assignments on nucleosomes reconstituted using 167 or 187 bp 601 DNA have been published ( Zhou et al . , 2012; Stützer et al . , 2016 ) . We confirmed assignments with NCP reconstituted using 147 bp 601 DNA because assignments have not been published with this construct . To obtain backbone assignments for native H3 within the context of the NCP , HNCACB , CBCAcoNH , and HNCO spectra were collected on a 320 μM 13C/15N-H3-NCP sample ( i . e . 640 μM of H3 component ) using a 600MHz Varian spectrometer at 45°C . Data was processed in NMRPipe ( Delaglio et al . , 1995 ) and analyzed using CcpNMR Analysis ( Vranken et al . , 2005 ) . The HNCACB and CBCAcoNH were collected with 32 and 24 scans , respectively , and 56 and 36 complex increments in the 13C- and 15N-dimensions , respectively . The HNCO was collected with 8 scans and 36 complex increments in both the 13C- and 15N-dimensions , respectively . Temperature titration was used to transfer assignments to 25°C and 37°C . 1H-15N HSQC spectra collected on an H3KC4me3-NCP sample were used to help confirm assignments of degenerate sections . Backbone assignments were made for DNA-bound 13C/15N-H3 ( 1–44 ) . The DNA-bound state was chosen to resolve degeneracy observed in the apo state . Data was collected on a sample containing 1 . 1 mM 13C/15N-H3 ( 1–44 ) and 1 . 4 mM DNA in 20 mM MOPS pH 7 , 150 mM KCl , 1 mM EDTA , 8% D2O using a 500 Bruker spectrometer at 10°C . Assignments were made based on HNCACB ( 40 scans with 43 and 40 complex increments in the 13C- and 15N-dimensions , respectively ) and HNcoCACB ( 8 scans with 40 complex increments in the 13C- and 15N-dimensions ) spectra . Temperature and DNA titrations were used to transfer assignments between conditions . 1H-15N HSQC spectra were collected on 15N-H3 ( 1–44 ) and 15N-H3-NCP samples at a range of KCl and MgCl2 concentrations and temperatures on an 800MHz Bruker spectrometer with cryogenic probe . Titrations of 21 bp DNA ( see above ) , NCP , and tlNCP into 15N-H3 ( 1–44 ) were carried out by collecting sensitivity enhanced ( SE ) SOFAST 1H-15N HMQC spectra on 15N-H3 ( 1–44 ) in the apo state and with increasing concentrations of substrate . Buffer conditions were 20 mM MOPS pH 7 , 150 mM KCl , 1 mM DTT , and 1 mM EDTA . The DNA titration was repeated with 1 mM MgCl2 and no EDTA to test for the effect of Mg2+ . Titrations were collected at H3 ( 1–44 ) :DNA ratios of 1:0 , 1:0 . 1 , 1:0 . 25 , 1:0 . 5 , 1:1 , 1:2 , and 1:4 and H3 ( 1–44 ) :NCP/tlNCP of roughly 1:0 , 1:0 . 2 , 1:0 . 5 , 1:1 , 1:2 , and 1:3 . The titrations were collected at 25°C on an 800MHz Bruker spectrometer with cryo probe . Titration data were processed in NMRPipe ( Delaglio et al . , 1995 ) and analyzed using CcpNmr Analysis ( Vranken et al . , 2005 ) . The composite chemical shift difference ( Δδ ) at each point in the titration is calculated by:Δδ= ( ΔδH ) 2+ ( 0 . 154ΔδN ) 2where ΔδH and ΔδN are the changes in the 1H and 15N chemical shift , respectively , at each titration point with respect to the apo chemical shifts . An N-terminal biotin tag was added to the BPTF PHD finger for BLI experiments . The Q5 mutagenesis kit ( New England Biolabs ) was used to introduce an AviTag and an additional linker ( GLNDIFEAQKIEWHEGSGS ) . The AviTag-PHD construct was grown and purified as described above , with the exception that this version of the BPTF PHD finger was not codon optimized and expression was done out of Rosetta 2 ( DE3 ) pLysS competent cells ( Novagen ) . The AviTag-PHD construct was biotinylated in vivo by endogenous BirA ( and confirmed via western blot , data not shown ) . Cells were grown in LB media , and 100 μM biotin was added at induction . All of the experiments were performed using biotin-PHD from a single protein prep so the population of biotinylated protein was constant between experiments . Experiments were run using an Octet RED96 Biolayer Interferometry ( BLI ) System ( Pall ForteBio , Menlo Park , CA ) . All samples were prepared in 20 mM MOPS pH 7 , 150 mM KCl , 1 mM DTT , 0 . 2 mg/mL BSA ( RPI albumin , bovine fraction V , molecular biology grade ) . Dip and Read Steptavidin Biosensors ( Pall ForteBio ) were used for all experiments and were hydrated in buffer for at least 30 min preceding data collection . Experiments were performed at 37°C in black 96-well plates ( Greiner Bio-One , Monroe , NC ) , shaking at 1000 rpm . Data were collected at 10 Hz with a scheme of 10 min temperature pre-equilibration followed by 180 s buffer equilibration , 300 s biotin-PHD loading , 120 s buffer baseline , 300 s analyte association , and 300 s analyte dissociation steps . Two data sets were collected with H3 ( 1–44 ) KC4me3 as analyte . For one of the data sets , double referencing was performed . This consisted of the standard single reference sensor that was loaded with biotin-PHD with the association phase performed using buffer alone and additionally a second set of sensors , not loaded with biotin-PHD , that were run through the same protocol for all analyte concentrations to test for optical property changes and non-specific binding . As no difference was observed between either referencing , the second data set was obtained with only the single reference of biotin-PHD against buffer . H3 ( 1–44 ) KC4me3 analyte concentrations used were 20 , 10 , 5 , 2 . 5 , 1 . 3 , and 0 . 6 μM , and the double referenced experiment additionally used 0 . 3 μM . Separate peptide dilutions were prepared for each experiment . Two data sets were also collected with H3KC4me3-NCP as analyte . Note that double referencing was needed for the NCP as a large offset in response signal was seen independent of the PHD finger , likely due to changes in the optical properties of the solution . H3KC4me3-NCP analyte concentrations used were 183 , 21 , and 10 μM for one experiment and 230 , 23 , and 11 μM for the other experiment . Data were analyzed using the Octet Analysis software . Data were processed by subtracting the single or double reference data , aligning to the last 5–10 s of the baseline , and applying a Savitzky-Golay smoothing filter . Fast kinetics precluded the use of kinetic fits to determine binding affinity . Instead , equilibrium dissociation values were calculated by taking the average of the last 20 s of the association phase ( req ) and plotting this against the analyte concentration ( x ) . This curve was fit to obtain the Kd value using a simple single-site binding equation:req=r0+rmax-r0xKd+x Data were fit using a nonlinear least-squares analysis in Igor ( Wavemetrics ) . Simulations of nucleosomes containing six modification states of the H3 tail were performed: an unmodified tail ( denoted un-NCP ) , H3K4me3 ( H3K4me3-NCP ) , and H3K14 , 18 , 23 , 27ac ( quadAc-NCP ) , H3K14 , 18 , 23 , 27ac ( 4xK-Q-NCP ) , a combination of H3K4me3 and H3K14 , 18 , 23 , 27Q ( H3K4me3/4xK-Q-NCP ) , and a combination of H3K4me3 and H3R8 , 17 , 26A ( H3K4me3/3xR-A-NCP ) . For each system , NCP simulations were initiated from three different initial H3 tail conformations: one based on the 1KX5 crystal structure , one in which the H3 tails were extended linearly from residues 1 to 40 , and one de novo structure predicted with the MODELLER software package ( Shen and Sali , 2006 ) . In the MODELLER calculations , a set of 25 possible H3 tail structures was created and used to construct a collection of 625 potential NCP models by substituting one of the 25 generated tail states for each copy of H3 in the 1KX5 NCP . These 625 conformations were then energy minimized in an implicit solvent environment ( Onufriev et al . , 2004 ) , and the initial conformation used for simulations was taken as the one with the most favorable energetics for the un-NCP . In all systems , the missing three residues of H2B ( 1PEP3 ) were extended linearly from the histone N-terminus using tLeap . By considering each of these three H3 tail conformations , simulations were conducted on a total of nine different systems . Protein and DNA parameters were based on the Amber14SB and bsc1 forcefields ( Maier et al . , 2015; Ivani et al . , 2016 ) , and PTM parameters were previously determined by Papamokos et al . ( Papamokos et al . , 2012 ) . All systems were neutralized and solvated in a TIP3P solution of 150 mM KCl ( Jorgensen et al . , 1983; Joung and Cheatham , 2009 ) . Heavy atom masses were repartitioned to their associated H atoms so that , in conjunction with SHAKE restraints , a four fs time-step could be used ( Hopkins et al . , 2015; Ryckaert et al . , 1977 ) . Simulations were conducted in the CUDA-enable pmemd engine ( v16 ) ( Salomon-Ferrer et al . , 2013; Le Grand et al . , 2013 ) , and five separate simulations for each combination of NCP system and tail conformation were performed , for a total of ninety independent simulations . For each simulation , energy minimization was performed for 5000 steps with solute heavy atoms restrained by a 10 kcal/mol/Å2 harmonic potential , followed by 5000 steps with no restraints . Then , each simulation was heated from 10 K to 300 K over 50 ps in the NVT ensemble with the restraints reinstated . Next , the restraints were gradually released over 250 ps in the NPT ensemble , using a Monte Carlo barostat with a target pressure of 1 atm and a relaxation time of 3 ps . Temperature was regulated using a Langevin thermostat ( Loncharich et al . , 1992 ) with a collision frequency of 3 . 0 ps−1 . Simulations were then conducted for 150 ns in the NPT ensemble with no restraints , yielding a cumulative sum of 13 . 5 µs of simulation time across all systems . Trajectories were recorded every 10 ps , and frames were visualized using VMD ( Humphrey et al . , 1996 ) and PyMol . Since 100 ns of simulation was required for the systems to equilibrate , only the last 50 ns of each simulation was used in the computational analysis ( 750 ns net for each NCP system ) . Interaction energies between the H3 tails and DNA in each system were determined from the sum of tail residue contributions to DNA binding according to an MM-GBSA ( Molecular Mechanics Generalized Born Surface Area ) analysis ( Miller et al . , 2012 ) . Reported energies are the average of all results from each single simulation of a set ( i . e . , tail chemistry or initial tail conformation ) . Each tail within a simulation is considered a separate observation of the H3 tail ensemble , thereby producing two samples per simulation . Errors in the calculations are presented as the standard error of the mean from the simulation set , which corresponds to the standard deviations within the 10 samples of each initial tail conformation for a particular tail chemistry ( a cumulative of 30 samples per tail chemistry ) . Residue secondary structures were calculated using the DSSP algorithm ( Kabsch and Sander , 1983 ) , as implemented in cpptraj ( Roe and Cheatham , 2013 ) . For simplicity , secondary structures are reported as one of four categories: helix ( DSSP types ‘G’ , ‘H’ , and ‘I’ ) , sheet ( ‘E’ , ‘B’ ) , turn ( ‘T’ ) , or unstructured coil ( none of the above ) . Total tail helicity or sheet values are reported as the average over all tail residues of each residue’s percentage of frames in the respective structure . The global H3 tail structure was monitored through the radius of gyration ( Rg ) of the whole H3 histone such that reduced values in Rg can be directly interpreted as tail compaction upon the core . Furthermore , the 3-D coordinates of heavy atoms in the tails are visualized as average occupancy within a grid of 1 . 0 Å3 voxels , in a method similar to the counter-ion condensation observations made by Materese et al ( Materese et al . , 2009 ) . Solvent exposure was calculated using the LPCO method , as implemented in cpptraj ( Weiser et al . , 1999 ) . Lastly , a comparative analysis of the root mean square deviation ( RMSD ) of tail residues in the final frame of each simulation was conducted . In this analysis , translations and rotations were removed by least squares fitting the backbone of H3 core residues , and the subsequent RMSD values consider only the H3 tail residue backbone atoms . | The human genome contains all the instructions needed to build the human body . However , each human cell does not read all of these instructions , which come in the form of genes encoded in the DNA . Instead , different subsets of genes are switched on in each type of cell , while the rest of the genes are switched off . DNA within human cells is wrapped around proteins called histones , to form hundreds of thousands of structures called nucleosomes . If the DNA that encodes a gene contains a lot of nucleosomes , the DNA is not very accessible and the gene will generally be off; removing the histones or rearranging the nucleosomes can turn the gene on . Each histone contains a region called a tail – because it protrudes like the tail of a cat – that can be chemically modified in dozens of different ways . Particular combinations of histone modifications are thought to signal how the nucleosomes should be arranged so that each gene is properly regulated . However , it is unclear how these combinations of modifications actually work because , historically , it has been difficult to study tails in the context of a nucleosome . Instead most studies had looked at tails that had been removed from the nucleosome . Now , Morrison et al . set out to investigate how one protein , called BPTF , recognizes a specific chemical modification on the tail of a histone , referred to as H3K4me3 , in the context of a human nucleosome . Unexpectedly , the experiments showed that the histone-binding domain of BPTF , which binds to H3K4me3 , was impeded when the tail was attached to the nucleosome but not when it was removed from the nucleosome . Morrison et al . went on to show that this was because the histone tail is tucked onto the rest of the nucleosome and not easily accessible . Further experiments revealed that additional chemical modifications made the tail more accessible , making it easier for the histone-binding domain to bind . Together these findings show that a combination of histone modifications acts to positively regulate the binding of a regulatory protein to H3K4me3 in the context of the nucleosome by actually regulating the nucleosome itself . The disruption of the histone signals is known to lead to a number of diseases , including cancer , autoimmune disease , and neurological disorders , and these findings could guide further research that may lead to new treatments . Yet first , much more work is needed to investigate how other histone modifications are recognized in the context of the nucleosome , and how the large number of possible combinations of histone signals affects this process . | [
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] | 2018 | The conformation of the histone H3 tail inhibits association of the BPTF PHD finger with the nucleosome |
The ability to speak coherently is essential for effective communication but declines with age: older people more frequently produce tangential , off-topic speech . The cognitive factors underpinning this decline are poorly understood . We predicted that maintaining coherence relies on effective regulation of activated semantic knowledge about the world , and particularly on the selection of currently relevant semantic representations to drive speech production . To test this , we collected 840 speech samples along with measures of executive and semantic ability from 60 young and older adults , using a novel computational method to quantify coherence . Semantic selection ability predicted coherence , as did level of semantic knowledge and a measure of domain-general executive ability . These factors fully accounted for the age-related coherence deficit . Our results indicate that maintaining coherence in speech becomes more challenging as people age because they accumulate more knowledge but are less able to effectively regulate how it is activated and used .
Engaging in a conversation is a complex cognitive act , in which a speaker must settle on a topic for discussion , generate a series of appropriate , relevant and hopefully interesting statements and monitor their speech as the discourse unfolds to ensure that they remain on-topic . Discourse that successfully navigates these challenges is said to be coherent: it consists of a series of well-connected statements all related to a shared topic , making it easy to comprehend ( Foltz , 2007; Glosser and Deser , 1992 ) . The ability to produce coherent speech tends to decline as people grow older . Older adults are more likely to produce tangential , off-topic utterances in conversation ( Arbuckle and Gold , 1993; Glosser and Deser , 1992 ) and to provide irrelevant information when telling a story ( Juncos-Rabadán et al . , 2005; Marini et al . , 2005 ) or describing an object ( Long et al . , 2018 ) . Such changes reduce the effectiveness of communication and the quality of older people’s verbal interactions ( Arbuckle et al . , 2000; Pushkar et al . , 2000 ) . Indeed , less coherent speech is associated with higher levels of stress and less satisfaction in social interactions ( Arbuckle and Gold , 1993; Gold et al . , 1988; Pushkar et al . , 2000 ) . Researchers have often made a distinction between local coherence ( LC ) , the degree to which adjoining utterances relate meaningfully to one another , and global coherence ( GC ) , the degree to which each utterance relates to the topic under discussion ( Glosser and Deser , 1992; Kintsch and van Dijk , 1978 ) . Most studies have reported larger declines in GC in later life , though reductions in LC have also been observed ( Glosser and Deser , 1992; Kemper et al . , 2010; Marini et al . , 2005; Wright et al . , 2014 ) . Explanations for age-related decline in coherence have typically focused on deterioration in domain-general cognitive control processes , which are assumed to be involved in the monitoring and selection of topics during speech ( Kintz et al . , 2016 ) . This view is supported by a handful of studies reporting that coherence in speech is predicted by performance on non-verbal tasks requiring cognitive control ( Gold et al . , 1988; Kintz et al . , 2016; North et al . , 1986 ) . One particular theory holds declines in coherence result from a reduced ability to inhibit irrelevant information , which means that older people are less able to prevent irrelevant or off-topic ideas from intruding into their discourse ( Arbuckle and Gold , 1993; Marini and Andreetta , 2016 ) . This view is supported by evidence that performance on cognitive tests requiring inhibition , such as the Stroop test and Trails test , predicts the level of coherence in older adults’ speech ( Arbuckle and Gold , 1993; Wright et al . , 2014 ) . However , this factor does not appear to provide a complete explanation for age-related coherence declines . This is likely because previous studies have overlooked the critical role that conceptual knowledge about the world ( i . e . , semantic knowledge ) plays in the generation of meaningful , coherent speech . Here , we propose and test the hypothesis that coherence depends not only on domain-general executive resources , but specifically on the ability to regulate the activation of semantic knowledge , ensuring that only the most relevant concepts are selected for inclusion in speech . All propositional speech relies on the retrieval and use of semantic knowledge . This is true at the lexical level , since the selection of words for production is guided by their meaning . But it is also true at the broader conceptual level , as the content of our speech is informed by our general semantic knowledge about the topic under discussion . For example , describing one’s favourite season requires access to stored semantic knowledge about the typical characteristics of each time of year , the events associated with them and so on . Thus , the coherence of an individual’s discourse is likely to be critically determined by ( a ) the quality of the semantic knowledge they have on the topic under discussion and ( b ) by their ability to retrieve and select the most appropriate information to talk about . It is important to note that these elements of semantic processing are served by distinct neural systems . Current theories hold that representations of semantic knowledge are centred on the anterior temporal cortices and that a separate ‘semantic control’ system provides top-down regulation of the activation and selection of concepts from this store , based on current situational demands ( Badre and Wagner , 2002; Hoffman et al . , 2018; Jefferies , 2013; Jefferies and Lambon Ralph , 2006; Ralph et al . , 2017; Yee and Thompson-Schill , 2016 ) . These principal components of semantic cognition – the store of representations and the control system – can be independently impaired following brain damage ( Jefferies , 2013; Ralph et al . , 2017 ) . Importantly for the present study , recent evidence suggests some aspects of semantic control are impaired in later life . Hoffman ( Hoffman , 2018 ) recently tested the verbal semantic abilities of 100 young and older adults . In common with many previous studies , older people were found to have a broader vocabulary , indicating a richer repository of semantic knowledge ( Grady , 2012; Nilsson , 2003; Nyberg et al . , 1996; Park et al . , 2002; Rönnlund et al . , 2005; Salthouse , 2004 ) . Unlike previous studies , we also probed semantic control abilities , using two experimental paradigms commonly used in cognitive neuroscience studies ( Badre et al . , 2005 ) ; Whitney , Kirk , Whitney et al . , 2012 ) . The first task probed the ability to engage in controlled search of the semantic knowledge store to detect weak associations between concepts . No age differences were found for this ability . The second tested the ability to select among competing semantic associations ( hereafter termed semantic selection ) . The semantic selection task required participants to inhibit prepotent but irrelevant semantic information in favour of task-relevant aspects of knowledge . Older adults were less successful than young people , indicating age-related decline in this aspect of semantic control . Evidence for age-related decline in controlled selection of semantic information is consistent with a meta-analysis of 47 functional neuroimaging studies indicating that older adults show reduced activity in the left prefrontal region most strongly linked with this ability ( Hoffman and Morcom , 2018 ) . The ability to select task-relevant semantic representations may be crucial in speech production because it may allow people to select the most relevant aspects of knowledge for use in speech and thus to avoid irrelevant shifts in topic . Recent studies therefore suggest that ageing is associated with both positive and negative changes in the function of the semantic system . Here , we tested whether these changes could account for age-related declines in the coherence of speech . Young and older adults were asked to produce samples of speech in response to a series of prompts and the coherence of these samples was estimated using a novel computational approach . We hypothesised in particular that individuals with reduced semantic selection abilities would produce less coherent speech , since they would be less able to prevent irrelevant semantic information from influencing their responses . Importantly , we tested whether semantic abilities had unique effects on coherence , after accounting for the effects of domain-general executive function . We used the Trails test as a measure of domain-general executive function because it is a well-established task which draws on various aspects of executive control including task-switching and inhibition ( Arbuthnott and Frank , 2000; Salthouse , 2011; Sánchez-Cubillo et al . , 2009 ) and also because it has previously been linked to poor coherence in speech ( Arbuckle and Gold , 1993; Wright et al . , 2014 ) . Finally , we also investigated the production of speech under conditions of divided attention , by including a dual-task condition in which participants completed a secondary manual task while speaking . We included this condition because a previous study has shown that people produce less coherent speech when their attention is divided and that this effect interacts with age ( Kemper et al . , 2010 ) .
Mean scores on a series of background cognitive tests are reported in Appendix 1—table 1 . Young people were faster to respond in the Trails test and produced more items in category fluency . Older people produced slightly more words in letter fluency , however . There were no group differences in MMSE scores , with all participants scoring at least 26/30 . Participants completed a series of semantic tasks that probed semantic selection ability , breadth of semantic knowledge and controlled retrieval of weak semantic associations . The full analysis of these tasks is reported in Appendix 5 , with mean scores in each condition presented in Appendix 5—figure 1 . The older group scored significantly higher on the vocabulary tests of semantic knowledge , indicating that they had a broader set of verbal semantic information available to them . Controlled retrieval was assessed by manipulating association strength during semantic judgements , since the detection of weak associations requires greater control over the retrieval of information from semantic memory ( Badre and Wagner , 2007 ) . This manipulation had similar effects in young and older people , suggesting that the ability to retrieve less salient semantic knowledge was equivalent in the two groups . Semantic selection was probed using a task in which participants were asked to match items based on particular semantic features ( e . g . , colour ) ( Thompson-Schill et al . , 1997 ) . Selection demands were highest when the correct target was incongruent with pre-existing semantic associations ( e . g . , salt goes with snow , not pepper ) . Older people showed a larger effect of the congruency manipulation , performing more poorly in the incongruent condition . This indicates that the older group had difficulty in selecting task-relevant semantic knowledge and inhibiting irrelevant associations . We now turn to analyses of the speech samples produced by participants . We first considered the effect of our experimental manipulations on rate of speech production ( number of words produced per minute ) . The results are shown in Figure 1A . Mixed effects modelling indicated that speech rate was influenced both by age group ( B = −7 . 74 , se = 3 . 81 , p=0 . 046 ) , with older participants tending to speak more slowly , and by task ( B = −1 . 93 , se = 0 . 79 , p=0 . 016 ) , with fewer words produced under dual-task conditions . We therefore included speech rate as a covariate in subsequent analyses , to ensure that effects on coherence were not attributable to this variable . Coherence of speech was assessed using a novel computational approach ( see Materials and methods and Figure 1 ) . Measures of global coherence ( GC ) and local coherence ( LC ) were computed . We began by assessing the internal reliability of the GC and LC measures over the fourteen prompts used to elicit speech samples . Cronbach’s alpha was high for both measures ( GC = 0 . 83; LC = 0 . 79 ) , indicating that stable individual differences in coherence were present over the various topics about which participants were asked to speak . GC and LC values were also strongly correlated with one another ( r = 0 . 79 ) , suggesting that both are closely linked , as found in previous studies . In the older group , age was negatively correlated with GC ( r = −0 . 64 ) and LC ( r = −0 . 56 ) . Next we investigated the effects of our experimental manipulations on the GC of participants’ speech . The first model included age group and task as predictors , with speech rate as an additional covariate ( see Table 1 for results ) . Age group was a strong predictor of GC: as predicted , older participants produced markedly less coherent speech than young people ( see Figure 2B ) . As shown in Table 1 , the dual-task manipulation had no effect on GC . The interaction between age and task fell just short of statistical significance ( p=0 . 052 ) . This suggests that there may be a weak tendency for the effect of the task manipulation to be larger in older people . Speech rate was a negative predictor of GC , indicating that participants who spoke more quickly showed a greater tendency to deviate from the topic being probed . The addition of Trails ratio scores ( Model 2 ) significantly improved the fit of the model ( χ2 ( 1 ) = 11 . 9 , p<0 . 001 ) . As expected , participants with a smaller ratio of Trails B to A ( indicating better executive ability ) had higher GC values . However , this was not sufficient to explain the lower GC values of older people: a significant difference between the young and older groups remained . The inclusion of semantic test scores ( Model 3 ) yielded a further improvement in model fit ( χ2 ( 3 ) = 10 . 1 , p=0 . 018 ) . The estimated effects of the test scores on GC are plotted in Figure 3 . Participants with higher scores on the semantic selection test produced more coherent speech . The effect of Trails ratio was also significant and there was also a tendency for individuals with higher semantic knowledge scores to produce less coherent speech . Weak association task scores were not a significant predictor of GC . Importantly , there was no remaining effect of age group in this model ( see Table 1 ) , suggesting that lower levels of GC in older adults can be explained in terms of their lower semantic selection and higher semantic knowledge scores . LC measurements were subjected to the same sequence of analyses , with broadly similar results ( see Table 1 ) . The first model revealed an effect of age group with no effect of task and a non-significant interaction ( see Figure 2C ) . In Model 2 , Trails ratio was again a significant predictor of coherence , but a significant age effect remained . In contrast , the age group effect was not significant once semantic scores were included ( Model 3 ) . Scores on the semantic selection task were a significant predictor of LC , with participants who performed poorly on this task tending to be less coherent ( see Figure 3 ) . The purpose of this analysis was to establish whether the observed effects of executive and semantic abilities on coherence were specific to this aspect of speech , or whether they would be observed for other characteristics of speech . Principal components analysis was used to reduce the nine properties of speech into four latent factors , shown in Figure 4A . These were the only factors with eigenvalues greater than one and together they explained 81% of the variance . GC and LC loaded exclusively on Factor 2 , confirming that coherence emerged as a discrete characteristic of speech . Factor 1 indexed the use of long , abstract , late-acquired words in speech , so appeared to reflect access to complex vocabulary . High scores on Factor 3 were associated with use of low frequency , concrete nouns that were low in semantic diversity . This factor may reflect the degree to which speech referenced highly specific concepts , so we labelled it semantic specificity . Finally , high scores on Factor 4 were characterised by high type:token ratio and a low proportion of closed-class words , which are indicative of greater lexical diversity . Scores on each factor were subjected to the same series of mixed effects analyses used for the analysis of GC and LC . The full results of these analyses are shown in Supplementary file 1 , while the effects of participants’ semantic and executive scores on each factor are presented in Figure 4B . The results for Factor 2 ( coherence ) were the same as previously observed for GC and LC separately: lower coherence was associated with poorer Trails and semantic selection performance but with better semantic knowledge . Importantly , no other factor showed the same pattern . The only other significant effects were that semantic knowledge was positively correlated with scores on factors 1 ( vocabulary ) and 4 ( lexical diversity ) , indicating that participants with broader semantic knowledge used a broader and more complex range of vocabulary when speaking . This analysis therefore confirms that the participants’ executive and semantic selection abilities had a specific effect on their coherence but not on other aspects of their speech production . Analysis of the secondary manual task is reported in Appendix 5 . In brief , older people had slower RTs and both groups were slower when the task was combined with speaking . Importantly , however , there was no interaction between these factors , indicating that the requirement to perform two tasks affected both groups equally ( see Appendix 5—figure 2 ) . Participants’ GC and LC scores did not predict performance on the secondary task , ruling out the possibility of a trade-off between secondary task performance and maintenance of coherence .
The ability to produce coherent speech is critical for effective communication but tends to decline in later life . Here , we investigated cognitive factors that predict this decline , using computational linguistic techniques to quantify the coherence of speech produced by a large group of young and older adults . We replicated previous findings indicating that individuals with greater domain-general executive ability produce more coherent speech , but this effect did not fully account for age differences in coherence . However , when we included semantic abilities as additional predictors of coherence , the age group difference was eliminated . Semantic selection ability emerged as a positive predictor of coherence while breadth of semantic knowledge was a negative predictor . These effects were specific to coherence and not to other characteristics of speech , and were not attributable to differences in speech rate . Our results indicate that older people produce less coherent speech ( a ) because they are less skilled at selecting the most relevant aspects of semantic knowledge to include in their speech and ( b ) because they have a larger set of semantic knowledge to select from . First and foremost , our results establish that the monitoring and control of discourse is influenced by the function of the semantic system , in addition to domain-general executive resources . In particular , we found that the ability to select task-relevant semantic information was a strong predictor of coherence in speech . The task we used to assess this ability is well-established as a measure of semantic control ( e . g . , Badre et al . , 2005; Thompson-Schill et al . , 1997; Whitney et al . , 2012 ) and required participants to attend to specific semantic features of objects while inhibiting strong but irrelevant semantic associations . Our data indicate that similar selection and inhibitory demands are present during the production of discourse . A conversational cue , such as ‘what’s your favourite season ? ' , initially causes a wide range of knowledge to become activated in the semantic system . Some of this information will be useful in answering the question and some less so . Coherent communication requires the speaker to select the subset of that information which is directly relevant at the current time , while suppressing aspects of knowledge that are activated but less pertinent . These demands grow as the narrative develops and new associations are activated . Of course , the knowledge that drives speech production is not solely semantic in nature – specific episodic memories and more general autobiographical knowledge will often be triggered as well . The prompts used in the present study were designed to elicit general knowledge rather than specific personal experiences . However , episodic and semantic memories are mutually interdependent ( Binder et al . , 2009 ) , and it was clear that participants drew on both in generating their responses . It is likely that selection mechanisms for these distinct types of memory are shared to some degree . Indeed , the left mid ventrolateral prefrontal cortex ( VLPFC ) , the brain region most closely associated with semantic selection , responds to selection demands in all three domains ( Badre and Wagner , 2007; Dobbins and Wagner , 2005; King et al . , 2005 ) . In particular , a large literature has examined brain regions implicated in the selection of task-relevant aspects of retrieved episodic memories . These processes are often referred to as ‘post-retrieval monitoring’ . The requirement to selectively recall particular details of an event drives greater activation in left mid VLPFC , suggesting that this area also mediates selection from episodic memory ( Badre and Wagner , 2007; Dobbins and Wagner , 2005 ) . However , the monitoring and selection of retrieved episodic memories is also associated with activation of dorsolateral prefrontal regions that are not implicated in selection from semantic knowledge ( Fletcher et al . , 1998; Rugg et al . , 2003 ) , which may indicate a degree of independence between semantic and episodic selection at the neural level . At a behavioural level , recent work indicates that patients with deficits in semantic selection also find it hard to resolve interference in episodic memory tasks ( Stampacchia et al . , 2018 ) . In addition , healthy older people often show increased interference from irrelevant events when retrieving episodic memories ( Campbell et al . , 2010; Ikier et al . , 2008 ) . Combined with our previous study ( Hoffman , 2018 ) , these findings point to a more general old-age deficit in selecting the most task-relevant aspects of retrieved semantic and episodic knowledge . We have demonstrated here that this selection deficit contributes to the loss of coherence in later life . This conclusion is consistent with age-related changes in the structure and function of the VLPFC region most associated with this ability . Lateral prefrontal cortex exhibits the greatest reductions in cortical volume as a function of age ( Fjell et al . , 2009; Raz et al . , 2004 ) . In addition , a recent meta-analysis of 47 functional neuroimaging studies found that older adults activated this region less strongly than young people during semantic processing ( Hoffman and Morcom , 2018 ) . Another important question is the extent to which the selection processes involved in regulating semantic knowledge overlap or interact with other , domain-general executive functions . This is an area of active debate , with researchers proposing that some aspects of the regulation of semantic knowledge are performed by domain-general systems for competition resolution while others require more specialised neural resources ( Badre et al . , 2005; Jefferies , 2013; Nagel et al . , 2008; Whitney et al . , 2012 ) . In this study , we employed a single measure of non-semantic executive ability , the Trails test . The measure of executive ability derived from this test was not correlated with performance on the semantic selection task ( young group: r = 0 . 08; older group: r = 0 . 02 ) and , although Trails performance was a strong predictor of coherence , we found that semantic selection had an additional , independent effect . This suggests that the relationship between coherence and semantic selection cannot simply be attributed to poorer general executive ability . However , it is unlikely that a single test can adequately capture all aspects of executive function . There are many different views on how executive functions are organised , but one common scheme proposes separate shifting , updating and inhibition components ( Miyake et al . , 2000 ) . The Trails test primarily taps shifting ( or task-switching ) ability ( Arbuthnott and Frank , 2000; Hedden and Yoon , 2006; Sánchez-Cubillo et al . , 2009 ) . In future , it will be important to probe potential contributions of other components of executive function , in particular inhibition . It is currently unclear whether suppression of irrelevant semantic information retrieved from memory involves the same executive resources as inhibition of overt behavioural responses , as measured by paradigms such as the Go/No Go task ( Verbruggen and Logan , 2008 ) . This is an important issue to resolve if we are to understand how semantic selection processes relate to domain-general executive function , particularly as semantic selection deficits in later life may be related to more general declines in inhibitory function , which have been reported across a range of tasks ( Borella et al . , 2008; Hasher and Zacks , 1988; Hoffman , 2018; Salthouse and Meinz , 1995 ) . We also found that the breadth of participants’ semantic knowledge influenced their coherence . Individuals with a wider range of lexical-semantic knowledge tended to be less coherent . This effect is consistent with the notion that selecting appropriately from activated knowledge is critical to maintaining coherence . This challenge becomes greater the more information one has in one’s semantic store , simply because more concepts are likely to be activated in response to any given cue . Thus , our data indicate that being more knowledgeable in itself brings greater challenges in identifying the most relevant aspects of knowledge to use in speech . It is very well-established that older people have greater semantic and general world knowledge , as was the case in our study ( Rönnlund et al . , 2005; Salthouse , 2004; Verhaeghen , 2003 ) , so this factor may also have contributed to age-related coherence declines . It is also worth noting that breadth of semantic knowledge also predicted the use of more sophisticated vocabulary ( i . e . , more late-acquired , abstract nouns ) and greater lexical diversity ( for a similar result , see Kemper and Sumner , 2001 ) . Therefore , it appears that quantity of semantic knowledge has more global effects on the characteristics of speech , unlike semantic selection , which impacts specifically on coherence . We found that conditions of divided attention had no overall effect on the coherence of speech , in contrast to the findings of Kemper et al . , 2010 . However , we note that our secondary task appeared less demanding that that used by Kemper and colleagues . Our secondary task produced a reduction in speech rate of around 5 WPM , compared with 20–40 WPM in Kemper et al . Despite this , we did find a non-significant trend ( p=0 . 056 ) towards an interaction of dual-task demands with age ( for the GC measure ) . There was a suggestion that the secondary task may have had a small effect on the coherence of older people , while young people appeared unaffected ( see Figure 2B ) . There remains a possibility therefore that divided attention has a particular detrimental effect on coherence in older adults . This could have important implications for conversations conducted in everyday situations in which speakers may simultaneously be engaged in other activities ( e . g . , talking while driving , shopping etc . ) . Future studies with more demanding concurrent tasks are needed to assess this possibility and its interaction with semantic abilities . Previous studies have found varying effects of age on coherence , depending on the speech elicitation task used . Typically , narratives elicited from verbal prompts , as in the present study , reveal the greatest decrements in coherence while tasks that elicit speech using visual stimuli , such as picture descriptions or story-telling from comic strips , produce smaller effects ( James et al . , 1998; Wright et al . , 2014 ) . These results fit well with our assertion that the ability to select relevant semantic content is a major determinant of coherence . When a pictured stimulus is used to cue speech , it acts as a source of constraint over semantic activation . Upon analysing the image , knowledge related to the objects and events depicted automatically comes to mind and can be used to drive speech production . If any irrelevant concepts become activated during this process , they can easily be eliminated on the basis that they are not present in the image . In contrast , constructing a response to a brief verbal prompt is a trickier proposition , since a wide range of potentially relevant information may be activated and no external cues are available to guide selection . Of course , the monologues elicited in the present study are a rather extreme example of this phenomenon . In everyday conversational speech , environmental cues are often available to guide the selection of speech content . For instance , a look of confusion from the speaker’s interlocutor can indicate when a loss of topic has occurred and a well-timed question could direct the speaker back to the topic in hand . Such cues can only be effective if speakers are sensitive to them , however , and evidence suggests that speakers with poor coherence are also less skilled at interpreting social cues ( Pushkar et al . , 2000 ) . Finally , it is important to consider an underlying assumption often made in the literature on coherence: that greater coherence is always a desirable characteristic for speech . Many situations do require specific information to be communicated quickly and efficiently and in these cases , it is beneficial to be able to provide the most germane information without digression to other topics . For example , there is evidence that individuals who are less coherent in conversation perform poorly at communicating task-related information to a partner in an experimental setting ( Arbuckle et al . , 2000 ) . In other situations , however , a less focused approach to speech may have its advantages . When the goal of a conversation is to entertain , rather than to convey specific information , the ability to shift focus away from the original topic may be beneficial . Indeed , older people are generally considered to produce more enjoyable stories than young people ( Ryan et al . , 1992 ) . One notable study collected responses of young and older people to questions about life events and asked judges to rate them on various dimensions ( James et al . , 1998 ) . Older people produced more off-topic speech than young people and their narratives were rated as less focused . However , while less coherent speakers were rated as less clear and focused , they were also considered to be more interesting and to have produced better stories . In summary , the ability to communicate coherently is critical in many but not all everyday conversations . It is possible that the most effective communicators are those who can tailor their selection of content to the current situational demands , focusing tightly on the subject at hand when required to but broadening their focus at other times . Little is known at present about how coherence interacts with these situational demands and this is one area where more research is needed .
Thirty young adults , aged between 18 and 30 ( mean = 19 . 3 ) , were recruited from the undergraduate Psychology course at the University of Edinburgh and participated in the study in exchange for course credit . Thirty older adults , aged between 61 and 91 ( mean = 76 . 0 ) , were recruited from the Psychology department’s volunteer panel . These participants were a subset of a group taking part in a larger study of semantic processing , some data from which have been reported elsewhere ( Hoffman , 2018 ) . All participants reported to be in good health with no history of neurological or psychiatric illness . Demographic information for each group is shown in Appendix 1—table 1 . Young and older adults did not differ significantly in years of education completed ( t ( 58 ) = 0 . 93 , p=0 . 36 ) . Sample size was selected to be similar to comparable studies in the literature . All participants provided informed consent and the study was approved by the University of Edinburgh Psychology Research Ethics Committee ( 120-1415/3 ) . Participants completed the following tests of general cognitive function and executive ability: Mini-Mental State Examination , Trail-making task , verbal fluency ( see Appendix 1 for details ) . As a measure of domain-general executive function , we computed the ratio of completion times for Trails part B to Trails part A . High ratios indicated disproportionately slow performance on part B , indicative of poor executive function . A ratio rather than a difference score was used as this measure minimises the influence of differences in general processing speed ( Arbuthnott and Frank , 2000; Salthouse , 2011 ) . Participants also completed two tasks probed breadth of semantic knowledge: lexical decision ( Baddeley et al . , 1992 ) and synonym matching ( adapted Mill Hill vocabulary scale; Raven et al . , 1989 ) ( for further details , see Appendix 1 ) . As scores on these tasks were strongly correlated , they were averaged to give a single measure of breadth of semantic knowledge . Semantic control was assessed using a 2 × 2 within-subjects experimental design that manipulated the need for semantic control in two different tasks ( Hoffman , 2018 ) ; following Badre et al . , 2005 ) . In the first task , participants made semantic decisions based on global semantic association . They were presented with a probe word and asked to select its semantic associate from either two or four alternatives ( see Figure 5 for examples ) . The strength of association between the probe and target was manipulated: the associate was either strongly associated with the probe ( e . g . , town-city ) or more weakly associated ( e . g . , iron-ring ) . The Weak Association condition was assumed to place greater demands on controlled retrieval of semantic information , because automatic spreading of activation in the semantic network would not be sufficient to identify the correct response ( Badre and Wagner , 2007 ) . In the second task , participants were asked to select items that matched on particular features . At the beginning of each block , participants were given a feature to attend to ( e . g . , Colour ) . On each trial , they were provided with a probe and were asked to select the item that was most similar on the specified feature . Trials manipulated the semantic congruency of the probe and target . On Congruent trials , the probe and target shared a pre-existing semantic relationship , in addition to matching on the currently relevant feature ( e . g . , cloud-snow ) . In contrast , on Incongruent trials the probe and target shared no meaningful relationship , other than matching on the specified feature ( e . g . , salt-dove ) . Furthermore , on these trials one of the foils had a strong semantic relationship with the probe , although it did not match on the currently relevant feature ( salt-pepper ) . Incongruent trials placed high demands on semantic selection processes for two reasons: first , because there was no pre-existing semantic relationship between probe and target to boost activation of the target and second , because the strong but irrelevant relationship between the probe and foil had to be ignored . Samples of speech were elicited under conditions of undivided and divided attention . On speech-only trials , participants were asked to speak for 60 s at a time in response to a written prompt ( for full list of prompts , see Appendix 2 ) . Prompts were designed to probe particular areas of semantic knowledge ( e . g . , What sort of things do you have to do to look after a dog ? ) . Participants read each prompt on a computer monitor and pressed a key when ready to begin speaking . After 60 s , a tone sounded to signal the end of the trial . Participants were instructed to continue speaking until they heard the tone . On dual-task trials , participants were asked to complete an attention-demanding secondary task while speaking ( Craik et al . , 1996 ) . On these trials , a horizontal array of four squares appeared on screen . Every 3 s , a red circle appeared in one of the squares and participants pressed a key corresponding to its location . This task was performed continuously throughout the speech elicitation period . Seven speech samples were obtained in the speech-only condition and seven in the dual-task condition . Finally , to obtain a baseline measure of secondary task performance , there were five secondary-only trials where the secondary task was performed without speech for 60 s . These trials were interspersed amongst the speech elicitation trials . Spoken responses were digitally recorded for later transcription . The main dependent variables analysed were computed measures of global and local coherence ( GC and LC ) , as described below . A number of other speech markers were also computed and were included in supplementary analyses ( see Appendix 3 for details ) . Measures of local and global coherence were generated using an automated computational linguistic approach . Analyses were implemented in R; the code is publicly available and can easily be applied to new samples ( https://osf . io/8atfn/ ) . Our approach used latent semantic analysis ( LSA ) ( Landauer and Dumais , 1997 ) , one of a number of computational techniques in which patterns of word co-occurrence are used to construct a high-dimensional semantic space . The LSA method utilises a large corpus of natural language divided into a number of discrete contexts . The corpus is used to generate a co-occurrence matrix registering how often each word appears in each context . Data reduction techniques are then applied to this matrix , with the result that each word is represented as a high-dimensional vector . Words that are used in similar contexts ( and are thus assumed to have related meanings ) are assigned similar vectors . Word similarities derived in this way are strong predictors of human judgements of semantic relatedness and human performance on a range of tasks ( Bullinaria and Levy , 2007; Recchia and Jones , 2009 ) . Importantly , vectors for individual words can be combined linearly to represent the meanings of whole sentences and passages of speech/text ( Foltz et al . , 1998 ) . A number of researchers have used this property to generate estimates of coherence for texts or spoken samples based on LSA similarity measures ( Elvevåg et al . , 2007; Foltz , 2007; Foltz et al . , 1998; Graesser et al . , 2004 ) . The present work builds on this approach . The overall strategy we took was to divide each speech sample into smaller windows ( of 20 words each ) and to use LSA to generate vector representations of the semantic content of each window . Coherence was assessed by measuring the similarity of the vector for each window with that of the previous window ( LC ) and with a vector representing the typical semantics of responses to the same prompt ( GC ) . This process is illustrated in Figure 1 . First , an LSA representation of each participant’s response was computed by averaging the LSA vectors of all the words they produced in response to the same prompt ( for details of the averaging method and vector space used , see Appendix 3 ) . These were averaged to give a composite vector that represented the typical semantic content produced in response to that prompt ( this step excluded the target response ) . Next , the target response was analysed using a moving window approach . The target response was divided into windows of 20 words in length . An LSA vector was computed for each window . Local coherence was defined as the cosine similarity of the semantic vector for the current window with that of the previous window . Therefore , in common with other researchers ( Elvevåg et al . , 2007; Foltz , 2007 ) , we define LC as the degree to which adjoining utterances convey semantically related content . A low LC value would be obtained if a participant switched abruptly between topics during their response . Global coherence was defined as similarity of the vector for each window with the composite vector derived from the other participants’ responses . Therefore , GC was a measure of how much the target response matched the typical semantic content of responses to that prompt . A low GC value would be obtained if a participant tended to talk about other topics that were semantically unrelated to the topic being probed . Thus , our measure of GC captured the degree to which participants maintained their focus on the topic under discussion , in line with the definition used by other researchers ( Glosser and Deser , 1992; Wright et al . , 2014 ) . Once GC and LC had been calculated , the window moved one word to the right and the process was repeated , until all windows had been assessed . GC and LC values were averaged across windows to give overall values for each response , which were multiplied by 100 for ease of presentation . Examples of responses with high vs . low coherence values are provided in Appendix 3-Table 1 . The LSA-based coherence measures were validated by comparing them with judgements of coherence provided by human raters for a subset of speech samples ( see Appendix 4 for details ) . There was a strong correlation between rated GC and LSA-based GC ( r = 0 . 68 ) and a somewhat weaker relationship between LSA-based LC and LC ratings ( r = 0 . 37 ) . Test-retest reliability was high ( see Appendix 4 ) . A series of linear mixed effects models were used to investigate the effects of the experimental manipulations and individual differences in semantic and executive ability on characteristics of speech . The dependent variable in the first analysis was speech rate in words per minute ( WPM ) . This was analysed in a linear mixed model with a 2 × 2 ( age group x task ) factorial design . We performed this analysis because previous studies have found that older people speak more slowly than young people and that speech rate is reduced under dual-task conditions ( Kemper et al . , 2003; Kemper et al . , 2010 ) . It was important to investigate this possibility in our data as speech rate might have an impact on coherence . For example , participants who spoke very quickly could cover a wider range of topics in 60 s , increasing the likelihood that their response would lose coherence . Since we found that speech rate was indeed influenced by both age and task , this variable was included as a covariate in later analyses . Our main hypotheses were tested with a series of nested models which used GC and LC as dependent variables ( in parallel ) . The first model included age group and task as predictors , as well as speech rate . Next , we added the Trails ratio score as an additional predictor , to test the hypothesis that general executive ability influences coherence . In the final model , we added three semantic task scores as predictors , to test the hypothesis that semantic abilities are an additional important determiner of coherence . The semantic task scores included were: Next , to establish whether the observed effects were specific to coherence , we investigated whether other characteristics of speech showed similar effects . We computed six measures of the lexical characteristics of the words produced in each speech sample ( see Figure 4 and Appendix 3 ) . Principal components analysis was performed on these ( along with the coherence measures ) and used to extract four underlying factors , which were promax-rotated . Scores on each of these factors were then analysed using the same series of nested mixed models employed in the main analysis of GC and LC . Finally , to analyse performance on the concurrent secondary task , we used a linear mixed model with group and task ( secondary-only vs . dual-task ) as predictors . The dependent variable was RT . We then added GC and LC values to the model as predictors to determine whether coherence was related to secondary task performance . All study data are available online ( https://osf . io/8atfn/ ) . Mixed effects models were constructed and tested using the recommendations of Barr et al . ( 2013 ) . We specified a maximal random effects structure , including random intercepts for participants and prompts as well as random slopes across participants for the effect of task and random slopes across prompts for task and age group . Continuous predictors were standardised prior to entry in the model . The significance of fixed effects was assessed by comparing the full model with a reduced model that was identical in every respect except for the exclusion of the effect of interest . Likelihood-ratio tests were used to determine whether the inclusion of the effect of interest significantly improved the fit of the model . | During a conversation , each person must plan and monitor what they say to make sure it is relevant to the discussion . This is called maintaining coherence during speech . Studies suggest that as people get older they find it harder to remain coherent , and become more likely to produce irrelevant or off-topic information when speaking . This reduces how effectively they communicate and can have negative effects on their social interactions . However , little is known about how thinking skills affect coherence in speech and why this declines in later life . To investigate , Hoffman et al . asked two groups of volunteers – a ‘younger’ group made up of people aged between 18 and 30 years old , and an ‘older’ group of people aged over 60 – to perform various speech-related tasks . For example , the volunteers were asked to speak when prompted and a computer analysis was used to evaluate how coherent they were . They also completed a speech test while distracted , and took part in tests to understand how well they can suppress irrelevant information . The results of the tests show that three factors influence how coherent people are during conversations: how well they could control and regulate their behaviour , how much general knowledge they had , and how skilled they were at selecting the most relevant information for the task they were doing . Having larger stores of knowledge to select from increases the challenge of staying on topic for older people . At the same time , they may experience age-related declines in the ability to suppress unnecessary information . This may help to explain why some people become less coherent as they get older and why some do not . | [
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] | 2018 | Poor coherence in older people's speech is explained by impaired semantic and executive processes |
Messenger RNA localization is important for cell motility by local protein translation . However , while single mRNAs can be imaged and their movements tracked in single cells , it has not yet been possible to determine whether these mRNAs are actively translating . Therefore , we imaged single β-actin mRNAs tagged with MS2 stem loops colocalizing with labeled ribosomes to determine when polysomes formed . A dataset of tracking information consisting of thousands of trajectories per cell demonstrated that mRNAs co-moving with ribosomes have significantly different diffusion properties from non-translating mRNAs that were exposed to translation inhibitors . These data indicate that ribosome load changes mRNA movement and therefore highly translating mRNAs move slower . Importantly , β-actin mRNA near focal adhesions exhibited sub-diffusive corralled movement characteristic of increased translation . This method can identify where ribosomes become engaged for local protein production and how spatial regulation of mRNA-protein interactions mediates cell directionality .
mRNA compartmentalization is a conserved mechanism cells use to spatially control protein translation through zipcode sequences often in the transcript’s 3'UTR ( Kislauskis and Singer , 1992; Holt and Bullock , 2009; Pratt and Mowry , 2012 ) . Previous work has shown that MS2 binding sites ( MBS ) inserted adjacent to the zipcode sequence in the 3'UTR of β-actin mRNA can be used to label and track the movement of single mRNA molecules ( Fusco et al . , 2003; Yamagishi et al . , 2009; Lionnet et al . , 2011; Katz et al . , 2012 ) . Previous studies identified that mRNA: ( 1 ) exhibited multiple movement profiles ( directed , diffusive , and corralled ) , ( 2 ) zipcode sequences facilitated directed transport of β-actin mRNA along microtubules , and ( 3 ) zipcode binding protein 1 ( ZBP1 ) delivered β-actin mRNA to leading edge adhesions where it could dwell for more than a minute . ZBP1 was previously shown to deliver its mRNA target in a translationally silenced state by blocking the 60S ribosomal subunit from initiating translation . Once the RNA-protein complex ( RNP ) localized to the cell periphery , Src kinase phosphorylated the 396 tyrosine to relax translation repression ( Hüttelmaier et al . , 2005 ) . How translating mRNA can be retained within this compartment is still unclear , although evidence has suggested that the elongation factor 1α ( EF1α ) may act to anchor mRNA to formins and thereby the leading edge actin cytoskeleton ( Liu et al . , 2002 ) . Accordingly , the actin cytoskeleton was previously shown to be necessary for mRNA localization and maintenance at the leading edge of migrating fibroblasts ( Sundell and Singer , 1991 ) . A recent report also argued that the RNP size is the main determinant of localization and therefore the architecture of the cytoplasmic microenvironment is a major factor in RNA retention at the leading edge ( Yamagishi et al . , 2009 ) . Our results suggest that delivery to a local site for increased translation may inherently increase β-actin mRNA dwell times and determine cell polarity and consequent directed motility ( Park et al . , 2012 ) . MBS bound by the GFP-fused coat proteins ( MCP ) make single molecule imaging of mRNA technically feasible ( Shav-Tal et al . , 2004 ) , but it has not been possible to image translation at the single molecule level . Recently , an approach was developed using binding sites in the coding region that indicated when the first ribosome passed through the mRNA , but this biosensor detected only the pioneer round of translation ( Halstead et al . , 2015 ) . Our goal in this work was to derive an approach that could be used to deduce the likelihood of the translational state at any given time of any fluorescently tagged mRNA . The work presented here describes a method for mapping the quantitative mobility features of a fluorescently tagged mRNA while it is associated with ribosomes to determine whether it is being translated , using multi-spectral live cell imaging . This approach mines data sets consisting of thousands of single mRNA tracks to extract the correlations between mRNA-polysome movements and the cytoplasmic nanoenvironment ( for instance , mRNA near focal adhesions were found to move in a more corralled fashion ) . A novel method for tracking ribosomes simultaneously with labeled mRNA resulted in a high-resolution spatial map of mRNAs actively interacting and moving with ribosomes . mRNAs that travel with ribosomes were likewise found to move slower and be more spatially confined . When translation inhibitors were used to dissociate ribosomes from mRNA , the average diffusion coefficient of β-actin mRNA increased . Therefore it is possible to map regions of active translation based on movement profiles within a given cellular compartment .
To investigate mRNA movement based on cytoplasmic location we first established a method to track and map all endogenous β-actin mRNA in live fibroblasts . Constitutive expression of tdMCP-GFP labeled all endogenous β-actin mRNA derived from a mouse in which 24 MS2 binding sites ( MBS ) were inserted into both alleles of the Actb gene ( Lionnet et al . , 2011 ) . Cells were transfected with paxillin-mCherry to label focal adhesions and spatially distinguish the cell’s leading edge ( Figure 1A and Figure 1—figure supplement 1A ) . We tracked both fast and slow moving mRNAs with TIRF excitation at a frame time of 35 ms ( Video 1 ) . We used mRNA trajectories with lifetimes beyond 105 ms ( three consecutive frames ) to build a β-actin mRNA diffusion map , in which all trajectories were given equal weight , regardless of their duration ( Figure 1F and 1G , see materials and methods for more details ) . Spatially averaged mRNA diffusion maps distinguish cell compartments that channel or restrict mRNA movement ( Figure 1F and Video 2 ) . Previous work suggested that a subset of dwelling mRNAs around focal adhesions represented sites of increased translation ( Katz et al . , 2012 ) . In order to verify that we could detect this effect in our assay , we identified the positions of each focal adhesion ( Figure 1D ) and partitioned tracks that localized to adhesion complexes . Diffusion maps revealed that mRNA movement around adhesions was indeed slower ( Figure 1B , C , and Figure 1—figure supplement 1B ) . A single-diffusive component fit of the cumulative distribution function ( CDF ) curves determined that β-actin mRNA at adhesions moved on average 37% slower ( Figure 1—figure supplement 2A vs . 2B ) . However , the CDF curves are much better fit by a linear combination of two states , a faster one with an apparent diffusion coefficient of 0 . 4 μm2/s , and a slower one with a coefficient of 0 . 1 μm2/s ( Figure 1B , Figure 1—figure supplement 2C , D ) . This two-component fit constitutes a straightforward method capable of de-convolving the entire dataset and estimating the percentage of the two mRNA species , fast and slow . Near focal adhesions , the population of fast mRNA species falls from 60% to 50% ( Figure 1B ) , which indicates that around focal adhesions mRNAs diffuse slower and dwell longer on average ( Figure 1—figure supplement 1C ) . Figure 1C depicts the mean square displacement ( MSD ) curves of adhesion-localized mRNAs , which plateaus at a much lower level . This plateau demonstrates a significant shift towards a corralled movement profile ( Figure 1C ) . This local confinement may indicate that β-actin mRNAs are trapped in areas of localized translation around focal adhesions . 10 . 7554/eLife . 10415 . 003Figure 1 . The diffusion properties of β-actin mRNA molecules around focal adhesions are slower and more corralled . ( A ) Overlay of a single frame from Video 1 of β-actin mRNA labeled with MS2-GFP and focal adhesions labeled with paxillin-mCherry . ( B ) The cumulative distribution function of all single molecule displacements for β-actin mRNA localized to focal adhesions is shifted left when compared to all other mRNA , which indicates a shift towards slower diffusion . ( C ) The mean square displacement ( MSD ) curves depict increased corralling of mRNAs localized at focal adhesions and reveal an exploration area of 0 . 1 μm2 . ( D ) Adhesion coordinates were localized relative to the tracked mRNAs displayed in panel ( E ) in order to define a population of mRNA tracks in the vicinity of adhesions . ( F ) The mRNA diffusion heat map indicates a shift towards slower diffusion at adhesions when compared to the total cytoplasmic population of mRNA trajectories ( G ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 00310 . 7554/eLife . 10415 . 004Figure 1—figure supplement 1 . Tracking of individual β-actin mRNA molecules at adhesion compartments . ( A ) The schematic depicts the side view of a fibroblast leading edge with focal adhesions labeled with paxillin-mCherry ( red stars ) and β-actin mRNA labeled with GFP tagged MS2 capsid proteins on 24 repeats of the MS2 stem-loop in the 3'UTR . ( B ) The distribution of mRNA apparent diffusion coefficients measured by single molecule tracking shifts significantly slower for mRNAs at adhesion compartments . ( C ) The distribution of single mRNA track lifetimes both near and away from focal adhesions . mRNAs near adhesions exhibit a shift towards longer track lifetimes , indicating a dwelling population near adhesions . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 00410 . 7554/eLife . 10415 . 005Figure 1—figure supplement 2 . Cumulative distribution function analysis of β-actin mRNA localized to focal adhesions . ( A ) β-actin mRNA localized to focal adhesions: The cumulative distribution function of all single molecule displacements for β-actin mRNA localized to focal adhesions is fit to a one component fit: P ( r , ∆t ) =1−exp ( −r2/4Dapp∆t ) . An apparent diffusion coefficient Dapp=0 . 12 μm2s−1 is obtained from the fit . ( B ) Cytoplasmic β-actin mRNA not localized to focal adhesions: The cumulative distribution function of all cytoplasmic β-actin mRNA not localized to focal adhesions is fit to a one component fit , resulting in an apparent diffusion coefficient Dapp=0 . 19 μm2s−1 . ( C ) β-actin mRNA localized to focal adhesions: A two-component fit of P ( r , ∆t ) =1−[A*exp ( −r2/4Dslow∆t ) + ( 1−A ) *exp ( −r2/4Dfast∆t ) ] ( dashed curve ) fits the experimentally obtained CDFs ( solid curve ) much better . A=0 . 51 is determined from the two-component fit ( 51% slow component and 49% fast component , dash-dotted curves ) . ( D ) Cytoplasmic β-actin mRNA not localized to focal adhesions: A two-component fit ( dotted curve ) fits the experimentally obtained CDFs ( solid curve ) much better . A=0 . 42 is determined from the two-component fit ( 42% slow component and 58% fast component , dash-dotted curves ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 00510 . 7554/eLife . 10415 . 006Video 1 . Mouse embryonic fibroblasts ( MEFs ) from the MBS mouse with 24 MS2 stem-loop binding sites in the β-actin 3'UTR are labeled with tdMCP-GFP . Focal adhesions are labeled with paxillin-mCherry . Live cell mRNA imaging was performed with TIRF excitation for 500 streaming frames at a frame exposure time of 35 ms . The movie is played at 30 frames per second . Scale bar = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 00610 . 7554/eLife . 10415 . 007Video 2 . The MBS MEF in Video 1 cropped and enhanced to highlight several β-actin mRNA molecules that persist at adhesions . The scale bar is 5 µm and the movie is played at 30 fps . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 007 We next hypothesized that mRNAs would exhibit restricted movement as ribosomes or other RNA binding proteins accumulate on the transcript , thereby increasing its mass and stokes radius . In order to test this , we dissociated ribosomes from β-actin mRNA with puromycin and hippuristanol , both drugs that disrupt mRNA-ribosome interactions ( Joklik and Becker , 1965; Bordeleau et al . , 2006; Darnell et al . , 2011; Wu et al . , 2015 ) . After treatment , β-actin mRNA trajectories showed a significant shift of movement towards the faster diffusing population ( Videos 3 and 4 ) . The average CDF from five separate acquisitions after puromycin treatment fit well with the linear combination of two CDFs , but now with ~90% of mRNAs being in the fast population state ( Figure 2A ) . Likewise , the MSD curve as well as the histogram of average apparent diffusion coefficients also displayed a significant shift to faster diffusion after puromycin treatment when compared to steady state β-actin mRNA movement ( Figure 2B and 2C ) . This global shift in mRNA diffusion behavior at the leading edge of cells is also apparent when comparing the local apparent diffusion maps between mRNA trajectories without puromycin treatment , as well as after addition of puromycin ( Figure 2D vs . 2E ) . The translation initiation inhibitor hippuristanol showed the same increase in mRNA diffusion after 1 μM treatment for 20 min ( Figure 2—figure supplement 1 , Video 4 ) . 10 . 7554/eLife . 10415 . 008Video 3 . β-actin mRNA is visualized 60 min after addition of puromycin . Compared to steady-state movement , mRNA without ribosomes is much faster and homogenous throughout the cytoplasm . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 00810 . 7554/eLife . 10415 . 009Video 4 . β-actin mRNA is visualized 20 min after addition of hippuristanol . Compared to the control ( left ) , the movement of mRNA without ribosomes ( right , same cell 20 min later ) is much faster and homogeneous throughout the cytoplasm . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 00910 . 7554/eLife . 10415 . 010Figure 2 . β-actin mRNA trajectories are faster and less-corralled after ribosome dissociation via puromycin treatment . ( A ) Treatment with puromycin dissociates ribosomes from mRNA and shifts β-actin mRNA trajectories significantly towards faster diffusion ( arrow ) . ( B ) Mean square displacement curves also indicate a shift towards faster movement ( arrow ) after puromycin treatment . ( C ) The histogram of average apparent diffusion coefficients shows the transition of the population to a faster movement profile of β-actin mRNA after ribosome dissociation . ( D ) A subset of trajectories from mRNA tracking and the resulting diffusion map indicates areas of slower movement . ( E ) After puromycin treatment most trajectories shift towards faster movement as reflected in the diffusion heat map . ( F ) sptPALM of ribosomes reflects similar movement changes after puromycin treatment . Immobilizing a subset of β-actin mRNA at focal adhesions with MS2-vinculin tethering shifts the ribosome CDF curve to the left , signifying tracked ribosomes are interacting with β-actin mRNA in the adhesion compartment . ( G ) The ribosome trajectory changes after puromycin and in mRNA tethering experiments are likewise reflected in MSD curves . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 01010 . 7554/eLife . 10415 . 011Figure 2—figure supplement 1 . β-actin mRNA trajectories are faster and less-corralled after addition of hippuristanol . ( A ) Treatment with hippuristanol inhibits translational initiation and shifts β-actin mRNA trajectories significantly towards faster diffusion ( arrow ) . The cumulative distribution function of all 28 , 065 trajectories obtained from three cells in steady state ( green curve ) is best fit by a two-component fit ( dashed curve , 43% in fast component ) . The cumulative distribution function of 26 , 921 β-actin mRNA trajectories of the same three cells after addition of hippuristanol ( red curve ) is best fit by a two-component fit with a shift towards the faster component ( dashed curve , 72% in fast component ) . ( B ) The corresponding mean square displacement curves also indicate a shift towards faster movement after hippuristanol addition ( arrow ) . ( C ) A map of the local apparent diffusion coefficients from all 10 , 084 β-actin mRNA trajectories . ( D ) A map of the local apparent diffusion coefficients from all 11 , 851 β-actin mRNA trajectories of the same cell as in ( C ) recorded 20 min after hippuristanol addition reflects the global shift towards faster β-actin mRNA movement after inhibition of translational initiation . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 01110 . 7554/eLife . 10415 . 012Figure 2—figure supplement 2 . Cumulative distribution function analysis of ribosome diffusion . ( A ) The cumulative distribution function of ribosomes for cells in steady state ( solid curve ) is best fit by two-components ( dashed curve ) composed of a slow ( 56% ) and a fast ( 44% ) diffusion component ( dash-dotted curves ) . A one-component single-exponential fit ( dotted curve ) does not adequately fit the experimentally obtained CDFs . ( B ) The cumulative distribution function of ribosomes after addition of puromycin is best fit by a two-component fit with a shift towards the faster component ( 73% fast ) . ( C ) The cumulative distribution function of ribosomes with β-actin mRNA tethered to focal adhesions ( see Figure 3—figure supplement 1 ) is best fit by a two-component fit with a shift towards the slower component ( 63% slow ) . ( D ) The cumulative distribution function of ribosomes co-moving with β-actin mRNA ( green curve ) is best fit by a two-component fit ( 62% slow ) . Ribosomes that were not co-moving with ribosomes are also best fit by a two-component fit ( in purple ) , but the ratio shifts towards the faster diffusion component ( 54% slow ) . ( E ) MSD curves of co-moving ribosomes ( in green ) depict increased corralling and an exploration area of 0 . 06 μm2 . MSD curves of non-co-moving ribosomes depict faster diffusion behavior and are less corralled ( purple curve ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 012 To image single ribosomes , the 60S large subunit protein L10A was labeled with PATagRFP for single particle tracking using photo-activated localization microscopy ( sptPALM ) . Stable expression of fluorescently labeled L10A has been shown to label ~40% of ribosomes ( Wu et al . , 2015 ) . Ribosomes could be tracked for an average lifetime of ~200 ms ( 6 . 2 frames ) after applying 405 nm activation energy . Since a small amount of activation was used in order to track single ribosome molecules , only a small fraction of total ribosomes were tracked . Ribosomes exhibited heterogeneity in diffusive motion , much as with β-actin mRNA ( Video 5 ) . Ribosome movement in cells at steady-state is best fit with the same two-component fit as mRNA: with a linear combination of a fast apparent diffusion coefficient of 0 . 4 μm2/s , and a slower one with a coefficient of 0 . 1 μm2/s . A two-component fit of experimentally obtained CDFs of ribosome trajectories reveals that ribosomes collectively show a shift towards a slower diffusion component as compared to β-actin mRNA ( 56% vs . 42% , respectively ) and suggests most ribosomes actively engage in translation throughout the cytoplasm ( compare Figure 2A vs . 2F ) . With the addition of puromycin , a significant increase in ribosome diffusion was observed just as with β-actin mRNA ( Figure 2F ) , and the population of faster ribosomes increased to 73% ( Figure 2—figure supplement 2B , Video 6 ) . 10 . 7554/eLife . 10415 . 013Video 5 . Ribosomes are visualized with photo-activated localization microscopy ( PALM ) in fibroblasts stably expressing the L10A 60s large subunit tagged with photo-activated TagRFP ( PATagRFP ) . 405-nm activation energy is controlled manually to limit the number of fluorescent molecules , enabling single particle tracking of ribosomes . Focal adhesions are labeled with vinculin-GFP . Ribosomes are tracked with a streaming acquisition at 35 ms per frame . The movie is played at 30 fps with a 5 µm scale bar . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 013 To demonstrate that a significant subset of ribosomes was engaged with β-actin mRNA , transcripts were tethered to adhesion plaques in live cells ( depicted in Figure 3E–G , Figure 3—figure supplement 1A and Video 7 ) . The adhesion protein vinculin was tagged with the MS2 capsid protein dimer ( vinculin-MCP ) ( Katz et al . , 2012 ) . MBS mRNA binds to vinculin-MCP , and allowed us to observe ribosome movement at sites where β-actin mRNA is immobilized . When β-actin mRNAs are tethered to focal adhesions , ribosomes diffused significantly slower ( Figure 3—figure supplement 1B ) . Tracking of ribosomes in cells with β-actin mRNA tethered to adhesions shifted the movement of ribosomes towards corralled diffusion ( 63% vs . 56% slow , see Figure 2F , G , Figure 2—figure supplement 2A vs . 2C , Figure 3G and Video 7 ) . Puromycin addition increased the movement of ribosomes in cells with mRNA tethered to adhesions ( Figure 3—figure supplement 1B and Video 8 ) . This is also the case for ribosomes in cells with non-tethered cytoplasmic β-actin mRNA ( Figure 2F , G , and Video 6 and 9 ) , suggesting that sptPALM of ribosomes in the TIRF field can identify ribosomes actively engaged and translating β-actin mRNA . In order to investigate this possibility further , we set out to perform dual-color tracking of β-actin mRNA and ribosomes . We reasoned that translating mRNAs should appear as mRNA particles moving while associated with a ribosome . Analyzing co-moving particles should therefore allow characterizing translation compartmentalization in migrating fibroblasts . 10 . 7554/eLife . 10415 . 014Figure 3 . Cellular maps of areas with increased β-actin translation . ( A ) Super-resolution PALM density map composed of all 63 , 940 β-actin mRNA localizations from 6 , 235 trajectories . The PALM density plot was generated with VISP ( El Beheiry and Dahan , 2013 ) from a simultaneous two-color movie with 500 consecutive frames . ( B ) Corresponding super-resolution PALM density map of all 19 , 420 ribosome localizations from 2 , 811 trajectories . ( C ) A map of the local apparent diffusion coefficients from all 6 , 235 β-actin mRNA trajectories . ( D ) A map of the local apparent diffusion coefficients from the 300 trajectories that were observed to be co-moving . The red arrows mark areas of enriched ribosome association with slowly diffusing β-actin mRNA . ( E ) Tracking of ribosome trajectories in cells where β-actin mRNA is tethered to focal adhesions . Super-resolution PALM density map composed of all detected localizations ( 1827 ) from 215 trajectories from 500 consecutive frames of ribosome imaging ( see Video 7 ) . ( F ) In shades of pink , overlay of the 33 trajectories ( 15 . 4% ) that co-localize with the focal-adhesion marker with tethered β-actin mRNA . ( G ) A map of the apparent diffusion coefficients of co-localized ribosome trajectories exhibits predominately slow diffusion characteristics when mRNA is tethered to focal adhesions . The red arrows mark the hotspots where the tethered mRNA is being actively translated . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 01410 . 7554/eLife . 10415 . 015Figure 3—figure supplement 1 . Tracking of individual ribosome molecules with β-actin mRNA tethered to focal adhesions . ( A ) The schematic depicts the side view of a fibroblast leading edge with focal adhesions integrating the mRNA tethering construct ( vinculin fused to the MS2 capsid protein ) . All β-actin mRNA tagged with 24 MS2 binding sites will tether directly to the adhesions . Ribosomes tagged with PATagRFP were visualized and tracked at tethering sites . ( B ) The distribution of apparent diffusion coefficients shifts to a slower population when β-actin mRNA is tethered to focal adhesions . This implies that a substantial fraction of ribosomes interacts with β-actin mRNAs . This population significantly decreases after puromycin treatment . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 01510 . 7554/eLife . 10415 . 016Figure 3—figure supplement 2 . Diffusion characteristics of β-actin mRNA co-moving with ribosomes and without ribosomes . ( A ) β-actin mRNAs that co-move with ribosomes exhibitslower apparent diffusion coefficients . ( B ) The mean square displacement curve of co-moving β-actin mRNA trajectories displays a shift towards corralled movement . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 01610 . 7554/eLife . 10415 . 017Figure 3—figure supplement 3 . mRNA/ribosome colocalization statistics in the absence and presence of puromycin . We generated a matrix of the distances dmRNA−Ribobetween all detected mRNA particles and all detected ribosome particles , and computed the corresponding histogram P ( dmRNA−Ribo ) . We then normalized the distance histogram to account for the fact the area covered by each distance bin grows with dmRNA−Ribo , and plotted the resulting normalized distribution of dmRNA−Ribo , equivalent to the average density of ribosome spots observed as a function of distance from an mRNA detection . If mRNAs and ribosomes do not colocalize , we would randomly detect ribosomes at all positions in the cell without regard for mRNA positions and therefore we would expect to observe a flat distribution for dmRNA−Ribo . In the case of mRNA/ribosome colocalization , we would expect an enrichment of short distances corresponding to comoving trajectories . Left panel: distribution of distances dmRNA−Ribo in the vicinity of focal adhesions observed in untreated cells ( dataset from Figure 4 ) . The distribution for the entire dataset ( gray histogram ) consists of a peak of short dmRNA−Ribo values ( <3 pixels ) above a flat baseline . The peak at short distances is the signature of colocalized mRNA/Ribosome trajectories , while the baseline reflects the concentration of fluorescent ribosomes non-associated with detected Actin mRNA molecules . We overlaid the distribution of distances dmRNA−Ribo generated from the colocalized trajectories selected by our co-movement algorithm ( red line ) . The algorithm efficiently selects the peak of colocalized trajectories . Right panel: distribution of distances dmRNA−Ribo observed in cells treated with puromycin ( dataset from Figure 2 ) . The distribution for the entire dataset ( gray histogram ) consists of essentially a flat baseline , with a modest increase at short distances . This result reflects the almost entire dissociation of ribosomes from mRNAs following puromycin treatment . The co-movement algorithm accordingly selects a very small fraction of colocalized trajectories . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 01710 . 7554/eLife . 10415 . 018Video 6 . Ribosomes visualized with L10A-PATagRFP after puromycin addition . Focal adhesions are labeled with vinculin-GFP . The stack was acquired at 35 ms per frame and played at 30 fps with a 5 µm scale bar . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 01810 . 7554/eLife . 10415 . 019Video 7 . Ribosomes visualized in cells with β-actin mRNA tethered to focal adhesions . Non-fluorescent β-actin mRNA with MS2 stem-loops in the 3'UTR becomes tethered to vinculin that is tagged with tdMCP-GFP . Ribosomes can be seen preferentially immobilizing at larger focal adhesions that have accumulated mRNA over time . Tethering mRNA to adhesions significantly slows the average diffusion of ribosomes and therefore shows a significant population of tracked ribosomes are interacting with β-actin mRNA . The stack was acquired at 35 ms per frame and played at 30 fps with a 10 µm scale bar . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 019 Simultaneous TIRF excitation of GFP labeled β-actin mRNA and PATagRFP labeled ribosomes ( fluorescently activated by a 405 nm laser ) enabled dual-color single molecule tracking using two synchronized EMCCD cameras ( Figure 3A–B ) . mRNA trajectories were spatially and temporally correlated with ribosome trajectories to identify a co-moving population for diffusion analysis ( see Methods and Figure 3—figure supplement 3 ) . Whereas all endogenous β-actin mRNA molecules in the cell are detected using our tdMCP-GFP labeled reporter system , only a few ribosomes ( on average 10 molecules/frame ) can be tracked at each given time using the sptPALM technique . Consistent with this experimental limit , only a subset of β-actin mRNA were identified as co-moving with ribosomes ( Figure 3C–D; Figure 4—figure supplement 3A ) . We hypothesized those mRNAs that co-moved with ribosomes were likely loaded with multiple ribosomes and may exhibit slower movement . Consistent with this , diffusion analysis showed a shift towards slower movement in the co-moving mRNA population ( Figure 3D; Figure 3—figure supplement 2B ) . mRNAs found to co-move with ribosomes shifted towards slower ( Figure 3—figure supplement 2A , Figure 4B ) and more corralled movement ( Figure 3—figure supplement 2B ) , an indication that these mRNAs were in polysomes and were more likely to dwell locally . Likewise , ribosomes found to co-move with β-actin mRNA trended slower and more corralled , reminiscent of mRNA localized to focal adhesions ( Figure 2—figure supplement 2E compared to Figure 1C ) . Together , the analysis of the mobility and co-movement of mRNA and ribosomes provide a detailed picture of the translational landscape inside live cells , capable of identifying translational hotspots ( Figure 3D , G ) . 10 . 7554/eLife . 10415 . 020Figure 4 . Multi-color live cell imaging and simultaneous tracking of β-actin mRNA and ribosomes at focal adhesions . ( A ) The image from a single frame from Video 10 depicts simultaneous tracking of β-actin mRNA ( green ) and ribosomes ( magenta ) in live mouse embryonic fibroblasts with focal adhesions labeled with paxillin-mCherry ( yellow ) . The adhesions were bleached prior to tracking mRNA and ribosomes . ( B ) Compartmentalized co-movement analysis of β-actin mRNA and ribosomes elucidates a shift in the displacement towards slower diffusion of mRNA visualized with ribosomes at adhesions . ( C ) β-actin mRNA trajectories were assessed by HMM-Bayesian analysis to identify diffusive state switching ( fast = blue , slow = green ) relative to ribosome trajectories ( magenta ) near adhesion proteins mapped in black . ( D ) One co-moving β-actin mRNA trajectory from C ) is displayed at higher magnification . HMM-Bayesian analysis clearly visualizes that the trajectory interconverts between the D1 and D2 states multiple times . Scale bar: 1 μm . ( E ) State-duration histogram for all fast states D1 as determined from all co-moving β-actin mRNA trajectories that sampled the state D1 ( from 382 state visits by co-moving mRNA trajectories ) . Insert: Histogram of only internal state durations of D1 ( excluding states at start and end of each track ) . ( F ) Corresponding state-duration histogram of all the slow state ( D2 ) durations as determined from 187 visits by co-moving β-actin mRNA trajectories to the state D2 . The insert depicts the internal state durations ( excluding states at start and end of each track ) for D2 . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 02010 . 7554/eLife . 10415 . 021Figure 4—figure supplement 1 . HMM-Bayesian analysis of co-moving mRNAs . ( A ) mRNAs that co-move with ribosomes are color-coded according to their diffusive state , as determined by HMM-Bayesian analysis ( co-moving ribosomes not shown ) . The fast state D1 ( individual states in shades of blue ) and the slow state D2 ( shades of green ) are displayed . Focal adhesions are depicted in white . Scale bar: 2 μm . ( B ) Two co-moving mRNA trajectories ( from A ) are highlighted and displayed at higher magnification . HMM-Bayesian analysis clearly visualizes that both trajectories interconvert between the D1 and D2 states multiple times . Also displayed is one of the co-moving ribosome trajectories ( in purple ) . For a map of all co-moving ribosomes please see Figure 4C and Figure 4—figure supplement 2 . Adhesions are depicted in white . Scale bar: 1 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 02110 . 7554/eLife . 10415 . 022Figure 4—figure supplement 2 . HMM-Bayesian analysis of co-moving ribosomes . ( A ) Ribosomes that co-move with β-actin mRNA are color-coded according to their diffusive state , as determined by HMM-Bayesian analysis . The fast state D1 ( individual trajectories in shades of yellow ) and the slow state D2 ( shades of purple ) are displayed . Focal adhesions are depicted in white . Scale bar: 5 μm . ( B ) State-duration histogram for all fast states D1 as determined from 329 visits by co-moving ribosome trajectories to the state D1 . Insert: histogram of only internal state durations of D1 ( excluding states at start and end of each track ) . ( C ) Corresponding state-duration histogram of all slow state ( D2 ) durations as determined from 149 visits by co-moving ribosome trajectories to the state D2 . The insert depicts the internal state durations ( excluding states at start and end of each track ) for D2 . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 02210 . 7554/eLife . 10415 . 023Figure 4—figure supplement 3 . HMM-Bayesian analysis of non-co-moving mRNAs . ( A ) Non-co-moving mRNA trajectories are color-coded according to their diffusive state , as determined by HMM-Bayesian analysis ( 7931 visits to the fast state D1 are displayed in shades of blue , and 2623 visits to the slow state D2 are displayed in shades of green ) . Focal adhesions are depicted in white . Scale bar: 5 μm . ( B ) State-duration histogram for all fast states D1 as determined from the trajectories displayed in ( A ) Insert: Histogram of only internal state durations of D1 ( excluding states at start and end of each track ) . ( C ) Corresponding state-duration histogram of all slow state ( D2 ) durations as determined from the trajectories displayed in ( A ) . The insert depicts the internal state durations ( excluding states at start and end of each track ) for D2 . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 023 Localization of mRNAs has long been proposed to spatially regulate translation , but this has not been directly observed in live cells . In order to address this question , we set out to test previous findings showing that β-actin mRNA localized to adhesion complexes , presumably synthesizing protein to enhance focal adhesion stabilization and directed migration ( Katz et al . , 2012 ) . In order to further investigate the localized translation near focal adhesions , expression of paxillin-mCherry in cells with mRNA and ribosomes allowed three-color colocalization analysis of co-moving trajectories near adhesions ( Video 10 ) . Adhesions were identified and bleached before tracking of mRNA and ribosomes ( Figure 4A , lower panel ) . This allowed us to analyze all co-moving mRNAs near adhesions separately . Consistent with this prediction , a majority of ribosome-bound mRNA near the focal adhesions diffused slower than ribosome-bound mRNA away from focal adhesions ( Figure 3—figure supplement 2B ) , and much slower than mRNA detected without ribosomes , where the two-component fit to the CDF revealed only 34% of slow-moving mRNAs ( Figure 4B ) . 10 . 7554/eLife . 10415 . 024Video 8 . Ribosomes in cells with β-actin mRNA tethered to adhesions after the addition of puromycin . Compared to the ribosomes in Video 7 , it is easy to see that puromycin dissociates ribosomes from mRNA tethered at focal adhesions . The stack was acquired at 35 ms per frame and played at 30 fps with a 5 µm scale bar . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 02410 . 7554/eLife . 10415 . 025Video 9 . β-actin mRNA and ribosomes are simultaneously visualized and tracked after puromycin addition . The stack was acquired at 35 ms per frame and played at 30 fps with a 10 µm scale bar . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 02510 . 7554/eLife . 10415 . 026Video 10 . β-actin mRNA and ribosomes are simultaneously visualized and tracked after the cell’s focal adhesions were identified with paxillin-mCherry and bleached . The stack was acquired at 35 ms per frame and played at 30 fps with a 5 µm scale bar . DOI: http://dx . doi . org/10 . 7554/eLife . 10415 . 026 We then sought to better characterize the diffusing subpopulations using a stochastic Hidden Markov modeling ( HMM ) -Bayes approach that annotates motion states with single-step resolution ( Monnier et al . , 2015 ) . HMM-Bayes fits two diffusive motion states with different diffusion coefficients and automatically annotates the individual steps of each trajectory as corresponding to either the high diffusion state ( blue ) or the low diffusion state ( green ) ( see Figure 4C to F and Figure 4—figure supplement 1 and 3 ) . In contrast to mean-square-displacement analysis that averages heterogeneous motion over predetermined temporal windows within each trajectory , HMM-Bayes automatically annotates stochastic motion dynamics , including the possibility of directed or active transport , without any user-supplied averaging parameters . Unlike our trajectory-averaged analysis , HMM-Bayes can provide insight into switching rates and lifetimes of the fast ( D1 ) and slow ( D2 ) diffusive states . It revealed that the slow diffusing state D2 is much longer lived for co-moving ribosomes ( 1 . 3 s ) as compared to mRNAs that are not observed to be co-moving ( 0 . 3 s ) , consistent with translating ribosomes having a higher propensity to dwell at sites of translation ( compare Figure 4—figure supplement 2C with 3C ) . Interestingly , all three species have fast diffusing states D1 with similar lifetimes of ~0 . 5 s ( Figure 4E and Figure 4—figure supplement 2B and 3B ) , which may suggest that away from sites of active translation all three species explore the cytoplasmic nanoenvironment in similar ways . As depicted in Figure 4C inset , ribosomes co-localize predominately with the slow diffusive mRNA state ( in green ) close to focal adhesions , and we hypothesize that the lower diffusion coefficient corresponds to a condition in which the mRNAs are loaded with polysomes within specific nanoenvironments of active translation . Interestingly , lifetimes of the slow state for co-moving ribosomes and mRNAs revealed by the HMM-Bayes are transient in nature and are much shorter than the translation time: given a translation rate of 5 . 6 amino acids per second ( Ingolia et al . , 2011 ) we estimate the time to translate one β-actin protein molecule to be 67 s . The transient slow state is both more abundant and more stable for co-moving ribosomes at focal adhesions , which suggests that translating ribosomes have higher propensity to dwell within specific nanoenvironments close to focal adhesions . While the microscopic mechanism of transient translational confinement is unknown , this local ribosome confinement can be enhanced by artificially tethering β-actin mRNA to focal adhesions ( Figure 2F , G , Figure 3—figure supplement 1B , Figure 3E–G and Video 7 ) . This indicates that local confinement of ribosomes is mediated by mRNAs that are dwelling at specific local nanoenvironments at focal adhesions . β-actin confinement close to focal adhesions has previously been shown to be functionally important for global cell locomotion ( Katz et al . , 2012 ) , and our analysis now indicates that weak and local transient interactions mediate this confinement .
The MS2 labeling system was previously used to track movements of reporter RNA transfected into live cells , and demonstrated that reporter mRNAs moved through a variety of states in the cell cytoplasm , including corralled diffusion and directed motion ( Fusco et al . , 2003; Yamagishi et al . , 2009 ) . Using a custom microscope and novel imaging methods , we were able to acquire high temporally and spatially resolved maps of the cytoplasmic diffusion of endogenous labeled β-actin mRNA . Simultaneously imaging PATagRFP labeled ribosomes along with the labeled mRNA allowed for co-movement analysis of single mRNAs and associated ribosomes . This method of mapping mRNA translation in live cells should be widely applicable to the field of translation , which currently lacks a method for single molecule imaging of mRNAs that are on polysomes . It has been well established that mRNA localization is a conserved mechanism utilized in multiple cell lines and species . Although it is assumed that localized mRNAs are translated , this level of regulation remains unexplored because of the lack of an appropriate method . We demonstrate an approach that can be applied to any mRNA in living cells to determine whether that mRNA is associated with polysomes at a precise location . This also allows for nanometer resolution on mRNA-protein interactions within the cell . HMM-Bayes analysis applied to the co-movement single molecule tracking data provides an additional level of information . For instance , unlike mRNA transport in the dendritic processes of neurons , active or directed transport mediated by molecular motors was not detected . We can now also identify the spatial location where individual mRNAs transition into a slower moving ( translating ) state . This differentiation is essential in order to investigate mechanisms of local regulation , for instance whether physical structures or proteins contact the molecule of interest . HMM-Bayes analysis has revealed that individual co-moving β-actin mRNA molecules and their ribosome trajectories can interconvert between slower and faster diffusing states on the timescale of seconds . The transient slow states are enriched at microenvironments close to focal adhesions , areas known for a dense meshwork of actin fibers and actin binding proteins . The translating complex may co-translationally associate with structures like filaments , adhesion associated proteins , chaperons , enzymes or other organelles ( Condeelis and Singer , 2005; Kramer et al . , 2009 ) . It is unlikely that the slow diffusing state represents a population of translating mRNAs that remain corralled for the entirety of protein synthesis since the lifetime is much shorter ( a few seconds ) than the time it takes to translate the actin mRNA ( ~ 1 min ) . However , the disappearance of the slow state upon ribosome disassembly indicates that the translation cycle may be involved . For instance , transitions between the slower and faster states occur on the same order as translation initiation rate ( ~ 0 . 1–0 . 5 s–1 ) for the β-actin mRNA ( Schwanhäusser et al . , 2011 ) . This suggests a potential connection to a single , short-lived state of the translation cycle , such as the deposition or release of initiation complex proteins . Interestingly , these functionally important yet transient interactions mediate local confinement at the cell leading edge and are reminiscent of the weak and transient nuclear interactions observed to play an important role in transcriptional initiation ( Cisse et al . , 2013 ) . Hence , single molecule tracking is a robust method to identify spatial heterogeneity of movement both in the nucleus and cytoplasm ( Izeddin et al . , 2014; Lu et al . , 2014; Spille et al . , 2015 ) . Understanding the frequency of state transitions and their local nanoenvironment will be essential in future live cell imaging studies . In addition , the method of mRNA tracking and mapping described here could by tagging another mRNA characterize multiple mRNA targets that may be localized and regulated in the same manner as β-actin mRNA . Since there are additional RNA labeling systems , like PP7 stem loops which work orthogonally to MS2 , it is now reasonable to test the dynamics of translational regulation for many of the reported target mRNAs ( Hocine et al . , 2013 ) . New results from microarray data , deep sequencing , and other biochemical approaches will require live cell tracking approaches to experimentally validate where and when these mRNAs localize ( Poon et al . , 2006; Sutton and Schuman , 2006; Patel et al . , 2012 ) . Development of sptPALM probes with greater photostability will enable longer-term imaging concomitant with MS2-based labeling , and future advances in imaging systems that can simultaneously track single molecules in three channels will allow any RNA of interest to be characterized . The PATagRFP-L10A ribosome-tracking construct can be used to map co-movement and diffusion characteristics of any uncharacterized mRNA . In particular , it would be advantageous to simultaneously track and map mRNA and ribosomes in neurons where local translation at the synapse is thought to contribute to information processing . Importantly , once the diffusion characteristics of mRNA in polysomes have been defined , it will not be necessary to use labeled ribosomes to verify that translation is occurring . Conversely , mapping ribosome diffusion in areas of the cell would reveal regions of higher translation . Therefore assessing diffusion coefficients using single particle tracking will provide new detailed information on translation . We have previously observed that β-actin mRNA localization at the leading edge of cells is highly correlated with motility ( Park et al . , 2012 ) . This localization appears to surround the focal adhesion complexes ( FACs ) nearest to the lamellipod ( Katz et al . , 2012 ) . By tethering the β-actin mRNA to all FACs indiscriminately to vinculin we inhibit cellular motility since mRNA localization cannot generate any asymmetry among the focal adhesions ( Katz et al . , 2012 ) . We have shown that β-actin is preferentially translated in the leading edge ( Wu et al . , 2015 ) . Here , we have investigated the translation of individual β-actin mRNAs by tracking their co-movement with ribosomes at high spatiotemporal resolution in live cells . The results corroborate and extend the hypothesis that actin translation in general is preferred around the focal adhesion complex . This localized translation could generate a more concentrated supply of not only actin , but also the many proteins associated with focal complex formation ( Kanchanawong et al . , 2010 ) , the proteins that nucleate actin such as Arp2/3 ( Mingle et al . , 2005 ) , and possibly FAC proteins as well . Hence the assembly of the complex starting with integrin association may be aided co-translationally , promoting 'waves' of actin polymerization ( Case and Waterman , 2011; Case and Waterman , 2015 ) . Therefore , we suggest that models of cell motility ( Danuser et al . , 2013 ) should consider the role of localized RNPs in facilitating cell motility by assembly of multipolypeptide complexes at adhesion sites .
Mouse embryonic fibroblasts ( MEFs ) derived from the MS2 β-actin knock-in mouse ( Lionnet et al . , 2011 ) were used for tracking endogenous β-actin mRNA as described previously . Briefly , cells were isolated from stage E14 . 5 embryos and immortalized after transformation by transfecting primary cells with SV40 large T-antigen . Focal adhesion label paxillin-mCherry was transiently expressed from pcDNA 3 . 1 plasmid after nucleofection ( Amaxa Biosystems , Gaithersburg , MD ) and imaged 24–48 hr later . The MS2 stem loops in the 3'UTR of endogenous β-actin mRNA were labeled with 2xMCP-GFP stably integrated into the cells by lentiviral infection . PATagRFP-L10A was also integrated through lentiviral infection . Cells were grown in DMEM high glucose with 10% FBS , penicillin and streptomycin . MEFs were imaged on MatTek ( Ashland , MA ) dishes or Bioptechs ( Butler , PA ) dishes and imaged in Leibovitz’s L15 medium ( without Phenol Red , Gibco , Gaithersburg , MD ) with 10% FBS . Glass coverslips were coated with 10 µg/ml fibronectin ( Sigma-Aldrich , St . Louis , MO ) 30 min before plating cells for imaging experiments . Puromycin ( Sigma-Aldrich ) was added at 100 µg/ml to disrupt translation and hippuristanol ( gift from Dr . Jerry Pelletier , McGill University ) was added at 1 μM to disrupt translation initiation . MEFs were imaged on a previously described custom-built microscope ( Grünwald and Singer , 2010; Katz et al . , 2012 ) . Simultaneous dual-color imaging experiments of mRNA and ribosomes were performed with an Olympus ( Waltham , MA ) 1 . 45 NA 150X objective with a resulting pixel size of 107 nm . Cobolt ( San Jose , CA ) Jive 50TM 561 nm and Cobolt Calypso 100 491-nm lasers were coupled into a single optical fiber , collimated , and delivered through the back port of the Olympus IX-71 stand . A tunable lens was inserted into the light path to produce objective base total internal reflection fluorescent excitation . A 405 nm laser ( Coherent , Santa Clara , CA ) was free-space-coupled into the back port of the microscope stand for full field photo-activation . Activation was manually controlled with a UNIBLITZ shutter ( Vincent Associates , Rochester , NY ) and purposely kept low ( ~1 . 5% of total possible output ) so only a few ribosomes were fluorescently activated for single particle tracking . Imaging experiments were run through MetaMorph Software ( Molecular Devices , Sunnyvale , CA ) . 16-bit Tiff stacks for each channel were exported from MetaMorph to DiaTrack software ( v . 3 . 03 , Semasopht , Switzerland ) , which identifies and fits the intensity spots of fluorescent particles with 2D Gaussian functions matched to the experimentally determined PSF . Images were background-subtracted and processed with a Gaussian filter of a half width at half max value of 1 . 3 pixels , which was user-determined to be the best value for particle identification . Thresholds for particle selection were determined for each data series . Care was taken to observe at least 50-100 frames of processing to ensure most particles were identified and there was no incidence of false detections due to background . The data were then tracked with a maximum particle displacement of 5 pixels ( ~533 nm ) between points in a track . Tracks with a lifetime of 3 frames or more were exported for co-movement analysis and 2D diffusion mapping . Diffusion mapping was performed with code written in Igor 6 . 2 ( WaveMetrics , Portland , OR ) . Briefly , the program extracted the trajectories from DiaTrack analysis and calculated local apparent diffusion coefficients evaluated in 20 nm x 20 nm grids from the mean square displacements over a timescale of 35 ms . Whenever five or more separate displacements originating within 80 nm of a given grid node were found , a local apparent diffusion coefficient was calculated and plotted . A heat map from all trajectories observed during the entire acquisition period was created to represent the average local apparent diffusion coefficient . All trajectories were also analyzed by calculating and fitting cumulative distribution functions ( CDFs ) of displacements to determine the average diffusion coefficient . The average mean square displacement ( MSD ) curve for all trajectories was determined over different time intervals . Error bars represent the experimental standard errors of the means . Co-movement analysis was performed in MatLab ( MathWorks , Natick , MA ) . Both RNA and ribosome stacks were tracked using the commercial tracking software DiaTrack ( v . 3 . 03 ) for single particle tracking analysis . Each trajectory coordinate was time encoded so tracks that moved within 320 nm ( 3 pixels ) of each other for at least 105 ms ( 3 frames ) were considered co-moving . A list of co-moving trajectories for each dataset were saved and input into the custom analysis routines written in Igor Pro for diffusion analysis and mapping . In our experiments all β-actin mRNAs are detected . However , translation events are only observed when a β-actin mRNA is translated by a ribosome containing L10A-PATagRFP , and when the fluorescence of this PATagRFP molecule is activated by our violet laser . Two potential concerns arise: ( 1 ) Only a small unrepresentative fraction of ribosomes might contain the PATagRFP-L10A . This is not the case: in a previous publication from our group ( Wu et al . , 2015 ) , we showed that labeled L10A is successfully incorporated into ~40% of the total ribosome population . ( 2 ) Only low numbers of PATagRFP-L10A molecules can be fluorescently activated at any given time . Since we must limit our photoactivation laser powers to a regime where we avoid overlap between individual ribosome molecules , we cannot observe all ribosomes simultaneously and this prevents us from observing the absolute number of ribosomes per mRNA , or any other metric that would depend on the degree of labeling of the various species . We first ensured through an ad-hoc co-moving detection algorithm that all co-movement events were stringently selected ( the algorithm was introduced by our group in an earlier publication: Halstead et al . , 2015 ) . This is important because false positives could affect our mobility measurements . Biologically specific co-moving events are apparent when looking at the pooled distances between mRNAs and ribosomes ( see Figure 3—figure supplement 3 ) . High stringency runs the risk of detecting too few trajectories to build robust statistics . We ensured this is the case by acquiring vast numbers of trajectories: in a typical experiment , we would detect ~9000 individual mRNA trajectories , out of which ~5% passed the co-moving test . This represents ~450 co-moving events , from a single movie . The aggregate data for co-movement analysis come from many movies in different cells , and therefore represent thousands of co-moving events . The trajectories were also analyzed with HMM-Bayes , which infers diffusive and directed motion states from observed particle displacements and annotates when and where each motion state occurs in space and time along each trajectory with single-step resolution ( Monnier et al . , 2015 ) . HMM-Bayes analysis was performed on pooled sets of trajectories; thus the motion parameters ( diffusion coefficients ) were inferred considering all trajectories together , but state sequence annotations were inferred separately for each trajectory . Motion state lifetimes were extracted from the inferred sequence of motion states along each trajectory , either including or excluding the durations of the states at the beginning and end of each trajectory as noted . | The instructions that a cell needs to build proteins are encoded within its genes . To make proteins , these instructions are first copied into molecules of messenger RNA ( or mRNA for short ) . Molecular machines called ribosomes then translate these mRNA molecules to produce proteins . Controlling when and where proteins are produced within a cell is important for tissue development , memory formation in the brain , and the outcome of certain cancers . Methods that can pinpoint the time and place that translation occurs will lead to an improved understanding of how biological processes take place . While current techniques can be used to track the movement of single mRNA molecules in individual cells , it has not yet been possible to determine whether ribosomes are actively translating these mRNAs . Katz , English et al . tracked the movements of mRNA molecules ( that encode a protein called β-actin ) and ribosomes at the same time , by using fluorescent tags with different colors . β-actin is an important component of the cell’s internal scaffolding and its localized translation enables cells to move in a given direction . Katz , English et al . confirmed that β-actin mRNA translation most often occurs near the leading edge of a moving cell , especially near sites where the cell attaches to its surroundings to help pull itself forward . The experiments also revealed that mRNAs that are being translated behaved differently to mRNAs that are not being translated . In particular , translating mRNAs and their associated ribosomes move much slower than non-translating mRNAs . Importantly , this method can be applied to any mRNA of interest , and once the movements of the mRNA have been defined it is possible to estimate whether or not it is being translated . In the future , a similar approach could provide a definitive answer concerning how mRNA molecules interact with proteins within different parts of the cell and will lead to future investigations on localized production of proteins in living cells . | [
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"biophysics"
] | 2016 | Mapping translation 'hot-spots' in live cells by tracking single molecules of mRNA and ribosomes |
JCVI-syn3A , a robust minimal cell with a 543 kbp genome and 493 genes , provides a versatile platform to study the basics of life . Using the vast amount of experimental information available on its precursor , Mycoplasma mycoides capri , we assembled a near-complete metabolic network with 98% of enzymatic reactions supported by annotation or experiment . The model agrees well with genome-scale in vivo transposon mutagenesis experiments , showing a Matthews correlation coefficient of 0 . 59 . The genes in the reconstruction have a high in vivo essentiality or quasi-essentiality of 92% ( 68% essential ) , compared to 79% in silico essentiality . This coherent model of the minimal metabolism in JCVI-syn3A at the same time also points toward specific open questions regarding the minimal genome of JCVI-syn3A , which still contains many genes of generic or completely unclear function . In particular , the model , its comparison to in vivo essentiality and proteomics data yield specific hypotheses on gene functions and metabolic capabilities; and provide suggestions for several further gene removals . In this way , the model and its accompanying data guide future investigations of the minimal cell . Finally , the identification of 30 essential genes with unclear function will motivate the search for new biological mechanisms beyond metabolism .
Establishing the core requirements of cellular life is a fundamental challenge of biology . The question of the minimal set of biochemical functions necessary for a cell to grow and replicate has been studied from a number of angles for more than 20 years . It has long been suggested ( Morowitz , 1984 ) that a model to study the basics of cellular life would be the mycoplasmas—a group of bacteria with small genomes ( 580–1350 kbp ( Herrmann , 1992; Fraser et al . , 1995 ) ) lacking a cell wall , which evolved via extreme genome reduction from low GC content Gram-positive ancestors ( Pollack et al . , 1997 ) . Mycoplasmas exist as parasites or saprotrophs and are adapted to scavenging nutrients and cellular building blocks from their niche environments , which enabled them to lose many metabolic capabilities . The genome of the human urogenital pathogen Mycoplasma genitalium ( 580 kbp , 525 genes overall , 482 for proteins , 43 for RNAs ) , sequenced in 1995 ( Fraser et al . , 1995 ) , is the smallest genome of any autonomously replicating cell found in nature and has thus been deemed a close approximation to a minimal genome ( Glass et al . , 2006 ) . In particular , different efforts have been undertaken to establish a minimal set of genes based on the near-minimal M . genitalium genome . A comparison of the first two sequenced bacterial genomes ( the Gram-positive M . genitalium ( Fraser et al . , 1995 ) and the Gram-negative Haemophilus influenzae ( Fleischmann et al . , 1995 ) ) yielded 256 orthologous genes that were suggested to approximate a minimal set of bacterial genes ( Mushegian and Koonin , 1996 ) ; a subsequent comparative study , including genomes from several free-living and endosymbiotic bacteria , proposed a minimal set of 206 genes ( Gil et al . , 2004 ) . A limitation of this approach lies in the possibility of the same function being fulfilled by non-orthologous proteins in different organisms , in which case it would not be captured by searching for orthologous genes . Transposon mutagenesis studies ( Hutchison et al . , 1999 ) avoid this drawback by directly probing the dispensability of individual genes in a single organism via random gene disruption , and testing for viability . In M . genitalium , such studies have suggested 382 out of the 482 protein-coding genes to be essential ( Glass et al . , 2006 ) . An important limitation of deriving a minimal gene set from essentiality information on individual genes lies in the fact that more than one gene can fulfill the same function , and while neither gene is essential individually , at least one of them has to be present in a functional minimal genome . Thus , while removal of either gene would be nonlethal , removing both would create a synthetic lethality . This problem can , in principle , be circumvented by sequential gene deletion starting from a given wild-type organism ( as partially done for Escherichia coli and Bacillus subtilis ( Juhas et al . , 2014; Pósfai et al . , 2006 ) ) , with testing for viability and growth rate after each deletion . In principle , this would not only yield the information on a minimal genome , but also would produce a living organism controlled by such a genome . However , the time and resource costs of minimizing a genome by serial deletion of dispensable genes are prohibitive . In 2016 , we developed a successful bottom-up approach to design a minimal genome and create a living cell controlled by it ( Hutchison et al . , 2016a ) . Starting with the gene sequence from the 1079 kbp genome of the ruminant pathogen Mycoplasma mycoides capri serovar LC GM12 , a minimal genome of 531 kbp was designed and constructed containing 473 genes ( 438 protein-coding genes and 35 genes for RNAs ) ( Hutchison et al . , 2016a ) . The resulting strain , JCVI-syn3 . 0 ( NCBI GenBank: https://www . ncbi . nlm . nih . gov/nuccore/CP014940 . 1 ( Hutchison et al . , 2016b ) ) , has a genome smaller than that of any independently-replicating cell found in nature and is considered to be our ‘working approximation to a minimal cell’ . This achievement was the culmination of a series of breakthroughs in synthetic biology . In 2007 , the successful transplantation of an M . mycoides capri LC GM12 genome into a Mycoplasma capricolum recipient cell was reported ( Lartigue et al . , 2007 ) , transforming the recipient cell to the species of the implanted DNA . In 2008 , the complete genome of M . genitalium was synthesized from scratch , starting with chemically synthesized oligonucleotides and stepwise recombination in vitro and subsequently in Saccharomyces cerevisiae ( yeast ) , using the available genetic manipulation tools to assemble the genome as a plasmid inside the yeast cell ( Gibson et al . , 2008 ) . These methods enabled the construction of JCVI-syn1 . 0 , the first cell controlled by a synthetic genome ( NCBI GenBank: https://www . ncbi . nlm . nih . gov/nuccore/CP002027 . 1 ) ( Gibson et al . , 2010a; Gibson et al . , 2010b ) . This was accomplished by synthesizing of a copy of the M . mycoides capri LC GM12 genome along with vector sequences that allowed cloning in E . coli and yeast , and its subsequent transplantation into M . capricolum recipient cells to yield JCVI-syn1 . 0 . These milestones enabled the synthesis of reduced versions of the JCVI-syn1 . 0 genome with subsequent transplantation into M . capricolum to test for viability . The genome reduction process was guided by transposon mutagenesis studies on the original JCVI-syn1 . 0 genome , as well as on intermediate reduced genome versions . Successful genome minimization depended on identifying both essential genes , whose disruption is immediately lethal , and quasi-essential genes , whose disruption causes an observable growth disadvantage . Quasi-essential genes were identified by observing if cells with potentially growth-reducing gene disruptions were outgrown during sufficiently long competition experiments , so that cells sampled from much later generations no longer contained the disrupted gene . Three cycles of genome design , assembly and growth testing yielded JCVI-syn3 . 0 ( Hutchison et al . , 2016a ) . JCVI-syn3 . 0 contains all the genes of JCVI-syn1 . 0 that are required for growth . This includes both essential and quasi-essential genes . Individually non-essential genes were removed in the design for JCVI-syn3 . 0 to the greatest extent possible without causing synthetic lethality or a major growth disadvantage from simultaneous knockouts . However , in a few cases , genes that appear to be non-essential were retained for ease of genome design and construction . Intriguingly , the role of a considerable fraction of the minimal genome of JCVI-syn3 . 0 could not be elucidated in spite of extensive bioinformatic analyses . At the time of publication of the minimal cell , 149 of the genes ( ∼31% of the genome ) could not be assigned a completely specific biological function . Assignment to a broad functional category could not even be made for a subset of 79 genes . These genes of unknown or poorly defined function potentially point toward required features of cellular life yet to be discovered . The original minimal cell JCVI-syn3 . 0 genome was assembled by combining individually minimized 1/8 chromosome segments ( Hutchison et al . , 2016a ) . Phenotypic traits of JCVI-syn3 . 0 included extensive filamentation and vesicle formation during growth , and a doubling time of 2–3 hr ( compared to the spheroidal morphology and 1 hr doubling time conferred by the JCVI-syn1 . 0 genome ) . To address these phenotypic disadvantages , an alternative design of segment six was found to restore consistent morphologic features and increase the growth rate , while retaining a near-minimal genome . This new design incorporated 19 additional genes from JCVI-syn1 . 0 segment six that were not present in JCVI-syn3 . 0 , including those encoding the cell partitioning proteins FtsZ and SepF along with others of unknown function; in addition , two other genes retained in JCVI-syn3 . 0 segment six were removed . The complete genome sequence of this strain , termed JCVI-syn3A , is available through NCBI under the accession number https://www . ncbi . nlm . nih . gov/nuccore/CP016816 . 2 . ( Glass , 2017 ) This entry contains 498 genomic features , however three of those are pseudo-genes and two are genes required for cloning in yeast . JCVI-syn3A has a doubling time of ∼2 hr and consistently forms spherical cells of approximately 400 nm in diameter . With a 543 kbp genome containing 493 genes of which 452 code for proteins and 38 for RNAs , JCVI-syn3A still has a smaller genome than any natural autonomously replicating cell while providing a robust and versatile platform to study the basics of life . In particular , this minimal cell opens up the possibility to pursue the construction of a complete in silico model including the function of all genes . The map of protein coding genes ( Figure 1 ) clearly shows the fundamental importance of Syn3A as a platform to study the principles of life . Among the model bacteria E . coli and the related and well-studied ( Güell et al . , 2009; Kühner et al . , 2009; Yus et al . , 2009; Maier et al . , 2011; Wodke et al . , 2013 ) Mycoplasma pneumoniae , JCVI-syn3A has the smallest ratio of genes involved in metabolism to those in genetic information processing . With 91 it also has the smallest number of genes that are considered to have no known ( unclear ) function compared to 311 and 1780 for M . pneumoniae and E . coli , respectively ( see Table 1 and Supplementary file 1C–1D for an itemized account of the functional categories for the three genomes ) . A model for ribosome biogenesis that includes DNA replication , transcription , translation , and ribosome assembly in slow growing E . coli has already been developed ( Earnest et al . , 2015; Earnest et al . , 2016 ) . As its components have on average 50% sequence identity to those genetic information processing genes in JCVI-syn3A , this model is assumed to be applicable to JCVI-syn3A as well . Hence , the next important step in modeling JCVI-syn3A is the reconstruction of its metabolic network . The metabolic reconstruction presented here is based on the curated genome annotation , extensive experimental information from the literature on M . mycoides capri and other mycoplasma species , and accompanying transposon insertion and proteomics data . Our model features 338 reactions organized in nine subsystems ( see Supplementary file 1B ) , involving 304 metabolites , catalyzed by gene products of 155 genes , thus covering a third of the genes of JCVI-syn3A . The reconstruction process enabled us to suggest several annotation refinements and updates , and yielded a metabolic network that is fairly complete . Together with the reconstructed biomass composition of JCVI-syn3A and estimates of its reaction flux constraints and energy expenses , the reconstructed metabolic network was cast into a flux-balance analysis ( FBA ) model ( Orth et al . , 2010 ) . FBA yields the set of steady-state reaction fluxes through a metabolic network that maximize a pre-defined objective function , for example production of cellular biomass . The solution space of possible fluxes is constrained not only by the steady-state assumption , but also by specific flux limits accounting for maximal uptake/secretion rates or cellular energy expenses . If these flux limits are not known , the network stochiometry predicts the biomass yield achieved by the cell , that is gram biomass produced/gram carbon source taken up ( or equivalently biomass production rate/carbon substrate uptake rate ) . If flux constraints , in particular substrate uptake rates are known or can be assumed , the yield as growth rate per uptake rate can be converted to an absolute growth rate . While measurements to derive such flux constraints are not available yet for JCVI-syn3A , some measurements are available from other mycoplasmas and bacteria that have the same high-affinity glucose transporter ( PtsG ) found in JCVI-syn3A . Using the glucose uptake rate measured in M . pneumoniae ( Wodke et al . , 2013 ) ( which is similar to the one measured in slow-growing E . coli ( Fuhrer et al . , 2005 ) ) and other constraint estimates allows us to provisionally predict a growth rate for JCVI-syn3A; this model growth rate is however sensitive to the assumed uptake rate ( see Section 'Sensitivity analysis' in Appendix 1 ) . In this article , the growth rate predicted by the model is presented with the understanding that for the aforementioned reasons , the prediction is provisional and comes with a degree of uncertainty . This uncertainty has no bearing on the prediction of in silico gene essentialities ( see below ) , which can be obtained by removing certain genes in the model and their associated reactions , and testing whether FBA still predicts a nonzero growth rate for the resulting in silico knockout . This FBA model for JCVI-syn3A allows for the analysis of the properties of minimized metabolism in JCVI-syn3A . In particular , gene essentiality can be compared between the model and experimental transposon mutagenesis data . Random gene disruption by bombardment with transposon insertions ( Hutchison et al . , 1999; Glass et al . , 2006 ) was used to identify non-essential genes in JCVI-syn1 . 0 that to the most part were removed during the construction of JCVI-syn3 . 0 ( Hutchison et al . , 2016a ) ; here , genome-scale transposon mutagenesis studies were carried out on JCVI-syn3A to survey the individual essentiality of all its remaining genes . We find that transposon- and model-derived gene essentiality agree well , with every in silico essential gene being at least quasi-essential in vivo ( i . e . removal might not be immediately detrimental , but give a growth disadvantage ) . The metabolic reconstruction allows us to rationalize the non-essentiality of some genes , and to propose possible further gene removals in JCVI-syn3A . These suggestions from the model are of particular interest as transposon mutagenesis experiments only probe the individual essentiality of genes and do not yield information on which genes could be removed simultaneously . The metabolic construction , on the other hand , allows us to suggest which genes might be simultaneously removed . At the same time , in silico and in vivo essentiality also show some discrepancies , which lead us to postulate new hypotheses about specific gene functions or metabolic capabilities . Protein expression profiles of essential and non-essential genes , classified by either transposon mutagenesis studies or FBA in silico gene knockouts , were not found to differ significantly–possibly indicating by and large the absence of expression regulation that would discriminate gene products based on their essentiality . Finally , the reconstruction process as well as the gene essentiality comparison have yielded a number of informed hypotheses and suggestions for specific experiments that will guide the ongoing experimental investigation of gene functions in the minimal cell .
Transposon insertion mutagenesis studies were performed in order to probe the dispensability of individual genes in JCVI-syn3A ( see Section 'Materials and methods' ) . In this experiment , transposons are randomly inserted into the chromosomes of a population of cells that is then plated under selection for a drug resistance gene carried by the transposon ( Hutchison et al . , 1999; Glass et al . , 2006 ) . After transferring to a liquid culture ( ‘passage zero’ , P0 ) , four serial passages are performed . DNA from the pooled colonies is isolated and sequenced to determine the location of transposon insertions within the genome at the beginning or at the end of the experiment . When determining transposon locations at the beginning of the experiment , P1 is used over P0 to limit any contamination from the DNA of non-viable cells . The number of insertions observed in a coding region can then be used to infer the importance of that gene . We note that not every insertion will necessarily obliterate a gene’s function . A graphical presentation of the essentiality classification along with the distribution of transposon insertions over a portion of the genome is presented in Figure 2 . It shows that secA/0095 is heavily hit with insertions in the 3' 25% of the gene ( but practically nowhere else ) ; however , SecA is certainly essential because it is a necessary component of the protein translocase , which inserts proteins such as transporters into the membrane . While the absence of gene products for genes carrying transposon inserts has not been confirmed experimentally , genes with relatively high insertion counts are more likely to be functionally disrupted and thus non-essential . Genes that are not required by the organism to grow in rich media will contain many transposon insertions ( ‘non-essential’ genes ) , whereas genes required for cell viability will be sparsely hit by transposon insertions . To identify genes whose disruption might not be immediately detrimental but might cause a growth defect apparent later on , sequencing of the transposon mutagenesis library was performed on P4 cells as well . Cells with a gene disruption that is not immediately lethal but causes a growth disadvantage will be outgrown after P4 , and the number of insertions for that gene will then significantly decrease from P1 to P4 . These genes are denoted ‘quasi-essential’ . A Poisson mixture model was used to partition the coding regions into two sets of genes based on the transposon insertion density . By comparing the assignment of genes into classes of sparse and dense transposon insertions between P1 and P4 , essentiality can be inferred . This classification method considers a gene to be essential if it has been classified to have sparse transposon insertions in both P1 and P4 , quasi-essential if it was classified to have dense transposon insertions for P1 and sparse insertions for P4 , and non-essential if the gene was classified as densely hit for both P1 and P4 . See Section 'Materials and methods' for a complete description of the classification method . Figure 2—figure supplement 3 shows the fit of the model to the distribution of transposon insertion counts per gene . In six out of 452 instances , the mixture model failed to classify the gene , either due to low assignment confidence or due to increased insertions from P1 to P4 . The short ribosomal proteins S9 ( rpsl/0637 ) , L27 ( rpmA/0499 ) , and L31 ( rpmE/0137 ) were manually assigned as essential since they are necessary to construct a functional ribosome . The gene secA/0095 could not be automatically classified since the mixture model predicted the gene to be more heavily hit with insertions in P4 than in P1; it was assigned as essential as it is a major component of the translocase assembly . The insertions occurred in the C-terminus linker domain considered to be important for binding to phospholipids and preprotein translocation . A gene of unclear function ( 0235 ) was predicted by the model to be essential at a slightly higher probability than quasi-essential ( 0 . 471 vs . 0 . 416 , respectively ) ; however , it was manually assigned to be quasi-essential following its previous assignment in JCVI-syn2 . 0 ( Hutchison et al . , 2016a ) . Thioredoxin ( trxA/0065 ) was assumed to be essential since its associated reductase ( trx/0819 ) was predicted to be essential by the mixture model . Only one gene was misclassified: the ATP synthase subunit ϵ ( atpC/0789 ) , initially classified as non-essential , was manually reassigned to essential since all other ATPase subunits ( atpD/0790 through atpB/0796 ) were essential according to the mixture model . The majority of transposon insertions in atpC/0789 are found in the 3′ region , similar to the pattern seen in secA . However , it is possible that the ϵ subunit may not actually be essential since in M . pneumoniae , transposon insertions into the atpC ( MPN597 ) lead to viable cells with decreased cytadherence activity ( Shimizu et al . , 2014 ) . The set of genes classified quasi-essential could potentially include essential genes which cannot be discriminated using these transposon insertion data . For these misclassified genes , it is possible that although expression of the gene product essential for cell growth has been halted , previously translated essential proteins remain in the cell in sufficient quantities to maintain cell growth and division up to P1 . A further discussion of this argument is presented in Section 'Completeness of the model' and Appendix 1 . The genes identified as non-essential by the Poisson mixture model may contain ‘weakly’ quasi-essential genes , that is disrupted genes which confer a minor growth disadvantage . This behavior would manifest as a decrease in transposon insertions between P1 and P4 , but not such a steep decline that the gene is observed with little to no insertions . To identify these ‘weakly’ quasi-essential genes , the genes classified as non-essential were subjected to further classification using k-means clustering of the ratio of the number of transposon insertions in P4 to P1 assuming two clusters ( see Figure 2—figure supplement 3 ) . Of the 118 genes initially classified non-essential , 42 were reclassified as quasi-essential . The assignment of essentiality classes and distribution of transposon insertions over the entire genome are presented in Figure 2—figure supplements 1 , 2 , and Supplementary file 3 . Genomic positions of transposon insertions are listed in Supplementary file 2 . Figure 3 summarizes the breakdown of the essential , quasi-essential , and non-essential genes according to the functional classes . Of the 452 coding genes in JCVI-syn3A , 60% are essential , 25% are quasi-essential , and 15% are non-essential by this analysis . The detailed breakdown of the JCVI-syn3A genome into these classes ( Table 1 ) shows that of the 91 genes of unclear function , 30 are essential , 32 are quasi-essential , and 29 are non-essential . Those 30 essential genes could represent new biological mechanisms not yet defined and should motivate the search to discover their function ( Alberts , 2011 ) . Since on average only one transposon insertion occurs per cell and the identification of insertion locations within the genome is performed over an ensemble of cells , these transposon mutagenesis studies can only reveal individual gene essentialities . To probe the essentiality of groups of genes , one would need to perform targeted multiple knockout studies . However , for metabolic genes , flux balance analysis of the metabolic reconstruction can predict the essentiality of groups of genes . In Targeted gene removal experiments , the individual gene essentiality results are expanded to include the assignment of essentiality to combinations of genes in silico , leading to potential combinations of genes to remove to further minimize the genome . The classifications of the genes used in the metabolic reconstruction are shown in Table 2 . Preliminary triple knockout experiments involving various sets of non-essential genes lead to cells with greatly impaired growth rates ( data not shown ) . The fact that ∼15% of the genes in JCVI-syn3A are individually non-essential is not inconsistent with the near-minimality of the genome as a whole: it is not possible to remove all non-essential genes without vastly decreasing the growth rate or outright killing the cell . Furthermore , a genome comprised only of essential and quasi-essential genes is non-viable as well , since the removal of a non-essential gene can cause a previously quasi-essential gene to become non-essential in the new construct . As JCVI-syn3A grows more slowly than JCVI-syn1 . 0 ( 2 hr doubling time vs . 1 hr ) , a gene disruption that in JCVI-syn1 . 0 led to outgrowth by unaffected competitor cells might still survive through passage four in JCVI-syn3A . As a result , genes that were classified as quasi-essential in JCVI-syn1 . 0 can appear non-essential in JCVI-syn3A , and could in principle be removed as well—for the price of some gradual further decrease in growth rate . This lack of a clear cutoff again underscores the ‘trade-off between genome size and growth rate’ taking place during genome minimization ( Hutchison et al . , 2016a ) . The cellular components of JCVI-syn3A fall into three categories: macromolecules , lipids and capsule , and small molecules and ions . Appendix 1—table 1 lists the mass fractions for all components included in the JCVI-syn3A biomass composition . These mass fractions are used to derive the coefficients in the biomass reaction depicted in Figure 4 for each component based on its molecular weight . The different biomass components are summarized below with the full discussion and derivation in Appendix 1 . The growth-associated maintenance ( GAM ) ATP cost shown in Figure 4 is described in Section 'GAM/NGAM' . The metabolic reconstruction of JCVI-syn3A features 338 reactions involving 304 metabolites , catalyzed by gene products of 155 genes , thus covering a third of the genome . The scope of the reconstruction includes all reactions associated with providing the components of the reconstructed biomass ( see Figure 4 ) . Not covered are metabolic functions outside the ‘core’ functions , in particular RNA modifications and damage repair reactions . While many RNA modification enzymes are known already , the prevalence of specific RNA modifications in the RNA pool is not yet known . A few RNA modification enzymes are however discussed with regard to folate metabolism in Section 'The role of folate metabolism' . The majority of damage reactions and possible repair thereof are mostly not yet known , and are hence omitted save for two genes in cofactor and nucleotide metabolism . Approximately 30 genes pertaining to RNA modification are listed in our KEGG ortholog search as ‘Genetic Information Processing’ and will be included in a future model for ribosome biogenesis and tRNA biogenesis . The model reactions are organized in nine subsystems , which are listed in Supplementary file 1B together with their respective number of reactions and genes included . Among these subsystems , ‘Biomass production’ contains the biomass reaction discussed in Section 'Biomass composition and reaction' . ‘Exchange’ contains the model reactions that describe metabolite exchange with the media . All other subsystems are discussed in detail in the following subsections . Figure 5 shows a global view of the metabolic network ( excluding biomass and exchange reactions ) . Individual maps for the subsystems for central , nucleotide , cofactor , lipid , macromolecule and amino acid metabolism are depicted in Figures 6–7 and Figures 9–12 . Table 2 lists all genes included in the reconstruction , together with their in vivo and in silico essentiality . Experimentally , JCVI-syn3A is grown in the rich and not fully defined SP4 media ( Glass et al . , 2015; Tully et al . , 1979 ) , since a defined media supporting its normal growth has yet to be obtained . Consequently , a rich in silico medium that provides for all biomass precursors the cell can take up is assumed , with glucose as the only energy source . With the chosen parameters , the model yields an in silico growth rate of μ = 0 . 34 hr-1 , corresponding to a doubling time of td = 2 . 02 hr ( td=ln ( 2 ) /μ ) ; this comes close to the experimental doubling time of ca . 105 min ( see Figure 14 ) . This exact agreement is sensitive to the choices for uptake/secretion and GAM parameters however ( see Section 'Sensitivity analysis' in Appendix 1 and Appendix 1—figure 2 ) , and the in silico growth rate should thus be more understood as a provisional prediction . This does not constitute a problem for the subsequent analyses; when the impact of in silico gene knockouts on growth rate is studied in Section 'In silico gene knockouts and mapping to in vivo essentiality' , nearly all knockouts either abolish the growth rate entirely ( lethal knockouts ) or do not affect it at all . Thus , this analysis is not affected by the rather qualitative nature of the growth rate prediction by the model . In the rest of this section , the steady-state fluxes produced by the model are compared to literature data , and to theoretical flux limits obtained from protein abundances and enzyme turnover numbers ( kcat ) . While no fluxomics data is yet available for JCVI-syn3A , some experimental fluxes from the literature allow for a few comparisons . The purine incorporation flux into RNA has been determined for M . mycoides capri LC Y ( Mitchell et al . , 1978 ) as 0 . 5–1 . 0 nmol/min/mg cellular protein , corresponding to 0 . 016–0 . 033 mmol gDW−1 h-1 . This is close to the in silico net flux of ATP and GTP into RNA of 0 . 047 and 0 . 043 mmol gDW−1 h-1 , respectively . Here , the net flux is defined as the difference between NTP consumed by the RNA polymerase reaction and NMP released by the RNA degradation reaction . The in silico K+ uptake of 0 . 29 mmol gDW−1 h-1 also falls within a factor of two of the experimental uptake rate of 0 . 49 mmol gDW−1 h-1 ( 15 nmol/min/mg cellular protein ) in M . mycoides capri PG3 ( Benyoucef et al . , 1982a ) . These comparisons serve as an internal consistency check on the model , demonstrating that the in silico uptake/incorporation rates as resulting from biomass composition and growth rate indeed reproduce the experimental values . Furthermore , it has been reported that M . mycoides capri LC Y spends 10% of its glucose uptake on polysaccharide capsule production ( Plackett , 1967a ) . The in silico fluxes through central metabolism are depicted in Figure 6—figure supplement 1 . As can be seen , the in silico flux through phosphoglucomutase ( PGMT , deoB/0733 , leading from glucose-6-phosphate to galactan and Gal-DAG in Figures 6 and 10 ) is lower than the experimental value , amounting to only 1 . 8% ( 0 . 135 mmol gDW−1 h-1 , from 7 . 4 mmol gDW−1 h-1 glucose taken up ) . However , the model does qualitatively reproduce the further splitting between galactan and Gal-DAG production , which gives a ratio of ∼22:1 ( UDP-galactofuranose consumption of 0 . 129 mmol gDW−1 h-1 vs . 0 . 006 mmol gDW−1 h-1 ) , compared to a ratio of ∼64:1 from 3H labeling ( Plackett , 1967a ) . Thus , with the chosen parameters , the model reproduces several experimental fluxes; with the only significant difference occurring in capsule production . FBA flux predictions were also compared to reaction flux bounds ( Vmax ) calculated from protein abundances and enzyme turnover numbers ( kcat ) ( Labhsetwar et al . , 2013; Labhsetwar et al . , 2017 ) . The protein abundances were derived from proteomics experiments ( see Section 'Abundances of essential and non-essential proteins' ) and the turnover numbers were extracted from the BRENDA database ( Scheer et al . , 2010 ) ( see Figure 15 , Figure 15—figure supplement 1 and Appendix 1 ) . Vmax values could be obtained for 105 ‘non-pseudo’ reactions ( i . e . excluding exchange , biomass and macromolecular reactions ) . Of these , 86 had non-zero fluxes . The zero-flux reactions include for example reactions pertaining to alternative sugars , which are unused in the assumed medium . Of the reactions with non-zero fluxes , only 19 reactions required fluxes in the FBA optimal solution higher than their proteomics-derived Vmax ( see Figure 15—figure supplement 1A ) . The reaction with the lowest Vmax/flux ratio is adenylate kinase , which is predicted to carry a flux of 2 . 23 mmol gDW−1 h-1 , compared to a proteomics-derived Vmax of only 0 . 01 mmol gDW−1 h-1 . However , the kcat for this enzyme as found for B . subtilis in BRENDA is 0 . 053 s-1 , which falls in the lower tenth percentile of the kcat data for all reactions in the model . The second-smallest Vmax to flux ratio is found for aspartate-tRNA synthetase ( 0 . 11 mmol gDW−1 h-1 in model vs . 0 . 005 mmol gDW−1 h-1 from proteomics/kcat ) ; other amino acyl-tRNA synthetases with significantly low Vmax/flux ratio ( < 0 . 25 ) are the ones for threonine and serine . These three amino acyl-tRNA synthetases have the smallest kcat numbers among all amino acyl-tRNA synthetases . The third-lowest Vmax to flux ratio is found for fructose bisphosphate aldolase ( 7 . 21 mmol gDW−1 h-1 in model , 0 . 39 mmol gDW−1 h-1 from proteomics/kcat ) . This protein has 227 copies in the cell on average , which places it among the least abundant proteins in central metabolism . Furthermore , the kcat value for this enzyme retrieved from BRENDA for Bacillus cereus is 2 . 95 s-1 , which is also one of the lowest values found among proteins in central metabolism . The only other reactions with a Vmax/flux ratio less than 0 . 25 are: DASYN ( cdsA/0304 ) , which produces the lipid intermediate CDP-diacylglycerol; ACPS ( acpS/0513 ) , which attaches the 4′-phosphopantetheine to apo-ACP; and GUAPRT ( hptA/0216 ) , which produces GMP from guanine . DASYN has a Vmax to flux ratio of 0 . 19 , that is only slightly decreased . ACPS has both the lowest kcat among the model enzymes ( 0 . 001 s-1 ) and one of the lowest protein abundances ( just one copy per cell per our proteomics data ) . If either value turned out to be not accurate , this could easily raise the Vmax/flux ratio above the current level of 0 . 07 . However , the discrepancy observed for GUAPRT is interesting in the light of the aforementioned mononucleotide uptake capabilities in M . mycoides capri ( Neale et al . , 1984; Youil and Finch , 1988 ) ( see Section 'Nucleotide metabolism' ) . While there is no other evidence for the possible conservation of this capability in JCVI-syn3A , this flux bound might suggest that this uptake capability still at least partially exists in JCVI-syn3A , and might be worth investigating . All other reactions with Vmax lower than the FBA flux differ by less than a factor of four; even though the FBA flux exceeds the estimated Vmax , the disagreement is rather modest . For the reactions that do show higher disagreement , we note that the kcat values obtained tend to be on the lower end either within the respective group of reactions , or across the model . This suggests that these kcat values might merit closer investigation . Overall , the proteomics-derived bounds are consistent with the FBA fluxes , with only a handful of reactions showing significant discrepancies . It should be noted that the rates of enzyme-catalyzed reactions in vivo are typically less than Vmax to allow the cell to respond to increases in substrate concentration . Accordingly , Vmax is expected to be greater than the metabolic flux necessary to sustain the cell , such that the flux required under typical growth conditions can be achieved without enzyme saturation . In line with this argument , a histogram of Vmax values for reactions in the model shows the bulk of reactions to have a Vmax1–3 orders of magnitudes higher than the flux required in the FBA solution ( see Figure 15—figure supplement 1B ) . Table 3 breaks down the energy consumption in JCVI-syn3A ( as percentage of total ATP consumption , see Section 'Materials and methods' ) . The upper five categories correspond to individual subsystems of the metabolic model . The lower five categories provide a breakdown of GAM and NGAM expenses into individual components . As discussed in Section 'GAM/NGAM' , a part of the protein and RNA synthesis ( and , by extension , the tRNA charging and nucleotide phosphorylation fluxes ) is routed through protein and RNA degradation , constituting the turnover-associated part of the NGAM; the resulting fraction of total ATP consumption is listed as ‘NGAMTurnover’ in Table 3 . Analogously , ‘NGAMATPase’ denotes the ATP expense for the ATPase-associated part of the NGAM . ‘GAMMacromolecules’ and ‘GAMtRNA charging’ denote the ATP expenses for growth-associated protein/RNA synthesis ( subsystem ‘Macromolecules’ in the model ) and tRNA charging ( subsystem ‘Amino acid metabolism’ ) , respectively . Analogously , ‘Nucleotide metabolism’ only includes ATP expenses beyond RNA turnover ( i . e . NTP production for growth-associated nucleic acid synthesis and nucleotide usage in other subsystems ) . ‘GAMNonquant’ denotes the non-quantifiable fraction of the GAM . In line with JCVI-syn3A relying heavily on uptake of pre-formed precursors and further conversion through salvage pathways only , it spends only ∼6% of ATP on ( small molecule ) metabolic processes ( i . e . lipids , cofactors and nucleotides , plus PRPP synthesis in the pentose phosphate pathway ) . The vast majority of energy ( 75% ) is spent directly on growth , that is macromolecular synthesis and tRNA charging and the non-quantifiable contribution to GAM . A modest fraction of ∼16% of cellular energy expenses falls to the NGAM ( macromolecular turnover and ATPase ) . These numbers stand in striking difference to M . pneumoniae , for which non-growth associated maintenance accounts for 71–88% of total cellular ATP consumption ( in the accounting for M . pneumoniae , the NGAM does not include protein/RNA turnover ) ( Wodke et al . , 2013 ) . This correlates with M . pneumoniae having a doubling time between 8 and 20 hr ( Yus et al . , 2009; Wodke et al . , 2013 ) , that is four to ten times slower than JCVI-syn3A . The ATP breakdown also reveals that in spite of the minimal cell’s heavy reliance on uptake of pre-formed precursors , transport processes only account for ∼3% of ATP consumption . While the optimal FBA solution only takes up amino acids through the permeases ( 0876 , 0878 , and gltP/0886 ) using proton symport reactions , the ATP expense on transport does not increase significantly ( only to ∼5% ) when forcing amino acid uptake through the ATP-consuming Opp peptide importer . This illustrates how JCVI-syn3A can maintain a relatively fast growth rate in spite of its extreme genome minimization and reliance on fermentative ATP production: By importing pre-formed precursors or recovering them through salvage reactions , the cell expends a minimal amount of energy to obtain the final macromolecular precursors and passes this savings in energy along to the production of biomass . The other important currency in the cell are reduction equivalents in the form of NADPH , which in JCVI-syn3A is produced by GapN ( GAPDP , gapdh/0451 ) and , in tiny amounts , by FolD ( MTHFD , folD/0684 ) . The only consumer of NADPH in the model is ribodinucleotide reductase ( RNDR , nrdE/0771 through nrdF/0773 ) . In vivo , however , NADPH is expected to also be needed for expenses not captured by the model , including RNA modification ( dihydrouridine synthesis ) and response to oxidative stress: The reduction of protein disulfide bonds formed by oxidative stress is mediated by thioredoxin ( Ben-Menachem et al . , 1997 ) , and coenzyme A disulfide reductase ( cdr/0887 ) serves to reduce coenzyme A disulfide dimers to the free thiol-carrying monomers . NADPH production through GAPDP diverts flux from the ATP-producing GAPD/PGK branch in glycolysis , effectively incurring an ATP cost for NADPH production . In order to probe the cellular capacity for NADPH production , Appendix 1—figure 2G shows a plot of in silico doubling time as a function of imposed NADPH consumption ( imposed via an artificial NAPDH oxidation reaction with O2 , introduced for testing purposes ) . Within a considerable margin , the doubling time rises shallowly with NADPH consumption: for example , at 3 . 5 mmol gDW−1 h-1 ( a quarter of the maximally possible flux through GAPDP ) , the model doubling time only rises by 25% to ∼2 . 5 hr . This suggests that even though NADPH usage is not fully captured by the model , the cell should be able to accommodate a considerable amount of NADPH demand without strong impact on the growth rate ( see also Section 'Sensitivity analysis' in the Appendix 1 ) . Finally , there is also some experimental information that allows for a comparison of cellular energetics , specifically of basal energy expenses . In Benyoucef et al . ( 1981b ) , the residual acid secretion in M . mycoides capri PG3 in a saline buffer after inhibition of ATPase has been measured to be around 110 nmol/min/mg cellular protein ( corresponding to ∼3 . 6 mmol gDW−1 h-1 ) , which can be compared to the corresponding in silico acid secretion ( which in turn is connected to ATP production ) . The measurements were performed in a saline buffer containing glucose but no other nutrients for growth . Under these conditions , the cell is not able to grow ( Leblanc and Le Grimellec , 1979 ) , but should be able to meet its basic energetic needs . Furthermore , since ATPase was inhibited with N , N′-dicyclohexylcarbodiimide ( which abolishes both proton transduction and ATPase activity ( Hermolin and Fillingame , 1989 ) ) , it should not consume ATP anymore under the experimental conditions . These conditions are simulated by setting the lower bound on ATPase proton extrusion to 0 . 0 mmol gDW−1 h-1 and changing the objective function in FBA from maximal growth rate to minimal glucose uptake . A residual acid secretion of 1 . 3–2 . 6 mmol gDW−1 h-1 results , which depends on the assumed lactate to acetate ratio , and falls within a factor of ∼2 of the experimental value . This suggests that the basic cellular energy expenses—protein degradation , RNA and protein synthesis under non-growth conditions—are described reasonably well by this model . At the same time , hypotheses can be made as to what energy expenses could account for the observed remaining discrepancy . One expected factor is the unknown actual NADPH demand ( and resulting effective ATP cost ) . In addition , a possibly significant energy sink not covered yet by the model are metabolite repair functions , of which thus far only two are included in the model , namely 5-formyl-THF cyclo-ligase ( ygfA/0443 ) and dUTPase ( dut/0447 ) . Metabolite repair usually consumes energy ( Linster et al . , 2013 ) , and it would be interesting to see to what extent this could account for the current underestimation of basal energy expenses . In addition to studying fluxes of the unperturbed model , the FBA framework also allows to study the impact of in silico gene disruptions by simulating knockouts in COBRApy ( Ebrahim et al . , 2013 ) , that is by removing all reactions associated with a gene of interest from the model and calculating the growth rate from the resulting model . A knockout is defined to be lethal if the resulting growth rate is zero or the FBA problem becomes infeasible . By this definition , 123 of the 155 genes included in the model are essential ( 79% ) . In this analysis , two genes are currently non-essential in silico for ‘technical’ reasons: metK/0432 ( methionine adenosyltransferase , MAT ) and mtnN/0381 ( S-adenosylhomocysteine ( SAH ) hydrolase , reaction ID: AHCi ) . These genes are part of the S-adenosylmethionine ( SAM ) pathway and would be connected through nucleic acid methylation reactions ( consuming SAM and producing SAH ) , which were not included in the model due to missing experimental information . As these two reactions currently cannot carry flux , it does not make sense to consider their in silico essentiality in the comparison to experiment . An individual breakdown of in silico gene essentialities is provided in Table 2 , which lists all genes modeled in silico , together with their catalyzed reaction ( or general description for genes with several reactions , like the peptidases ) , and their essentiality in silico and in vivo . Genes non-essential in silico are found in amino acid , central and nucleotide metabolism as well as transport reactions , and only one gene in lipid metabolism . Some non-essentialities are functionally connected . For example , as the peptide importer Opp ( oppB/0165 through oppA/0169 ) is non-essential in silico , the downstream peptidases have to be non-essential as well . A further analysis of in silico essentialities is featured in Sections 'Interpretation of individual gene essentialities' , 'The role of folate metabolism' , and 'A partial bypass to the pentose phosphate pathway' . These in silico essentialities can be compared to the experimental transposon data ( see Section 'Transposon mutagenesis experiments probe in vivo gene essentiality' ) . Figure 16 shows an overall mapping between in silico and in vivo essentiality ( including all genes in JCVI-syn3A , including RNA genes , pseudogenes and the two ‘technical non-essentials’ ) . A more detailed analysis for the genes included in the model is presented in Table 4 , which displays the confusion matrix for the in silico to in vivo comparison , that is the distribution of model genes among the in silico and in vivo classifications . The left table represents the breakdown for all model genes except the two technical non-essentials , while the right table shows the breakdown if genes related to amino acid utilization are also excluded ( see below ) . Whilst the model only distinguishes essential and non-essential genes , the experimental classification includes quasi-essentiality , which falls somewhere in between essentiality and non-essentiality . Thus , for any evaluation of predictive performance of the model , some assumption has to be made with regard to the in vivo quasi-essential genes . Table 5 summarizes several statistics obtained for specific cases , discussed in the following . Two limiting cases of interest are treating all quasi-essential genes as either in vivo essential ( 1 ) or in vivo non-essential ( 2 ) . Given that the identification of quasi-essential genes was crucial for the successful genome minimization in JCVI-syn3 . 0 ( Hutchison et al . , 2016a ) , treating these genes as essential might be the biologically more relevant assumption . If all quasi-essential genes are considered essential ( i . e . adding the numbers in the second row in Table 4 , left matrix to the first row ) , the model displays an accuracy of 88% . ( Accuracy = ( TP+TN ) /total; we opt to define essential genes as ‘positive’ and non-essential genes as ‘negative’ , so that a true positive gene ( TP ) is essential in model and experiment; a false positive ( FP ) is essential in the model but non-essential in experiment; a true negative ( TN ) is non-essential in model and experiment; and a false negative ( FN ) is non-essential in the model but essential in experiment . ‘Total’ is the sum of all genes included in the analysis . ) The resulting sensitivity ( TP/ ( TP+FN ) ) is 87% , while the specificity ( TN/ ( TN+FP ) ) is 100%: All in silico essential genes are at least quasi-essential in vivo , so there are no ‘strong’ false positive predictions ( of genes to be essential that are actually non-essential in vivo ) . If , alternatively , all quasi-essential genes are considered non-essential in vivo ( adding the numbers in the second row to the third row in the left confusion matrix in Table 4 ) , the accuracy comes out a bit lower at 83%; the sensitivity increases to 96% while the specificity drops to 54% . This low specificity can be explained by considering the comparatively low number of in vivo non-essentials among the genes included in the model ( 12 ) : Considering all quasi-essentials ( two thirds of which are essential in the model ) to be non-essential as well then leads to a large relative fraction of ‘non-essentials’ not detected by the model , even though the overall accuracy does not change much compared to case ( 1 ) . As a more balanced measure of model prediction performance , Table 5 also features the Matthews correlation coefficient ( MCC ) in the last column , which can range from −1 . 0 ( perfect disagreement ) via 0 . 0 ( same agreement as a random model ) to 1 . 0 ( perfect agreement ) . For both cases described above ( treating quasi-essentials as either all essential or all non-essential ) , the MCC comes out to ∼0 . 59 . While this does not amount to perfect agreement , we note that the quasi-essentials in the middle row in Table 4 ( upper confusion matrix ) actually encompass the vast majority of false model predictions . Thus , in addition to the two limiting cases presented above , it is also instructive to consider the prediction performance when including only those genes that can be classified as essential or non-essential in vivo , that is those genes that can be compared to the model classification without further assumptions . In this case , the specificity reaches 100% as in case ( 1 ) above , as there are again no false positives; the sensitivity reaches the same value as in case ( 2 ) above ( 96% ) as there are only four false negatives; and the accuracy increases to 97% in this case . The MCC also comes out higher at 0 . 85 . This demonstrates that the lower MCC and other metrics obtained before really arise from the large number of quasi-essential genes included in the model , that are inherently difficult to describe in an FBA model: For example , nucleic acid stabilization by polyamines is a known essential process , and the minimal media for both M . mycoides capri LC Y ( Rodwell , 1969 ) and M . pneumoniae ( Yus et al . , 2009 ) hence include spermine , which is thus a biomass component in the model for JCVI-syn3A . While this renders the corresponding uptake genes ( potC/0195 through potA/0197 ) essential in the model , they are only quasi-essential in vivo ( see Table 2 ) . Similarly , it is of interest to consider one set of genes whose functionality is difficult to capture precisely based on the currently available information , namely the genes pertaining to uptake and utilization of amino acids ( in free or peptide form ) : As can be seen in Table 2 , from the overall 14 ‘weak’ false negative predictions ( in silico non-essential genes that are quasi-essential in vivo ) , 10 comprise the peptide importer Opp ( oppB/0165 through oppA/0169 ) , two amino acid permeases ( 0878 and gltP/0886 ) and three of the four peptidases ( 0305 , 0444 and 0479 ) . As further discussed in Section 'Interpretation of individual gene essentialities' , the in vivo essentiality of these genes is likely affected by their exact substrate profiles and maximal uptake rates . If all 12 genes related to amino acid utilization ( i . e . the genes above plus ietS/0133 and 0876 ) are excluded from the prediction comparison , the right confusion matrix in Table 4 is obtained . The resulting metrics are listed in the last two rows of Table 5 , where the remaining quasi-essentials are again included in the in vivo essentials ( upper row ) or in the non-essentials ( lower row ) . As can be seen , in the first case , the accuracy and sensitivity both rise to 94% compared to the full set of genes ( 88% and 87% , row one in Table 5 ) ; the MCC rises to 0 . 72 . In the second case , the specificity drops from 54% to 39% compared to the full set of genes ( row two in Table 5 ) , and the MCC decreases to 0 . 46 . However , this must be seen in the light of the excluded genes comprising mainly weak false negatives , that is quasi-essential genes that are non-essential in silico , and no weak false positives ( quasi-essentials that are essential in silico ) . Thus , even though genes are excluded that show disagreement between model and experiment , the agreement worsens because these genes happened to be classified as ‘true negatives’ in case ( 2 ) . The improved model metrics in case ( 1 ) for excluding amino acid genes thus seem more relevant . In summary , this analysis demonstrates an overall good agreement between model and experiment , which is mainly impacted by the in vivo quasi-essential genes , whose essentiality is inherently difficult to capture in an FBA model . The disagreements observed ( quasi-essential genes , and a few strong false negatives ) are discussed in detail in Sections 'Interpretation of individual gene essentialities' , 'The role of folate metabolism' , and 'A partial bypass to the pentose phosphate pathway' . Some of them can be rationalized , while others lead to new hypotheses . Finally , performing in silico double knockouts ( Figure 16—figure supplement 1 ) yields just one synthetic lethality ( i . e . lethality of a two-gene knockout where the individual knockouts are non-lethal ) –namely , a double knockout of the two amino acid permeases 0876 and 0878 , which prevents the cell from acquiring cysteine . Absolute cellular abundances ( molecules per average cell ) of JCVI-syn3A proteins were obtained from mass spectrometry based proteomics and the assumed protein dry mass fraction . Relative and absolute protein abundances were used in the reconstruction of the JCVI-syn3A biomass composition ( see Section 'Biomass composition and reaction' ) and estimates of the Vmax for reactions in the metabolic model . They also served for the further study of the JCVI-syn3A proteome , both with respect to the fraction of proteins with known functions , and in regard to expression of essential vs . non-essential proteins . Comparing the overall JCVI-syn3A proteomics breakdown in Figure 17a to the genome breakdown in Figure 1 shows that the ‘Unclear’ fraction is even smaller in the proteome than in the genome , suggesting that at least a generic function can be immediately assigned to >90% of the proteome . Furthermore , proteins classified as ‘Metabolism’ alone account for ∼25% of the proteome . Considering all proteins included in the FBA model ( i . e . also the synthetases classified as ‘Genetic Information Processing’ ) covers a subset of 40% of the proteome . Thus , studying expression features for genes in the model should yield relevant insights into the proteome as a whole . Figure 17b compares distributions of absolute protein abundances between in silico essential , in silico non-essential and all proteins . Figure 17c shows the same comparison based on the transposon mutagenesis classification of essentiality ( also including quasi-essential genes ) . As can be seen , the expression profiles for essential and non-essential proteins are qualitatively similar both to each other and to the expression profile of all proteins in JCVI-syn3A . This holds for both the genome-wide transposon data-based comparison ( Figure 17c ) and the comparison for the subset of ( mostly ) metabolic FBA genes ( Figure 17b ) . While this does not yet allow for strong conclusions , it does suggest the presence of little regulation , if at all , that would discriminate gene products based on their essentiality . This conclusion would be in line with the small number of identified regulatory proteins left in the genome of JCVI-syn3A .
The creation of the first minimal bacterial cell JCVI-syn3 . 0 in 2016 provided a powerful platform for understanding the basics of life . As a first step along this road , we have combined the genetic information of JCVI-syn3A with the extensive amount of experimental information available for the natural M . mycoides capri and assembled a metabolic reconstruction and FBA model for the minimal cell . The majority of reactions in this model are supported by experimental evidence on the parent organism and related mycoplasmas . The model is near-complete with regard to accounting for the biomass components , describes cellular energy expenses well , shows good agreement with experimental transposon insertion data , and importantly has relatively few non-essential metabolic genes . It thus provides a foundation to study the features of the minimal metabolic network . The metabolic networks of lipids and cofactors are both functionally nearly minimal and in their reconstruction nearly complete . The reconstructed lipid network is consistent with all membrane components known from the biomass composition ( save for the small fraction of triacylglycerol , which might or might not still be produced in JCVI-syn3A ) and contains no redundant features ( except for one more glycosyltransferase than required by the current reconstruction ) . The only remaining gap in lipid metabolism is the missing gene for phosphatidate phosphatase . In cofactor metabolism , the remaining questions are the substrate specificities of the EcfS transporter subunits , and the proposed lipoate uptake mechanism . Amongst the ion transport reactions , a gene for the Na+/H+ antiporter remains to be identified . Central and nucleotide metabolism display a number of potential redundancies ( see Table 2 ) , and several important reactions not accounted for by a gene yet . In central metabolism , these include the in silico essential transaldolase ( TALA ) reaction; and the reactions PDH_E1 , NOX and export of lactate and acetate , all of which ( except for lactate export ) are required in the model to maintain the experimental doubling time of ∼2 hr . In nucleotide metabolism , nucleobase uptake is an essential function still unaccounted for . Even so , we obtain a number of gap-filled reactions of only 21—a fraction of 6% of all model reactions , or 9% of the 244 ‘non-pseudo’ reactions . ( Non-pseudo reactions are the subset of individual chemical or transport reactions in the model . This includes all model subsystems except for the artificial exchange and biomass reactions; and the macromolecule reactions , which describe non-metabolic processes taking place in all cells that are therefore not relevant for the number of gap fills ) . These 21 gap fills are obtained from the total number of 35 non-pseudo reactions without assigned gene after subtraction of 14 passive transport reactions assumed to take place without protein mediation ( see e . g . discussion on passive permeative glycerol uptake in Appendix 1 ) . This number of gap fills is considerably lower than in comparable models ( see Section 'Comparison to M . pneumoniae' ) . Furthermore , from these 21 gap fills , only four are not supported by experimental evidence . Hence , 98% of all non-pseudo reactions are justified through gene assignments and/or experimental evidence , or are assumed to be passive . Table 6 summarizes the overall features of the model . We also note that there are good candidates for many of the missing functions: The NOX reaction could conceivably be carried out by an oxidoreductase of unspecified function . Both 0029 and fre/0302 code for putative oxidoreductases . The gene fre/0302 in particular has been suggested to be the missing NADH oxidase ( Danchin and Fang , 2016 ) and might be a candidate for investigation . While no gene for transaldolase has thus far been identified in any mycoplasma , the alternative route proposed in Vanyushkina et al . ( 2014 ) would just require a phosphatase reaction , which could plausibly be carried out by one of a number of hydrolases in JCVI-syn3A of thus far unknown function . The same holds for further phosphatase reactions , including the phosphatidate phosphatase ( PAPA ) reaction in lipid metabolism and a number of hydrolase reactions in nucleotide metabolism . Substrate screening , informed by the metabolic reconstruction , might therefore be of interest for the hydrolases of unknown function . Finally , some of the reactions without assigned gene are transport processes ( e . g . lactate/acetate export , nucleobase uptake ) . It stands to reason that these processes might be carried out by some of the many membrane proteins in JCVI-syn3A whose function could not be identified yet . Our metabolic model of JCVI-syn3A thus features an overall quite complete metabolic network , and even though a small percentage of reactions could currently not yet be assigned to a gene , the presence of genes catalyzing these reactions is plausible and the majority of these reactions are supported by experimental evidence . We therefore believe that comparing our model to the experimental transposon mutagenesis data is informative . In the following we discuss the comparison of in silico and in vivo ( transposon mutagenesis-based ) essentiality . While a number of genes can be discussed individually ( see Section 'Interpretation of individual gene essentialities' ) , two pathways need to be discussed as a whole: the folate cycle ( see Section 'The role of folate metabolism' ) and the pentose phosphate pathway ( see Section 'A partial bypass to the pentose phosphate pathway' ) . Overall , the analysis suggests a few new hypotheses and even yields suggestions for some genes or groups of genes that could still be removed from the genome of JCVI-syn3A to minimize the genome even further . In this way , it complements the transposon mutagenesis data that can only probe individual essentialities—and simultaneous knockouts prove challenging in experiment ( see Section 'Transposon mutagenesis experiments probe in vivo gene essentiality' ) . As presented in the preceding sections , the comparison between in silico and in vivo essentiality yielded a number of hypotheses and suggested several possible gene removal experiments ( Table 7 ) . Similarly , the metabolic reconstruction itself yielded a number of informed hypotheses , as well as raised specific questions . While the minimal genome has been experimentally obtained , understanding all genetic functions both individually and as a system remains an ongoing challenge . Thus , the hypotheses and questions raised in this work provide invaluable help in the ongoing effort to completely understand the minimal genome . In Table 8 , we provide a list of suggested experiments other than gene removal/knockout studies , sorted by category and providing a rationale for each experiment . M . pneumoniae is an important systems biology model organism that has been extensively studied ( Güell et al . , 2009; Kühner et al . , 2009; Yus et al . , 2009; Maier et al . , 2011; Wodke et al . , 2013 ) so a comparison to its metabolic map should be of interest . With a published metabolic reconstruction ( iJW145 ( Wodke et al . , 2013 ) ) that includes 304 reactions involving the products of 145 genes it is similar in size to the metabolic reconstruction of the minimal cell JCVI-syn3A with its 338 reactions and 155 genes . Utilizing the vast experimental information on M . mycoides capri , the natural precursor of JCVI-syn3A , as well as information on JCVI-syn3A homologs in other organisms , enabled us to obtain a smaller percentage of gap fills ( i . e . model reactions assumed to be enzymatic yet having no gene assigned ) of 6% out of all model reactions , compared to 25% in the M . pneumoniae model iJW145; or 9% for JCVI-syn3A vs . 32% , if exchange , macromolecular and biomass reactions are excluded from the total number of reactions in each model . The JCVI-syn3A model yields a higher degree of in silico essentiality ( 79% vs . 56% for the 131 ‘metabolic proteins’ in the M . pneumoniae model ( Wodke et al . , 2013 ) ) —reflecting the minimization of the JCVI-syn3A genome . This higher degree of essentiality is also reflected in the differences in individual reactions presented in Supplementary file 6 ( see also Section 'Materials and methods' for details on the model comparison ) . Excluding exchange , macromolecular and biomass reactions , a core of 126 reactions is shared between the models , including glycolysis , the pentose phosphate pathway , reactions from nucleotide , cofactor and lipid salvage pathways , and tRNA charging . However , M . pneumoniae has 116 reactions not present in JCVI-syn3A , which mainly includes uptake and utilization of additional sugar sources , further nucleotide conversions , more extensive cofactor salvage reactions , and additional lipid-related reactions . Some of these reactions were present in JCVI-syn1 . 0 but were removed during minimization of the genome to JCVI-syn3A . Furthermore , some of the differences are technical in nature , for example the choice to model amino acid uptake as ABC import reactions in M . pneumoniae , or the decision to include amino acid secretion reactions there . Interestingly , in spite of the much smaller genome of JCVI-syn3A , its reconstruction still contains 120 reactions not present in the M . pneumoniae model . While a number of these arise from a more detailed description of various transport processes ( nucleosides , peptides and ions ) , we note the presence of some functionalities not present or known in M . pneumoniae . These include the production of a polysaccharide capsule ( in addition to the monogalactosyl-lipid ) , some alternative sugar sources specific to JCVI-syn3A and also specific nucleotide conversion and breakdown reactions , perhaps most notably the presence of the essential damage preemption enzyme dUTPase ( dut/0447 , see Figure 8 ) . We have presented a comprehensive metabolic reconstruction and FBA model of the minimal cell JCVI-syn3A , informed by the extensive experimental information available for the natural precursor , M . mycoides capri , in vivo transposon mutagenesis and proteomics data . The metabolic model is near complete with regard to accounting for all biomass components , with known metabolic functions not included mainly pertaining to damage repair/pre-emption and RNA modification . The high quality of the model is exemplified by the strong support for the network , with 98% of enzymatic reactions in the model justified through gene assignments and/or experimental evidence; and by its good agreement with experimental transposon mutagenesis data showing 92% of the genes included in the model to be essential or quasi-essential . The essential metabolism of this minimal cell consists of only a few subsystems that are only minimally connected with each other . The subsystems for lipids , amino acids , nucleotides and cofactors contain only salvage pathways . An energy analysis shows how this reliance on salvage pathways enables the cell to only spend 9% of its produced ATP on precursor transport and processing while maintaining a doubling time of 2 hr . The experimental transposon mutagenesis data probe individual gene essentialities , which together with the metabolic model point to a few possible remaining redundancies . Comparison with M . mycoides capri further suggests that folate metabolism only became quasi-essential by removal of other genes , underlining how different routes of genome minimization could yield different minimal genomes . Model and accompanying experimental data thus not only reveal properties of the minimal metabolic network , but also yield an extensive list of suggested experiments to test the resulting hypotheses . The model , together with the accompanying transposon mutagenesis and proteomics data , provides an excellent foundation for further studies of the minimal cell .
A genome-scale FBA model requires the reconstruction of the network of metabolic reactions , the assembly of the cellular biomass composition and necessary reaction constraints ( e . g . substrate uptake and ATP consumption ) . The biomass composition of JCVI-syn3A was assembled based on experimental information available for Mycoplasma mycoides capri ( in a few instances using information from other organisms ) . The reconstruction of the metabolic network began with the curated annotation published for JCVI-syn3 . 0 ( Hutchison et al . , 2016a ) ( which also contained annotations for all genes removed from JCVI-syn1 . 0 ) . As done in other models ( Suthers et al . , 2009 ) , an existing curated model was used as a reference to construct a first draft reconstruction . Initially , an FBA model for M . pneumoniae ( Wodke et al . , 2013 ) was used , keeping all reactions whose enzymes had an equivalent in JCVI-syn3A . Information from MetaCyc ( Caspi et al . , 2008 ) , KEGG ( Kanehisa et al . , 2015 ) , and an extensive evaluation of primary literature was then used to add reactions for the remaining metabolism-related genes in JCVI-syn3A , as well as reactions without a gene but supported by experimental evidence ( including the assembled biomass composition ) . Experimental evidence was also used to exclude certain candidate reactions . Finally , a few reactions were added as gap-fills to complete the respective pathways . Metabolite and reaction IDs were matched to BiGG IDs ( Schellenberger et al . , 2010; King et al . , 2016 ) when possible , otherwise IDs akin to BiGG IDs were assigned . Additionally , KEGG compound IDs were assigned to metabolites using the KEGG API; and InChI keys were assigned using the API for the Chemical Translation Service ( Wohlgemuth et al . , 2010 ) . Flux constraints for certain reactions were based on in vivo measurements , other models or physicochemical parameters . Reaction reversibilities were based on information from MetaCyc ( Caspi et al . , 2008 ) and eQuilibrator ( Noor et al . , 2013 ) , inferred by analogy ( e . g . , fatty acid kinase was set as reversible like acetate kinase ) or determined from biochemical context ( e . g . , H+ diffusive influx is set to irreversible , in accordance with in vivo flow direction ) . Model assembly and flux-balance analysis ( Orth et al . , 2010 ) were carried out in COBRApy ( Ebrahim et al . , 2013 ) , a Python module for constraint-based modeling . In flux-balance analysis , a system of n reaction equations featuring in total m reactants is represented as a stoichiometric matrix S of dimensions m×n , where the element Sij denotes the stoichiometric coefficient of reactant i in reaction j ( negative for reactants , positive for products ) . A given set of fluxes through each reaction in the system is represented as a flux vector ν→ of length n . Any steady-state flux vector then belongs to the solution space of the equation S⋅ν→=0→ . This solution space is further constrained by any other constraints imposed on individual fluxes of the form Vmin , j<νj<Vmax , j . A default upper bound Vmax of 1000 mmol gDW−1 h-1 was used for all reactions and default lower bounds of −1000 mmol gDW−1 h-1 and 0 mmol gDW−1 h-1 were used for reversible and irreversible reactions , respectively . Specific constraints were chosen to account for uptake , secretion and ATP consumption restrictions . An optimal flux vector or set of flux vectors within the constrained solution space is then found by maximizing a particular objective function by means of linear programming . We picked biomass production as our objective function , so that the optimal flux vector describes the optimal growth under the chosen constraints . As the solution to the flux optimization may not be unique , parsimonious FBA ( pFBA ) ( Lewis et al . , 2010 ) is employed to obtain a unique solution . In pFBA , the optimal growth rate obtained by using the original objective function ( biomass production in our case ) is subsequently set as a constraint and a new objective function is defined with a coefficient of -1 for all reactions not part of the original objective function . Optimizing the flux vector under this objective function then yields the solution with the smallest sum of individual fluxes , corresponding to minimal enzyme usage in a biological context . Reversible reactions are split into two irreversible reactions for this purpose so as to avoid negative fluxes being maximized rather than minimized . In order to analyze the energy consumption in the metabolic model for JCVI-syn3A , the consumption of ATP equivalents per subsystem was calculated . The term ‘ATP equivalent’ is used to account for the fact that phosphorylation of all dinucleotides in JCVI-syn3A is assumed to be carried out by the glycolytic enzymes phosphoglycerate kinase and pyruvate kinase , so that the phosphate donors are 1 , 3-diphosphoglycerate ( 1 , 3-DPG ) and phosphoenolpyruvate ( PEP ) instead of ATP ( whose role in dinucleotide phosphorylation is effectively bypassed ) . For all model reactions not involving 1 , 3-DPG or PEP , the production or consumption of ATP equivalents was calculated from the number of phosphate bonds formed or broken in each reaction producing or consuming ATP multiplied by the flux through that reaction in the FBA solution . Interconversion of ATP and ADP produces/consumes one phosphate bond . Hydrolysis of ATP to AMP ( e . g . in tRNA charging ) was counted as consuming two phosphate bonds , since the free pyrophosphate can only be hydrolyzed further to two individual phosphates . Consumption of the ATP moiety as a whole ( e . g . in NAD+ synthesis ) was also counted as consuming two phosphate bonds , accounting for the phosphorylation steps from AMP to ATP; the energy spent in AMP is already accounted for in other reactions ( nucleoside uptake and PRPP synthesis for adenine phosphoribosylation ) . The flux through adenylate kinase ( ADK1 ) phosphorylating AMP to ADP is already accounted for by counting ATP–>AMP hydrolysis as two phosphate bonds; it is thus ignored to avoid double-counting . To properly account for 1 , 3-DPG and PEP as phosphate donors for trinucleotide production , the fluxes for the ATP-producing PGK and PYK reactions were set equal to the sum of all PGK or PYK fluxes , respectively , in order to obtain the total number of ATP equivalents produced . In turn , the PGK and PYK model reactions phosphorylating dinucleotides other than ADP were counted as consuming one ATP equivalent each . Accuracy and correct accounting of the calculated ATP equivalent creation and consumption fluxes were verified by confirming that all individual fluxes thus calculated added up to zero . To obtain the total ATP equivalent consumption percentage per category in Table 3 , the consumption fluxes for all reactions in a given category were added up and normalized by the total ATP equivalent consumption flux in the model . ( Central metabolism as the only source of ATP is not included in Table 3 . ) In doing so , an own category ‘NGAMTurnover’ was introduced to include all energy expenses attributable to protein and RNA turnover . This includes the ATP spent on protein degradation itself , as well as the fractions of protein synthesis , RNA synthesis , tRNA charging and phosphorylation of mononucleotides to trinucleotides that produce protein and RNA for turnover only ( as determined from the protein and RNA degradation reaction constraints ) . The remainder of the protein and RNA synthesis fluxes then produces protein and RNA to be consumed in the biomass equation; hence , the associated energy consumption is part of the quantifiable fraction of the growth-associated maintenance ( GAM ) cost , and is hence included in ‘GAMMacromolecules’ in Table 3 ( DNA synthesis being the other cost included ) . Similarly , ‘GAMtRNA charging’ is the fraction of energy expense in tRNA charging attributable to growth-associated protein synthesis . The consumption of the ATP moiety in RNA and biomass production was included in nucleotide metabolism , in order to stay consistent with the definition of the GAM to only include the ATP hydrolyzed for growth ( including macromolecular synthesis ) , but not the consumption of the ATP moiety as a precursor ( see also Figure 4 ) . Accordingly , the ATP hydrolyzed in RNA synthesis was included under ‘GAMMacromolecules’ . Finally , PRPPS ( PRPP synthase ) is part of central metabolism in the model but as a reaction is independent from energy production in glycolysis . It was hence assigned its own subsystem ( ‘Pentose phosphate pathway’ ) for the purposes of energy usage breakdown . Reactions between the models for JCVI-syn3A and for M . pneumoniae ( iJW145 ) ( Wodke et al . , 2013 ) were compared programmatically by associating with each reaction in either model a set of involved metabolites , excluding water , Pi , and H+ . By comparing only the involved metabolites , differences in stoichiometry , reversibility , and mass balance between the two models are not considered . To develop a common language of metabolites between the two models , a mapping from the chemical name in the model SBML file to a KEGG compound identifier ( C number ) was constructed . The KEGG API was used to search for a C number based on the substrate description in the SBML files . When a C number was not found for a particular substrate , the mapping was created by hand . The name to compound map was verified manually by comparing the name given in the model to the name given in the KEGG database to that C number . A reaction in the JCVI-syn3A model was determined to be equivalent to a reaction in the iJW145 model if the metabolite sets associated with each reaction were equal . The resulting grouping of reactions into common or model-specific reactions was then manually curated to distinguish reactions where different directionalities/reversibilities between models arose from different roles of these reactions in the model ( irreversible amino acid influx in JCVI-syn3A vs . irreversible amino acid efflux in iJW145 , which has a separate set of ATP-driven amino acid uptake reactions ) . Growth and rate measurements of minimized synthetic cells have been described in detail elsewhere ( Hutchison et al . , 2016a ) . Briefly , cells were grown in SP4 medium to mid-late log phase in static cultures , then diluted in fresh pre-warmed ( 37 °C ) medium . Subsequent samples obtained over time were centrifuged to remove medium , cells were lysed with dilute detergent , and released dsDNA was measured using the fluorescent stain Quant-iT PicoGreen ( Molecular Probes , Eugene , OR ) . Fluorescence was measured in a 96-well format using a FlexStation 3 fluorimeter ( Molecular Devices , San Jose , CA ) . The net relative fluorescence units ( RFU ) of samples ( after subtracting RFU from a medium control lacking cells ) , were plotted as ln ( RFU ) vs . time from which the doubling times , τd were calculated from the slopes of exponential regression curves ( k ) asτd=ln2k . Rates were measured from log–linear portions of the growth curve . To avoid minor variables such as batch differences among medium preparations and temperature fluctuations , strains with different genomes were compared under identical conditions and within a single experiment . The accuracy and reproducibility of the measurements ( reflected in the observed R2 values , see Figure 14 ) allowed the use of single samples , as also observed previously ( Hutchison et al . , 2016a ) . Voronoi treemaps ( Figures 1 , 3 and 17a ) were constructed following ( Liebermeister et al . , 2014 ) . Briefly , the genetic loci were associated with a KEGG orthology ( KO ) identifier ( Kanehisa et al . , 2015 ) . Mappings between KO identifiers and locus tags were acquired from KEGG Genomes for M . pneumoniae ( T00006 ) and E . coli ( T00944 ) . A mapping between genes and KO identifiers for JCVI-syn3A was derived from the locus tag/KO map for M . mycoides capri LC str . 95010 ( T01478 ) by matching M . mycoides capri genes to JCVI-syn3A genes using a reciprocal best hit BLASTp search ( Altschul et al . , 1990 ) . Since the KO identifier , in general , can associate multiple functionality to a single ortholog , it was necessary to choose a single function for each ortholog . Initially , the KO/function assignment was taken from Liebermeister et al . ( 2014 ) . Mycoplasma specific genes were then added to this hierarchy manually . Genes for which no ortholog could be assigned , but were well annotated in the genome were also added to the hierarchy manually . Voronoi treemaps were constructed by first using the freely available software described by Nocaj and Brandes ( 2012 ) to generate the vertices of the polygons comprising the Voronoi tessellation , then rendering the resulting treemap using Cairo ( Packard et al . , 2018 ) . | One way that researchers can test whether they understand a biological system is to see if they can accurately recreate it as a computer model . The more they learn about living things , the more the researchers can improve their models and the closer the models become to simulating the original . In this approach , it is best to start by trying to model a simple system . Biologists have previously succeeded in creating ‘minimal bacterial cells’ . These synthetic cells contain fewer genes than almost all other living things and they are believed to be among the simplest possible forms of life that can grow on their own . The minimal cells can produce all the chemicals that they need to survive – in other words , they have a metabolism . Accurately recreating one of these cells in a computer is a key first step towards simulating a complete living system . Breuer et al . have developed a computer model to simulate the network of the biochemical reactions going on inside a minimal cell with just 493 genes . By altering the parameters of their model and comparing the results to experimental data , Breuer et al . explored the accuracy of their model . Overall , the model reproduces experimental results , but it is not yet perfect . The differences between the model and the experiments suggest new questions and tests that could advance our understanding of biology . In particular , Breuer et al . identified 30 genes that are essential for life in these cells but that currently have no known purpose . Continuing to develop and expand models like these to reproduce more complex living systems provides a tool to test current knowledge of biology . These models may become so advanced that they could predict how living things will respond to changing situations . This would allow scientists to test ideas sooner and make much faster progress in understanding life on Earth . Ultimately , these models could one day help to accelerate medical and industrial processes to save lives and enhance productivity . | [
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] | 2019 | Essential metabolism for a minimal cell |
Mutation of the Wiskott–Aldrich syndrome protein and SCAR homology ( WASH ) complex subunit , SWIP , is implicated in human intellectual disability , but the cellular etiology of this association is unknown . We identify the neuronal WASH complex proteome , revealing a network of endosomal proteins . To uncover how dysfunction of endosomal SWIP leads to disease , we generate a mouse model of the human WASHC4c . 3056C>G mutation . Quantitative spatial proteomics analysis of SWIPP1019R mouse brain reveals that this mutation destabilizes the WASH complex and uncovers significant perturbations in both endosomal and lysosomal pathways . Cellular and histological analyses confirm that SWIPP1019R results in endo-lysosomal disruption and uncover indicators of neurodegeneration . We find that SWIPP1019R not only impacts cognition , but also causes significant progressive motor deficits in mice . A retrospective analysis of SWIPP1019R patients reveals similar movement deficits in humans . Combined , these findings support the model that WASH complex destabilization , resulting from SWIPP1019R , drives cognitive and motor impairments via endo-lysosomal dysfunction in the brain .
Neurons maintain precise control of their subcellular proteome using a sophisticated network of vesicular trafficking pathways that shuttle cargo throughout the cell . Endosomes function as a central hub in this vesicular relay system by coordinating protein sorting between multiple cellular compartments , including surface receptor endocytosis and recycling , as well as degradative shunting to the lysosome ( Chiu et al . , 2017; Cullen and Steinberg , 2018; Raiborg et al . , 2015; Simonetti et al . , 2019 ) . How endosomal trafficking is modulated in neurons remains a vital area of research due to the unique degree of spatial segregation between organelles in neurons , and its strong implication in neurodevelopmental and neurodegenerative diseases ( Follett et al . , 2014; Lane et al . , 2012; Mukherjee et al . , 2019; Poët et al . , 2006; Zimprich et al . , 2011 ) . In non-neuronal cells , an evolutionarily conserved complex , the Wiskott–Aldrich syndrome protein and SCAR homology ( WASH ) complex , coordinates endosomal trafficking ( Derivery and Gautreau , 2010; Linardopoulou et al . , 2007 ) . WASH is composed of five core protein components: WASHC1 ( aka WASH1 ) , WASHC2 ( aka FAM21 ) , WASHC3 ( aka CCDC53 ) , WASHC4 ( aka SWIP ) , and WASHC5 ( aka Strumpellin ) ( encoded by genes Washc1-Washc5 , respectively ) , which are broadly expressed in multiple organ systems ( Alekhina et al . , 2017; Kustermann et al . , 2018; McNally et al . , 2017; Simonetti and Cullen , 2019; Thul et al . , 2017 ) . The WASH complex plays a central role in non-neuronal endosomal trafficking by activating Arp2/3-dependent actin branching at the outer surface of endosomes to influence cargo sorting and vesicular scission ( Gomez and Billadeau , 2009; Lee et al . , 2016; Phillips-Krawczak et al . , 2015; Piotrowski et al . , 2013; Simonetti and Cullen , 2019 ) . WASH also interacts with at least three main cargo adaptor complexes – the Retromer , Retriever , and COMMD/CCDC22/CCDC93 ( CCC ) complexes – all of which associate with distinct sorting nexins to select specific cargo and enable their trafficking to other cellular locations ( Binda et al . , 2019; Farfán et al . , 2013; McNally et al . , 2017; Phillips-Krawczak et al . , 2015; Seaman and Freeman , 2014; Singla et al . , 2019 ) . Loss of the WASH complex in non-neuronal cells has detrimental effects on endosomal structure and function , as its loss results in aberrant endosomal tubule elongation and cargo mislocalization ( Bartuzi et al . , 2016; Derivery et al . , 2009; Gomez et al . , 2012; Gomez and Billadeau , 2009; Phillips-Krawczak et al . , 2015; Piotrowski et al . , 2013 ) . However , whether the WASH complex performs an endosomal trafficking role in neurons remains an open question , as no studies have addressed neuronal WASH function to date . Consistent with the association between the endosomal trafficking system and pathology , dominant missense mutations in WASHC5 ( protein: Strumpellin ) are associated with hereditary spastic paraplegia ( SPG8 ) ( de Bot et al . , 2013; Valdmanis et al . , 2007 ) , and autosomal recessive point mutations in WASHC4 ( protein: SWIP ) and WASHC5 are associated with syndromic and non-syndromic intellectual disabilities ( Assoum et al . , 2020; Elliott et al . , 2013; Ropers et al . , 2011 ) . In particular , an autosomal recessive mutation in WASHC4 ( c . 3056C>G; p . Pro1019Arg ) was identified in a cohort of children with non-syndromic intellectual disability ( Ropers et al . , 2011 ) . Cell lines derived from these patients exhibited decreased abundance of WASH proteins , leading the authors to hypothesize that the observed cognitive deficits in SWIPP1019R patients resulted from disruption of neuronal WASH signaling ( Ropers et al . , 2011 ) . However , whether this mutation leads to perturbations in neuronal endosomal integrity , or how this might result in cellular changes associated with disease , are unknown . Here we report the analysis of neuronal WASH and its molecular role in disease pathogenesis . We use in vivo proximity proteomics ( iBioID ) to uncover the neuronal WASH proteome and demonstrate that it is highly enriched for components of endosomal trafficking . We then generate a mouse model of the human WASHC4c . 3056c>g mutation ( SWIPP1019R ) ( Ropers et al . , 2011 ) to discover how this mutation may alter neuronal trafficking pathways and test whether it leads to phenotypes congruent with human patients . Using an adapted spatial proteomics approach ( Davies et al . , 2018; Geladaki et al . , 2019; Hirst et al . , 2018; Shin et al . , 2019 ) , coupled with a system-level analysis of protein covariation networks , we find strong evidence for substantial disruption of neuronal endosomal and lysosomal pathways in vivo . Cellular analyses confirm a significant impact on neuronal endo-lysosomal trafficking in vitro and in vivo , with evidence of lipofuscin accumulation and progressive apoptosis activation , molecular phenotypes that are indicative of neurodegenerative pathology . Behavioral analyses of SWIPP1019R mice at adolescence and adulthood confirm a role of WASH in cognitive processes and reveal profound , progressive motor dysfunction . Importantly , retrospective examination of SWIPP1019R patient data highlights parallel clinical phenotypes of motor dysfunction coincident with cognitive impairments in humans . Our results establish that loss of WASH complex function leads to alterations in the neuronal endo-lysosomal axis , which manifest behaviorally as cognitive and movement impairments in mice .
While multiple mutations within the WASH complex have been identified in humans ( Assoum et al . , 2020; Elliott et al . , 2013; Ropers et al . , 2011; Valdmanis et al . , 2007 ) , how these mutations lead to neurological dysfunction remains unknown ( Figure 1A ) . Given that previous work in non-neuronal cultured cells and non-mammalian organisms have established that the WASH complex functions in endosomal trafficking , we first aimed to determine whether this role was conserved in the mouse nervous system ( Alekhina et al . , 2017; Jia et al . , 2010; Derivery et al . , 2009; Gomez et al . , 2012; Gomez and Billadeau , 2009 ) . To discover the likely molecular functions of the neuronal WASH complex , we utilized an in vivo BioID ( iBioID ) paradigm developed in our laboratory to identify the WASH complex proteome from brain tissue ( Uezu et al . , 2016 ) . BioID probes were generated by fusing a component of the WASH complex , WASH1 ( gene: Washc1 ) , with the promiscuous biotin ligase , BioID2 ( WASH1-BioID2 , Figure 1B ) , or by expressing BioID2 alone ( negative control , solubleBioID2 ) under the neuron-specific , human Synapsin-1 promoter ( Kim et al . , 2016 ) . We injected adenoviruses ( AAV ) expressing these constructs into the cortex of wild-type postnatal day zero ( P0 ) mice ( Figure 1B ) . Two weeks post-injection , we administered daily subcutaneous biotin for 7 days to biotinylate in vivo substrates . The viruses displayed efficient expression and activity in brain tissue , as evidenced by colocalization of the WASH1-BioID2 viral epitope ( HA ) and biotinylated proteins ( Streptavidin ) ( Figure 1C–F ) . For label-free quantitative LC-MS/MS analyses , whole-brain samples were collected at P22 , snap frozen , and processed as previously described ( Uezu et al . , 2016 ) . A total of 2102 proteins were identified across all three experimental replicates , which were further analyzed for those with significant enrichment in WASH1-BioID2 samples over solubleBioID2 negative controls ( Figure 1—figure supplement 1D , Supplementary file 1 ) . The resulting neuronal WASH proteome included 175 proteins that were significantly enriched ( fold-change≥4 . 0 , Benjamini–Hochberg FDR<0 . 05 , Figure 1G; Benjamini and Hochberg , 1995 ) . Of these proteins , we identified all five WASH complex components ( Figure 1H ) , as well as 13 previously reported WASH complex interactors ( Figure 1I; McNally et al . , 2017; Phillips-Krawczak et al . , 2015; Simonetti and Cullen , 2019; Singla et al . , 2019 ) , which provided strong validity for our proteomic approach and analyses . Additional bioinformatic analyses of the neuronal WASH proteome identified a network of proteins implicated in vesicular trafficking , including 23 proteins enriched for endosomal functions ( Figure 1J ) and 24 proteins enriched for endocytic functions ( Figure 1K ) . Among these endosomal and endocytic proteins were components of the recently identified endosomal sorting complexes , CCC ( CCDC93 and COMMD9 ) and Retriever ( VPS35L ) ( Phillips-Krawczak et al . , 2015; Singla et al . , 2019 ) , as well as multiple sorting nexins important for recruitment of trafficking regulators to the endosome and cargo selection , such as SNX1-3 and SNX16 ( Kvainickas et al . , 2017; Maruzs et al . , 2015; Shin et al . , 2019; Simonetti et al . , 2017 ) . These data demonstrated that the WASH complex interacts with many of the same proteins in neurons as it does in yeast , amoebae , flies , and mammalian cell lines . Furthermore , there were 31 proteins enriched for cytoskeletal regulatory functions ( Figure 1L ) , including actin-modulatory molecules such as the Arp2/3 complex subunit ARPC5 , which is consistent with WASH’s role in activating this complex to stimulate actin polymerization at endosomes for vesicular scission ( Jia et al . , 2010; Derivery et al . , 2009 ) . The WASH1-BioID2 isolated complex also contained 27 proteins known to localize to the excitatory post-synapse ( Figure 1M ) . This included many core synaptic scaffolding proteins , such as SHANK2-3 and DLGAP2-4 ( Chen et al . , 2011; Mao et al . , 2015; Monteiro and Feng , 2017; Wan et al . , 2011 ) , as well as modulators of synaptic receptors such as SYNGAP1 and SHISA6 ( Barnett et al . , 2006; Clement et al . , 2012; Kim et al . , 2003; Klaassen et al . , 2016 ) , which was consistent with the idea that vesicular trafficking plays an important part in synaptic function and regulation . Taken together , these results support a major endosomal trafficking role of the WASH complex in mouse brain . To determine how disruption of the WASH complex may lead to disease , we generated a mouse model of a human missense mutation found in children with intellectual disability , WASHC4c . 3056c>g ( protein: SWIPP1019R ) ( Ropers et al . , 2011 ) . Due to the sequence homology of human and mouse Washc4 genes , we were able to introduce the same point mutation in exon 29 of murine Washc4 using CRISPR ( Derivery and Gautreau , 2010; Ropers et al . , 2011 ) . This C>G point mutation results in a Proline>Arginine substitution at position 1019 of SWIP’s amino acid sequence ( Figure 2A ) , a region thought to be critical for its binding to the WASH component , Strumpellin ( Jia et al . , 2010; Ropers et al . , 2011 ) . Western blot analysis of brain lysate from adult homozygous SWIPP1019R mutant mice ( referred to from here on as MUT mice ) displayed significantly decreased abundance of two WASH complex members , Strumpellin and WASH1 ( Figure 2B ) . These results phenocopied data from the human patients ( Ropers et al . , 2011 ) and suggested that the WASH complex is unstable in the presence of this SWIP point mutation in vivo . To test whether this mutation disrupted interactions between WASH complex subunits , we compared the ability of wild-type SWIP ( WT ) and SWIPP1019R ( MUT ) to co-immunoprecipitate with Strumpellin and WASH1 in HEK cells . Compared to WT , MUT SWIP co-immunoprecipitated significantly less Strumpellin and WASH1 ( IP: 54 . 8% and 41 . 4% of WT SWIP , respectively ) , suggesting that the SWIPP1019R mutation hinders WASH complex formation ( Figure 2—figure supplement 1 ) . Together these data support the notion that SWIPP1019R is a damaging mutation that not only impairs its function , but also results in significant reductions of the WASH complex as a whole . Next , we aimed to understand the impact of the SWIPP1019R mutation on the subcellular organization of the mouse brain proteome using spatial proteomics . Conceptually , spatial proteomics encompasses a variety of methodological and analytical approaches , which share a common goal: predicting the subcellular localization of proteins . Most often this is done by combining subcellular fractionation of a biological sample with proteomic profiling of the resultant fractions ( Breckels et al . , 2016; Crook et al . , 2019; Crook et al . , 2018; Geladaki et al . , 2019; Itzhak et al . , 2017; Itzhak et al . , 2016; Jean Beltran et al . , 2016 ) . We performed spatial proteomics by subcellular fractionation , MS profiling , and subsequent clustering analysis . Clusters ( modules ) in the spatial proteomics network represent predicted subcellular compartments composed of proteins whose abundance covaries together in subcellular space ( Geladaki et al . , 2019; Mulvey et al . , 2017 ) . We analyzed differential abundance of individual proteins , as well as of protein groups ( modules ) identified in the spatial proteomics network to evaluate how the pathogenic SWIPP1019R mutation may perturb the organization of the neuronal subcellular proteome . This approach enabled us to study protein changes at a network level , which provided more biologically relevant insight than would be possible by assessing only protein-level differences . Brains from 10-month-old mice were gently homogenized to release intact organelles , followed by successive centrifugation steps to enrich subcellular compartments into different biological fractions ( BioFractions ) based on their density ( Figure 3A; Geladaki et al . , 2019 ) . Seven WT and seven MUT fractions ( each prepared from one brain , 14 samples total ) were labeled with unique isobaric tandem-mass tags and concatenated . We also included two sample pooled quality controls ( SPQCs ) , which allowed us to assess experimental variability and perform normalization between experiments . By performing this experiment in triplicate , deep coverage of the mouse brain proteome was obtained – across all 48 samples we quantified 86 , 551 peptides , corresponding to 7488 proteins . After data pre-processing , normalization , and filtering , we retained 6919 reproducibly quantified proteins in the final dataset ( Supplementary file 2 ) . We used MSstatsTMT to assess differential protein abundance for intra-fraction comparisons between WT and MUT genotypes and for overall comparisons between WT and MUT groups across all BioFractions ( Figure 3—figure supplement 4F; Huang et al . , 2020 ) . In the first analysis , there were 65 proteins with significantly altered abundance in at least one of the seven subcellular fractions ( Benjamini–Hochberg FDR<0 . 05 , Supplementary file 2 ) . Five proteins were differentially abundant between WT and MUT in all seven fractions , including four WASH proteins ( WASHC1 , WASHC2 , WASHC4 , WASHC5 ) and RAB21A – a known WASH interactor that functions in early endosomal trafficking ( Figure 3D; Del Olmo et al . , 2019; Simpson et al . , 2004 ) . The abundance of the remaining WASH complex protein , WASHC3 , was found to be very low and was only significantly reduced in BioFraction 10 ( F10 ) and the overall ( ‘Mutant-Control’ ) comparison . These data affirm that the SWIPP1019R mutation destabilizes the WASH complex . Next , to evaluate global differences between WT and MUT brain , we analyzed the average effect of genotype on protein abundance across all fractions using MSstatsTMT ( Huang et al . , 2020 ) . At this level , there were 728 differentially abundant proteins between WT and MUT brain ( Benjamini–Hochberg FDR<0 . 05 ) ( Supplementary file 2 ) . We then aimed to place these differentially abundant proteins into a more meaningful biological context using a spatial proteomics approach . Network-level analyses of spatial proteomic datasets can generally be performed in one of two ways: a top-down approach where proteins are grouped into organellar compartments learned from a predefined set of marker proteins , or a bottom-up approach where proteins are first clustered together based on covariation across biological fractions , and then analyzed for organellar enrichment ( Breckels et al . , 2016; Crook et al . , 2019; Crook et al . , 2018; Itzhak et al . , 2019; Itzhak et al . , 2017; Jean Beltran et al . , 2016; Orre et al . , 2019 ) . For our network-based analyses , we chose to use a bottom-up approach , where we clustered the protein covariation network defined by the pairwise Pearson correlations between all proteins ( Freedman et al . , 2007 ) . Our data-driven , quality-based approach used Network Enhancement ( Wang et al . , 2018 ) to remove biological noise from the covariation network and optimized partitions of the graph by maximizing the Surprise quality statistic ( Aldecoa and Marín , 2013; Traag et al . , 2015 ) . Clustering of the protein covariation graph identified 49 modules of proteins that strongly covaried together ( see Materials and methods for complete description of clustering approach ) . To test for module-level differences between WT and MUT brain , we extended the LMM framework provided by MSstatsTMT to perform statistical inference at the level of protein groups ( Huang et al . , 2020 ) . To identify systematic differences in the abundance of protein groups ( modules ) , we fit the protein-level data for each module with a linear mixed-model expressing the mixed effect term , Protein , representing variation among a module’s constituent proteins . We then performed a contrast of condition means given the fitted model , as described by Huang et al . , 2020 . Twenty-three of the 49 modules exhibited significant differences in WT versus MUT brain ( Bonferroni p-adjust < 0 . 05; Supplementary file 3; Benjamini and Hochberg , 1995; Hochberg , 1988 ) . Of note , the module containing the WASH complex , M38 , was predicted to have endosomal function by annotation of protein function ( UniProt: ‘Early Endosome’ , hypergeometric test p-adjust < 0 . 05 , Supplementary file 4 ) . Similar to the WASH iBioID proteome ( Figure 1 ) , M38 contained many endosomal proteins , including components of the CCC ( CCDC22 , CCDC93 , COMMD1-3 , and COMMD6-7 ) and Retriever sorting complexes ( VPS26C and VPS35L ) ( Figure 3B ) . It also contained core subunits of the CORVET and HOPS vesicular tethering complexes , which enable fusion of vesicles within the endo-lysosomal system ( VPS11 , VPS16 , VPS18 , and VPS33A ) ( van der Beek et al . , 2019 ) . Across all fractions , the abundance of M38 was significantly lower in MUT brain compared to WT , providing evidence that the SWIPP1019R mutation reduces the stability of this protein subnetwork and impairs its function ( Figure 3C ) . We also observed another module , M36 , that was enriched for lysosomal protein components ( hypergeometric test p-adjust <0 . 05 ) ( Geladaki et al . , 2019 ) and contained all eight subunits of the exocyst complex ( CORUM ) , a vesicular trafficking complex involved in lysosomal secretion ( Giurgiu et al . , 2019; Sáez et al . , 2019 ) . In contrast to the decreased abundance of the WASH complex/endosome module ( M38 ) , M36 exhibited increased abundance in MUT brain ( Figure 4C ) . M36 ( Figure 4B ) contained several lysosomal cathepsin proteases ( CTSA , CTSB , CTSS , and CTSL ) as well as key lysosomal hydrolases ( HEXA , GBA , GLB1 , MAN2B1 , and MAN2B2 ) ( Eng and Desnick , 1994; Mayor et al . , 1993; Mok et al . , 2003; Moon et al . , 2016; Patel et al . , 2018; Regier and Tifft , 1993; Rosenbaum et al . , 2014 ) . Notably , M36 also contained the lysosomal glycoprotein progranulin ( GRN ) , which is integral to proper lysosome function and whose loss is widely linked with neurodegenerative pathologies ( Baker et al . , 2006; Pottier et al . , 2016; Tanaka et al . , 2017; Zhou et al . , 2018 ) . The overall increase in abundance of module 36 , and these key lysosomal proteins ( Figure 4C–E ) , may therefore reflect an increase in flux through degradative lysosomal pathways in SWIPP1019R brain . Besides these endo-lysosomal changes , module-level alterations were evident for an endoplasmic reticulum ( ER ) module ( M6 ) , supporting a shift in the proteostasis of mutant neurons ( Figure 4—figure supplement 1C–D ) . Notably , within the ER module , M6 , there was increased abundance of chaperones ( e . g . HSPA5 , PDIA3 , PDIA4 , PDIA6 , and DNAJC3 ) that are commonly engaged in presence of misfolded proteins ( Bartels et al . , 2019; Kim et al . , 2020; Montibeller and de Belleroche , 2018; Synofzik et al . , 2014; Wang et al . , 2016 ) . This elevation of ER stress modulators can be indicative of neurodegenerative states , in which the unfolded protein response ( UPR ) is activated to resolve misfolded species ( Garcia-Huerta et al . , 2016; Hetz and Saxena , 2017 ) . These data demonstrate that loss of WASH function not only alters endo-lysosomal trafficking , but also causes increased stress on cellular homeostasis . In addition , we also observed a synaptic module ( M27 ) that was reduced in MUT brain ( Figure 4—figure supplement 1E–F ) . This module included core excitatory post-synaptic proteins such as SHANK2 and DLGAP4 ( also identified in WASH1-BioID , Figure 1 ) , consistent with endosomal WASH influencing synaptic regulation . Decreased abundance of these modules indicates that loss of the WASH complex may result in failure of these proteins to be properly trafficked to the synapse . In line with these findings , we observed fewer excitatory synapses in adult MUT brain compared to WT ( Figure 4—figure supplement 2 ) , validating that these module-level differences correlate with cellular alterations in vivo . Combined , the proteomics data strongly suggested that endo-lysosomal pathways are altered in adult SWIPP1019R mutant mouse brain . Next , we analyzed whether structural changes in this system were evident in primary neurons . Cortical neurons from littermate WT and MUT P0 pups were cultured for 15 days in vitro ( DIV15 , Figure 5A ) , then fixed and stained for established markers of early endosomes ( early endosome antigen 1 [EEA1]; Figure 5B and C ) and lysosomes ( Cathepsin D [CathD]; Figure 5D and E ) . Reconstructed three-dimensional volumes of EEA1 and Cathepsin D puncta revealed that MUT neurons display larger EEA1+ somatic puncta than WT neurons ( Figure 5G and J ) , but no difference in the total number of EEA1+ puncta ( Figure 5F ) . This finding is consistent with a loss-of-function mutation , as loss of WASH activity prevents cargo scission from endosomes and leads to cargo accumulation ( Bartuzi et al . , 2016; Gomez et al . , 2012 ) . Conversely , MUT neurons exhibited significantly less Cathepsin D+ puncta than WT neurons ( Figure 5H ) , but the remaining puncta were significantly larger than those of WT neurons ( Figure 5I and K ) . These data support the finding that the SWIPP1019R mutation results in both molecular and morphological abnormalities in the endo-lysosomal pathway . As there is strong evidence that dysfunctional endo-lysosomal trafficking and elevated ER stress are associated with neurodegenerative disorders , adolescent ( P42 ) and adult ( 10 month old , 10mo ) WT and MUT brain tissues were analyzed for the presence of cleaved caspase-3 , a marker of apoptotic pathway activation , in four brain regions ( Boatright and Salvesen , 2003; Porter and Jänicke , 1999 ) . Very little cleaved caspase-3 staining was present in WT and MUT mice at adolescence ( Figure 6A and B , and Figure 6—figure supplement 1 ) . However , at 10mo , the MUT motor cortices displayed significantly greater cleaved caspsase-3 staining compared to age-matched WT littermate controls ( Figures 6D , E and H ) . Furthermore , this difference appeared to be selective for the motor cortex , as we did not observe significant differences in cleaved caspase-3 staining at either age for hippocampal , striatal , or cerebellar regions ( Figure 6—figure supplement 1 ) . Consistent with these findings , there were no significant differences in dopaminergic cell number in the substantia nigra pars compacta or in dopaminergic innervation of the striatum in adult brain , suggesting that the motor cortex was the primary movement-related region altered in SWIPP109R brain ( Figure 6—figure supplement 2 ) . These data suggested that neurons of the motor cortex were particularly susceptible to disruption of endo-lysosomal pathways downstream of SWIPP109R , perhaps because long-range corticospinal projections require high fidelity of trafficking pathways ( Blackstone et al . , 2011; Slosarek et al . , 2018; Wang et al . , 2014 ) . To further examine the morphology of primary motor cortex neurons at a subcellular resolution , samples from age-matched 7-month-old WT and MUT mice ( 7mo , three animals each ) were imaged by transmission electron microscopy ( TEM ) . Strikingly , we observed large electron-dense inclusions in the cell bodies of MUT neurons ( arrows , Figure 6L; pseudo-colored region , 6N ) . These dense structures were associated electron-lucent lipid-like inclusions ( asterisk , Figure 6N ) , which supported the conclusion that these structures were lipofuscin accumulation at lysosomal residual bodies ( Poët et al . , 2006; Valdez et al . , 2017; Yoshikawa et al . , 2002 ) . Lipofuscin is a by-product of lysosomal breakdown of lipids , proteins , and carbohydrates , which naturally accumulates over time in non-dividing cells such as neurons ( Höhn and Grune , 2013; Moreno-García et al . , 2018; Terman and Brunk , 1998 ) . However , excessive lipofuscin accumulation is thought to be detrimental to cellular homeostasis by inhibiting lysosomal function and promoting oxidative stress , often leading to cell death ( Brunk and Terman , 2002; Powell et al . , 2005 ) . As a result , elevated lipofuscin is considered a biomarker of neurodegenerative disorders , including Alzheimer’s disease , Parkinson’s disease , and neuronal ceroid lipofuscinoses ( Moreno-García et al . , 2018 ) . Therefore , the marked increase in lipofuscin area and number seen in MUT electron micrographs ( Figure 6O and P , respectively ) is consistent with the increased abundance of lysosomal proteins observed by proteomics and likely reflects an increase in lysosomal breakdown of cellular material . Together these data indicate that SWIPP1019R results in pathological lysosomal function that could lead to neurodegeneration . To observe the functional consequences of the SWIPP1019R mutation , we next studied WT and MUT mouse behavior . Given that children with homozygous SWIPP1019R point mutations display intellectual disability ( Ropers et al . , 2011 ) and SWIPP1019R mutant mice exhibit endo-lysosomal disruptions implicated in neurodegenerative processes , behavior was assessed at two ages: adolescence ( P40–50 ) , and mid-late adulthood ( 5 . 5–6 . 5 mo ) . Interestingly , MUT mice performed equivalently to WT mice in episodic and working memory paradigms , including novel object recognition and Y-maze alternations ( Figure 7—figure supplement 1 ) . However , in a fear conditioning task , MUT mice displayed a significant deficit in cued fear memory ( Figure 7 ) . This task tests the ability of a mouse to associate an aversive event ( a mild electric footshock ) with a paired tone ( Figure 7A ) . Freezing behavior of mice during tone presentation is attributed to hippocampal or amygdala-based fear memory processes ( Goosens and Maren , 2001; Maren and Holt , 2000; Vazdarjanova and McGaugh , 1998 ) . Forty-eight hours after exposure to the paired tone and footshock , MUT mice showed a significant decrease in conditioned freezing to tone presentation compared to their WT littermates ( Figure 7B , C ) . To ensure that this difference was not due to altered sensory capacities of MUT mice , we measured the startle response of mice to both electric foot shock and presented tones . In line with intact sensation , MUT mice responded comparably to WT mice in these tests ( Figure 7—figure supplement 2 ) . These data demonstrate that although MUT mice perceive footshock sensations and auditory cues , it is their memory of these paired events that is significantly impaired . Additionally , this deficit in fear response was evident at both adolescence and adulthood ( top panels , and bottom panels , respectively , Figure 7B and C ) . These changes are consistent with the hypothesis that SWIPP109R is the cause of cognitive impairments in humans . Because SWIPP1019R results in endo-lysosomal pathology consistent with neurodegenerative disorders in the motor cortex , we next analyzed motor function of the mice over time . First , we tested the ability of WT and MUT mice to remain on a rotating rod for 5 min ( Rotarod , Figure 8A–C ) . At both adolescence and adulthood , MUT mice performed markedly worse than WT littermate controls ( Figure 8C ) . Mouse performance was not significantly different across trials , which suggested that this difference in retention time was not due to progressive fatigue , but more likely due to an overall difference in motor control ( Mann and Chesselet , 2015 ) . To study the animals’ movement at a finer scale , the gait of WT and MUT mice was also analyzed using a TreadScan system containing a high-speed camera coupled with a transparent treadmill ( Figure 8D; Beare et al . , 2009 ) . Interestingly , while gait parameters of the mice were largely indistinguishable across genotypes at adolescence , a striking difference was seen when the same mice were aged to adulthood ( Figure 8E–G ) . In particular , MUT mice took slower ( Figure 8E ) , longer strides ( Figure 8F ) , stepping closer to the midline of their body ( track width , Figure 8—figure supplement 1 ) , and their gait symmetry was altered , so that their strides were no longer perfectly out of phase ( out of phase=0 . 5 , Figure 8G ) . While these differences were most pronounced in the rear limbs ( as depicted in Figure 8E–G ) , the same trends were present in front limbs ( Figure 8—figure supplement 1 ) . These findings demonstrate that SWIPP1019R results in progressive motor function decline that was detectable by the rotarod task at adolescence , but which became more prominent with age , as both gait and strength functions deteriorated . These marked motor findings prompted us to re-evaluate the original reports of human SWIPP1019R patients ( Ropers et al . , 2011 ) . While developmental delay or learning difficulties were the primary impetus for medical evaluation , all patients also exhibited motor symptoms ( mean age=10 . 4 years old , Figure 8H ) . The patients’ movements were described as ‘clumsy’ with notable fine motor difficulties , dysmetria , dysdiadochokinesia , and mild dysarthria on clinical exam ( Figure 8H ) . Recent communication with the parents of these patients , who are now an average of 21 years old , revealed no notable symptom exacerbation . It is therefore possible that the SWIPP1019R mouse model either exhibits differences from human patients or may predict future disease progression for these individuals , given that we observed significant worsening at 5–6 months old in mice ( which is thought to be equivalent to ~30–35 years old in humans ) ( Dutta and Sengupta , 2016; Zhang et al . , 2019 ) .
Taken together , the data presented here support a mechanistic model whereby SWIPP1019R causes a loss of WASH complex function , resulting in endo-lysosomal disruption and accumulation of neurodegenerative markers , such as upregulation of unfolded protein response modulators and lysosomal enzymes , as well as build-up of lipofuscin and cleaved caspase-3 over time . To our knowledge , this study provides the first mechanistic evidence of WASH complex impairment having direct and indirect organellar effects that lead to cognitive deficits and progressive motor impairments ( Figure 9 ) . Using in vivo proximity-based proteomics in wild-type mouse brain , we found that the WASH complex closely interacts with the CCC ( COMMD9 and CCDC93 ) and Retriever ( VPS35L ) cargo selective complexes ( Bartuzi et al . , 2016; Singla et al . , 2019 ) . Interestingly , we did not find significant enrichment of the Retromer sorting complex , a well-known WASH interactor ( Figure 1 ) , which may be the by-product of using WASH1 rather than another WASH subunit for BioID tagging . Future studies on these protein candidates may clarify how these molecular interactions occur and influence WASH function in mouse brain . These data are consistent with our spatial proteomics analyses of SWIPP1019R mutant brain , which clustered the WASH , CCC , Retriever , and CORVET/HOPS complexes together in M38 ( Figure 3 ) and the Retromer complex in a different endosomal-enriched module , M22 ( Figure 4—figure supplement 1A ) . Spatial proteomics analyses also revealed that disruption of these WASH–CCC–Retriever–CORVET/HOPS interactions may have multiple downstream effects on the endosomal machinery , since both endosomal-enriched modules exhibited significant decrease in SWIPP1019R brain ( M38 and M22 , Figure 3 and Figure 4—figure supplement 1A ) . These modules include corresponding decreases in the abundance of endosomal proteins including Retromer subunits ( VPS29 , VPS26B , and VPS35; M22 ) , associated sorting nexins ( SNX27; M22 ) , known WASH interactors ( FKBP15; M38 ) , and cargos ( e . g . LRP1; M22 ) ( Figure 3 and Figure 4—figure supplement 1; Del Olmo et al . , 2019; Farfán et al . , 2013; Fedoseienko et al . , 2018; Halff et al . , 2019; Harbour et al . , 2012; McNally et al . , 2017; Pan et al . , 2010; Ye et al . , 2020; Zimprich et al . , 2011 ) . While previous studies have indicated that Retromer and CCC influence the endosomal localization of WASH ( Harbour et al . , 2012; Phillips-Krawczak et al . , 2015; Singla et al . , 2019 ) , our findings demonstrate both protein- and module-level decreases in abundance of these proteins , pointing to a cascade of endosomal dysfunction . Future studies defining the hierarchical interplay between the WASH , Retromer , Retriever , and CCC complexes in neurons could provide clarity on how these mechanisms are organized . Of note , some of the lysosomal enzymes with elevated levels in MUT brain ( GRN , HEXA , and GLB1 – M36; Figure 4 ) are also implicated in lysosomal storage disorders , where they generally have decreased , rather than increased , function or expression ( Boles and Proia , 1995; Regier and Tifft , 1993; Smith et al . , 2012; Ward et al . , 2017 ) . This divergent lysosomal effect in our SWIPP1019R model compared to other degenerative models could represent either a distinct endo-lysosomal disruption that culminates in similar cellular pathology or a transient compensatory state that may ultimately lead to declined lysosomal function in SWIPP1019R neurons . We speculate that loss of WASH function in our mutant mouse model may lead to increased accumulation of cargo and associated machinery at early endosomes ( as seen in Figure 5 , enlarged EEA1+ puncta ) , eventually overburdening early endosomal vesicles and triggering transition to late endosomes for subsequent fusion with degradative lysosomes ( Figure 9 ) . This would effectively increase delivery of endosomal substrates to the lysosome compared to baseline , resulting in enlarged , overloaded lysosomal structures , and elevated demand for degradative enzymes . For example , since mutant neurons display increased abundance of a lysosomal module ( Figure 4 ) , and larger lysosomal structures ( Figures 5 and 6 ) , they may require higher quantities of progranulin ( GRN , M36; Figure 4 ) for sufficient lysosomal acidification ( Tanaka et al . , 2017 ) . Our findings that SWIPP1019R results in reduced WASH complex stability and function , which may ultimately drive lysosomal dysfunction , are supported by studies in non-mammalian cells . For example , expression of a dominant-negative form of WASH1 in amoebae impairs recycling of lysosomal V-ATPases ( Carnell et al . , 2011 ) and loss of WASH in Drosophila plasmocytes affects lysosomal acidification ( Gomez et al . , 2012; Nagel et al . , 2017; Zech et al . , 2011 ) . Moreover , mouse embryonic fibroblasts lacking WASH1 display abnormal lysosomal morphologies , akin to the structures we observed in cultured SWIPP1019R MUT neurons ( Gomez et al . , 2012 ) . Consistent with the idea that WASH regulates lysosomal V-ATPase function either directly or indirectly , we observed a significant decrease in the overall abundance of module M35 , a module containing 6 of the 13 components of the vacuolar-associated ATPase complex subunits ( CORUM: ATP6V1A , ATP6V1E1 , ATP6V0C , ATP6V1F , ATP6V1C1 , and ATP6V0A1; Supplementary files 3–4 ) . The overall significant decrease in this module resonates with previous studies linking WASH to V-ATPase acidification of lysosomes . In addition to lysosomal dysfunction , endoplasmic reticulum ( ER ) stress is commonly observed in neurodegenerative states , where accumulation of misfolded proteins disrupts cellular proteostasis ( Cai et al . , 2016; Hetz and Saxena , 2017; Montibeller and de Belleroche , 2018 ) . This cellular strain triggers the adaptive unfolded protein response ( UPR ) , which attempts to restore cellular homeostasis by increasing the cell’s capacity to retain misfolded proteins within the ER , remedy misfolded substrates , and trigger degradation of persistently misfolded species . Involved in this process are ER chaperones that we identified as increased in SWIPP1019R mutant brain including BiP ( HSPA5 ) , calreticulin ( CALR ) , calnexin ( CANX ) , and the protein disulfide isomerase family members ( PDIA1 , PDIA4 , PDIA6; M6 Figure 4—figure supplement 1C–D; Garcia-Huerta et al . , 2016 ) . Many of these proteins were identified in the ER protein module found to be significantly altered in MUT mouse brain ( M6 ) , supporting a network-level change in the ER stress response ( Figure 4—figure supplement 1D ) . One notable exception to this trend was the chaperone endoplasmin ( HSP90B1 , M22 ) , which exhibited significantly decreased abundance in SWIPP1019R mutant brain ( Supplementary file 2 ) . This is surprising given that endoplasmin has been shown to coordinate with BiP in protein folding ( Sun et al . , 2019 ) ; however , it may highlight a possible compensatory mechanism . Additionally , prolonged UPR can stimulate autophagic pathways in neurons , where misfolded substrates are delivered to the lysosome for degradation ( Cai et al . , 2016 ) . These data highlight a potential pathogenic relationship between ER and endo-lysosomal disturbances as an exciting avenue for future research . Strikingly , we observed modules enriched for resident proteins corresponding to all 10 of the major subcellular compartments mapped by Geladaki et al . , 2019: nucleus , mitochondria , golgi apparatus , ER , peroxisome , proteasome , plasma membrane , lysosome , cytoplasm , and ribosome; Figure 3—figure supplement 5 . The greatest dysregulations , as quantified by log2Fold-Change between genotypes , were in lysosomal , endosomal , ER , and synaptic modules , supporting the hypothesis that SWIPP1019R primarily results in disrupted endo-lysosomal trafficking . While analysis of these dysregulated modules informs the pathobiology of SWIPP1019R , our spatial proteomics approach also identified numerous biologically cohesive modules , which remained unaltered ( Figure 3—figure supplement 5 ) . Given that many of these modules contained proteins of unknown function , we anticipate that future analyses of these modules and their protein constituents have great potential to inform our understanding of protein networks and their influence on neuronal cell biology . It has become clear that preservation of the endo-lysosomal system is critical to neuronal function , as mutations in mediators of this process are implicated in neurological diseases such as Parkinson’s disease , Huntington’s disease , Alzheimer’s disease , frontotemporal dementia , neuronal ceroid lipofuscinoses ( NCLs ) , and hereditary spastic paraplegia ( Baker et al . , 2006; Connor-Robson et al . , 2019; Edvardson et al . , 2012; Follett et al . , 2019; Harold et al . , 2009; Mukherjee et al . , 2019; Pal et al . , 2006; International Parkinsonism Genetics Network et al . , 2013; Seshadri et al . , 2010; Tachibana et al . , 2019; Valdmanis et al . , 2007 ) . These genetic links to predominantly neurodegenerative conditions have supported the proposition that loss of endo-lysosomal integrity can have compounding effects over time and contribute to progressive disease pathologies . In particular , mutations associated with Parkinson’s disease have been found in a close endosomal interactor of the WASH complex – the retromer protein VPS35 ( VPS35D620N and VPS35R524W ) – and have been linked to pathological α-synuclein aggregation in vitro ( Chen et al . , 2019; Follett et al . , 2014; Tang et al . , 2015 ) . While α-synuclein ( SNCA ) was highly enriched in our WASH1-BioID assay in WT brain ( Figure 1 ) , its protein abundance was not found to be significantly different in SWIPP1019R mutant brain fractions compared to WT in our TMT spatial proteomic analysis ( Supplementary file 2 ) . In addition , unlike many Parkinson’s disease models , which display specific deficits in dopaminergic cells , we did not observe any dopaminergic cell-specific changes in SWIPP1019R brain ( Figure 6—figure supplement 2 ) . This suggests that the motor pathology of SWIPP1019R mice diverges from that of α-synuclein-driven Parkinson’s mouse models . The more parsimonious explanation may be that α-synuclein’s enrichment in the WASH1-BioID proteome results from its colocalization with the WASH complex at the endosome and throughout the endo-vesicular system in neurons ( Boassa et al . , 2013; Bodain , 1965; Burre et al . , 2010; Iwai et al . , 1995; Lee et al . , 2005 ) . While our SWIPP1019R model appears to diverge in pathology from Parkinson’s disease models , it does exhibit parallels to NCL models . NCLs are lysosomal storage disorders primarily found in children rather than adults , with heterogenous presentations and multigenic causations ( Mukherjee et al . , 2019 ) . The majority of genes implicated in NCLs affect lysosomal enzymatic function or transport of proteins to the lysosome ( Mukherjee et al . , 2019; Poët et al . , 2006; Ramirez-Montealegre and Pearce , 2005; Yoshikawa et al . , 2002 ) . Most patients present with marked neurological impairments , such as learning disabilities , motor abnormalities , vision loss , and seizures , and have the unifying feature of lysosomal lipofuscin accumulation upon pathological examination ( Mukherjee et al . , 2019 ) . While the human SWIPP1019R mutation has not been classified as an NCL ( Ropers et al . , 2011 ) , findings from our mutant mouse model suggest that loss of WASH complex function leads to phenotypes bearing strong resemblance to NCLs , including lipofuscin accumulation ( Figures 5–8 ) . As a result , our mouse model could provide the opportunity to study these pathologies at a mechanistic level , while also enabling preclinical development of treatments for their human counterparts . Currently , there is an urgent need for greater mechanistic investigations of neurodegenerative disorders , particularly in the domain of endo-lysosomal trafficking . Despite the continual increase in identification of human disease-associated genes , our molecular understanding of how their protein equivalents function and contribute to pathogenesis remains limited . Here we employ a system-level analysis of proteomic datasets to uncover biological perturbations linked to SWIPP1019R . We demonstrate the power of combining in vivo proteomics and system network analyses with in vitro and in vivo functional studies to uncover relationships between genetic mutations and molecular disease pathologies . Applying this platform to study organellar dysfunction in other neurodegenerative and neurodevelopmental disorders may facilitate the identification of convergent disease pathways driving brain disorders .
We generated Washc4 mutant ( SWIPP1019R ) mice in collaboration with the Duke Transgenic Core Facility to mimic the de novo human variant at amino acid 1019 of human WASHC4 . A CRISPR-induced CCT>CGT point mutation was introduced into exon 29 of Washc4 . Fifty nanograms per microliter of the sgRNA ( 5′-ttgagaatactcacaagaggagg-3′ ) , 100 ng/µL Cas9 mRNA , and 100 ng/µL of a repair oligonucleotide containing the C>G mutation were injected into the cytoplasm of B6SJLF1/J mouse embryos ( Jax #100012 ) ( see Key Resources Table for the sequence of the repair oligonucleotide ) . Mice with germline transmission were then backcrossed into a C57BL/6J background ( Jax #000664 ) . At least five backcrosses were obtained before animals were used for behavior . We bred heterozygous SWIPP1019R mice together to obtain age-matched mutant and wild-type genotypes for cell culture and behavioral experiments . Genetic sequencing was used to screen for germline transmission of the C>G point mutation ( FOR: 5′-tgcttgtagatgtttttcct-3′ , REV: 5′-gttaacatgatcctatggcg-3′ ) . All mice were housed in the Duke University′s Division of Laboratory Animal Resources or Behavioral Core facilities at two to five animals per cage on a 14:10 hr light:dark cycle . All experiments were conducted with a protocol approved by the Duke University Institutional Animal Care and Use Committee in accordance with NIH guidelines . We retrospectively analyzed clinical findings from seven children with homozygous WASHC4c . 3056C>G mutations ( obtained by Dr . Rajab in 2010 at the Royal Hospital , Muscat , Oman ) . The original report of these human subjects and parental consent for data use can be found in Ropers et al . , 2011 . HEK293T cells ( ATCC #CRL-11268 ) were purchased from the Duke Cell Culture facility and were tested for mycoplasma contamination . HEK239T cells were used for co-immunoprecipitation experiments and preparation of AAV viruses . Primary neuronal cultures were prepared from P0 mouse cortex . P0 mouse pups were rapidly decapitated and cortices were dissected and kept individually in 5 mL Hibernate A ( Thermo #A1247501 ) supplemented with 2% B27 ( Thermo #17504044 ) at 4°C overnight to allow for individual animal genotyping before plating . Neurons were then treated with Papain ( Worthington #LS003120 ) and DNAse ( VWR #V0335 ) -supplemented Hibernate A for 18 min at 37°C and washed twice with plating medium ( plating medium: Neurobasal A [Thermo #10888022] supplemented with 10% horse serum , 2% B-27 , and 1% GlutaMAX [Thermo #35050061] ) , and triturated before plating at 250 , 000 cells/well on poly-l-lysine-treated coverslips ( Sigma #P2636 ) in 24-well plates . Plating medium was replaced with growth medium ( Neurobasal A , 2% B-27 , 1% GlutaMAX ) 2 hr later . Cell media was supplemented and treated with AraC at DIV5 ( 5 uM final concentration/well ) . Half-media changes were then performed every 4 days . For immunoprecipitation experiments , a pmCAG-SWIP-WT-HA construct was generated by PCR amplification of the human WASHC4 sequence , which was then inserted between NheI and SalI restriction sites of a pmCAG-HA backbone generated in our lab . Site-directed mutagenesis ( Agilent #200517 ) was used to introduce a C>G point mutation into this pmCAG-SWIP-WT-HA construct for generation of a pmCAG-SWIP-MUT-HA construct ( FOR: 5′-ctacaaagttgagggtcagacggggaacaattatatagaaa-3′ , REV: 5′-tttctatataattgttccccgtctgaccctcaactttgtag-3′ ) . For iBioID experiments , an AAV construct expressing hSyn1-WASH1-BioID2-HA was generated by cloning a Washc1 insert between SalI and HindIII sites of a pAAV-hSyn1-Actin Chromobody-Linker-BioID2-pA construct ( replacing Actin Chromobody ) generated in our lab . This backbone included a 25 nm GS linker-BioID2-HA fragment from Addgene #80899 , generated by Kim et al . , 2016 . An hSyn1-solubleBioID2-HA construct was created similarly , by removing Actin Chromobody from the above construct . Oligonucleotide sequences are reported in Key Resources Table . Links to sequences of the plasmid DNA constructs are available in Supplementary file 5 . AAV preparations were performed as described previously ( Uezu et al . , 2016 ) . The day before transfection , HEK293T cells were plated at a density of 1 . 5 × 107 cells per 15 cm2 plate in DMEM media with 10% fetal bovine serum and 1% Pen/Strep ( Thermo #11965–092 , Sigma #F4135 , Thermo #15140–122 ) . Six HEK293T 15 cm2 plates were used per viral preparation . The next day , 30 µg of pAd-DeltaF6 helper plasmid , 15 µg of AAV2/9 plasmid , and 15 µg of an AAV plasmid carrying the transgene of interest were mixed in OptiMEM with PEI-MAX ( final concentration 80 µg/mL , Polysciences #24765 ) . Two milliliters of this solution were then added dropwise to each of the 6 HEK293T 15 cm2 plates . Eight hours later , the media was replaced with 20 mL DMEM+10%FBS . Seventy-two hours post-transfection , cells were scraped and collected in the media , pooled , and centrifuged at 1500 rpm for 5 min at RT . The final pellet from the six cell plates was resuspended in 5 mL of cell lysis buffer ( 15 mM NaCl , 5 mM Tris–HCl , pH 8 . 5 ) , and freeze-thawed three times using an ethanol/dry ice bath . The lysate was then treated with 50 U/mL of Benzonase ( Novagen #70664 ) , for 30 min in a 37°C water bath , vortexed , and then centrifuged at 4500 rpm for 30 min at 4°C . The resulting supernatant containing AAV particles was added to the top of an iodixanol gradient ( 15% , 25% , 40% , 60% top to bottom ) in an Optiseal tube ( Beckman Coulter #361625 ) . The gradient was then centrifuged using a Beckman Ti-70 rotor in a Beckman XL-90 ultracentrifuge at 67 , 000 rpm for 70 min , 18°C . The purified viral solution was extracted from the 40%/60% iodixanol interface using a syringe and placed into an Amicon 100 kDa filter unit ( #UFC910024 ) . The viral solution was washed in this filter three times with 1× ice-cold PBS by adding 5 mL of PBS and centrifuging at 4900 rpm for 45 min at 4°C to obtain a final volume of approximately 200 µL of concentrated virus that was aliquoted into 5–10 µL aliquots and stored at −80°C until use . HEK293T cells were transfected with pmCAG-SWIP-WT-HA or pmCAG-SWIP-MUT-HA constructs for three days , as previously described ( Mason et al . , 2011 ) . Cells were lysed with lysis buffer ( 25 mM HEPES , 150 mM NaCl , 1 mM EDTA , 1% NonidetP-40 , pH 7 . 4 ) containing protease inhibitors ( 5 mM NaF , 1 mM orthovanadate , 1 mM AEBSF , and 2 μg/mL leupeptin/pepstatin ) and centrifuged at 1700 g for 5 min . Collected supernatant was incubated with 30 µL of pre-washed anti-HA agarose beads ( Sigma #A2095 ) on a sample rotator ( 15 rpm ) for 2 hr at 4°C . Beads were then washed three times with lysis buffer , and sample buffer was added before subjecting to immunoblotting as described above . The protein-transferred membrane was probed individually for WASH1 , Strumpellin , and HA . Data were collected from four separate preparations of WT and MUT conditions . Adult ( 7mo ) WT and MUT SWIPP1019R mice were deeply anesthetized with isoflurane and then transcardially perfused with warmed heparinized saline ( 25 U/mL heparin ) for 4 min , followed by ice-cold 0 . 15 M cacodylate buffer pH 7 . 4 containing 2 . 5% glutaraldehyde ( Electron Microscopy Sciences #16320 ) , 3% paraformaldehyde , and 2 mM CaCl2 for 15 min . Brain samples were dissected and stored on ice in the same fixative for 2 hr before washing in 0 . 1 M sodium cacodylate buffer ( three changes for 15 min each ) . Samples were then post-fixed in 1 . 0% OsO4 in 0 . 1 M sodium cacodylate buffer for 1 hr on a rotator . Samples were then washed in three 15 min changes of 0 . 1 M sodium cacodylate . Samples were then placed into en bloc stain ( 1% uranyl acetate ) overnight at 4°C . Subsequently , samples were dehydrated in a series of ascending acetone concentrations including 50% , 70% , 95% , and 100% for three cycles with 15 min incubation at each concentration change . Samples were then placed in a 50:50 mixture of epoxy resin ( Epon ) and acetone overnight on a rotator . This solution was then replaced twice with 100% fresh Epon for at least 2 hr at room temperature on a rotator . Samples were embedded with 100% Epon resin in BEEM capsules ( Ted Pella ) for 48 hr at 60°C . Samples were ultrathin sectioned to 60–70 nm on a Reichert Ultracut E ultramicrotome . Harvested grids were then stained with 2% uranyl acetate in 50% ethanol for 30 min and Sato’s lead stain for 1 min . Micrographs were acquired using a Phillips CM12 electron microscope operating at 80 kV , at 1700× magnification . Micrographs were analyzed in Adobe Photoshop 2019 , using the ‘magic wand’ tool to demarcate and measure the area of electron-dense and electron-lucent regions of interest ( ROIs ) . Statistical analyses of ROI measurements were performed in GraphPad Prism ( version 8 ) software . The experimenter was blinded to genotype for image acquisition and analysis . AAV2/9 viral probes , hSyn1-WASH1-BioID2-HA or hSyn1-solubleBioID2-HA , were injected into wild-type CD1 mouse brains using a Hamilton syringe ( #7635–01 ) at age P0–P1 to ensure viral spread throughout the forebrain ( Glascock et al . , 2011 ) . Fifteen days post-viral injection , biotin was subcutaneously administered at 24 mg/kg for seven consecutive days for biotinylation of proteins in proximity to BioID2 probes . Whole brains were extracted on the final day of biotin injections , snap frozen , and stored in liquid nitrogen until protein purification . Seven brains were used for protein purification of each probe , and each purification was performed three times independently ( 21 brains total for WASH1-BioID2 , 21 for solubleBioID2 ) . We performed all homogenization and protein purification on ice . A 2 mL Dounce homogenizer was used to individually homogenize each brain in a 1:1 solution of Lysis-R:2X-RIPA buffer solution with protease inhibitors ( Roche cOmplete tablets #11836153001 ) . Each sample was sonicated three times for 7 s and then centrifuged at 5000 g for 5 min at 4°C . Samples were transferred to Beckman Coulter 1 . 5 mL tubes ( #344059 ) and then spun at 45 , 000 rpm in a Beckman Coulter tabletop ultracentrifuge ( TLA-55 rotor ) for 1 hr at 4°C . SDS was added to supernatants ( final 1% ) , and samples were then boiled for 5 min at 95°C . We next combined supernatants from the same condition together ( WASH1-BioID2 vs . solubleBioID2 ) in 15 mL conical tubes to rotate with 30 µL high-capacity NeutrAvidin beads overnight at 4°C ( Thermo #29204 ) . The following day , all steps were performed under a hood with keratin-free reagents . Samples were spun down at 6000 rpm , 4°C for 5 min to pellet the beads and remove supernatant . The pelleted beads then went through a series of washes , each for 10 min at RT with 500 µL of solvent , and then spun down on a tabletop centrifuge to pellet the beads for the next wash . The washes were as follows: 2% SDS twice , 1% TritonX100–1% deoxycholate-25 mM LiCl2 once , 1 M NaCL twice , 50 mM ammonium bicarbonate ( Ambic ) five times . Beads were then mixed 1:1 with a 2× Laemmli sample buffer that contained 3 mM biotin/50 mM Ambic , boiled for 5 min at 95°C , vortexed three times , and then biotinylated protein supernatants were stored at −80°C until LC–MS/MS . We gave the Duke Proteomics and Metabolomics Shared Resource ( DPMSR ) six eluents from streptavidin resins ( 3× WASH1-BioID2 , 3× solubleBioID2 ) , stored on dry ice . Samples were reduced with 10 mM dithiolthreitol for 30 min at 80°C and alkylated with 20 mM iodoacetamide for 30 min at room temperature . Next , samples were supplemented with a final concentration of 1 . 2% phosphoric acid and 256 μL of S-Trap ( Protifi ) binding buffer ( 90% MeOH/100 mM triethylammonium bicarbonate [TEAB] ) . Proteins were trapped on the S-Trap , digested using 20 ng/μL sequencing grade trypsin ( Promega ) for 1 hr at 47°C , and eluted using 50 mM TEAB , followed by 0 . 2% formic acid ( FA ) , and lastly using 50% acetonitrile ( ACN ) /0 . 2% FA . All samples were then lyophilized to dryness and resuspended in 20 μL 1%TFA/2% ACN containing 25 fmol/μL yeast alcohol dehydrogenase ( UniProtKB P00330; ADH_YEAST ) . From each sample , 3 μL was removed to create a pooled QC sample ( SPQC ) which was run analyzed in technical triplicate throughout the acquisition period . Quantitative LC/MS/MS was performed on 2 μL of each sample , using a nanoAcquity UPLC system ( Waters ) coupled with a Thermo QExactive HF-X high-resolution accurate mass tandem mass spectrometer ( Thermo ) via a nanoelectrospray ionization source . Briefly , the sample was first trapped on a Symmetry C18 20 mm × 180 μm trapping column ( 5 μL/min at 99 . 9/0 . 1 vol/vol water/ACN ) , after which the analytical separation was performed using a 1 . 8 μm Acquity HSS T3 C18 75 μm × 250 mm column ( Waters ) with a 90 min linear gradient of 5–30% ACN with 0 . 1% formic acid at a flow rate of 400 nL/min with a column temperature of 55°C . Data collection on the QExactive HF-X mass spectrometer was performed in a data-dependent acquisition ( DDA ) mode of acquisition with a r=120 , 000 , 000 ( @ m/z 200 ) full MS scan from m/z 375–1600 with a target AGC value of 3e6 ions followed by 30 MS/MS scans at r=15 , 000 , 000 ( @ m/z 200 ) at a target AGC value of 5e4 ions and 45 ms . A 20 s dynamic exclusion was employed to increase depth of coverage . The total analysis cycle time for each sample injection was approximately 2 hr . We performed three independent fractionation experiments with one adult SWIP mutant brain and one WT mouse brain fractionated in each experiment . Each mouse was sacrificed by isoflurane inhalation and its brain was immediately extracted and placed into a 2 mL Dounce homogenizer on ice with 1 mL isotonic TEVP homogenization buffer ( 320 mM sucrose , 10 mM Tris base , 1 mM EDTA , 1 mM EGTA , 5 mM NaF , pH7 . 4 [Hallett et al . , 2008] ) . A complete mini protease inhibitor cocktail tablet ( Sigma #11836170001 ) was added to a 50 mL TEVP buffer aliquot immediately before use . Brains were homogenized for 15 passes with a Dounce homogenizer to break the tissue , and then this lysate was brought up to a 5 mL volume with additional TEVP buffer . Lysates were then passed through a 0 . 5 mL ball-bearing homogenizer for two passes ( 14 µm ball , Isobiotec ) to release organelles . Final brain lysate volumes were approximately 7 . 5 mL each . Lysates were then divided into replicate microfuge tubes ( Beckman Coulter #357448 ) to perform differential centrifugation , following Geladaki et . al’s LOPIT-DC protocol ( Geladaki et al . , 2019 ) . Centrifugation was carried out at 4°C in a tabletop Eppendorf 5424 centrifuge for spins at 200 g , 1000 g , 3000 g , 5000 g , 9000 g , 12 , 000 g , and 15 , 000 g . To isolate the final three fractions , a tabletop Beckman TLA-100 ultracentrifuge with a TLA-55 rotor was used at 4°C with speeds of 30 , 000 g , 79 , 000 g , and 120 , 000 g , respectively . Samples were kept on ice at all times , and pellets were stored at −80°C . Pellets from seven fractions ( 5000 g–120 , 000g ) were used for proteomic analyses . The Duke Proteomics and Metabolomics Shared Resource ( DPMSR ) processed and prepared fraction pellets from all 42 frozen samples simultaneously ( seven fractions per brain from three WT and three MUT brains ) . Due to volume constraints , each sample was split into three tubes , for a total of 126 samples , which were processed in the following manner: 100 µL of 8 M urea was added to the first aliquot then probe sonicated for 5 s with an energy setting of 30% . This volume was then transferred to the second and then third aliquots after sonication in the same manner . All tubes were centrifuged at 10 , 000 g , and any residual volume from tubes 1 and 2 were added to tube 3 . Protein concentrations were determined by BCA on the supernatant in duplicate ( 5 μL each assay ) . Total protein concentrations for each replicate ranged from 1 . 1 mg/mL to 7 . 8 mg/mL with total protein quantities ranging from 108 . 3 to 740 . 81 µg . 60 µg of each sample was removed and normalized to 52 . 6 µL with 8 M urea and 14 . 6 µL 20% SDS . Samples were reduced with 10 mM dithiolthreitol for 30 min at 80°C and alkylated with 20 mM iodoacetamide for 30 min at room temperature . Next , they were supplemented with 7 . 4 μL of 12% phosphoric acid and 574 μL of S-Trap ( Protifi ) binding buffer ( 90% MeOH/100 mM TEAB ) . Proteins were trapped on the S-Trap , digested using 20 ng/μL sequencing grade trypsin ( Promega ) for 1 hr at 47°C , and eluted using 50 mM TEAB , followed by 0 . 2% FA , and lastly using 50% ACN/0 . 2% FA . All samples were then lyophilized to dryness . Each sample was resuspended in 120 μL 200 mM TEAB , pH 8 . 0 . From each sample , 20 µL was removed and combined to form a pooled quality control sample ( SPQC ) . Fresh TMTPro reagent ( 0 . 5 mg for each 16-plex reagent ) was resuspended in 20 μL 100% ACN and was added to each sample . Samples were incubated for 1 hr at RT . After the 1 hr reaction , 5 μL of 5% hydroxylamine was added and incubated for 15 min at room temperature to quench the reaction . Each 16-plex TMT experiment consisted of the WT and MUT fractions from one mouse , as well as the two SPQC samples . Samples corresponding to each experiment were concatenated and lyophilized to dryness . Samples were resuspended in 800 µL 0 . 1% formic acid . 400 µg was fractionated into 48 unique high-pH reversed-phase fractions using pH 9 . 0 20 mM ammonium formate as mobile phase A and neat ACN as mobile phase B . The column used was a 2 . 1 mm × 50 mm XBridge C18 ( Waters ) , and fractionation was performed on an Agilent 1100 HPLC with G1364C fraction collector . Throughout the method , the flow rate was 0 . 4 mL/min and the column temperature was 55°C . The gradient method was set as follows: 0 min , 3%B; 1 min , 7% B; 50 min , 50%B; 51 min , 90% B; 55 min , 90% B; 56 min , 3% B; 70 min , 3% B . 48 fractions were collected in equal time segments from 0 to 52 min , then concatenated into 12 unique samples using every 12th fraction . For instance , fractions 1 , 13 , 25 , and 37 were combined , fractions 2 , 14 , 26 , and 38 were combined , etc . Fractions were frozen and lyophilized overnight . Samples were resuspended in 66 μL 1% TFA/2% ACN prior to LC–MS analysis . Quantitative LC/MS/MS was performed on 2 μL ( 1 μg ) of each sample , using a nanoAcquity UPLC system ( Waters ) coupled with a Thermo Orbitrap Fusion Lumos high-resolution accurate mass tandem mass spectrometer ( Thermo ) equipped with a FAIMS Pro ion-mobility device via a nanoelectrospray ionization source to enhance precursor ion selectivity and quantitative accuracy without losing the depth of coverage . Briefly , the sample was first trapped on a Symmetry C18 20 mm × 180 μm trapping column ( 5 μL/min at 99 . 9/0 . 1 vol/vol water/ACN ) , after which the analytical separation was performed using a 1 . 8 μm Acquity HSS T3 C18 75 μm × 250 mm column ( Waters ) with a 90 min linear gradient of 5–30% ACN with 0 . 1% formic acid at a flow rate of 400 nL/min with a column temperature of 55°C . Data collection on the Fusion Lumos mass spectrometer was performed for three different compensation voltages ( CV: −40 V , −60 V , −80 V ) . Within each CV , a DDA mode of acquisition with a r=120 , 000 , 000 ( @ m/z 200 ) full MS scan from m/z 375–1600 with a target AGC value of 4e5 ions was performed . MS/MS scans were acquired in the Orbitrap at r=50 , 000 , 000 ( @ m/z 200 ) from m/z 100 with a target AGC value of 1e5 and max fill time of 105 ms . The total cycle time for each CV was 1 s , with total cycle times of 3 s between like full MS scans . A 45 s dynamic exclusion was employed to increase depth of coverage . The total analysis cycle time for each sample injection was approximately 2 hr . Following UPLC–MS/MS analyses , data were imported into Proteome Discoverer 2 . 4 ( Thermo Scientific ) . The MS/MS data were searched against a SwissProt Mouse database ( downloaded November 2019 ) plus additional common contaminant proteins , including yeast alcohol dehydrogenase ( ADH ) , bovine casein , bovine serum albumin , as well as an equal number of reversed-sequence ‘decoys’ for FDR determination . Mascot Distiller and Mascot Server ( v 2 . 5 , Matrix Sciences ) were utilized to produce fragment ion spectra and to perform the database searches . Database search parameters included fixed modification on Cys ( carbamidomethyl ) and variable modification on Met ( oxidation ) , Asn/Gln ( deamindation ) , Lys ( TMTPro ) , and peptide N-termini ( TMTPro ) . Data were searched at 5 ppm precursor and 0 . 02 product mass accuracy with full trypsin enzyme rules . Reporter ion intensities were calculated using the Reporter Ions Quantifier algorithm in Proteome Discoverer . Percolator node in Proteome Discoverer was used to annotate the data at a maximum 1% protein FDR . Behavioral tests were performed on age-matched WT and homozygous SWIPP1019R mutant littermates . Male and female mice were used in all experiments . Testing was performed at two time points: P42–55 days old as a young adult age and 5 . 5 months old as mid-adulthood , so that we could compare disease progression in this mouse model to human patients ( Ropers et al . , 2011 ) . The sequence of behavioral testing was as follows: Y-maze ( to measure working memory ) , object novelty recognition ( to measure short- and long-term object recognition memory ) , TreadScan ( to assess gait ) , and steady-speed rotarod ( to assess motor control and strength ) for 40–55 day old mice . Testing was performed over 1 . 5 weeks , interspersed with rest days for acclimation . This sequence was repeated with the same cohort at 5 . 5–6 months old , with three additional measures added to the end of testing: fear conditioning ( to assess associative fear memory ) , a hearing test ( to measure tone response ) , and a shock threshold test ( to assess somatosensation ) . Of note , a separate , second cohort of mice was evaluated for fear conditioning , hearing , and shock threshold testing at adolescence . After each trial , equipment was cleaned with Labsan to remove residual odors . The experimenter was blinded to genotype for all behavioral analyses . Working memory was evaluated by measuring spontaneous alternations in a three-arm Y-maze under indirect illumination ( 80–90 lux ) . A mouse was placed in the center of the maze and allowed to freely explore all arms , each of which had different visual cues for spatial recognition . Trials were 5 min in length , with video data and analyses captured by EthoVision XT 11 . 0 software ( Noldus Information Technology ) . Entry to an arm was define as the mouse being >1 body length into a given arm . An alternation was defined as three successive entries into each of the different arms . Total % alternation was calculated as the total number of alternations/the total number of arm entries minus 2 × 100 . One hour before testing , mice were individually exposed to the testing arena ( a 48×22×18 cm white opaque arena ) for 10 min under 80–100 lux illumination without any objects . The test consisted of three phases: training ( day 1 ) , short-term memory test ( STM , day 1 ) , and long-term memory test ( LTM , day 2 ) . For the training phase , two identical objects were placed 10 cm apart , against opposing walls of the arena . A mouse was placed in the center of the arena and given full access to explore both objects for 5 min and then returned to its home cage . For STM testing , one of the training objects remained ( the now familiar object ) , and a novel object replaced one of the training objects ( similar in size , different shapes ) . The mouse was returned to the arena 30 min after the training task and allowed to explore freely for 5 min . For LTM testing , the novel object was replaced with another object , and the familiar object remained unchanged . The LTM test was also 5 min in duration , conducted 24 hr after the training task . Behavior was scored using Ethovision 11 . 0 XT software ( Noldus ) and analyzed by a blind observer . Object contact was defined as the mouse’s nose within 1 cm of the object . We analyzed both number of nose contacts with each object and duration of contacts . Preference scores were calculated as follows: ( duration contactnovel− duration contactfamiliar ) /total duration contactnovel+familiar . Positive scores signified a preference for the novel object , whereas negative scores denoted a preference for the familiar object , and scores approaching zero indicated no preference . A TreadScan forced locomotion treadmill system ( CleversSys Inc , Reston , VA ) was used for gait recording and analysis . Each mouse was recorded walking on a transparent treadmill at 45 days old and again at 5 . 5 months old . Mice were acclimated to the treadmill chamber for 1 min before the start of recording to eliminate exploratory behavior confounding normal gait . Trials were 20 s in length , with mice walking at speeds between 13 . 83 and 16 . 53 cm/s ( P45 WT average 15 . 74 cm/s; P45 MUT average 15 . 80 cm/s; 5 . 5mo WT average 15 . 77 cm/s; 5 . 5mo MUT average 15 . 85 cm/s ) . A high-speed digital camera attached to the treadmill-captured limb movement at a frame rate of 100 frames/s . We used TreadScan software ( CleversSys ) and representative WT and MUT videos to generate footprint templates , which were then used to identify individual paw profiles for each limb . Parameters such as stance time , swing time , step length , track width , and limb coupling were recorded for the entire 20 s duration for each animal . Output gait tracking was verified manually by a blinded experimenter to ensure consistent limb tracking throughout the duration of each video . A 5-lane rotarod ( Med Associates , St . Albans , VT ) was used for steady-speed motor analysis . The rod was run at a steady speed of 32 rpm for four 5 min trials , with a 40 min inter-trial interval . We recorded mouse latency to fall by infrared beam break or manually for any mouse that completed two or more rotations on the rod without walking . Mice were randomized across lanes for each trial . Animals were examined in contextual and cued fear conditioning as described by Rodriguiz and Wetsel , 2006 . Two separate cohorts of mice were used in testing the two age groups . A 3-day testing paradigm was used to assess memory: conditioning on day 1 , context testing 24 hr post-conditioning on day 2 , and cued tone testing 48 hr post-conditioning on day 3 . All testing was conducted in fear conditioning chambers ( Med Associates ) . In the conditioning phase , mice were first acclimated to the test chamber for 2 min under ~100 lux illumination . Then a 2900 Hz , 80 dB tone ( conditioned stimulus , CS ) played for 30 s , which terminated with a paired 0 . 4 mA , 2 s scrambled foot shock ( unconditioned stimulus , US ) . Mice were removed from the chamber and returned to their home cage 30 s later . In the context testing phase , mice were placed in the same conditioning chamber and monitored for freezing behavior for a 5 min trial period , in the absence of the CS and US . For cued tone testing , the chambers were modified to different dimensions and shapes , contained different floors and wall textures , and lighting was adjusted to 50 lux . Mice acclimated to the chamber for 2 min , and then the CS was presented continuously for 3 min . Contextual and cued fear memory was assessed by freezing behavior , captured by automated video software ( CleversSys ) . We tested mouse hearing using a startle platform ( Med Associates ) connected to Startle Pro Software in a sound-proof chamber . Mice were placed in a ventilated restraint cylinder connected to the startle response detection system to measure startle to each acoustic stimulus . After 2 min of acclimation , mice were assessed for an acoustic startle response to seven different tone frequencies , 2 kHz , 3 kHz , 4 kHz , 8 kHz , 12 kHz , 16 kHz , and 20 kHz that were randomly presented three times each at four different decibels , 80 , 100 , 105 , and 110 dB , for a total of 84 trials . A random inter-trial interval of 15–60 s ( average 30 s ) was used to prevent anticipation of a stimulus . An animal’s reaction to the tone was recorded as startle reactivity in the first 100 ms of the stimulus presentation , which was transduced through the platform’s load cell and expressed in arbitrary units ( AU ) . Mouse somatosensation was tested by placing mice in a startle chamber ( Med Associates ) connected to Startle Pro Software . Mice were placed atop a multi-bar cradle within a ventilated plexiglass restraint cylinder , which allows for horizontal movement within the chamber , but not upright rearing . After 2 min of acclimation , each mouse was exposed to 10 different scrambled shock intensities , ranging from 0 to 0 . 6 mA with randomized inter-trial intervals of 20–90 s . Each animal’s startle reactivity during the first 100 ms of the shock was transduced through the platform’s load cell and recorded as area under the curve ( AUC ) in AU . Experimental conditions , number of replicates , and statistical tests used are stated in each figure legend . Each experiment was replicated at least three times ( or on at least three separate animals ) to assure rigor and reproducibility . Both male and female age-matched mice were used for all experiments , with data pooled from both sexes . Data compilation and statistical analyses for all non-proteomic data were performed using GraphPad Prism ( version 8 , GraphPad Software , CA ) , using a significance level of alpha=0 . 05 . Prism provides exact p-values unless p<0 . 0001 . All data are reported as mean ± SEM . Each data set was tested for normal distribution using a D’Agostino–Person normality test to determine whether parametric ( unpaired Student’s t-test , one-way ANOVA , two-way ANOVA ) or non-parametric ( Mann–Whitney , Kruskal–Wallis , Kolmogorov–Smirnov ) tests should be used . Parametric assumptions were confirmed with the Shapiro–Wilk test ( normality ) and Levine’s test ( error variance homogeneity ) for ANOVA with repeated-measures testing . The analysis of iBioID and TMT proteomics data are described below . All proteomic data and analysis scripts are available online ( see key resources table ) . For EEA1+ and CathepsinD+ puncta analyses , coverslips were imaged on a Zeiss LSM 710 confocal microscope . Images were sampled at a resolution of 1024 × 1024 pixels with a dwell time of 0 . 45 µs using a 63×/1 . 4 oil immersion objective , a 2 . 0 times digital zoom , and a z-step size of 0 . 37 µm . Images were saved as ‘ . lsm’ formatted files , and quantification was performed on a POGO Velocity workstation in the Duke Light Microscopy Core Facility using Imaris 9 . 2 . 0 software ( Bitplane , South Windsor , CT ) . For analyses , we first used the ‘surface’ tool to make a solid fill surface of the MAP2-stained neuronal soma and dendrites , with the background subtraction option enabled . We selected a threshold that demarcated the neuron structure accurately while excluding background . For EEA1 puncta analyses , a 600 × 800 µm selection box was placed around the soma in each image and surfaces were created for EEA1 puncta within the selection box . Similarly , for CathepsinD puncta analyses , a 600 × 600 µm selection box was placed around the soma ( s ) in each image for surface creation . The same threshold settings were used across all images , and individual surface data from each soma were exported for aggregate analyses . The experimenter was blinded to sample conditions for both image acquisition and analysis . Z-stack images were acquired on a Zeiss 710 LSM confocal microscope . Images were sampled at a resolution of 1024 × 1024 pixels with a dwell time of 1 . 58 µs , using a 63×/1 . 4 oil immersion objective ( for cortex , striatum , and hippocampus ) or 20×/0 . 8 dry objective ( cerebellum ) , a 1 . 0 times digital zoom , and a z-step size of 0 . 67 µm . Images were saved as ‘ . lsm’ formatted files and then converted into maximum intensity projections ( MIPs ) using Zen 2 . 3 SP1 software . Quantification of CC3 colocalization with DAPI was performed on the MIPs using the Particle Analyzer function in FIJI ImageJ software . The experimenter was blind to sample conditions for both image acquisition and analysis . Z-stack images of the motor cortex were acquired on a Zeiss 710 LSM confocal microscope . Images were sampled at a resolution of 1024 × 1024 pixels with a dwell time of 1 . 58 µs , using a 63×/1 . 4 oil immersion objective , a 1 . 0 times digital zoom , and a z-step size of 0 . 34 µm , acquiring five steps per image . Images were saved as ‘ . lsm’ formatted files and then converted into maximum intensity projections ( MIPs ) using Zen 2 . 3 SP1 software . We selected 250 µm x 250 µm regions in the MIPs for analyses . Quantification of bassoon and homer1 colocalization was performed using the Particle Analyzer function of FIJI ImageJ software . The experimenter was blind to sample conditions for both image acquisition and analysis . Z-stack images of the substantia nigra and striatum were acquired on a Zeiss 710 LSM confocal microscope . Images were sampled at a resolution of 1024 × 1024 pixels with a dwell time of 1 . 58 µs , using a 40×/1 . 3 oil immersion objective or 10×/0 . 45 dry objective , a 1 . 0 times digital zoom , and a z-step size of 0 . 67 µm , acquiring five steps per image . Images were saved as ‘ . lsm’ formatted files and then converted into maximum intensity projections ( MIPs ) using Zen 2 . 3 SP1 software . Quantification of Tyrosine Hydroxlyase+ ( TH+ ) neurons was performed using the Particle Analyzer function of FIJI ImageJ software . Quantification of dopaminergic innervation of the striatum was obtained by measuring the mean TH+ signal intensity for each image . The experimenter was blind to sample conditions for both image acquisition and analysis . Following UPLC–MS/MS analyses , data was imported into Proteome Discoverer 2 . 2 ( Thermo Scientific Inc ) and aligned based on the accurate mass and retention time of detected ions ( ‘features’ ) using Minora Feature Detector algorithm in Proteome Discoverer . Relative peptide abundance was calculated based on AUC of the selected ion chromatograms of the aligned features across all runs . The MS/MS data was searched against the SwissProt Mus musculus database ( downloaded in April 2018 ) with additional proteins , including yeast ADH1 , bovine serum albumin , as well as an equal number of reversed-sequence ‘decoys’ for false discovery rate ( FDR ) determination . Mascot Distiller and Mascot Server ( v 2 . 5 , Matrix Sciences ) were utilized to produce fragment ion spectra and to perform the database searches . Database search parameters included fixed modification on Cys ( carbamidomethyl ) , variable modifications on Meth ( oxidation ) , and Asn and Gln ( deamidation ) and were searched at 5 ppm precursor and 0 . 02 Da product mass accuracy with full trypsin enzymatic rules . Peptide Validator and Protein FDR Validator nodes in Proteome Discoverer were used to annotate the data at a maximum 1% protein FDR . Protein-level intensities were exported from Proteome Discoverer and processed using custom R scripts . Carboxylases , keratins , and mitochondrial proteins ( Calvo et al . , 2016 ) were removed from the identified proteins as known contaminants . Sample loading normalization was performed to account for technical variation between the nine individual MS runs . In brief , this is done by multiplying intensities from each MS run by a scaling factor , such that total run intensities are equal . We created a pooled QC sample by pooling equivalent aliquots of peptides from each biological replicate and analyzed this in technical duplicate in each experiment . We performed sample pool normalization to SPQC samples to standardize protein measurements across all samples and correct for batch effects between MS analyses . Sample pool normalization adjusts the protein-wise mean of all biological replicates to be equal to the mean of all SPQC replicates . Finally , proteins that were identified by a single peptide and/or identified in less than 50% of samples were removed . Any remaining missing values were inferred to be missing not-at-random due to the left shifted distribution of proteins with missing values and imputed using the k-nearest neighbors algorithm using the impute . knn function in the R package impute ( impute::impute . knn ) . Normalized protein data were then fit with a simple linear model to derive a model-based statistical comparison of WASH iBioID and solubleBioID2 control groups ( Huang et al . , 2020 ) . To consider a protein enriched in the WASH interactome , we required that a protein exhibit a fold-change greater than 4 over the negative control with a Benjamini−Hochberg false discovery rate ( FDR ) less than 0 . 05 ( Benjamini and Hochberg , 1995 ) . With these criteria , 175 proteins were identified as WASH1 interactome proteins . The statistical results can be found in Supplementary file 1 . Proteins that function together often interact directly . We compiled experimentally determined protein–protein interactions ( PPIs ) among the WASH1 interactome from the HitPredict database ( López et al . , 2015 ) using a custom R package , getPPIs ( available online at twesleyb/getPPIs ) . We report PPIs among the WASH1 interactome in Supplementary file 1 . Bioinformatic GO analysis was conducted by manual annotation of identified proteins and confirmed with Metascape analysis ( Zhou et al . , 2019 ) of WASH1-BioID2-enriched proteins using the 2102 proteins identified in the mass spectrometry analysis as background . PSM-level data were exported from Proteome Discover 2 . 2 and prepared for analysis with MSstatsTMT , an R package for data normalization and hypothesis testing in multiplex TMT proteomics experiments ( Huang et al . , 2020 ) . MSstatsTMT performs statistical inference in two steps . First , each protein in the dataset is fit with a LMM expressing the major sources of variation in the experimental design . Second , given the fitted model , a model-based comparison is made between pairs of treatment conditions . Given our experimental design , MSstatsTMT fits the following LMM to each protein-level subset of the data: ( 1 ) Ymcbt=μ+Conditionc+Mixturem+ϵmcb The model’s constraints delimit the response as a function of fixed and mixed effects: ( 2 ) ∑c=1CConditionc=0Mixturem∼iid N ( 0 , σM2 ) ϵmcb∼iid N ( 0 , σ2 ) Condition is a fixed effect and represents the 14 combinations of Genotype and BioFraction in our experimental design . The term Mixture is a mixed effect and represents variation between the three TMT mixtures . Mixed effects are normally and independently distributed ( i . i . d . ) . The term epsilon ( ε ) is a mixed effect and represents both biological and technical variations , quantifying any remaining error . Pairwise contrasts between MUT and WT conditions are obtained by comparing estimates obtained from the LMM fit by restricted maximum likelihood ( Bates et al . , 2015 ) . We are interested in testing the null hypothesis: lT*β=0 . Where the contrast , lT is a vector of sum 0 specifying the positive and negative coefficients of the contrast . Beta ( β ) is the model-based estimates of Mutant and Control conditions . A test statistic for such a two-way contrast is given by Kuznetsova et al . , 2017: ( 3 ) t=lTβ^lσ2V^lT We obtain the model’s estimates ( β ) , error ( σ2 ) , and variance–covariance matrix ( V^ ) from the fit LMM . The numerator is the log fold-change of a comparison . Together , the denominator represents the standard error of the comparison . The degrees of freedom for the contrast are derived using the Satterthwaite moment of approximation method ( Kuznetsova et al . , 2017 ) . Finally , a p-value is calculated given the t-statistic and degrees of freedom . p-values for all tests of a given contrast are adjusted using the FDR method ( Huang et al . , 2020 ) . Using MSstatsTMT we assessed two types of contrasts . Statistical results for both intra-BioFraction and the overall ‘Mutant-Control’ comparison are found in Supplementary file 2 . Peptide-level data from the spatial proteomics analysis of SWIPP1019R WT and MUT brain were exported from Proteome Discoverer ( version 2 . 4 ) and analyzed using custom R scripts . Peptides from contaminant and non-mouse proteins were removed . First , we performed sample loading normalization , normalizing the total ion intensity for each TMT channel within an experiment to be equal . Sample loading normalization corrects for small differences in the amount of sample analyzed and labeling reaction efficiency differences between individual TMT channels within an experiment . We found that in each TMT experiment there were a small number of missing values ( mean percent missing=1 . 6±0 . 17% ) . Missing values were inferred to be missing at random imputed using the k-nearest neighbor algorithm in the R package impute ( impute::impute . knn ) . Missing values for SPQC samples were not imputed . Peptides with any missing SPQC data were removed . Following sample loading normalization , SPQC replicates within each experiment should yield identical measurements . As peptides with irreproducible QC measurements are unlikely to be quantitatively robust , and their inclusion may bias downstream normalization , we sought to remove them . To assess intra-batch peptide variability , we adapted the method described by Ping et al . , 2018 . Briefly , peptides were binned into five groups based on the average intensity of the two SPQC replicates . For each pair of SPQC measurements , the log ratio of SPQC intensities was calculated . To identify outlier QC peptides , we plotted the distribution of these log ratios binned into five intensity bins . Peptides with ratios that were more than 4 standard deviations away from the mean of its intensity bin were considered outliers and removed . Proteins were summarized as the sum of all unique peptide intensities corresponding to a unique UniProtKB Accession identifier , and sample loading normalization was performed across all three experiments to account for inter-experimental technical variability . In a TMT experiment , the peptides selected for MS2 fragmentation are partially random , especially at lower signal-to-noise ratios . This stochasticity means that proteins are typically quantified by different peptides in each experiment . Thus , although SPQC samples should yield identical protein measurements in each of the three experiments ( as it is the same sample analyzed in each experiment ) , the observed protein measurements exhibit variability due to their quantification by different peptides . To account for this protein-level bias , we utilized the internal reference scaling ( IRS ) approach described by Plubell et al . , 2017 . IRS normalization scales the protein-wise geometric average of all SPQC measurements across all experiments to be equal and simultaneously adjusts biological replicates . In brief , each protein is multiplied by a scaling factor , which adjusts its intra-experimental SPQC values to be equal to the geometric mean of all SPQC values for the three experiments . This normalization step effectively standardizes protein measurements between different mass spectrometry experiments . Before downstream analyses , we removed irreproducible proteins . This included proteins that were quantified in less than 50% of all samples , proteins that were identified by a single peptide , and proteins that had missing SPQC values . Across all 42 biological replicates , we observed that a small number of proteins had potential outlier measurements that were either several orders of magnitude greater or less than the mean of its replicates . In order to identify and remove these proteins , we assessed the reproducibility of protein measurements within a fraction in the same manner used to identify and filter SPQC outlier peptides . A small number of proteins were identified as outliers if the average log ratio of their three QC technical replicates was more than 4 standard deviations away from the mean of its intensity bin . In total , we retained 5897 proteins in the final spatial proteomics dataset . To construct a protein covariation graph , we assessed the pairwise covariation ( correlation ) between all 5897 proteins quantified in all 42 biological samples using the Pearson correlation statistic ( Freedman et al . , 2007 ) . The resulting complete , signed , weighted , and symmetric adjacency matrix was then re-weighted using ‘Network Enhancement’ . We implemented network enhancement in R based on microbma’s translation of the original Matlab code ( https://github . com/microbma/neten ) . Network enhancement removes noise from the graph and facilitates downstream community detection ( Wang et al . , 2018 ) . The enhanced adjacency matrix was clustered in Python using the Leiden algorithm ( Traag et al . , 2019 ) , a recent extension and improvement of the well-known Louvain algorithm ( Mucha et al . , 2010 ) . The Leiden algorithm functions to optimize the partition of a graph into modules by maximizing a quality statistic . We utilized the ‘Surprise’ quality statistic ( Traag et al . , 2015 ) to identify optimal partitions of the protein covariation graph . Clustering of the network resulted in the identification of 49 modules . To evaluate modules that were changing between WT and MUT genotypes , we extended MSstatsTMT’s LMM framework . In this statistical design , we were interested in the average effect of genotype on the common response of all proteins in a module . After scaling normalized protein intensity measurements , we fit each module-level subset of the data with a linear mixed-model expressing the term Protein as a random effect: ( 4 ) log2 ( Relative Protein Intensity ) =μ+Condition+Protein+ ϵ When fitting the module-level models , we omitted the term Mixture as the variance attributable to Mixture after normalization is negligible ( Figure 3—figure supplement 4 ) . The response variable is the log2-transformed scaled ( sum-normalized ) protein intensity measurements for all proteins in a spatial proteomics module . An overall comparison is assessed given the fitted model and a contrast vector specifying a comparison between WT and MUT groups , as described above , for protein-wise comparisons . We utilized the Bonferroni method to adjust p-values for k=49 module comparisons and considered modules with an adjusted p-value less than 0 . 05 significant ( n=23 ) . For plotting , log2-transformed relative protein intensity measurements were scaled into the range of 0–1 to avoid plotting negative numbers . Modules were analyzed for enrichment of the WASH interactome ( this paper , Figure 1 ) , Retriever complex ( McNally et al . , 2017 ) , CORUM protein complexes ( Giurgiu et al . , 2019 ) , and subcellular predictions generated by Geladaki et al . , 2019 using the hypergeometric test with Bonferroni p-value correction for multiple comparisons . The union of all clustered and pathway proteins was used as background for the hypergeometric test . In addition to analysis of these general cellular pathways , we analyzed modules for enrichment of neuron-specific subcellular compartments – this included the presynapse ( Takamori et al . , 2006 ) , excitatory post-synapse ( Uezu et al . , 2016 ) , and inhibitory post-synapse ( Uezu et al . , 2016 ) . Gene set enrichment results are found in Supplementary file 4 and are available online at https://github . com/soderling-lab/SwipProteomics . Network graphs were visualized in Cytoscape ( Version 3 . 7 . 2 ) . Node location was manually adjusted to visualize the module more compactly . Node size was set to be proportional to the weighted degree centrality of a node in its module subgraph . Node size thus reflects node importance in the module . Visualizing co-expression or covariation networks is challenging because every node is connected to every other node ( the graph is complete ) . To aid visualization of module topology , we removed weak edges from the graphs . A threshold for each module was set to remove the maximal number of edges before the module subgraph split into multiple components . This strategy enables visualization of the strongest paths in a network . | Cells in the brain need to regulate and transport the proteins and nutrients stored inside them . They do this by sorting and packaging the contents they want to move in compartments called endosomes , which then send these packages to other parts of the cell . If the components involved in endosome trafficking mutate , this can lead to ‘traffic jams’ where proteins pile up inside the cell and stop it from working normally . In 2011 , researchers found that children who had a mutation in the gene for WASHC4 – a protein involved in endosome trafficking – had trouble learning . However , it remained unclear how this mutation affects the role of WASCH4 and impacts the behavior of brain cells . To answer this question , Courtland , Bradshaw et al . genetically engineered mice to carry an equivalent mutation to the one identified in humans . Experiments showed that the brain cells of the mutant mice had fewer WASHC4 proteins , and lower levels of other proteins involved in endosome trafficking . The mutant mice also had abnormally large endosomes in their brain cells and elevated levels of proteins that break down the cell’s contents , resulting in a build-up of cellular debris . Together , these findings suggest that the mutation causes abnormal trafficking in brain cells . Next , Courtland , Bradshaw et al . compared the behavior of adult and young mice with and without the mutation . Mice carrying the mutation were found to have learning difficulties and showed abnormal movements which became more exaggerated as they aged , similar to people with Parkinson’s disease . With this result , Courtland , Bradshaw et al . reviewed the medical records of the patients with the mutation and discovered that these children also had problems with their movement . These findings help explain what is happening inside brain cells when the gene for WASHC4 is mutated , and how disrupting endosome trafficking can lead to behavioral changes . Ultimately , understanding how learning and movement difficulties arise , on a molecular level , could lead to new therapeutic strategies to prevent , manage or treat them in the future . | [
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] | 2021 | Genetic disruption of WASHC4 drives endo-lysosomal dysfunction and cognitive-movement impairments in mice and humans |
The robustness and limited plasticity of the master circadian clock in the suprachiasmatic nucleus ( SCN ) is attributed to strong intercellular communication among its constituent neurons . However , factors that specify this characteristic feature of the SCN are unknown . Here , we identified Lhx1 as a regulator of SCN coupling . A phase-shifting light pulse causes acute reduction in Lhx1 expression and of its target genes that participate in SCN coupling . Mice lacking Lhx1 in the SCN have intact circadian oscillators , but reduced levels of coupling factors . Consequently , the mice rapidly phase shift under a jet lag paradigm and their behavior rhythms gradually deteriorate under constant condition . Ex vivo recordings of the SCN from these mice showed rapid desynchronization of unit oscillators . Therefore , by regulating expression of genes mediating intercellular communication , Lhx1 imparts synchrony among SCN neurons and ensures consolidated rhythms of activity and rest that is resistant to photic noise .
Circadian clocks generate ∼24 hr rhythms in behavior and physiology which allow an organism to anticipate and adjust to environmental changes accompanying the earth's day/night cycle . These rhythms are generated in a cell-autonomous manner by transcription–translation based feedback loops which are composed of clock proteins , such as PERIOD ( PER1 , PER2 , and PER3 ) , CRYPTOCHROME ( CRY1 and CRY2 ) , CLOCK , BMAL1 , REV-ERB ( REV-ERBα and REV-ERBβ ) , and ROR ( RORα , RORβ , and RORγ ) in mammals ( Mohawk et al . , 2012 ) . These oscillatory loops reside in almost all tissue types and regulate their downstream effectors to generate oscillations in the steady-state mRNA levels of thousands of genes in a tissue-specific manner . Identification of tissue-specific circadian transcripts in peripheral organs has elucidated the mechanism by which circadian clocks dictate the temporal regulation of organ function . The tissue level clocks are organized in a hierarchical manner . The hypothalamic suprachiasmatic nucleus ( SCN ) composed of ∼20 , 000 densely packed neurons acts as the master clock by orchestrating molecular oscillations in peripheral tissues ( Welsh et al . , 2010 ) . Tight intercellular communication among SCN neurons ( coupling ) drives synchronous oscillations . This , in turn , imparts overt rhythms in activity-rest and dependent rhythms in physiology and metabolism of the whole organism . While coupling between the SCN neurons buffers against the noise in oscillations of the constituent neurons , it is plastic enough to allow adaptive resetting of the phase of the SCN oscillator in response to changes in the environment . Light is the principal cue for entraining the SCN circadian clock to environmental cycles . Light stimuli are perceived in the retina and transmitted to the SCN via melanopsin ( OPN4 ) -expressing retinal ganglion cells ( mRGCs ) ( Hatori and Panda , 2010 ) . The time-of-the-day specific response ( called ‘gating’ ) of the SCN to light pulses properly adjusts the phase of the circadian clock . In mice held under constant darkness , light administered at subjective daytime , subjective evening , or subjective late night causes no shift , phase delay or phase advance of the behavioral rhythm respectively . This phase-dependent light response is conserved across species ( Pittendrigh , 1967; Zatz et al . , 1988; Schwartz and Zimmerman , 1990 ) . It is known that illumination at night triggers extensive chromatin remodeling in the mouse SCN ( Crosio et al . , 2000 ) , which likely results in changes in the levels of a large number of transcripts . However , except for a few dozen transcripts including the clock genes Per1 and Per2 ( Zhu et al . , 2012; Jagannath et al . , 2013 ) , the extent of light-triggered transcriptional changes in the SCN is largely unknown . These acute transcriptional changes impinge on the molecular oscillator to adjust the phase of the mRNA rhythms in the SCN . It is becoming increasingly clear that coupling among the SCN neurons buffers against phase shifts , and the transient weakening of such coupling facilitates large phase shifts . SCN neurons exhibit tight intercellular communication imposed by paracrine peptidergic signals such as VIP ( Vasoactive intestinal polypeptide ) , AVP ( Arginine vasopressin ) , and GRP ( Gastrin-releasing peptide ) ( Welsh et al . , 2010; Hogenesch and Herzog , 2011 ) . As the deficiency of Vip or its receptor Vpac2r causes desynchronization among SCN neurons ( Aton et al . , 2005 ) , this peptide-mediated coupling mechanism is a unique and necessary feature of the SCN in order to generate robust synchronous rhythms . Weaker coupling among the SCN neurons is suggested to facilitate rapid and large phase shifts of the overt rhythms ( Herzog 2007; An et al . , 2013 ) . Therefore , transcription factors that regulate expression of the SCN coupling agents are central to the unique function of the SCN . Here , using a combination of behavioral , genetic , and genomic tools , we vastly expand the understanding of the dynamic transcriptional landscape of the SCN . We conducted comprehensive analysis of the light-regulated , circadian , and tissue-enriched protein-coding transcriptome of the mouse SCN in order to understand the specificity of the master circadian clock . We found that the SCN-enriched transcription factor Lhx1 ( LIM homeobox 1 ) is required for expression of a number of genes including Vip whose protein product participates in intercellular signaling . The SCN-specific loss of Lhx1 attenuates cell to cell coupling of cell-autonomous oscillators in the SCN , abolishing circadian behavioral activity consolidation in vivo .
To comprehensively identify circadian- , light-regulated , and SCN-enriched protein-coding transcripts in the adult SCN , following 2 weeks of entrainment to 12 hr light:12 hr dark ( LD ) cycles male C57BL6/J mice were transferred to constant darkness ( DD ) , and the SCN was collected every 2 hr over 48 hr . Light at subjective night , but not during subjective day is known to cause a behavioral phase shift . To assess the gene expression effect of a phase-shifting pulse of light , a subset of mice were exposed to a 1 hr white light pulse delivered at 30 hr , 40 hr , and 46 hr after the onset of DD ( Figure 1A , Figure1—figure supplement 1 ) representing subjective daytime ( CT6 ) , early evening ( CT16 ) , and late night ( CT22 ) . We discovered 1412 genes ( Figure 1—figure supplement 1; Supplementary file 1 ) that show circadian oscillation in transcript abundance ( pMMCβ <0 . 05 , pFGT <0 . 05 , and median temporal expression >100 ) . Gene expression measured at 1 hr , 2 hr , and 4 hr after the beginning of the 1 hr light pulse revealed 508 probesets whose levels changed ( up- or down-regulated ) in response to at least one of the three light pulses ( Figure 1A , B , Figure 1—figure supplement 1; Supplementary file 2A ) . However , only 84 ( 17% ) were also rhythmic ( Figure 1—figure supplement 1; Supplementary file 2B ) . 10 . 7554/eLife . 03357 . 003Figure 1 . Light-regulated transcripts of the SCN . ( A ) Heatmap rendering of light-regulated SCN transcripts . For each time point , fold change between respective light treated and dark control was plotted . ( B ) Circadian gating of light-modulated transcripts . Cutoffs of two fold were set for up-regulation ( blue ) or suppression ( red ) after light pulse , and the number of probesets that satisfy each cutoff was plotted for each point . Quantitative RT-PCR ( qRT-PCR ) expression confirmation of genes detected as light-regulated by microarray . Examples of genes ( C ) induced or ( D ) repressed by light pulses at three different time points . ( Mean +s . e . m . , n = 4 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 00310 . 7554/eLife . 03357 . 004Figure 1—figure supplement 1 . Transcriptional profiling of the mouse SCN . ( A ) Sampling schedule for the collection of SCN . C57BL/6J male mice were entrained to 12 hr light:12 hr darkness for 2–3 weeks and transferred to constant darkness . From Circadian Time ( CT ) 18 , 30 hr after lights off , four mice at each time point were collected every 2 hr in dark over two complete days till CT64 . From CT30 , CT40 , or CT46 , one group of mice was exposed to 1 hr light , while the control group was maintained in dark , then both groups stayed in dark after 1 hr . SCNs were collected 1 , 2 , or 4 hr after the beginning of 1 hr light pulse . ( B ) Heatmap rendering of circadianly expressed transcripts in the mouse SCN . Each horizontal line represents one probeset from MOE430 high density array . ( C ) Venn diagram for the overlap of light-regulated and cycling transcripts in the SCN . Numbers shown are for probesets . ( D ) Venn diagram of light induced and suppressed transcripts showing that the light pulse at CT16 that causes maximal phase shift also affects the expression of a large number of SCN transcripts . DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 00410 . 7554/eLife . 03357 . 005Figure 1—figure supplement 2 . Light-induced changes in SCN gene expression correlate with the known effect of light on phase shift in different genetic models of light signaling . qRT-PCR quantification of ( A ) Per1 , ( B ) Nr4a2 , ( C ) Nr4a3 , ( D ) Klf4 , and ( E ) JunB mRNA in the SCN of dark reared or 2 hr after a 1-hr light pulse delivered at CT16 are shown . ( Mean + s . e . m . , n = 4 ) . The adult rd mice show outer retina degeneration , yet light resets their circadian clock as effectively as of the WT mice ( Foster et al . , 1991 ) . Opn4−/− mice lack melanopsin and their circadian clock shows an attenuated light-induced phase shift ( Panda et al . , 2002; Ruby et al . , 2002 ) . Opn4−/−;rd mice lack rod , cone , and melanopsin photopigments and show no response to light ( Panda et al . 2003 ) . Opn4Cre/+;R26iDTR/+ mice treated with DT specifically lose melanopsin-expressing retinal ganglion cells and show no phase shifting effect of light ( Hatori et al . , 2008 ) . All mice were dark reared for at least 7 days and their activity onset was used to calculate CT16 . DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 00510 . 7554/eLife . 03357 . 006Figure 1—figure supplement 3 . SCN enriched ( not SCN-exclusive ) transcripts . ( A ) Criteria to find SCN-enriched genes among 83 mouse tissues revealed 230 probesets among which 13 were transcription factors . ( B ) Expression patterns of SCN-enriched transcripts in 83 mouse tissues . Except SCN , duplicate data sets were used for other 82 tissues . The value used for the SCN was the ( normalized ) median of all the circadian values ( 24 in total ) for the given probeset . Affymetrix probeset IDs and raw data for each gene are shown in Supplementary file 3 . ( C ) The SCN is the only tissue showing overlapping expression of Lhx1 and Rorα . Lhx1 ( Affymetrix IDs 1421951_at and 1450428_at ) and Rorα ( 1436325_at ) in Figure 1—figure supplement 3C were extracted from Figure 1—figure supplement 3B . DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 006 We tested whether the number of light-modulated transcripts parallels the phase shifting effect of light . Light pulse delivered during subjective day ( CT6 ) triggered changes in a small number of transcripts , while the same pulse at CT16 causes a large phase shift accompanied by a large number of transcripts changing >2 fold ( Figure 1B , Figure 1—figure supplement 1; Supplementary file 2C ) . The magnitude of light modulation of transcripts also paralleled the known phase shifting effect of light in genetic models of light input perturbation . Compared to wild type ( WT ) C57BL6/J , the light response was unchanged in mice lacking rod/cone photoreceptors , attenuated in melanopsin ( Opn4 ) -deficient mice and completely abolished in mice lacking all three photopigments or those with specific ablation of melanopsin expressing retinal ganglion cells ( Opn4Cre/+;R26iDTR/+ + diphtheria toxin [DT] ) ( Figure 1—figure supplement 2 ) . Overall , the light-induced transcriptional responses in the SCN closely correlate with the extent of light-induced behavioral phase shift ( Hatori and Panda , 2010 ) and hence are informative of the mechanism and consequences of the phase shift . The light-induced genes include the circadian clock components e . g . , Per1 , Per2 , and Bhlhe40 ( Dec1 ) and genes involved in the CREB and MAPK signaling pathways ( Zhu et al . , 2012; Jagannath et al . , 2013 ) , which are implicated in intracellular signaling leading to resetting the phase of cell autonomous oscillators ( Figure 1C ) . The novel group of light repressed transcripts was enriched for those involved in inter-cellular communication including Vip , Avpr1a , Rasd1 , and Npy6r ( Figure 1D , Supplementary file 2C ) , suggesting that an effective phase shift of the SCN clock rests both on resetting the phase of cell autonomous clock and on light-induced relaxation of the intercellular coupling . The data for circadian and light-dependent gene expression are also being made available in a user friendly searchable web interface at http://scn . salk . edu . The database can be queried using a gene symbol or a probeset identifier as the keyword . Since the tight intercellular coupling of circadian oscillators is largely an SCN specific phenomenon ( Herzog 2007 ) , we reasoned that light likely suppresses the expression of an SCN-enriched factor that coordinates intercellular communication . We employed three-step enrichment criteria comparing the transcriptome of the SCN with that of 82 other mouse tissues including 14 different neural tissues ( Su et al . , 2004 ) ( Figure 1—figure supplement 3 ) . This analysis identified 213 SCN-enriched ( not SCN-exclusive ) genes ( Figure 1—figure supplement 3 , Supplementary file 3 ) , including Rorα , Rorβ , Vip , Grp , Rgs16 , and Prokr2 which are known to play important roles in SCN function ( Welsh et al . , 2010; Doi et al . , 2011; Kasukawa et al . , 2011; VanDunk et al . , 2011 ) . Among the 13 SCN-enriched transcription factors discovered ( Figure 1—figure supplement 3 ) , Lhx1 mRNA was suppressed by light ( Figure 1D ) , raising the possibility that it regulates the expression of SCN synchronizing agents and that the lack of Lhx1 might render the SCN prone to desynchrony . However , Lhx1 is a necessary factor for differentiation of several tissue types as Lhx1−/− embryos die ∼ E10 ( Shawlot and Behringer , 1995 ) . In the hypothalamus , Lhx1 expression begins at E11 and parallels that of Six6 ( VanDunk et al . , 2011 ) , whose function is necessary for normal SCN development ( Clark et al . , 2013 ) . Since Rorα expression is also SCN enriched ( Figure 1—figure supplement 3 ) and its developmental expression follows Lhx1 expression in the SCN region , we generated RorαCre;Lhx1loxP mice for testing the role of Lhx1 in SCN function . The overlap between Rorα and Lhx1 expression in the adult brain is largely restricted to the SCN ( Figure 1—figure supplement 3 ) and the dLGN ( Chou et al . , 2013 ) . The Rorα/Lhx1 double positive dLGN neurons constitute the thalamocortical pathway for conveying visual information to the brain ( Chou et al . , 2013 ) . Since this circuit is not implicated in circadian photoentrainment ( Mure and Panda , 2012 ) , we reasoned the RorαCre;Lhx1loxP mice will be appropriate for testing the role of Lhx1 in SCN . Rorα is an essential component of the cell autonomous circadian oscillator ( Sato et al . , 2004 ) . Its mRNA becomes detectable from E15 onward ( VanDunk et al . , 2011 ) . Therefore , in the early developmental stage between E11 and E15 , Lhx1 is expected to be expressed in the RorαCre;Lhx1loxP double mutant mice , which would allow normal Lhx1 function ( if any ) during early SCN differentiation , while uncovering the post-developmental role of Lhx1 in SCN function . The RorαCre mouse has an IRES;Cre cassette knocked-in downstream of the Rorα locus ( Chou et al . , 2013 ) which permits normal expression of Rorα and co-expression of Cre . RorαCre;R26R mice ( Figure 2A and Figure 2—figure supplement 1 ) or RorαCre;Z/AP mice ( Figure 2B and Figure 2—figure supplement 1 ) showed robust Cre-dependent LacZ or ALPP ( alkaline phosphatase ) expression in the SCN region of the hypothalamus . ALPP staining of RorαCre;Z/AP mice revealed uniform staining of SCN neurons along dorso-ventral and rostro-caudal axes ( Figure 2—figure supplement 1 ) . As opposed to the developmental and circadian dysfunction in Rorα mutant ( Sato et al . , 2004 ) , the RorαCre allele did not compromise Rorα function as the RorαCre mice exhibited normal development and circadian activity rhythm in light–dark cycles and in constant darkness that are indistinguishable from those of wild-type mice ( see below ) . 10 . 7554/eLife . 03357 . 007Figure 2 . Loss of Lhx1 expression in the SCN renders faster synchronization with change in LD regimes . Enriched expression of a Rorα-driven marker in the SCN in RorαCre;R26R mice . ( A ) Ventral view of a whole brain ( magnified view on the right ) of adult RorαCre;R26R shows LacZ staining of the SCN . ( B ) Coronal section through the mid-SCN region ( scale bar , 1 mm ) and the magnified view of the SCN ( scale bar , 100 µm ) showing LacZ expression or ( C ) alkaline phosphatase expression in RorαCre;Z/AP mice . ( D ) qRT-PCR estimate of Lhx1 expression in the SCN ( mean +s . e . m , n = 5 ) . ( E ) Normal SCN innervation of the retinal ganglion cells in the WT mice as revealed by monocular injection of CTB-conjugated fluorescent marker is intact in ( F ) Lhx1SCN−KO mice . A 1 hr light pulse at CT16 causes ( G ) upregulation of light-induced genes ( Per1 , Per2 , cFos , JunB ) , while ( H ) the light-suppressed transcripts ( Lhx1 , Vip , Avpr1a ) in the WT SCN show reduced expression in the Lhx1SCN-KO mice . Mice were in DD for 2 days before the light pulse . Representative actograms of ( I ) RorαCre/Cre , ( J ) RorαCre/+;Lhx1loxP/loxP , and ( K ) RorαCre/Cre;Lhx1loxP/loxP mice subjected to 8 hr phase advance and 8 hr delay . ( I ) Average ( +s . e . m . , n = 5–8 ) activity onset and ( K ) average ( +s . e . m . ) number of days to re-entrain to advance or delay in light onset in three genotypes . Color codes in L and M correspond to the labels in I–K . DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 00710 . 7554/eLife . 03357 . 008Figure 2—figure supplement 1 . Histology of the adult SCN . Serial coronal brain sections of adult . ( A ) RorαCre;R26R or ( B ) RorαCre;Z/AP mice showing LacZ or alkaline phosphatase staining in the SCN . Scale bar , 100 µm . ( C ) Serial coronal hypothalamic brain section of an adult RoraCre/Cre;Lhx1fl/fl mouse intra-ocularly injected with Cholera toxin B ( CTB ) conjugated Alexa Fluor 488 ( green ) or 594 ( Red ) showing normal innervation of the SCN . DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 00810 . 7554/eLife . 03357 . 009Figure 2—figure supplement 2 . Activity profile under light-dark condition . ( A–F ) Activity profiles and ( G–L ) Chi-squared periodograms of representative mice of indicated genotypes during LD cycles . The period length ( H ) is shown inside panels of ( G–L ) . Respective actograms showing wheel-running activity during LD are shown in Figure 3A–F . ( M ) Quantitation of the amounts of RorαCre;Lhx1loxP wheel running activity . Activity counts in LD cycles ( L; light , D; dark and T; total ) were plotted . Error bars indicate standard error of the mean . Activity during light , activity during dark and the total daily activity among these three genotypes were not significantly different . DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 009 A single copy of Cre in RorαCre/+:Lhx1loxP/loxP mice reduced Lhx1 mRNA levels by nearly 40% , while in RorαCre/Cre:Lhx1loxP/loxP ( Lhx1SCN-KO ) mice Lhx1 expression in the SCN is severely reduced ( Figure 2D ) . The gross morphology of the SCN in these mice remains intact ( Figure 2—figure supplement 1 ) suggesting that the conditional loss of Lhx1 post early development in the SCN does not severely affect its differentiation unlike the loss of Six3 , Six6 , or Math5 ( Wee et al . , 2002; VanDunk et al . , 2011; Clark et al . , 2013 ) . The activity pattern of these mice entrains normally to an imposed LD cycle ( Figure 2—figure supplement 2 ) implying functional innervation of the SCN by the mRGCs , which is known to occur postnatally ( McNeill et al . , 2011 ) . Accordingly , anterograde labeling using a Cholera Toxin B conjugated fluorescent marker indicated normal innervation of the SCN by RGCs ( Figure 2E , F and Figure 2—figure supplement 1 ) . Light-induced upregulation of immediate early genes and clock components including c-Fos , JunB , Per1 , and Per2 in the SCN of Lhx1SCN-KO mice was comparable to that in WT mice ( Figure 2G ) . However , light suppressed transcripts involved in intercellular communication such as Vip and Avpr1a showed reduced basal expression under DD ( Figure 2H ) . This acute induction of the phase-resetting branch of light input along with potentially weak intercellular coupling suggested that the Lhx1SCN-KO mice may be more susceptible to light-induced phase shifts . Accordingly , in response to an 8 hr phase advance or delay of the LD cycle , the activity onset of RorαCre/+;Lhx1loxP/loxP and RorαCre/Cre;Lhx1loxP/loxP mice readjusted much faster than the WT mice to the new LD regime irrespective of the direction of the shift ( Figure 2I–L ) . Next , we tested the consequence of the potentially weak intercellular communication in the absence of light . Under constant darkness ( DD ) , the circadian locomotor activity rhythms of the Rorα+/+; Lhx1+/+ , Rorα+/+;Lhx1loxP/loxP , RorαCre/+;Lhx1+/+ , or RorαCre/Cre;Lhx1+/+ mice were comparable ( Figure 3A–D , Table 1 ) . The RorαCre/+;Lhx1loxP/loxP mice showed normal activity rhythm for up to 3 weeks under DD , after which the activity consolidation deteriorated with no apparent ∼24 hr rhythm ( Figure 3E ) . The RorαCre/Cre;Lhx1loxP/loxP mice showed circadian activity rhythm for up to 4 days under constant darkness , after which they became arrhythmic ( Figure 3F , Figure 3—figure supplement 1 ) . The lack of circadian locomotor activity rhythm in Lhx1SCN-KO mice under DD does not result from the disruption of the cell autonomous circadian oscillator , since the median expression of core clock components Per1 and clock output gene Dbp largely remained equivalent in the SCN of Lhx1SCN-KO and wild-type cohorts ( Figure 3G , H ) . Both transcripts showed a significantly dampened rhythm with reduced peak levels and increased expression at the trough , suggestive of oscillator desynchrony . On the other hand , transcripts participating in intercellular communication including Vip , Avpr1a , Rasd1 , Pde7b , Creb3l1 , and a cell matrix associated cell–cell interaction mediator Nov were significantly reduced in the Lhx1SCN-KO mice ( Figure 3I ) . 10 . 7554/eLife . 03357 . 010Figure 3 . Lhx1 sustains normal circadian activity rhythms by regulating expression of synchronizing factors . ( A–F ) Representative wheel running activity pattern over several days of LD followed by DD in wild type and mice lacking Lhx1 in the SCN . Double-plotted qRT-PCR quantification ( average +s . e . m , n = 3–4 mice ) of ( G ) Per1 and ( H ) Dbp in the SCN of DD adapted WT and Lhx1SCN−KO mice . ( I ) Average ( +s . e . m . 8 time points every 3 hr over 24 hr ) expression of several factors involved in intercellular communication or circadian clock in the SCN of dark adapted WT and Lhx1SCN−KO mice . ( J ) Average mRNA ( +s . e . m . , n = 3–6 mice , *p < 0 . 05 ) expression or ( K ) immunoreactivity of VIP is reduced in the SCN of Lhx1-deficient mice . ( L ) Transcriptional activation of mouse Vip promoter by mouse LHX1 . pGL3-promoter vector was used as a control promoter vector . Values are mean +s . e . m , ANOVA **p<0 . 01 , ***p<0 . 001 vs 0 ng ( white bar ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 01010 . 7554/eLife . 03357 . 011Figure 3—figure supplement 1 . Activity profile under constant darkness . ( A–F ) Activity profiles and ( G–L ) Chi-squared periodograms of representative mice of indicated genotypes during DD cycles . The period length ( H ) is shown inside panels of ( G–L ) . Respective actograms showing wheel-running activity during DD are shown in Figure 3A–F . The insets in Ea and Eb show activity profile during the first 2 weeks in DD and last week of DD when the mice were rhythmic and arrhythmic respectively . Similarly , the insets in Ka and Kb show the respective chi-square periodogram . Average period lengths are shown in Table 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 01110 . 7554/eLife . 03357 . 012Figure 3—figure supplement 2 . Lhx1 activates Vip transcription . ( A ) Human Lhx1 activates Luciferase expression from Vip:Luc but not from SV40 promoter in a dose-dependent manner . ( B ) Amino acid sequence alignment of DNA binding region of LHX1 and LHX3 showing the N230 residue in mouse Lhx1 that is critical for normal transcriptional activation function of Lhx1 . DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 01210 . 7554/eLife . 03357 . 013Table 1 . Circadian running wheel activity period length of various mouse strains under constant darkness ( n = 7–22 ) DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 013Average ( h ) SEM ( h ) Rora+/+;Lhx1+/+23 . 780 . 23Rora+/+;Lhx1loxP/loxP23 . 640 . 05RoraCre/Cre;Lhx1+/+23 . 400 . 14RoraCre/+;Lhx1+/+23 . 860 . 07RoraCre/+;Lhx1loxP/loxP23 . 510 . 13RoraCre/Cre;Lhx1loxP/loxPNANAMice showing arrhythmic activity were excluded from the analysis . ( NA = Not Applicable ) . Among the downregulated genes , the mRNA levels of Vip were undetectable in the Lhx1SCN−KO mice ( Figure 3J ) , and VIP protein level was also reduced in the SCN ( Figure 3K ) . Hence , we tested whether LHX1 regulates Vip expression . Transcription from a 1 kb promoter region of mouse Vip driving a luciferase reporter was activated by wild-type mouse and human Lhx1 in a dose-dependent manner ( Figure 3L and Figure 3—figure supplement 2 ) . Such transcriptional regulation was dependent on the DNA binding function of LHX1 because the LHX1N230S mutant failed to activate transcription from Vip:luc reporter ( Figure 3L ) . This mutant ( Figure 3—figure supplement 2 ) carries a missense mutation in a highly conserved asparagine residue known to disrupt DNA binding ( Thaler et al . , 2002 ) . Furthermore , wild-type LHX1 also failed to activate transcription from SV40 promoter , supporting the idea that Vip expression is specifically activated by LHX1 and that this requires the DNA-binding activity of LHX1 . Collectively , these results demonstrate that LHX1 regulates expression of Vip , which along with sufficient expression of other synchronizing agents maintains synchrony among SCN neurons . Coupling among SCN neurons supports synchronous oscillations of individual oscillators that otherwise show variation in intrinsic period lengths ( Aton et al . , 2005 ) . To directly test the role of Lhx1 in maintaining SCN synchrony , we recorded electrophysiological activity of SCN slices using a multielectrode array . SCNs of both WT and Lhx1SCN-KO mice showed no apparent difference in the firing frequency , supporting the notion that Lhx1SCN-KO has no discernible developmental defects in the SCN . The WT SCN from LD reared animals showed synchronous firing rhythm , as characterized by both robust oscillation of multiunit activity as well as synchrony of peak phase of activity among different channels . The Lhx1SCN-KO SCN , on the other hand , showed remarkable dampening of firing rhythm as well as phase dispersion on day 1 and became almost asynchronous within 3 days ( Figure 4A–E ) . This parallels the timeline of emergence of behavioral arrhythmicity in Lhx1SCN-KO mice when they are released from LD to DD ( Figure 3F ) . Similarly , in WT mice maintained in DD for 2 weeks , the SCN displayed circadian oscillation of the multi-unit activity with a similar peak phase among the different electrodes examined ( Figure 4—figure supplement 1 ) . In contrast , the slices from RorαCre/Cre;Lhx1loxP/loxP animals showed widely dispersed phases of the multi-unit activity peak , a finding coherent with the arrhythmic locomotor activity observed in these animals in DD ( Figure 3A ) . Since VIP expression is severely reduced in the Lhx1SCN-KO mice , we tested if extrinsic supplementation of VIP can restore the synchrony of the SCN neurons . Daily application of VIP for 1 hr on SCN slices from DD adapted Lhx1SCN-KO mice restored the normal synchrony and waveforms of the SCN firing rhythm ( Figure 4F , G ) . Finally , the gradual dampening of SCN multiunit activity and desynchrony in the LD-adapted SCN slice from Lhx1SCN-KO mouse can be reversed by the daily application of VIP ( Figure 4H , I ) . 10 . 7554/eLife . 03357 . 014Figure 4 . Lhx1 maintains synchrony among SCN neurons partly via VIP . ( A ) Average ( +s . e . m . ) normalized multiunit activity ( MUA ) recorded from representative SCN slices of LD-adapted WT ( n = 40 , black ) and Lhx1SCN−KO ( n = 12 , orange ) mice . Data were binned every 60 min . Representative normalized MUAs and peak phase of activity from WT SCN ( B and C , n = 40 ) and Lhx1SCN−KO mouse ( D and E , n = 19 ) . For C and E , left and right panels are respectively for days 1 and 3 . ( F ) Average MUA of a DD adapted Lhx1SCN−KO SCN that received 1 hr perfusion of VIP daily for up to 7 days . ( G ) Peak phases of activity are gradually synchronized over 7 days . ( H ) Representative MUA from the SCN of an LD-adapted Lhx1SCN−KO mouse over several days . During the first 4 days , the activity dampened , which was rescued by daily application of VIP . Down arrows in F and H indicate the time of VIP application . ( I ) Peak phases of activity in WT and Lhx1SCN−KO SCN at the end of 7 days are shown . DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 01410 . 7554/eLife . 03357 . 015Figure 4—figure supplement 1 . Normalized multi-unit activity recorded from DD adapted WT and Lhx1SCN-KO ( mean ± SEM ) . Peak time of multiunit activity from each channel shows relative synchrony in the WT mouse that is dispersed in the Lhx1SCN-KO mouse . DOI: http://dx . doi . org/10 . 7554/eLife . 03357 . 015
Tissue-specific gene expression and temporal changes in transcript levels govern and facilitate tissue function . Although the SCN has long been recognized as the master circadian oscillator and the principal target for light modulation of circadian rhythms in mammals , the molecular basis for these at the protein-coding transcript level have not been comprehensively identified . In this study , we present a thorough analysis of transcriptional oscillations in the SCN , its response to light at different phases of the circadian oscillator and a glossary of SCN-enriched genes . This detailed description of the transcriptional landscape of the SCN has a variety of implications for understanding SCN function . In this study , we focused on the relevance of oscillator synchrony in circadian rhythms and light-induced phase shift in behavior . Under natural conditions , time-of-the-day dependent interaction of the SCN with light input from the retina specifies the phase of overt activity-rest rhythms ( Hatori and Panda , 2010 ) . Light during the subjective night induces expression of several genes including immediate early genes c-fos , activates MAPK pathway , acutely upregulates the Per1 transcript and resets the phase of overt behavioral rhythm ( Obrietan et al . , 1998 , 1999; Dziema et al . , 2003; Cheng et al . , 2004; Butcher et al . , 2005 ) . We found that global transcriptional changes in the SCN parallel the sensitivity of the activity rhythms to light . During the subjective day , when light is ineffective in resetting overt rhythms , a very small fraction of the transcriptome changed in response to light . Conversely , during the subjective night , extensive changes in transcripts correlated with the large phase shifting effect of light , suggesting that changes in multiple pathways accompany the shift in the phase of the SCN oscillator network . Among the transcripts that change both during daytime and nighttime , their magnitude of expression was attenuated during the daytime . The magnitude of transcript changes also reflected the effectiveness of retinal photoreceptors in entraining the clock . As seen in the Opn4−/− mice ( Panda et al . , 2002b ) , attenuated light-induced change in overt rhythms paralleled the reduced transcriptional responses to light . Mice lacking mRGCs or those lacking rod , cone and melanopsin photopigments exhibited no behavioral phase shift in response to light ( Hatori and Panda , 2010 ) . They also showed no significant mRNA expression changes in transcripts in the SCN ( Figure 1—figure supplement 2 ) . Collectively , these observations support the notion that a light signal perceived through retina photoreceptors causes transcriptional changes in the SCN that parallel the behavioral phase-shifting effect of light . Included among the light-induced transcripts were several components of intracellular signaling cascades , kinases , phosphatases , and transcription factors ( MAPK pathway genes , SIK1 , PER1 , PER2 , DEC1 , EGR1 , and EGR2 ) , which likely function at the different signaling steps needed to transduce light information received at the plasma membrane to generate an appropriate change in the phase of the core clock ( Obrietan et al . , 1998 , 1999; Dziema et al . , 2003; Cheng et al . , 2004; Butcher et al . , 2005; Jagannath et al . , 2013 ) . The light-suppressed transcripts on the other hand included intercellular signaling agents , cell surface receptors , and associated signaling components . This indicated that stimulus-induced downregulation of the tight coupling among SCN neurons is a likely mechanism to allow desynchronization and then resynchronization of cellular clocks to reset the SCN to a new phase . Since tight intercellular communication among the SCN neurons is a characteristic feature of the SCN , we further focused on the subset of transcripts that are light-suppressed as well as enriched in the SCN . The search for SCN-specific genes among 82 different mouse tissues did not yield any protein coding gene that is exclusively expressed in the SCN , suggesting that a combinatorial gene expression signature specifies SCN development and function . LHX1 is one of the SCN-enriched and light-suppressed transcription factors , and hence we reasoned that LHX1 might regulate transcription of at least a subset of the intercellular communication agents that specify SCN neuronal network features . During the preparation of this manuscript , another study showed Six3-Cre dependent loss of Lhx1 expression severely affected terminal differentiation as well as peptidergic outputs of the SCN ( Bedont et al . , 2014 ) . By using Rora-Cre in our study , the loss of Lhx1 expression likely occurred after the onset of Rorα expression at E14 . 5 , thereby allowing any terminal differentiation function of Lhx1 in SCN development to occur normally . In both studies , the loss of Lhx1 in the SCN did not affect the overall expression level of any of the clock components , while the expression of several genes implicated in cellular synchrony including Vip and Avpr1a were downregulated . AVP , VIP , and their cognate receptors have been implicated in maintaining inter-cellular communication among SCN neurons ( Herzog , 2007 ) . Light pulse also reduced the expression of Avpr1a and Vip mRNA . Mice carrying loss-of-function alleles of Vip or vasopressin receptors exhibit a weakened SCN network , progressive desynchronization of SCN neurons and increased sensitivity to light-induced phase shifts ( Herzog , 2007; Yamaguchi et al . , 2013 ) . Similarly , the loss of AVP receptor weakens the network and increases the sensitivity of the behavioral rhythm to changes in the light regime ( Herzog , 2007 ) . While these observations have indicated that AVP- and VIP-mediated intercellular communication constitutes the framework for the SCN network , the transcription factor ( s ) that determines this SCN-specific property was unknown . Direct transcriptional induction from the VIP promoter by LHX1 , the severe loss of VIP mRNA and immunoreactivity in the Lhx1−/− SCN along with a desynchronous SCN now establishes Lhx1 as a critical regulator of VIP production in the SCN . In summary , we have discovered that Lhx1 is a master regulator of multiple factors including VIP that maintain robust coupling among SCN neurons after their differentiation into oscillator neurons . While the loss of Vip ( Colwell et al . , 2003 ) , Avpr1a ( Wersinger et al . , 2007; Li et al . , 2009 ) , or Rasd1 alone causes a mild alteration in the circadian organization or light responses ( Cheng et al . , 2004 ) , the parallel perturbation of multiple intercellular signaling components in the Lhx1SCN-KO mice indicates a critical role for Lhx1 in determining the specific feature of SCN neurons that impart coupling among neurons . The intercellular coupling is thus as important as the cell-autonomous oscillations for maintaining the consolidated rhythm of activity-rest that can resist abrupt changes in the ambient light conditions .
All animal experiments were carried out in accordance with the guidelines of the Institutional Animal Care and Use Committee of the Salk Institute . Mice were housed under 12 hr light: 12 hr dark ( LD ) cycles . Food and water were available ad libitum . C57BL/6J , C3H/HeJ strain ( rd ) carrying Pdebrd1 mutation , Cre-dependent lacZ reporter strain ( R26R ) ( Soriano , 1999 ) , and Cre-dependent human ALPP reporter strain ( Z/AP ) ( Lobe et al . , 1999 ) were obtained from the Jackson Laboratory . Opn4−/− mice ( Panda et al . , 2002b ) were bred to rd/rd to generate rdrd;Opn4−/− . Both Opn4Cre and Opn4Cre;R26iDTR were described in Hatori et al . ( 2008 ) . The floxed Lhx1 allele ( Lhx1loxP ) mice were originally generated in Kwan and Behringer ( 2002 ) . RorαCre mouse was generated by knocking in an IRES;Cre cassette 3′ downstream of the Rorα locus ( Chou et al . , 2013 ) . Both Lhx1loxP and RorαCre mice were back-crossed to C57BL/6J strain for at least eight generations . 168 male C57BL/6J mice of 6 weeks of age were maintained for 3 weeks on a 12 hr light:12 hr dark cycle . For circadian gene expression profiling , after being placed in DD for 2 days , four animals were sacrificed every 2 hr , beginning at hour 30 of DD , which corresponds to CT18 , for two complete 24 hr cycles ( Figure 1A ) . For light-regulated gene profiling , mice were maintained in DD then exposed to 1 hr light at CT30 , 40 , or 46 , while control ( no light pulse ) mice were left in dark . After 1 hr , all mice were returned to DD , and four animals each were collected at 1 , 2 , and 4 hr from the beginning of light exposure from each CT . Mice were sacrificed by cervical dislocation , and the optic nerves were cut under dim red light . The SCN was quickly dissected and four individual SCNs were pooled to be rapidly frozen on dry ice . Total RNA was extracted by RNeasy mini column ( Qiagen , CA , USA ) . For each time point , 100 ng of total RNA was used as starting material for Affymetrix MOE430 high density arrays ( Affymetrix , CA , USA ) . For RorαCre;Lhx1loxP mice , the SCNs were collected every 4 hr . Total RNA was processed for qRT-PCR analyses following standard protocols . Procedures for alkaline phosphatase staining for RorαCre;Z/AP brain , X-gal staining for RoraCre;R26R brain , and anterograde tracing with fluorescent cholera toxin subunit B ( CTB ) have been described previously ( Hatori et al . , 2008; Chou et al . , 2009; Brown et al . , 2010 ) . Daily locomotor activity of mice individually housed in wheel running cages was measured following standard methodology ( Siepka and Takahashi , 2005 ) . Typically , 6- to 10-week-old mice kept in cages were placed inside light tight boxes with independent illumination . During the light phase , the mice received ∼150 lux of white light from fluorescent light source . Wheel running activity in 5 min bins was continuously collected and later analyzed by ClockLab software ( Actimetrics , Evanston , IL , USA ) . All routine animal husbandry care during the dark phase was performed under dim red light . Mice were sacrificed by cervical dislocation followed by rapid dissection . The brains were cooled down in an ice-cold ACSF solution ( 125 mM NaCl , 25 mM KCl , 1 mM MgCl2 , 1 . 25 mM NaH2PO4 , 2 mM CaCl2 , 20 mM Glucose , 26 mM NaHCO3 , Penicillin 5000 IU/ml , and Streptomycin 5000 µg/ml ) saturated with 95% O2/5% CO2 . Coronal slices ( ∼300 μm ) were then prepared using a tissue chopper and trimmed to ∼10 mm2 slices containing both nuclei . Finally , the slices were transferred to the multi electrodes arrays ( MEA ) . The MEA consists of a glass recording chamber , on the bottom of which are engraved 256 electrodes ( 10 μm in diameter , situated every 60 μm ) and arranged in a 16 × 16 square grid ( Multichannel Systems , Reutlingen , Germany ) . The chamber was continuously perfused with heated ( 35°C ) and oxygenated ACSF-containing antibiotics . Extracellular electrical activity was continuously monitored ( signal was acquired from all 256 channels , 10 kHz ) and spikes crossing a threshold set at 3 times the standard deviation of the noise on each channel were recorded and stored for off-line analysis . Just prior to placing the MEA on the amplifier , a bright field picture of the slice position on the electrode was rapidly taken to assess SCN placement . The electrodes covering the SCN were then continuously recorded from 2 to 4 days . Channels displaying noise or monotonically decreasing activity were excluded from subsequent analysis . We recorded from animals housed for at least 2 weeks either in DD or LD ( 12/12 ) conditions . Animals were handled under dim red light until the optic nerve was cut . Data analysis and display were performed using NeuroExplorer ( Plexon Denton , TX ) , Oriana ( Kovach Computing Services , UK ) , and custom software written in MATLAB ( MathWorks , Natick , MA ) . Peaks of firing were determined after smoothing of data ( rloess , MATLAB ) and fitting with a sinusoidal function . Daily administration of VIP was realized by switching the perfusion from the tank containing the medium described earlier to a tank containing the same medium supplemented with VIP ( Calbiochem , EMD Millipore , MA ) at the concentration of 25 nM for 1 hr . The coding regions corresponding to full-length mouse and human Lhx1 were amplified by PCR from pineal cDNA and subcloned into pcDNA3 . 1-TOPO ( Invitrogen , CA , USA ) to yield expression plasmids mouse Lhx1/pcDNA3 . 1 and human Lhx1/pcDNA3 . 1 , respectively . Site-directed mutagenesis ( Stratagene , CA , USA ) was performed to construct mouse Lhx1/pcDNA3 . 1 to generate a point mutation changing asparagine ( amino acid 230 ) to serine . A DNA fragment corresponding to ∼1 kb of mouse Vip promoter was amplified by PCR from mouse genomic DNA and cloned into pGL3 basic vector ( Promega ) to yield the Vip reporter vector . 293T cells were cultured in DMEM supplemented with 10% FBS . The cells ( 40 , 000 cells ) in 96-well plates were transfected by using TransIT-LT1 ( Mirus Bio , WI , USA ) with various amounts of expression plasmid ( total amount was adjusted to 250 ng by adding empty vector pcDNA3 . 1 ) , and 5 ng of firefly luciferase reporter plasmid . The cell lysates were prepared 46 hr after the transfection and subjected to dual-luciferase assay by luminometry ( Promega , WI , USA ) . | As anyone who has experienced jet lag can testify , our sleeping pattern is normally synchronized with the local day–night cycle . Nevertheless , if a person is made to live in constant darkness as part of an experiment , they still continue to experience daily changes in their alertness levels . In most individuals , this internal ‘circadian rhythm’ repeats with a period of just over 24 hr , and exposure to light brings it into line with the 24-hr clock . The internal circadian rhythm is generated by a structure deep within the brain called the suprachiasmatic nucleus ( SCN ) , which is essentially the ‘master clock’ of the brain . However , each cell within the SCN also contains its own clock , and can generate rhythmic activity independently of its neighbors . Cross-talk between these cells results in the production of a single circadian rhythm . Now , Hatori et al . have identified the master regulator that controls this cross-talk . When mice living in 24-hr darkness were exposed to an hour of light in the early evening , they showed changes in the levels of proteins associated with many SCN genes . But one gene in particular , known as Lhx1 , stood out because it was strongly suppressed by light . Mice with a complete absence of Lhx1 die in the womb . However , mice that lose Lhx1 during embryonic development survive , although they struggle to maintain circadian rhythms when kept in complete darkness . This is not because their SCN cells fail to generate circadian rhythms . Instead , it is because the loss of Lhx1—a transcription factor that controls the expression of many other genes—means that the SCN cells do not produce the proteins they need to synchronize their outputs . As well as identifying a key gene involved in the generation and maintenance of circadian rhythms , Hatori et al . have underlined the importance of cell-to-cell communication in these processes . These insights may ultimately have therapeutic relevance for individuals with sleep disturbances caused by jet lag , shift work or certain sleep disorders . | [
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] | 2014 | Lhx1 maintains synchrony among circadian oscillator neurons of the SCN |
Understanding the computations that take place in brain circuits requires identifying how neurons in those circuits are connected to one another . We describe a technique called TRACT ( TRAnsneuronal Control of Transcription ) based on ligand-induced intramembrane proteolysis to reveal monosynaptic connections arising from genetically labeled neurons of interest . In this strategy , neurons expressing an artificial ligand ( ‘donor’ neurons ) bind to and activate a genetically-engineered artificial receptor on their synaptic partners ( ‘receiver’ neurons ) . Upon ligand-receptor binding at synapses the receptor is cleaved in its transmembrane domain and releases a protein fragment that activates transcription in the synaptic partners . Using TRACT in Drosophila we have confirmed the connectivity between olfactory receptor neurons and their postsynaptic targets , and have discovered potential new connections between neurons in the circadian circuit . Our results demonstrate that the TRACT method can be used to investigate the connectivity of neuronal circuits in the brain .
Comprehensively mapping the connectivity of diverse neural circuits across many brain regions and many organisms is a major goal of modern neuroscience ( Denk et al . , 2012; Bargmann and Marder , 2013; Swanson and Lichtman , 2016 ) . In addition , recent research indicates that aberrant neuronal wiring may be the cause of several neurodevelopmental disorders ( Peça and Feng , 2012; Rubinov and Bullmore , 2013; Mevel and Fransson , 2016 ) , further emphasizing the importance of identifying the wiring diagrams of brain circuits . To address this issue , several new approaches have been developed . Each of these methods has strengths and limitations , which are discussed in detail in a recent review ( Lee et al . , 2017 ) . In a previous work , we demonstrated that a strategy based on ligand-induced intramembrane proteolysis can be used to monitor cell-cell interactions in the nervous system of transgenic Drosophila ( Huang et al . , 2016 ) . In this strategy , cells expressing an artificial ligand ( ‘donor’ cells ) bind to and activate an artificial receptor on their interacting partners ( ‘receiver’ cells ) via interactions across the intercellular space . We previously used this method to investigate the interactions between neurons and glial cells in the Drosophila central nervous system ( Huang et al . , 2016 ) . However , in its original implementation , the system was not useful to trace neuronal circuits because it revealed all forms of cell-cell contact , including non-synaptic contacts . To allow this strategy to trace neuronal circuits it is necessary to ensure that the interaction of ligand and receptor occurs exclusively across synapses . To prevent activation of the system between neurons that had non-synaptic cell-cell contacts , we engineered the ligand so that it would be selectively located at the presynaptic cleft ( Südhof , 2012; Van Vactor and Sigrist , 2017 ) . To validate that the optimized ligands revealed neurons specifically connected by synapses , we tested the system on the Drosophila antennal lobe , an olfactory center with well-understood connectivity established by light and electron microscopy , and electrophysiological methods ( Grabe et al . , 2016; Stocker et al . , 1990; Wilson , 2013; Rybak et al . , 2016 ) . We observed that adding domains from the synaptic proteins synaptobrevin ( nSyb ) or syndecan ( sdc ) was sufficient to achieve reliable synaptic tracing in the antennal lobe . In addition , we used TRACT to investigate the connectivity of the Drosophila circadian system , a compact brain circuit with a small number of neurons ( Dubowy and Sehgal , 2017; Nitabach and Taghert , 2008 ) , and discovered new candidate postsynaptic targets for the PDF neurons of the circadian system in the central brain , some of which that express the circadian-related gene , per . These results demonstrate that the TRACT method will be a useful addition to the arsenal of methods available to investigate the connectivity of brain circuits .
In our previously published work , we described an artificial ligand-receptor system based on intramembrane proteolysis to investigate connections between cells ( Huang et al . , 2016 ) . In short , upon ligand-receptor interaction at sites of cell-cell contact the transmembrane domain of an engineered receptor is cleaved by intramembrane proteolysis and releases a protein fragment that regulates transcription in the interacting partners ( Figure 1a , Gordon et al . , 2015; Morsut et al . , 2016; Huang et al . , 2016 ) . In its original implementation , we used a ligand called CD19mch that contains the extracellular and transmembrane domains ( ECD and TMD ) of the mouse lymphocyte antigen CD19 ( Fujimoto et al . , 1998 ) fused to the red fluorescent protein mCherry in its intracellular domain ( ICD ) . The original artificial receptor is called SNTG4 and contains the following domains ( in N-to-C terminal order ) : ( i ) the ECD from a single chain antibody ( ID3 ) that recognizes mouse CD19 ( Kochenderfer et al . , 2009 ) , ( ii ) the Notch regulatory region ( NRR ) and the TMD from Drosophila Notch ( spanning from EGF repeat 36 until the ICD ) ( Kovall et al . , 2017 ) , and ( iii ) esn , a simplified version of the yeast transcriptional activator Gal4 ( Figure 1a ) ( Sprinzak et al . , 2010 ) . In this configuration , expression of the ligand and receptor in neurons leads to the distribution of these two proteins uniformly throughout the membrane , including cell body , dendrite branches , and axons . To detect the activation of the receptor upon interaction with its ligand in vivo in Drosophila , we included a UAS-GFP reporter transgene , which expresses GFP in response to Gal4 activity ( Brand and Perrimon , 1993 ) . To test whether the expression of the ligand in donor neurons could reveal the subset of receiver neurons that receive synaptic input from them , we focused on the antennal lobe , the second-order olfactory processing area in the Drosophila brain that receives direct input from the primary olfactory receptor neurons ( ORNs ) ( Figure 2a , Wilson , 2013; Laissue and Vosshall , 2008 ) . Synapses in the antennal lobe are organized into discrete compartments , called glomeruli , and each glomerulus corresponds to a distinct ORN class ( defined by the odorant receptor it expresses ) ( Berck et al . , 2016 ) . All ORNs of a specific class project their axons to a common glomerulus ( Vosshall et al . , 1999; Fishilevich and Vosshall , 2005; Couto et al . , 2005 ) . The principal second-order olfactory neuron is called a projection neuron ( PN ) . The vast majority of PNs are uniglomerular PNs ( uniPNs ) ( Figure 2b ) , and each of these uniPNs have dendrites that branch into single glomeruli where they receive direct synaptic input from a single class of ORNs . Axonal output from uniPNs projects to two major third-order olfactory areas , the mushroom body and the lateral horn , via the medial antennal lobe tract ( mALT , formerly iACT ) ( Ito et al . , 2014 ) , ( Figure 2a ) . In addition , a smaller subset of PNs , multiglomerular PNs ( multiPNs ) ( Figure 2c ) ( Stocker et al . , 1990; Parnas et al . , 2014; Liang et al . , 2013 ) , have dendritic arbors which pool input across multiple glomeruli in the antennal lobe , and send their output to the lateral horn only via the mediolateral ALT ( mlALT , formerly the mACT ) ( Figure 2a ) ( Tanaka et al . , 2012 ) . Finally , a large set of local neurons ( LNs ) send and receive synaptic input from both ORNs and PNs , densely interconnecting the glomeruli ( Figure 2d ) ( Chou et al . , 2010; Hong and Wilson , 2015; Seki et al . , 2010 ) . Expression of the original TRACT ligand and receptor ( without any domains that would localize them to synaptic sites ) in neurons failed to reveal any synaptic connections in the antennal lobe . For instance , when the CD19mch ligand is expressed in the majority of ORNs using the Orco-lexA driver ( Stocker et al . , 1997 ) and the SNTG4 receptor is expressed from the elav enhancer ( Yao and White , 1994 ) , we predicted labeling of both PNs and LNs , the major postsynaptic targets in the antennal lobe . However , in flies carrying Orco > CD19 mch , elav-SNTG4 , UAS-GFP transgenes , no GFP induction was observed in the antennal lobe ( data not shown ) , consistent with the findings from another publication ( He et al . , 2017 ) , suggesting that the system needed adjustments to detect synaptic connections . To generate a genetic system that could be used to reliably identify synaptically connected neurons we tested a number of different modifications with the ligand , receptor , the drivers used to direct their expression , and reporters ( discussed in full detail in the supplementary methods ) . The following modifications proved to be effective to achieve specific transneuronal labeling: To test the ability of TRACT to reveal the connectivity originating from ORNs , we generated flies carrying orco>nSybCD19 or orco>CD19 sdc , nSybE-nlgSNTG4 , and UAS-CD4tdGFP . In these flies , we observed GFP expression in dozens of neurons in the antennal lobe . Roughly half the GFP+ neurons were PNs , which can be identified by their axons projecting into the mushroom body and lateral horn , consistent with the known connectivity between ORNs and PNs ( Figure 2e , top panels ) . In addition , the cell bodies of these axon-bearing neurons were located in the anterodorsal , lateral , and ventral sectors of the antennal lobe , consistent with them being PNs . Finally , most of the putative PNs were immunopositive for CHAT , a known marker of PNs ( Figure 2 , Figure 2—figure supplement 5 ) . Around half the GFP+ neurons in the antennal lobe did not have axons , were immunopositive for GABA , and their cell bodies were located dorsolateral and ventrolateral , all features consistent with them being LNs ( Figure 2e , top panels and Figure 2 , Figure 2—figure supplement 5 ) ( Okada et al . , 2009 ) . The experiments with the orco driver revealed that the TRACT system can be used to activate gene expression in neurons known to be connected by synapses , but it did not prove that the LNs and PNs that were highlighted by TRACT were strictly connected by synapses to the ORNs expressing ligand ( Figure 1b ) . In principle , any type of cell-to-cell contact , including non-synaptic membrane-to-membrane contact , could be sufficient to activate the TRACT receptor in the receiver neurons . We took advantage of the anatomy of the Drosophila antennal lobe to investigate whether TRACT could be used to reveal neurons solely connected by synapses . In Drosophila , all ORNs expressing the same olfactory receptor project their axons into two bilaterally symmetric glomeruli in the antennal lobe ( Grabe et al . , 2016; Tanaka et al . , 2012 ) . In addition , the dendrites of individual uniPNs branch into single glomeruli where they make synapses with the axons of ORNs ( Figure 2a and b ) . Due to this feature of the connectivity of uniPNs , it is possible to unambiguously confirm that a given uniPN is connected to a single type of ORN through synaptic contacts that will exclusively occur in an identified glomerulus . Thus , to test the synaptic specificity of TRACT , we focused on the connectivity between ORNs and uniPNs . To selectively express the receptor in PNs , we generated transgenic flies in which the nlgSNTG4 receptor is driven by the GH146 enhancer , which is expressed in the majority of antennal lobe PNs ( Stocker et al . , 1997 ) ( Figure 2e bottom panels , 3 and 4 ) . To investigate the connectivity between ORNs and PNs , we generated flies carrying orco>nSybCD19 , GH146-nlgSNTG4 , and UAS-CD4::tdGFP ( Han et al . , 2011 ) . In these flies we observed dozens of PNs ( identifiable by their axons projecting into the mushroom body and lateral horn ) with GFP expression in the antennal lobe , consistent with the known connectivity between ORNs and PNs ( Figure 2e , bottom panels ) . To investigate the ability of TRACT to reveal neurons exclusively connected by synapses we crossed GH146-nlgSNTG4 flies with flies that expressed the nSyb::CD19 and CD19::sdc ligands in identified glomeruli . If TRACT exclusively revealed neurons connected by synaptic contacts , the uniPNs whose receptors were activated by interaction with the ligand would have GFP+ dendrites that would selectively branch in the glomeruli onto where the ORNs would converge their axons . We crossed the GH146-nlgSNTG4 flies with flies expressing the nSyb::CD19 and CD19::sdc ligand in glomeruli VC1 and DA1/VA6/VA1lm under the enhancers of R28H10 and R17H02 LexA drivers , respectively ( http://www . virtualflybrain . org ) ( Figure 3 ) . We observed that with the nSyb::CD19 and CD19::sdc ligands , the uniPNs labeled projected their dendrites selectively into the glomeruli where the ligand was expressed , consistent with previous work ( Figure 3 ) ( Grabe et al . , 2016 ) . We counted the number of PNs induced by the nSyb::CD19 or CD19::sdc ligands when expressed in identified glomeruli , and found that the observed numbers matched those from previously published work ( Grabe et al . , 2016 ) ( Table 1a and b ) . For example , a study using photoactivatable GFP ( paGFP ) indicated that between 8 and 10 PNs receive input from the DA1 glomerulus ( Grabe et al . , 2016 ) , and when we expressed the nSyb::CD19 ligand with the R17H02-LexA driver in the DA1 glomerulus , we observed ~11 labeled uniPNs ( Figure 3a and Table 1a ) . Similarly , previous work indicated that between 1–3 and 1–2 PNs receive input from the VA6 and VC1 glomeruli , respectively , and with the nSyb::CD19 ligand , we observed that one uniPN expressed GFP in each of these glomeruli ( Figure 3a and Table 1a ) ( Grabe et al . , 2016 ) . In the VA1lm glomerulus , we did not observe any induction in PNs with the nSyb::CD19 , which may be due to the low level of the receptor expression in this glomerulus ( Figure 3a ) . When using the CD19::sdc ligand in identified glomeruli , we observed one PN labeled in VA6 and VC1 and ~12 PNs labeled in DA1 ( Figure 3b and Table 1b ) . A previous work demonstrated that , in addition to the uniPNs , the GH146 enhancer drives expression of transgenes into one multiPN ( Marin et al . , 2002 ) . With the CD19::sdc and nSyb::CD19 , in addition to the uniPNs described above , one multiPN was labeled with both R17H02 and R28H10 drivers , which has been identified previously ( Figures 3b and 4 and Table 1a and b ) ( Marin et al . , 2002 ) . The dendrites of this neuron almost cover the whole antennal lobe , and its axon projects first to the lateral horn and then anterodorsally toward the midline . ( Figure 3c and d ) . Although we did not observe any differences in the number or distribution of uniPNs when the ligands were CD19::Sdc or nSyb::CD19 , we observed that the labeling of multiPNs had different requirements for sdc and nSyb . Whereas we could achieve labeling of multiPNs with a single copy of CD19::sdc ( in heterozygote animals ) ( Figures 3b and 4b ) , we could only detect multiPNs when the nSyb::CD19 ligand was present in two copies ( homozygote animals ) ( Figures 3a and 4a ) . This observation suggests first , that the amount of ligand may be a limiting factor for transneuronal labeling , and second , that in some cases , the CD19::sdc could be more effective at revealing some synaptic partners than nSyb::CD19 . These results indicate that both the nSyb::CD19 and CD19::sdc ligands selectively reveal neurons connected by synaptic contacts between ORNs and PNs , match the available data from previously published works , and confirm that TRACT can be used to perform anterograde transneuronal tracing of brain circuits in the Drosophila brain . To explore the ability of the TRACT method to trace neuronal circuits in other brain areas , we investigated the brain circuits involved in the control of Drosophila circadian behavior ( Figure 5a ) ( Konopka and Benzer , 1971; Hall , 1998; Rosbash et al . , 2003; Young , 1998; Nitabach and Taghert , 2008 ) . There are two key advantages of the circadian circuit to test the usefulness of the TRACT system: ( i ) it consists of a relatively low number of neurons ( around 150 ) , and ( ii ) the function and anatomy of many of those neurons are known in some detail , although their connectivity is not fully understood ( Beckwith and Ceriani , 2015; Peschel and Helfrich-Förster , 2011; Shafer et al . , 2006 ) . As a test case , we explored the ability of TRACT to reveal the connectivity of the PDF neurons , a well-characterized set of cells in the Drosophila brain that are critical regulators of circadian rhythm ( Figures 5b and 6 and Figure 5 , Figure 5—figure supplement 1; Renn et al . , 1999; Lear et al . , 2009 ) . There are two types of PDF neurons in the Drosophila brain: ( i ) s-LNv neurons , which project their axons into the dorsal regions of the central brain , and ( ii ) l-LNv neurons , which have large dendrites that branch in the optic lobe , and an axon that projects to the contralateral optic lobe ( Figure 5a ) . In addition to pdf , the period ( per ) gene is another critical regulator of circadian function in Drosophila ( Konopka and Benzer , 1971; Bargiello and Young , 1984; Reddy et al . , 1984 ) . Previous works using GRASP suggested that s-LNv neurons were connected to the DN1p ( Cavanaugh et al . , 2014; Seluzicki et al . , 2014 ) and DN2 neurons ( Tang et al . , 2017 ) in the adult fly . In addition , it has been reported that s-LNv neurons make weak connections with LNd neurons that vary throughout the day ( Gorostiza et al . , 2014 ) . We tested whether we could use TRACT , first , to confirm the connection between s-LNv and DN1 and/or LNd neurons , and second , to identify new synaptic partners of s-LNvs in other brain areas . We used a pdf-lexA driver to express the nSyb::CD19 or CD19::sdc ligands , performed immunostaining against the OLLAS tag and confirmed that the ligands were selectively expressed in the axon terminals of the PDF neurons ( Figures 5b and 6 and Figure 5—figure supplement 1 ) . In the flies carrying pdf>nSyb::CD19 , nSybE-nlgSNTG4 and UAS-CD4::tdGFP , we observed several neurons located in the central brain that expressed GFP ( Figures 5b and 6 and Table 1c ) . Consistent with a previous work ( Tang et al . , 2017 ) indicating the s-LNvs make synaptic contacts with DN2 neurons , we performed immunocytochemistry using an antibody against the PER protein and observed GFP+ , PER+ neurons in the DN2 cluster in 10 out of 18 hemispheres ( Figures 5b and 6b , and Table 1c ) . There are two PER neurons in the DN2 cluster ( Helfrich-Förster et al . , 2007 ) . In our experiments , in seven hemispheres we observed one GFP+ , PER+ DN2 neuron , and in three hemispheres we observed two GFP+ , PER+ DN2 neurons ( Figures 5b and 6b , and Table 1c ) . In contrast to previous works , we did not observe any GFP+ cells in the DN1 cluster but we observed several GFP+ cells that were PER- , close to the DN1 cluster ( Figures 5b and 6a ) ( Cavanaugh et al . , 2014 ) ( Seluzicki et al . , 2014 ) . In addition , we did not detect any induction in the LNd neurons ( Figure 5b ) ( Gorostiza et al . , 2014 ) . Finally , we observed GFP induction in some of the DN3 neurons in all the brains we analyzed ( Table 1c ) , and we confirmed that all the GFP+ cells in DN3s were also PER+ ( Figures 5b and 6b ) . We observed that whereas some of the sets of neurons that were identified as potential synaptic partners with CD19::sdc were similar to those detected with nSyb:CD19 , there were also some differences ( Figures 5b and 6 , and Figure 5 , Figure 5—figure supplement 1 ) . Like nSyb::CD19 , CD19::sdc also revealed potential synaptic connections with neurons in the DN2 and DN3 cluster ( Figure 5 , Figure 5—figure supplement 1 ) . However , the numbers of neurons labeled with CD19::sdc were fewer than the ones with nSyb::CD19 . This might be due to the fact that CD19::sdc driven by the pdf driver was expressed at very high levels in the cell body , but at much lower levels in the presynaptic terminals ( Figure 5 , Figure 5—figure supplement 1 ) . In contrast , nSyb::CD19 was clearly detectable in the presynaptic terminals ( Figure 5b ) . In addition , nSyb::CD19 , but not CD19::sdc , detected some PER- neurons near the DN1 cluster , and , with CD19::sdc , but not with nSyb::CD19 , we observed GFP induction in the l-LNvs and s-LNvs ( the pdf neurons themselves ) ( Figures 5b and 6a and Figure 5 , Figure 5—figure supplement 1 ) . Finally , we observed that the patterns of GFP induction between animals in this circuit were more consistent with nSyb::CD19 than with CD19::sdc ( Figure 5 , Figure 5—figure supplement 1 ) . These observations indicate that TRACT can be used to discover candidate synaptic partners in an unbiased manner . Whereas the overall pattern of transneuronal labeling observed is broadly comparable when using the sdc or nSyb ligands , in some cases there may be potential synaptic partners that may be specifically revealed by one of the ligands , and that further experiments will be required to validate these putative connections .
Our experiments demonstrate that it is possible to take advantage of the molecular mechanisms of ligand-induced intramembrane proteolysis to trace neuronal circuits . We have generated transgenic animals in which neurons expressing an artificial ligand ( ‘donor’ cells ) activate a genetically modified receptor on their synaptic partners ( ‘receiver’ cells ) . Using this system , called TRACT ( for TRansneuronal ACtivation of Transcription ) we have shown that expressing the ligand in a set of donor neurons can activate transcription in synaptically connected neurons in the Drosophila brain , in an anterograde manner . Using TRACT , we have confirmed the connectivity between ORNs and PNs in the antennal lobe , and have discovered new potential connections between PDF and PER neurons in the circadian circuit . There are several advantages of transneuronal tracing based on ligand-induced membrane proteolysis . ( 1 ) TRACT is fully genetically-encoded , it only requires three constructs ( a ligand , a receptor , and a reporter ) and can be used with high reproducibility in transgenic animals . Moreover , as we demonstrate here , the ligand and/or the receptor can be driven with promoters specific to selective neuronal populations to reveal circuits of synaptically connected neurons . ( 2 ) In principle , it can be used in any species amenable to transgenesis . This feature is particularly advantageous for Drosophila ( Bellen et al . , 2010; St Johnston , 2002 ) , mice ( Anderson and Ingham , 2003; Kile and Hilton , 2005 ) and zebrafish ( Fetcho and Liu , 1998; Patton and Zon , 2001 ) , three model organisms of great interest to neurobiologists , with an extensive arsenal of genetic tools . ( 3 ) The synaptically connected neurons can be studied in vivo with electrophysiological recordings , live imaging , and optical monitoring of activity , or in fixed tissue , combined with light or electron microscopy . ( 4 ) The system can be used in high-throughput experiments because , unlike electron microscopy ( Mikula and Denk , 2015; Bock et al . , 2011; Tapia et al . , 2012 ) , it is not labor intensive or time-consuming . This feature would be very useful to perform genetic screens seeking to identify mutations that affect neuronal connectivity , and to investigate how neuronal connections change in response to behavioral experience , diseases , or exposure to different environmental variables , such as drugs or toxic chemicals . ( 5 ) The system can be used to induce the expression of transgenes in the synaptically connected cells that allow for imaging of neuronal activity , such as genetically encoded Ca2+ sensors ( Chen et al . , 2013 ) or for experimental modification of neuronal activity , such as optogenetic tools ( Kim et al . , 2017 ) or ion channels ( Lin et al . , 2010 ) . ( 6 ) TRACT could be used to control neuronal function by regulating endogenous genes indirectly through nuclear translocation of molecules such as Cre , Flp , LexA , QF2 , or TetA ( Riabinina et al . , 2015; Sauer and Henderson , 1988; Dymecki , 1996; Lewandoski , 2001; Venken et al . , 2011; del Valle Rodríguez et al . , 2011 ) or directly by fusing endogenous transcription factors to the artificial receptor . ( 7 ) In the experiments presented here , we localized the ligand in presynaptic sites and observed anterograde tracing , in a presynaptic to postsynaptic neuron direction . In principle , it should be possible to design a retrograde tracing system by localizing the ligand selectively in the postsynaptic sites ( Sheng and Kim , 2011 ) , and/or the receptor in the presynaptic site ( Südhof , 2012 ) . ( 8 ) Finally , we demonstrate that directing expression of the ligand into a subset of donor neurons localized to a restricted area of the nervous system activates transcription in a very selective subset of neurons that receive synaptic contact from those donor neurons . For example , we were able to activate transcription selectively in specific subsets of antennal lobe PNs that receive synaptic input from individual glomeruli . This observation indicates that even if there are no specific promoters capable of directly driving expression of transgenes into certain neuronal types ( such as antennal lobe uniPNs that receive synaptic input from glomerulus VC1 and DN3 neurons ) , this strategy makes it possible to genetically manipulate highly specific populations of neurons based not on the genes that they express , but on the cells from which they receive synaptic input . In any strategy for mapping synaptic connectivity there are two key problems that are important to be aware of . First , there may be neurons connected by synapses but the system fails to detect that they are connected ( false negatives ) . For example , it is probably easier to miss the connection between neurons that have very few synapses between them . Second , there may be neurons that are not connected by synapses , but the system indicates that they are connected ( false positives ) . For example , it is possible that some of the connections revealed by the tracing system could be between neurons that are close to each other , but not connected by synapses . This is a problematic scenario , as designing models of computations by brain circuits will be completely inadequate if they include synaptic connections that do not exist . This indicates that with any of the currently available methods to study neuronal connectivity , it is advisable to confirm that any connections revealed are validated by other complementary methods . Our results of the connectivity between ORNs and the antennal lobe with TRACT are consistent with the published literature . First , with the nSyb and sdc-targeted ligand , we never observed any uniPN whose dendrites projected outside of the glomeruli where the ligand was expressed , thus indicating that TRACT has a very low rate of false positives . Second , with these ligands we consistently observed a number of uniPNs consistent with the published works , suggesting that for the connectivity between ORNs and uniPNs , TRACT has a very low rate of false negatives . However , we could only detect the expected multiPN ( Marin et al . , 2002 ) ( Figures 3b and 4 and Table 1 ) in approximately 50% of the cases . Several reasons could account for the variable labeling of multiPNs that we observed in our data . First , we do not know whether the GH146 enhancer drives expression of the nlgSNTG4 receptor into this multiPN in a consistent manner . If the expression of the receptor is variable between animals , this could explain why we detect the multiPN in some brains but not others . Second , whereas uniPNs have all their synapses concentrated in a single glomerulus , multiPNs have synapses distributed throughout multiple glomeruli . This means that the number of synapses between the multiPN and the ligands presented by ORN axons in an individual glomerulus is likely lower than between those of uniPNs and ORNs . This suggests that TRACT probably can detect contacts between ORNs and uniPNs with a higher sensitivity than it can between ORNs and multiPNs . Our analysis of the connectivity of the PDF neurons in the circadian circuit using TRACT confirmed a recent report indicating a connection between PDF and DN2 neurons ( Tang et al . , 2017 ) . In addition , using TRACT with the nSyb::CD19 and CD19::sdc ligand revealed that DN3 neurons are new potential postsynaptic targets for PDF neurons , an observation consistent with recent studies demonstrating that applying PDF causes a delay in the circadian phases of Ca2+ activity in DN3 ( Liang et al . , 2017 ) . The role of DN3s in the control of circadian rhythm is still poorly understood , and it would be interesting to use TRACT to selectively manipulate these labeled DN3s to investigate their function in regulating circadian behavior . It is important to note that although the axons from the PDF neurons ( l-LNvs and s-LNvs ) project very widely through the brain , we identified a handful of putative synaptic targets in brain regions that are consistent with a circadian function . These observations suggest that TRACT probably has a low rate of false positives , consistent with our data on the connectivity of the antennal lobe . Finally , our experiments suggest that TRACT may have some false negatives as it failed to reveal the connections between s-LNvs and DN1s , and between s-LNvs and LNds that have been postulated by previous works using GRASP ( Cavanaugh et al . , 2014; Seluzicki et al . , 2014; Gorostiza et al . , 2014 ) . Several reasons could account for this discrepancy . First , in the previous published works , neither of the two components of the GRASP system were localized to synaptic sites , so it is possible that the GRASP signal that was detected could be due to proximity between axons of PDF and DN1 or LNd neurons that were not connected by synapses . Resolving this issue will require validating the connectivity between PDF and DN1 neurons using complementary methods , including electrophysiological methods or electron microscopy . Second , although the nSybE driver is supposed to be pan-neuronally homogeneous , it is likely that it may express transgenes in certain neuronal populations at weaker levels . For example , If the expression of the receptor is weak in DN1 and LNd neurons , this could explain why we did not detect any connections between s-LNvs and DN1 and LNd neurons . Third , it is possible that in our current implementation of the system , TRACT can reveal neurons connected by certain synapses but not others . PDF neurons are characterized by the expression of the neuropeptide PDF , and it is possible that the secretion of neurotransmitters and neuropeptides could occur at different presynaptic sites . It is possible that in this current implementation , TRACT may be able to detect synaptic contacts mediated by neurotransmitter vesicles , but perhaps it may fail to detect neuropeptide release sites . Further tests of the system in multiple circuits would be required to fully assess the sensitivity of TRACT . Our results indicate that the choice of the domain used to target the ligand into the presynaptic sites determines the specific potential synaptic partners identified by TRACT . In initial experiments ( described in the supplementary materials ) we observed that fusing the CD19 ligand to the intracellular domains of several molecules known to be localized in presynaptic sites ( including syntaxin , dpr-10 , dip- ɣ , and neurexin ) was sufficient to enrich the localization of the TRACT ligand into presynaptic compartments . However , when using the stringent test afforded by the anatomical specificity of uniPNs ( whose dendrites branch into a single glomerulus where they receive synapses from ORNs expressing a single olfactory receptor molecule ) , we observed that using these ligands we could detect GFP expression in uniPNs whose dendrites branched in glomeruli other than the one where the TRACT ligand was expressed . These results suggest that the domains that we used from syntaxin , dpr-10 , dip- ɣ , and neurexin were not useful to restrict transneuronal labeling between neurons that were exclusively connected by synapses ( as opposed to any other non-synaptic forms of cell-cell contact , including mere proximity between their respective plasma membranes ) . In contrast , we observed that fusing the CD19 ligand to the intracellular domains from sdc and nSyb enables reliable transneuronal tracing that was restricted to neurons connected by synapses , as assessed by the stringent test of the branching of uniPNs’ dendrites into single glomeruli . We did not observe any difference in the number or distribution of uniPNs labeled with nSyb::CD19 or CD19::sdc in the antennal lobe . However , we observed that whereas a single copy of CD19::sdc was sufficient to label multiPNs , two copies of nSyb::CD19 were necessary to produce any multiPN labeling . This observation indicates that both ligands are effective at detecting both uni- and multiPNs . However , in some cases the amount of the ligand could be a limiting factor to the intensity of the labeling . In the circadian circuit we observed that whereas the overall pattern of transneuronal labeling revealed by TRACT was similar with nSyb::CD19 and CD19::sdc , there were some differences . First , we observed that the patterns of GFP induction were more consistent between animals with nSyb::CD19 than with CD19::sdc ( Figures 5b and 6 and Figure 5 , Figure 5—figure supplement 5 ) . Second , we observed that some neuronal populations were labeled with one of the ligands , but not the other . Most notably , nSyb::CD19 ( but not CD19::sdc ) labeled some PER- neurons near the DN1 cluster and CD19::sdc ( but not nSyb::CD19 ) labeled l-LNvs , and s-LNVs ( the pdf neurons themselves ) . Several reasons could account for these differences , including the different biological functions of nSyb and sdc . nSyb is a molecule predominantly localized into the synaptic vesicles , which is displayed on the plasma membrane after synaptic vesicle fusion . Sdc is a heparan sulfate proteoglycan that in neurons is enriched in presynaptic sites . Thus , given the different biological functions of sdc and nSyb , and their specific subcellular localizations , it is expected that their domains may target expression of the TRACT ligands into slightly different locations in the presynaptic site , or they may influence its abundance on the synaptic cleft . Finally , it is possible that creating a hybrid molecule that combines , for example , some of the domains from a presynaptic marker and CD19 may perturb its targeting to the intended synaptic compartment , its abundance , or stability . The two methods currently available to unambiguously confirm neuronal connectivity are dual single-cell electrophysiological recordings and serial electron microscopy . However , because single-cell recordings have a low throughput ( in a typical experiment , only a handful of connections can be tested every day ) , this method is well suited to confirm the connections suggested by a different method , but it is not feasible to identify neuronal connections in an exploratory manner . Serial electron microscopy is an extremely powerful method to identify neuronal connections , but currently it has two main limitations . First , because it has a low throughput it cannot be used to study the connectivity of brain circuits across multiple animals . Second , the area that can be acquired in a single imaging event is restricted to a maximum of around 1 mm2 . This makes it well suited to investigate local connectivity , but extremely challenging to follow the connections between neurons located far away from each other , because they cannot be observed in a single imaging acquisition . The results shown here demonstrate that TRACT allows investigators to discover potential synaptic targets for genetically identified neurons in an unbiased manner , across multiple animals , thus allowing for high-throughput approaches . Then , the candidate synaptic partners identified by TRACT can be validated using complementary low-throughput methods that allow for unambiguous confirmation of connectivity , including dual single-cell electrophysiological recordings and/or electron microscopy . While the present work was under review another work describing a genetic strategy to identify synaptically connected neurons called trans-TANGO was published ( Talay et al . , 2017 ) . Trans-TANGO is the implementation of the TANGO system with a membrane-bound ligand , so that it can be applied to the study of neuronal connectivity . Both TRACT and trans-TANGO depend of ligand-induced proteolysis and subsequent release of a membrane anchored transcription factor . TRACT is based on intramembrane proteolysis by the endogenous ɣ-secretase , which is ubiquitously present in the plasma membrane of metazoan cells . Trans-TANGO depends on the cleavage and release of a transcription factor by the reconstitution of the viral protease TEV , which is triggered by the interaction between arrestin and the activation of an exogenous , engineered GPCR upon interaction with its ligand . The initial report of trans-TANGO also studied the connectivity between ORNs and antennal lobe neurons in Drosophila to validate its specificity . Our results in the Drosophila antennal lobe with TRACT , when the ligand was expressed into individual glomeruli , revealed connections between ORNs and uniPNs in the antennal lobe that are consistent with the published literature , but we only detected multiPNs in around 50% of the brains tested . In the trans-TANGO report with ligand expression into individual glomeruli , there was no evidence of detection of multiPNs , and the number of postsynaptic uniPNs observed with transTANGO was higher than those reported in previously published works . The authors concluded that the higher numbers of PNs observed with trans-Tango could represent false-positive signals that might have resulted from inefficient synaptic localization of the ligand due to its overexpression . Further experiments will clarify the respective rate of false positives and false negatives by TRACT and transTANGO , and will allow for the optimization of these systems to enable the reliable application of these strategies for the investigation of neuronal connections in brain circuits . In recent years , there has been a surge in interest for new methods to investigate synaptic connectivity in brain circuits ( Meinertzhagen and Lee , 2012; Denk et al . , 2012; Bargmann and Marder , 2013; Swanson and Lichtman , 2016 ) . Identifying how neurons are connected is a valuable guide towards understanding how computations take place in the brain . In addition , recent research indicates that abnormal neuronal wiring might be the cause of several neurodevelopmental and psychiatric disorders , including autism and schizophrenia ( Peça and Feng , 2012; Rubinov and Bullmore , 2013; Mevel and Fransson , 2016; Narr and Leaver , 2015 ) . We anticipate that the advantages of TRACT will make it a useful addition to the arsenal of tools available to identify the wiring diagrams of brain circuits , and will open new avenues for enabling the genetic manipulation of neurons connected by synapses .
elav-nlgSNTG4: The SNTG4 cassette is described in detail in our previous publication ( Huang et al . , 2016 ) , and it contains a single chain antibody domain ( S ) , the NRR domain from Drosophila Notch ( N ) , the transmembrane domain from Drosophila Notch ( T ) , and the esn variant of Gal4 ( G4 ) . nlgSNTG4 constructs were generated by ligating three PCR fragments of SNT , dNlg2 ICD and Gal4esn ( esn , for short ) . The SNT and esn fragment was amplified by PCR from elav-SNTG4 ( Huang et al . , 2016 ) , and the dNlg2 ICD was from Drosophila EST RH63339 . These three fragments were subcloned into a P-element vector carrying the elav enhancer ( Yao and White , 1994 ) using standard procedures . Transgenic elav-nlgSNTG4 flies were produced by standard P-element integration , were screened by GAL4 immunostaining , and the lines with the highest expression level of SNTG4 were chosen for subsequent experiments . Line #1 , 2 and 4 were tested , and had similar results . Line #4 was used in this study . GH146-nlgSNTG4: The nlgSNTG4 with V5 epitope was generated by amplifying nlgSNTG4 from elav-nlgG4 with a reverse primer that included the V5 epitope sequence , and inserted into pCasper-GH146QF ( gift from C . Potter , Johns Hopkins University ) . Transgenic GH146-nlgSNTG4 flies were produced by standard P-element integration , were screened by V5 immunostaining , and the lines with the highest expression level of SNTG4 with PN specification , line #1 , were chosen for subsequent experiments . nSyb-nlgSNTG4: The nlgSNTG4 fragment with V5 epitope was directly amplified from GH146-nlgSNTG4 , and was subcloned into pattNSYBBN ( Addgene ) . Transgenic nSyb-nlgSNTG4 flies were produced by attb site-specific integration in the attP40 site . LexAop-Syx::CD19: A fragment comprising the intracellular and transmembrane domains of syx was synthesized ( Gene blocks , IDT inc . ) , fused to a fragment containing the extracellular domain of CD19 and the OLLAS epitope ( Gene blocks , IDT inc . ) , and inserted into the LexAop pJFRC19 vector . Transgenic flies were produced by attb site-specific integration in attP2 site . LexAop-nSyb::CD19: A fragment comprising the intracellular and transmembrane domains of nSyb was synthesized ( Gene blocks , IDT inc ) , fused a fragment containing the extracellular domain of CD19 and the OLLAS epitope ( Gene blocks , IDT inc . ) , and inserted into the LexAop pJFRC19 vector . Transgenic flies were produced by attb site-specific integration in attP2 site . LexAop-CD19::Nrx1: A fragment comprising the CD19 extracellular domain followed by the OLLAS tag ( Gene blocks , IDT inc . ) was fused to a synthetic DNA fragment comprising the transmembrane and the intracellular domains of nrx1 ( Gene blocks , IDT inc . ) and inserted into pJFRC19 . Transgenic flies were produced by attb site-specific integration in attP2 site . LexAop-CD19::Dip: The extracellular domain of CD19 was fused to a synthetic full length open reading frame ( ORF ) of DIP ɣ followed by the OLLAS epitope , and inserted into the LexAop pJFRC19 vector . Transgenic flies were produced by attb site-specific integration in attP2 site . LexAop-CD19::Dpr: The extracellular domain of CD19 was fused to the full length ORF of Dpr10 followed by the OLLAS epitope , and inserted into the LexAop pJFRC19 vector . Transgenic flies were produced by attb site-specific integration in attP2 site . LexAop-CD19::Sdc: The extracellular domain of CD19 was fused to the full length ORF of DIP syndecan followed by the OLLAS epitope , and inserted into the LexAop pJFRC19 vector . Transgenic flies were produced by attb site-specific integration in attP2 site . - 5xUAS-mCD8::GFP and 5xUASCD4::tdGFP reporters were gifts from Dr . Freeman , Oregon Health and Science University . pdf-LexA ( 7M ) : gift from Dr . Rosbash , Brandeis University orco-LexA::VP16: gift from Dr . Lee , Janelia Research Campus HHMI Janelia LexA driver lines: GMR17H02 , and GMR28H10-LexA were requested from Bloomington fly stocks . Figure 2e top: 5XUAS-CD4::tdGFP , nSybE-nlgSNTG4/nSybE-nlgSNTG4; orco-LexA::VP16/LexAop-nSyb::CD19 Figure 2e bottom: 5XUAS-CD4::tdGFP/CyO; orco-LexA::VP16/LexAop-nSyb::CD19 , GH146-nlgSNTG4#4 Figure 3a top: 5XUAS-CD4::tdGFP/GMR17H02-LexA; LexAop-nSyb::CD19/GH146-nlgSNTG4#4 . We noticed that the male R17H02 has lower LexA activity in DA1 than the female . Therefore , only the female flies were analyzed . Figure 3a bottom: 5XUAS-CD4::tdGFP/GMR28H10-LexA; LexAop-nSyb::CD19/GH146-nlgSNTG4#4 Figure 3b top: 5XUAS-CD4::tdGFP/GMR17H02-LexA; LexAop-CD19::Sdc/GH146-nlgSNTG4#4 . Only the female flies were analyzed . Figure 3b bottom: 5XUAS-CD4::tdGFP/GMR28H10-LexA; LexAop-CD19::Sdc/GH146-nlgSNTG4#4 Figure 4a 5XUAS-CD4::tdGFP/GMR28H10-LexA; LexAop-nSyb::CD19/GH146-nlgSNTG4#4 , LexAop-nSyb::CD19 Figure 4b and d 5XUAS-CD4::tdGFP/GMR28H10-LexA; LexAop-CD19::Sdc/GH146-nlgSNTG4#4 Figure 5b top: nSyb-nlgSNTG4 , 5XUAS-CD4::tdGFP/nSyb-nlgSNTG4; LexAop-nSyb::CD19/LexAop-nSyb::CD19 Figure 5b bottom and 8: nSyb-nlgSNTG4 , 5XUAS-CD4::tdGFP/nSyb-nlgSNTG4 , pdf-LexA; LexAop-nSyb::CD19/LexAop-nSyb::CD19 . All crosses were maintained in a 25C incubator with 12 hr-12hr dark-light cycles , and were repeated at least three times . After incubation at 29C for one day , the adult Drosophila were dissected , and the brains were removed in 1x PBS under a dissection microscope . For PER immunostaining , the flies were dissected one hour before the lights turned on ( around 7 AM ) . Brains were fixed by immersing them in a 4% paraformaldehyde solution in PBS for 15 min at room temperature . Brains were washed in PBS three times for 10 mins each , followed by permeabilization with PBS + 0 . 5% triton X-100 ( PBST ) for 30 mins and blocking with 5% horse serum in PBST for 30 mins . The brain samples were stained with antibodies against GFP ( rabbit polyclonals ( AB3080 1:1000 , AB3080P 1:1500 ( Millipore ) ) , or chicken polyclonal ( Abcam ) , diluted at 1:1000 ) , mcherry ( rat monoclonal 5F8 ( Chromotek ) diluted at 1:1 , 000 ) , Brp ( mouse monoclonal nc82 ( DSHB ) diluted at 1:50 ) , ChAT ( mouse monoclonal 4B1 ( DSHB ) diluted at 1:200 ) , GABA ( rabbit polyclonal A2052 ( Sigma ) 1:200 ) , PER ( guinea pig polyclonal PA1140 , gift from Dr . Sehgal , University of Pennsylvania ) , V5 ( mouse monoclonal R960-25 ( invitrogen ) diluted at 1:300 ) , OLLAS ( rat monoclonal L2 NBP106713 ( Novus ) diluted at 1:300 ) diluted in 5% horse serum/PBST . Brains were Incubated with primary antibodies overnight at 4C , washed three times in PBST , incubated with secondary ( goat secondary antibodies ( Life Technologies ) 1:500 , except for rabbit anti-GFP AB3080P ( Millipore ) , where the secondary was used at 1:750 ) for 1 . 5 hr at room temperature , washed in PBST and mounted on glass slides with a clearing solution ( Slowfade Gold antifade reagent ( Invitrogen ) ) . Stained brains were imaged with Confocal microscopes ( Olympus Fluoview 300 or Zeiss 710 ) under 20x or 40X objectives . In a typical experiment , we imaged 150 sections with an optical thickness of 0 . 3–0 . 5 µm from dorsal or ventral sides . Confocal stacks were processed with Fiji to obtain maximal projections . All constructs were generated using either PCR with NEB Phusion polymerase or IDT gene blocks . Cloning into transgenic vector plasmids was performed using Gibson assembly mix from NEB . The sequences of the constructs used to generate the data shown in main text ( ID3 dNRR dNotch1 TMD dnlng2 esn V5 , nSyb::CD19 , and CD19::sdc ) are shown in Supplementary file 1 , including maps indicating their key elements ) . | One of the main obstacles to understanding how the brain works is that we know relatively little about how its nerve cells or neurons are connected to one another . These connections make up the brain’s wiring diagram . Current methods for revealing this wiring all have limitations . The most popular method – serial electron microscopy – can reveal the connections in a small region of the brain in great detail , but it cannot show connections between neurons that are far apart . Huang et al . have now created a genetic system for visualizing these connections . For neurons to communicate , one neuron must produce a signal called a ligand . This ligand can then bind to and activate its partner neuron . Huang et al . modified the DNA of neurons so that every time those cells produced a specific ligand , they also produced a red fluorescent protein . Similar modifications ensured that every time the ligand activated a partner neuron , the activated neuron produced a green fluorescent protein . Viewing the red and green neurons under a microscope enabled Huang et al . to see which cells were communicating with which others . While these experiments took place in fruit flies , the same approach should also work in other laboratory animals , including fish , mice and rats . Once we know the wiring diagram of the brain , the next step is to investigate the role of the various connections . To understand how a computer works , for example , we might change the connections between its circuit components and look at how this affects the computer’s output . With this new method , we can change how neurons communicate with one another in the brain , and then look at the effects on behavior . This should provide insights into the workings of the human brain , and clues to what goes wrong in disorders like schizophrenia and autism . | [
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Vascular remodeling under conditions of growth or exercise , or during recovery from arterial restriction or blockage is essential for health , but mechanisms are poorly understood . It has been proposed that endothelial cells have a preferred level of fluid shear stress , or ‘set point’ , that determines remodeling . We show that human umbilical vein endothelial cells respond optimally within a range of fluid shear stress that approximate physiological shear . Lymphatic endothelial cells , which experience much lower flow in vivo , show similar effects but at lower value of shear stress . VEGFR3 levels , a component of a junctional mechanosensory complex , mediate these differences . Experiments in mice and zebrafish demonstrate that changing levels of VEGFR3/Flt4 modulates aortic lumen diameter consistent with flow-dependent remodeling . These data provide direct evidence for a fluid shear stress set point , identify a mechanism for varying the set point , and demonstrate its relevance to vessel remodeling in vivo .
Homeostasis , one of the central concepts in physiology ( Cannon , 1929 ) , posits that physiological variables have an optimum value or set point such that deviations from that set point activate responses that return those variables toward their original value . For example , changes in central body temperature trigger sweating , altered blood flow to the skin or shivering to restore normal temperature . In the vasculature , arteries remodel under sustained changes in blood flow , with increased or decreased flow triggering outward or inward remodeling , respectively , to adjust lumen diameters accordingly ( Thoma , 1893; Kamiya and Togawa , 1980; Kamiya et al . , 1984; Langille and O'Donnell , 1986; Langille et al . , 1989; Langille , 1996; Tronc et al . , 1996; Tuttle et al . , 2001 ) . These studies have given rise to the concept that the endothelium encodes a fluid shear stress set point that governs remodeling responses ( Rodbard , 1975; Cardamone and Humphrey , 2012 ) ( Figure 1A ) . While appealing , there is no direct evidence for such a mechanism . Moreover , if it exists , the set point must itself be variable , since different types of vessels , for example , arteries , veins and lymphatics , generally have very different magnitudes of fluid shear stress ( Lipowsky et al . , 1980; Dixon et al . , 2006; Suo et al . , 2007 ) . 10 . 7554/eLife . 04645 . 003Figure 1 . Testing the set point hypothesis . ( A ) Definition of the ‘shear stress set point’ . ( B ) Picture of a silicone gasket used in the gradient flow chamber with the corresponding calculation of the theoretical shear stress level across the channel with two different conditions of gasket thickness and flow rate . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 003 Arterial remodeling is crucial in normal physiological adaptation to growth and exercise , and is a major determinant of outcomes in cardiovascular disease ( Kohler et al . , 1991; Corti et al . , 2011; Padilla et al . , 2011 ) . Outward remodeling of atherosclerotic vessels helps to maintain lumen diameter and blood flow , whereas inward remodeling leads to ischemia associated with angina and peripheral vascular disease ( Ward et al . , 2000 ) . Additionally , flow-dependent remodeling of small blood vessels near sites of myocardial infarction provides collateral circulation that plays a major role in restoring cardiac function ( Heil and Schaper , 2004 ) , whereas failure to remodel is a major factor in progression to heart failure . Flow-dependent remodeling is initiated by inflammatory activation of the endothelium , leading to recruitment of leukocytes that assist with remodeling in several ways including secretion of matrix metalloproteinases , cytokines and extracellular matrix proteins ( Silvestre et al . , 2008; Schaper , 2009; Silvestre et al . , 2013 ) . Once the remodeling phase is completed , inflammation is resolved and the vascular wall stabilized . NF-κB plays a major role in the initial inflammatory activation ( Castier et al . , 2009; Sweet et al . , 2013 ) , whereas signaling through TGF-β is critical in the anti-inflammatory , stabilization phase ( Walshe et al . , 2009 ) . These considerations led us to investigate the existence of a fluid shear stress set point and its relevance to vascular remodeling . Our results provide strong evidence for a fluid shear stress set point in vascular endothelium . They also show that vascular and lymphatic endothelium have different set points , that this difference is mediated by differences in expression of VEGFR3 , and provide evidence that this pathway controls artery remodeling in vivo .
To test the existence of a shear stress set point , we built a flow chamber that creates a gradient of shear stress along a single culture slide . Following a previous design ( Usami et al . , 1993 ) , the width of the chamber progressively decreases to yield a linear gradient ( Figure 1B ) . We then measured several biological responses associated with fluid shear stress and vascular remodeling . To assay responses as a function of shear stress , we took successive microscopic images along the chamber . Depending on localization , these responses correlated with calculated values of shear stress . Changing the gasket thickness and flow rate allowed us to control the range of shear stress for each experiment ( Figure 1B ) . We first measured endothelial cell alignment in flow , which is a well-studied response associated with vessel stabilization and suppression of inflammatory pathways ( Levesque and Nerem , 1985; Wang et al . , 2012; Baeyens et al . , 2014 ) . Alignment was quantified by measuring the angle between the major axis of the nucleus and the flow direction ( Baeyens et al . , 2014 ) . Human umbilical vein endothelial cells ( HUVECs ) were subjected to 16 hr of laminar shear stress ranging from 2 to 60 dynes . cm−2 . HUVECs aligned in the direction of the flow , between approximately 10 and 20 dynes . cm−2 , but were misaligned or oriented perpendicularly , against the flow direction , outside this range ( Figure 2A , Figure 2—figure supplement 1 ) . This result agrees with previous studies showing perpendicular alignment of endothelial cells under very high shear stress ( Viggers et al . , 1986; Dolan et al . , 2011; Dolan et al . , 2012 ) . 10 . 7554/eLife . 04645 . 004Figure 2 . Set point for shear stress . ( A ) Cell orientation: the average orientation of HUVEC nuclei was measured in each picture , to obtain average orientation at a given shear stress . ( n = 16 , Mean ± SEM , ANOVA: F = 15 . 02 , p < 0 . 0001 ) . With no flow , cell orientation was random ( average = 45° ) . ( B ) NF-κB activation: p65 nuclear translocation in HUVEC was measured either in no flow ( dotted line: average ) or after 16 hr of flow in the gradient chamber ( n = 6 , Mean ± SEM , ANOVA: F = 10 . 97 , p < 0 . 0001 ) . ( C ) Smad1 activation: Smad1 nuclear translocation in HUVECs was measured without flow ( dotted line: average ) or after 16 hr of flow in the gradient chamber ( n = 6 , Mean ± SEM , ANOVA: F = 13 . 47 , p < 0 . 0001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 00410 . 7554/eLife . 04645 . 005Figure 2—figure supplement 1 . ( A ) Quantification of cell orientation , p65 nuclear translocation or smad1 nuclear translocation without flow or after 16 hr laminar flow at the indicated values ( NS: not significant , *: p < 0 . 05 , **: p < 0 . 01 , ****: p < 0 . 0001 ) . ( B ) Representative pictures of HUVECs with labeled nuclei ( DAPI ) , actin stress fibers ( phalloidin ) , p65 or Smad1 without flow or after 16 hr of flow . Flow direction is from left to right . Scale bar = 50 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 005 Next , to assess NF-κB activation , we measured the nuclear translocation of the p65 subunit of NF-κB . NF-κB showed baseline activation in cells without flow , which decreased between approximately 10 and 25 dynes . cm−2 , and dramatically increased at very high shear ( Figure 2B , Figure 2—figure supplement 1 ) . The suppression of NF-κB translocation in this range is consistent with previous observations that sustained laminar flow is anti-inflammatory ( Mohan et al . , 1997; Berk , 2008 ) . Lastly , we measured the activation of TGFβ/SMAD signaling by assaying nuclear translocation of Smad1 . Strikingly , flow induced Smad translocation with a sharp maximum between 10 and 20 dynes . cm−2 and repressed translocation at higher values ( Figure 2C , Figure 2—figure supplement 1 ) . The results obtained with the gradient chamber were validated by examining 2 , 12 and 50 dynes . cm−2 using normal parallel flow chambers ( Figure 2—figure supplement 1 ) . Taken together , these results show that HUVECs have a biphasic response to shear stress such that anti-inflammatory , stabilization pathways are activated between approximately 10 and 20 dynes . cm−2 , while lower or higher shear stress is pro-inflammatory . This behavior is consistent with a shear stress set point within the range of 10 and 20 dynes . cm−2 for these cells . An essential aspect of the set point hypothesis is that it must differ between different types of vessels . In vivo , average shear stress in lymphatic vessels is much lower than in arteries or veins ( Lipowsky et al . , 1980; Dixon et al . , 2006; Suo et al . , 2007 ) . We therefore examined the behavior of human dermal lymphatic endothelial cells ( HDLEC ) , using modified chamber parameters to obtain values of shear stress from 0 . 5 to 20 dynes . cm−2 ( Figure 1 ) . In these experiments , HUVECs aligned between 8 and 20 dynes . cm−2 , ( Figure 2A and Figure 3A ) whereas HDLEC aligned maximally between 4 and 6 dynes . cm−2 ( Figure 3A , Figure 3—figure supplement 1 ) . The minimum for NF-κB translocation also shifted to between 4 and 10 dynes . cm−2 ( Figure 3B , Figure 3—figure supplement 1 ) , which corresponds well to in vivo measurements ( Dixon et al . , 2006 ) . These results indicate that lymphatics have a higher sensitivity to shear stress compared to HUVECs , consistent with the set point concept . 10 . 7554/eLife . 04645 . 006Figure 3 . Set point in HUVECs vs lymphatic endothelial cells . ( A ) The average orientation of venous cell ( HUVEC ) or lymphatic cell ( HDLEC ) nuclei across the slide was measured as in Figure 2A . ( n = 11 , Mean ± SEM ) . The difference between HUVECs and HDLECs is statistically significant ( ANOVA Two-way , p < 0 . 0001 ) . ( B ) NF-κB activation: p65 nuclear translocation in HDLEC was measured either in no flow ( dotted line: average ) or after 16 hr of flow in the gradient chamber ( n = 4 , Mean ± SEM , ANOVA: F = 34 . 32 , p < 0 . 0001 ) . ( C ) Expression of VE-cadherin , PECAM-1 , VEGFR2 and VEGFR3 , proteins involved in the shear stress mechanotransduction through the junctional complex . Actin was used as a loading control . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 00610 . 7554/eLife . 04645 . 007Figure 3—figure supplement 1 . Representative pictures of HDLEC probed for DAPI and p65 at 5 and 20 dynes . cm−2 . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 007 A number of shear stress responses , including cell alignment and NF-κB activation , require mechanotransduction via VEGFR2 , whose ligand-independent transactivation by flow requires PECAM-1 and VE-cadherin ( Tzima et al . , 2005 ) . We therefore considered whether differences in expression of these proteins might account for the difference in flow sensitivity between HUVECs and HDLECs . However , no major differences in levels of these proteins were observed ( Figure 3C ) . VEGFR3 , a close homolog of VEGFR2 , is highly expressed in lymphatic cells ( Kaipainen et al . 1995 ) and recent work in our lab showed that it is activated by flow in vascular endothelial cells similarly to VEGFR2 ( Coon et al . , 2015 ) . These considerations prompted us to examine levels of this receptor as well , which showed approximately 10-fold higher expression in lymphatic ECs compared to HUVECs ( Figure 3C ) . We therefore considered whether VEGFR3 levels might be responsible for the higher flow sensitivity of lymphatic ECs . HDLECs were therefore transfected with VEGFR3 siRNA , which reduced its expression to approximate the level in HUVECs ( Figure 4A ) . We also transduced HUVECs with adenovirus coding for hVEGFR3-GFP ( Figure 4A ) , which increased levels by ∼10-fold and infected >90% of the cells ( Figure 4—figure supplement 1 ) . Cell alignment in flow was then analyzed . Depletion of VEGFR3 in HDLECs shifted the optimal alignment to between 10 to 20 dynes . cm−2 ( Figure 4B , Figure 4—figure supplement 2 ) , similar to HUVECs . Conversely , over-expression of VEGFR3 in HUVECs decreased the optimal response toward the lower shear stress levels seen with lymphatic ECs ( Figure 4C , Figure 4—figure supplement 2 ) . Taken together , these results show that VEGFR3 levels are a major determinant of the difference in shear stress sensitivity between HUVECs and HDLECs . 10 . 7554/eLife . 04645 . 008Figure 4 . VEGFR3 expression controls the shear stress set point . ( A ) Western Blot of VEGFR3 and GFP in HDLECs with and without VEGFR3 siRNA ( 10 nM ) , and in HUVECs with and without adenoviral expression of hVEGFR3-GFP . Actin serves as a loading control . ( B ) Effect of VEGFR3 siRNA in HDLECs on set point . Cell alignment was assayed after shear stress for 16 hr ( n = 6 ) . Data were smoothed with a LOWESS fit to improve visualization ( mean ± SEM; HDLEC vs HDLC + VEGFR3 siRNA: p = 0 . 004; HDLEC + VEGFR3 siRNA vs HUVEC: p = 0 . 45 ) . ( C ) Effect of VEGFR3 over-expression on set point . Alignment after 16 hr flow was assayed in HUVECs infected with adenovirus expressing mCherry or hVEGFR3-GFP as before . Data were smoothed with a LOWESS fit to improve visualization ( n = 10 , values are means ± SEM; HUVEC + mCherry vs HUVEC + VEGFR3-GFP: p < 0 . 0001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 00810 . 7554/eLife . 04645 . 009Figure 4—figure supplement 1 . ( A ) Representative pictures of HUVEC cells expressing hVEGFR3-GFP ( GFP signal displayed ) after 16 hr of stimulation at 5 and 20 dynes . cm−2 . Flow direction is from left to right . ( B ) FACS analysis of the GFP signal from HUVEC ( grey ) and HUVEC infected with VEGFR3-GFP ( GFP+ ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 00910 . 7554/eLife . 04645 . 010Figure 4—figure supplement 2 . Non-smoothened data of the graphs displayed in Figure 4B , C . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 010 We also confirmed VEGFR3 activation by flow in lymphatic endothelial cells . Onset of flow stimulated VEGFR3 phosphorylation maximally at 6 dynes . cm−2 in HDLEC ( Figure 5 ) , which corresponds well to the set point of around 5 dynes . cm−2 in these cells . HUVECs , by contrast , exhibited a weaker response that was shifted to higher shear , consistent with their higher set . 10 . 7554/eLife . 04645 . 011Figure 5 . VEGFR3 activation by shear stress . HDLECs ( left ) and HUVECs ( right ) were stimulated for 15 min with shear stress at the indicated levels . VEGFR3 transactivation was assayed by phosphorylation on Y1230 , detected by Western blotting with pY1230 antibody ( n = 5 independent experiments; *: p < 0 . 05 , **: p < 0 . 01 , ****: p < 0 . 0001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 011 To test whether VEGFR3 levels control sensitivity to shear stress and vascular remodeling in vivo , we examined Danio rerio ( zebrafish ) . This system has the advantage that development proceeds normally without blood flow , thus , fluid shear stress can be altered or even stopped without affecting viability ( Langheinrich et al . , 2003 ) . The notion that levels of VEGFR3 ( Flt4 in zebrafish ) determine the shear stress set point predicts that reducing VEGFR3 expression will induce inward remodeling of the vessels in order to increase shear stress and restore normal signaling . We used a strain in which blood vessels are labeled by expression of kdrl:mCherry ( VEGFR2 ) and flt4:Citrine ( VEGFR3 ) reporters . kdrl:mCherry was highly visible in the dorsal aorta and the posterior cardinal vein , whereas flt4:Citrine was low ( though detectable ) in the dorsal aorta and higher in the cardinal posterior vein and the developing thoracic duct ( Figure 6 , Figure 6—figure supplement 1 ) . Flt4/VEGFR3 and its ligand , VEGF-C , are associated with development of lymphatic vasculature and segmental arteries in zebrafish ( Covassin et al . , 2006; Kuchler et al . , 2006 ) . To assay the effect of FLT4 and VEGFC dosage on vessels diameter , we injected zebrafish embryos at the one cell stage with previously validated VEGFC and FLT4 morpholinos at two different concentrations . These antisense oligos target the respective mRNAs and induce a dose dependent loss of function ( Nicoli et al . , 2012; Villefranc et al . , 2013 ) . At 72 hr post fertilization ( hpf ) , the progressive inhibition of VEGFC did not perturb the remodeling of blood vessel or vessel diameter but as expected inhibited the development of the thoracic duct , the first zebrafish lymphatic vessel ( Yaniv et al . , 2006 ) ( Figure 6 , white stars ) . By contrast , progressive inhibition of FLT4 reduced the diameter of the dorsal aorta with loss of thoracic duct evident at a higher dose of FLT4 morpholino ( Figure 6 ) . These results suggested that VEGF-C-independent Flt4 activation is required for artery diameter and exclude an indirect effect of lymphatic development on the artery development . Interestingly , a similar decrease of the dorsal aorta diameter can be observed in a recent paper ( Kwon et al . , 2013 ) . Although these authors focused on the growth of motoneurons , the dorsal aorta is readily visible in images of Flt1 mCherry reporter embryos; its diameter is obviously smaller in expando embryos expressing a kinase dead Flt4 , as well as in wildtype embryos treated with Flt4 morpholino or VEGFR3 inhibitors but not after injection with VEGFC morpholino , in accordance with our own observations . 10 . 7554/eLife . 04645 . 012Figure 6 . VEGFR3 ( Flt4 ) controls blood vessel caliber in zebrafish . Representative pictures of the dorsal aorta ( DA ) , posterior cardinal vein ( PCV ) and thoracic duct ( white * ) at 72 hr post-fertilization ( hpf ) in wild type zebrafish embryos or embryos injected with Flt4 ( VEGFR3 ) morpholino at 0 . 06 or 0 . 1 mM , or with VEGF-C morpholino at 0 . 06 or 0 . 1 mM . The mCherry reporter driven by the KDR ( VEGFR2 ) promoter ( kdrl:mCherry ) is depicted in red and the citrine reporter driven by the Flt4 promoter ( flt4:citrine ) is depicted in green . Scale = 20 µm and applies to all pictures . n = 6-15 fishes for each condition , whiskers represents the minimum and maximum data point ( NS: non-significant , ***: p < 0 . 001 and ****: p < 0 . 0001 , ANOVA ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 01210 . 7554/eLife . 04645 . 013Figure 6—figure supplement 1 . Representative pictures of the dorsal aorta ( DA ) and posterior cardinal vein ( PCV ) and developing thoracic duct ( * ) in wild type zebrafish embryos with a citrine reporter associated to Flt4 promoter ( flt4:citrine ) before and approximatively 2 hr after nifedipine treatment . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 013 To test the role of flow in this process , embryos were treated with 40 μM nifedipine , a voltage-dependent calcium channel blocker that stops the heart and thus blood flow ( Langheinrich et al . , 2003 ) . Blocking flow led to a decreased vessel diameter ( Figure 6 , Figure 6—figure supplement 1 ) , supporting the role of shear stress in determining lumen size . Interestingly , lumen diameter was similar in embryos treated with high dose Flt4 morpholino and with nifedipine . To test whether Flt4 acts on a flow pathway , we then combined these treatments . Strikingly , in the absence of flow , neither Flt4 nor VEGF-C morpholinos caused further changes in vessel size . Taken together , these results support the conclusion that VEGF-C-independent activation of VEGFR3 by flow may determine the endothelial cell sensitivity to flow and vessel remodeling , consistent with the existence of a fluid shear stress set point . Interestingly , ligand-independent responses for VEGFR3 are consistent with developmental mouse phenotypes: deletion of VEGF-C and VEGF-D does not affect the development and maturation of blood vessels during mice development , while deletion of VEGFR3 does ( Haiko et al . , 2008 ) . Ligand-dependent responses are thus required for lymphangiogenesis but probably not for flow responses . Lastly , we investigated whether VEGFR3 controls artery remodeling in mice in a similar manner . Expression of VEGFR3 in adult arteries has been reported to be low ( Gu et al . , 2001; Witmer et al . , 2002; Tammela et al . , 2008 ) , thus , we first verified its transcription in the thoracic aorta . Using a transgenic Vegfr3::YFP reporter mouse ( Calvo et al . , 2011 ) , expression of YFP was readily detected , confirming Vegfr3 expression in adult arteries ( Figure 7A ) . We confirmed this observation by staining a longitudinal section of the thoracic aorta with an anti-VEGFR-3 antibody ( Figure 7B ) . Interestingly , VEGFR3 expression was not uniform: weaker expression was detected in the outer curvature or some portions of the carotid artery , associated with higher shear stress , while stronger expression was observed in the inner curvature , associated with low shear stress ( Figure 7—figure supplement 1 ) . 10 . 7554/eLife . 04645 . 014Figure 7 . Transient vascular remodeling in EC iΔR3 mice . ( A ) Longitudinal paraffin section of the thoracic aorta of a Vegfr3::YFP ( VEGFR3 reporter ) mouse . YFP was detected with an anti-GFP antibody . Scale bar = 50 µm . ( B ) VEGFR3 and DAPI staining of a longitudinal section of the thoracic aorta of Vegfr3fl/fl ( WT ) or EC iΔR3 mice , 3 weeks after Tx injection . Scale bar: 50 µm . ( C ) Lyve1 and VEGFR3 staining of the lymphatic network in ear skin from Vegfr3fl/fl ( WT ) or EC iΔR3 mice . Pictures were taken 1 week after Tx injection . Scale bar = 50 µm . ( D ) Aorta from oil injected-EC iΔR3 ( WT ) or Tx-injected EC iΔR3 mice , 2 weeks after Tx injection . Scale bar = 1 mm . ( E ) Diameters ( graph on right ) were measured in thoracic aortas ( images on left ) from Vegfr3fl/fl ( WT ) or EC iΔR3 mice , 3 weeks ( WT: n = 8 and EC iΔR3: n = 7 ) and 7 weeks ( WT: n = 6 and EC iΔR3: n = 5 ) after Tx treament ( whiskers indicate the minimum and maximum data point , ***: p < 0 . 001 , ANOVA ) . The measurement was performed right after the curvature , 1 mm below the subclavian artery bifurcation . ( F ) Longitudinal paraffin sections of the thoracic aorta from Vegfr3fl/fl ( WT ) or EC iΔR3 mice or EC iΔR3 mice , probed for MMP9 ( blue ) and nuclei ( red ) after injection of Tx for the indicated time ( WT is 1 week post-injection ) . Distribution of the area under the curve of MMP9 fluorescence from the media is plotted on the left ( n ≥ 3 mice for each condition , whiskers are 10–90% , cross is the arithmetic mean ) . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 01410 . 7554/eLife . 04645 . 015Figure 7—figure supplement 1 . VEGFR3 and DAPI staining of a longitudinal section different portions of the aorta . Scale bar: 50 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 04645 . 015 Because deletion of Vegfr3 in mice leads to major cardiovascular defects and embryonic lethality ( Dumont et al . , 1998 ) , we used an inducible knock out strategy in adult Vegfr3fl/fl mice ( Haiko et al . , 2008 ) that also contain an endothelium-specific , tamoxifen-inducible Cre ( Cdh5-CreERT2 ) allele ( Wang et al . , 2010 ) . Cdh5 CreERT2 , Vegfr3fl/fl mice , referred as EC iΔR3 , grow normally without any defect prior to tamoxifen injection . Two month old Vegfr3fl/fl ( wild-type , WT ) and EC iΔR3 mice were injected with tamoxifen and examined at 1 , 2 , 3 or 7 weeks . 1 week after tamoxifen injection , no VEGFR3 expression was visible in the thoracic aorta ( Figure 7B ) and in the ear skin lymphatics of EC iΔR3 mice ( Figure 7C ) . 3 weeks after deletion of Vegfr3 , the dermal lymphatic network in the skin was completely intact but vessel diameter was dramatically decreased ( WT: 38 ± 5 µm and EC iΔR3: 22 ± 2 µm , n = 4 , p < 0 . 001 ) . We also observed a ∼15% reduction of the diameter of the descending aorta ( Figure 7D , E ) . No further change was observed when mice were examined at 7 weeks ( Figure 7E ) , indicating that vessels remodeled and then stabilized . No change in body weight was observed 3 weeks after injection ( 28 . 4 g ± 2 for WT and 28 . 3 g ± 2 . 7 for EC iΔR3 mice ) . The curvature of the aortic arch was also reproducibly decreased after excision , an unexpected result that we have not further investigated . To investigate the role of remodeling pathways , we stained longitudinal sections of the thoracic aorta for MMP9 , a matrix metalloprotease involved in flow-dependent vascular remodeling ( Bond et al . , 1998; Godin et al . , 2000; Magid et al . , 2003 ) . Following Vegfr3 deletion , MMP9 in the thoracic aorta was highly elevated at 1 week but decreased to baseline at later times ( Figure 7F ) . This observation strongly supports the notion that Vegfr3 deletion induces inward remodeling of the thoracic aorta which is followed by stabilization . Increased MMP9 expression may be induced through NF-κB ( Sun et al . , 2007 ) . We hypothesize that elevating the set point causes the endothelium to signal low shear , which induces inward remodeling . Together , these data support the concept that vessel lumen diameter is controlled by a VEGFR3-dependent shear stress set point .
Living organisms have developed an extensive repertoire of mechanisms to adapt to stresses and maintain homeostasis . For more than a century , investigators have observed effects suggesting that the blood flow controls vascular diameter ( Thoma , 1893; Langille and O'Donnell , 1986; Langille et al . , 1989; Langille , 1996 ) , a mechanism that would optimize perfusion by adjusting vascular morphology in response to tissue demand . It has been hypothesized that , as for thermoregulation , there is an optimal value of flow which is maintained through feedback mechanisms to prevent deviation from this value . This is what we term the ‘shear stress set point’ theory ( Rodbard , 1975 ) . The current data show that HUVECs align in the direction of flow , inhibit NF-κB and activate Smads within a narrow range of fluid shear stress magnitudes . This range corresponds to the physiological flow within the umbilical vein estimated at around 8 . 4 to 12 . 5 dynes . cm−2 ( ( Kiserud and Rasmussen , 1998; Boito et al . , 2002; Christensen et al . , 2014 ) ; shear stress = 8 × viscosity ( velocity/diameter ) , with viscosity = 0 . 06–0 . 09 poisse , velocity = 7 . 1 cm . s−1 and diameter = 4 . 1 mm ) . These results imply that physiological flow inhibits inflammatory pathways and activates anti-inflammatory/stabilization pathways . By contrast , cells in low or high flow fail to align , activate NF-κB and suppress Smads . We propose that these responses are involved in the vessel remodeling that reestablishes optimal blood flow . It is known that inflammation is a critical component of flow-dependent as well as other forms of vessel remodeling ( Silvestre et al . , 2008; Schaper , 2009; Silvestre et al . , 2013 ) . It has been recently demonstrated that inhibiting NF-κB impairs outward remodeling associated with increased shear stress as well as aneurysm formation ( Saito et al . , 2013 ) . On the other hand , defective Smad1 signaling in the endothelium is associated with hereditary haemorrhagic telengiectasia ( HHT ) , which is characterized by the development of unstable , arteriovenous malformations ( Dupuis-Girod et al . , 2010 ) . Interestingly , these malformations are preceded by increased vascular lumen diameter , which occurs in a flow dependent manner ( Corti et al . , 2011 ) . These observations , combined with ours , suggest that these two signaling pathways contribute to balanced control of the vessel caliber . Fluid shear stress varies among different types of vessels , and to some extent even within the same vessel , suggesting that different cells must have different set points for shear stress , depending on their location . Relevant to our experiments , the shear stress in the human umbilical vein is estimated at around 8 . 4–12 . 5 dynes/cm−2 whereas lymphatic vessels have highly pulsatile flow with peaks values at around 4–8 dynes . cm−2 and averages that are much lower ( Dixon et al . , 2006 ) . The shear stress set point model therefore predicts that these cell types will have different set points , which was borne out in our studies . Furthermore , we found that this difference can be largely accounted for by differences in VEGFR3 expression . This receptor , a close homolog of VEGFR2 , is also activated in response to flow . Both expression levels in vivo ( Witmer et al . , 2002 ) and our functional experiments in vitro lead to the conclusion that high expression of VEGFR3 increases sensitivity to shear to give a low shear stress set point , while low expression of VEGFR3 is associated with higher set points . However , it is highly likely that other mechanotransducers or mediators influence set point values . While we did not observe any major difference in PECAM-1 and VE-cadherin expression between HDLEC and HUVEC , these two proteins can vary between different vascular beds ( Pusztaszeri et al . , 2006; Herwig et al . , 2008 ) , which might also affect the set point . We used HUVECs as a model for blood endothelial cells because they are readily available and their response to shear stress is well characterized . However , it has been recently showed that arterial and venous markers greatly diminish in culture ( Aranguren et al . , 2013 ) , thus , whether they fully represent typical venous cells in vivo should be treated with caution . Comparing fresh primary cells from veins and arteries will be an interesting direction for future work . Mechanotransducers apart from the junctional complex are also likely to be important . There must also be pathways that distinguish high and low shear to initiate outward vs inward remodeling . Future work will be required to explore these pathways in more detail and their relevance to vascular remodeling . The notion that vascular remodeling is governed by a shear stress set point , which is itself set by activation of various receptors and signaling pathways , may be relevant to a number of applications . Recovery from atherosclerotic luminal narrowing or myocardial infarction is thought to proceed in part via flow-dependent vessel remodeling ( Heil and Schaper , 2004 ) . Vascular graft adaptation also requires activation of signaling pathways activated by high shear stress to promote healing of the graft by preventing intimal proliferation ( Kohler et al . , 1991 ) . Arteriovenous malformations are also thought to have a flow-dependent component ( Corti et al . , 2011 ) . Thus , further understanding of the molecular sensors and downstream signaling pathways that control flow-dependent remodeling is relevant to a broad range of vascular dysfunction .
Human Umbilical Vein Endothelial Cells ( HUVECs ) pooled from three different donors were obtained from the Yale Vascular Biology and Therapeutics program and cultured in M199 medium supplemented with 20% Fetal Bovine Serum , 50 µg . ml−1 of Endothelial Cell growth Supplement ( ECGS ) prepared from bovine hypothalamus , 100 μg . ml−1 heparin , 100 U . ml−1 penicillin and 100 μg . ml−1 streptomycin . They were used between passage 3 and 7 . Human Dermal Lymphatic Endothelial Cells ( HDLECs ) were obtained from Lonza ( Basel , Switzerland ) and cultured in EGM-2 MV medium and used from passage 5 to 7 . Cells were starved in M199 medium supplemented with 5% FBS and 100 U . ml−1 penicillin and 100 μg . ml−1 streptomycin for a minimum of 4 hr before further treatments . Cells were seeded on tissue culture plastic slides cut from 150 mm tissue culture dishes ( Falcon ) , coated with 20 μg . ml−1 fibronectin . Confluent cells were subjected to steady laminar shear stress in a modified parallel plate flow chamber ( Figure 1 ) in which the gasket was a silicon sheet of either 0 . 8 or 1 . 6 mm height ( Grace Bio-Labs , Bend , OR , #664172 and #664283 ) cut to generate a linear gradient of shear stress , calculated from ( Usami et al . , 1993 ) . Flow was applied for 16 hr in starvation medium . Cells were then fixed with 4% formaldehyde in PBS for 10 min , permeabilized with 0 . 5% Triton x-100 in PBS for 10 min , blocked with Startingblock buffer ( ThermoScientific ) for 30 min at room temperature and probed overnight at 4°C with a primary antibody diluted in Startingblock buffer . Slides were stained with Hoechst 33342 to label nuclei , with rabbit anti-p65 antibody ( Cell Signaling ) to label NF-κB , and with rabbit anti-Smad1 antibody ( Cell Signaling ) . Images were acquired with a Perkin Elmer spinning disk confocal microscope equipped with an automated stage which was used to take successive pictures along the chamber channel . Masks of the images were made using a combination of an adaptive histogram equalization algorithm with intensity and size thresholding . Cell orientation was calculated by taking the masks of the cell nuclei , fitting to an ellipse , and finding the angle between the flow direction and the major-axis of the ellipse . Nuclear translocation was computed by taking the mask of the nucleus and determining the integrated intensity of the transcription factor stain ( Smad1 or p65 ) in the nucleus and in the whole cell . The ‘translocation factor’ ( TF ) was calculated by dividing the integrated intensity in the nucleus by the value for the whole cell . If the entire signal is localized to the nucleus , TF = 1 , while if the entire signal is cytoplasmic , TF = 0 . Depletion of VEGFR3 was achieved by transfecting 10 nM siRNA ( L-003138-00 OnTarget Smartpool Human FLT4 , ThermoScientific ) with Lipofectamine RNAi Max ( Invitrogen ) , following the manufacturer's instructions . Transfection efficiency was assessed by Western-blot . Human VEGFR3-GFP was cloned in adenoviral ( pAd ) expression vector . Cells were infected with the virus in medium with polybrene ( 5 mg/ml ) overnight and used 48 hr later . GFP expression in HUVEC or HUVEC infected with VEGFR3-GFP was assayed on a Stratedigm S1000EX ( Stratedigm , San Jose , CA ) . Data were analyzed with the FlowJo software ( TreeStar , Ashland , OR ) . Cells were washed with cold PBS and proteins extracted with Laemmli's buffer . Samples were run on 10 or 12% SDS-PAGE and transferred onto nitrocellulose membranes . The membranes were blocked with StartingBlock buffer ( ThermoScientific ) and probed with primary antibodies overnight at 4°C: VEGFR3 ( R&D systems ) , phospho-VEGFR3 ( Cell Applications ) , VEGFR2 ( Cell Signaling ) , PECAM-1 ( Abcam ) , VE-cadherin ( Santa Cruz ) , GFP ( Invitrogen ) and actin ( Santa Cruz ) . DyLight conjugated fluorescent secondary antibodies ( 680 nm and 800 nm , Thermoscientific ) or HRP-conjugated antibodies were used to detect primary antibodies . Bands were detected and quantified with an Odyssey infrared imaging system for DyLight antibodies ( Li-Cor ) or a BioRad western blot imaging system ( Bio Rad ) . Zebrafish were grown and maintained according to protocols approved by the Yale University Animal Care . The Tg ( kdrl:mCherry; flt4:citrine ) was used ( Bussmann and Schulte-Merker , 2011 ) . Morpholinos ( Nicoli et al . , 2012 ) were injected at the indicated concentrations and morphants were observed in a confocal microscope ( SP5 Leica Microsystems ) . Images captured using Leica application suite software . Chemical treatment with nifedipine 40 µM was performed as previously described , 4 hr prior imaging ( Bussmann et al . , 2011 ) . All animal experiments were approved by the Institutional Care and Use Committee of Yale University . The Vegfr3::YFP ( Calvo et al . , 2011 ) , Cdh5CreERT2 ( Pitulescu et al . , 2010; Wang et al . , 2010 ) , Vegfr3flox/flox ( Haiko et al . , 2008 ) mice were described previously . Cdh5CreERT2 mice were crossed with Vegfr3flox/flox mice to generate endothelial-specific inducible Vegfr3 mutant mice . 6–8 weeks old Vegfr3flox/flox mice , with or without the Cre recombinase , were injected intra-peritoneally with 2 mg tamoxifen ( TX; at 20 mg/ml in peanut oil ( Sigma ) with 10% Ethanol ) once per day for 5 consecutive days ( induction period ) . Mice were euthanized then fixed by perfusion with 3 . 7% formaldehyde 1 , 2 , 3 or 7 weeks after induction . Ear tissue was fixed overnight in 3 . 7% formaldehyde . The ear skin was removed , cleaned of connective tissue and cartilage , and permeabilized for 4 hr in permeabilization buffer ( 1% BSA , 1% NGS , 0 . 5% Tween in PBS ) . The skin was then incubated with antibody against LYVE-1 or VEGFR3 , in 50% permeabilization buffer/50% PBS for 2 days at 4°C . After washing , the skin was incubated with secondary antibodies overnight 4°C in the same buffer . The skin was then flat mounted in Fluoromount G ( Southern Biotech ) and imaged with a Perkin Elmer spinning disk confocal microscope with a 20× objective . The aorta was removed , cleaned of all connective tissue , fixed overnight in 3 . 7% formaldehyde at 4°C and embedded in paraffin . Paraffin embedding and sectioning were performed by the Yale Pathology department , in the Tissue Microarray facility . Aortas were cut longitudinally , paraffin was removed in xylene baths and sections progressively rehydrated before antigen retrieval for 30 min at 95°C in citrate buffer ( 10 mM sodium citrate , 0 . 05% Tween , pH = 6 ) . Sections were blocked for 30 min in StartingBlock blocking buffer ( ThermoScientific ) and probed either with anti-MMP9 antibody ( Abcam , 1/400 ) , anti-VEGFR3 antibody ( R&D ) or anti-GFP antibody ( Invitrogen , 1/400 ) . Slides were then washed 3× in PBS-Tween and once in PBS , then incubated with donkey-anti rabbit AlexaFluor 647 secondary antibody ( Molecular Probes , 1 hr at RT , 1/500 ) . Slides were washed 3× in PBS-Tween and once in PBS , then mounted in Fluoromount G ( Southern Biotech ) . Slides were imaged with a Nikon Eclipse 80i epifluorescence microscope . Image analysis of MMP9 staining was performed by measuring the area under the curve of the fluorescence signal coming from the media in 4 different 20× pictures for each individual aorta . The fluorescence profile was obtained with MeasureEndo , an ImageJ macro . Values indicated in the text are mean ± SD . At least three independent experiments were performed for each condition . Statistical tests were performed by using either analysis of variance tests ( ANOVA ) or unpaired Student's t-tests . The ANOVA test performed on Figures 2 and 3 tested the null hypothesis that shear stress magnitude does not have an effect on either cell orientation or p65 and Smad1 nuclear translocation . | Blood and lymphatic vessels remodel their shape , diameter and connections during development , and throughout life in response to growth , exercise and disease . This process is called vascular remodeling . The endothelial cells that line the inside of blood and lymphatic vessels are constantly exposed to the frictional force from flowing blood , termed fluid shear stress . Changes in shear stress are sensed by the endothelial cells , which trigger vascular remodeling to return the stress to the original level . It has been proposed that remodeling is governed by a preferred level of fluid shear stress , or set point , against which deviations in the shear stress are compared . Thus , changing the fluid flow through a blood vessel increases or decreases shear stress , which results in the vessel remodeling to restore the original level of shear stress . Like all remodeling , this process involves inflammation to recruit white blood cells , which assist with the process . Baeyens et al . investigated whether such a shear stress set point exists and what its biological basis might be using cultured endothelial cells from human umbilical veins . These cells remained stable and in a resting state when a particular level of shear stress was applied to them; above or below this shear stress level , the cells produced an inflammatory response like that seen during vascular remodeling . This suggests that these cells do indeed have a set point for shear stress . The same response occurred in human lymphatic endothelial cells , although in these cells the shear stress set point was much lower , correlating with the low flow in lymphatic vessels . Baeyens et al . then discovered that the shear stress set point is related to the level of a protein called VEGFR3 in the cells , which was recently found to participate in shear stress sensing . Endothelial cells from lymphatic vessels normally produce much greater quantities of VEGFR3 than those from blood vessels . Reducing the amount of VEGFR3 in lymphatic endothelial cells increased the set point shear stress , while increasing the levels in blood vessel cells decreased the set point . This suggests that the levels of this protein account for the difference in the response of these two cell types . Baeyens et al . then tested this pathway by reducing the levels of VEGFR3 in zebrafish embryos and in adult mice . In both animals , this caused arteries to narrow , showing that VEGFR3 levels also control sensitivity to shear stress—and hence vascular remodeling—inside living creatures . Understanding in detail how vascular remodeling is regulated could help improve treatments for a wide range of cardiovascular conditions . To do so , further work will be needed to develop methods to control the sensitivity of endothelial cells to shear stress and to identify other proteins that might specifically control the narrowing or the expansion of vessels in human patients . | [
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] | 2015 | Vascular remodeling is governed by a VEGFR3-dependent fluid shear stress set point |
The joints of mammals are lined with cartilage , comprised of individual chondrocytes embedded in a specialized extracellular matrix . Chondrocytes experience a complex mechanical environment and respond to changing mechanical loads in order to maintain cartilage homeostasis . It has been proposed that mechanically gated ion channels are of functional importance in chondrocyte mechanotransduction; however , direct evidence of mechanical current activation in these cells has been lacking . We have used high-speed pressure clamp and elastomeric pillar arrays to apply distinct mechanical stimuli to primary murine chondrocytes , stretch of the membrane and deflection of cell-substrate contacts points , respectively . Both TRPV4 and PIEZO1 channels contribute to currents activated by stimuli applied at cell-substrate contacts but only PIEZO1 mediates stretch-activated currents . These data demonstrate that there are separate , but overlapping , mechanoelectrical transduction pathways in chondrocytes .
In diarthrodial joints , which allow a large degree of movement , the surfaces of the opposing bones are lined with hyaline cartilage which reduces friction . This tissue is avascular and non-innervated and comprised of individual chondrocytes embedded in an extracellular matrix ( ECM ) . Production and homeostatic maintenance of cartilage structure is dependent on chondrocytes ( Hall et al . , 1996 ) . Chondrocytes sense changes in the physical microenvironment and mechanical loading within the joints and adjust the balance of anabolic and catabolic processes to maintain the integrity and physical properties of the ECM ( Buckwalter and Mankin , 1997a; Goldring and Marcu , 2009 ) . Disrupting these homeostatic processes can lead to osteoarthritis ( OA ) whereby inappropriate activation of catabolic pathways leads to cartilage degradation ( Buckwalter and Mankin , 1997b ) . It is therefore important to define how chondrocytes respond to mechanical stimuli and to understand how the sensitivity of the mechanotransduction pathways is modulated as both excessive and insufficient mechanical loading of the joint can lead to joint dysfunction . Chondrocytes are embedded within a complex , viscoelastic environment formed by specialized ECM , proteoglycans and water ( Sophia Fox et al . , 2009; Mow et al . , 1984 ) . Physiologically , the cartilage is subjected to a spectrum of mechanical inputs ( Sanchez-Adams and Athanasiou , 2011 ) . Cartilage is regularly impacted by compressive forces that are initially carried by the fluid phase , before being transferred to the elastic ECM molecules within the tissue ( Mow et al . , 1980 ) . The movement of fluid within the joints also generates shear forces ( Wong et al . , 2008 ) , whereas tensile forces are transmitted to chondrocytes via the surrounding pericellular matrix ( PCM ) ( Guilak et al . , 2006 ) . Given the biomechanical complexity of this system , it is difficult to model precisely how these various mechanical inputs are experienced by the cells; however , in the simplest terms , cells will experience mechanical stimuli propagated both via the fluid phase and via the matrix to which the cells are bound . Cellular mechanotransduction depends on a number of distinct processes ( Roca-Cusachs et al . , 2012 ) including channel-mediated ionic flux across the membrane ( Nilius and Honoré , 2012; Martinac , 2004; Arnadóttir and Chalfie , 2010 ) , integrin-mediated signaling ( Chen et al . , 2004; Schwartz , 2010 ) , action of strain gauge proteins ( Hirata et al . , 2008 ) or cytoskeleton-mediated transfer of mechanical signals from the plasma membrane to the nucleus ( Maniotis et al . , 1997 ) . In chondrocytes , a number of these pathways have been implicated in the mechanotransduction that is required for homeostasis; however , in this study , we focus on the role of mechanically gated ion channels . We refer here to channel-mediated mechanotransduction as mechanoelectrical transduction in order to distinguish this process from parallel mechanotransduction mechanisms . It has long been proposed that ion channels play a role in the process of chondrocyte mechanotransduction . Hyperpolarization of chondrocytes on application of mechanical loads is inhibited ( Wright et al . , 1996 ) and matrix production is altered ( Mouw et al . , 2007 ) in the presence of GdCl3 , a non-specific inhibitor of mechanically gated ion channel activity . Blocking the TRPV4 ion channel using a specific antagonist ( GSK205 ) inhibits matrix production in response to compressive mechanical stimulation and the TRPV4 agonist , GSK1016790A , stimulates matrix production in the absence of mechanical stimulation ( O'Conor et al . , 2014 ) . Additionally , mutations in the human TRPV4 gene can lead to joint dysfunction ( Lamandé et al . , 2011; Loukin et al . , 2010 ) . In mouse models , a global Trpv4-/- knockout leads to an increased susceptibility to obesity-induced ( O'Conor et al . , 2013 ) and age-related OA ( Clark et al . , 2010 ) , whereas conditional knockout of Trpv4 in adult cartilage decreases the risk of age-related OA ( O'Conor et al . , 2016 ) . Despite this growing body of evidence that TRPV4 is directly involved in chondrocyte mechanotransduction , no evidence for gating of TRPV4 by mechanical stimuli ( other than osmotic stimuli ( Lechner et al . , 2011 ) , and membrane-stretch in Xenopus laevis oocytes ( Loukin et al . , 2010 ) ) has been presented . More recently , it has been shown that Ca2+ spikes in isolated porcine chondrocytes ( detected using Ca2+ imaging ) are reduced when the mechanically gated Piezo1 and Piezo2 channel transcripts are knocked down using siRNA ( Lee , 2014 ) . Both PIEZO1 and PIEZO2 have been demonstrated to mediate mechanically gated ion currents in neuronal cells and neuronal cell lines ( Coste et al . , 2012; Ranade et al . , 2014a ) . Beyond the nervous system , PIEZO1 has been found to be functionally relevant in the vasculature ( Li et al . , 2014; Ranade et al . , 2014b ) , urothelium ( Miyamoto et al . , 2014 ) , tubal epithelial cells ( Peyronnet et al . , 2013 ) , erythrocytes ( Zarychanski et al . , 2012 ) , as well as in porcine chondrocytes ( Lee , 2014 ) . However , in these non-neuronal cell types there has , to date , only been one publication that has directly measured mechanical activation of ion channels in intact cells and a reduction in channel gating when PIEZO1 is absent ( Peyronnet et al . , 2013 ) . What has been lacking is: ( 1 ) a direct demonstration of mechanically gated channel activity in chondrocytes; ( 2 ) a quantitative analysis of the relative contributions of distinct mechanically gated ion channels in chondrocyte mechanotransduction and ( 3 ) an analysis of how chondrocytes respond to distinct mechanical stimuli . Here , we have used an experimental approach wherein we apply mechanical stimuli at cell-substrate contact points and concurrently monitor membrane currents using whole-cell patch-clamp ( Poole et al . , 2014 ) . This approach allows us to measure channel activity in response to mechanical stimuli that are applied via connections to the substrate . Using this approach , we show that we can measure mechanically gated currents in intact chondrocytes . To the best of our knowledge , these measurements represent the first direct demonstration of mechanically gated ion channel activity in primary chondrocytes . We have further demonstrated that both the TRPV4 and PIEZO1 channels contribute to this current and that , in particular for TRPV4 , the nature of the membrane environment and applied stimulus are crucial for channel gating .
To study mechanically gated ion channels in chondrocytes , we prepared primary cells from mouse articular cartilage isolated from the knees and femoral heads of 4- to 5-day-old mouse pups . A fraction of these cells were encapsulated in alginate beads and the remainder seeded in 2D tissue culture flasks . The chondrocytes cultured in alginate beads retained the chondrocyte phenotype ( high levels of Sox9 transcript , spherical morphology and staining for SOX9 and Collagen X [Lefebvre et al . , 1997 , 2001; Dy et al . , 2012; Poole et al . , 1984; Ma et al . , 2013] ) ( Figure 1A–B ) . The cells seeded in tissue culture flasks dedifferentiated away from the chondrocyte phenotype , as reflected in reduced levels of Sox9 transcript , a fibroblast-like morphology ( Caron et al . , 2012 ) and negative staining for SOX9 and Collagen X ( Figure 1B ) . Dedifferentiated cells from tissue culture flasks were redifferentiated back into the chondrocyte phenotype by encapsulating them in alginate for 7 days ( Figure 1 , Figure 1—figure supplement 1 ) . We found that SOX9-positive cells exhibited a spherical morphology and that the average diameter of these cells was 11 . 7 ± 2 . 0 µm ( mean ± s . d . , n = 77 cells ) ( Figure 1—figure supplement 1 ) . Accordingly , the cells with a chondrocyte phenotype could be distinguished on the basis of their morphology and selected for study using bright-field microscopy in a live , 2D culture . 10 . 7554/eLife . 21074 . 003Figure 1 . Primary , murine chondrocyte culture . ( A ) Transcript levels of the transcription factor Sox9 in just harvested chondrocytes , dedifferentiated cells ( post 7 days in monolayer culture ) and redifferentiated chondrocytes ( recovered from 2D plastic and encapsulated in alginate for 7 days ) . Data are displayed as mean ± s . e . m . Note , significantly less Sox9 transcript was detected in the population of dedifferentiated cells ( one-way ANOVA , Tukey Post-hoc test *p=0 . 035; n ≥ 3 . ) ( B ) Phase contrast and epi-fluorescent images representative of the morphological differences between chondrocytes , dedifferentiated and redifferentiated cells . SOX9 was detected in the nucleus and Collagen X at the membrane of chondrocytes and redifferentiated cells , but not the dedifferentiated population ( inverted images and overlay ) . Scale bar 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 00310 . 7554/eLife . 21074 . 004Figure 1—figure supplement 1 . Schematic diagram of the isolation and culture of primary murine chondrocytes . ( A ) Knees and femoral heads were isolated from 4- to 5-day-old mouse pups to obtain chondrocytes . A fraction of the isolated cells were encapsulated in alginate to maintain their differentiation state and the rest were expanded in 2D tissue-culture flasks . When passaged less than three times , the cells could be recovered from the flasks and redifferentiated by encapsulation in 3D alginate beads . For pillar array experiments , cells were recovered by dissolving the alginate and then seeded on arrays . The dedifferentiated population of the studied cells were those that dedifferentiated in situ . ( B ) Chondrocytes with circular morphology exhibit a higher SOX9 intensity . After redifferentiation in alginate , chondrocytes were recovered , seeded on glass coverslips , fixed and labelled with anti-SOX9 antibody . Samples were imaged using epifluorescent and bright-field imaging . A measure of the morphology of the cells ( degree of circularity determined from bright-field images where 1 defines a perfect circle ) was plotted against the SOX9 signal ( derived from epifluorescent imaging where intenstity was normalized to the highest intensity measured in each sample ) . Data are displayed as individual values of intensity and circularity . n = 144 cells . ( C ) Representative images of cells corresponding to the marked points ( red and blue ) within the graph . On the basis of the morphology and SOX9 signal , we refer to the round cells as chondrocytes ( red point ) and the fibroblast-like as dedifferentiated cells ( blue point ) . Scale bar 10 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 004 Within the cartilage , mechanical stimuli are transferred to chondrocytes via the surrounding PCM ( Guilak et al . , 2006 ) . We tested whether the regions of the membrane that form the cell-substrate interface constitute an important compartment for mechanoelectrical transduction . We seeded chondrocytes on an elastomeric pillar array cast in polydimethylsiloxane ( PDMS ) where each element of the array had defined dimensions and each cell-substrate contact point was 10 µm2 ( Figure 2A ) ( Poole et al . , 2014 ) . A glass probe ( driven by a Piezo-electric element ) was used to deflect an individual pilus in order to apply a series of fine deflection stimuli to the cell directly at the cell-substrate interface ( for range of deflections see Figure 2A ) . 10 . 7554/eLife . 21074 . 005Figure 2 . Mechanoelectrical transduction currents in primary cells isolated from mouse cartilage . ( A ) Deflection stimuli applied via cell-matrix contact points . Left panel: cartoon of pillar array experiment , stimuli are applied by deflecting a pilus subjacent to a cell that is concurrently monitored using whole-cell patch-clamp ( blue indicates stimulator probe and orange the patch pipette . ) Right panel: bright-field image of a chondrocyte seeded on the pillar array . Successive images of the movement of the highlighted pilus demonstrate the degree of movement corresponding to the stimuli used in this study ( B ) Deflection-gated mechanoelectrical transduction currents in chondrocytes . Bright-field image of a chondrocyte and corresponding example traces of deflection-gated currents ( red ) . ( C ) Deflection-gated mechanoelectrical transduction currents in dedifferentiated cells . Bright-field image of a dedifferentiated cell and representative traces of deflection-gated currents ( blue ) . ( D ) Comparison of current kinetics . Left panel indicates values measured ( latency ( magenta ) , activation time constant ( τ1 , blue ) and current decay ( τ2 , green ) ) . Data are displayed as individual values ( chondrocytes: red , dedifferentiated cells: cyan ) , mean ± s . e . m . superimposed in black . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 00510 . 7554/eLife . 21074 . 006Figure 2—source data 1 . Electrophysiological characteristics of WT chondrocytes and WT dedifferentiated cells . Chondrocytes were isolated from C57Bl/6 mice . For each sample ( chondrocyte phenotype and dedifferentiated phenotype ) the number of litters , recorded cells and number of cells that respond to pillar deflections are shown along with the total number of stimulation points ( corresponding to the number of distinct pili deflected ) and the total number of measurements ( i . e . individual deflections ) . For each recorded current , the latency and the current amplitude were measured , and the activation time constant and current decay were obtained from a mono-exponential fit of the data . The mean ± s . e . m . and the median are displayed for each kinetic parameter . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 006 In order to analyze chondrocyte mechanoelectrical transduction , cells were released from alginate and seeded over pillar arrays coated with poly-ι-lysine ( PLL ) . The cells attached and initially exhibited the spherical morphology typical of chondrocytes . Within 3 hr , the morphology of a subset of cells became more fibroblast-like as the cells dedifferentiated . We investigated whether the chondrocytes and the cells that had dedifferentiated in situ exhibited similar mechanoelectrical transduction properties in order to determine if these cells with distinct morphologies could be treated as a coherent sample . The application of stimuli to the chondrocytes evoked deflection-gated inward currents in 88 . 9% of cells ( Figure 2B ) ( 24/27 cells ) . Deflection-gated currents were also observed in dedifferentiated cells ( Figure 2C ) ( 88 . 2% ( 15/17 cells ) ) . The kinetics of these currents suggested a channel directly gated by mechanical stimuli ( chondrocyte currents: latency = 3 . 6 ± 0 . 3 ms , activation time constant ( τ1 ) = 1 . 7 ± 0 . 3 ms , dedifferentiated cell currents: latency = 3 . 1 ± 0 . 3 ms , τ1 = 1 . 4 ± 0 . 3 ms , mean ± s . e . m . , n = 99 and 109 currents , measured across 24 chondrocytes and 15 dedifferentiated cells ) ( Figure 2D ) . We found that both the latency and the τ1 values were significantly faster for currents measured in the dedifferentiated cells ( Mann-Whitney U test , p=0 . 018 , p=0 . 04 , respectively ) . In addition , whilst no significant difference was noted in the τ2 values ( p=0 . 19 ) , the variance in the τ2 of currents measured in dedifferentiated cells was significantly higher compared to chondrocytes ( F test , p<0 . 0001 , n = 109 and 99 currents , respectively ) . These data demonstrate ion channel-mediated mechanoelectrical transduction in chondrocytes . Such measurements have previously proven impossible due to application of techniques incompatible with simultaneous patch-clamp analysis or that result in the destruction of cellular integrity before any mechanical activation of ion channels can be observed , such as cellular indentation of chondrocytes ( Lee , 2014 ) . An advantage of applying stimuli via pillar arrays is that the stimuli are applied to a defined area of membrane . We therefore quantified the magnitude of each applied stimulus , and compared the sensitivity of mechanoelectrical transduction in distinct subsets of cells . Each individual pilus acts as a light guide , such that the center can be calculated from a 2D Gaussian fit of intensity values within a bright-field image ( du Roure et al . , 2005 ) . An image was taken before , during and after the stimulus , and the magnitude of each deflection was subsequently calculated from the difference between the coordinates of the center of the pilus in successive images . In order to collect stimulus-response data , we applied stimuli across the range 1–1000 nm to each cell and measured the currents that were evoked . To compare the sensitivity of the mechanoelectrical transduction in chondrocytes versus dedifferentiated cells , our analysis included only those cells that responded to at least one stimulus within the 1–1000 nm range . We binned current amplitude data by stimulus size and averaged across cells for each bin ( Figure 3A ) . We found that stimuli within the ranges of 10–50 nm and 250–500 nm produced significantly larger currents in the dedifferentiated cells , in comparison with chondrocytes ( Mann-Whitney test , for the range 10 nm to 50 nm p=0 . 02 and for 100 nm to 250 nm p=0 . 004 ) ( Figure 3A ) . When the stimulus-response data was compared using two-way ANOVA , the response of the chondrocytes was significantly different to that of the dedifferentiated cells ( Figure 3A; 24 chondrocytes vs 15 dedifferentiated cells , p=0 . 03 ) . In addition , the smallest stimulus required to gate currents was significantly lower for the dedifferentiated cells , compared to chondrocytes ( 59 ± 13 nm ( mean ± s . e . m . , 15 cells ) ; 252 ± 68 nm ( mean ± s . e . m . , 24 cells ) , Mann-Whitney test p=0 . 028 ) ( Figure 3B ) . We conclude that , compared to chondrocytes , the dedifferentiated cells were more sensitive to deflection stimuli applied at cell-substrate contact points . 10 . 7554/eLife . 21074 . 007Figure 3 . Chondrocytes and dedifferentiated cells display distinct mechanosenstivity to substrate deflections . ( A ) Stimulus-response graph of deflection-gated currents in chondrocytes ( red circles ) and dedifferentiated cells ( cyan squares ) . Measurements from an individual cell were binned according to stimulus size and current amplitudes were averaged within each bin , then across cells , data are displayed as mean ± s . e . m . For stimuli between 10–50 and 100–250 nm , the dedifferentiated cells exhibit significantly larger currents . ( Mann-Whitney test *p=0 . 02 and **p=0 . 004 , respectively , n = 24 chondrocytes and 15 dedifferentiated cells . ) Additionally , an ordinary two-way ANOVA indicates that the cell-types differ in their overall response ( *p=0 . 03 ) . ( B ) Chondrocytes and dedifferentiated cells display distinct deflection thresholds to substrate deflections . A threshold was calculated by averaging the smallest deflection that resulted in channel gating , for each cell . The threshold for chondrocytes , 252 ± 68 ( mean ± s . e . m . , n = 24 ) was significantly higher than that calculated for dedifferentiated cells 59 ± 13 ( mean ± s . e . m . , n = 15 ) ( Mann-Whitney , *p=0 . 028 ) . ( C ) Representative traces from HSPC recordings of stretch-activated currents from outside-out patches pulled from chondrocytes ( upper panel ) and dedifferentiated cells ( lower panel ) . ( D ) Stimulus-response curve of pressure-gated currents in chondrocytes ( red ) and dedifferentiated cells ( cyan ) , normalized to maximal amplitude measured for each sample . ( Data are displayed as mean ± s . e . m . , n = 12 chondrocytes , 13 dedifferentiated cells . ) . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 00710 . 7554/eLife . 21074 . 008Figure 3—source data 1 . Statistical comparison of mechanoelectrical transduction currents , chondrocytes vs dedifferentiated cells . ( A ) Statistical comparison of deflection-gated mechanoelectrical transduction responses . For each individual cell , currents were binned in the indicated size ranges ( in nm ) and the current amplitudes within each bin averaged and then averaged across cells . Bins were subsequently tested for normal distribution and subsequently compared with a Student’s t-test ( parametric data sets ) or a Mann Whitney test ( non-parametrical data ) . The p values are shown for significant comparisons , ‘NS’ indicates no significant differences and ‘NA’ is shown when all measurements within a bin were equal to zero . The number of compared points is shown in brackets . An ordinary two-way ANOVA was used to compare the cellular response over the range of stimuli , reported are the p value and F statistic ( including DFn , DFd ) . ( B ) Statistical comparison of stretch-gated mechanoelectrical transduction in chondrocytes . Chondrocytes were isolated from WT mice , expanded and encapsulated in alginate . After deposition on coverslips for measurement , cells were analyzed using HSPC . For each condition , the number of litters , recorded membrane patches and maximal current ( pA ) are shown . Data are displayed as mean ± s . e . m . Conditions were compared with Student’s t-test ( parametric data sets ) , and the p values are shown for significant comparisons , ‘NS’ indicates no significant . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 008 Many cell-types exhibit stretch-activated currents when pressure-stimuli are applied to membrane patches ( Sachs , 2010 ) . Using high-speed pressure-clamp ( HSPC ) on outside-out patches , we detected stretch-activated currents in both chondrocytes and dedifferentiated cells ( Figure 3C ) . Analysis of the P50 showed that there was no significant difference between the sensitivity of stretch-activated currents in chondrocytes ( 87 . 1 ± 6 . 0 mmHg , mean ± s . e . m . , n = 12 ) compared to dedifferentiated cells ( 78 . 7 ± 7 . 4 mmHg , mean ± s . e . m . , n = 13 ) ( Figure 3D ) . These data suggest that the pressure-generated mechanoelectrical transduction in membrane patches is a separable phenomenon from deflection-gated currents observed when stimuli are applied at cell-substrate contact points . Due to the significant differences in mechanoelectrical transduction in response to deflection stimuli in chondrocytes versus dedifferentiated cells all further experiments were conducted on the population of cells exhibiting the chondrocyte phenotype . We used RT-qPCR analysis to determine if Piezo1 and Piezo2 transcript could be detected in murine chondrocytes and to confirm the presence of Trpv4 transcript in these cells . We found significant levels of Trpv4 and Piezo1 transcript; however , Piezo2 transcript could not be reliably detected in our samples , in contrast to the observations made for porcine chondrocytes ( Lee , 2014 ) ( Figure 4—figure supplement 1 ) . In order to directly test whether the PIEZO1 channels are involved in chondrocyte mechanoelectrical transduction , we used validated miRNA constructs ( Poole et al . , 2014 ) to reduce PIEZO1 levels and examined the resulting effect on deflection-gated mechanoelectrical transduction currents . We transfected dedifferentiated cells with a plasmid encoding the Piezo1-targeting miRNA or a scrambled miRNA . Cells were recovered from culture flasks and redifferentiated in alginate beads , before harvesting and seeding onto pillar arrays . Cells expressing the GFP marker were selected for measurement . The percentage of cells that responded to stimuli within the 1–1000 nm range was significantly reduced when chondrocytes were treated with Piezo1-targeting miRNA ( 50% , 6/12 cells ) , in comparison with those cells treated with the scrambled miRNA ( 19/22 cells , Fisher’s exact test , p=0 . 04 ) ( Figure 4A ) . These data show that knocking down the levels of the PIEZO1 channel reduces the likelihood of evoking deflection-gated currents . When the stimulus-response data was plotted , the PIEZO1 knockdown cells showed a tendency for reduced mechanoelectrical transduction , compared to control cells ( Figure 4B ) . 10 . 7554/eLife . 21074 . 009Figure 4 . Substrate-deflection gated currents are mediated by PIEZO1 and TRPV4 . ( A ) Fraction of chondrocytes that responded with at least with one mechanically gated current in response to deflection stimuli . Knockdown of Piezo1 resulted in significantly fewer responsive cells compared with cells treated with non-targeting miRNA ( scrambled ) ( Fisher´s exact test , *p=0 . 04 ) . Trpv4-/- chondrocytes were significantly less likely to respond to deflection stimuli compared with WT cells ( Fisher’s exact test , *p=0 . 03 ) . When the miRNA against Piezo1 was expressed in Trpv4-/- chondrocytes , the response further decreased compared with the WT chondrocytes transfected with a scrambled miRNA ( ***p=0 . 002 , Fisher’s exact test ) . ( B ) Stimulus-response graph of the deflection-gated currents in chondrocytes transfected with a scrambled miRNA ( gray open circles , n = 22 cells ) or Piezo1-targeting miRNA ( green open circles , n = 12 cells ) . Data are displayed as mean ± s . e . m . , and a representative trace of the mechanosensitive currents is shown as insert ( green line ) . ( C ) Cells isolated from a Trpv4-/- mouse are significantly less sensitive to deflections , in comparison with WT cells . Stimulus-response graph of the mechanically gated currents triggered by pillar deflections in WT chondrocytes ( black open circles , n = 27 cells ) and Trpv4-/- chondrocytes ( magenta open circles , n = 13 cells ) . The Trpv4-/- cells are significantly less responsive to substrate deflections ( ordinary two-way ANOVA , *p=0 . 04 ) . Data are displayed as mean ± s . e . m . A representative trace is shown as insert ( magenta line ) . ( D ) Stimulus-response graph of Trpv4-/- chondrocytes transfected with a Piezo1-targeting miRNA . Data are displayed as mean ± s . e . m . ( n = 11 cells ) . Chondrocytes from the Trpv4-/- mouse treated with Piezo1-targeting miRNA were significantly less sensitive to substrate deflections , in comparison with WT cells treated with scrambled miRNA ( ordinary two-way ANOVA , *p=0 . 04 ) . A representative trace is shown as insert ( black line ) . ( E ) Flourometric calcium imaging of chondrocyte responses to Yoda1 and GSK1016790A . Cells were perfused with ATP ( 10 µM ) , Yoda1 ( 10 µM ) and GSK1016790A ( GSK101 , 50 nM ) as indicated by black bars and changes in [Ca2+] were monitored by using the Ca2+ responsive dye , Cal520 . In the left panel , traces correspond to intensity changes in individual cells and in the right panel is a plot representing the average of all cells ( as mean ± s . e . m . ) . Example images are presented of cells before activation , during application of Yoda1 and of GSK1016790A . Scale bar 20 µm . Each cell that responded to ATP was included in the analysis ( 400 cells , two preparations ) . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 00910 . 7554/eLife . 21074 . 010Figure 4—source data 1 . Electrophysiological characteristics of WT , Trpv4-/- and miRNA-treated chondrocytes . ( A ) Electrophysiological characteristics of WT , Trpv4-/- and miRNA-treated chondrocytes . Chondrocytes were isolated from C57Bl/6 and Trpv4-/- mice , expanded , transfected ( in the case of Scrambled and Piezo1 miRNA constructs ) and encapsulated in alginate . For each condition , the number of litters , recorded cells and number of cells that respond to pillar deflections are shown . The total number of stimulation points ( corresponding to the number of distinct pili deflected ) and the total number of measurements ( i . e . individual deflections ) are displayed . For each recorded current , the latency and the current amplitude were measured , and the activation time constant and current decay were obtained from a mono-exponential fit of the data . The mean ± s . e . m . and the median are displayed for each kinetic parameter . ( B ) Statistical comparison of deflection-gated mechanoelectrical transduction responses . For each individual cell , currents were binned in the indicated size ranges ( in nm ) and the current amplitudes within each bin averaged and then averaged across cells . Bins were tested for normal distribution and subsequently compared with a Student’s t-test ( parametric data sets ) or a Mann-Whitney test ( non-parametrical data ) . The p values are shown for significant comparisons , ‘NS’ indicates no significant differences and ‘NA’ is shown when all measurements within a bin were equal to zero or data were not enough to perform the comparison . The number of compared points is shown in brackets . An ordinary two-way ANOVA was used to compare the cellular response over the range of stimuli , reported are the p value and F statistic ( including DFn , DFd ) . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 01010 . 7554/eLife . 21074 . 011Figure 4—figure supplement 1 . Normalized transcript levels of Piezo1 , Piezo2 and Trpv4 in primary chondrocytes . Knees and femoral heads were isolated from litters of C57Bl/6 mouse pups to obtain isolated chondrocytes . The chondrocytes from one litter were pooled to obtain mRNA , which was retro-transcribed to cDNA for RT-qPCR analysis . In these freshly-harvested chondrocytes , Piezo1 and Trpv4 transcripts were detected; however , Piezo2 was not reliably detected in our samples . The transcript levels were normalized against β-actin . n = 5 preparations . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 011 TRPV4 has been proposed to play a role in chondrocyte mechanoelectrical transduction ( Clark et al . , 2010; Leddy et al . , 2014; Dunn et al . , 2013 ) . We therefore studied deflection-gated currents in chondrocytes isolated from Trpv4-/- mice ( Suzuki et al . , 2003 ) ( back-crossed onto a C57Bl/6 background ) . Cells were isolated and cultured in the same fashion as wild-type ( WT ) cells . We found that deflection-gated currents could be observed in a subset of Trpv4-/- chondrocyte yet only 46 . 2% ( 6/13 cells ) responded to deflections within the range of 1–1000 nm , significantly less than the percentage of responsive WT cells , 88 . 9% ( 24/27 cells ) ( Fisher’s exact test , p=0 . 03 ) ( Figure 4A ) . It was challenging to characterize the kinetics of the few , remaining currents . However , the latency between stimulus and channel gating was significantly longer in Trpv4-/-chondrocytes ( 7 . 8 ± 1 . 6 ms ) compared with WT chondrocytes ( 3 . 6 ± 0 . 3 ms ) ( mean ± s . e . m . , n = 12 and 99 currents , respectively , Mann-Whitney test , p=0 . 015 ) . The stimulus-response plot was significantly different in WT chondrocytes vs Trpv4-/- chondrocytes ( two-way ANOVA , p=0 . 04 ) ( Figure 4C ) . These data clearly indicate that both PIEZO1 and TRPV4 are required for normal mechanoelectrical transduction in murine chondrocytes in response to deflections applied at cell-substrate contact points . However , it is also clear that neither PIEZO1 nor TRPV4 are essential to this process , as deflection-gated currents were detected in Trpv4-/- cells and in chondrocytes treated with Piezo1-targeting miRNA . As such , we determined whether removal of both PIEZO1 and TRPV4 had an additive effect on chondrocyte mechanoelectrical transduction , using miRNA to knockdown Piezo1 transcript in Trpv4-/- chondrocytes . In this case , significantly fewer cells ( 2/11 ) responded to deflection stimuli , compared with the WT chondrocytes treated with scrambled miRNA ( Fisher’s exact test , p=0 . 0002 ) ( Figure 4A ) . The stimulus-response plot of Trpv4-/--Piezo1-KD chondrocytes was significantly different to that of scrambled miRNA-treated WT chondrocytes ( Two-way ANOVA , p=0 . 04 ) . In addition , the stimulus-response plot for Trpv4-/--Piezo1-KD cells highlights how little current activation was observed in the cells that responded to at least one stimulus ( Figure 4D ) . These residual currents likely resulted from an incomplete knockdown of Piezo1 transcript . We then asked whether these data reflect two subpopulations of cells , expressing either TRPV4 or PIEZO1 , using calcium imaging experiments . Chondrocytes were loaded with the Cal520 calcium-sensitive dye and perfused with 10 µM ATP to test for viability . After ATP washout , cells were perfused with the PIEZO1 activator Yoda1 ( 10 µM ) . All the cells that had responded to ATP also exhibited an increase in Ca2+ signal when treated with Yoda1 . Following Yoda1 washout , the cells were then perfused with the TRPV4 agonist , GSK1016790A ( 50 nM ) . All the analyzed cells exhibited an increase in Ca2+ signal when treated with GSK1016790A ( 400 cells , from two separate chondrocyte preparations; Figure 4E ) . These data clearly demonstrate that both PIEZO1 and TRPV4 are expressed and active in the membrane of all of the viable chondrocytes isolated from the articular cartilage . In order to definitively test whether TRPV4 is activated in response to substrate deflections , we used the TRPV4-specific antagonist GSK205 ( Vincent and Duncton , 2011 ) . We found that acute application of GSK205 ( 10 µM ) reversibly blocked deflection-gated ion channel activity ( n = 12 WT cells from five preparations ) ( Figure 5A ) . In the presence of GSK205 , deflection-gated current amplitudes were significantly smaller , 13 ± 6% ( mean ± s . e . m . ) of pre-treatment values . After washout of the TRPV4 antagonist , current amplitudes recovered to 97 ± 28% of pretreatment values ( Figure 5B ) ( one-way ANOVA , matched measures with Dunnett’s post-hoc test for multiple comparisons . p=0 . 01 treated vs pre-treated ) . These data indicate that TRPV4 directly mediates a large fraction of deflection-activated currents in WT chondrocytes . 10 . 7554/eLife . 21074 . 012Figure 5 . TRPV4 directly mediates deflection-gated currents in primary chondrocytes . ( A ) Representative traces of currents gated by pillar deflections before , during and after the wash out of the TRPV4 antagonist GSK205 ( 10 µM , 3 min ) . ( B ) Quantification of the inhibition of the current during the GSK205 application , the current amplitude was normalized against pre-treatment currents . Data represent average of 12 measurements . ( One-way ANOVA , matched measures with Dunnett’s post-hoc test for multibple comparisons . **p=0 . 01; ns = not significant ) . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 012 Recently , evidence was provided from calcium-imaging experiments that TRPV4 in chondrocytes is activated by hypo-osmotic stimuli ( O'Conor et al . , 2014 ) . Hypo-osmotic stimuli induce cell swelling ( Lechner et al . , 2011 ) , and it has thus been postulated that TRPV4 is activated by the resulting membrane stretch . Accordingly , we investigated stretch-activated currents in outside-out membrane patches isolated from chondrocytes . We first tested chondrocytes transfected with either a scrambled miRNA or the Piezo1-targeting miRNA . It was not possible to generate a pressure-response curve using this second data set , as there was insufficient current activation over the range of applied stimuli . As such , we compared the peak current amplitude measured in outside-out patches . We found that when Piezo1 was knocked down the stretch-mediated peak current amplitude measured using HSPC was 4 . 1 ± 0 . 8 pA ( mean ± s . e . m . , n = 10 ) , significantly smaller than that measured in patches pulled from chondrocytes transfected with scrambled miRNA , 72 . 8 ± 14 . 3 pA ( mean ± s . e . m . , n = 11 ) ( Student’s t-test , p=0 . 0002 ) ( Figure 6A ) . 10 . 7554/eLife . 21074 . 013Figure 6 . Murine chondrocytes display a stretch-sensitive current dependent on PIEZO1 but not TRPV4 . ( A ) Comparison of maximal current induced by membrane stretch in outside-out patches isolated from chondrocytes . HSPC experiments were performed in membrane patches isolated from chondrocytes that were either: WT transfected with scrambled miRNA , WT transfected with Piezo1-targeting miRNA , WT , Trpv4-/- or Trpv4-/- transfected with Piezo1-targeting miRNA . WT chondrocytes transfected with Piezo1-targeting miRNA display significantly smaller maximal current amplitudes than WT chondrocytes transfected with scrambled miRNA ( n = 11 and 10 patches , respectively , unpaired Student’s t-test , ***p=0 . 0002 ) . In contrast , peak current amplitude in Trpv4-/- chondrocytes was indistinguishable from that measured in WT chondrocytes . The treatment of Trpv4-/- chondrocytes with Piezo1-targeting miRNA led to a significant reduction in peak current amplitude compared to WT cells treated with scrambled miRNA ( n = 7 and 11 patches , respectively , unpaired Student’s t-test , **p=0 . 002 ) . Number of Trpv4-/--Piezo1-KD chondrocytes: 11 scrambled-miRNA; 10 Piezo1-miRNA; 11 WT; 7 Trpv4-/-; 7 Trpv4-/-: Piezo1-miRNA . ( B ) Example traces of currents measured using HSPC in outside-out patches . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 01310 . 7554/eLife . 21074 . 014Figure 6—source data 1 . Statistical comparison of stretch-gated mechanoelectrical transduction in chondrocytes . Chondrocytes were isolated from WT and Trpv4-/- mice , expanded , transfected ( in the case of Scrambled and Piezo1 miRNA constructs ) and encapsulated in alginate . For each condition , the number of litters , recorded membrane patches and maximal current ( pA ) are shown . Data are displayed as mean ± s . e . m . Conditions were compared with Student’s t-test ( parametric data sets ) and the p values are shown for significant comparisons , ‘NS’ indicates no significant . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 01410 . 7554/eLife . 21074 . 015Figure 6—figure supplement 1 . The P50 measured in WT and Trpv4-/- chondrocytes using HSPC is not significantly different . Stimulus-response curves obtained from outside-out patches pulled from WT or Trpv4-/- chondrocytes and stimulated with positive pressure steps ( 10-150 mmHg ) . Individual normalized responses were pooled and averaged . Individual fits to a sigmoidial equation were averaged to obtain P50 values . The P50 for WT and for Trpv4-/- chondrocytes is 87 . 1 ± 6 . 0 mmHg ( n = 12 ) and 88 . 2 ± 9 . 3 mmHg ( n = 7 ) respectively . Data are presented as mean ± s . e . m . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 01510 . 7554/eLife . 21074 . 016Figure 6—figure supplement 2 . WT chondrocytes respond to the TRPV4 agonist GSK101 but not chondrocytes isolated from a Trpv4-/- mouse . ( A ) WT chondrocytes respond to the application of TRPV4 agonist GSK1016790A ( GSK101 ) . Intensiometric epifluorescent imaging was used to demonstrate the presence of functional TRPV4 in WT , murine chondrocytes . Cells were loaded with the fluorescent calcium dye Cal520-AM , stimulated with ATP ( 100 μM , addition indicated by a cyan line ) as a positive control to demonstrate viability and then treated with GSK101 ( 100 nM , application indicated by red line ) . The average ratio of fluorescence signal intensity: the ratio of the intensity of the fluorescence signal/baseline was plotted against time . The right panel displays example images of the fluorescence intensity before and during treatment with GSK101 . n = 4 different preparations . Scale bar 50 μm . ( B ) Trpv4-/- chondrocytes do not respond to the addition of GSK101 . Trpv4-/- chondrocytes were treated with ATP to confirm their viability and subsequently with GSK101; the average signal shows that the Trpv4-/- chondrocytes respond to ATP but not to the TRPV4 agonist ( cyan line indicates the application of ATP to the sample and the red of GSK101 ) . Example images are presented in the right hand panel . Scale bar 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 016 We then compared outside-out patches isolated from WT chondrocytes to those isolated from Trpv4-/- mice . We found that patches pulled from WT chondrocytes exhibited robust currents to applied pressure , with a P50 of 87 . 1 ± 6 . 0 mmHg ( mean ± s . e . m . , n = 12 ) . However , we observed comparable stretch-activated currents in patches isolated from Trpv4-/- cells with a mean P50 for activation ( 88 . 2 ± 9 . 3 mmHg ( mean ± s . e . m . , n = 7 ) ) ( Figure 6—figure supplement 1 ) . In addition , there was no significant difference in peak current amplitude measured in these sample sets ( Trpv4-/- , 51 . 4 ± 12 . 9 pA , n = 7; WT , 45 . 2 ± 7 . 5 pA , n = 12; mean ± s . e . m . ) ( Figure 6A ) . We confirmed that these cells lacked functional TRPV4 using the TRPV4-agonist GSK1016790A ( Figure 6—figure supplement 2 ) . When we treated Trpv4-/- cells with Piezo1-targeting miRNA we found that peak current amplitude ( 5 . 2 ± 0 . 9 pA , n = 7; mean ± s . e . m . ) was significantly reduced , in comparison with the WT chondrocytes treated with scrambled miRNA ( Student’s t-test , p=0 . 002 ) . The example traces presented in Figure 6B clearly demonstrate the loss of the stretch-activated current when Piezo1 was knocked down . These data demonstrate that PIEZO1 is largely responsible for the stretch-activated current in chondrocytes , whilst TRPV4 does not seem to play a role in this specific mechanoelectrical transduction pathway . In addition , the fact that stretch-activated currents in WT and Trpv4-/- cells were indistinguishable supports the hypothesis offered above that stretch-gated and deflection-gated currents represent distinct phenomena . TRPV4 is a polymodal channel ( Nilius et al . , 2004; Darby et al . , 2016 ) that has been shown to be gated by diverse inputs , including temperature , osmotic and chemical stimuli ( Vriens et al . , 2005 ) . In addition , TRPV4 has been demonstrated to play a role in mechanotransduction pathways in a variety of cells and tissues , including chondrocytes ( O'Conor et al . , 2014 ) , vascular endothelium ( Thodeti et al . , 2009 ) and urothelium ( Miyamoto et al . , 2014; Mochizuki et al . , 2009 ) , yet it remains unclear whether TRPV4 is directly gated by mechanical stimuli or is activated down-stream of a force sensor ( Christensen and Corey , 2007 ) . In order to address this question , we asked whether the TRPV4 channel can be gated by various mechanical stimuli ( applied using HSPC , cellular indentation or pillar deflection ) when expressed in heterologous cells . We first confirmed that we could measure robust PIEZO1-mediated currents in outside-out patches isolated from HEK-293 cells , where PIEZO1 was overexpressed . PIEZO1 exhibited large amplitude ( >50 pA ) and robust macroscopic currents in response to pressure-stimuli ( Figure 7B , left panel ) . We also confirmed that PIEZO1 responds to indentation stimuli ( Figure 7B , center panel ) , in accordance with published data ( Coste et al . , 2012; Peyronnet et al . , 2013; Gottlieb et al . , 2012; Cox et al . , 2016 ) . As shown previously ( Poole et al . , 2014 ) and confirmed here , PIEZO1 was also efficiently gated by deflection stimuli ( Figure 7B , right panel ) . In previous studies , TRPV4 has been shown to respond to membrane-stretch when overexpressed in X . laevis oocytes ( Loukin et al . , 2010 ) , but similar activity was not observed when TRPV4 was overexpressed in HEK-293 cells ( Strotmann et al . , 2000 ) . We found that currents were observed in response to membrane-stretch but only in a subset of membrane patches ( 55% , 5/9 patches ) . Additionally , in those patches that did respond to pressure stimuli , we were unable to determine a P50 , as the currents putatively mediated by TRPV4 were not particularly robust ( Figure 7C , left panel ) . In cell-free patches , TRPV4 is no longer activated by warm temperatures ( Watanabe et al . , 2002 ) . These data indicate that outside-out patches lack functional molecular components necessary for some modes of TRPV4 activation . As such , we next tested whether TRPV4 was activated by stretch in cell-attached patches . Similar to the results obtained in outside-out patches , TRPV4 did not respond to stretch stimuli applied using HSPC ( Figure 7—figure supplement 1 ) . These data demonstrate that PIEZO1 is more efficiently gated by membrane-stretch than TRPV4 , in a heterologous cell system . 10 . 7554/eLife . 21074 . 017Figure 7 . TRPV4 is efficiently gated by substrate deflections . ( A ) HEK-293 cells were used as a heterologous system to test stretch- indentation- and deflection-mediated currents . In the left panel is a cartoon of the HSPC experiment ( stretch ) , in the center , indentation and on the right of the pillar array experiment ( deflection ) . ( B ) PIEZO1 is efficiently gated by membrane stretch , indentation and substrate deflection . Left panel: example trace of PIEZO1-mediated current in an HSPC experiment , center panel: example trace from indentation experiment , right panel: example trace of PIEZO1-mediated current activated by substrate deflection . ( cyan ) ( C ) TRPV4 is efficiently gated by substrate deflection . Left panel: example trace ( black ) of HSPC of TRPV4 in outside-out patches isolated from HEK-293 cells . Center panel: example trace from HEK-293 cells expressing TRPV4 in response to indentation . Right panel: TRPV4 activation by substrate deflections in HEK-293 cells . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 01710 . 7554/eLife . 21074 . 018Figure 7—figure supplement 1 . Mechanical stimulation of cell-attached patches in HEK-293 cells overexpressing PIEZO1 or TRPV4 . ( A ) Schematic of the cell-attached configuration together with the HSPC . ( B ) Example traces of mechanically gated currents elicited by HEK-293 cells expressing PIEZO1 , TRPV4 or eGFP as a negative control . Membrane potential was held at −60 mV and pressure steps between 10 and 200 mmHg were applied . PIEZO1 , n=6 cells; TRPV4 , n=10 cells; eGFP , n=8 cells . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 01810 . 7554/eLife . 21074 . 019Figure 7—figure supplement 2 . Mechanical indentation of HEK cells overexpressing PIEZO1 or TRPV4 . ( A ) Schematic of mechanical indentation in whole-cell patch clamp configuration . ( B ) Example traces of indentation-mediated currents from HEK-293 cells expressing PIEZO1 , TRPV4 or LifeAct-mCherry as negative control . ( C ) Categorical plot of the number of cells that responded to the indentation stimuli with at least one current ( Fisher’s exact test , ***p<0 . 0001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 019 We next tested whether cellular indentation could activate TRPV4 currents . We compared channel activity in HEK-293 cells measured using whole-cell patch-clamp in cells expressing PIEZO1 , TRPV4 or LifeAct as a negative control . PIEZO1-mediated currents were measured in all cells ( 12 cells ) , in response to indentations of 0 . 5–11 µm , in accordance with published data ( Coste et al . , 2012; Gottlieb et al . , 2012; Coste et al . , 2010 ) . In contrast , the response of HEK-293 cells expressing TRPV4 was indistinguishable from the negative control ( Figure 7C , center panel; Figure 7—figure supplement 2 ) . TRPV4-expressing HEK-293 cells exhibited large currents in response to deflection stimuli in 87% transfected cells measured ( 39/45 ) , in contrast to the lack of TRPV4 activation by pressure or indentation stimuli ( Figure 7C , right panel ) . In order to confirm that the current observed in cells overexpressing TRPV4 was mediated by this channel , we acutely applied GSK205 ( 10 µM ) and noted that with similar deflection stimuli the current was blocked . After wash-out of the TRPV4-specific antagonist , the amplitude of the mechanoelectrical transduction current was restored to pre-treatment levels ( Figure 8A ) . These data clearly indicate that the deflection-gated current in HEK-293 cells overexpressing TRPV4 is mediated by the TRPV4 channel . 10 . 7554/eLife . 21074 . 020Figure 8 . Deflection-mediated activation of TRPV4 . ( A ) The deflection-gated current observed in HEK-293 cells expressing TRPV4 is reversibly blocked by the TRPV4-specific antagonist , GSK205 ( 10 μM , 3 min ) ( B ) TRPV4 is more sensitive to substrate deflections than PIEZO1 , in HEK-293 cells . Stimulus-response plots of current amplitude vs the magnitude of pillar deflection demonstrates that with stimulus sizes of 10–50 , 50–100 , 100–250 , 250–500 nm cells expressing TRPV4 ( black squares ) ( n = 8 cells ) respond with significantly larger current amplitude than cells expressing PIEZO1 ( cyan circles ) ( n = 12 cells ) ( see source data for details ) . In addition , the TRPV4 response is significantly different to the PIEZO1 response ( two-way ANOVA , ****p<0 . 0001 ) . ( C ) The kinetics of the deflection-gated currents . No differences were observed in the latency of current activation . However , both current activation ( τ1 ) and current decay ( τ2 ) values were significantly faster for TRPV4-mediated currents than PIEZO1-mediated currents . ( Student’s t-test; *p=0 . 04; **p=0 . 005 ) . ( D ) Inactivation of TRPV4 at positive and negative potentials . Deflection stimuli were applied to HEK-293 cells expressing TRPV4 at – 60 mV and +60 mV . The current decay ( τ2 ) and current amplitude values did not depend on the membrane holding potential ( n = 67 currents , −60 mV; 30 currents , 60 mV; Mann-Whitney U test ) . Data collected from 16 cells over five experiments . Example traces are presented in the right hand panel . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 02010 . 7554/eLife . 21074 . 021Figure 8—source data 1 . Electrophysiological characteristics of HEK-293 cells overexpressing either TRPV4 or PIEZO1 . ( A ) Electrophysiological characteristics of HEK-293 cells overexpressing either TRPV4 or PIEZO1 . HEK-293 cells were transiently transfected with a plasmid encoding either TRPV4 or PIEZO1 . For each condition the number of transfections , total number of recorded cells and number of stimuli are indicated . For each recorded current , the latency and the current amplitude were measured , and the activation time constant and current decay were obtained from a mono-exponential fit . Every kinetic parameter is shown as mean ± s . e . m . ( B ) Statistical comparison of deflection-gated mechanoelectrical transduction responses . For each individual cell , currents were binned in the indicated size ranges ( in nm ) and the current amplitudes within each bin averaged and then averaged across cells . Bins were subsequently tested for normal distribution and subsequently compared with a Student’s t-test ( parametric data sets ) or a Mann Whitney test ( non-parametrical data ) . The p values are shown for significant comparisons , ‘NS’ indicates no significant differences . The number of compared points is shown in brackets . An ordinary two-way ANOVA was used to compare the cellular response over the range of stimuli , reported are the p value and F statistic ( including DFn , DFd ) . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 02110 . 7554/eLife . 21074 . 022Figure 8—figure supplement 1 . PLA2 is not involved in the activation by pillar-deflection of TRPV4 ( A ) Scheme of the TRPV4 signaling pathway activated after hypotonic stress compared with TRPV4 activation by pillar displacement . After the hypotonic change , PLA2 produces arachidonic acid ( AA ) , which can activate TRPV4 . Subsequently , AA is used as a substrate by the P450 epoxygenase to produce 5’ , 6’-EET , which also activates TRPV4 . This activation takes place in seconds . On the right side of the panel , TRPV4 activation by pillar deflections is shown; in this case , the latency of activation is in the milliseconds range . ( B ) N- ( p-Amylcinnamoyl ) anthranilic acid ( ACA ) does not inhibit the activation of mechanically gated currents triggered by pillar deflections . Stimulus-response graphs of HEK cells overexpressing TRPV4 ( green circles ) compared with TRPV4 in the presence of ACA ( black circles , at least 3 min of incubation after breaking through the cell; 20 μM in the intracellular solution ) . On the right panel , example traces of the mechanically gated currents are shown . ( C ) Kinetics of the deflection-gated currents from B . The kinectics were compared using Mann-Whitney test . DOI: http://dx . doi . org/10 . 7554/eLife . 21074 . 022 We compared the sensitivity of TRPV4 versus PIEZO1 and found that HEK-293 cells overexpressing TRPV4 exhibited larger currents in response to stimuli up to 500 nm , compared to HEK-293 cells overexpressing PIEZO1 ( Figure 8B ) . The overall TRPV4 stimulus-response data were significantly different than for PIEZO1 ( two-way ANOVA , p<0 . 0001 ) . When we analyzed the current kinetics ( Figure 8C ) , we found that both the activation time constant ( τ1 ) and current decay ( τ2 ) were significantly faster for TRPV4-mediated currents , compared to PIEZO1-mediated currents; however , there was no significant difference in the latencies for current activation . These data are pertinent because the latency and the activation time constant ( namely , latencies below 5 ms and activation time constants faster than 1 ms ) have been used as parameters to classify channels that may be directly gated by a mechanical stimulus , in this case a deflection stimulus . These rapid latencies ( <5 ms ) are distinct from the latencies of seconds to minutes measured for TRPV4 activation by osmotic stimuli , cell swelling , chemical activators ( 4αPDD ) or heat ( Lechner et al . , 2011; Nilius et al . , 2004 , 2003 ) . We next measured the current amplitude of deflection-activated TRPV4 currents at negative and positive potentials ( −60 mV versus 60 mV ) to determine if these currents were outwardly rectifying , as observed for activation of TRPV4 by 4αPDD , swelling and heat ( Nilius et al . , 2003 ) . The peak amplitude of the outward currents measured at 60 mV ( 157 ± 23 pA , mean ± s . e . m . ; n = 30 currents ) was not significantly different to the peak amplitude of the inward current measured at –60 mV ( 147 ± 15 pA , mean ± s . e . m . ; n = 67 currents; Mann-Whitney U test ) . In addition , the current decay of the outward currents , measured at 60 mV ( 11 . 7 ± 4 . 8 ms , mean ± s . e . m . ; n = 30 currents ) , was indistinguishable from inward currents measured at a holding potential of –60 mV ( 12 . 2 ± 2 . 7 , mean ± s . e . m . ; n = 67 currents; Mann-Whitney U test ) ( Figure 8D ) . These data stand in contrast to PIEZO1 , where current decay values increased at positive membrane potentials ( Coste et al . , 2012 , 2010; Gottlieb and Sachs , 2012 ) . Activation of TRPV4 in response to cell swelling depends on phospholipase 2 ( PLA2 ) enzymatic activity , releasing arachidonic acid from the membrane ( Vriens et al . , 2004 ) . In order to determine whether such a second messenger system is required for deflection-mediated activation of TRPV4 , we repeated the stimulus-response analysis in the presence of ACA , a PLA2 inhibitor . There were no significant changes in TRPV4-mediated currents in the presence of ACA , suggesting that gating of TRPV4 in response to cell swelling is distinguishable from the activation of TRPV4 in response to substrate deflections ( Figure 8—figure supplement 1 ) . We conclude that TRPV4 directly mediates currents in response to deflection stimuli applied at cell-substrate contact points and that , in this transmembrane compartment , TRPV4 can be directly gated by the mechanical stimulus , as opposed to indirectly via a second messenger cascade . We propose that this mechanical activation of TRPV4 is distinct from the mechanisms of gating by heat , chemical activators and cell swelling .
We have addressed the questions of whether mechanically gated channel activity can be directly measured in primary murine chondrocytes , which channels mediate this process and how the specific type of mechanical stimulus affects mechanoelectrical transduction . In situ , chondrocytes are subjected to physical stimuli propagated via the fluid phase of the cartilage , as well as via contacts between the cells and ECM . Mechanical loading within the joints leads to chondrocyte deformations and changes in cell volume , applying strain to the cells in situ ( Guilak et al . , 1995; Alexopoulos et al . , 2005; Madden et al . , 2013 ) . The transfer of mechanical loading to the chondrocytes themselves is modulated by the local mechanical environment , i . e . the local ECM structure and properties of the PCM ( Madden et al . , 2013 ) . In vivo there exists a functional relationship between the PCM and the chondrocyte , together forming the chondron and changes in the composition or the mechanical properties of the PCM can lead to the development of OA ( Alexopoulos et al . , 2009; Zelenski et al . , 2015 ) . In this study , we have investigated mechanoelectrical transduction in isolated chondrocytes in response to deflections applied at the cell-substrate interface ( to model stimuli transferred to the cells via matrix contacts ) and to stretch applied to patches of membrane . We chose to directly monitor channel activity using electrophysiological techniques . Given that such an experimental approach requires access to the cell membrane , our studies have been conducted on chondrocytes in a 2D environment , as opposed to the 3D environment found in vivo . Using pillar arrays , we were able to determine that the average substrate-deflection required for channel gating in chondrocytes was 252 ± 68 nm . Accordingly , chondrocyte mechanoelectrical transduction sensitivity to stimuli applied at the cell-substrate interface does not rival that of mechanoreceptor sensory neurons ( known for their low mechanical threshold ) but is comparable with the higher mechanoelectrical transduction threshold of nociceptive sensory neurons ( Poole et al . , 2014 ) . Within the cartilage , chondrocytes are subjected to deformation but these shape changes are markedly different depending on the specific joint region ( Madden et al . , 2013; Gao et al . , 2015 ) . However , changes of 10–15% along the chondrocyte height axis in response to mechanical loading have been measured ( Amini et al . , 2010 ) . Given that such changes represent average differences in cell length of >1 µm , this threshold lies within the range of conceivable membrane displacements that would occur in situ . There is variation in the amplitude of the mechanically gated currents measured in response to pillar deflections , resulting in data with large error bars . We have noted this variability in all systems tested to date: sensory mechanoreceptive neurons , sensory nociceptive neurons , Neuro2A cells and HEK-293 cells heterologously expressing either PIEZO1 or PIEZO2 . There are two likely reasons for this variability . Firstly , the pillar deflection stimuli are applied to a 10 μm2 contact area between the cell and the pilus , restricting the number of potentially activated domains and resulting in noisier data than methods where stimuli are applied over a larger area , e . g . indentation . Secondly , stimuli are applied via dynamic cell-substrate contact points , likely introducing additional confounding factors such as changes in the local mechanical environment dictated by adhesion molecules and the cytoskeleton . It is interesting to note that , despite clear differences in mechanosensitivity between chondrocytes and dedifferentiated cells measured using pillar arrays , no differences were observed when HSPC was used to apply pressure-stimuli to membrane patches . This phenomenon may reflect differences in the mechanical environment of the cell matrix contact points in the spherical chondrocytes versus the flattened edges of the dedifferentiated cells that display a more fibroblast-like morphology . These data suggest that the behavior of mechanically gated channels in response to membrane stretch cannot be directly related to channel function when stimuli are applied via cell-substrate contact points and suggests that distinct pathways may mediate mechanoelectrical transduction within the cartilage in response to applied forces that stretch the membrane versus those forces propagated via movements within the matrix . The elements of the pillar arrays are elastomeric cylinders , i . e . springs , meaning that the deflection of each pilus can be converted into a corresponding restoring force , using Hooke’s Law ( see Materials and methods ) . When we applied this conversion to our deflection data we obtained an average threshold for current activation of 63 nN in chondrocytes when deflection stimuli are applied to a 10 µm2 patch of membrane , i . e . approximately 2% of the cell surface . These data do not indicate the force that is transferred to the mechanically gated ion channel , and this value for the restoring force will also be influenced by the mechanical properties of the cell at the cell-pilus contact . However , given that the elasticity of chondrocytes ( approx . 1 kPa ( Trickey et al . , 2000; Shieh and Athanasiou , 2006 ) ) is three orders of magnitude lower than that of the substrate ( 2 MPa ( Poole et al . , 2014 ) ) , the influence of the mechanical properties of the cell on the restoring force will be minimal . These data allow a first comparison with earlier studies that investigated chondrocyte responses to compression . The calculated threshold for transduction in response to pillar deflection is almost 10x smaller than the compressive forces , applied to the whole cell , required in order to generate a robust Ca2+ signal ( 500 nN , ( Lee , 2014 ) ) . This comparison suggests that current activation is more sensitive to deflections applied at the cell-substrate interface than to whole-cell compression . We have found that both TRPV4 and PIEZO1 are involved in mediating deflection-gated currents in chondrocytes . In the light of recent work on TRPV4 and PIEZO1 in porcine chondrocytes , it has been proposed that TRPV4 responds to fine mechanical stimuli and PIEZO1 to injurious stimuli ( Boettner et al . , 2014 ) . In contrast , studies using Ca2+ imaging to measure mechanotransduction in response to substrate-stretch in urothelial cells found that PIEZO1 mediates cellular mechanosensitivity in response to smaller stimuli than TRPV4 ( Miyamoto et al . , 2014 ) . In both cases , the ‘readout’ of mechanotransduction is down-stream of the mechanoelectrical transduction event , monitoring alterations in matrix production ( O'Conor et al . , 2014 ) or changes in intracellular Ca2+ levels ( O'Conor et al . , 2014; Lee , 2014; Miyamoto et al . , 2014 ) . As such , the relative differences in mechanosenstivity that depend on TRPV4 or PIEZO1 expression in the two systems could either reflect ( a ) differential modulation of channel sensitivity in distinct tissues by accessory molecules ( as previously demonstrated for PIEZO1 [Poole et al . , 2014] ) or ( b ) that the pathways downstream of the channel event amplify the signal in a differential fashion . These two possibilities are also not mutually exclusive . Our data suggest that , in chondrocytes , it is the downstream amplification of the original mechanoelectrical transduction current that differs , as we observed very similar effects on mechanoelectrical transduction sensitivity when either TRPV4 or PIEZO1 levels were ablated . Some care does need to be taken with this interpretation due to the fact that a specific TRPV4-antagonist acutely and reversibly blocked 87% of the deflection-gated current , yet chondrocytes from Trpv4-/- mice did not display a similar reduction in current amplitude . We conclude that the chronic loss of one mechanosensitive channel in chondrocytes can be compensated for by other molecules , particularly given the fact that both TRPV4 and PIEZO1 were found to be active in all viable chondrocytes isolated from the articular cartilage . Such a conclusion supports the theory that there are multiple redundancies in mechanoelectrical transduction pathways ( Arnadóttir and Chalfie , 2010 ) and highlights the possibility that potentially more mechanically gated channels await discovery . Whilst both TRPV4 and PIEZO1 are required for normal mechanoelectrical transduction in response to substrate deflections , only PIEZO1 is required for normal current activation in HSPC measurements . A recent paper has demonstrated that PIEZO1 gating can be directly mediated by changes in membrane tension in membrane blebs ( Cox et al . , 2016 ) , suggesting an underlying mechanism for this stretch-mediated channel gating . In our experiments , when Piezo1 transcript levels in chondrocytes were knocked-down using miRNA , stretch-activated currents largely disappeared , whereas a complete absence of TRPV4 did not significantly change the peak current amplitude nor the P50 , in comparison with WT chondrocytes . This is a clear demonstration that current activation in response to membrane stretch cannot be used as an indicator of the overall mechanoelectrical transduction pathways within a cell . In addition , this observation highlights the impact of quantitative measurements of channel activity when precise stimuli are applied directly to a specific membrane environment , such as the cell-substrate interface . Our data suggest that both PIEZO1 and TRPV4 similarly contribute to mechanoelectrical transduction of nanoscale deflection-stimuli in chondrocytes , whilst differing in their response to membrane stretch . We therefore addressed whether the two channels behave similarly in a heterologous system . We confirmed that TRPV4 , unlike PIEZO1 , is not efficiently gated by pressure-induced membrane-stretch , and demonstrated that TRPV4 is not activated by cellular indentation . It has previously been shown that TRPV4 can be gated by membrane-stretch in X . laevis oocytes ( Loukin et al . , 2010 ) ; however , the recording conditions used to demonstrate this effect all promote TRPV4 channel gating ( holding potential + 50 mV , 20 mM Sodium Citrate and a pH of 4 . 5 ) . Taken together with our observations , these data suggest that whilst TRPV4 can be gated by pressure stimuli , this process is not particularly efficient . However , we observed that HEK-293 cells expressing TRPV4 are more sensitive to mechanical stimuli applied at cell-substrate contact points than HEK-293 cells expressing PIEZO1 . For TRPV4-expressing cells , the latency between stimulus and response ( <2 ms , indistinguishable from PIEZO1 expressing cells ) and the activation time constant ( <0 . 5 ms , significantly faster than PIEZO1-expressing cells ) suggest that , in response to deflection stimuli , TRPV4 is directly gated by the mechanical stimulus . These data directly address the long-standing question of whether TRPV4 is a mechanically gated channel ( Christensen and Corey , 2007 ) . A number of criteria have been proposed to determine whether a channel is mechanically gated: the latency of current activation should be less than 5 ms ( Christensen and Corey , 2007 ) , the channel should be present in mechanosensitive cells , ablation of the channel should eliminate the response , expression of the channel in a heterologous system should produce mechanically gated currents and there should be an effect on mechanotransduction processes in vivo when the channel is deleted ( Arnadóttir and Chalfie , 2010 ) . As shown in this study , TRPV4-mediated current activation occurs with sufficiently rapid latencies . TRPV4 is expressed in the chondrocytes ( along with other mechanosensory cells ) : its deletion leads to a reduction in mechanotransduction , in WT chondrocytes mechanotransduction currents are largely blocked by a TRPV4 antagonist and Trpv4-/- mice are more likely to develop OA ( although given the polymodal nature of TRPV4 these changes do not definitively reflect changes in mechanoelectrical transduction ) . In addition , we demonstrate here that TRPV4 mediates mechanically-gated currents in response to substrate deflections in a heterologous system . Whilst the loss of this channel does not produce a complete loss of current , the observed redundancy in mechanoelectrical transduction pathways suggests that this criterion is too stringent . We propose that studying how mechanically gated channels function when stimuli are applied at cell-substrate contact points will prove instrumental in elucidating the role of both TRPV4 and PIEZO1 in mechanosensing pathways in additional cell types . PIEZO1 has recently been shown to be inherently mechanosensitive ( Syeda et al . , 2016 ) . In contrast , the data that we present here suggests that TRPV4 mechanosensitivity depends on the type of stimulus and the membrane compartment to which stimuli are applied . We speculate that differences in channel gating in response to physical stimuli applied to distinct membrane compartments represents a mechanism by which cells can promote mechanoelectrical transduction events to changes in the surrounding matrix without increasing cellular sensitivity to localized membrane stretch . As such , the direct measurement of mechanically gated ion channel activity in response to stimuli applied via cell-substrate contact points is essential in order to understand how cells respond to changes in their immediate physical environment .
The mouse-TRPV4 in pcDNA3 plasmid was a kind gift from Dr . Veit Flockerzi ( Wissenbach et al . , 2000 ) . For RT-qPCR experiments , total RNA was extracted using Trizol reagent ( Ambion , Carlsand , CA , 15596018 ) according to manufacturer’s instructions , contaminating genomic DNA was digested using the TURBO DNA-free kit ( Ambion , AM1907 ) and 2 µg of RNA was reverse transcribed using random primers and SuperScript III ( Invitrogen , Germany , 18080–044 ) according to manufacturer’s instructions . The RT-qPCR reactions were performed in an Abi 7900 Sequence Detection System ( Applied Biosystems , Germany ) using probes from the Universal Probe Library Set ( Roche , Germany , 04 683 641 001 ) . Relative expression levels were calculated using the 2-∆Ct method with the house-keeping genes β-actin and Hprt1[75] . ( Primer sequences , listed as 5’ to 3’: β-actin , AAGGCCAACCGTGAAAAGAT , GTGGTACGACCAGAGGCATAC; Hprt1 , TCCTCCTCAGACCGCTTTT , CCTGGTTCATCATCGCTAATC; Piezo1 , GACGCCTCACGAGGAAAG , GTCGTCATCATCGTCATCGT; Piezo2 , ACGGTCCAGCTTCTCTTCAA , CTACTGTTCCGGGTGCTTG; Trpv4 , CCACCCCAGTGACAACAAG , GGAGCTTTGGGGCTCTGT; Sox9 , TATCTTCAAGGCGCTGCAA , TCGGTTTTGGGAGTGGTG . ) When recovering cells from alginate beads due to the low number of cells , RNA was extracted using RNeasy microkit ( Qiagen GmbH , Germany , 74004 ) . miRNA sequences targeting Piezo1 were previously cloned using the Block-iT Pol II miR RNAi system ( Invitrogen ) and validated along with a scrambled miRNA that does not target any known vertebrate gene was used as a control ( Poole et al . , 2014 ) . The sequence of the selected miRNAs were: Piezo1 targeting , top strand: 5’-TGCTGTAAAGATGTCCTTCAGGTCCAGTTTTGGCCACTGACTGACTGGACCTGGGACATCTTTA-3' , bottom strand: 5'-CCTGTAAAGATGTCCCAGGTCCAGTCAGTCAGTGGCCAAAACTGGACCTGAAGGACATCTTTAC-3' . Primary chondrocytes from mice ( aged 4–5 days ) were cultured as described previously ( Gosset et al . , 2008 ) . Briefly , the knees and femoral heads were removed , mildly chopped and rinsed with PBS . The rinsed cartilage was treated with collagenase D ( 3 mg/ml , Roche 11 088 882001 ) in chondrocyte basal medium ( Lonza , Walkersville , MD , CC-3217 ) for 1 hr . The cartilage was treated overnight with collagenase D ( 0 . 5 mg/ml ) in medium with 10% FBS at 37°C . The suspension was then centrifuged at 400 g for 10 min and the resulting pellet was incubated for 10 min with 0 . 05% trypsin-EDTA ( Biotech GmbH , Germany , P10-023100 ) at 37°C . Chondrocytes were washed and harvested , then plated in flasks or encapsulated in alginate to maintain their differentiated state . Chondrocytes cultured in flasks were used only until passage 3 . Mouse strains used in this study were WT C57Bl/6 from Charles River or Trpv4-/- ( Jackson Laboratory , MGI ID: 2667379 ) on a C57Bl/6 background . To confirm a lack of functional TRPV4 in the Trpv4-/- mice each litter was genotyped using the suggested protocol from Jackson Laboratory and a sample of cells from each preparation was treated with the TRPV4 agonist GSK1016790A ( Sigma Aldrich , G07898 , 100 nM ) and monitored using functional Ca2+ analysis ( Figure 6—figure supplement 1 ) . Chondrocytes were transfected with Lipofectamine LTX and Plus Reagent ( Invitrogen , 15338 ) according to manufacturer´s instructions . All experiments involving mice were carried out in accordance with protocols approved by the German Federal authorities ( State of Berlin ) . Primary mouse chondrocytes were encapsulated in alginate ( Brand et al . , 2012 ) . Briefly , chondrocyte density was adjusted to 8 × 105 cells/ml and cells were then mixed with 1 ml of alginate ( 1 . 2% w/v in solution: 25 mM HEPES , 118 NaCl , 5 . 6 KCl , 2 . 5 MgCl2 , pH 7 . 4 ) and passed dropwise through a 22-gauge needle into gelation solution ( 22 mM CaCl2 , 10 mM Hepes , pH 7 . 4 ) . The encapsulated cells were cultured in Chondrocyte Differentiation Medium ( Lonza , CC-3225 ) . To recover the cells , the alginate matrix was dissolved using 55 mM Na citrate ( Sigma Aldrich , Germany , 6132 ) at 37°C . HEK-293 cells were used as a heterologous cell line to study TRPV4 and PIEZO1 activity . This cell line was chosen as it has previously been shown to exhibit little mechanoelectrical transduction in response to deflection stimuli within the 1–1000 nm range ( Poole et al . , 2014 ) . HEK-293 cells were cultured in DMEM media containing 10% fetal calf serum and 1% penicillin , streptomycin . To transfect HEK-293 cells , FuGeneHD ( Promega , Madison , WI , E231A ) was used as per manufacturer’s instructions . HEK-293 cells were tested regularly to confirm absence of mycoplasma , using a luminescence kit from Epo GmBH ( Germany ) , as per manufacturer’s instructions . The identity of the cultured cells was authenticated by Eurofins Medigenomix Forensik GmbH ( Germany ) , using PCR-single-locus-technology using 21 independent PCR reactions . For immunofluorescence staining , cells were fixed with 4% PFA . When labeling intracellular components , cells were permeabilized with 0 . 25% Triton-X 100 ( Sigma-Aldrich , X-100 ) . Fetal goat serum ( 3% ) in PBS was used as a blocking agent before labeling with primary antibody ( anti-Sox9 ( Abcam , UK , ab59265; at 1:500 ) , anti-Collagen X ( Abcam , ab49945; at 1:2000 ) ) . Secondary antibodies were all used at a dilution of 1:2000 ( Life Technologies , Germany , A11034 , A31630 , A21050 ) . Pillar arrays were prepared as described previously ( Poole et al . , 2014 ) ; briefly , a silanized negative master was coated with degassed PDMS mixed at a ratio of 10:1 . After 30 min , the still-liquid PDMS was covered with a glass coverslip ( thickness , 2 ) and the coated master placed at 110°C for 1 hr . After curing , the pillar array was gently peeled away from the master . Before use , pillar arrays were either coated with PLL or activated by plasma cleaning ( Deiner Electronic GmbH , Germany ) and cells were allowed to attach . The individual elements in the pillar arrays were cylinders made of PDMS , as such , they could be modeled as a spring . Therefore , deflection measured ( d ) could be converted into a corresponding force using Hooke’s law ( Equation 1 ) , ( 1 ) F=−kd , where k is the spring constant of each pilus in the array . The spring constant is dependent on the elasticity ( E ) of the material , and the dimensions of the cylinder , according to Equation 2: ( 2 ) k= 34 ⋅π ⋅E⋅\ r4L3 The arrays were cast under curing conditions that result in an elasticity of the PDMS equal to 2 . 1 MPa and the dimensions of the elements within the array were: radius = 1 . 79 µm; length = 5 . 87 µm ( Poole et al . , 2014 ) . The spring constant of each individual pilus was therefore 251 pN/nm . To generate quantitative data on mechanoelectrical transduction an individual pilus subjacent to a cell was deflected using a polished glass probe ( approx . 2 µm in diameter ) driven by a MM3A micromanipulator ( Kleindiek Nanotechnik , Germany ) . The electrical response of the cells was monitored using whole-cell patch-clamp and to quantitate the magnitude of the stimulus , a bright-field image was taken before pillar deflection , during the applied stimulus and after the release of the stimulus . Bright-field images were taken using a 40x objective and a CoolSnapEZ camera ( Photometrics , Tucson , AZ ) . To calculate the pillar deflection , the center point of the relevant pilus was determined from a 2D Gaussian fit of the intensity values in the relevant images ( Igor , Wavemetrics , TIgard , OR ) ; the distance that this center point moves represents the stimulus magnitude . The estimated error of the calculated stimulus size was 7 nm , as previously described ( Poole et al . , 2014 ) . Whole-cell patch-clamp recordings were performed at room temperature . The resistance of the recording pipettes ranged between 3 and 5 MΩ . Currents were acquired at 10 kHz and filtered at 3 kHz using an EPC-10 amplifier with Patchmaster software ( HEKA , Elektronik GmbH , Germany ) in combination with a Zeiss 200 inverted microscope and were analyzed using FitMaster software ( HEKA , Elektronik GmbH ) . The bath solution contained ( in mM ) 140 NaCl , 4 KCl , 2 CaCl2 , 1 MgCl2 , 4 glucose and 10 HEPES , adjusted to pH 7 . 4 with NaOH . The internal solution contained ( in mM ) 110 KCl , 10 NaCl , 1 MgCl2 , 1 EGTA and 10 HEPES , adjusted to pH 7 . 3 with KOH . The membrane potential was held at −40 mV in chondrocyte measurements ( Sánchez and Wilkins , 2003; Sánchez et al . , 2006 ) and −60 mV for HEK-293 cell measurements . GSK205 ( Calbiochem , Billerica , MA , 616522 ) was used at a concentration of 10 µM and cells were treated for 3 min . ACA ( Calbiochem , 104550 ) was used at a concentration of 20 µM and applied directly via the patch pipette . We allowed solution exchange for at least 3 min before collecting data . Cellular indentation studies were performed as described previously ( Hu and Lewin , 2006 ) . Briefly , cells were indented using a fire-polished glass probe , with a diameter of approximately 2 µm . The probe was moved toward the cell until compression of the surface was observed . Indentation stimuli ( between 0 . 5–11 µm ) were then applied by driving the glass probe into the cell using the MM3A micromanipulator . Cellular responses were simultaneously monitored using whole-cell patch-clamp . Outside-out patches were pulled from cells and currents were elicited by applying positive pressure to the patch via the patch pipette using a High Speed Pressure Clamp ( ALA Scientific , Farmingdale , NY ) . Within 30 s of pulling the patch a protocol of pressure steps ( duration 600 ms , application 0 . 1 Hz ) ranging from 10 mmHg to 150 mmHg , in 20 mmHg steps were applied while holding the patch at −60 mV . The sensitivity of stretch-activated channels for each patch was estimated by fitting individual pressure response curves to the Boltzmann equation . Extracellular solution had the following composition ( in mM ) : 150 NaCl , 5 KCl , 10 Hepes , 10 glucose , 1 MgCl2 , 2 CaCl2 . The intracellular solution contained ( in mM ) : 140 KCl , 10 Hepes , 1 EGTA , 1 MgCl2 . Thick walled electrodes ( Harvard apparatus 1 . 17 mm x 0 . 87 mm , external and internal diameter respectively ) were pulled with a DMZ puller ( Germany ) polished to a final resistance of 6 to 8 MΩ . Cell-attached recordings were performed using the extracellular solution in the pipette and at a holding voltage of −60 mV . Negative pressure steps were applied with the same frequency amplitude as for outside-out patches . Chondrocytes were plated on PLL-coated glass coverslips and loaded with Cal-520 ( 5 µM ) for 1 hr ( AAT-Bioquest , Sunnyvale , CA ) . Cells were placed in 200 µl of solution in a chamber that allows laminar flow and a fast solution exchange . Calcium images were acquired using a DG4 ( Sutter Instruments , Novato , CA ) as a light source and were acquired and analyzed using Metafluor ( Molecular Devices , Sunnyvale , CA ) . Fluorescent images were acquired every 5 s . The initial ( background ) fluorescence was acquired for 10 cycles and used to normalize the fluorescence of the whole experiment . Fluorescence values were calculated and plotted according to the formula DF/F = ( F-F0 ) / ( F0 ) where F0 is baseline fluorescence for Cal-520 . Yoda1 ( 10 µM ) was applied for 90s , followed by wash-out period of 5 min , whereas GSK1016790A ( 50 nM ) was applied for 15 s . The stimulus-response data collected from experiments performed in the pillar arrays have variation in x ( deflection ) and y ( current amplitude ) ; therefore , the response was grouped in bins of different sizes in order to compare it . The size of the bins is as follows: 0–10 , 10–50 , 100–250 , 250–500 and 500–1000 nm . For each cell , the current amplitudes within the bins are averaged , and then these data averaged across cells . All data sets were tested for normality: parametric data sets were compared using a two-tailed , Student’s t-test , paired or unpaired depending on the experimental setup , nonparametric data sets were compared using a Mann-Whitney test . In order to compare the overall response of samples to deflection stimuli , we conducted two-way ANOVA . Categorical data were compared using Fisher’s exact test . One-way ANOVA and Tukey post-hoc test were used to compare RT-qPCR data sets . | Cartilage is a flexible tissue that cushions the joints in our body , allowing them to move smoothly . It is made of cells called chondrocytes that are surrounded by a scaffold of proteins known as the extracellular matrix . Chondrocytes regularly experience mechanical forces , which can arise from the movement of fluid within the joints or be transmitted to chondrocytes via the extracellular matrix . These cells sense mechanical forces by a process known as mechanotransduction , which allows chondrocytes to alter the composition of the extracellular matrix in order to maintain an appropriate amount of cartilage . If mechanotransduction pathways are disrupted , the cartilage may become damaged , which can result in osteoarthritis and other painful joint diseases . The membrane that surrounds a chondrocyte contains proteins known as ion channels that are responsible for sensing mechanical forces . The channels open in response to mechanical forces to allow ions to flow into the cell . This movement of ions generates electrical signals that result in changes to the production of extracellular matrix proteins . However , there is little direct evidence that mechanical forces can activate ion channels in chondrocytes and it not known how these cells respond to different types of forces . To address these questions , Servin-Vences et al . exposed chondrocytes from mice to mechanical forces either at the point of contact between the cell and its surrounding matrix , or to stretch the cell membrane . The experiments show that two ion channels called PIEZO1 and TRPV4 both generate electrical currents in response to forces transmitted between cells and the extracellular matrix . However , only PIEZO1 generates a current when the cell membrane is stretched . Thus , chondrocytes are able to distinguish between different types of mechanical forces . More work is needed to understand how mechanical forces are able to activate these ion channels . Understanding how these processes work at the molecular level will hopefully lead to new therapies that boost cartilage production to treat joint diseases . | [
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] | 2017 | Direct measurement of TRPV4 and PIEZO1 activity reveals multiple mechanotransduction pathways in chondrocytes |
In solid tumors , targeted treatments can lead to dramatic regressions , but responses are often short-lived because resistant cancer cells arise . The major strategy proposed for overcoming resistance is combination therapy . We present a mathematical model describing the evolutionary dynamics of lesions in response to treatment . We first studied 20 melanoma patients receiving vemurafenib . We then applied our model to an independent set of pancreatic , colorectal , and melanoma cancer patients with metastatic disease . We find that dual therapy results in long-term disease control for most patients , if there are no single mutations that cause cross-resistance to both drugs; in patients with large disease burden , triple therapy is needed . We also find that simultaneous therapy with two drugs is much more effective than sequential therapy . Our results provide realistic expectations for the efficacy of new drug combinations and inform the design of trials for new cancer therapeutics .
The current wave of excitement about targeted cancer therapy ( Sawyers , 2004; Sequist et al . , 2008; Kwak et al . , 2010; Chapman et al . , 2011; Gonzalez-Angulo et al . , 2011 ) was initiated by the success of imatinib in the treatment of chronic myeloid leukemia ( CML ) ( Druker et al . , 2006; Gambacorti-Passerini et al . , 2011 ) . Four decades of research passed between the discovery of the Philadelphia chromosome and the first treatment to target an activated oncogene in a human cancer . Targeted therapies against many different types of cancer are now being developed at a fast pace . These include gefitinib and erlotinib for non-small-cell lung cancer patients with EGFR mutations ( Sequist et al . , 2008 ) , panitumumab and cetuximab for metastatic colon cancer ( Amado et al . , 2008 ) , vemurafenib for patients with melanomas harboring BRAF mutations ( Chapman et al . , 2011 ) , and crizotinib for lung cancer patients with EML4-ALK translocations ( Kwak et al . , 2010 ) . At present , dozens of other targeted cancer therapies have either been approved or are being evaluated in clinical trials . Although targeted agents have prolonged the lives of cancer patients , clinical responses are generally short-lived . In most patients with solid tumors , the cancer evolves to become resistant within a few months ( Amado et al . , 2008; Sequist et al . , 2008; Gerber and Minna , 2010; Chapman et al . , 2011 ) . Understanding the evolutionary dynamics of resistance in targeted cancer treatment is crucial for progress in this area and has been the focus of experimental ( Engelman et al . , 2007; Corcoran et al . , 2010; Bivona et al . , 2011; Diaz et al . , 2012; Ellis et al . , 2012; Misale et al . , 2012; Straussman et al . , 2012; Wilson et al . , 2012; Khorashad et al . , 2013 ) and theoretical studies ( Dewanji et al . , 2005; Komarova and Wodarz , 2005; Michor et al . , 2005 , 2006; Haeno et al . , 2007; Dingli et al . , 2008; Katouli and Komarova , 2010; Lenaerts et al . , 2010; Beckman et al . , 2012; Bozic et al . , 2012 ) . One of the most important conclusions of these studies is that a small number of cells resistant to any targeted agent are always present in large solid tumors at the start of therapy and that these cells clonally expand once therapy is administered . Tumor recurrences are thus a fait accompli when single agents are delivered ( Diaz et al . , 2012 ) . How can one overcome the near-certainty of disease recurrence following therapy with such agents ? Conceptually , there are two paths: treat tumors when they are very small , before a sufficient number of mutant cells conferring resistance have developed , or treat tumors with two or more drugs that target different pathways . In reality , the first option is usually not feasible , as clinicians have little or no control over the size of lesions in their patients at presentation . The second option , however , will become possible as more targeted agents are developed . The potential of combination therapy with targeted agents is buttressed by the success of conventional chemotherapeutic agents in leukemias and other cancers ( DeVita , 1975 ) and of combination therapies for infectious diseases such as HIV ( Porter et al . , 2003 ) . But the potential therapeutic utility of combination therapies targeting different pathways in solid tumors cannot be inferred from these prior studies , as the anatomic and evolutionary characteristics of solid tumors are far different from those of liquid tumors ( leukemias ) or infectious diseases . In this work , we have formulated a mathematical model to predict the effects of combined targeted therapies in realistic clinical scenarios and attempted to answer the question posed at the beginning of this paragraph .
Our model is based on a multitype branching process ( see ‘Materials and methods’ ) . Similar mathematical modeling has successfully predicted the dynamics of acquired resistance , including the timing of treatment failure , in colorectal cancer patients treated with the EGFR inhibitor panitumumab ( Diaz et al . , 2012 ) , and has led to specific recommendations for combination therapies to treat CML ( Komarova et al . , 2009; Katouli and Komarova , 2010 ) . Our current work builds on these previous studies by using recent advances in the mathematical theory of branching processes ( Antal and Krapivsky , 2011 ) , which enable us to obtain results that are exact in the biologically relevant limit of many tumor cells and small mutation rate . To obtain key parameters for our model , we have studied the dynamics of 68 index lesions in 20 melanoma patients receiving the BRAF inhibitor vemurafenib . The data from six patients that represented distinct patterns of responses are shown in Figure 1 . Patients P1 and P2 achieved complete responses , and their lesions became undetectable . Patient P3 had stable disease , with tumors remaining approximately the same size throughout treatment . Patients P4 to P6 all had partial remissions , with some lesions shrinking and others unchanging or regrowing during treatment . As expected , the smallest lesions were the ones most likely to become undetectable when the agent was effective . 10 . 7554/eLife . 00747 . 003Figure 1 . Variability in treatment response to monotherapy among six patients . Patients were treated with the BRAF inhibitor vemurafenib . Patients P1 and P2 achieved a complete response . Patient P3 had stable disease . Patients P4 , P5 , and P6 had partial responses . The minimal detection size ( indicated by discontinuous red line ) was assumed to be ≈63 × 106 cells . DOI: http://dx . doi . org/10 . 7554/eLife . 00747 . 00310 . 7554/eLife . 00747 . 004Figure 1—source data 1 . Response to vemurafenib . DOI: http://dx . doi . org/10 . 7554/eLife . 00747 . 004 For 21 lesions in our vemurafenib dataset , two pretreatment measurements were available . Using these data , we calculate the average net growth rate of these lesions to be 0 . 01 per day , which is consistent with previous reports ( Friberg and Mattson , 1997; Eskelin et al . , 2000 ) . The estimated average time between cell divisions in the absence of cell death in melanoma cells is 7 days ( Rew and Wilson , 2000 ) , implying a birth ( cell division ) rate of b = 0 . 14 per day . We set this as the typical birth rate , and additionally explore birth rates that correspond to a wide range of 1–14 days between cell divisions ( Supplementary file 3 ) . To achieve the observed net growth rate , we set the cell death rate to d = b − 0 . 01 ( typical d = 0 . 13 ) . We assume that these birth and death rates are valid for all cell types prior to treatment . For simplicity , we assume that these birth and death rates remain constant for all cell types prior to treatment , and neglect variations in the growth rate due to spatial and metabolic constraints in solid tumors ( Bozic et al . , 2012 ) . A given cancer therapy will reduce the birth rate and/or increase the death rate of tumor cells . A cell type is defined as sensitive if the treatment in question would cause its death rate to exceed its birth rate; otherwise , it is resistant . The key parameters describing a particular combination treatment are its effects on the birth and death rates of cells and the number of point mutations that have the potential to confer resistance . Consider a treatment with two drugs , 1 and 2 . We denote by n1 ( respectively , n2 ) the number of point mutations that have the potential to confer resistance to drug 1 alone ( respectively , drug 2 alone ) . We denote by n12 the number of point mutations that have the potential to confer resistance to both drug 1 and drug 2 ( cross-resistance mutations ) . We assume that drugs in a combination treatment are given at concentrations tolerable by patients , and define the numbers of resistance mutations ( n1 , n2 , n12 ) relative to these concentrations ( Katouli and Komarova , 2010 ) . A crucial quantity for the effects of combination therapy is the expected number , X , of resistant cells at the start of treatment in a lesion containing M cells . From the dynamics of our branching process model ( see Supplementary file 1 ) , we obtainX≈M[n12μ+ ( n1n2+n122 ( n1+n2−n12 ) ) μ2] . Here μ=uslog ( Ms ) ( log denotes the natural logarithm ) , where s = 1 − d/b is the survival probability of the branching process initiated with a single cell and u is the point mutation rate , ∼10−9 for most cancers . As µ is small , this formula can be further simplified . If there is at least one possible mutation that could in principle confer resistance to both drugs , n12 ≥ 1 , then X ≈ M n12 µ . In this case , the expected number of cells resistant to both drugs is independent of the numbers of mutations , n1 and n2 , that have the potential to confer resistance to each individual drug . Intuitively , this means that tumor cells are much more likely to become resistant to dual therapy through the occurrence of one mutation conferring resistance to both drugs simultaneously than through sequential mutations conferring resistance to each drug separately . If there is no mutation that could confer resistance to both drugs simultaneously ( no cross-resistance ) , then n12 = 0 and we obtain X ≈ M n1n2 µ2 . This quantity scales with the square of the point mutation rate , so the number of resistant cells in a tumor will be much smaller than for the case n12 > 0 . In general , the expected number of cells resistant to combination therapy with k drugs , with no cross-resistance , is X ≈ M n1n2 … nk µk ( proof in Supplementary file 1 ) . We emphasize , however , that resistance is the outcome of random mutation , division , and death events , and consequently may arise in one lesion but not in another , even if these lesions are otherwise identical . We therefore also obtain formulas for the probability that resistance to combination therapy is present at the time of detection . This probability can be computed as pres=1−p1p2 . Here , p1 is the probability that there is no resistance at detection that arose in a single mutational step , due to one of the n12 possible cross-resistance mutations . p2 is the probability that no such resistance arises in two mutational steps . These probabilities can be expressed as follows ( proofs in Supplementary file 1 , Section 4 ) :p1=exp ( Mun12log ( s ) 1−s ) p2≈exp[Mu2 ( 2n1n2+n12 ( n1+n2 ) ) log ( s ) log ( Ms ) s ( 1−s ) ] . As above , s = 1 − d/b is the survival probability of the branching process initiated with a single cell . The quantity 2n1n2 + n12 ( n1 + n2 ) in the expression for p2 represents the number of possible two-step mutational paths to dual resistance . We turn now to the dynamics of the treatment response . Once treatment starts , sensitive cells decline , but resistant cells continue to grow . We assume that resistant cells maintain the pretreatment birth and death rates , b and d , respectively , during treatment . To obtain estimates for the birth rate b′ and death rate d′ of sensitive cells during treatment , we calculate that the 68 lesions in our dataset declined at median rate b′ − d′ = −0 . 03 per day ( −0 . 01 and −0 . 07 being 10th and 90th percentile , respectively ) . Thus , we set the typical death rate of sensitive cells during treatment to d′ = b′ + 0 . 03 , and additionally explore cases when treatment is less ( d′ = b′ + 0 . 01 ) or more effective ( d′ = b′ + 0 . 07 ) . As a default in our simulations , we suppose that treatment affects only the death rate ( b′ = b ) , but our mathematical analysis applies also to the case that treatment affects the birth rate . Figure 2 shows computer simulations of single lesions in response to targeted therapies . Previous studies ( Engelman et al . , 2007; Corcoran et al . , 2010; Diaz et al . , 2012; Ellis et al . , 2012; Misale et al . , 2012; Straussman et al . , 2012; Wilson et al . , 2012 ) suggest that about 50 different mutations can confer resistance to a typical targeted therapeutic agent . Assuming that there are 50 or more potential resistance mutations , monotherapy will eventually fail in all lesions that can be detected by conventional imaging ( Figure 2A , B ) even when the death rate d’ conferred by the therapy is far higher than usually observed in practice ( Figure 2C ) . Small lesions , however , can decrease below the detection limit and appear to be eradicated for years before re-emerging ( Figure 2B , C ) . This result is important , as it explains why tumors can recur after long periods of remission without the need to invoke processes involving cancer stem cells , angiogenesis , or immune escape ( Hensel et al . , 2012 ) . Note that results similar to those obtained by simulation are observed in several of the individual lesions from actual patients graphed in Figure 1 . 10 . 7554/eLife . 00747 . 005Figure 2 . Tumor response to mono and dual therapy . The tumor grows exponentially until a certain detection size , M , is reached , at which point treatment is initiated . The number of point mutations that could in principle confer resistance to monotherapy is n = 50 . For dual therapy , the number of point mutations that could confer resistance to drugs 1 and 2 separately is given by n1 = 50 and n2 = 50 . The number of point mutations that could confer resistance to both drugs simultaneously is given by n12 . The point mutation rate was assumed to be u = 10−9 and the rate of cell division b = 0 . 14 per day and is unaffected by treatment . The rate of cell death before treatment is d = 0 . 13 per day; it is increased to d’ for sensitive cells during treatment . ( A ) – ( C ) For clinically detectable sizes ( M = 1010 , 109 , 108 , depending on the location of the tumors and the detection methods used ) , monotherapy leads to a temporary shrinkage of the tumor but is always followed by tumor regrowth . ( D ) Due to stochastic fluctuations the few resistant cells present at the start of treatment go extinct and the lesion is eradicated . ( E ) Treatment leads to a temporary shrinkage of the tumor followed by regrowth . ( F ) The tumor decreases slowly in response to dual therapy , but resistant cells eventually evolve and cause treatment failure . DOI: http://dx . doi . org/10 . 7554/eLife . 00747 . 005 The results predicted to occur with dual therapy are shown in Figure 2D–F . Here , we also assume that there are 50 mutations that have the potential to confer resistance to either drug alone , but also that there is at least one mutation that can confer resistance to both drugs simultaneously . Intuitively , one might imagine that the existence of even a single cell resistant to both drugs at the start of therapy will automatically result in treatment failure . However , our results show that this is not necessarily true , and that the response depends on the size of the lesion , the number of cross-resistant cells , and the effects of the therapy on the balance between cell birth and cell death . Three examples illustrate these points . In Figure 2D , there is a small number of cells resistant to both drugs at the initiation of dual therapy , but these cells are lost by stochastic drift and the lesion is eradicated . In Figure 2E , there is a greater , but still relatively small number ( ∼100 ) , of cells resistant to both drugs . The lesion shrinks at first , but eventually progresses due to preexisting cross-resistance mutations within it . In the third lesion , the few cells resistant to both drugs at the initiation of therapy are lost to stochastic drift , but the cytolytic effects of the drug combination are less pronounced than in the other two cases ( d’ = 0 . 15 instead of 0 . 17 or 0 . 21 ) . The relatively slow decrease in lesion size enables the generation of de novo resistance mutations during treatment and the lesion eventually recurred ( Figure 2F ) . In summary , treatment failure can be caused either by the preexistence of resistance to both drugs in a small number of tumor cells ( Figure 2E ) or the emergence of resistant cells during treatment ( Figure 2F ) . Taking both of these possibilities into account , the probability , perad , that dual therapy eradicates a lesion containing M cells at the start of treatment is given by ( 1 ) perad=p1↑p1↓p2↑p2↓ . p1↑ is the probability that no 1-step resistant lineage arises ( and survives ) prior to treatment . p1↓ is the probability that no 1-step resistant lineage arises ( and survives ) during treatment . p2↑ is the probability that no 2-step resistant lineage arises ( and survives ) prior to treatment . p2↓ is the probability that no 2-step resistant lineage arises ( and survives ) during treatment . Here , ‘steps’ refers to the number of mutations ( one or two ) needed to achieve dual resistance , and ‘lineage’ refers to the descendants of a single cell that has achieved dual resistance via a particular mutational path . The therapy is successful if there is no resistant lineage arising in any of these four scenarios; since these are independent events , the overall success probability is obtained by multiplying the corresponding probabilities as shown in equation ( 1 ) . The probabilities that no 1-step resistant lineages arise before ( p1↑ ) or during treatment ( p1↓ ) and survive are given by Komarova and Wodarz ( 2005 ) p1↑=exp ( −Mun12 ) and Michor et al . ( 2006 ) p1↓=exp ( Mun12ss' ) . Here s = 1 − d/b as above , and s′ = 1 − d′ /b′ , where b′ and d′ are birth and death rates of cells sensitive to at least one drug during treatment ( note that s’<0 ) . The probabilities that no 2-step resistant lineages arise before ( p2↑ ) or during ( p2↓ ) treatment and survive can be calculated as:p2↑=exp[Mu2s'−sss' ( n1 ( n2+n12 ) log ( 1sM+u ( n2+n12 ) s'−sss' ) +n2 ( n1+n12 ) log ( 1sM+u ( n1+n12 ) s'−sss' ) ) ]andp2↓=exp ( −Mu2 ( 2n1n2+n12 ( n1+n2 ) ) ss'2 ) . The proofs of these results are provided in Supplementary file 1 , Section 5 . Excellent agreement between equation ( 1 ) and simulation results is shown in Figure 3 . 10 . 7554/eLife . 00747 . 006Figure 3 . Probability of tumor eradication for two-drug combination therapy . A single mutation conferring cross-resistance to both drugs ( n12 = 1 ) can prohibit any hope for a successful dual therapy . Solid curves show analytical results for dual therapy and dashed curve shows analytical results for a typical monotherapy , both are calculated using equation ( 1 ) . Markers ( square , triangle , circle , diamond ) indicate simulation results ( averages of 106 runs ) . Parameter values: birth rate b = 0 . 14 , death rate d = 0 . 13 , death rate for sensitive cells during treatment d’ = 0 . 17 , point mutation rate u = 10−9 . DOI: http://dx . doi . org/10 . 7554/eLife . 00747 . 006 Although modeling of single neoplastic lesions is the norm in theoretical studies , most patients with advanced cancers have multiple lesions and curing a patient requires eradication of all lesions . Equation ( 1 ) can be used to evaluate which combination treatments will be successful in typical patients with multiple metastatic lesions . To determine the total extent of disease in typical patients who enroll for clinical trials , we quantified all radiographically detectable metastases in 22 such patients: 7 with pancreatic ductal adenocarcinomas , 11 with colorectal carcinomas , and 6 with melanomas—a different cohort than that depicted in Figure 1 , in which only index lesions ( those easiest to measure ) were evaluated . The number of metastatic lesions in the 22 patients described in Table 1 ranged from 1 to 30 , and their total tumor burden ranged from 9 × 108 to 3 × 1011 cells ( see Supplementary file 2 ) . 10 . 7554/eLife . 00747 . 007Table 1 . Probability of treatment failure for combination therapy in patientsDOI: http://dx . doi . org/10 . 7554/eLife . 00747 . 007PatientPrimary tumor typeNumber of metastasesTotal tumor burden ( number of cells ) Probability of treatment failureMonotherapyDual therapy: n12 = 1Dual therapy: n12 = 0N1Pancreas182 . 6 × 1011110 . 283N2Colon252 . 3 × 1011110 . 26N3Melanoma261 . 7 × 1011110 . 203N4Melanoma301 . 4 × 1011110 . 172N5Colon211 . 0 × 1011110 . 128N6Melanoma89 . 8 × 1010110 . 12N7Colon259 . 1 × 1010110 . 112N8Pancreas87 . 4 × 1010110 . 092N9Pancreas236 . 4 × 1010110 . 08N10Pancreas55 . 5 × 1010110 . 069N11Colon145 . 4 × 1010110 . 068N12Rectal234 . 8 × 1010110 . 061N13Melanoma94 . 1 × 1010110 . 052N14Pancreas134 . 1 × 1010110 . 051N15Pancreas83 . 3 × 1010110 . 042N16Melanoma72 . 2 × 1010110 . 028N17Melanoma102 . 1 × 1010110 . 027N18Colon42 . 0 × 1010110 . 026N19Melanoma91 . 8 × 1010110 . 023N20Colon31 . 6 × 10910 . 8810 . 002N21Melanoma211 . 3 × 10910 . 8280 . 002N22Pancreas18 . 5 × 10810 . 6770 . 001For monotherapy , we assume that 50 point mutations ( n = 50 ) can in principle confer resistance to the drug . With dual therapy , we assume that 50 point mutations can in principle confer resistance to each drug individually ( n1 = n2 = 50 ) . Two scenarios are modeled: in the first , there is one mutation that can in principle confer resistance to both drugs ( i . e . , cross-resistance , n12 = 1 ) . In the other case , there are no possible mutations that can confer resistance to both drugs ( n12 = 0 ) . Parameter values: birth rate , b = 0 . 14 , death rate , d = 0 . 13 , death rate for sensitive cells during treatment , d′ = 0 . 17 , point mutation rate u = 10−9 . Colon: colonic adenocarcinoma; Rectal: rectal adenocarcinoma; Pancreas: pancreatic ductal adenocarcinoma . For each of these 22 patients , we used equation ( 1 ) to calculate the probability that monotherapy or dual therapy would eradicate all the patients’ lesions . We find that monotherapy will fail in all 22 patients ( Table 1 and Supplementary file 3 ) , as expected from the simulations in Figure 2A–C and from clinical experience . If there is even one possible mutation that can in principle confer resistance to both drugs , then our model shows that dual therapy has also only a small chance of curing patients , even those with the smallest tumor burden . In our cohort of 22 patients , none are expected to be cured under these circumstances ( Table 1 ) . Only if there are no potential mutations that can confer cross-resistance will dual therapy be successful in eradicating all lesions . In the cohort described in Table 1 , we calculate that eight patients ( those with the smallest tumor burden ) would have >95% probability of cure . Those with the largest tumor burden would still have a >20% probability of tumor recurrence . Additional simulations show that therapy with three agents will also not cure patients if there is even one mutation that can confer resistance to all three agents . Similar conclusions hold if we vary parameter values within a reasonable range ( Supplementary file 3 ) . We note that in patients whose tumors have high cell turnover ( time between cell divisions of 1 day , corresponding to b = 1 ) , even dual therapy with no cross-resistance mutations would be expected to fail in 37% of patients described in Table 1 ( Supplementary file 3 ) . Graphical representations of the simulated responses of two patients with multiple metastatic lesions are shown in Figure 4 . With monotherapy in patient N1 ( Figure 4A ) , all lesions are predicted to regress , but then recur within a year or so after the initiation of therapy ( Figure 4B , left panel ) . Treatment failure in most lesions would also occur after dual therapy when there is at least one mutation that could confer resistance to both agents , although the length of remission will be longer than with monotherapy ( Figure 4B , middle panel ) . In patient N11 , with less disease burden , dual therapy will fail to eradicate several of the lesions when there is a possibility of a single cross-resistance mutation , but there is hope of cure if no such cross-resistance mutations are possible ( Figure 4C , D ) . 10 . 7554/eLife . 00747 . 008Figure 4 . Treatment response dynamics to monotherapy and dual therapy in two patients . ( A ) Depiction of all 18 detectable metastases in patient N1 , who had a particularly heavy tumor burden ( scale 1:4 ) . ( B ) Simulated treatment of patient N1 , comparing monotherapy with n = 50 resistance mutations and dual therapy with n1 = n2 = 50 resistance mutations to the individual drugs and one ( n12 = 1 ) or no ( n12 = 0 ) cross-resistance mutations to both drugs . ( C ) Depiction of all 14 detectable metastases in patient N11 , who had a more typical tumor burden ( scale 1:4 ) . ( D ) Simulated treatment of patient N11 . Parameter values for simulations in ( B ) and ( D ) : birth rate b = 0 . 14; death rate d = 0 . 13; death rate for sensitive cells during treatment d′ = 0 . 17; point mutation rate u = 10−9 . DOI: http://dx . doi . org/10 . 7554/eLife . 00747 . 008 In current clinical practice , it is common to administer targeted agents sequentially: once relapse occurs , a second , often experimental , agent is administered . The model described above can also be used to predict the relative effectiveness of sequential vs simultaneous therapies of a single lesion with two drugs . When there is a possibility of a single mutation conferring resistance to both drugs , sequential combination therapy will ‘always’ fail . In ∼74% of lesions , the failure is due to mutations that were present prior to the treatment with the first drug , whereas in ∼26% of the lesions , failure is due to the development of cells resistant to drug 2 during treatment with drug 1 ( Figure 5A and Figure 5—figure supplement 1 ) . With simultaneous treatment , it is possible to eradicate ∼26% of the lesions even when cross-resistance mutations are possible ( Figure 5B ) . When there is no possibility of a mutation conferring cross-resistance to both drugs , the differences are even more striking: sequential therapy fails in 100% of cases ( Figure 5C ) , whereas simultaneous therapy succeeds in >99% of lesions of the identical size ( Figure 5D ) . 10 . 7554/eLife . 00747 . 009Figure 5 . Sequential vs simultaneous therapy with two drugs . ( A ) If there is even a single mutation that confers cross-resistance to both drugs ( n12 = 1 ) , then sequential therapy will fail in all cases . In 73 . 7% of the cases , this failure is due to the exponential growth of fully resistant cells that were present at the start of treatment . In the remaining 26 . 3% of cases , the failure is due to resistance mutations that developed during therapy with the first drug . ( B ) With simultaneous therapy , 26 . 3% of patients can be cured under the same circumstances . In the remaining patients ( 73 . 7% ) , cross-resistant mutations existed prior to the therapy and their expansive growth will ensure treatment failure whether treatment is simultaneous or sequential ( see Figure 5—figure supplement 1 for further details ) . ( C ) and ( D ) If the two drugs have no resistance mutations in common ( n12 = 0 ) , then simultaneous therapy is successful with a probability of 99 . 9% while sequential therapy still fails in all cases . DOI: http://dx . doi . org/10 . 7554/eLife . 00747 . 00910 . 7554/eLife . 00747 . 010Figure 5—figure supplement 1 . Examples for the evolution of resistance during sequential therapy . Two drugs are available for treatment where n1 = 50 and n2 = 50 point mutations confer resistance to each drug individually and one mutation confers resistance to both drugs simultaneously ( n12 = 1 ) . ( A ) A typical example of a tumor relapsing in the second wave of panel ( A ) in Figure 5 . The few fully resistant cells go extinct due to stochastic fluctuations at the start of treatment . The cells resistant only to drug 1 produce cross-resistant cells during the treatment with the first drug . The cells resistant to both drugs received sequentially two mutations . ( B ) A typical example of a tumor relapsing in the first wave of panel ( A ) in Figure 5 . The fully resistant cells are already present at the start of treatment . These cells received the cross-resistance mutation and are therefore immediately resistant to both drugs . The exponential growth of these fully resistant cells cause the relapse; their growth is unaffected by whether treatment is simultaneous or sequential . Parameter values: birth rate b = 0 . 14 , death rate d = 0 . 13 , death rate for sensitive cells during treatment d' = 0 . 17 , point mutation rate u = 10−9 , detection size of tumor ( for start of treatment and relapse ) M = N = 109 cells . DOI: http://dx . doi . org/10 . 7554/eLife . 00747 . 010 One of the most important aspects of the cancer stem cell hypothesis revolves around therapeutic resistance . Evidence to date does not indicate that cancer stem cells are innately resistant to either single drugs or drug combinations . However , the precise proportion of cancer stem cells ( among all cancer cells ) has a dramatic effect on the development of resistance . This effect can be studied using our model if we use the number of cancer stem cells as an effective population size in our formulas and adjust other parameters to account for the stem cell dynamics ( Tomasetti and Levy , 2010 ) ( i . e . , the birth rate should correspond to the rate of symmetric renewal , the rate of symmetric differentiation should be added to the death rate , and an effective mutation rate for stem cells should be introduced to account for mutations that occur during asymmetric division ) . For example , if cancer stem cells represent only 0 . 1% of cancer cells , then the development of resistance to single agents or combinations is roughly 0 . 1% as likely as if 100% of the cancer cells have the capacity to repopulate the tumor . The fraction of cancer stem cells appears to be this low in CML , perhaps explaining the remarkable success of imatinib ( Michor et al . , 2005 ) . In solid tumors , however , the fraction of cancer stem cells seems much higher , usually higher than 5% and in some cases close to 100% ( Shackleton et al . , 2009 ) . This issue is further complicated by the fact that the situation is plastic , with non-stem cells converting to cancer stem cells under certain conditions ( Gupta et al . , 2009 ) . As better approaches to quantify cancer stem cells in solid tumors become available , our estimates of the likelihood of therapeutic success will be improved . If resistance has a fitness cost , then we expect a smaller number of resistant cells at the start of treatment and correspondingly a higher chance of treatment success . We used computer simulations to verify our results in the case when there is a cost for resistance , by assuming that each resistance mutation decreases the net growth rate of the cell by up to 10% . The results are shown in Table 2 . For combination therapies with drugs that have resistance mutations in common , the probability of eradicating a lesion is only marginally affected by costly resistance . For dual therapies with no cross-resistance mutations , treatment has a high chance of eradicating all but the largest lesions , whether or not resistance is costly . In the case of large lesions with high cell turnover rates ( the case in which even dual therapies with no cross-resistance might fail ) , costly resistance increases the chance of treatment success . For example , if each resistance mutation decreases the net growth rate of cells by 10% , the probability that dual therapy with no cross-resistance mutations will eradicate a lesion of size 1011 in which cells divide on average every day is 68% ( compared with 47% in the case of neutral resistance ) . 10 . 7554/eLife . 00747 . 011Table 2 . Simulation results for the probability of treatment failure when resistance is costlyDOI: http://dx . doi . org/10 . 7554/eLife . 00747 . 011Dual therapy:Number of cellsBirth rateProbability of treatment failuren1 = n2n12c = 0%c = 1%c = 5%c = 10%5001090 . 140 . 00 . 00 . 00 . 050010910 . 010 . 010 . 010 . 05011090 . 140 . 740 . 730 . 720 . 750110910 . 740 . 740 . 720 . 750010110 . 140 . 120 . 110 . 080 . 06500101110 . 530 . 510 . 420 . 3250110110 . 141 . 01 . 01 . 01 . 0501101111 . 01 . 01 . 01 . 0Each resistance mutation reduces the net growth rate by a factor c via a decrease of the birth rate b . Parameter values are death rate , d = b − 0 . 01 , death rate for sensitive cells during treatment , d’ = b + 0 . 03 , point mutation rate , u = 10−9 . The simulation results are averages over 106 runs per parameter combination . Some therapies may directly eliminate tumor cells ( d′ > d ) , whereas others may impede their division ( b′ < b ) . Our formulas account for both of these possibilities . Overall , the rate b′ − d′ of tumor decline is of primary importance , and whether this is achieved by eliminating cells or suppressing division has only a minor effect on treatment outcomes . For example , consider a dual therapy with n1 = n2 = 50 , n12 = 1 , applied to a lesion of size M = 109 , with other parameters as inferred from our dataset . If this therapy shrinks the tumor at rate −0 . 03 per day by increasing cell death , the eradication probability is 26% . If the therapy instead suppresses division , this probability increases to 29% , because there are fewer chances for resistance mutations during treatment . While our typical parameter values are derived from the melanoma dataset , our analytical results can accommodate parameter values from any other type of cancer , once they become available . Furthermore , our results are qualitatively robust across a wide range of birth and death rates ( Supplementary file 3 ) . The parameters with the strongest effects on the success of combination treatments—apart from the number of cross-resistance mutations—are lesion sizes and point mutation rate . Thus , we expect that combination treatments will be more effective in cancers with small fractions of tumor stem cells ( small effective population size of lesions ) and less effective in cancers with significantly increased point mutation rates .
Our conclusions are highly relevant for the expanding development and use of targeted agents for cancer therapy . Most importantly , they show that even if there is one genetic alteration within any of the 6 . 6 billion base pairs present in a human diploid cell that can confer resistance to two targeted agents , therapy with those agents will not result in sustained benefit for the majority of patients with advanced disease . The same result is obtained with triple therapy; if there is the possibility of a mutation conferring cross-resistance to three drugs , lesions of the size commonly observed in patients with advanced cancers will always recur . Similar conclusions were reached by Komarova et al . ( 2009 ) , who showed that a combination of three current targeted drugs for CML will not be beneficial over a combination of two such drugs due to cross-resistance . Our formulas could be used to develop an optimum in vitro assay to detect the existence of cross-resistance mutations for a given drug combination . The development of drugs that act through distinct pathways will therefore be essential for the success of combination therapies in the clinic . Although this seems feasible in principle , there are a number of observations suggesting that it will be difficult in practice . For example , it has been shown that the increased expression of growth factors ( such as hepatocyte growth factor ) can confer resistance to a variety of drugs that inhibit kinases functioning through different pathways ( Straussman et al . , 2012; Wilson et al . , 2012 ) . Moreover , it is well known that mutations in several different genes , including those encoding ABC transporters , can confer resistance to many different drugs ( Lavi et al . , 2012 ) . Drugs that have very different chemical structures , in addition to distinct mechanisms of action , may be required to circumvent these resistance mechanisms . Our results are not readily applicable to therapies that rely on the immune destruction of tumors ( Kirkwood et al . , 2012 ) , such as those employing CTLA-4 ( Hodi et al . , 2010 ) , PD1 ( Topalian et al . , 2012 ) , or CD19-CARs ( Grupp et al . , 2013 ) . This promising line of therapy relies on an ongoing battle between cancer cells and the immune system . The immune system , unlike small molecule compounds , can replicate and evolve , and the factors underlying therapeutic success or failure are not sufficiently understood to allow useful modeling at this point . Once the mechanisms underlying the failures of immune modulators become more apparent , it will be important to try to understand why long-term control of disease is more common with these therapies than with small molecule drugs . Our results on sequential vs simultaneous therapy with two or more agents ( Figure 5 ) are in agreement with previous results ( Katouli and Komarova , 2011 ) and have immediate practical implications even while new combinations are being developed . Sequential administration of targeted agents is often used to treat patients , for a variety of medical and economic reasons . Our data show that this sequential administration precludes any chance for cure—even when there are no possible mutations that can confer cross-resistance ( Figure 5C ) . And when there are potential mutations conferring cross-resistance to two or more agents , simultaneous administration offers some hope for cure while there is no hope with sequential therapy ( Figure 5A ) . The realization of the advantages of simultaneous vs sequential dual therapy will hopefully stimulate efforts to combine agents much earlier in the drug development process .
We model tumor growth and evolution as a continuous time multitype branching process ( Athreya and Ney , 1972; Goldie and Coldman , 1998; Komarova and Wodarz , 2005 ) . In the case of two drugs , there are four possible types: 00 , 01 , 10 , and 11 , where zeros indicate sensitivity to a drug and ones indicate resistance . For example , type 01 is sensitive to drug 1 and resistant to drug 2 . Our model includes two phases: pretreatment and treatment . The pretreatment phase is initiated with a single fully sensitive cell ( type 00 for two drugs ) . During this phase , all cell types reproduce at rate b and die at rate d . The offspring of a type 00 cell has probability un1 of being type 10 , un2 of being type 01 , un12 of being type 11 , and otherwise is of type 00 . The offspring of a type 10 cell has probability u ( n2 + n12 ) of being of type 11 and otherwise is of type 10; similar probabilities apply to type 01 . Type 11 cells produce only type 11 . These formulas generalize in straightforward manner to combination therapy with three or more drugs . The pretreatment phase ends , and the treatment phase begins , when there are a total of M cells . During the treatment phase , all cell types that are sensitive to one or more drugs have birth rate b’ and death rate d’; fully resistant cells maintain the pretreatment birth and death rates . Mutation probabilities are unchanged . Our mathematical analysis of dual therapy is based in part on a recently discovered exact solution to the two-type branching process ( Antal and Krapivsky , 2011 ) . Detailed proofs of all results are provided in Supplementary file 1 . We use Monte Carlo computer simulations to confirm our analytical results and improve our understanding of the evolutionary dynamics during cancer treatment . The developed tool is an enhanced version of TTP ( Tool for Tumor Progression ) where the discrete time branching processes are replaced by continuous time branching processes ( Reiter et al . , 2013 ) . Moreover , the new version also simulates tumor dynamics during treatment with several drugs . The simulations implement a multitype birth–death branching process using the specified parameter values . For cell subpopulations with less than 104 cells , the process is simulated exactly; for larger subpopulations , a deterministic ( exponential growth ) approximation is used in the interest of efficiency . Within this deterministic approximation , the timing of appearances of new mutations is simulated using an adapted version of the Gillespie algorithm ( Gillespie , 1977 ) . Between 106 and 108 runs are used for each parameter combination . To study the consequences of costly resistance , we suppose that each resistance mutation reduces the cell division rate such that the net growth rate is decreased by a factor c representing the metabolic costs of resistance . For example , cells with two resistance mutations divide at rate ( b − d ) ( 1 − c ) 2 + d . | As medicine becomes increasingly personalized , more and more emphasis is being placed on the development of therapies that target specific cancer-causing mutations . But while many of these drugs are effective in the short term , and do extend patient lives , tumors tend to evolve resistance to them within a few months . The key problem is that large tumors are genetically diverse . This means that for any given treatment , there is likely to be a small population of cells within the tumor that is resistant to the effects of the drug . When the drug is given to a patient , these cells will survive and multiply and this will lead ultimately to treatment failure . Given that a single drug is therefore highly unlikely to eradicate a tumor , combinations of two or more drugs may offer a higher chance of cure . This approach has been effective in the treatment of HIV as well as certain forms of leukemia . Here , Bozic et al . present a mathematical model designed to predict the effects of combination targeted therapies on tumors , based on the data obtained from 20 melanoma ( skin cancer ) patients . Their model revealed that if even 1 of the 6 . 6 billion base pairs of DNA present in a human diploid cell has undergone a mutation that confers resistance to each of two drugs , treatment with those drugs will not lead to sustained improvement for the majority of patients . This confirms the need to develop drugs that target distinct pathways . The model also reveals that combination therapy with two drugs given simultaneously is far more effective than sequential therapy where the drugs are used one after the other . Indeed , the model of Bozic et al . indicates that sequential treatment offers no chance of a cure , even when there are no cross-resistance mutations present , whereas combination therapy offers some hope of a cure , even in the presence of cross-resistance mutations . By emphasizing the need to develop drugs that target distinct pathways , and to administer them in combination rather than sequentially , the study by Bozic et al . offers valuable advice for drug development and the design of clinical trials , as well as for clinical practice . | [
"Abstract",
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"Results",
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"methods"
] | [
"evolutionary",
"biology",
"cancer",
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] | 2013 | Evolutionary dynamics of cancer in response to targeted combination therapy |
Perturbations in neural circuits can provide mechanistic understanding of the neural correlates of behavior . In M71 transgenic mice with a “monoclonal nose” , glomerular input patterns in the olfactory bulb are massively perturbed and olfactory behaviors are altered . To gain insights into how olfactory circuits can process such degraded inputs we characterized odor-evoked responses of olfactory bulb mitral cells and interneurons . Surprisingly , calcium imaging experiments reveal that mitral cell responses in M71 transgenic mice are largely normal , highlighting a remarkable capacity of olfactory circuits to normalize sensory input . In vivo whole cell recordings suggest that feedforward inhibition from olfactory bulb periglomerular cells can mediate this signal normalization . Together , our results identify inhibitory circuits in the olfactory bulb as a mechanistic basis for many of the behavioral phenotypes of mice with a “monoclonal nose” and highlight how substantially degraded odor input can be transformed to yield meaningful olfactory bulb output .
Odorants in the environment are detected by a large repertoire of odorant receptor , expressed on the dendrites of sensory neurons in the olfactory epithelium ( Buck and Axel , 1991; Zhang and Firestein , 2002 ) . In mice , each olfactory sensory neuron expresses only one of ~1300 odorant receptor genes , and each of these receptors interacts with multiple odorants ( Chess et al . , 1994; Malnic et al . , 1999 ) . Neurons expressing a given receptor are distributed randomly across large zones of the olfactory epithelium , but project to two spatially invariant glomeruli in the olfactory bulb , the first processing center of olfactory information in the mammalian brain ( Ressler et al . , 1994; Vassar et al . , 1994 ) . Thus , the distributed pattern of neural activity that is evoked by the binding of an odorant to a given receptor in the olfactory epithelium is transformed into a topographically organized , invariant map of glomerular activity at the level of the olfactory bulb ( Bozza et al . , 2004; Meister and Bonhoeffer , 2001; Rubin and Katz , 1999; Uchida et al . , 2000; Wachowiak and Cohen , 2001 ) . The principal neurons of the olfactory bulb , mitral and tufted cells , extend their apical dendrite into a single glomerulus , and thus only receive direct input from sensory neurons expressing a single odorant receptor . Electrophysiological and imaging experiments have revealed that , consistent with this anatomical organization , mitral cells tend to be narrowly tuned and only respond to a small number of odorants ( Davison and Katz , 2007; Tan et al . , 2010 ) . The spatiotemporal patterns of mitral cell firing are strongly shaped by the activity of local inhibitory neurons , including periglomerular cells , EPL interneurons , and granule cells ( Banerjee et al . , 2015; Fukunaga et al . , 2014; Kato et al . , 2013; Luo and Katz , 2001; Miyamichi et al . , 2013; Yokoi et al . , 1995 ) . Ultimately , mitral and tufted cells relay this odor information to several higher brain regions , including the piriform cortex , amygdala , and entorhinal cortex , via a dense elaboration of axonal projections ( Ghosh et al . , 2011; Igarashi et al . , 2012; Luskin and Price , 1982; Miyamichi et al . , 2011; Nagayama et al . , 2010; Sosulski et al . , 2011 ) . How the patterns of activity evoked by odor stimulation in the cells of the olfactory bulb ultimately relate to odor perception , discrimination , and behavior , however , remains largely undefined . A major challenge for the olfactory system is that it must function across a wide range of stimulus intensities . For example , salient odor cues must reliably be detectable against strong and highly dynamic background odors . To explore potential neural mechanisms that can mediate such signal amplification and noise reduction we used previously generated M71 transgenic mice with a 'monoclonal nose' ( Fleischmann et al . , 2008 ) . In these mice , more than 95% of all olfactory sensory neurons express a single odorant receptor , M71 . As a consequence of this genetic manipulation , the frequency of sensory neurons expressing endogenous odorant receptor genes is reduced 20-fold , and the canonical glomerular odor map observed in wild-type mice disappears: most odorants now fail to elicit detectable levels of glomerular activity , while the majority of glomeruli respond to acetophenone , a known M71 ligand ( Figure 1—figure supplement 1 ) . Surprisingly , despite this striking alteration of odor-evoked neural activity , M71 transgenic mice are able to smell a variety of odors . They can detect and discriminate several odorants in a go/no go operant conditioning task , although their performance in this task decreases compared to controls when M71 transgenic mice are required to discriminate mixtures of structurally and perceptually similar odorants . Moreover , M71 transgenic mice fail to discriminate acetophenone , a known strong M71 ligand , from air in this go/no go discrimination assay , despite the fact that acetophenone activates the vast majority of sensory neurons and glomeruli in these mice . This apparent discrepancy between molecular alteration and receptor neuron physiology on the one side and behavioral phenotype on the other now allows us to investigate the neural mechanisms at play: What does allow M71 transgenic mice to detect and discriminate most odorants despite the 20-fold decrease in the expression of endogenous odorant receptors ? Conversely , what underlies the inability of these mice to detect the pervasive glomerular activity evoked by acetophenone ? To explore the link between odor-evoked sensory neuron activity and behavior we analyzed the activity of olfactory bulb mitral cells , the main output neurons of the olfactory bulb . Two-photon calcium imaging and whole cell patch-clamp recordings of mitral cells revealed that mitral cell odor responses in M71 transgenic mice greatly resembled the responses observed in wild-type mice . Indeed , the fraction of responsive mitral cells and odor-evoked changes in firing rates were indistinguishable from controls . Calcium imaging and whole cell recordings further indicated that much of this normalization of odor-evoked activity is achieved through inhibition by periglomerular interneurons . Finally , we found that M71 transgenic mice exhibit spontaneous sniff adaptation in response to acetophenone exposure , suggesting that while they consistently fail to discriminate acetophenone from air in a go/no-go operant conditioning tasks they are indeed able to detect the presence of acetophenone . Together , our data reveal that odor-evoked patterns of glomerular activity can be substantially transformed by olfactory bulb neural circuits , to extract meaningful odor information from massively degraded sensory input and point towards a key role of glomerular inhibition .
Previous behavioral experiments using a go/no go operant conditioning task indicated that M71 transgenic mice failed to discriminate acetophenone from its diluent , mineral oil , but could detect and discriminate other odorants ( Fleischmann et al . , 2008 ) ( Figure 1—figure supplement 1 ) . To better understand the link between odor-evoked neural activity and behavior we first replicated these behavioral observations with an independent cohort of mice . Consistent with our initial observations , we found that M71 transgenic mice consistently failed to detect acteophenone in this task ( acetophenone versus mineral oil , repeated measure ANOVA , ( block x genotype ) F ( 9 , 90 ) = 5 . 43 , p<0 . 001 ) , yet readily discriminated other pairs of odorants ( ethyl acetate versus mineral oil , citronellol , or carvone , ( block x genotype ) F ( 9 , 90 ) = 1 . 49 , p=0 . 17 , Figure 1A and B ) . Individual experiments consisted of 10 blocks of 20 odor presentations , and all 15 M71 transgenic mice failed to reach a 'correct lick ratio' surpassing 75% . In contrast , the same 15 M71 transgenic mice all successfully discriminated ethyl acetate from mineral oil , citronellol , or carvone ( Figure 1A and B , Figure 1—figure supplement 2 ) . Thus , in a go/no-go operant conditioning task , M71 transgenic mice are consistently unable to discriminate acetophenone from air . 10 . 7554/eLife . 16335 . 003Figure 1 . The ability of M71 transgenic mice to detect acetophenone is task-dependent . ( A , B ) In a go/no go operant conditioning task , M71 transgenic mice fail to discriminate acetophenone from mineral oil ( left panels ) . In contrast , M71 transgenic mice readily discriminate other pairs of odorants ( ethyl acetate vs . mineral oil , citronellol , or carvone , right panels ) . ( A ) Original results reported in Fleischmann et al . ( 2008 ) . ( B ) Repeat experiment with an additional cohort of mice . Thin lines: learning curves for individual mice . Thick lines: averaged learning curves . Error bars: 95% CI of the mean . ( C ) Sniff adaptation: schematic of the experimental configuration . ( D ) Example sniff traces during first 3 ( 1st , 2nd , and 3rd ) presentations of hexanone ( shaded area ) from a control mouse . Lighter colored traces signify later presentations . ‘FV’ trace shows opening of final valve directing odorized air to the mouse , ‘flow’ trace shows the output from the olfactometer , and ‘PID’ trace shows signal evoked by odorized air from a photo-ionization detector . ( E ) Example moving averages of instantaneous sniff frequency during first 3 presentations of hexanone ( window = 500 ms , plotted against leading edge ) . Black traces: controls , red traces: M71 transgenic mice . ( F , G ) Mean instantaneous sniff frequency responses to first vs . the average of the 2nd and 3rd presentation of an odor for control ( black , F ) and M71 transgenic ( red , G ) mice . Pooled non-acetophenone odorants: hexanone , ethyl acetate , heptanal , and an odor mixture . Lighter colors: individual trials , thick lines: averages . Error bars: SD . Black dotted lines on the M71 plots show the means for the corresponding data from controls . DOI: http://dx . doi . org/10 . 7554/eLife . 16335 . 00310 . 7554/eLife . 16335 . 004Figure 1—figure supplement 1 . Schematic representation of the perturbation of the glomerular map of M71 transgenic mice with a 'monoclonal nose' . ( A ) In wild-type mice , odors bind to subsets of odorant receptors ( ORs ) , which results in the activation of subsets of olfactory sensory neurons ( OSNs ) and glomeruli in the olfactory bulb ( OB ) . This activity is transformed into sparse patterns of mitral cell activity , which transmits odor information to higher olfactory centers to drive behavior . ( B and C ) In M71 transgenic mice , odor-evoked patterns of glomerular activity are massively perturbed . The pervasive expression of the M71 OR , instead of a large repertoire of endogenous OR , results in the pervasive activation of OSNs and glomeruli in response to acetophenone , an M71 receptor ligand . In contrast , most other odorants do not elicit detectable glomerular activity . Surprisingly , behavioral experiments using a go/no go operant conditioning task showed that M71 transgenic mice could detect and discriminate most odorants , but not acetophenone . DOI: http://dx . doi . org/10 . 7554/eLife . 16335 . 00410 . 7554/eLife . 16335 . 005Figure 1—figure supplement 2 . M71 transgenic mice fail to detect acetophenone in a go/no go operant conditioning task . ( A ) Wild-type mice consistently discriminate acetophenone from its diluent mineral oil ( black lines ) . Wild-type mice also discriminate between other pairs of odorants ( ethyl acetate versus mineral oil , citronellol , or carvone , dotted lines ) . ( B ) M71 transgenic mice consistently fail to discriminate acetophenone from mineral oil , but readily discriminate between the other pairs of odorants . DOI: http://dx . doi . org/10 . 7554/eLife . 16335 . 005 We next asked if the failure to detect acetophenone was specific for this go/no go operant conditioning task , or whether it could similarly be observed in a different behavioral test . To address this question we measured exploratory sniffing behavior in response to novel odors , a simple , spontaneous test for odor perception , which does not require training ( Welker , 1964; Wesson et al . , 2008 ) . As previously described , wild-type mice exhibited increased sniff frequencies when exposed to a novel odorant ( mean response = 2 . 1 Hz , SD = 1 . 6 Hz ) that then decreased upon repeated presentation of the same odorant ( mean response for 2nd and 3rd presentation = 0 . 3 Hz , SD = 1 . 2 Hz , p=3 . 3 x10-7 , paired t-test 1st presentation versus mean of 2nd and 3rd , n = 27 mouse-odor pairs , n = 7 mice , Figure 1C–F ) . Consistent with their ability to detect and discriminate most odorants , M71 transgenic mice exhibited an initial increase in sniff frequency to ethyl acetate , hexanone , heptanal , or a mixture of isoamyl acetate , 2-nonanone and cyclohexanol ( referred to as 'non-acetophenone' odors in Figure 1F , mean response = 2 . 7 Hz , SD = 1 . 4 Hz ) . This response was indistinguishable from controls ( p=0 . 12 , t-test ) , and similarly displayed a significant decrease in sniff frequencies upon re-exposure ( p=1 . 7 x 10–8 , paired t-test , n = 29 mouse-odor pairs , n = 8 mice , Figure 1G ) . Surprisingly , similar results were obtained for acetophenone presentations: both control and M71 transgenic mice displayed initial high frequency responses ( control: mean = 3 . 0 Hz , SD = 1 . 6 Hz; M71 transgenic: mean = 3 . 8 Hz , SD = 1 . 9 Hz , p=0 . 38 , t-test ) , and reductions in response frequencies during the second and third acetophenone presentation ( Figure 1F and G , right panels ) . Together , these results indicate that in contrast to the aforementioned go/no go operant conditioning task , M71 transgenic mice can identify acetophenone in a spontaneous test for odor detection . Thus , in M71 transgenic mice the strong M71 ligand acetophenone results in major behavioral perturbations - while acetophenone is spontaneously detected , it cannot reliably be discriminated from background in an operant conditioning experiment . The ability to probe the cellular processes that underlie changes in olfactory-driven behaviors in this massively perturbed system can provide important general insights into how odor information is normally processed in the olfactory bulb . We therefore next asked how perturbed glomerular inputs in M71 transgenic mice are transformed into olfactory bulb outputs . We developed an in vivo imaging approach that permits the visualization of odor-evoked responses specifically in mitral cells , the main output neurons of the olfactory bulb . We used a replication-deficient recombinant rabies virus to drive the expression of the calcium-sensitive indicator of neural activity GCaMP3 ( RVΔG-4GCaMP3 ) ( Tian et al . , 2009; Wickersham et al . , 2010 ) in large populations of mitral cells . We made multiple injections of this rabies-GCaMP3 virus into the olfactory cortex underneath the lateral olfactory tract ( Figure 2A ) . After injections , mice were allowed to recover for 5–7 days before two-photon imaging of neural activity was performed under ketamine/xylazine anesthesia . Because this modified rabies virus lacks the gene encoding its viral glycoprotein , it is unable to spread transsynaptically , thereby restricting the expression of GCaMP3 to neurons directly infected via their axonal terminations ( Wickersham et al . , 2007; 2010 ) . However , because the virus retains its ability to replicate in infected cells , we found that infected cells began exhibit clear signs of toxicity after 12 days ( not shown ) . We therefore performed all imaging experiments within 5–7 days after virus injections . 10 . 7554/eLife . 16335 . 006Figure 2 . Normalization of odor-evoked mitral cell activity in M71 transgenic mice . ( A–I ) Two-photon in vivo imaging of mitral cell odor responses in anesthetized mice . ( A ) Schematic of rabies-GCaMP3 injection into the lateral olfactory tract ( LOT ) and two-photon imaging of olfactory bulb mitral cells . ( B ) Mitral cells expressing GCaMP3 in a coronal slice of the olfactory bulb after injection of rabies-GCaMP3 . Note the restriction of labeled cell bodies to the mitral cell layer . Scale bar = 100 µM . ( C ) Higher magnification of mitral cells expressing GCaMP3 throughout the neuron , including the apical and lateral dendrites . Scale bar = 20 µM . ( D ) Two-photon micrograph showing GCaMP3 expression in mitral cell of a single imaging site . Scale bar = 30 µM . ( E ) Example traces of the responses of 4 mitral cells ( circled in ( D ) ) to 4 different odorants . Traces represent responses to 4 individual odorant exposures , non-responsive trials are shown in grey , responsive trials in black . Horizontal bar indicates odorant application . ( F , G ) Representative maps of odor-evoked mitral cell activity elicited by a panel of 5 different odorants at a single imaging site in a control ( F ) and M71 transgenic ( G ) mouse . Cells responding to at least 2 out of 4 trails are color-coded . Overlap: cells responsive to more than one odorant are shown in blue . ( H , I ) Mean fraction of cells ( horizontal line ) responding to a given odorant at 0 . 01% vol . /vol . dilution , in control ( H ) and M71 transgenic ( I ) mice . Dots represent the fraction of responding cells for a given imaging site . Controls: 14 imaging sites in 7 mice , n ( median number of cells per site ) = 35; M71 transgenic: 7 imaging sites in 4 mice , n = 28 . Error bars = 95% CI of the mean . DOI: http://dx . doi . org/10 . 7554/eLife . 16335 . 006 Using this method , we were able to selectively express GCaMP3 in hundreds of mitral cells in the olfactory bulb ( Figure 2B–D ) . GCaMP3-expressing mitral cells were uniformly distributed across the olfactory bulb . The cell bodies of GCaMP3-expressing neurons were exclusively located in the mitral cell layer , and we often observed multiple GCaMP3-expressing mitral cells projecting to the same glomerulus ( Figure 2C ) . While we cannot exclude the possibility that GCaMP3 is also expressed in some tufted cells , these results demonstrate that rabies-GCaMP3 virus permits the highly efficient and selective labeling of mitral cells projecting to the piriform cortex . Mitral cells infected with rabies-GCaMP3 displayed robust stimulus-locked responses to odorants , which could vary with respect to their response magnitudes ( e . g . peak ΔF/F values ) , duration and trial-to-trial variability ( Figure 2E ) . In wild-type mice , we found that odorants at low concentrations ( 0 . 01% , or 1/10 , 000 vol . /vol . dilution ) typically evoked sparse , spatially distributed patterns of activity in ~15% of mitral cells ( mean = 14 . 5% , standard deviation ( SD ) = 11 . 2%; Figure 2F and H ) . These observations are consistent with recent results using adeno-associated virus ( AAV ) -mediated and transgenic GCaMP3 expression ( Blauvelt et al . , 2013; Kato et al . , 2012; Wachowiak et al . , 2013 ) . We observed mitral cell responses to a variety of structurally and perceptually diverse odorants , regardless of whether the neurons were located in the posterior , medial , or anterior dorsal olfactory bulb ( 13 odorants at 0 . 01% vol . /vol . dilution; Figure 2F and H , and data not shown ) . Furthermore , mitral cells responsive to a given odorant were typically distributed across the imaging site and did not exhibit the segregated patterns observed in odor-evoked glomerular activity . We next performed these same imaging experiments using M71 transgenic mice . Remarkably , we found that the fraction and distribution of odor-responsive mitral cells in M71 transgenic mice and their wild-type littermate controls were strikingly similar ( Figure 2I ) . Interestingly , our test set of odorants includes several odorants that have been reported to not activate the M71 receptor . Ethyl acetate or eugenol , for example , do not activate M71-expressing olfactory sensory neurons at all concentrations tested ( Bozza et al . , 2002 ) , and do not elicit detectable glomerular activity in M71 transgenic mice ( Fleischmann et al . , 2008 ) . However , we found that all test odorants including ethyl acetate and eugenol evoked mitral cell responses . Moreover , the fractions of odor-responsive neurons were indistinguishable from wild-type littermate controls ( mixed-effect ANOVA ( genotype x odorants ) , F ( 13 , 242 ) = 0 . 58 , p=0 . 87 , Figure 2H and I ) . Thus , at least at the gross level of overall activation , different odorants including odorants that barely engage the large population of M71 expressing olfactory sensory neurons , result in the excitation of a population of mitral cells that is similar to wild-type mice . Given that 95% of all olfactory sensory neurons in these mice express the M71 receptor , we next examined mitral cell responses to acetophenone , a known strong M71 receptor ligand ( Bozza et al . , 2002 ) that evoked pervasive glomerular activation of the dorsal surface of the olfactory bulb of M71 transgenic mice ( Fleischmann et al . , 2008 ) . We observed two surprising findings: first , the fraction of mitral cells activated by acetophenone was virtually identical to littermate controls , and second , the fraction of mitral cells responding to acetophenone was highly similar to the fractions of mitral cells responding to other odorants ( Figure 2H , I right ) . Given the massively altered glomerular input and essentially normal mitral cell output , these imaging data indicate that the OB circuitry profoundly normalizes activity , strengthening the weakened input from odorants that do not activate the M71 receptor , and suppressing the overt excitation due to the M71 ligand acetophenone , resulting in responses that – on the crude level of overall activation – were indistinguishable from wild-type mice . A more detailed analysis of our imaging data , however , did reveal subtle differences between the response properties of M71 transgenic mitral cells and wild-type littermate controls . In controls , individual mitral cells generally displayed narrow stimulus tuning at low odor concentrations , in accord with previously published results from electrophysiological and optical recordings ( Figure 3A ) ( Davison and Katz , 2007; Kato et al . , 2012; Tan et al . , 2010 ) . Approximately half of the neurons ( 46 . 1% ) did not respond to any of the 13 odorants in the stimulus set used to probe selectivity , while the majority of odor-responsive neurons ( 43 . 9% of all neurons ) displayed significant increases in fluorescence to only 1–5 odor stimuli . A small subpopulation ( 10 . 0% ) of mitral cells were more broadly tuned . Moreover , the majority of stimulus-evoked mitral cell response magnitudes were small , with peak ΔF/F values below 35% , although we could observe a small number of large stimulus-evoked responses with ΔF/F values of up to 200% ( Figure 3C ) . Finally , the fluorescence levels of most odor-responsive neurons ( >80% ) returned to baseline within 8 s after response onset ( Figure 3D ) . In M71 transgenic mice , mitral cells tended to be more broadly tuned ( Figure 3A and B ) , but this difference did not reach statistical significance ( Chi-squared test: χ2 = 17 . 7 , p=0 . 17 ) . For acetophenone and the 12 other odorants , the distribution of the response magnitudes of mitral cell was shifted towards smaller peak ΔF/F values , with a particularly large reduction in the number of strongly responding neurons ( i . e . those reaching ΔF/F values of greater than 50%; Figure 3C , median ΔF/F: control: 0 . 36 , M71 transgenic: 0 . 32; mean ΔF/F: control: 0 . 47 , M71 transgenic: 0 . 38 , Two-sample Kolmogorov-Smirnov test: D4116 , 1639 = 0 . 12 , p<0 . 01 , and Figure 3—figure supplement 1 ) . In contrast , the average response duration of M71 transgenic mitral cells to acetophenone and the 12 other odorants was significantly increased ( Figure 3D , Two-sample Kolmogorov-Smirnov test: D4116 , 1639 = 0 . 24 , p<0 . 01 , and Figure 3—figure supplement 1 ) . 10 . 7554/eLife . 16335 . 007Figure 3 . Normalization of odor-evoked neural activity in M71 transgenic mice results in changes in response magnitudes and duration , and trial-to-trial variability . ( A–F ) Two-photon in vivo imaging of mitral cell odor responses in anesthetized mice . ( A , B ) Odor tuning: the fraction of mitral cells responding to N odorants out of the 13 odorant test panel in control ( A , black ) and M71 transgenic ( B , red ) mice . Error bars = 95% CI . ( C ) Peak DF/F values for odor-evoked responses in control ( black ) and M71 transgenic ( red ) mice . The fraction of responses with high peak DF/F values is reduced in M71 transgenic mice . ( D ) Response durations are increased in M71 transgenic mice ( red ) compared to controls ( black ) . ( E , F ) Trial-to-trial variability . Pearson’s correlation coefficients for individual odor presentations ( 13 odorants , 4 trials per odorant ) . The similarities of response patters to 4 presentations of the same odorant is reduced in M71 transgenic mice . DOI: http://dx . doi . org/10 . 7554/eLife . 16335 . 00710 . 7554/eLife . 16335 . 008Figure 3—figure supplement 1 . Response magnitudes and durations , and trial-to-trial variability of mitral cell odor responses in M71 transgenic mice . ( A–I ) Two-photon in vivo imaging of mitral cell odor responses in anesthetized mice . ( A ) Cumulative frequency plots of the peak F/F values for ethyl acetate- and acetophenone-evoked mitral cell responses in control ( black ) and M71 transgenic ( red ) mice . ( B ) Cumulative frequency plots of response durations for ethyl acetate- and acetophenone-evoked mitral cell responses in control ( black ) and M71 transgenic ( red ) mice . ( C ) Percent of neurons responding to 1 , 2 , 3 , or 4 out of 4 odorant exposures in control ( black ) and M71 transgenic ( red ) mice . Note that the fraction of neurons responding on 4 out of 4 trials in strongly reduced in M71 transgenic mice . Error bars = SEM . ( D–G ) Trial-to-trial variability of mitral cell odor responses decreases with increasing odorant concentrations . ( C , D ) Percent of neurons responding to 1 , 2 , 3 , or 4 out of 4 odorant exposures to ethyl acetate ( D ) and acetophenone ( E ) at increasing concentrations in control mice . ( F , G ) Percent of neurons responding to 1 , 2 , 3 , or 4 out of 4 odorant exposures to ethyl acetate ( F ) and acetophenone ( G ) at increasing concentrations in M71 transgenic mice . Error bars = SEM . ( H , I ) Pearson’s correlation coefficients for individual acetophenone and ethyl acetate presentations at increasing concentrations . Note that response patterns to acetophenone at increasing concentrations are highly dissimilar from responses to acetophenone at low concentrations , and to responses to ethyl acetate . DOI: http://dx . doi . org/10 . 7554/eLife . 16335 . 008 Finally , we analyzed the trial-to-trial variability of mitral cell responses following the repeated delivery of the same odorant . Mice were presented with the same odorant 4 times ( average inter-trial interval ~10 min ) , and the presentation of each odorant was interleaved with other odorants to avoid habituation . In littermate controls , 56% of responsive mitral cells responded on only one out of 4 trials , 20% of cells responded twice , 10% three times , and 14% of cells responded on all 4 out of 4 trials ( Figure 3—figure supplement 1 ) . In M71 transgenic mice , the fraction of neurons responding on all 4 out of 4 repeat presentations of the same odorant was reduced from 14% in controls to 2 . 9% in M71 transgenic mice . Furthermore , we calculated the Pearson correlation coefficients of the activity patterns after odor onset . We found that for acetophenone and the 12 other odorants , the mean correlation of response patterns to individual exposures of the same odorant was reduced in M71 transgenic mice compared to controls ( controls: mean across 13 odorants = 0 . 75 ± 0 . 11; M71: mean = 0 . 25 ± 0 . 17 , Figure 3E and F ) . Taken together , these data suggest that the neural circuits of the olfactory bulb of M71 transgenic mice can greatly amplify weak odor-evoked signals while suppressing overly strong signals . Such amplification may explain how M71 transgenic mice can still detect and discriminate most odorants . However , an increase in the trial-to-trial variability of mitral cell responses will degrade the fidelity of the odor representation , and may underlie the impairments in odor discrimination that M71 transgenic mice exhibit with more challenging assays . Our calcium imaging experiments provide information about the patterns of odor-evoked activity in large ensembles of mitral cells . We next sought to obtain more detailed information about the network mechanisms underlying the normalization of odor-evoked mitral cell activity , using whole cell recordings from mitral and tufted cells ( MTCs ) in awake head-fixed mice . First , we characterized the intrinsic properties of MTCs , including resting membrane potentials , input resistance , membrane time constant tau , and baseline firing rates . These biophysical properties of MTCs were , on average , similar in M71 transgenic mice ( n = 6 cells from 6 mice ) and controls ( n = 7 cells from 5 mice , Figure 4A–G ) . Interestingly , however , we observed that MTCs in M71 transgenic mice appeared to be less heterogeneous compared to wild-type , in particular for baseline firing rate ( Control: 5 . 8 ± 6 . 2 Hz , M71: 3 . 0 ± 1 . 0 Hz , p=0 . 003 Bartlett test ) and theta modulation strength ( Control: 0 . 4 ± 0 . 4 mV , M71: 0 . 2 ± 0 . 1 mV , p=0 . 01 Bartlett test ) , which might reflect their more homogeneous olfactory inputs and thus developmental history ( Angelo et al . , 2012 ) . 10 . 7554/eLife . 16335 . 009Figure 4 . Intrinsic and odor-evoked mitral cell activity in M71 transgenic mice . ( A–M ) In vivo whole cell recordings in awake mice: comparison of physiological properties of mitral cells in control ( black , n = 7 ) and M71 transgenic mice ( red , n = 6 ) . ( A ) Schematic of the experimental configuration . ( B ) Resting membrane potential ( mV ) , ( C ) input resistance ( MΩ ) , ( D ) membrane time constant tau ( ms ) , ( E ) baseline firing rate ( Hz ) , ( F ) strength of modulation of baseline Vm by the sniff cycle ( theta coupling ) ( mV ) , and ( G ) phase-preference of baseline Vm within the sniff cycle ( rad ) . ( H , I ) Example trace showing single 1 s odor presentation ( shaded area ) during mitral cell recordings from control ( H ) and M71 transgenic ( I ) mice . ( J , K ) Histograms of mean odor-evoked membrane potential ( J ) and firing rate ( K ) responses in control cells to non-acetophenone odors , n = 27 cell-odor pairs from 7 cells . ( L , M ) Histograms of mean odor-evoked membrane potential ( L ) and firing rate ( M ) responses in M71 transgenic cells , n = 20 cell-odor pairs from 6 cells . Black dotted lines in ( L ) and ( M ) show corresponding data from controls . DOI: http://dx . doi . org/10 . 7554/eLife . 16335 . 00910 . 7554/eLife . 16335 . 010Figure 4—figure supplement 1 . Patch clamp mitral cell recordings in anesthetized mice reveal increased acetophenone-evoked inhibition in M71 transgenic mice . ( A ) Schematic: whole cell recordings in anesthetized mice . ( B , C ) Example traces showing single 1 s ethyl acetate presentations ( shaded area ) during mitral cell recordings from control ( black ) and M71 transgenic ( red ) mice . ( D , E ) Histograms of mean odor-evoked membrane potential responses in control and M71 transgenic cells . ( F , G ) Example traces showing single 1 s acetophenone presentations during mitral cell recordings from control and M71 transgenic mice . ( H , I ) Histograms of mean acetophenone-evoked membrane potential responses in control and M71 transgenic cells . Arrows indicate the median evoked Vm . DOI: http://dx . doi . org/10 . 7554/eLife . 16335 . 010 Next , we sought insight into how responses to odorants other than acetophenone are able to evoke largely normal levels of mitral cell activity , despite the dramatic reduction in the expression of endogenous odorant receptors in M71 transgenic mice . We measured evoked MTC response profiles to a one-second pulse of 4 non-acetophenone stimuli ( 3 monomolecular odorants - hexanone , heptanal , ethyl acetate - and a mixture of isoamyl acetate , 2-nonanone and cyclohexanol , at a concentration of 1% of absolute vapor pressure ) . As previously reported ( Cury and Uchida , 2010; Kollo et al . , 2014; Shusterman et al . , 2011 ) , MTC activity in these awake , head-fixed mice was modulated by odor in a diverse manner , with prominent excitatory as well as inhibitory responses ( Figure 4H , J and K ) . On average , odor exposure resulted in a moderate hyperpolarization ( mean ΔVm = -1 . 8 mV , SD = 2 . 1 mV , 27 cell/odor pairs ) , and a small increase in the firing rate ( mean Δ firing rate = 2 . 2 Hz , SD = 13 . 3 Hz , 27 cell/odor pairs ) . Consistent with our imaging data , we found that overall firing rate distributions to the same 4 stimuli were more compact in M71 transgenic mice; mean changes in firing rate were indistinguishable from controls ( mean Δ firing rateM71 = 0 . 75 Hz , SD = 2 . 2 Hz , 20 cell/odor pairs , p=0 . 67 , Rank-sum , Figure 4M ) , yet the fraction of excitatory responses was similar ( control: 32% , M71: 25% ) . Odor presentation generally resulted in both excitatory and inhibitory responses , with only a small change in the average membrane potential ( mean ΔVm = 0 . 10 mV , SD = 1 . 1mV , 20 cell/odor pairs; Figure 4I , J and M ) . However , both excitatory and inhibitory responses were generally weaker ( Δ|Vm|M71 = 0 . 5+[-0 . 3 0 . 8] mV , Δ|Vm|cntrl = 1 . 3+[-0 . 6 2 . 5] mV , p=0 . 006 , Wilcoxon rank sum; Δ |firing rate|M71 = 0 . 8+[-0 . 3 1 . 6]yy Hz , Δ|firing rate|cntrl = 6 . 0+[-3 . 0 6 . 2] Hz , p<0 . 001 , Wilcoxon rank sum , median +[lower quartile , upper quartile] ) , and strong excitatory responses notably absent in M71 transgenic mice ( Figure 4M ) . Most prominently , inhibitory responses were substantially reduced compared to littermate controls ( p=0 . 003 , Rank-sum , Figure 4L ) . Taken together , calcium imaging experiments and in vivo whole cell recordings reveal overall surprisingly normal mitral cell odor responses in M71 transgenic mice , despite massive changes in odor-evoked sensory input . Importantly , however , responses to odorants other than acetophenone result in slightly weaker , more variable responses and in particular - as apparent from the subthreshold analysis - substantially less hyperpolarizing responses . Finally , as in the awake case , in anaesthetized M71 transgenic mice responses to non-acetophenone odors were weaker than in controls ( Figure 4—figure supplement 1 ) . In contrast to all our other test odorants , acetophenone strongly activates the vast majority of sensory neurons , resulting in pervasive glomerular activity in M71 transgenic mice ( Fleischmann et al . , 2008 ) . Despite this widespread glomerular activation , our calcium imaging experiments have demonstrated that acetophenone activates similar numbers of mitral cells in both control and M71 transgenic mice . One mechanism behind this apparent normalization could be inhibition that is increased concomitantly with the massively increased excitatory input . To directly test this prediction we also examined acetophenone-evoked mitral cell activity in M71 transgenic mice using whole cell patch-clamp recordings . Strikingly , in M71 transgenic mice , acetophenone exposure caused a massive and prolonged increase in theta modulation of the membrane potential ( Figure 5C and D ) . Phasic odor responses , however , were highly similar in control and M71 transgenic mice: mean firing rate change induced by acetophenone was again not significantly different between M71 transgenic mice and controls ( control: -0 . 2 ± 7 . 5 Hz , M71: -0 . 5 ± 2 . 7 Hz , p=0 . 89 , t-test , Figure 5F and H ) , consistent with results obtained in calcium imaging experiments . However , acetophenone generally induced prolonged hyperpolarizations in M71 transgenic mice , whereas responses were more transient in littermate controls ( responses over 5 s window: control: -0 . 7 ± 0 . 5 mV , M71: -2 . 2 ± 1 . 9 mV , p<0 . 05 , 1-tailed t-test . Figure 5E , G and I , and Figure 5—figure supplement 1 ) . 10 . 7554/eLife . 16335 . 011Figure 5 . Increased acetophenone-evoked inhibition and theta coupling in M71 transgenic mice . ( A–H ) In vivo whole cell recordings in awake mice . ( A ) Schematic of the experimental configuration . ( B , C ) Example trace of a 1% acetophenone presentation to a mitral cell in a control ( B , black ) and M71 transgenic ( C , red ) mouse . Note the differences in the duration of inhibition , and the great amplification in theta coupling of the M71 transgenic cell after response offset . ( D ) Quantification of strength of theta coupling before and after acetophenone presentation for control ( black ) and M71 transgenic ( red ) cells . ( E–H ) Histograms of mean 1 s odor-evoked membrane potential and firing rate responses to acetophenone presentation in control ( E , F ) and M71 transgenic ( G , H ) mice . ( I ) Comparison of control and M71 transgenic mean Vm responses calculated over different time windows: 1 s , 2 s and 5 s from the first inhalation post odor-onset . ( J–P ) Two-photon in vivo imaging of periglomerular cell activity in anesthetized mice . ( J ) Schematic of the experimental configuration . ( K ) Expression of GCaMP3 ( in green ) in inhibitory neurons after injection of conditional AAV-flex-GCaMP3 into the olfactory bulb of a Gad2-Cre transgenic mouse . Purple: nuclear counterstain . Scale bar = 100 µM . ( L ) Higher magnification of periglomerular ( PG ) cells ( examples indicated by white arrowheads ) expressing GCaMP3 . Scale bar = 20 µM . ( M ) Two-photon micrograph showing GCaMP3 expression in PG cells of a single imaging site . Scale bar = 20 µM . ( O , P ) Example traces of the responses of 4 PG cells to acetophenone and ethyl acetate in control ( O ) and M71 transgenic ( P ) mice . Traces represent responses to 4 individual odorant exposures , non-responsive trials are shown in grey , responsive trials in black . Note the difference in the scale of the y-axis between genotypes . DOI: http://dx . doi . org/10 . 7554/eLife . 16335 . 01110 . 7554/eLife . 16335 . 012Figure 5—figure supplement 1 . Individual acetophenone response traces . In vivo whole cell recordings in awake mice . Mean spike-clipped traces in response to acetophenone at a concentration of 1% of absolute vapor pressure , averaged across all trials aligned to first inhalation post odor onset for each MTC . In black are traces from each cell recorded in control littermates , in red are traces from M71 transgenic cells . The shaded area shows the 1 s odor stimulus . The dotted line at 2 s is for comparison of the long-duration component of the response . Note the long inhibitory transients seen in 4 of the 6 transgenic MTCs , compared to the much more transient responses in controls . DOI: http://dx . doi . org/10 . 7554/eLife . 16335 . 012 Thus , while supra-threshold responses in mitral cells are highly similar between control and M71 transgenic mice , whole-cell recordings in awake animals reveal a potential source of this normalization: hyperpolarizing , inhibitory responses are increased for the M71 receptor ligand acetophenone but reduced for other odorants compared to control animals ( cf . Figure 4J and L and Figure 5I ) . These alterations in inhibition were not a consequence of altered sampling behavior e . g . due to anxiety ( Glinka et al . , 2012 ) or other behavioral state changes , as whole-cell recordings in anaesthetized mice showed a similarly profound and selective increase in inhibitory responses to acetophenone exposure ( Figure 4—figure supplement 1 ) . Previous work indicated that such slow , odor-evoked phasic inhibition likely originates in the glomerular layer ( Fukunaga et al . , 2014 ) , and the position and connectivity of PG cells make them prime candidates to mediate both presynaptic and feedforward inhibition in response to acetophenone in M71 transgenic mice . Therefore , we performed two-photon imaging experiments in mice engineered to selectively express GCaMP3 in PG cells . Selectivity was achieved by injecting Cre-dependent AAV ( AAV5 . hSynap . Flex . GCaMP3 . WPRE . SV40 ) into the olfactory bulbs of either M71 transgenic mice that also carried a Gad2-Cre transgene ( Taniguchi et al . , 2011 ) , or littermate controls expressing the Gad2-Cre transgene only . AAV injections resulted in the labeling of large numbers of GABA-positive interneurons in the glomerular layer , with extensive processes extending into individual glomeruli ( Figure 5K–M ) . In control mice , only a fraction of PG cells displayed responses to either acetophenone or ethyl acetate ( acetophenone: 10 . 9%; ethyl acetate: 12 . 1% , Figure 5O and Figure 6G ) . The magnitudes of these odor-evoked responses were small , with more than 80% of peak ∆F/F values below 10% ( data not shown ) . Unlike mitral cells , where responses in M71 transgenic mice and their littermate controls were often indistinguishable , odor responses were strikingly different in PG cells . Exposure of M71 transgenic mice to acetophenone , even at the lowest concentration ( 0 . 01% vol . /vol . ) , resulted in pervasive , strong and persistent activity in over 48% of PG cells , significantly higher than in littermate controls ( Figure 5P and Figure 6H , Rank-sum test nco = 10 , nM71 transgenic = 9 , U = 83 , p<0 . 01 ) . Furthermore , the magnitude and duration of acetophenone-evoked PG cell activity was significantly increased in M71 transgenic mice compared to controls ( data not shown ) . In contrast to these robust and pervasive responses to acetophenone , ethyl acetate elicited PG cell activity in only a small population of neurons ( <10% , Figure 5P and Figure 6H ) , and response magnitudes were consistently below 10% peak ∆F/F values ( data not shown ) . These responses tended to be fewer and with smaller ∆F/F values than those observed in littermate controls , but this observation did not reach statistical significance . Taken together , electrophysiology and imaging experiments indicate that pervasive and strong glomerular excitation is balanced by similarly pervasive and strong periglomerular inhibition to normalize olfactory bulb mitral cell output . 10 . 7554/eLife . 16335 . 013Figure 6 . The olfactory bulb excitation/inhibition balance in M71 transgenic mice breaks down at high acetophenone concentrations . ( A–D ) Two-photon in vivo imaging of mitral cell odor responses in anesthetized mice . ( A , B ) Representative maps of odor-evoked mitral cell activity elicited by acetophenone and ethyl acetate at increasing odorant concentrations in a control ( A ) and M71 transgenic ( B ) mouse . Weak to strong responses are color-coded in blue to red . ( C , D ) Mean fraction of mitral cells that respond to acetophenone and ethyl acetate at increasing odorant concentrations in control ( C , black ) and M71 transgenic ( D , red ) mice . Grey circles represent the fraction of responsive cells of a single imaging site . Controls: 19 imaging sites in 8 mice , n ( median number of cells per site ) = 57; M71 transgenics: 10 imaging sites in 4 mice , n = 28 . red line: linear fit onto concentration . r: coefficient of correlation . ( E–H ) Two-photon in vivo imaging of periglomerular cell activity in anesthetized mice . ( E , F ) Representative maps of odor-evoked periglomerular ( PG ) cell activity elicited by acetophenone and ethyl acetate at increasing odorant concentrations in a control ( E ) and M71 transgenic ( F ) mouse . Note that the heatmaps predominantly reflect dendritic responses of PG cells in glomeruli . ( G , H ) Mean fraction of PG cells that respond to acetophenone and ethyl acetate at increasing odorant concentration in control ( G , black ) and M71 transgenic ( H , red ) mice . Grey circles represent the fraction of responsive cells of a single imaging site . Controls: 10 imaging sites in 5 mice , n ( median number of cells per site ) = 46; M71 transgenics: 9 imaging sites in 5 mice , n = 51 . Error bars = SD . DOI: http://dx . doi . org/10 . 7554/eLife . 16335 . 01310 . 7554/eLife . 16335 . 014Figure 6—figure supplement 1 . Response magnitudes and durations strongly increase with increasing acetophenone concentrations in M71 transgenic mice . ( A–H ) Two-photon in vivo imaging of mitral cell odor responses in anesthetized mice . ( A , B ) Cumulative frequency plot of the peak DF/F values for mitral responses in control ( A ) and M71 transgenic ( B ) mice to increasing acetophenone concentrations . Light , intermediate and dark colored curves represent responses to low ( 1:10 . 000 vol . /vol . ) , intermediate ( 1:1 . 000 vol . /vol . ) and high ( 1:100 vol . /vol . ) odorant concentration . ( C , D ) Cumulative frequency plot of the peak DF/F values for mitral responses in control ( C ) and M71 transgenic ( D ) mice to increasing ethyl acetate concentrations . ( E , F ) Cumulative frequency plot of response durations of mitral cells in control ( E ) and M71 transgenic ( F ) mice to increasing acetophenone concentrations . ( G , H ) Cumulative frequency plot of response durations of mitral cells in control ( G ) and M71 transgenic ( H ) mice to increasing ethyl acetate concentrations . DOI: http://dx . doi . org/10 . 7554/eLife . 16335 . 014 We next attempted to upset this balance of mitral cell excitation and PG cell inhibition by increasing odorant concentration . As mentioned above , acetophenone and ethyl acetate at low concentrations ( 0 . 01% vol . /vol . ) activate approximately 10% of mitral cells in both M71 transgenic mice and controls ( Figure 6C and D ) . In control mice , a 10- and 100-fold increase in acetophenone or ethyl acetate concentration only caused a modest increase in mitral cell activity: about 15% of mitral cells responded to either odorant at 0 . 1% dilutions , and ~20% of mitral cells responded at 1% dilutions ( Figure 6A and C ) . Response magnitudes and durations increased slightly while trial-to-trial variability decreased with increasing odor concentrations ( Figure 6—figure supplement 1 ) . In marked contrast to controls , increasing concentrations of acetophenone in M71 transgenic mice dramatically increased the fraction of responsive mitral cells: acetophenone at 0 . 1% activated over 28% of mitral cells ( mean = 28 . 7% ± 17 . 1% ) , while over 50% ( mean = 50 . 1% ± 25 . 8% ) of mitral cells responded at 1% acetophenone ( Figure 6B and D ) . The number of responsive neurons was highly correlated to acetophenone concentration ( n = 10 , Pearson’s correlation coefficient = 0 . 71 ) , indicating that the strong dependence of acetophenone responses on concentration is consistently observed across all imaging sites . Furthermore , although response magnitudes sharply increased with increasing acetophenone concentrations , response durations were reduced ( Figure 6—figure supplement 1 ) . The striking sensitivity to acetophenone concentration was not observed for ethyl acetate . A ten-fold increase in ethyl acetate concentration resulted in only a ~1 . 5-fold increase in the fraction of responsive mitral cells , and a hungred-fold increase in ethyl acetate activated ~2 times more cells , essentially identical to what we observed in controls ( Figure 6B and D ) . We did not observe robust concentration-dependent changes in the distributions of peak ∆F/F values and response durations to ethyl acetate ( Figure 6—figure supplement 1 ) . To quantify these differences , we calculated the difference in concentration-driven linear change between stimuli ( ∆LC = LCacetophenone – LCethyl acetate ) in control and M71 transgenic mice . A ∆LC of 0 indicates no difference in the effect of concentration on the response to the two stimuli , while a positive ∆LC indicates the greater linear change in acetophenone-evoked response density with increasing concentration . We found no difference in linear change between ethyl acetate and acetophenone responses for controls ( ∆LC = -0 . 04 ) , indicating that control mice responded similarly to increasing concentrations of these 2 stimuli . In contrast , we found a positive ∆LC in M71 transgenic mice ( ∆LC = 0 . 18 ) , highlighting that the density of neural responses to acetophenone is strongly modulated by concentration in these mice . Earlier we suggested that PG cell-mediated feedforward inhibition plays an important role in normalizing olfactory bulb output ( Figure 5 ) . We therefore next asked how increasing odorant concentration affects the activity of PG cells in both M71 transgenic mice and controls . While PG cells were already quite responsive to low acetophenone concentrations in M71 transgenic mice , increasing acetophenone concentrations dramatically increased PG cell activity in these mice ( Figure 6F and H ) : 0 . 1% activated over 85% ( mean 85 . 1% ± 13% ) of PG cells in M71 transgenic mice compared to only 10 . 8% ( ± 6 . 6% ) observed in controls . A further ten-fold increase in concentration ( 1% ) increased the fraction of responsive neurons to 94% ( ± 8% ) in M71 transgenic mice . Increasing acetophenone concentration also markedly increased response magnitudes and durations , which was not observed in controls ( data not shown ) . Interestingly , exposure of M71 transgenic mice to intermediate concentrations of ethyl acetate at ( 0 . 1% ) activated only 14 . 6% ( ± 11% ) of PG cells , similar to the 18 . 2% ( ± 9 . 3% ) responsive PG cells observed in littermate controls ( Mann-Whitney test nco = 10 , nM71 transgenic = 9 , U = 35 , p=0 . 45 ) . Interestingly , however , a hundred-fold increase in ethyl acetate concentration ( 1% ) activated 77 . 9% ( ± 20% ) of PG cells , more than double that observed in controls . Although ethyl acetate has previously been reported not to activate the M71 receptor ( Bozza et al . , 2002 ) , these data suggest that ethyl acetate may in fact be a weak M71 receptor agonist that activates M71 at high concentrations . This speculation is supported by the observation that while odorants activate segregated , glomerulus-specific clusters of PG cells in wild-type mice , PG cell responses to both acetophenone and ethyl acetate were pervasive and not restricted to individual glomeruli in M71 transgenic mice , consistent with the pervasive glomerular innervation by M71-expressing sensory inputs ( Figure 6E and F ) . Taken together , these data demonstrate that the pervasive glomerular activity elicited by acetophenone in M71 transgenic mice results in strong , stimulus-specific PG cell activation , even at low concentrations . At higher concentrations , acetophenone-evoked PG cell activity appeared to saturate in M71 transgenic mice while the proportion of responsive mitral cells dramatically increased . These data therefore suggest that at high acetophenone concentrations , periglomerular inhibition fails to counterbalance excitation .
Our previous behavioral analyses of M71 transgenic mice using a go/no go operant conditioning task indicated that M71 transgenic mice could readily detect and discriminate most odorants . In contrast , M71 transgenic mice failed to discriminate the M71 receptor ligand acetophenone from air . We have replicated these initial findings with an independent cohort of mice . In total , 15 M71 transgenic mice were tested in this task , and all 15 mice failed to reach a correct lick ratio of above 75% . Why do M71 transgenic mice fail this test ? We previously hypothesized that pervasive glomerular activation elicits inhibition at multiple stations along the olfactory pathway , which could entirely suppress acetopheone-evoked neural activity . However , our calcium imaging and electrophysiological recordings do not support this hypothesis . Instead , we observe that acetophenone-evoked suprathreshold mitral cell activity in M71 transgenic mice is surprisingly similar to that observed in wild-type mice: the fraction of acetophenone-responsive neurons , and acetophenone-induced changes in mitral cell firing rates are indistinguishable from controls . Furthermore , alterations in mitral cell response magnitude , duration , and changes in the dynamic range of mitral cell responses do not provide a simple explanation for the observed behavioral deficit . The most striking acetophenone-specific effect we observe is a massive amplification of theta oscillations . A single one second puff of acetophenone elicits strong theta oscillations that last for at least 20 s . The behavioral consequences of this oscillatory activity are unknown , but it is possible that these prolonged network perturbations interfere with the discrimination of acetophenone from clean air in the go/no go operant conditioning task . Alternatively , we cannot exclude that our clean air stimulus contains contaminating traces of acetophenone , and that acetophenone at extremely low concentrations is sufficient to elicit neural activity in M71 transgenic mice that makes it indistinguishable from the acetophenone odor puff . This would suggest that the observed behavioral deficit results from the inability of M71 transgenic mice to discriminate between different acetophenone concentrations , rather than from the inability to detect it . Finally , strong oscillatory network activity may precede an epileptic state ( Nguyen and Ryba , 2012 ) , which could obstruct odor discrimination . In contrast to their inability to discriminate acetophenone from air in the go/no go operant conditioning task , M71 transgenic mice adapt their spontaneous sniffing behavior in response to acetophenone exposure , suggesting that they can indeed detect acetophenone in this test . Spontaneous sniff adaptation does not require that mice accurately discriminate between successive presentations of two different stimuli . Therefore , while acetophenone-induced network perturbations may interfere with odor discrimination , mice may be able to detect and recognize acetophenone as a previously encountered stimulus in this test . Additionally , the heightened level of anxiety in M71 transgenic mice ( Glinka et al . , 2012 ) might amplify sniff responses to novel stimuli including to non-olfactory components of the stimulus . Another important difference between the two behavioral tasks is that unlike spontaneous sniff adaptation , operant conditioning requires extensive training . It is possible that training , which is performed with non-acetophenone odorants , may shape the processing of odor-evoked activity , and that such plastic changes may underlie the task dependency of the behavioral deficit . Changes in the expression of odorant receptor genes in M71 transgenic mice have two major consequences for odor-evoked glomerular activity . First , exposure to the M71 receptor ligand acetophenone activates the vast majority of sensory neurons and elicits pervasive glomerular activity . Second , the number of sensory neurons responsive to most odorants , i . e . those that do not activate the M71 receptor , is strikingly reduced , resulting in glomerular activity that is below the detection threshold of in vivo synapto-pHluorin imaging experiments ( Fleischmann et al . , 2008 ) . Despite these massive perturbations in odor-evoked olfactory bulb input , both our calcium imaging experiments and electrophysiological recordings reveal that mitral cell responses to acetophenone and non-acetophenone odorants are highly similar . Thus , M71 transgenic mice provide an exaggerated genetic setting in which we have examined neural circuit mechanisms for generating normalized sensory output for downstream targets that direct olfactory behaviors . The fraction of acetophenone-responsive mitral cells as well as acetophenone-evoked changes in mitral cell firing rates are indistinguishable between M71 transgenic mice and controls . This observation immediately suggests that powerful inhibitory mechanisms must exist within the olfactory bulb to prevent massive excitation evoked by pervasive inputs to the glomerular layer . We now show that PG cells are pervasively and robustly activated by acetophenone in M71 transgenic mice . Furthermore , mitral cell patch-clamp recordings reveal that acetophenone evokes strong and prolonged phasic inhibition in M71 transgenic mitral cells , reminiscent of PGC-mediated feedforward inhibition described previously ( Fukunaga et al . , 2014 ) . This feedforward inhibitory activity is likely to provide an effective mechanism to transform pervasive glomerular activity into sparse mitral cell responses . Interneuron populations in the deeper layers of the olfactory bulb could further modify neural activity evoked by acetophenone . Candidates include superficial dopaminergic interneurons that have previously been suggested to mediate signal normalization ( Banerjee et al . , 2015 ) , as well as parvalbumin-expressing interneurons , which reside in the external plexiform layer and receive direct input from widely distributed mitral cells ( Kato et al . , 2013; Miyamichi et al . , 2013 ) . In addition , deep layer granule cells , which form dendrodendritic synapses on the lateral dendrites of nearby mitral cells can modulate neighboring mitral cell output by means of powerful feedback and feedforward inhibition ( Abraham et al . , 2010; Isaacson and Strowbridge , 1998; Jahr and Nicoll , 1982; Margrie et al . , 2001; Rall et al . , 1966 ) . Altogether , these interglomerular inhibitory networks can normalize response magnitudes across a range of input intensities and enhance contrast between patterns of odor-evoked glomerular activity , and feedforward inhibition from primary onto secondary olfactory neurons represents an olfactory circuit function that appears highly conserved in evolution ( Olsen and Wilson , 2008; Zhu et al . , 2013 ) . However , we found that increasing acetophenone concentration dramatically increased the fraction of responsive mitral cells , resulting in dense neural odor representations similar to the dense patterns of acetophenone-evoked glomerular activity . Thus , at high acetophenone concentrations , excitation may override inhibition , exposing the limits of olfactory bulb circuit mechanisms to normalize glomerular activity . We observed that all odorants tested elicit sparse and unique , overlapping patterns of mitral cell activity in M71 transgenic mice . Strikingly , the fraction of responsive neurons to a panel of 13 odorants ( including acetophenone ) was not significantly different from what was observed in littermate controls . While individual ligand-receptor interactions remain incompletely characterized , calcium imaging experiments suggest that most odorants do not activate M71-expressing olfactory sensory neurons , or do so only at high odorant concentrations ( Bozza et al . , 2002; Fleischmann et al . , 2008 ) . Thus , our data provide a striking example of signal amplification by olfactory bulb neural circuits . Whole cell recordings of mitral cells reveal that odorants commonly evoke phasic inhibition in wild-type mice ( as described previously e . g Fukunaga et al . , 2014; Margrie et al . , 2001 ) , and that this inhibition in response to odorants other than acetophenone is significantly reduced in M71 transgenic mice . This result suggests that olfactory bulb inputs ordinarily evoke non-specific inhibition that can only be overcome by strong and specific input . Such inhibition coupled with specific excitation can increase signal-to-noise ratios and enhance contrast ( Cleland and Sethupathy , 2006 ) . By contrast , in the M71 transgenic mice , non-acetophenone inputs are too weak to recruit inhibition , allowing these weak inputs to evoke responses . This may at least partially explain why responses are prolonged , and why trial-to-trial variability of responses is increased in these animals . However , weak signals may also be actively amplified . For example , electrical coupling electrical coupling between mitral cells connected to the same glomerulus , and self-excitation of intraglomerular mitral cell assemblies , can further facilitate the detection of weak odor signals ( Christie et al . , 2005; Isaacson , 1999; Margrie et al . , 2001; Murphy et al . , 2005; Schoppa and Westbrook , 2001 ) . However , a multi-synaptic pathway involving olfactory bulb tufted cells may also directly amplify the output of mitral cells in response to weak inputs ( De Saint Jan et al . , 2009; Fukunaga et al . , 2012; Gire et al . , 2012; Najac et al . , 2011 ) . These different neural mechanisms are likely to cooperate in improving the ability of M71 transgenic mice to detect odorants that do not activate the M71 receptor . Interestingly , however , we observed that the patterns of mitral cell activity are more variable across multiple odorant presentations . The fraction of mitral cells responding reliably to the same stimulus is significantly reduced in M71 transgenic mice , and the variation of the average fraction of responsive neurons for a given trial is increased . Odorants will therefore activate more variable ensembles of cells . One important source of variability in neural responses to sensory stimuli is noise , and neural circuit mechanisms to reduce variability due to noise often rely on averaging signals from neurons carrying redundant information ( Faisal et al . , 2008 ) . Large numbers of sensory neurons expressing the same odorant receptor and converging onto only two glomeruli in the olfactory bulb provide a striking example of this principle . In M71 transgenic mice , the number of sensory neurons expressing a given odorant receptor are strongly reduced , thus limiting the power of averaging to reduce variability . We speculate that this increased variability of a neural odor representation will affect the accuracy of odor discrimination in M71 transgenic mice , a model consistent with the olfactory discrimination deficits observed for difficult to discriminate odorant mixtures . In conclusion , we report a number of ways in which the olfactory bulb can modify substantially altered primary inputs to generate meaningful odor representations . Amplification of weak signals and suppression of strong , pervasive input patterns are likely to be crucial under normal circumstances , for example by allowing the system to tune to odors with considerable variations in vapor pressures .
Adult ( 6–10 week-old ) mice on a mixed 129SvEv; C57BL/6 genetic background were used for all experiments . Omp-ires-tTA and teto-M71-ires-lacZ mouse lines were bred to generate hemizygous Omp-ires-tTA /teto-M71-ires-lacZ transgenic mice ( referred to as M71 transgenic mice ) . Wild-type and Omp-ires-tTA heterozygous littermates were used as controls . To generate compound Omp-ires-tTA; teto-M71-ires-lacZ; Gad2-Cre transgenic mice ( Taniguchi et al . , 2011 ) , Omp-ires-tTA / teto-M71-ires-lacZ females were bred with homozygous Gad2-Cre males . Gad2-Cre littermates were used as controls . All experiments were performed according to Columbia University , College de France , and the Francis Crick Institute institutional animal care guidelines . Go/no go operant conditioning experiments were performed in a liquid dilution , eight channel olfactometer ( Knosys , Lutz , Florida ) as described previously ( Bodyak and Slotnick , 1999; Fleischmann et al . , 2008 ) . Briefly , mice were water-restricted ( 1–1 . 5 ml water/day ) and maintained on a reverse 12 hr light/dark cycle . Initial training was performed with ethyl acetate , citronellol , and carvone . All odorants were used at 1% vol . /vol . dilution in mineral oil . Individual experiments consisted of at least 200 trials and typically lasted for ~30 min . Individual trials consisted of a 2 s odor sampling period , followed by an inter-trial interval of at least 4 s . The median time from the end of one odor presentation ( closing of the odor valve ) to the beginning of the next was 6 . 3 ± 0 . 5 s ( mean and SD across 7 animals , 300 trials each ) . Discrimination accuracy was calculated as the percent correct licks during a two second interval following valve opening for blocks of 20 trails each . Behavioral data were analyzed in R by fitting a linear mixed-effects model to test the effect of genotype on the fraction of correct licks ( fraction correct lick ~ genotype * block + 1 | mouse Id / block ) . Sniff behavior was measured in head-fixed passive mice using a fast mass flow sensor ( FBAM200DU , Sensortechnics , Puchheim , Germany ) externally placed in close proximity to the left nostril . Baseline sniff frequencies for each trial were calculated by taking the inverse of the mean inter-sniff interval ( time between successive inhalation peaks ) during the 2 s prior to odor period . Responses were calculated by subtracting the baseline sniff frequency from the sniff frequency measured similarly for all sniff cycles beginning and ending within the odor period . Inter-trial interval was 10 s and 8 odor stimuli were presented in a fixed order ( 1% acetophenone , ethyl acetate , 0 . 5% acetophenone , mixture 1 , 0 . 1% acetophenone , hexanone , 0 . 05% acetophenone , heptanal ) . This block was repeated at least 3 times . The sequence of odors was randomized between animals ensuring alternating acetophenone and non-acetophenone odors . No blank controls were presented . For analysis , sniff responses were analyzed for the first and second/third acetophenone presentation irrespective of concentration . Deletion-mutant rabies virus was generated as described in Wickersham et al . ( 2010 ) . Mice were anaesthetized with ketamine/xylazine ( 100 mg/kg / 10 mg/kg , Sigma Aldrich ) and body temperature was maintained at 37°C using a feedback-controlled heating pad ( Fine Science Tools ) . The scalp was removed , and the membrane overlying the skull was cleared using a microblade ( Roboz ) . An aluminum headpost was attached to the skull using RelyX luting cement ( Henry Schein ) . The skin overlying the cheek and zygomatic bone was removed , and vessels over the zygomatic bone were sealed using a cauterizing iron ( Fine Science Tools ) . The muscle above and attached to the zygomatic bone was peeled away , and the bone was removed with microscissors ( Roboz ) . The membrane and muscle holding the jawbone and associated tissue in place were then slightly peeled back to allow access to the skull underneath . A dental drill was used to thin the bone directly overlying the lateral olfactory tract ( LOT ) , and fine forceps ( Fine Science Tools , USA ) were used to remove the thinned skull and dura underneath . Using a micromanipulator and injection assembly kit ( Narishige; WPI ) , 3000–3500 nL of rabies-GCaMP3 virus was slowly pressure injected via a pulled glass pipette at five locations; three approximately equidistant locations directly underneath the LOT ( normal to the surface of the brain ) , and two locations ~500 µm deep to the surface of the brain in the anterior portion of the exposed area . The craniotomy was covered with silicone sealant ( WPI ) , and the surgical exposure was covered with a layer of lidocaine jelly ( Henry Schein Veterinary ) followed by a layer of silicone sealant . No signs of virus toxicity , such as highly fluorescent or blebbing cells could occasionally be observed before 9 days post-infection , but were clearly evident after 2 weeks post-infection . Therefore , mice all imaging experiments were performed 5–7 days post-infection . Animals were deeply anaesthetized with ketamine/xylazine and transcardially perfused with 10 ml PBS , followed by 10 ml 4% paraformaldehyde . The brain was removed and postfixed in 4% paraformaldehyde at 4°C overnight . A vibratome was used to cut 85 µm-thick coronal slices through the olfactory bulb , and slices were counterstained overnight in 1/1000 NeuroTrace 435 ( Invitrogen ) in PBS and mounted in Vectashield ( Vector Labs ) for imaging on a Zeiss 710 confocal microscope ( Zeiss ) using a 10x water immersion objective ( Zeiss 0 . 45 NA ) . Mice were anaesthetized using ketamine/xylazine ( 100 mg/kg / 10 mg/kg , Sigma Aldrich ) and the skull overlying the olfactory bulb was thinned using a dental drill and removed with forceps , and the dura was peeled back using fine forceps . A small circular coverslip cut from a cover glass ( Corning #2870–18 ) using a diamond scriber ( VWR ) was placed over the exposed bulb and sealed in place using 2% agarose to minimize movement of the brain . Animals were then moved to a two-photon microscope ( Ultima , Prairie Technologies , or Leica SP5 ) for imaging . A 16x objective at 2x zoom ( Ultima ) or a 25x ( Leica SP5 ) was used to focus on the glomerular layer ( ~150 µm below the pial surface ) or the mitral cell layer ( ~300–400 µm below the pial surface ) , and a Ti-Sapphire laser ( Coherent ) was tuned to 910 nm for experiments . Images ( 256 x 256 pixels ) were acquired at a frame rate of 2 . 53 Hz ( Ultima ) or 2 . 9 Hz ( Leica SP5 ) . Odors were delivered at a flow rate of 1L/min for 2 s with inter-trial intervals of ~60 s . Odor stimuli for a given experiment consisted of one of two odor sets , delivered through a 16 channels olfactometer ( Automate Scientific ) : a set of 13 monomolecular odorants ( purchased from Sigma-Aldrich with the highest purity available ) diluted at 1/10 000 vol . /vol . dilution in mineral oil ( Sigma-Aldrich ) , and a set of 'concentration series' consisting of three odors of ten fold increasing concentrations ( 1/100 , 1/1 000 and 1/10 000 vol . /vol . dilutions of acetophenone , ethyl acetate , and hexanone ) . Odorants were presented 4 times each , in pseudorandomized order . A total of 2–3 spatially distinct sites ( often consisting of the posterior , medial , and anterior dorsal surface of the bulb ) were imaged in each mouse . For anaesthetized recordings , male and female M71 transgenic mice and their littermate controls ( 6–9 weeks old ) were anaesthetized intraperitoneally with ketamine and xylazine ( 100 mg/kg and 20 mg/kg , respectively for induction; xylazine concentration was reduced to 10 mg/kg for maintenance ) and kept warm ( 37°C; DC temperature controller , FHC , Bowdoin ME , USA ) for the duration of the experiments . A small craniotomy and duratomy of approximately 1–2 mm in diameter was made over the dorsal right olfactory bulb , which was submerged in Ringer solution containing ( in mM ) : NaCl ( 135 ) , KCl ( 5 . 4 ) , HEPES ( 5 ) , MgCl2 ( 1 ) , CaCl2 ( 1 . 8 ) , and its pH adjusted to 7 . 2 and 280 mOsm/kg . Whole-cell recordings were made with borosilicate glass pipette filled with ( in mM ) : KMeSO4 ( 130 ) , HEPES ( 10 ) , KCl ( 7 ) , ATP2-Na ( 2 ) , ATP-Mg ( 2 ) , GTP ( 0 . 5 ) , EGTA ( 0 . 05 ) , biocytin ( 10 ) , with pH and osmolarity adjusted to 7 . 3 and 275–80 mOsm/kg , respectively . Signals were amplified and filtered at 30 kHz by an Axoclamp 2B ( Molecular Devices , Sunnyvale , CA , USA ) and digitized at 20 kHz with a micro 1401 ( Cambridge Electronic Design , Cambridge , UK ) . Odors were presented to the animals using a custom-made flow-dilution olfactometer at approximately 1% of saturated vapor with an inter-trial interval of 10 s ( awake ) or 20–25 s ( anaesthetized ) . All recordings were done blindly with respect to the genotype of the animals . Data were analysed in Matlab ( MathWorks , Natick , Massachusetts , USA ) . To calculate the evoked membrane potential ( Vm ) in anaesthetized animals , voltage traces were first aligned to expiration peaks of respiration rhythms ( chest distension , see Fukunaga et al . , 2012; Schaefer et al . , 2006 ) . The average waveform from the baseline period was subtracted from the aligned voltage trace from first complete sniff-cycle after odor valve opening . Evoked Vm for each cell was the mean of this subtracted component , averaged across trials . Responses were defined as significantly hyperpolarizing or depolarising if the evoked Vm deviated by more than -2 or 2 standard deviations from baseline fluctuations , respectively . For two-sample KS test , the test statistic , D , was max ( | F1 ( x ) - F2 ( x ) | ) , where F ( x ) is the cumulative distribution function for each dataset . For awake recordings , head plate surgery and craniotomy was performed either directly preceding the electrophysiological recording or up to 2 days before ( cf . Kollo et al . , 2014 ) under isoflurane anaesthesia ( 5% for induction , 1 . 5–3% for maintenance in 95% oxygen ) , with local ( 0 . 5% mepivicaine s . c . ) and general anaesthesia ( 5mg/kg carprofen , s . c . ) administered . Recordings , solutions and analysis were as described above for anaesthetized animals . The only exception was that in awake animals , where sniff length is more variable , for each trial , the baseline Vm was calculated as the mean Vm during the 2 s prior to odor onset , and this was subtracted from the Vm during odor period . Evoked response was directly calculated as the mean Vm during the 1 s odor period , averaged over all trials . For FR responses , FR was calculated within each 0 . 25 s time bin aligned to the first inhalation post odor onset . Baseline FR was calculated on each trial as the mean within the 2 s prior to odor onset , and this was subtracted from the odor period . FR response was calculated as the mean FR in all time bins of the odor period across all trials . To test whether FR responses were significant , a paired T-test was performed between FR calculated during baseline in the 2 s prior to odor onset , and those calculated during the 1 s odor stimulus for all trials . Theta tuning was calculated from Vm during sniffs of durations >0 . 2 s and <0 . 32 s within the inter-trial intervals as described previously ( Fukunaga et al . , 2012 ) . Data analysis was conducted in ImageJ and Matlab . Motion artifacts were first corrected by using a subpixel translational-based discrete Fourier analysis ( Guizar-Sicairos and Fienup , 2008 ) . ROIs were then manually drawn on an average image of the imaging site , and the pixel gray values averaged in each ROI were used to estimate the fluorescence of single cells at each time frame . For each trial , the change in fluorescence ( ∆F/F0 ) was calculated as ( F-F0 ) / F0 , where F0 is the median value between seconds 2 and 6 of the pre-odor period . We estimated the baseline fluctuation at a given trial as the standard deviation ( SD ) of ∆F/F0 during the baseline period . Odor responses were assessed over a 10 s period following odor onset . A cell was deemed responsive if it exceeded response threshold ( 3 . 2 x SD for mitral cells , false positive rate ( FPR ) = 1 . 3% , 3 . 4 x SD for periglomerular cells , FPR = 3 . 8% ) during at least 3 frames in this period . Using a more stringent response criterion for mitral cells ( 3 . 8 x SD ) yielded reduced numbers of odor-responsive neurons , but did not change the relative distributions of odor-responsive neurons between M71 transgenic mice and controls , or mitral cell response variability ( data not shown ) . The percent of responding neurons to each stimulus was calculated as the average number of active neurons across 4 trials . To construct the odor spot maps and to calculate the tuning curves , only cells that responded at least 2 out of the 4 trials were included . To build the cross-correlation matrix of the patterns of activity we combined ∆F/F0 responses of all mitral cells , averaged over the 4 s following odor onset into a single mitral cell x odor trial matrix . We then calculated the cross-trial correlations of the patterns of mitral cell activity . All descriptive statistics in the text are mean ± SD . Before performing parametric statistical tests ( ANOVA ) , homogeneity of variance within datasets was tested by computing the maximum variance ratio Max ( s2 ) /Min ( s2 ) between groups . Homogeneity of variance was assumed if the maximum variance ratio was below 4 . To explore the variability of mitral cell odor representation density across genotypes and odorants ( Figure 1H–I ) , we used a mixed-effect ANOVA with genotype and odor as fixed-effect categorical factors , and imaging site as a random effect variable to account for repeated measure of the same imaging site in the course of an experiment . To quantify the differences in the concentration dependence of neural responses across stimuli and genotypes ( Figure 4 ) , we regressed the number of responsive neurons on stimulus intensity , and calculated the difference in linear change between stimuli ( ∆LC = LCacetophenone – LCethyl acetate ) as the difference between their regression slopes . | The lining of the nose contains cells called olfactory sensory neurons that allow different smells to be detected . Odor molecules bind to receptor proteins that are embedded in the surface of the olfactory sensory neuron . Different receptors respond to different odors , and the nose contains hundreds of different receptors that work together to distinguish thousands of scents . When an odor molecule binds to a receptor , it triggers a pattern of electrical activity in the neuron . These patterns are the building blocks that allow smells to be recognized and if necessary , acted upon – by not eating food that smells rancid , for example . In 2008 , researchers genetically engineered mice so that nearly all of their olfactory sensory neurons produced the same type of olfactory receptor . Unexpectedly , these mice could still detect and discriminate between many different smells . Now , Roland , Jordan , Sosulski et al . – including several of the researchers involved in the 2008 study – have tracked the brain activity of these mice as they were exposed to various smells to find out how they can recognize such a wide range of odors with such a limited repertoire of receptors . The results of the experiments revealed that neural circuits in the brains of these modified mice still produce largely normal patterns of activity in response to an odor . This ‘normalization’ of activity relies on a fine balance between ‘excitatory’ processes that increase the activity of neurons and ‘inhibitory’ processes that reduce this activity . Overall , the findings of Roland , Jordan , Sosulski et al . provide a link between how a scent is detected and how this information is processed in the brain . In future experiments , it will be important to determine how this processing of odor information is influenced by learning and experience to generate the long-lasting odor memories that guide behavior . | [
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] | 2016 | Massive normalization of olfactory bulb output in mice with a 'monoclonal nose' |
Can replication and translation emerge in a single mechanism via self-assembly ? The key molecule , transfer RNA ( tRNA ) , is one of the most ancient molecules and contains the genetic code . Our experiments show how a pool of oligonucleotides , adapted with minor mutations from tRNA , spontaneously formed molecular assemblies and replicated information autonomously using only reversible hybridization under thermal oscillations . The pool of cross-complementary hairpins self-selected by agglomeration and sedimentation . The metastable DNA hairpins bound to a template and then interconnected by hybridization . Thermal oscillations separated replicates from their templates and drove an exponential , cross-catalytic replication . The molecular assembly could encode and replicate binary sequences with a replication fidelity corresponding to 85–90 % per nucleotide . The replication by a self-assembly of tRNA-like sequences suggests that early forms of tRNA could have been involved in molecular replication . This would link the evolution of translation to a mechanism of molecular replication .
We designed a set of cooperatively replicating DNA strands using the program package NUPACK ( Zadeh et al . , 2011 ) . The sequences are designed to have self-complementary double hairpins and are pairwise complementary within the molecule pool , such that the 3’ hairpin of one strand is complementary to the 5’ hairpin of the next . Their structure resembles the secondary structure of proto-tRNAs proposed by stereochemical theories ( Figure 1a ) , comprising two hairpin loops that surround the anticodon with a few neighboring bases ( Krammer et al . , 2012 ) . The lengths of 82–84 nt of the double hairpins are that of average tRNA molecules ( Sharp et al . , 1985 ) , with stem loops consisting of 30–33 nt and the information-encoding interjacent domains of 15 nt . As the replication mechanism is based on hybridization only , it is expected to perform equally well for DNA and RNA . Here , we implemented the system with DNA and not RNA as done previously ( Krammer et al . , 2012 ) . Both , in the design and the implementation we did not see significant differences between the two versions . Because of the simpler and more inexpensive synthesis of the 82–84 nt long sequences we now implemented the replicator in DNA . Due to short heating times and moderate magnesium concentrations , we estimate that an RNA version could survive for days if not weeks ( Li and Breaker , 1999; Mariani et al . , 2018 ) . The most critical step regarding the RNA stability would be the initial temperature spike to 95 °C , which remains unchanged from our previous study ( Krammer et al . , 2012 ) and did not prove critical . We also show that an RNA version behaves structurally identical to the implemented DNA version ( Figure 1—figure supplement 1 ) . The replication mechanism is a template-based replication , where instead of single nucleotides , information is encoded by a succession of oligomers . The domain , at the location of the anticodon in tRNA , is the template sequence and thus contains the information to be replicated . We therefore term it information domain . The goal is to replicate the succession of information domains . To allow longer replicates , we chose the resulting meta-sequences to be periodic with a periodicity of four different hairpins . This makes the minimal cyclic meta-sequence large enough to keep the information domains accessible even in cyclic configuration . The information domains feature a binary system and contain sequences marked by '0' and '1' ( blue/red ) . For replication , two sets of strands replicate strings of codons in a cross-catalytic manner ( Figure 1b ) , using complementary information domains ( light/dark colors ) . The replication is driven by thermal oscillations and operates in four steps ( Figure 1b ) : ( 0 ) Fast cooling within seconds brings the strands to their activated state with both hairpins closed . ( 1 ) At the base temperature , activated strands with complementary information domains can bind to an already assembled template . ( 2 ) Thermal fluctuations cause open-close fluctuations of the hairpins . When strands are already bound to a template at the information domain , those fluctuations permit adjacent complementary hairpins of different strands to bind . In this way , the succession of information domains is replicated . ( 3 ) Subsequent heating splits the newly formed replicate from the template at the information domains . Due to their higher melting temperatures , the backbone of hairpin strands remains stable . Both , replicate and template , are available for a new replication round . This makes both the replicate and the template replication cross-catalytic in a subsequent step . Later , high temperatures spikes can unbind and recycle all molecules for new rounds of replication . Because of the initial fast cooling , all hairpins are closed in free solution . This inhibits the formation of replicates without template . While the binding of adjacent hairpins with template happens within minutes , hairpins in free solution connect without template only on timescales slower than hours and thus give false positives at a very low rate . The basic principle of this replication mechanism was previously explored by Krammer et al . using a set of four hairpins using half a tRNA sequence ( 36 nt ) that amplified into dimers ( Krammer et al . , 2012 ) . This amplification could not encode information and suffered from a high rate ( >50 % ) of unspecific amplification without template ( Figure 4 therein ) . Here , in contrast , we demonstrate exponential amplification , and the replicator can now encode sequence information ‘0’ and ‘1’ with four bits . Moreover , the strands making up the new replicator are double hairpins with the sequence structure and length of tRNA . The replicator now shows a significantly decreased unspecific amplification without template of approximately 10 % ( Figure 5a ) .
Native polyacrylamide gel electrophoresis ( PAGE ) showed that the double hairpins assembled as intended ( Figure 2 ) . Comparing different subsets of strands allowed to identify all gel bands . All complexes were formed at concentrations of 200 nM of each strand and could be resolved despite their branched tertiary structure . Friction coefficients of complexes of two to four strands were 1 . 6–1 . 8-fold higher than for linear dsDNA , and 2 . 4-fold higher for larger complexes ( 4:4 configuration , ca . 660 nt , Figure 2—figure supplement 1 ) . This agrees with the branched structure of the suggested strand assembly geometry ( Figure 1a ) . Partially assembled complexes of two or three strands bound to a four-strand template could be resolved ( Figure 6—figure supplement 1 ) . Complexes containing single bound information domains were not stable during electrophoresis ( Figure 2 , lanes 2 , 7 and Figure 6—figure supplement 1 ) . This allowed to differentiate fully assembled complexes from those where individual strands are bound to a template but have not formed backbone duplexes . Covalent end labels and two reference lanes on each gel were used to quantify concentrations from gel intensities using image analysis as described in Materials and methods . For a replicator to be autonomous , there must be a mechanism in place to select , assemble and ( re- ) accumulate its molecular components purely at one location . We argue that DNA hydrogels could offer such a solution . While DNA often , also in our case , assembles into agglomerates , DNA hydrogels have been shown to be able to form fluid phases if gaps of single bases were added to create flexible linkers between molecules ( Nguyen and Saleh , 2017 ) . We combined eight matching hairpin sequences of design as introduced in Figure 1 at moderately elevated concentrations and cooled the system to only 25 °C after separating the molecules at 95 °C ( Figure 3 ) . We found the spontaneous formation of agglomerates that were large enough to sediment under gravity . The initial homogeneous fluorescence turned into micrometer-sized grains and sedimented within hours . The fluorescence was provided by a covalently attached label to either strand 0A or 1A . Since the double hairpins have a periodic boundary condition , they can create large assemblies ( Figure 3a ) . It is evident from Figure 3—video 1 that the sedimentation was very selective . When only seven of the eight matching hairpins were present , sedimentation was much weaker and , in most cases , undetectable ( Figure 3b , c ) . For the full system , the sedimentation kinetics showed to be strongly concentration dependent ( Figure 3—figure supplement 1b ) . Analogous experiments with random sequences ( random pool of 84 nt strands ) at equal concentration did not show agglomeration nor sedimentation ( Figure 3—figure supplement 1c ) . We have previously found that similar hairpin molecules provided the shortest sequences capable of forming agglomerates ( Morasch et al . , 2016 ) . The above results suggest that agglomeration could serve as an efficient way to assemble matching hairpins from much less structured and selected sequences in an autonomous way . After the molecules have been assembled as sedimented agglomerates , a convection flow can carry the large assemblies into regions of warmer temperatures , where the molecules would be disassembled by heat and activated for replication with a cooling step . Similar recycling behavior is seen in thermal gradient traps ( Morasch et al . , 2016 ) , which were also found to enhance the molecular assembly ( Mast et al . , 2013 ) with characteristics that can match the above scenario . Hybridization between stems of neighboring hairpins ( Figure 1b , step 2 ) was catalyzed by the presence of already assembled complexes 0¯A0¯B0¯C0¯D , confirming its role as a template . Assembly kinetics at 45 °C were recorded in reactions containing 200 nM of each strand for a range of template concentrations . At 120 nM template concentration , 40 % yield was achieved within 10 min ( Figure 4b , black line ) . The untemplated , spontaneous reaction proceeded significantly slower ( 1 . 4 % yield , light gray line ) . Assembly rates showed a strong dependence on incubation temperature ( Figure 4c ) . At 39 °C , the reaction proceeded significantly slower than at 42 °C or 45 °C . This is because the hairpins are predominantly in closed configuration and cannot bind to neighboring molecules in the assembly . Binding between complementary information domains still occurs , but the formation of bonds between neighboring strands becomes rate limiting . Above the melting temperature of the information domain ( 48 °C ) ( see Figure 4—figure supplement 1 ) , template-directed assembly becomes slower . However , the slower kinetics of template-directed product formation are partially superposed by the spontaneous product formation lacking an initial template ( Figure 4c , small circles ) , which becomes an additional reaction channel due to the now open hairpins . As intermediate step toward replication , we studied amplification reactions under thermal oscillations ( Figure 5 ) . The amplification reactions only contained strands encoding for information domain '0' , that is 0A , 0¯A , 0B , 0¯B , … , 0¯D . The strands were subjected to thermal oscillations between Tbase = 45 °C and Tpeak = 67 °C . The lower temperature was held for 20 min , the upper for one second with temperature ramps amounting to 20±1 s in each full cycle . This asymmetric shape of the temperature cycle accords with differences in kinetics of the elongation step and the melting of the information domain . It is typical for trajectories in thermal convection settings with local heating ( Braun et al . , 2003 ) . The growth of molecular assemblies with different initial concentrations of template 0¯A0¯B0¯C0¯D revealed an almost linear dependence of the reaction velocity on the initial amount of template ( Figure 5a , b ) . This confirms the exponential nature of the replication . The cross-catalytic replication kinetics can be described by a simplistic model that only considers the concentrations ct of the template 0A0B0C0D and its complement c-t of 0¯A0¯B0¯C0¯D: ( 1 ) ddtc ( t ) =k⋅c¯ ( t ) +k0 , ddtc¯ ( t ) =k⋅c ( t ) +k0 Here , k is the rate of cross-catalysis and k0 the spontaneous formation rate . For c ( t ) ≈c¯ ( t ) , the model corresponds to simple exponential growth on a per-cycle basis . The model can be solved in closed form but does not account for saturation effects from the depletion of monomers . Therefore , it is not valid for concentrations similar to the total concentration of each strand . Fitting the model to the amplification reactions with 0–45 nM of template 0¯A0¯B0¯C0¯D revealed rate constants of k = 0 . 16 cycle−1 and k0 = 0 . 4 nM cycle−1 ( Figure 5b ) . Amplification was robust with regard to the peak temperature of the oscillations . For Tpeak below 74 °C , the reaction remained almost unaffected ( Figure 5c ) . Above , the temperature is too close to the melting transitions of the hairpin-hairpin duplexes , ranging from 76 to 79 °C ( Figure 4—figure supplement 1 ) . The ability to withstand consecutive dilutions is characteristic for exponentially growing replicators and was tested for in serial transfer experiments . Strands encoding for '0' ( i . e . 0A , 0¯A , 0B , etc . ) were thermally cycled with 30 nM of template 0¯A0¯B0¯C0¯D . After three cycles each , samples were diluted one to one with buffer containing all eight strands as monomers at 200 nM each ( Figure 5d ) . This high frequency of dilutions prevented the reaction from transitioning into the saturating regime . The cross-catalytic model was fitted to the data with the dilution factor as single free parameter , that was found to be 0 . 43 . The difference from the theoretical value of 0 . 50 was likely due to strands sticking to the reaction vessels before dilution . As a control , a reaction with the same initial concentration of template 0¯A0¯B0¯C0¯D , but without monomers 0¯A , 0¯B , 0¯C , 0¯D , was subjected to the same protocol . As the control could not grow exponentially , it gradually died out ( Figure 5d , open circles ) . The above-mentioned reactions did amplify , but not replicate actual sequence information , as they only contained strands with 0/0¯ information domains . To study the replication of arbitrary sequences of binary code , replication reactions with all 16 strands encoding for '0' and '1' were performed . To discriminate sequences encoded in equally sized complexes and deduce error rates , we compared these results to those from different reaction runs with defects , that is lacking one or two of the hairpin sequences required for the faithful replication of a particular template . Reference reactions contained all 16 strands ( 0A , 0¯A , 1A , 1¯A , 0B , … , 1¯D ) at 100 nM each , and were run for each of three different template sequences ( 0¯A0¯B0¯C0¯D , 0¯A1¯B0¯C1¯D , and 0¯A0¯B1¯C1¯D ) ( Figure 6 ) . The product yields were quantified from reaction time traces , extracted by integrating the intensities of all gel bands containing tetramers with the labeled strand 0A . Leaving out a single strand ( reaction label “+++−” , for example omitting 0D for template 0¯A0¯B0¯C0¯D ) reduced the yield of full-size product to about 40 % ( Figure 6a , b ) . The non-zero product yield with a missing strand is most likely due to the incorporation of the corresponding strand with an information domain mismatch ( here 1D ) . This type of mismatch allows the hairpin backbone to form regardless , and the unfaithful product can propagate since both strands needed for an amplification of '1' at position D ( 1D and 1¯D ) are provided . In particular during the first few cycles , mostly complex 0A0B0C:0¯A0¯B0¯C0¯D ( 3:4 ) was detected in the gel , instead of the desired tetramer product ( Figure 6—figure supplement 1 ) . This was expected given the lack of strand 0D and provides an upper limit on the error rate of the full replication . The fact that the full reaction produced almost no complexes 3:4 or 4:3 indicates that the incomplete product was indeed caused by the lack of a particular strand . Removal of a further strand either directly next to the previous one ( '++−−' , missing strands 0Cand0D ) or not ( '+−+−' , missing strands 0Band0D ) reduced the yield of product tetramers even further . Due to the periodic design those two variants represent all defective sets with two missing strands . Replication of the other two templates 0¯A1¯B0¯C1¯D and 0¯A0¯B1¯C1¯D produced very similar results . Product concentrations after six cycles are given in Figure 6c for each of the three templates as well as an average over the template sequences ( horizontal lines ) . A single defect reduced the yield of tetramer complexes to about 40 % , two defects to 15–20 % , which is close to 0 . 4×0 . 4=0 . 16≃15−20 % , that is the combined probability of two independent mismatches . The observed rate of erroneous product formation can be attributed to the spontaneous background rate ( Figure 4b , c , Figure 5a , b and Figure 6b ) . The reaction ‘+−+−' ( dark green ) amplified similarly to the untemplated reference reaction ( solid line ) , as it did not contain any strands that could bind next to each other to the template and form a backbone duplex ( Figure 6b ) . For the templated reactions '+++−' and '++−−' , templating worked for partial sequences , producing intermediate yields . The reduction in yield caused by a single defect ( i . e . missing strand ) to ~40 % ( and to ~16 % for two defects ) translates into a replication fidelity per information domain of ~60 % . The exact value for the replication fidelity is 62 % and can be calculated from Figure 6b by extracting the endpoint concentrations ( blue vs . yellow line ) and calculating 1−14nM37nM=0 . 62 . However , this is a worst-case estimation , and the replication fidelity is likely higher due to binding competition . The mutations caused by a single defect ( '+++-' ) in Figure 6b were imposed by not providing strand 0D for a template ending with 0¯D and only leaving the option to incorporate 1D instead . For the full system ( '++++' ) , however , with the presence of the matching strand , there is a binding competition for position D . Since the matching strand preferentially binds , the unfaithful incorporation of the wrong strand would be reduced . A similar effect of competition was observed in a protein-catalyzed ligation reaction ( Toyabe and Braun , 2019 ) . There , a comparable binding competition lead to a sevenfold decrease of the inferior ligation reaction in the presence of competition ( Figure 2a , b therein ) . Therefore , we expect the real fidelity to be better than above lower bound estimate . It is interesting to project and compare this per information domain replication fidelity to a per nucleotide replicator ( i . e . polymerization ) . To do so , we define a threshold in the decrease of melting temperature per information domain as the criterion for when the replication mechanism is still functional . Then , we estimate how many point mutations in the information domain can maximally be tolerated to stay within this range of decrease in melting temperature . From this , we can calculate a hypothetical , corresponding per nucleotide fidelity to the measured information domain fidelity . We compared the properties of the duplex 0:0¯ to duplexes 0:0¯* , where 0¯* differs from 0¯ by K point mutations . We assumed that within the temperature range of this replication mechanism ( Figure 7b , gray box ) a reduction in information domain melting temperature Tm of the mutated duplex 0:0¯* by up to 10 °C compared to the original duplex 0:0¯ would be tolerated by the replication reaction . This was inferred from the width of the melting transition of duplex 0:0¯ ( Figure 7b ) , where a shift of 10 °C corresponds to an increase of the unbound fraction from 0 . 08 at Tbase = 45 °C to 0 . 66 at 55 °C . In terms of free energies of the information domain duplex , this difference corresponds to ΔG ( 0:0¯* ) ≥ −12 . 5 kcal/mol compared to ΔG ( 0:0¯ ) = −15 . 4 kcal/mol . 99 % of all duplexes 0:0¯* , with 0¯* containing three point mutations , met that criterion ( Figure 7a ) . Therefore , up to K=3 point mutations can be allowed . We will assume that the replication did not differentiate between information domain 0¯ and any information domain 0¯* if 0¯ and 0¯* differ by less than K point mutations . The fidelity per information domain pKN is given by a cumulative binomial distribution: ( 2 ) pK ( N ) =∑k=0K−1 ( Nk ) pN−k ( 1−p ) k Here , N is the information domain length , and p the per nucleotide replication fidelity . The reduction in binding energy of the information domain duplex 0:0¯* and subsequent change in melting temperature was used as criterion to define the functionality of the replicator and to translate between a per information domain and a per nucleotide approach . As justified above , we calculate with K=3 mutations within the N=15 bases of the information domain , that is the replication can tolerate up to three mismatches in the information domain . From Figure 6 we extracted a per information domain fidelity of p3 ( 15 ) =0 . 62 , and deduce a per nucleotide fidelity of p=85 % . In fact , information domain duplexes 0:0¯* with mutations at two internal bases all show similar properties as information domains with a total of three mutations ( Figure 7—figure supplement 1 ) . This refinement ( p2 ( 13 ) =0 . 62 ) would increase the per nucleotide fidelity to p=90 % . We therefore estimate that a per nucleotide replication process would need a replication fidelity of 85–90 % to produce sequences with an error rate equivalent to the presented mechanism . Detailed calculations of the per nucleotide fidelities can be found in the supplementary information .
A cross-catalytic replicator can be made from short sequences and without covalent bonds under a simple non-equilibrium setting of periodic thermal oscillations . The replication is fast and proceeds within a few thermal oscillations of 20 min each . This velocity is comparable to other replicators ( Kindermann et al . , 2005 ) , cross-ligating ribozymes ( Robertson and Joyce , 2014 ) , or autocatalytic DNA networks ( Yin et al . , 2008 ) . The required thermal oscillations can be obtained by laminar convection in thermal gradients ( Braun et al . , 2003; Salditt et al . , 2020 ) , which also accumulates oligonucleotides ( Mast et al . , 2013 ) . Depending on the envisioned environment , the mechanism could also be driven by thermochemical oscillations ( Ball and Brindley , 2014 ) or convection in pH gradients ( Keil et al . , 2017 ) . It should however be noted , that with the current state-of-the-art prebiotic chemistry regarding polymerization and ligation , the creation of >80 nt RNA is not yet understood . It is likely that a slower prebiotic ligation chemistry could later fix the replication results over long timescales . Such an additional non-enzymatic ligation ( Stadlbauer et al . , 2015 ) that joins successive strands would relax the constraint that backbone duplexes must not melt during high-temperature steps . Early on , this is difficult to achieve in aqueous solution against the high concentration of water . In order to overcome this competition and to favor the reaction entropically by a leaving group , individual bases are typically activated by triphosphates ( Attwater et al . , 2013; Horning and Joyce , 2016 ) or imidazoles , which are especially interesting in this context since they can replicate RNA directly ( O'Flaherty et al . , 2019; Zhou et al . , 2019 ) . However , the required chemical conditions of enhanced Mg2+ concentration hinder strand separation . The overall replication fidelity is limited by the spontaneous bond formation rate between pairs of hairpin sequences , caused by the interaction of strands in free solution . At lower concentrations , as one would imagine in a prebiotic setting , this rate would decrease at the expense of an overall slower reaction . To some degree and despite ongoing design efforts , such a background rate is inherent to hairpin-fuelled DNA or RNA reactions ( Green et al . , 2006; Krammer et al . , 2012; Yin et al . , 2008 ) . The replication mechanism is expected to also work with shorter strands , as long as the order of the melting temperatures of the information domain and the backbone duplexes is preserved . Smaller strands would also be easier to produce by an upstream polymerization process , simply because they contain less nucleotides . In addition , binding of shorter information domain duplexes could discriminate even single base mismatches , resulting in an increased selectivity . It is not straightforward to estimate a minimal sequence length for the demonstrated mechanism . However , it is worth noting that it has been suggested that tRNA arose from two proto-tRNA sequences ( Hopfield , 1978 ) . Pre-selection of nucleic acids for the presented hairpin-driven replication mechanism can be provided by highly sequence-specific gelation of DNA . This gel formation has been shown to be most efficient with double hairpin structures very similar to the tRNA-like sequences used in this study ( Morasch et al . , 2016 ) . For our replication system , we have demonstrated this in Figure 3 by showing the spontaneous formation of agglomerates and sedimentation under gravity if all molecules of the assembly are present . This self-selection shows a possible pathway how the system can emerge from random or semi-random sequences , for example in a flow or a convection system where the molecules are selected as macroscopic agglomerate ( Mast et al . , 2013 ) . Another selection pressure could stem from the biased hydrolysis of double-stranded nucleotide backbones , which favors assembled complexes over the initial hairpins ( Obermayer et al . , 2011 ) . The replication mechanism could serve as a mutable assembly strategy for larger functional RNAs ( Mutschler et al . , 2015; Vaidya et al . , 2012 ) . As an evolutionary route toward a more mRNA-like replication product with chemically ligated information domains , the mechanism would be supplemented by self-cleavage next to the information domains that cuts out the non-coding backbone duplexes , followed by ligation of the information domains . Both operations could potentially be performed by very small ribozymatic centers ( Dange et al . , 1990; Szostak , 2012; Vlassov et al . , 2005 ) . The proposed replication mechanism of assemblies from tRNA-like sequences allows to speculate about a transition from an autonomous replication of successions of information domains to the translation of codon sequences encoded in modern mRNA ( Figure 1a ) . Short peptide-RNA hybrids ( Griesser et al . , 2017; Jauker et al . , 2015 ) , combined with specific interactions between 3’-terminal amino acids and the anticodons , could have given rise to a primitive genetic code . The spatial arrangement of tRNA-like sequences that are replicated by the presented mechanism would translate into a spatial arrangement of the amino acid or short peptide tails that are attached to the strands in a codon-encoded manner ( Schimmel and Henderson , 1994 ) . The next stage would then be the detachment and linking of the tails to form longer peptides . Eventually , tRNA would transition to its modern role in protein translation . The mechanism thus proposes a hypothesis for the emergence of predecessors of tRNA , independent of protein translation . This is crucial for models of the evolution of translation , because it could justify the existence of tRNA before it was utilized in an early translation process . However , many questions around the evolutionary steps that created translation are still unclear . Therefore , replication and translation could have , at an early stage , emerged along a common evolutionary trajectory . This supports the notion that predecessors of tRNA could have featured a rudimentary replication mechanism: starting with a double hairpin structure of tRNA-like sequences , the replication of a succession of informational domains would emerge . The interesting aspect is , that the replication is first encoded by hybridization and can later be fixed by a much slower ligation of the hairpins . The demonstrated mechanism could therefore jumpstart a non-enzymatic replication chemistry , which was most likely restricted in fidelity due to working on a nucleotide-by-nucleotide basis ( Robertson and Joyce , 2012; Szathmáry , 2006 ) .
DNA double-hairpin sequences were designed using the NUPACK software package ( Zadeh et al . , 2011 ) . In addition to the secondary structures of the double-hairpins , the design algorithm was constrained by all target dimers . Candidate sequences were selected for optimal homogeneity of binding energies and melting temperatures . Backbone domains connecting consecutive strands ( e . g . 0A0B0C ) had to be the most stable bonds in the system , in particular more stable than between a template and a newly formed product complex ( e . g . 0B:0¯B ) . On the other hand , hairpin melting temperatures had to be low enough to allow for a sufficient degree of thermal fluctuations . To reconcile this with the length of the strands , mismatches were introduced in the hairpin stems . The sequences of all strands are listed in Supplementary file 1 . All reactions were performed in salt 20 mM Tris-HCl pH 8 , 150 mM NaCl with added 20 mM MgCl2 . DNA oligonucleotides ( Biomers , Germany ) were used at 200 nM concentration per strand in reactions containing a fixed-sequence subset of eight strands ( e . g . 0/0¯ only ) and 100 nM per strand in reactions containing all 16 different strands . Thermal cycling was done in a standard PCR cycler ( Bio-Rad C1000 ) . Reaction kinetics were obtained by running each reaction for different run times or numbers of cycles in parallel . The products were analyzed using native PAGE . The time between thermal cycling and PAGE analysis was minimized to exclude artifacts from storage on ice . Template sequences were prepared using a two-step protocol . Annealing from 95°C to 70°C within 1 hr , followed by incubation at 70 °C for 30 min . Afterwards , samples were cooled to 2 °C and stored on ice . When assembling complexes containing paired information domains ( Figure 2 ) , samples were slowly cooled down from 70 to 25 °C within 90 min before being transferred onto ice . DNA double hairpins were quenched into monomolecular state by heating to 95 °C and subsequent fast transfer into ice water . DNA complexes were analyzed using native polyacrylamide gel electrophoresis ( PAGE ) in gels at 5 % acrylamide concentration and 29:1 acrylamide / bisacrylamide ratio ( Bio-Rad , Germany ) . Gels were run at electric fields of 14 V/cm at room temperature . Strand 0A/1A was covalently labeled with Cy5 . Cy5 fluorescence intensities were later used to compute strand concentrations . As an additional color channel , strands were stained using SYBR Green I dye ( New England Biolabs ) . Complexes were identified by comparing the products obtained from annealing different strand subsets . To correctly identify bands in the time-resolved measurements , gels were run with a marker lane . The marker contained strands 0A ( 200 nM ) , 0B ( 150 nM ) , 0C ( 50 nM ) , and 0D ( 100 nM ) , and was prepared using the two-step annealing protocol from 95 to 70 °C . The unequal strand concentrations ensured that the sample contained a mixture of mono- , di- , tri- , and tetramers . Electrophoresis gels were imaged in a multi-channel imager ( Bio-Rad ChemiDoc MP ) , image post processing , and data analysis were performed using a self-developed LabVIEW software . Post-processing corrected for inhomogeneous illumination by the LEDs , image rotation , and distortions of the gel lanes if applicable . Background fluorescence was determined from empty lanes on the gel , albeit generally low in the Cy5 channel . For the determination of reaction yields , the intensities of all gel bands containing strands of the sequence length of interest were added up . For strings of four strands , these were the single tetramer as well as its complex with di- and tri- and tetramers . Single strands separated from their complements during electrophoresis ( Figure 2 and Figure 6—figure supplement 1 ) . Thermal melting curves were measured using either UV absorbance at 260 nm wavelength in a UV/Vis spectrometer ( JASCO V-650 , 1 cm optical path length ) , via quenching of the Cy5 label at the 5'-end of strand 0A ( excitation: 620–650 nm , detection: 675–690 nm ) , or using fluorescence of the intercalating dye SYBR Green I ( excitation: 450–490 nm , detection: 510–530 nm ) . Fluorescence measurements were performed in a PCR cycler ( Bio-Rad C1000 ) . Samples measured via fluorescence were at 200 nM of each strand , those measured via UV absorption contained 1 µM total DNA concentration to improve the signal-to-noise ratio . Before analysis of the melting curves ( Mergny and Lacroix , 2003 ) , data were corrected for baseline signals from reference samples containing buffer and intercalating dye , if applicable . The samples were mixed in the replication buffer ( 150 mM NaCl , 20 mM MgCl2 , 20 mM Tris-HCl pH 8 ) at a total oligomer concentration of 5 µM , that is varying concentration per strand depending on the number of different strands in the configuration ( 4 , 7 , or 8 ) . The microfluidic chamber was assembled with a custom cut , 500 µm thick , Teflon foil placed between two plane sapphires ( Figure 3—figure supplement 2 ) . Three Peltier elements ( QuickCool QC-31–1 . 4-3 . 7AS , purchased from Conrad Electronics , Germany ) were attached to the backside of the chamber to provide full temperature control . The chamber was initially flushed with 3M Novec7500 ( 3M , Germany ) to avoid bubble formation . The samples were pipetted into the microfluidic chamber through the 0 . 5 mm channels using microloader pipette tips ( Eppendorf , Germany ) . The chamber was then sealed with Parafilm and heated to 95 °C for 10 s to fully separate the strands and cooled rapidly ( within 30 s ) to 25 °C . Assembly and sedimentation were monitored for 20 hr on a fluorescence microscope ( Axiotech Vario , Zeiss , Germany ) with two LEDs ( 490 nm and 625 nm , Thorlabs , Germany ) using a 2 . 5 x objective ( Fluar , Zeiss , Germany ) . The observed sedimentation was independent of the attached dye and its position ( Figure 3—figure supplement 1c ) . Prior to image analysis the image stacks were stabilized using an ImageJ plugin ( Li , 2008 ) . The ratio of sedimented fluorescence relative to the first frame after heating was used to quantify sedimentation ( Figure 3 ) . The sedimentation time-traces ( Figure 3b ) were fitted with a Sigmoid function to determine the final concentration increase c/c0 ( Figure 3c ) . The experiment was also performed with random 84 nt DNA strands at 5 µM total concentration to exclude unspecific agglomeration ( Figure 3—figure supplement 1c ) . | The genetic code stored within DNA contains the instructions for manufacturing all the proteins organisms need to develop , grow and survive . This requires molecular machines that ‘transcribe’ regions of the genetic code into RNA molecules which are then ‘translated’ into the string of amino acids that form the final protein . However , these molecular machines and other proteins are also needed to replicate and synthesize the sequences stored in DNA . This presents evolutionary biologists with a ‘chicken-and-egg’ situation: which came first , the DNA sequences needed to manufacture proteins or the proteins needed to transcribe and translate DNA ? Understanding the order in which DNA replication and protein translation evolved is challenging as these processes are tightly intertwined in modern-day species . One theory , known as the ‘RNA world hypothesis’ , suggests that all life on Earth began with a single RNA molecule that was able to make copies of itself , as DNA does today . To investigate this hypothesis , Kühnlein , Lanzmich and Braun studied a molecule called transfer RNA ( or tRNA for short ) which is responsible for translating RNA into proteins . tRNA is assumed to be one of the earliest evolved molecules in biology . Yet , why it was present in early life forms before it was needed for translation still remained somewhat of a mystery . To gain a better understanding of tRNA’s role early in evolution , Kühnlein , Lanzmich and Braun made small changes to its genetic code and then carried out tests on these tRNA-like sequences . The experiments showed these ‘early’ forms of tRNA can actually self-assemble into a molecule which is capable of replicating the information stored in its sequence . It suggests early forms of tRNA could have been involved in replication before modern tRNA developed its role in protein translation . With these experiments , Kühnlein , Lanzmich and Braun have identified a possible evolutionary link between DNA replication and protein translation , suggesting the two processes emerged through one shared pathway: tRNA . This deepens our understanding about the origins of early life , while taking biochemists one step closer to their distant goal of recreating self-replicating molecular machines in the laboratory . | [
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] | 2021 | tRNA sequences can assemble into a replicator |
Innate lymphoid cells ( ILCs ) are recently identified lymphocytes that limit infection and promote tissue repair at mucosal surfaces . However , the pathways underlying ILC development remain unclear . Here we show that the transcription factor NFIL3 directs the development of a committed bone marrow precursor that differentiates into all known ILC lineages . NFIL3 was required in the common lymphoid progenitor ( CLP ) , and was essential for the differentiation of αLP , a bone marrow cell population that gives rise to all known ILC lineages . Clonal differentiation studies revealed that CXCR6+ cells within the αLP population differentiate into all ILC lineages but not T- and B-cells . We further show that NFIL3 governs ILC development by directly regulating expression of the transcription factor TOX . These findings establish that NFIL3 directs the differentiation of a committed ILC precursor that gives rise to all ILC lineages and provide insight into the defining role of NFIL3 in ILC development .
Innate lymphoid cells ( ILCs ) are a recently identified family of lymphocytes that perform a variety of immune functions at barrier surfaces ( Spits and Cupedo , 2012 ) . Although ILCs share a common developmental origin with B- and T-cells , they lack antigen-specific receptors . Instead , they exert their immune functions through cytokine secretion in a manner similar to T helper cells ( Spits et al . , 2013 ) . Despite their important contributions to immunity , the pathways that regulate ILC development remain poorly understood . There are three known ILC groups . ILC1 , which include conventional NK ( cNK ) cells , require the transcription factors T-BET and/or EOMES , produce interferon-γ ( IFNγ ) ( Kiessling et al . , 1975; Gordon et al . , 2012; Fuchs et al . , 2013 ) , and promote immunity to intracellular pathogens ( Yokoyama et al . , 2004; Klose et al . , 2014 ) . ILC2 require the transcription factor GATA-3 , produce IL-5/13 and amphiregulin ( Moro et al . , 2010; Neill et al . , 2010; Monticelli et al . , 2011; Hoyler et al . , 2012 ) , and promote tissue repair and anti-helminth immunity ( Monticelli et al . , 2012 ) . ILC3 , which include lymphoid tissue inducer ( LTi ) cells , depend on the transcription factor RORγt and secrete IL-17/22 ( Eberl and Littman , 2003; Satoh-Takayama et al . , 2008; Luci et al . , 2009; Takatori et al . , 2009 ) . ILC3 are especially important for the defense of barrier surfaces as they promote anatomical containment of commensal bacteria ( Sonnenberg et al . , 2012 ) , regulate CD4+ T cell responses to commensal bacteria ( Hepworth et al . , 2013; Qiu et al . , 2013 ) , and stimulate epithelial cells to produce antibacterial proteins ( Sanos et al . , 2011 ) . All ILC differentiate from the common lymphoid progenitor ( CLP ) , which resides in the bone marrow and also gives rise to B- and T-lymphocytes ( Possot et al . , 2011; Hoyler et al . , 2012 ) . Committed ILC progenitors that are positioned developmentally downstream of the CLP have been identified , and give rise to various ILC subsets . For example , an Id2 ( inhibitor of DNA binding 2 ) -expressing progenitor , known as the common ‘helper-like’ innate lymphoid progenitor ( CHILP ) , gives rise to ‘helper-like’ ILC lineages including ILC2 , ILC3 and a subgroup of ILC1 ( Klose et al . , 2014 ) . PLZF-positive progenitors , termed ILCP , differentiate into non-NK ILC1 , ILC2 , and ILC3 ( Constantinides et al . , 2014 ) . However , these progenitors do not differentiate into cNK cells ( Constantinides et al . , 2014; Klose et al . , 2014 ) , suggesting that a precursor that gives rise to all ILC subtypes remains to be identified . NFIL3 ( also known as E4BP4 ) is a basic leucine zipper transcription factor that controls a number of different immune processes , including cytokine expression ( Kashiwada et al . , 2011; Kobayashi et al . , 2011; Motomura et al . , 2011 ) , IgE class switching ( Kashiwada et al . , 2010 ) , and TH17 cell differentiation ( Yu et al . , 2013 ) . It was identified several years ago as an essential transcription factor in the differentiation of cNK cells ( Gascoyne et al . , 2009; Kamizono et al . , 2009 ) . More recently , NFIL3 has been shown also to be required for the development of non-NK ILC1 ( Klose et al . , 2014 ) , ILC2 ( Geiger et al . , 2014; Seillet et al . , 2014a ) , ILC3 ( Geiger et al . , 2014; Klose et al . , 2014; Kobayashi et al . , 2014; Seillet et al . , 2014a ) , and LTi cells ( Geiger et al . , 2014; Seillet et al . , 2014a ) . Thus , NFIL3 is essential for the development of all ILC lineages . Here we show that NFIL3 is required for the development of a common ILC progenitor from the CLP . The progenitor population is marked by CXCR6 , and resides in the α4β7+ αLP bone marrow population , which can give rise to all ILC lineages . Clonal differentiation assays show that the CXCR6+ precursors are committed ILC progenitors that differentiate into all ILC lineages but not B- or T-cells . Finally , we show that NFIL3 directs progenitor differentiation by directly regulating the expression of TOX , a known driver of ILC differentiation . These findings provide new insight into the defining role of NFIL3 in the differentiation of innate lymphoid cells .
NFIL3 has recently been shown to be essential for the development of all ILC lineages ( Geiger et al . , 2014; Seillet et al . , 2014a ) . Consistent with these findings , we observed that Nfil3−/− mice had lowered frequencies and absolute numbers of ILC2 , ILC3 ( including the NKp46+ subtype ) , cNK cells , and non-NK ILC1 ( Figure 1A; Figure 1—figure supplement 1 ) . Nfil3−/− mice also had fewer and smaller Peyer's patches in the small intestine and remaining Peyer's patches contained fewer LTi cells ( RORγt+ LTβ+ ) than wild-type mice ( Figure 1—figure supplement 2 ) , indicating a deficiency in LTi cells that is consistent with the prior reports ( Geiger et al . , 2014; Seillet et al . , 2014a ) . These data support the conclusion that NFIL3 is required for the development of all ILC lineages . 10 . 7554/eLife . 04406 . 003Figure 1 . NFIL3 is required for innate lymphoid cell development in a cell-intrinsic manner . ( A ) Nfil3−/− mice show reduced frequencies ( left panel ) and numbers ( right panel ) of major ILC types , including conventional NK ( cNK ) , non-NK ILC1 , ILC2 and ILC3 . Lymphocytes were isolated from the small intestinal lamina propria and the liver and were stained as described in Materials and methods . Gating strategies are depicted in Figure 1—figure supplement 1 . cNK cells were identified as CD45+ Lin ( CD3ε , CD19 , CD5 , TCRβ , TCRγδ ) - NK1 . 1+ T-BET+ EOMES+; non-NK ILC1 as CD45+ Lin ( CD3ε , CD19 , CD5 , TCRβ , TCRγδ ) - NK1 . 1+ T-BET+ EOMES−; ILC2 as CD45+ Lin ( CD3ε , CD19 ) − GATA3+ Sca1+ KLRG1+; and ILC3 as CD45+ Lin ( CD3ε , CD19 ) - RORγt+ CD127+ . The NK receptor-expressing subtype of ILC3 ( also known as NK22 cells ) was identified by additional staining for NKp46 . ( B ) NFIL3 regulates ILC development in a bone-marrow progenitor intrinsic manner . Equal numbers of wild-type ( CD90 . 2+ CD45 . 1+ ) and Nfil3−/− ( CD90 . 2+ CD45 . 2+ ) LSK cells were co-transplanted into lethally irradiated CD90 . 1+ mice . Liver CD90+ NK and non-NK ILC1 and intestinal ILC2 and ILC3 were analyzed 4-6 weeks later . The ratios of ILCs derived from wild-type ( CD45 . 1+ ) and Nfil3−/− ( CD45 . 2+ ) donor cells were calculated and plotted . Significant variation from 1 . 0 is indicated by * . sm . int . , small intestine . ( C ) Nfil3 regulates ILC development in a CLP-intrinsic manner . Equal numbers of wild-type ( CD45 . 1+ ) and Nfil3−/− ( CD45 . 2+ ) CLPs were co-transplanted into sublethally irradiated alymphoid Rag2−/−;Il2rg−/− mice . ILCs were analyzed 4–6 weeks later as for the LSK experiment . Groups were compared by two-tailed student's t-test ( A ) , one-sample t-test ( B , LSK ) or Wilcoxon signed rank test ( B , CLP ) . Means ± SEM are shown . * , p < 0 . 05 , ** , p < 0 . 01 , *** , p < 0 . 001 , **** , p < 0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 00310 . 7554/eLife . 04406 . 004Figure 1—figure supplement 1 . Gating strategy for ILC analysis . ( A ) Small intestinal lamina propria lymphocytes ( LPLs ) were gated on CD45+ to remove non-hematopoietic cells and then on lineage markers ( CD3ε and CD19 ) to exclude B and T cells ( upper panel ) . ILC2 were identified as CD45+ Lin− GATA3+ Sca1+ and were more stringently gated on expression of KLRG1 . Total ILC3 were identified as CD45+ Lin− RORγt+ CD127+ and the NKp46+ ILC3 ( also known as NK22 ) were examined by staining for NKp46 . ( B ) Liver lymphocytes were first gated on CD45+ as above and then on lineage ( CD3ε , CD19 , CD5 , TCRβ , TCRγδ ) and NK1 . 1 . Lin− NK1 . 1+ cells were further examined for T-BET and EOMES expression . Conventional NK ( cNK ) cells were identified as CD45+ Lin− NK1 . 1+ T-BET+ EOMES+ and non-NK ILC1 as CD45+ Lin− NK1 . 1+ T-BET+ EOMES− . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 00410 . 7554/eLife . 04406 . 005Figure 1—figure supplement 2 . Nfil3−/− mice are deficient in Peyer's patches and lymphoid tissue inducer cells . ( A ) Peyer's patches were examined in wild-type and Nfil3−/− mice by hematoxylin and eosin ( H&E ) staining ( upper panel ) and immunofluorescence ( lower panel ) . Anti-lymphotoxin β ( LTβ ) and anti-RORγt were used to detect lymphoid tissue inducer ( LTi ) cells and anti-B220 was used to identify B cells . Scale bars = 50 μm . ( B ) LTi cells were enumerated as a function of Peyer's patch area . N = 4–5 mice/group . ( C ) Peyer's patches were enumerated in the small intestines of wild-type and Nfil3−/− mice . ( D ) Peyer's patch size was measured by determining the area of the section of tissue at the center ( necessitated by the irregular shape of some Peyer's patches ) . N = 4–5 mice/group . Statistical analysis was performed with the two-tailed student's t-test . Means ± SEM are shown . *** , p < 0 . 001; **** , p < 0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 00510 . 7554/eLife . 04406 . 006Figure 1—figure supplement 3 . LSK cells are not deficient in Nfil3−/− mice . ( A ) Enrichment of Lineage-negative bone marrow cells by negative selection . Due to low frequencies of hematopoietic progenitor cells in the bone marrow of adult mice , lineage-negative cells were enriched by MACS-mediated negative selection prior to analysis or purification by FACS . To examine the efficiency of negative selection , samples before selection ( upper panel ) and after selection ( lower panel ) were subjected to CD45 and Lineage marker staining . At least 10-fold enrichment of Lineage-negative cells was routinely obtained during this process . Lineage markers used here include CD3ε , B220 , CD11b , Gr-1 , Ter119 , CD5 , TCRγδ , and NK1 . 1 . ( B–D ) LSK cell frequencies and numbers are unaltered in Nfil3−/− mouse bone marrow . Lin− cKit+ Sca1+ ( LSK ) cells roughly represent the hematopoietic stem cells ( HSC ) . Bone marrow cells were isolated from femur and tibia from wild-type and Nfil3−/− mice . Lineage marker ( Lin ) -negative cells were first enriched by negative selection and then stained with antibodies against Sca1 and cKit . Typical flow plots are shown in ( B ) and cell frequencies and absolute cell numbers from multiple mice are pooled in ( C ) and ( D ) . Comparison between genotypes was done with the two-tailed student's t-test . Means ± SEM are shown . ns , not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 006 ILCs develop from common lymphoid progenitors ( CLPs ) in the bone marrow ( Possot et al . , 2011; Hoyler et al . , 2012 ) . To gain insight into the cellular origin of the broad ILC deficiency in Nfil3−/− mice , we first examined undifferentiated bone marrow precursors that were enriched by negative selection ( Figure 1—figure supplement 3 ) . In agreement with previous findings ( Male et al . , 2014; Seillet et al . , 2014b ) , wild-type and Nfil3−/− littermates harbored similar frequencies of LSK cells ( Lin− Sca1+ cKit+ ) ( Figure 1—figure supplement 3 ) , which include hematopoietic stem cells ( HSC ) that give rise to all lymphoid and non-lymphoid hematopoietic cells . To test whether the requirement for NFIL3 was intrinsic to bone marrow precursors , we co-transferred wild-type and Nfil3−/− LSK cells into lethally irradiated mice and examined ILC subsets 5 weeks later . Nfil3−/− LSK cells generated fewer ILC2 , ILC3 , and NK1 . 1+ ILC3 in the small intestine , and fewer cNK and non-NK ILC1 in the liver ( Figure 1B ) . These data indicate that the requirement for NFIL3 in ILC development is intrinsic to bone marrow progenitors . Like LSK cells , CLP cells are also present in normal numbers in the bone marrow of Nfil3−/− mice ( Figure 2A ) ( Male et al . , 2014; Seillet et al . , 2014b ) . To further determine whether the NFIL3 requirement was CLP intrinsic , we co-transferred wild-type and Nfil3−/− CLP cells into sublethally irradiated alymphoid Rag2−/−;Il2rg−/− mice . 5 weeks later , Nfil3−/− CLP had generated fewer ILC2 , ILC3 in the small intestine and fewer CD90+ cNK and non-NK ILC1 in the liver ( Figure 1C ) . ( Because of the lower proliferation potential of CLP compared to LSK cells , progeny cells were fewer and we were not able to reliably enumerate NK1 . 1+ ILC3 following CLP co-transfer . ) These findings reveal that the requirement for NFIL3 in ILC development is intrinsic to the CLP and are consistent with the findings of Seillet et al . ( Seillet et al . , 2014a ) . 10 . 7554/eLife . 04406 . 007Figure 2 . Nfil3−/− mice are deficient in bone marrow ILC precursors downstream of the CLP . ( A ) Nfil3−/− mice have comparable CLP frequencies but show deficiencies in αLP . Bone marrow cells were isolated from femur and tibia of wild-type and Nfil3−/− mice . Lineage marker ( CD3ε , B220 , CD11b , Gr-1 , Ter-119 , CD5 , TCRγδ , NK1 . 1 ) -negative ( Lin− ) cells were first enriched by negative selection and then stained with antibodies to identify CLP ( Lin− cKitlow CD127+ Sca1low Flt3+ α4β7- ) and αLP ( Lin− cKitlow CD127+ Sca1low Flt3- α4β7+ ) ( Possot et al . , 2011 ) . Gating strategy and representative flow plots are shown on the left and combined data for the frequencies and absolute numbers of CLP and αLP are shown on the right . ( B ) Nfil3−/− mice are deficient in common ‘helper-like’ innate lymphoid progenitor ( CHILP ) cells and ILC2P ( Klose et al . , 2014 ) . Bone marrow cells were processed as above and CHILPs and ILC2Ps were identified as Lin− CD127+ α4β7+ CD25- Flt3- and Lin− CD127+ α4β7+ CD25+ Sca1+ , respectively . ( C ) Expression of key transcription factors involved in ILC development in CLP , αLP and CHILP . Bone marrow cells were isolated from Id2GFP/+ and RorgtGFP/+ mice to examine Id2 and RORγt expression . Expression of GATA3 , PLZF , T-BET and EOMES were examined in C57BL/6 mice with specific antibodies . Statistical analysis was performed with two-tailed student's t-test . Means ± SEM are shown . ns , not significant; *** , p < 0 . 001 , **** , p < 0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 00710 . 7554/eLife . 04406 . 008Figure 2—figure supplement 1 . Gating strategies for bone marrow lymphoid progenitor analysis . ( A ) Gating of common lymphoid progenitor ( CLP ) cells and α4β7 integrin-expressing CLP ( αLP ) in the bone marrow . Bone marrow cells were released from femur and tibia and differentiated cells were removed by negative selection . Cells were then stained with anti-biotin ( Lineage ) , cKit , Sca1 , CD127 , Flt3 and α4β7 . CLPs are identified as Lin− cKitlo Sca1lo CD127+ Flt3+ α4β7− and αLP as Lin− cKitlo Sca1lo CD127+ Flt3− α4β7+ . ( B ) Gating of CHILP and ILC2P cells . Lineage-negative cells were prepared as above and stained with anti-biotin ( Lineage ) , CD25 , Sca1 , CD127 , Flt3 and α4β7 . CHILPs are identified as Lin− CD127+ α4β7+ CD25− Flt3− and ILC2P as Lin− CD127+ α4β7+ CD25+ Sca1+ . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 008 The CLP gives rise to all lymphoid cells , including ILCs , T cells , and B cells . In contrast to ILC numbers , overall T and B cell numbers are not altered in Nfil3−/− mice ( Kashiwada et al . , 2010 ) . The general requirement for NFIL3 in ILC development therefore suggested that NFIL3 might be essential for the development of ILC-committed precursors downstream of the CLP . To further investigate the cellular origin of the ILC developmental deficiency in Nfil3−/− mice , we analyzed various bone marrow precursor populations downstream of the CLP in wild-type and Nfil3−/− mice . Nfil3−/− mice had markedly fewer Flt3- α4β7+ CLPs ( known as αLPs ) ( Figure 2A; Figure 2—figure supplement 1A ) , which have been shown to differentiate into ILC3 and NK cells ( Possot et al . , 2011 ) . Nfil3−/− mice also had fewer previously identified precursor cells that have a more restricted differentiation potential . These cells include ILC2 progenitor cells ( ILC2P , Lin− α4β7+ CD127+ Sca1+ CD25+ ) that only differentiate into ILC2 ( Hoyler et al . , 2012 ) ( Figure 2B; Figure 2—figure supplement 1B ) , and the CHILP that can give rise to non-NK ILC1 , ILC2 and NK1 . 1+ NKp46+ ILC3 ( Figure 2B; Figure 2—figure supplement 1B ) . Similarly , NFIL3 has been found to be critical for generation of the earliest NK-committed precursors ( PreNKP ) ( Male et al . , 2014; Seillet et al . , 2014b ) . Thus , Nfil3−/− mice have reduced numbers of precursors that give rise to cNK cells , non-NK ILC1 , ILC2 and ILC3 . Together , these data indicate that NFIL3 is required for generation of ILC precursors in the bone marrow . It is thought that ILCs differentiate from a common ILC progenitor population ( Spits et al . , 2013; Tanriver and Diefenbach , 2014 ) . Prior studies have identified progenitor populations that develop into most , but not all , subtypes of known ILC lineages ( Hoyler et al . , 2012; Constantinides et al . , 2014; Klose et al . , 2014 ) . The CHILP , identified through Id2 lineage tracing studies , can differentiate into non-NK ILC1 , ILC2 and NK1 . 1+ NKp46+ ILC3 but not cNK cells ( Klose et al . , 2014 ) , and the PLZF-dependent ILCP gives rise to all ILCs except cNK cells ( Constantinides et al . , 2014 ) . These findings accord with the partial ILC deficiencies seen in mice lacking Id2 and Zbtb16 ( encoding PLZF ) ( Boos et al . , 2007; Savage et al . , 2008 ) . In particular , cNK cell development is not impaired in Zbtb16−/− mice , while Id2−/− mice show cNK developmental defects only during NK maturation ( Boos et al . , 2007 ) . Similarly , ILC2Ps are lineage-specified progenitors of ILC2s with no appreciable potential to differentiate into cNK cells or ILC3 ( Hoyler et al . , 2012 ) . The broad ILC deficiency ( including cNK cells ) and impaired ILC precursor development in Nfil3−/− mice thus suggested that the NFIL3 might be required for the generation of a common ILC progenitor that lies developmentally upstream of the previously identified ILC precursors . We therefore sought to identify NFIL3-dependent precursor populations that differentiate into all ILC lineages . In contrast to CHILP , ILCP , and ILC2P , αLP cells can differentiate into both cNK cells and ILC3 ( Yoshida et al . , 2001; Possot et al . , 2011 ) and thus likely represent an earlier stage of ILC development . This idea is supported by expression profiles of key transcription factors known to be involved in ILC development ( Figure 2C ) . Similar to CLP and CHILP , αLPs do not express transcription factors that specify ILC lineages , such as RORγt , GATA3 , T-BET and EOMES , suggesting an undifferentiated phenotype . However , in contrast to CHILPs , which uniformly express high levels of ID2 , only a small fraction of αLPs are ID2+ ( Figure 2C ) . The majority of αLPs express ID2 at levels that are markedly lower than those in CHILPs . Because ID2 is virtually undetectable in CLPs , this suggests that αLPs may represent a transitional stage between CLP and CHILP . This is further supported by the fact that αLPs do not express PLZF while a major fraction of CHILPs express PLZF ( Klose et al . , 2014 ) , which defines another group of ILC precursors ( ILCP ) that lack cNK cell differentiation potential ( Constantinides et al . , 2014 ) . To determine whether αLP can also give rise to ILC2 , we co-cultured purified αLP with bone marrow stromal OP9 cells ( OP9-GFP ) or OP9 cells expressing the Notch ligand Delta-like 1 ( OP9-DL1 ) , which support ILC differentiation in vitro ( Holmes and Zúñiga-Pflücker , 2009; Possot et al . , 2011; Hoyler et al . , 2012 ) . When co-cultured with OP9-DL1 cells in the presence of ILC2-inducing cytokines , αLPs readily developed into ILC2 as the majority of progeny cells expressed ILC2 markers ( GATA3+ Sca1+ ) ( Figure 3A ) . When OP9-GFP cells ( not expressing Notch ligand ) were used in this assay , only a small fraction of progeny cells became ILC2 ( Figure 3A ) , confirming that Notch signaling is important for ILC2 differentiation in vitro ( Wong et al . , 2012; Yang et al . , 2013 ) . In agreement with a prior study ( Possot et al . , 2011 ) , αLP differentiated into ILC3 and RORγt− NK1 . 1+ cells under ILC3-inducing conditions ( Figure 3A ) . 10 . 7554/eLife . 04406 . 009Figure 3 . αLPs can differentiate into ILC2 in vitro and in vivo , and can thus give rise to all known ILC lineages . ( A ) αLPs can differentiate into ILC2 in vitro . αLPs were purified by FACS and ∼25 cells were co-cultured with a bone marrow stromal cell line OP9 ( OP9-GFP ) or OP9 cells stably expressing the Notch ligand Delta-like 1 ( OP9-DL1 ) for 14 days in the presence of ILC2-inducing ( IL-2 ) or ILC3-inducing ( IL-23 ) cytokines . Cells were then stained and analyzed by flow cytometry . ILC2 cells were identified as CD45+ CD3ε− CD19- GATA3+ Sca1+ , ILC3 as RORγt+ NK1 . 1- , and ILC1 ( including NK and non-NK ILC1 ) as CD45+ CD3ε− CD19- RORγt− NK1 . 1+ . Typical flow plots are shown on the left and combined data are shown on the right . ( B and C ) αLPs can differentiate into ILC2 , ILC3 , cNK , and non-NK ILC1 in vivo . αLP cells were purified from wild-type ( CD45 . 1+ ) mice and ∼1000 αLP cells were transplanted into sublethally irradiated Rag2−/−;Il2rg−/− ( CD45 . 2+ ) mice . ILCs in the small intestine and colon ( B ) or liver ( C ) were examined 4–6 weeks later . ( D ) αLPs failed to differentiate into B cells both in the small intestine and spleen . There were small numbers of T cells in both the small intestine and spleen . Statistical analysis was performed with two-tailed student's t-test . Means ± SEM are shown . N . D . , not detected; * , p < 0 . 05; *** , p < 0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 009 To assess the potential of αLPs to differentiate into ILC2 in vivo , we transferred ∼1000 purified αLPs ( CD45 . 1+ ) into sublethally irradiated Rag2−/−;Il2rg−/− mice ( CD45 . 2+ ) . After 5 weeks , ILC2 that had differentiated from engrafted αLPs were detected in small intestine and colon of the recipient mice ( Figure 3B ) . We noted that GATA3+ ILC2 comprised a small fraction of CD127+ ILCs in the small intestine but were the majority in the colon while RORγt+ ILC3 showed the reverse tissue distribution pattern ( Figure 3B ) . This suggests that tissue-specific microenvironment influences ILC development or recruitment . Consistent with the previously reported cNK cell differentiation potential of αLPs ( Possot et al . , 2011 ) , donor cells gave rise to cNK cells in the liver , and also differentiated into non-NK ILC1 ( Figure 3C ) . Differentiation of ILCs from αLPs was not caused by contamination of αLPs with CLPs , as no donor-derived B cells were detected in the spleen and small intestine of recipient mice ( Figure 3D ) . This accords with the loss of B cell differentiation potential in αLPs ( Yoshida et al . , 2001; Possot et al . , 2011 ) . However , there were small numbers of donor-derived T cells , consistent with prior findings that αLP retain some T cell differentiation potential ( Possot et al . , 2011 ) . Thus , αLPs can give rise to all known ILC lineages in vitro and in vivo . Given the more restricted differentiation potential of CHILP , ILCP , and ILC2P , αLPs are therefore likely to be developmentally upstream of these progenitors , and defective αLP development in Nfil3−/− mice can thus explain the general ILC deficiency in these mice . The residual T cell differentiation potential of αLPs suggested that this population includes cells that are not fully committed ILC precursors . Prior studies have shown that when αLP cells acquire CXCR6 expression , they continue to give rise to cNK cells and ILC3 but lose T cell differentiation potential ( Possot et al . , 2011 ) . CXCR6+ cells comprised 3–4% of the αLP population in the bone marrows of adult wild-type and Nfil3−/− mice ( Figure 4A , Figure 4—figure supplement 1 ) . Their absolute numbers were diminished in Nfil3−/− mice ( Figure 4B ) , in parallel with the decrease in total αLP numbers ( Figure 2A ) . We therefore hypothesized that CXCR6+ αLPs might include NFIL3-dependent committed ILC precursors that give rise to all ILC lineages . To assess the developmental potential of CXCR6+ αLP cells , we isolated cells by flow cytometry and cultured individual cells with OP9-DL1 feeder cells in the presence of non-polarizing SCF and IL-7 ( Figure 4C , D ) . In contrast to CXCR6- αLP cells , which retained the ability to differentiate into T cells , CXCR6+ αLP cells failed to give rise to T cells in any of the clones examined ( Figure 4C ) . 10 . 7554/eLife . 04406 . 010Figure 4 . CXCR6+ αLP cells are ILC-committed precursors . ( A ) αLP can be divided into two subpopulations based on CXCR6 expression . ( B ) Enumeration of CXCR6+ cells in Nfil3−/− mice . Bone marrow progenitor cells were enriched by negative selection with Lin− cell counts ranging from 8–12 . 5 × 106 per mouse . Lin− cells were then stained and analyzed by flow cytometry . The numbers of CXCR6+ αLP cells in 3 . 3 × 106 Lin− cells are plotted . ( C ) CXCR6+ αLP cells lack T cell differentiation potential . CXCR6- and CXCR6+ αLP cells were individually sorted into the wells of a 96-well plate with an irradiated OP9-DL1 feeder cell monolayer . Cells were cultured in the presence of 20 ng/ml SCF and 20 ng/ml IL-7 for 3 weeks . T cells were detected by CD3ε staining . Data are shown as the percentages of CD45+ cell-containing wells in which T cells were detected . ( D ) CXCR6+ αLP cells are multipotent precursors to cNK cells , non-NK ILC1 , ILC2 and ILC3 in vitro . Individual CXCR6+ αLP cells were sorted and cultured as above ( 240 cells in total , pooled from two independent experiments ) . ILCs were analyzed by flow cytometry . cNK cells were detected as CD45+ CD3ε− CD19- RORγt− GATA3- NK1 . 1+ T-BET+ EOMES+; non-NK ILC1 as CD45+ CD3ε− CD19- RORγt− GATA3- NK1 . 1+ T-BET+ EOMES− , ILC2 as CD45+ CD3ε− CD19- GATA3+; and ILC3 as CD45+ CD3ε− CD19- RORγt+ . Each well is presented as a column , with detected ILC lineages highlighted in blue . ( E ) CXCR6+ αLP cells differentiate into cNK cells , non-NK ILC1 , ILC2 and ILC3 in vivo . ∼1000 CXCR6+ αLP cells were purified from 20 CD45 . 1+ mice by FACS sorting and were transplanted into sublethally irradiated Rag2−/−;Il2rg−/− ( CD45 . 2+ ) mice . T cells and B cells in the blood and ILCs in the small intestine and liver were examined 4–6 weeks later . Data shown are representative of two independent experiments . Statistical analysis was performed with two-tailed student's t-test . Means ± SEM are shown . ** , p < 0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 01010 . 7554/eLife . 04406 . 011Figure 4—figure supplement 1 . Frequencies of CXCR6+ cells in wild-type and Nfil3−/− αLP cell populations . Bone marrow progenitors from wild-type and Nfil3−/− mice were enriched by negative selection and stained to detect CXCR6+ αLP cells . The frequencies of CXCR6+ cells among αLP cells are plotted . Statistical analysis was performed by two-tailed student's t-test . Means ± SEM are shown . ns , not significant . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 011 To determine if a single CXCR6+ αLP cell could give rise to all ILC lineages , we assessed the clonal differentiation potential of the CXCR6+ αLP cells in vitro . While no T cells were detected , mixtures of cNK , non-NK ILC1 , ILC2 , and ILC3 cells were present in the progeny populations of single CXCR6+ αLP cells ( Figure 4C , D ) . Approximately 60% of wells with clonal growth contained multiple ILC lineages . In particular , 43 . 3% of the wells differentiated from individual CXCR6+ αLP cells contained two ILC lineages , and 11 . 7% three ILC lineages ( Figure 4C ) . Importantly , 2 . 5% of wells contained all four ILC lineages , demonstrating that individual CXCR6+ αLP cells can differentiate into all known ILC lineages . In agreement with the in vitro differentiation data , CXCR6+ αLP cells differentiated into cNK , non-NK ILC1 , ILC2 and ILC3 , but not B cells or T cells , when transferred into sublethally-irradiated Rag2−/−;Il2rg−/− mice ( Figure 4E ) . Thus , CXCR6+ αLP cells include committed ILC precursors that can differentiate into all major ILC lineages in vitro and in vivo . To identify potential mechanisms underlying NFIL3-dependent ILC development , we isolated CLPs from wild-type and Nfil3−/− mice and surveyed their transcriptomes by Illumina BeadArrays . Nfil3 expression was readily detected in CLPs ( Figure 5A ) , which accords with previous reports ( Geiger et al . , 2014; Seillet et al . , 2014b ) and is consistent with the finding that NFIL3 regulates ILC development in a CLP-intrinsic manner ( Figure 1C; Seillet et al . , 2014a ) . However , there was no detectable expression in CLPs of other transcription factors that are known to govern ILC development ( Figure 5A ) . These factors include Id2 ( Hoyler et al . , 2012; Male et al . , 2014; Seillet et al . , 2014b ) , Zbtb16 ( Constantinides et al . , 2014 ) , Eomes ( Male et al . , 2014; Seillet et al . , 2014b ) , Tcf7 ( encoding TCF-1 ) ( Yang et al . , 2013 ) , Rora ( Wong et al . , 2012 ) , Rorc ( Eberl and Littman , 2003; Sawa et al . , 2010 ) , Gata3 ( Hoyler et al . , 2012 ) and Tbx21 ( Gordon et al . , 2012; Rankin et al . , 2013 ) . In contrast , the high mobility group ( HMG ) transcriptional regulator Tox , which is known to regulate NK and ILC3 development ( Aliahmad et al . , 2010 ) , was expressed at a detectable level in wild-type CLPs and was down-regulated in Nfil3−/− CLPs ( Figure 5A , B ) . This suggested that NFIL3 might regulate Tox expression in CLPs . 10 . 7554/eLife . 04406 . 012Figure 5 . NFIL3-dependent ILC development is mediated by Tox . ( A and B ) Tox expression is lower in Nfil3−/− CLPs than in wild-type cells . ( A ) Heatmap comparing expression levels of transcription factors in wild-type and Nfil3−/− CLPs ( left ) . Factors included Nfil3 , Tox and other transcription factors that are known to be involved in ILC development . The absolute signal values and detection p values for each transcription factor in Illumina BeadArrays are also shown ( right ) . Note that only Nfil3 and Tox expression can be reliably detected in wild-type CLP cells . ( B ) Q-PCR analysis of Tox expression in wild-type and Nfil3−/− CLPs . ( C–E ) NFIL3 activates Tox expression by binding directly to the Tox promoter . ( C ) Tox expression was determined by Q-PCR following shRNA knockdown of NFIL3 ( left ) , and NFIL3 overexpression ( right ) in EL4 cells . ( D ) ChIP analysis of EL4 cells using an NFIL3-specific antibody or IgG control . Tox promoter ( nt −2105 to −1867 ) enrichment was calculated as the ratio of the NFIL3-specific antibody pull-down to the IgG control pull-down . The left panel shows results with endogenous NFIL3 levels and the right panel shows results with NFIL3 overexpression . ( E ) Luciferase reporter assay . A 2 . 8 kb fragment of the Tox promoter was cloned and fused with the firefly luciferase gene to generate a Tox-luciferase reporter . HEK293T cells were co-transfected with the reporter and an empty vector or an NFIL3-encoding vector . Luciferase activity was normalized to cells transfected with vector-only controls . ( F ) Restoring Tox expression in Nfil3−/− progenitors rescues ILC development in vivo . Nfil3−/− LSK cells ( CD45 . 2+ ) were retrovirally transduced with either an empty vector ( MSCV-IRES-hCD2 ) , a TOX-encoding vector ( MSCV-Tox-IRES-hCD2 ) , or an NFIL3-encoding vector ( MSCV-Nfil3-IRES-hCD2 ) and then transferred into lethally irradiated wild-type ( CD45 . 1+ ) mice . ILCs were examined 5–6 weeks later . The frequencies of total ILC2 and ILC3 within CD45 . 2+ hCD2+ cells are shown . Statistical comparisons between groups were performed with two-tailed student's t-test ( B–E ) , nonparametric one-way ANOVA test with posttests ( F ) . Means ± SEM are shown . ns , not significant; * , p < 0 . 05; ** , p < 0 . 01; *** , p < 0 . 001; **** , p < 0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 01210 . 7554/eLife . 04406 . 013Figure 5—figure supplement 1 . Knockdown of Nfil3 by shRNA . ( A ) shRNA constructs were screened for Nfil3 knockdown . HEK293T cells were co-transfected with an Nfil3-coding plasmid ( CDS only ) and shRNA constructs . NFIL3 protein levels were examined by Western blotting 36 hr later . A scrambled shRNA was used as a negative control and ACTIN was used as a loading control during Western blotting . ( B ) Nfil3 was knocked down by shRNAs , sh39 and sh40 , in EL4 cells . HEK293T cells were co-transfected with shRNA constructs , pVSVG and pCMVDR9 . Cell culture supernatants were harvested 48 hr later and lentiviral particles were concentrated by ultracentrifugation . EL4 cells were transduced with shRNA-encoding lentivirus by spinoculation in the presence of 4 μg/ml polybrene . Cells stably expressing shRNAs were selected with 8 μg/ml puromycin and live cells ( propidium iodide-negative ) were purified by FACS . Nfil3 mRNA levels were examined by Q-PCR with Gapdh as an internal control . Statistical analysis was performed with one-way ANOVA with post-tests . Means ± SEM are shown . **** , p < 0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 01310 . 7554/eLife . 04406 . 014Figure 5—figure supplement 2 . Experimental design and gating strategy for the Tox rescue experiment . ( A ) Schematic illustrating the experimental design . Nfil3−/− LSK ( CD45 . 2+ ) cells were retrovirally transduced with empty ( MSCV-IRES-hCD2 ) , TOX-encoding ( MSCV-Tox-IRES-hCD2 ) or NFIL3-encoding ( MSCV-Nfil3-IRES-hCD2 ) vectors . Cells were then transplanted into lethally irradiated wild-type mice ( CD45 . 1+ ) and ILCs were examined 4–6 weeks later . ( B ) Gating strategy for examining ILCs in the recipient mice . Live cells were first electronically gated as ZombieGreen-negative and cells transduced by retrovirus were identified as CD45 . 2+ hCD2+ . ILC2 , ILC3 , cNK and non-NK ILC1 were gated as Lineage ( CD3 , CD19 , CD5 , TCRβ , TCRγδ ) − CD127+ GATA3+ , Lin− CD127+ RORγt+ , Lin− NK1 . 1+ T-BET+ EOMES+ and Lin− NK1 . 1+ T-BET+ EOMES− , respectively . The results of the experiment are summarized in Figure 5F . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 014 CLPs are present in small numbers in adult mice ( Klose et al . , 2014 ) , making it challenging to perform biochemical studies of Tox regulation by NFIL3 using these cells . As an alternative , we found that NFIL3 regulates Tox expression in EL4 cells , a mouse lymphoma cell line ( Figure 5C ) . Knockdown of NFIL3 in EL4 cells with two independent shRNA constructs led to dose-dependent down-regulation of Tox expression ( Figure 5C; Figure 5—figure supplement 1 ) . Conversely , overexpression of NFIL3 in EL4 cells increased Tox expression ( Figure 5C ) , indicating that Tox expression is sensitive to NFIL3 levels in EL4 cells in a manner similar to CLPs . A chromatin immunoprecipitation ( ChIP ) assay with an NFIL3-specific antibody ( Yu et al . , 2013 ) demonstrated that NFIL3 directly bound to the Tox promoter ( nt −2105 to −1867 ) and that overexpression of NFIL3 enhanced this binding ( Figure 5D ) . Finally , NFIL3 activated Tox promoter activity as assessed by a luciferase reporter assay ( Figure 5E ) . Thus , NFIL3 activates Tox expression by directly binding to its promoter . EL4 cells are derived from T lymphocytes , a CLP-derived lineage , and thus we cannot exclude the possibility that the regulatory relationship between Nfil3 and Tox differs between T lymphocytes and CLPs . Nevertheless , our studies on CLPs and EL4 cells both support the idea that NFIL3 is an activator of Tox expression . Because Tox is known to be essential for cNK and ILC3 development ( Aliahmad et al . , 2010 ) , we postulated that lowered Tox expression leads to the broad ILC deficiency in Nfil3−/− mice and that restoring Tox expression would rescue ILC development . To test this idea , we cloned Tox coding sequences into a bicistronic vector ( MSCV-IRES-hCD2 ) , which allowed expression of the native form of TOX and also marked cells with the cell surface marker hCD2 . We then delivered the TOX-encoding plasmid or the empty vector into purified Nfil3−/− LSK cells ( CD45 . 2+ ) by retroviral transduction ( Zheng et al . , 2012; Spencer et al . , 2014 ) , followed by transfer of these cells into lethally irradiated wild-type mice ( CD45 . 1+ ) ( Figure 5—figure supplement 2 ) . Compared to the empty vector control , transduction of the TOX-encoding plasmid led to increased numbers of cNK cells in spleen , non-NK ILC1 cells in liver , and ILC2 and ILC3 in the small intestines of recipient mice ( Figure 5F ) . We observed that rescue of ILC development from Nfil3−/− LSK cells by Tox was largely comparable to rescue by Nfil3 in the same setting , supporting the idea that Tox acts downstream of Nfil3 in ILC development . Though ILC2 cells developing from Tox-rescued LSK cells were generally fewer than those from Nfil3-rescued LSK cells , the difference between the two groups was not statistically significant . Thus , ILC development is rescued by restoring Tox expression in Nfil3−/− progenitors , indicating that NFIL3 drives ILC development in part by regulating Tox expression . IL-22 is produced both by ILC3 and TH17 cells and is essential for protection against Citrobacter rodentium infection ( Satoh-Takayama et al . , 2008; Zheng et al . , 2008 ) . Nfil3−/− mice show elevated susceptibility to intestinal pathogens such as Citrobacter rodentium ( Geiger et al . , 2014 ) . However , Nfil3−/− mice retain TH17 cells , which are elevated relative to wild-type mice ( Yu et al . , 2013 ) . To rule out confounding effects of TH17 cells , we first crossed Nfil3−/− mice with Rag1−/− mice to create Nfil3−/−;Rag1−/− mice , which lack T and B cells in addition to ILCs . Nfil3−/−;Rag1−/− mice were more susceptible to oral C . rodentium infection than Rag1−/− mice as measured by weight loss ( Figure 6 ) . These data thus suggest that NFIL3-dependent ILC development is essential for host immune defense against a mucosal pathogen . 10 . 7554/eLife . 04406 . 015Figure 6 . Nfil3 deficiency results in increased susceptibility to C . rodentium infection in mice . Nfil3−/− mice were crossed with Rag1−/− mice to generate Nfil3−/−;Rag1−/− mice to eliminate the effects of adaptive immune cells , especially TH17 cells . Rag1−/− and Nfil3−/−;Rag1−/− mice were orally challenged with 5 × 109 CFU of C . rodentium and mouse weight loss was monitored . 4 Rag1−/− mice and 5 Nfil3−/−;Rag1−/− mice were analyzed . Comparisons were carried out with or two-way ANOVA with posttests . Means ± SEM are shown . * , p < 0 . 05 . DOI: http://dx . doi . org/10 . 7554/eLife . 04406 . 015
Innate lymphoid cells are essential players in the immune response to various infections and in maintenance of barrier function in mucosal tissues . ILCs arise in the bone marrow from the CLP , and thus share a common developmental origin with T- and B-cells . The pathways that govern ILC differentiation downstream of the CLP have recently begun to be unraveled . Here we present new insight into the fundamental role of the basic leucine zipper transcription factor NFIL3 in ILC development . We show that NFIL3 directs the development of bone marrow precursors , derived from the CLP , which give rise to all known ILC lineages including cNK cells . Additionally , we show that NFIL3 regulates the expression of the transcription factor TOX , and provide evidence that a NFIL3-TOX transcription factor cascade is central to the development of all ILC lineages . Several transcription factors are known to play essential roles in ILC development . For example , all ILC subsets express Id2 , an antagonist of E proteins that control B- and T- cell commitment ( Kee , 2009; Hoyler et al . , 2012 ) . Deletion of Id2 in mice abrogates the development of multiple ILC lineages ( Yokota et al . , 1999; Hoyler et al . , 2012 ) , although NK developmental defects arise only during NK maturation ( Boos et al . , 2007 ) . The HMG factor TOX is required for the development of cNK cells and ILC3 ( Aliahmad et al . , 2010 ) , and the transcription factor PLZF is required for the differentiation of non-NK cell ILC subsets ( Constantinides et al . , 2014 ) . Most recently , the basic leucine zipper transcription factor NFIL3 was found to be essential for the development of all known ILC lineages , including cNK cells ( Geiger et al . , 2014; Seillet et al . , 2014a ) . Lineage tracing studies with Id2 and Zbtb16 ( encoding PLZF ) reporter mice have identified two distinct progenitor populations that develop into multiple ILC lineages but not cNK cells ( Hoyler et al . , 2012; Constantinides et al . , 2014; Klose et al . , 2014 ) . The PLZF-dependent ILCP differentiates into non-NK ILC1 , ILC2 , and ILC3 , but not cNK cells ( Constantinides et al . , 2014 ) . Similarly , the common ‘helper-like’ innate lymphoid progenitor ( CHILP ) can differentiate into non-NK ILC1 , ILC2 and NK1 . 1+ NKp46+ ILC3 but not cNK cells ( Klose et al . , 2014 ) . This suggests that the ILCP and the CHILP may lie downstream of a common ILC progenitor that gives rise to all ILC lineages including cNK cells . We have provided evidence that NFIL3 is required for the differentiation of a multipotent ILC precursor population , the αLP , from the CLP . αLP gave rise to all ILC lineages including cNK cells . Although αLP lack B cell differentiation potential , they retain some residual T cell differentiation potential ( Possot et al . , 2011 ) . However , the CXCR6+ αLP subpopulation , which accounted for ∼4% of αLP cells in adult bone marrow , differentiated into all ILC lineages including cNK cells , but not B- or T-cells . This suggests that CXCR6+ αLP represent committed ILC precursors that give rise to all ILC lineages including cNK cells . Thus , these cells are likely to lie developmentally upstream of the described ILCP and CHILP populations . Our findings suggest that NFIL3 activation of TOX expression is a key mechanism by which NFIL3 directs ILC development . NFIL3 is required for Tox expression in the CLP , and directs Tox expression through direct binding to the Tox promoter . Since TOX has been shown to direct the development of multiple ILC lineages , including cNK cells and ILC3 ( Aliahmad et al . , 2010 ) , this suggests that activation of TOX expression is a key mechanism by which NFIL3 influences ILC development . This idea is supported by our finding that forced Tox expression in Nfil3−/− bone marrow progenitors rescues the ILC developmental defect and restores differentiation of cNK , non-NK ILC1 , ILC2 , and ILC3 . Together , these data support the idea that a NFIL3-TOX transcription factor cascade plays a fundamental role in the development of all ILC lineages . Recent studies have shown that forced expression of Eomes can rescue cNK cell development from Nfil3−/− hematopoietic progenitors ( Male et al . , 2014; Seillet et al . , 2014b ) . However , because Eomes is not expressed in CLPs ( Figure 5A; Figure 5—figure supplement 1 ) and Eomes deficiency only impacts cNK cells but no other ILCs ( Klose et al . , 2014 ) , Eomes is unlikely to mediate the NFIL3-dependent development of non-NK ILCs , and may lie developmentally downstream of NFIL3-TOX during NK cell development . Although NFIL3 is required for the development of the major ILC types and their precursors , some ILC subtypes appear to be NFIL3-independent . For example , certain NK cells , including salivary gland NK cells ( Cortez et al . , 2014 ) and tissue-resident NK cells ( Sojka et al . , 2014 ) , are not impacted by Nfil3 deficiency , in contrast to conventional NK cells . Extramedullary development of thymic NK cells is also independent of NFIL3 ( Crotta et al . , 2014 ) . In addition , during mouse cytomegalovirus infection , NK cells in Nfil3−/− mice expand to numbers similar to those in wild-type mice through an IL-15-dependent mechanism ( Firth et al . , 2013 ) . This suggests that the requirement for NFIL3 can be overridden by cytokine signaling during infection . Finally , despite the strict requirement for NFIL3 in bone marrow ILC precursor development ( αLP and CHILP ) , Nfil3−/− mice appear to have normal lymph nodes ( Spits and Di Santo , 2011; Seillet et al . , 2014b ) and only moderately impaired Peyer's patch development ( Figure 1—figure supplement 2 ) ( Geiger et al . , 2014 ) . One possibility is that fetal LTi cell function may be preserved in the absence of Nfil3 , which could account for the presence of lymph nodes in Nfil3−/− mice . Nfil3 is regulated by the circadian clock and thus its expression varies diurnally in multiple tissues and cells ( Duez et al . , 2008; Yu et al . , 2013 ) . We previously showed that Nfil3 expression varies diurnally in T cells and that NFIL3 synchronizes TH17 lineage specification to the day-night light cycle ( Yu et al . , 2013 ) . Synchronization is essential for TH17 cell homeostasis , as circadian disruption by chronic light cycle perturbation elevates intestinal TH17 cell frequencies and increases susceptibility to intestinal inflammation ( Yu et al . , 2013 ) . The finding that NFIL3 is required for the development of committed ILC precursors suggests that precursor differentiation may also be synchronized with diurnal light cycles through a similar mechanism . Future studies will examine whether Nfil3 expression is diurnally regulated in the CLP , whether precursor generation is synchronized to circadian light cycles in an NFIL3-dependent manner , and whether disruption of circadian light cycles leads to dysregulated ILC development . Altogether , our findings provide new insight into the defining role of NFIL3 in ILC development . Identification of a committed pan-ILC precursor should allow further insight into the developmental pathways that drive ILC cell fate decisions . Because of the general importance of ILCs in immune defense , NFIL3-dependent pathways may provide new targets for treatment of inflammatory and infectious diseases .
Nfil3−/− mice were obtained from Dr . Paul B . Rothman ( Johns Hopkins University ) ( Kashiwada et al . , 2010 ) , and were maintained by heterozygous breeding in the Specific Pathogen Free ( SPF ) mouse facility at the University of Texas Southwestern Medical Center at Dallas . Rag1−/− mice ( B6 . 129S7-Rag1tm1Mom/J ) , CD90 . 1+ mice ( B6 . PL-Thy1a/CyJ ) , CD45 . 1+ mice ( B6 . SJL-Ptprca Pepcb/BoyJ ) , Id2-eGFP reporter mice ( B6 . 129S ( Cg ) -Id2tm2 . 1Blh/ZhuJ ) , and RORγt-GFP reporter mice ( B6 . 129P2 ( Cg ) -Rorctm2Litt/J ) were purchased from the Jackson Laboratory , Bar Harbor , Maine . Nfil3−/− mice were intercrossed with Rag1−/− mice to create Nfil3−/−;Rag1−/− double knockout mice . Rag2−/−;Il2rg−/− mice ( B10;B6-Rag2tm1Fwa II2rgtm1Wjl ) were purchased from Taconic Farms , New York . All procedures described in this study were performed in accordance with protocols approved by the Institutional Animal Care and Use Committees ( IACUC ) of the UT Southwestern Medical Center . Lamina propria lymphocytes ( LPLs ) were isolated from the intestine as previously described ( Yu et al . , 2013 ) . Briefly , intestines were dissected from mice and Peyer's patches were removed . Intestines were cut into small pieces and thoroughly washed with ice-cold PBS . Epithelial cells were removed by incubating intestinal tissues in Hank's buffered salt solution ( HBSS ) supplemented with EDTA and DTT , followed by extensive washing with PBS . Residual tissues were digested by Collagenase IV ( Sigma , St . Louis , Missouri ) , DNase I ( Sigma ) and Dispase ( BD Biosciences , San Jose , California ) for 1 hr at 37°C . Cells were filtered through 100 μm cell strainers and applied onto a 40%:80% Percoll gradient ( GE Healthcare , Pittsburgh , Pennsylvania ) , in which lamina propria lymphocytes were found at the interface of 40% and 80% fractions . Livers were dissected from mice and cut into small pieces , followed by digestion with Collagenase IV ( Sigma ) , DNase I ( Sigma ) and Dispase ( BD Biosciences ) for 1 hr at 37°C . Residual tissues were forced through 100 μm cell strainers . Cells were spun down and applied onto a 40%:80% Percoll gradient as for the LPLs . Isolated lymphocytes were washed with PBS with 2 mM EDTA and 3% fetal bovine serum ( FBS ) and Fc receptors were blocked with α-CD16/32 ( 2 . 4G2 ) . Cells were then stained with antibodies against cell surface markers including α-CD3ε ( 500A2 ) , α-CD19 ( ebio1D3 ) , α-CD5 ( 53-7 . 3 ) , α-TCRβ ( H57-597 ) , α-TCRγδ ( GL3 ) , α-NK1 . 1 ( PK136 ) , α-Sca1 ( D7 ) , α-KLRG1 ( 2F1 ) , α-NKp46 ( 29A1 . 4 ) , α-CD45 ( 30-F11 ) , α-CD45 . 1 ( A20 ) , α-CD45 . 2 ( 104 ) , α-hCD2 ( RPA-2 . 10 ) , and α-CD127 ( A7R34 ) . Cells were fixed/permeabilized with eBiosciences ( San Diego , California ) Mouse Regulatory T Cell Staining Kit #3 per the manufacturer's instructions , and subjected to nuclear staining with α-RORγ ( AFKJS-9 ) , α-GATA3 ( TWAJ ) , α-T-BET ( 4B10 ) and α-EOMES ( Dan11mag ) . Cells were analyzed with an LSRII ( BD Biosciences , San Jose , California ) or CyAn ADP ( Beckman Coulter , Jersey City , New Jersey ) flow cytometer and data were processed with FlowJo software ( Tree Star , Ashland , Oregon ) . Femur and tibia were dissected from adult mice and bone marrow cells were released in PBS buffer containing 2 mM EDTA and 3% FBS with a mortar and pestle . Cells were filtered through 70 μm cell strainers and blocked with α-CD16/32 ( 2 . 4G2 ) , followed by incubation with biotinylated lineage markers ( Lin ) antibodies including α-CD3ε ( 145-2C11 ) , α-B220 ( RA3-6B2 ) , α-CD11b ( M1/70 ) , α-Gr1 ( RB6-8C5 ) , α-Erythroid Cells ( TER119 ) , α-CD5 ( 53-7 . 3 ) , α-TCRγδ ( GL3 ) , and α-NK1 . 1 ( PK136 ) . Cells were then washed and incubated with α-biotin magnetic microbeads ( Miltenyi Biotec , San Diego , California ) . Lineage-negative cells were enriched by an autoMACS sorter with the ‘Depletes’ setting . Surface staining was performed with antibodies including α-biotin ( Bio3-18E7 ) , α-CD45 ( 30-F11 ) , α-cKit ( 2B8 ) , α-CD127 ( A7R34 ) , α-Sca1 ( D7 ) , α-Flt3 ( A2F10 ) and α-α4β7 integrin ( DATK32 ) and α-CXCR6 ( 221002 ) . FACS sorting was performed with a FACSAria cell sorter ( BD Biosciences ) while flow cytometry analysis was carried out with an LSRII ( BD Biosciences ) . In both cases , LSK cells were identified as Lin− Sca1+ cKit+ , CLP as Lin− cKitlow CD127+ Sca1low Flt3+ α4β7− , αLP as Lin− cKitlow CD127+ Sca1low Flt3- α4β7+ , ILC2P as Lin− CD127+ α4β7+ CD25+ Sca1+ , and CHILPs as Lin− CD127+ α4β7+ CD25- Flt3- . Data were processed with FlowJo software ( Tree Star ) . αLPs were purified from CD45 . 1+ mice by FACS sorting as described above . In order to obtain a large number ( ∼1000 ) CXCR6+ αLP , femur and tibia from 20 CD45 . 1+ mice were pooled together for the cell isolation . Rag2−/−;Il2rg−/− recipient mice were sublethally irradiated with a dose of 4 . 2 Gy on the same day with an XRAD320 irradiator ( Precision X-ray , Inc , North Branford , Connecticut ) . Cells were transplanted into recipient mice by retro-orbital injection . ILCs in recipient mice were examined 4–6 weeks later . For CLP co–transfer experiments , wild-type and Nfil3−/− CLP cells were purified by FACS sorting and mixed at a 1:1 ratio before transplantation into sublethally irradiated Rag2−/−;Il2rg−/− recipient mice . For LSK co-transfer , CD90 . 1+ recipient mice were lethally irradiated with two doses of 5 Gy on the same day . FACS-purified LSK cells from wild-type and Nfil3−/− mice were mixed at a 1:1 ratio and transplanted into recipient mice by retro-orbital injection . αLPs were purified by FACS sorting as described above . For bulk culture , ∼25 cells were co-cultured on a monolayer of OP9 cells ( OP9-GFP ) or OP9 cells stably expressing the Notch ligand Delta-like 1 ( OP9-DL1 ) in αMEM media . To induce ILC2 , the culture medium was supplemented with 20 ng/ml Stem Cell Factor ( SCF , PeproTech , Rocky Hill , New Jersey ) , 20 ng/ml IL-7 ( BioLegend , San Diego , California ) and 20 ng/ml IL-2 ( BioLegend ) . To induce ILC3 , 20 ng/ml SCF , IL-7 and IL-23 ( BioLegend ) were added to the medium . The culture medium was replaced every 3–4 days and , after 14 days , cells were stained and analyzed by flow cytometry . For clonal differentiation , OP9-DL1 cells were irradiated at 1500 rad and seeded at a density of 10 , 000 cells per well in a 96-well plate . On the following day , CXCR6+ αLP cells were individually sorted into the wells and cultured in αMEM media supplemented with 20 ng/ml SCF and IL-7 . Cells were analyzed by flow cytometry 3 weeks later . In total , 240 cells from two independent experiments were analyzed . CLPs from wild-type and Nfil3−/− mice were purified as described above . Total RNA was isolated with the PicoPure RNA Isolation Kit ( Life Technologies , Grand Island , New York ) . RNA quality and quantity were determined with a Bioanalyzer ( Agilent Genomics ) with a Pico chip . Samples with RNA integrity numbers ( RIN ) larger than 8 were subjected to further processing and hybridized to the Mouse WG-6 V2 BeadChips ( Illumina , San Diego , California ) by the UT Southwestern Microarray Core Facility . Microarray images were processed and annotated with GenomeStudio ( Illumina ) . Differential gene expression analysis was performed using R together with bioConductor and the Limma package ( Smyth et al . , 2005; Ritchie et al . , 2011 ) . Briefly , signal intensities were first log2-transformed , followed by background correction and quantile normalization with the NEQC function . Empirical reliabilities of samples were estimated by the arrayWeights function , which gave each sample a weight score accordingly . Samples were then fitted into a weighted linear model by lmFit to detect differentially expressed genes . TOX-coding or NFIL3-coding sequences ( CDS ) were cloned by PCR from total mouse thymus cDNA into the bicistronic retroviral vector MSCV-IRES-hCD2 ( a gift from Dr Chandrashekhar Pasare at UT Southwestern ) to generate a TOX-encoding plasmid , MSCV-Tox-IRES-hCD2 and an NFIL3-encoding plasmid , MSCV-Nfil3-IRES-hCD2 . The MSCV-IRES-hCD2 , MSCV-Tox-IRES-hCD2 , and MSCV-Nfil3-IRES-hCD2 plasmids were transfected into the Plat-E packaging cell line ( Morita et al . , 2000 ) with FugeneHD ( Promega , Madison , Wisconsin ) to produce retroviral particles . Cell culture supernatant was harvested 48 and 72 hr post transfection . Cell debris was first cleared by spinning at 400×g for 10 min , followed by passage through 0 . 2 μm sterile filters . LSK cells were purified from Nfil3−/− mice by FACS sorting as described above and seeded into round-bottom 96-well plates at a density of 10 , 000 cells/well in STEMSPAN Serum-Free Expansion Medium ( SFEM ) ( Stemcell Technologies , Vancouver , Canada ) ( Zheng et al . , 2012 ) . During retroviral transduction , cells were mixed with an equal volume of cleared retrovirus-containing cell culture supernatant , supplemented with 2 U/ml Heparin ( Sigma ) , 10 ng/ml mouse Stem Cell Factor ( SCF , Peprotech ) , 20 ng/ml mouse Thrombopoietin ( TPO , Peprotech ) , 10 ng/ml mouse Fibroblast Growth Factor ( FGF-1 , Life Technologies ) and 4 μg/ml polybrene ( Sigma ) . Spinoculation was carried out at 1200×g for 90 min at 32°C to enhance retroviral transduction . 3 hr later , cell media was replaced with fresh STEMSPAN SFEM media supplemented with the above cytokines but without polybrene . Transduction was performed on two consecutive days using retroviral supernatant harvested 48 and 72 hr post transfection , respectively . On day 3 , CD45 . 1+ wild-type recipient mice were lethally irradiated at two doses of 5 Gy as described above . LSK cells were collected from the 96-well plate with Cell Dissociation Buffer ( Life Technologies ) and washed with sterile PBS . 2000–4000 cells were transferred into recipient mice in 200 μl sterile PBS by retro-orbital injection . ILCs in recipient mice were examined 5–6 weeks later . Five independent shRNA constructs ( sh38-sh42 ) targeting mouse NFIL3 and a control construct containing scramble sequences ( pLKO . 1-scramble ) were purchased from Sigma . To identify shRNA constructs that could effectively knock down NFIL3 , 1 μg of shRNA plasmid and 1 μg of NFIL3-encoding plasmid ( Yu et al . , 2013 ) were co-transfected into HEK293T cells in a 6-well plate with FugeneHD ( Promega ) . Cells were harvested 36 hr later , lysed and used for western blotting with anti-NFIL3 antibody . Two shRNA constructs that could effectively knock down NFIL3 were identified: sh39 and sh40 . These constructs as well as the pLKO . 1-scramble vector were each co-transfected with the packaging plasmids pCMVDR9 and pVSVG into HEK293T cells . Cell culture supernatants were harvested 48 and 72 hr later and cleared by spinning and filtering as described above . Lentiviral particles were concentrated by ultracentrifugation at 75 , 000×g for 2 hr and resuspended in RPMI media . EL4 cells were mixed with lentiviral particles in the presence of 4 μg/ml polybrene , and spinoculated at 1200×g for 90 min at 32°C . 2 days later , EL4 were selected with 8 μg/ml puromycin for 2 weeks . Live cells were sorted with a FACSAria cell sorter as they excluded propidium iodide . NFIL3 coding sequences were subcloned into MSCV-IRES-hCD2 to generate MSCV-Nfil3-IRES-hCD2 . MSCV-IRES-hCD2 and MSCV-Nfil3-IRES-hCD2 were then transfected into Plat-E cells to produce retroviral particles as described above , which were then used to transduce EL4 cells . 3 days after transduction , EL4 cells were stained with anti-hCD2 and hCD2+ EL4 cells were purified with a FACSAria cell sorter . Sorted cells were maintained in RPMI media for another 3–4 days , followed by staining and sorting again . The resulting cells were expanded and Nfil3 expression was examined by SYBR green-based real-time PCR . ChIP experiments were carried out as previously described ( Yu et al . , 2013 ) . Briefly , EL4 cells or NFIL3-overexpressing EL4 cells were cultured in RPMI medium at ∼0 . 8 × 106 cells/ml . Cells were harvested and fixed with 1% formaldehyde for 10 min in the dark , which was quenched by adding glycine to a final concentration of 0 . 15 M . Nuclei were released with a Dounce homogenizer ( Wheaton , Millville , New Jersey ) in Nuclear Isolation Solution containing 10 mM Tris pH 7 . 4 , 5 mM MgCl2 , 25 mM KCl and 250 mM sucrose , and purified by spinning at 1000×g for 10 min over Hypertonic Solution containing 10 mM Tris pH 7 . 4 , 5 mM MgCl2 , 25 mM KCl and 30% ( wt/vol ) sucrose . Purified nuclei were used for ChIP with the Magna ChIP assay kit ( Millipore , Billerica , Massachusetts ) per the manufacturer's instructions . The Tox promoter was detected by SYBR green-based real-time PCR with specific primers: Tox-ChIPF6: 5′-GACACTGACAGCAAGGACCA-3′ and Tox-ChIPR6: 5′-CAGGGCTTCATAGCACCGAT-3′ , targeting nucleotide −2105 to nucleotide −1867 in the Tox promoter . Enrichment of the Tox promoter was determined by normalizing the level of the Tox promoter in DNA pulled down with an anti-NFIL3 antibody to that pulled down with an IgG control . A 2 . 3 kb fragment ( −2133 to 232 ) of the Tox promoter was cloned into the pGL3-Basic vector to drive firefly luciferase expression ( the Tox-luciferase reporter ) . HEK293T cells were cultured in a 96-well plate overnight and were co-transfected with the Tox-luciferase reporter and an empty or NFIL3-encoding vector . A pCMV-Renilla-Luciferase reporter was co-transfected into HEK293T cells to serve as an internal control . Luciferase activities were detected using the Dual-Glo Luciferase Assay kit ( Promega ) and measured with a SpectraMax M5e plate reader ( Molecular Devices , Sunnyvale , California ) . Firefly luciferase activities in each sample were first normalized against Renilla luciferase activities in the same sample and then normalized against that in cells transfected with the empty vector . The C . rodentium ( DBS100 ) strain was originally obtained from ATCC ( Manassas , Virginia ) . To infect mice , C . rodentium ( DBS100 ) was first inoculated into Luria-Bertani ( LB ) broth overnight at 37°C with shaking in the presence of 50 μg/ml nalidixic acid , and was subcultured into fresh LB media the next morning until OD600 = ∼0 . 8–1 . 0 . Bacteria were then harvested by centrifugation and resuspended in sterile PBS . Rag1−/− and Nfil3−/−;Rag1−/− mice were deprived of food the night before infection and were orally gavaged with 5 × 109 CFU in 200 μl sterile PBS . The number of viable C . rodentium ( DBS100 ) in the inoculum was confirmed by retrospective plating on nalidixic acid-containing LB-agar plates . Mouse disease conditions were monitored by weight loss . | The mucus-covered tissues that line the nose , mouth , and the digestive tract play an important role in protecting the body from infection . These mucosal tissues are the first line of defense against any pathogens we inhale or ingest , and help to keep communities of helpful bacteria—such as those that aid digestion—in place so that they can perform their beneficial functions without causing disease . A special group of immune cells called innate lymphoid cells helps to prevent infection in the mucosal tissues and to repair damage to these tissues . There are several different types of innate lymphoid cells , with each type performing a different function . All innate lymphoid cells originate from precursor cells in the bone marrow . Some of these precursor cells had been identified previously , but were able to develop into only some of the different innate lymphoid cell types . Scientists suspected that a precursor cell existed that could develop into all types of innate lymphoid cell , but the identity of this cell had remained elusive . Yu , Wang et al . now identify a precursor cell in the bone marrow that can produce all of the currently known different types of innate lymphoid cells . A protein called NFIL3 coaxes stem cells in the bone marrow into becoming these precursor cells , which only develop into innate lymphoid cells , and not into other immune cell types such as B cells and T cells . Yu , Wang et al . find that NFIL3 causes some of these previously identified precursor cells to become dedicated producers of innate lymphoid cells by regulating another protein called TOX . Furthermore , gene therapy using NFIL3- or TOX-encoding DNA can help to restore normal numbers of innate lymphoid cells in mice whose bone marrow progenitor cells lack the NFIL3 gene . These new details about how bone marrow stem cells develop into innate lymphoid cells may help scientists looking for new ways to treat infections or diseases that hamper the innate immune system . | [
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] | 2014 | The basic leucine zipper transcription factor NFIL3 directs the development of a common innate lymphoid cell precursor |
Genomic analyses of microbial populations in their natural environment remain limited by the difficulty to assemble full genomes of individual species . Consequently , the chromosome organization of microorganisms has been investigated in a few model species , but the extent to which the features described can be generalized to other taxa remains unknown . Using controlled mixes of bacterial and yeast species , we developed meta3C , a metagenomic chromosome conformation capture approach that allows characterizing individual genomes and their average organization within a mix of organisms . Not only can meta3C be applied to species already sequenced , but a single meta3C library can be used for assembling , scaffolding and characterizing the tridimensional organization of unknown genomes . By applying meta3C to a semi-complex environmental sample , we confirmed its promising potential . Overall , this first meta3C study highlights the remarkable diversity of microorganisms chromosome organization , while providing an elegant and integrated approach to metagenomic analysis .
Microbial species have for a long time been studied individually , leading to the development of applications in fields as diverse as agronomy , environment , or medicine . Sequencing and analyzing the genetic material of microbial communities directly collected from various natural environments such as skin , gut , soil , and water have dramatically improved our knowledge and understanding of their diversity and interrelations ( Handelsman et al . , 1998; Guermazi et al . , 2008; The Human Microbiome Jumpstart Reference Strains Consortium , 2010; Mackelprang et al . , 2011; Lundberg et al . , 2012; Le Chatelier et al . , 2013 ) . A number of techniques have been developed to improve the resolution and accuracy of individual genome assembly in mixed populations , for instance by separating the species prior to sequencing ( Fitzsimons et al . , 2013 ) or by refining the clustering and scaffolding procedures used to process sequence data ( Albertsen et al . , 2013 ) . However , these approaches remain generally limited by the use of complex technologies and/or by the need to construct multiple libraries . In parallel , an interesting development in the field of genome assembly has recently arisen from the realization that the physical properties of chromosomes contain information regarding their linear structure . The frequency with which two chromosomal segments come in contact , as measured in genome-wide chromosome conformation capture ( 3C ) experiments ( Dekker et al . , 2002; Lieberman-Aiden et al . , 2009 ) , obeys to some extent the laws of polymer physics ( Rippe , 2001; Wong et al . , 2012 ) . Contact frequencies can be used to identify synteny between DNA segments up to a few 100s kilobases ( kb ) apart , and two studies recently applied this concept to refining the human genome sequence ( Burton et al . , 2013; Kaplan and Dekker , 2013 ) . Such approach therefore shows great promise as a general method for improving genome assemblies . Using chromosomal contacts data in order to generate the precise scaffold of a given genome is however a difficult task: hence , we developed GRAAL , a robust and explicit statistical method to tackle this issue at the highest resolution possible ( Marie-Nelly et al . , 2014a; Figure 1A ) . Since this approach enables the identification of individual chromosomes within the same nuclei , we hypothesized that tridimensional ( 3D ) organization holds enough specific information to distinguish as well DNA segments of chromosomes present within different organisms ( Figure 1A ) . In other words , if the interactions between chromosomes contained within a human or a fungal nucleus can be resolved , then it may be possible to resolve the chromosomes of various species mixed together . In the present work , we show that this is indeed the case: a single meta3C experiment , performed on a mix of species , allows the de novo assembly and scaffolding of the various genomes present in the mixture without prior knowledge of their genome sequences . We also show that the method allows deciphering the average 3D organization of these genomes in space , unveiling a remarkable diversity of chromosome organization in microorganisms . Therefore , meta3C paves the way to the integrated characterization and analysis of metagenomes in complex populations . 10 . 7554/eLife . 03318 . 003Figure 1 . meta3C experiment on a controlled mix of bacterial species . ( A ) Schematic representation of the principle of a meta3C experiment . For a single species , one can ( i ) generate a genome-wide contact map but also ( ii ) use the genomic contact data to reorder the contact matrix of a poorly assembled genome ( top ) in order to scaffold its contigs into a more likely structure ( bottom left ) . For a mixture of species , one can similarly generate a contact map directly from the mix ( iii ) and then use it to characterize the genomes of the species contained in the mixture ( iv ) . ( B ) Chromosomal contact map of a mixture of three bacteria . The darker the shade in the matrix , the higher the contact frequency ( color scale in log ) . Blue and green arrowheads: origins of replication of the chromosomes . Red and orange arrowheads: termination of replication . Each chromosome arm is represented with a color code . The percentages of interactions that occurred between different species are indicated within the matrix . We detected frequent interactions between a F plasmid sequence ( black arrowhead ) and a large duplicated region of the E . coli chromosome ( dotted line ) . ( C ) 3D reconstruction of the entire contact matrix . Each replichore of the four chromosomes is represented with a different color , with the colored arrowheads indicating Ori positions . Black arrowhead: F’ plasmid and E . coli chromosome when contacts with the duplication are taken into account; see Figure 1—figure supplement 2 for more details . ( D ) Two different views of the 3D reconstruction of each of the three bacteria taken individually . Ter and Ori are indicated with colored arrowheads ( see above ) . E . coli and B . subtilis domains are also shown . The F’ plasmid is positioned according to its 3D contacts with the E . coli genome , without taking into account the duplication ( dotted line within the E . coli genome ) . ( E ) Networks of interactions between the different contigs for the mix of three bacteria represented with a force-directed graph-drawing algorithm . Each node represents a contig from the de novo assembly . Each link in gray represents at least one 3C contact . Each color corresponds to one community detected by the Louvain algorithm . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 00310 . 7554/eLife . 03318 . 004Figure 1—figure supplement 1 . Numbers of intra-specific and inter-specific ( chimeric ) pairs of reads from the meta3C experiment performed on the bacterial mix . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 00410 . 7554/eLife . 03318 . 005Figure 1—figure supplement 2 . Analysis of the organization of the F’ plasmid in the E . coli strain used in this study . ( A ) Frequencies of interaction between the F’ plasmid and the three bacterial genomes . ( B ) Contact frequencies between the F’ plasmid and the E . coli genome , normalized by the read coverage to take into account the replication . The dotted region encompassing a duplication with frequent contacts with the F’ plasmid corresponds to the region of interest analyzed in C . ( C ) Correlation analysis of the contact frequency between the F’ plasmid ( in pink ) and the duplicated region ( in blue ) along the E . coli genome ( in green ) . A strong shift in the correlation score occurred for a region indicated with the red dotted lines , indicating that the plasmid F’ is in frequent contact with this region but not beyond it ( and therefore must be carrying one copy of the duplicated region ) . This analysis allowed to position duplication breakpoints at coordinates ∼240 , 000:380 , 000 along the chromosome . ( D ) PFGE analysis of the E . coli genome and corresponding Southern blots hybridized with probes from the genome ( duplicated and non-duplicated region ) and from the F’ plasmid ( see Table 2 for the coordinates of the probe ) . Orange triangle: migration front of the degraded DNAs of both E . coli genome and plasmid . ( E ) 3D reconstruction of genome-wide contact maps of the E . coli genome when ( i ) the duplicated region is considered as a single copy region of E . coli genome , ( ii ) the duplicated region is removed from the analysis , but not the plasmid F’ , and ( iii ) a region of a size similar to the duplicated region is removed from the analysis and replaced with a small DNA segment of the same size as the F’ plasmid . ( F ) Schematic representation of the contacts between the F plasmid sequence ( pink segment ) and duplicated segments ( blue ) of the E . coli genome ( green ) that accounts for all the observations in panels A–E . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 00510 . 7554/eLife . 03318 . 006Figure 1—figure supplement 3 . Pearson correlation matrix of the meta3C bacterial experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 006
We first processed a controlled mix of three bacterial species—Escherichia coli ( Gram− ) , Vibrio cholerae ( Gram− ) , and Bacillus subtilis ( Gram+ ) —into a metagenomic 3C ( meta3C ) library and sequenced it on an Illumina platform ( Hiseq2000 − Paired End − 2 × 104 bp ) . The reads were aligned on the three reference genome sequences to generate a chromosome contact map of the whole population ( Figure 1B ) . To construct this map , each genome was divided into bins of 30 kb , and the contact frequencies were normalized so that their sum equaled one for each bin ( see ‘Materials and methods’; Cournac et al . , 2012 ) . As in a typical 3C experiment squares appeared on the diagonal of the matrix , revealing individual chromosomes: the circular chromosomes of both E . coli and B . subtilis were recovered as separate entities , whereas the two chromosomes of V . cholerae yielded two squares exhibiting higher contact frequencies with each other than with the chromosomes of the other species—as expected since these chromosomes share the same cellular compartment ( Figure 1B ) . The background generated by cross-species interactions was low ( 0 . 37% of the total interactions ) , indicating that few chimeric pairs of reads ( in which one read came from the genome of one species and the other read from the genome of another species ) had been generated during the construction of the library ( Figure 1—figure supplement 1 ) . The meta3C contact matrix was subsequently converted into a 3D structure ( ‘Materials and methods’ , Figure 1C , Animation 1 ) . In this representation , bins are shown as beads and the distance between each pair of beads is optimized in proportion to the inverse of their measured contact frequency ( Lesne et al . , 2014 ) . Using this approach , we recovered three populations of bins that corresponded to the three bacterial genomes , with the two chromosomes of V . cholerae being visualized in close vicinity to each other . The continuity and circularity of each genome was clearly apparent in the reconstructed structures . 10 . 7554/eLife . 03318 . 007Animation 1 . 3D reconstruction of the bacterial meta3C contact matrix . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 007 A sequence corresponding to a F plasmid ( the fertility factor of E . coli ) was characterized in the reads and appeared to interact with the genome of the E . coli HB101 strain used in the analysis ( Figure 1B , black arrow ) . The 3D data provided us with the opportunity to carefully investigate the structure and spatial positioning of this plasmid with respect to the E . coli chromosome . We first noticed that a large ( ∼140 kb ) region of the E . coli genome exhibited a two fold increase of the read coverage , indicating a segmental duplication . This region was also enriched in contacts with the F plasmid sequence ( Figure 1—figure supplement 2A , B ) , prompting us to hypothesize that one of the two copy was actually carried by a F’ plasmid ( i . e . , a F plasmid carrying bacterial sequences ) . A correlation analysis of the contacts between these two regions of interest ( a chromosome region encompassing the duplication and the plasmid ) revealed clearly that , on the one hand , a copy of the duplication is in close contact with the plasmid ( with an IS2 at one the boundaries [coord . 390 , 063] and an IS3 within ) , and that on the other hand the plasmid contacts with the E . coli genome drop sharply past the duplication boundaries , in agreement with an integration of one copy of the duplication within a F’ plasmid ( Figure 1—figure supplement 2C ) . This hypothesis was confirmed experimentally by a Southern blot of a pulsed-field gel ( Figure 1—figure supplement 2D ) . Having deciphered the linear structure of the F’ plasmid within this strain , we investigated its 3D organization with respect to the E . coli genome ( Figure 1—figure supplement 2E ) . The frequent contacts each copy of the duplication makes with both the chromosome and the F’ plasmid resulted in an artifactual co-localization of the plasmid with the chromosome , since the contacts within each of the copies cannot be discriminated and are all positioned along the genome ( see Figure 1—figure supplement 2E , i and Animation 2 ) . To alleviate this artifact , we generated a contact map were all the contacts involving the duplicated region were removed ( Figure 1—figure supplement 2E , ii and Animation 3 ) . In the corresponding 3D structure , the bin representing the plasmid appeared now well isolated from the chromosome ( Figure 1D ) . As a supplementary control , we verified that removing from the genome a region of a size similar to the duplication did not impair the 3D positioning of a DNA segment of a size similar to the F’ plasmid , positioned within this deleted region ( blue dot; Figure 1—figure supplement 2E , iii ) . Interestingly , besides this duplication , two regions along the E . coli chromosome appeared enriched in contacts with the F’ plasmid: the replication origin ( Ori ) and , to a lower extent , the termination ( Ter ) regions ( Figure 1—figure supplement 2B ) . This indicates that the F’ plasmid is positioned preferentially in the vicinity of these regions in fast growing cells , as expected given the preferred location of the F plasmid in mid , 1/4 , and 3/4 positions in the cell , which are also preferred positions for Ori/Ter regions during the cell cycle ( Gordon et al . , 1997; Niki and Hiraga , 1997 ) . These results illustrate the high specificity of the meta3C approach , which allows the identification of genomic regions belonging to each species , and the power of using DNA physical contacts to decipher complex structures in individual genomes . 10 . 7554/eLife . 03318 . 008Animation 2 . 3D reconstruction of the E . coli genome with a plasmid F’ carrying a 140 kb segmental duplication . The contacts made by the duplicated region are taken into account . Because of the frequent contacts of one copy of this region with the chromosome and the other copy with the plasmid and of the impossibility to distinguish those two copies , the plasmid is now artificially dragged into the vicinity of the E . coli chromosome . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 00810 . 7554/eLife . 03318 . 009Animation 3 . 3D reconstruction of the E . coli genome with a plasmid F’ . The contacts made by the 140 kb duplication are not taken into account , so the position of the plasmid depends only on its contacts with the rest of the genome . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 009 The remaining two genomes were also considered individually and their average 3D reconstructions generated , unveiling a remarkable diversity of global chromosomal organization in these three bacterial species during exponential growth ( Figure 1D , Animation 4 , and Animation 5 ) . Interestingly , this diversity appeared rooted in shared principles . One structural feature shared by these bacterial genomes was the global symmetry of the replichores , that is , the two chromosomal arms between the Ori and Ter . In B . subtilis and V . cholerae this symmetry was made clearly visible , in the growth conditions used in this experiment , by the presence of a counter-diagonal in the map ( running opposite to the continuous and strong signal that accounts for the contact between adjacent DNA segments; see also the correlation version of the contact map on Figure 1—figure supplement 3 ) . This global symmetry as well as this second opposite diagonal , reflecting a longitudinal organization of the two replichores extending along parallel axes within the cell , were also reported for Caulobacter crescentus ( Umbarger et al . , 2011; Le et al . , 2013 ) . The two chromosomes of V . cholera were found in two different conformations , with chromosome 1 exhibiting a more ‘open’ configuration and the replichores of chromosome 2 appearing paired ( Figure 1D ) . The two arms of the E . coli chromosome exhibited an open 3D structure , with no visible diagonal in the matrix . 10 . 7554/eLife . 03318 . 010Animation 4 . 3D reconstruction of the B . subtilis genome . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 01010 . 7554/eLife . 03318 . 011Animation 5 . 3D reconstruction of the V . cholerae genome . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 011 Another 3D feature shared by these bacteria consisted in the presence of at least one well-defined domain centered either on the Ori or Ter region . The 3D reconstruction of the B . subtilis genome revealed a relatively compact domain at the Ori region that overlapped with the region of the genome containing the centromere-like parS sites , in agreement with the structural role described for the ParS/Spo0J/Smc complex in the literature ( Gruber and Errington , 2009; Sullivan et al . , 2009 ) . By contrast , the E . coli chromosome presented a strong Ter domain that was clearly identifiable in the contact map and exhibited fewer contacts with the rest of the genome , reflecting most likely the structuring role of the MatP/matS system ( Mercier et al . , 2008 ) . For V . cholerae , the Ter regions of chromosomes 1 and 2 were in closer proximity than the two Ori regions , reflecting the controlled segregation mechanisms of these two unequal-sized chromosomes ( Val et al . , 2008 ) . Interchromosomal contacts did not span the entire length of chromosome 1 but started between the Ori macrodomain of chromosome 2 and positions located at about one-third of chromosome 1 arms ( black dotted squares , Figure 1D ) . In order to visualize the interplay between replication and genomic organization , we applied a color code to represent the read coverage of the genome ( reflecting the average progression of the replication fork and thus the relative timing of replication ) atop the genome structure of V . cholerae ( Animation 6 ) . Whereas the two Ter regions appeared of the same color , consistent with the fact that both chromosomes I and II achieve replication synchronously ( Rasmussen et al . , 2007 ) , the Ori regions presented , as expected , different coverages: chromosome I initiates replication first whereas chromosome II starts only to replicate later . This analysis provides a glimpse on the spatial and temporal articulation of the replication program of V . cholerae , which would be an interesting example of organization-dependent function . Overall , the chromosome organizations of the three bacteria analyzed ( under the exponential , rich-medium growth conditions used in the experiment ) appeared remarkably different but shared similar principles , such as the presence of well-defined domains . In order to see if those shared features are conserved across bacteria species , it will be interesting to use a similar high-resolution 3C approach to investigate the organization of bacterial linear chromosomes . 10 . 7554/eLife . 03318 . 012Animation 6 . 3D reconstruction of the V . cholerae genome . The color scale now represents the read coverage along the genome , hence displaying the differential replication timing of the two chromosomes , from early ( over-covered ) replicated regions ( in blue ) to terminal regions ( in red ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 012 The low amount of ‘background interaction’ , that is , chimeric religation events between restriction fragments belonging to different species , in the experiment incited us to test whether the genomes of these three bacterial species could be directly assembled de novo from the meta3C data by taking advantage of the presence of ∼80% ‘regular’ paired-end reads in the library . This relatively high percentage results from the fact that , unlike the Hi-C protocol ( Lieberman-Aiden et al . , 2009 ) , meta3C does not involve an enrichment step for religated fragments ( ‘Materials and methods’ ) . Using the assembly program IDBA-UD ( Peng et al . , 2012 ) , a set of 2 , 436 contigs was generated from the 3C read pairs ( N50 = 55 kb , total length 12 . 5 Mb ) . The quality of these contigs was assessed by comparing them with published reference genomes , which showed that the assembled contigs covered respectively 96% , 98% , and 93% of the V . cholerae , E . coli , and B . subtilis reference genomes and that only 0 . 7% of the contigs ( 52 , 373 bp total ) were chimeric . The meta3C reads were then realigned on the de novo contigs and the contact information was used to pool the contigs into communities sharing similar contact behavior ( hence likely belonging to the same genome ) using the Louvain algorithm ( Blondel et al . , 2008 ) . Three communities of contigs were generated , each corresponding to a different species and covering respectively 96 , 98 , and 92% of the genomes of V . cholerae , E . coli , and B . subtilis , respectively ( Figure 1E ) . This shows that meta3C does not only allow convenient high-throughput analysis of the 3D organization of a mix of bacterial species but also provides an efficient way to assemble de novo the genomes of these species . To see if this approach could be applied to a more complex mix of eukaryotic species , we pooled 11 yeast species and performed a meta3C experiment directly on this mixture ( Figure 2A ) . The meta3C contact matrix of the 11 reference genomes put side by side presented discrete squares on the diagonal , each corresponding to a species from the mix ( Figure 2A; see also Figure 2—figure supplement 2 ) . The 3D representation of this contact map revealed , again , a very low level of background interactions in the experiment ( Figure 2B , Animation 7; see also Figure 2—figure supplement 1 ) . In each of the squares , the co-localization of centromeres resulting from the Rabl configuration was clearly visible ( Figure 2C , blue arrowheads; see also Figure 2—figure supplement 4; Duan et al . , 2010; Marie-Nelly et al . , 2014b ) . In contrast to the diversity of structures observed for the bacterial species analyzed above , the Rabl configuration appeared as the primary driver of yeast genome organization , as illustrated by the individual 3D structures of the genomes of Yarrowia lipolytica and Naumovozyma castellii ( Figure 2C , Animation 8 , 9 ) . To test the potential of using meta3C reads for assembling de novo such a complex mix of yeast genomes , we assembled them using IDBA-UD ( N50 = 6 , 914 bp , total length 138 Mb ) . The breadth of coverage of the 11 genomes by the resulting contigs ranged from 89 . 8% ( Candida albicans ) to 98 . 3% ( N . castellii ) , with chimeric contigs ( misassemblies ) representing ∼20% of the total ( 37 Mb ) . This high percentage of chimera contrasted with the very low level of misassemblies observed previously for the mix of three bacterial genomes . To monitor the influence of chimeric pairs of reads ( originating from intergenomic 3D contacts ) on the generation of chimeric contigs , we performed an assembly on the same library using the Velvet software that considers all reads as independents ( i . e . , without pairing them; Zerbino and Birney , 2008 ) . This assembly exhibited a dramatic increase ( 73% , 69 Mb ) in chimeric contigs , demonstrating that these misassemblies were not caused by intergenomic 3C paired-end reads but rather by the frequent occurrence of identical or near-identical genome regions ( such as transposable elements ) in those eukaryotic genomes . The presence of chimeric contigs in the IDBA_UD assembly did not impede the clustering of the most contigs based on their contact frequencies as determined using the Louvain algorithm . The clustering procedure resulted in 13 sub-groups: one for each of the 11 yeast species , one corresponding to an E . coli contaminant , and one comprising various misassembled fragments of mitochondrial genomes . In total , only 1% of the assembly ( 2% of the contigs ) could not be attributed to a given species ( Figure 2D ) . A de novo assembly followed by scaffolding using our dedicated program GRAAL ( Marie-Nelly et al . , 2014a ) was then applied to the pool of contigs identified as belonging to the genome of N . castellii , for which a high-quality reference sequence was available ( Figure 2E; Gordon et al . , 2011 ) . Since this algorithm involves a splitting procedure of the contigs into restriction fragments , chimeric contigs were broken into non-chimeric parts thereby correcting the assembly errors mentioned above . After processing , 11 superscaffolds were recovered , with the reordered contact map presenting the typical co-localized centromeres regions expected from the Rabl configuration ( Figure 2E; Animation 10 ) . Overall these 11 scaffolds covered 94 . 5% of the reference sequence of the 10 chromosomes ( chromosome 3 was split into two scaffolds because of the presence of the large , unassembled rDNA array on this chromosome ) , illustrating the ability of this de novo analysis to correctly assemble and scaffold unknown genomes ( see Figure 2—figure supplement 3 ) . The same approach was applied to other species in the mixture , such as Saccharomyces bayanus ( Cliften et al . , 2003; Scannell et al . , 2011; Figure 2—figure supplement 4 ) . Our de novo assembly of the genome of S . bayanus was more fragmented than the one of N . castellii , probably as a result of the lower sequencing depth for this species in the mixture ( Figure 2—figure supplement 4C; Animation 11 ) . Interestingly , not only did de novo assembly and scaffolding from meta3C reads allow genome assembly from a mixed population , but the 3D organization of the assembled genomes was also apparent in the resulting contact maps . For instance , interchromosomal signals corresponding to centromere clustering were clearly visible in the matrix , emphasizing that 3D signals could also be used to annotate the centromeres of unknown species isolated from meta3C data ( Figure 2F; Figure 2—figure supplement 4B; Marie-Nelly et al . , 2014b ) . 10 . 7554/eLife . 03318 . 013Figure 2 . meta3C experiment on a controlled mix of yeasts species . ( A ) meta3C contact map of the mix of eleven species . ( B ) 2D projection of the 3D reconstruction of the entire meta3C contact matrix . Each genome occupies an isolated position in space ( the 2D projection induces a visual overlap for some species ) . The color code is the same as for the schematic yeasts on the left panel in ( A ) . ( C ) Close-up on the contact maps of three species , with the 3D representation of the matrix in vis-à-vis . ( D ) Quantification of the assembly performed using meta3C reads . The first number indicates the amount of total DNA in the community . The two numbers that follow indicates the proportion of the contigs of each community in regards to the total assembly ( % of total kb , % of total number of contigs ) . ( E ) Top: contact map of the contigs present within the community containing mostly sequences from N . castellii . The bottom contact map corresponds to the maps recovered following the GRAAL scaffolding . 11 large scaffolds were retrieved , in near-perfect agreement with the known number of chromosomes of this species . Blue triangles: inter-scaffold signal corresponding most likely to the 10 centromeric interactions ( Marie-Nelly et al . , 2014b ) . ( F ) Corresponding 3D structure of the N . castellii de novo meta3C assembly combined with GRAAL processing . The collinearity between two of the resulting superscaffolds and their corresponding reference chromosome sequences is represented in the right panels . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 01310 . 7554/eLife . 03318 . 014Figure 2—figure supplement 1 . Number of intra-specific and inter-specific ( chimeric ) pairs of reads in the meta3C contact map . Mitochondrial genomes behaved as separate entities . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 01410 . 7554/eLife . 03318 . 015Figure 2—figure supplement 2 . Contact matrices of the genomes of Y . lipolytica , K . thermotolerans , N . castellii , C . lusitaniae , S . bayanus , C . glabrata , C . albicans , K . lactis , L . kluyveri , and D . hansenii . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 01510 . 7554/eLife . 03318 . 016Figure 2—figure supplement 3 . Scaffolding using GRAAL of N . castellii meta3C contigs . ( A ) Comparison between the scaffolds generated by GRAAL and the reference sequences of the N . castellii chromosomes: y-axis: coordinates along the new scaffolds . x-axis: coordinates along the reference chromosome . The position of the rDNA cluster is indicated . ( B ) The eleven largest scaffolds are aligned against the concatenated reference genome , covering 94 . 5% of the total sequence . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 01610 . 7554/eLife . 03318 . 017Figure 2—figure supplement 4 . Scaffolding using GRAAL of S . bayanus meta3C contigs . ( A ) Contact map obtained from a draft genome assembly of S . bayanus . ( B ) Contact map and corresponding 3D structure of the S . bayanus de novo meta3C assembly combined with GRAAL processing . Blue triangles: inter-scaffold signal corresponding most likely to the 10 centromeric interactions . ( C ) Comparison between the scaffolds generated by GRAAL and the reference sequences of S . bayanus chromosomes . The main scaffold is indicated in bold , whereas the other ones correspond to other scaffolds and are shown here for information only . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 01710 . 7554/eLife . 03318 . 018Animation 7 . 3D reconstruction of the 11 yeasts meta3C contact matrix . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 01810 . 7554/eLife . 03318 . 019Animation 8 . 3D reconstruction of the Y . lipolytica genome . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 01910 . 7554/eLife . 03318 . 020Animation 9 . 3D reconstruction of the N . castellii genome . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 02010 . 7554/eLife . 03318 . 021Animation 10 . 3D reconstruction of the N . castellii genome after meta3C de novo assembly and GRAAL scaffolding . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 02110 . 7554/eLife . 03318 . 022Animation 11 . 3D reconstruction of the S . bayanus genome after meta3C de novo assembly and GRAAL scaffolding . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 022 As an even more challenging test of the meta3C approach , we confronted it with an environmental sample of unknown and presumably complex composition . For this purpose , sediments were collected from an affluent of the Seine river near Paris . Because environmental genomes usually contain a tremendous diversity of species , including many eukaryotes of large genome sizes , we decided to enrich our sample for prokaryotic organisms by cultivating it for 14 hr in Luria broth and filtrating it prior to meta3C library construction and sequencing . As an internal control of the assembly process , reads from a V . cholerae 3C library were added to the meta3C sequences before running IDBA-UD ( N50 = 1 . 2 kb ) . Contigs generated from the meta3C sediment library were clustered using the Louvain algorithm , yielding 184 significant communities ( total size 111 Mb , median size 300 kb , with 19 communities containing more than 1 Mb ) . The largest 11 ones were included in a contact matrix , revealing again squares along the diagonal ( Figure 3A ) . Each community was analyzed using MG-RAST ( Glass et al . , 2010 ) , showing relatively homogeneous taxonomic compositions in most communities ( Figure 3—figure supplement 1 ) . For each community , more than 80% of the genes identified by MG-RAST within the contigs were attributed to the same taxonomic class . Going down to family level , 8 out of the 11 largest communities were >80% homogeneous , with the remaining three presenting more complex patterns that will require further investigation and development . One community was composed of contigs covering 95% of the V . cholerae genome control ( for a total amount of 3 . 9 Mb congruent with the expected size of the genome; Figure 3B , i ) , confirming the ability of this approach to pool DNA regions according to their genome of origin . Other communities contained contigs belonging to other discrete species , related for instance to the ubiquitous bacteria Aeromonas veronii or Exiguobacterium sp . ( for total amounts of 4 Mb and 3 . 1 Mb , aligning to 72% and 36% of the reference genomes of 4 . 5 Mb and 3 Mb total sizes , respectively; Figure 3B , i ) . Several species belonging to the classes Bacilli and Enterobacteria were also present in the mix ( probably favored by the LB enrichment step ) . In some instances , weak interactions between communities from these clades suggested that a genome had been split into more than one community or that two communities contained mixtures of closely related species ( see dotted square on Figure 3A ) . In spite of this problem the approach seemed to perform relatively well , as reflected by the analysis of the contacts with plasmids . Whether integrated in a genome or under circular forms ( see Figure 1B ) , plasmids are expected to present mostly contacts with their host genome since they share the same cellular compartment ( see also the contacts of plasmid F with the E . coli genome in Figure 1—figure supplement 1A ) . In this study , plasmids annotated as belonging to Bacillus megaterium were retrieved almost entirely within a single Bacilli community ( Figure 3B , ii ) , suggesting that this community presented indeed a relatively homogeneous content and therefore validating our approach . Although the limited sequencing depth of our experiment restrained our ability to scaffold optimally the contigs of the communities using GRAAL , mapping the reads present in the community related to A . veronii against the reference genome of this species revealed a 3D structure reminiscent of those of B . subtilis and C . crescentus ( Figure 3C; Animation 12 ) . Hence , the 3D information contained within the chromosome of this species was efficiently captured during the meta3C experiment , suggesting that increasing the sequencing depth of a meta3C library will likely make it possible to generate de novo scaffolds and 3D clusters for genomes entirely unknown or underrepresented , shedding light at the same time onto their overall organization . 10 . 7554/eLife . 03318 . 023Figure 3 . meta3C analysis of a complex environmental sample . ( A ) meta3C contact map of the largest 11 communities of contigs in the matrix before ( bottom left ) and after ( upper right ) clustering . Each square corresponds to a community grouping contigs that exhibit significantly more contact with each other than with other communities . For each community , an index , the sum of the sequences , and candidate classes are indicated on the right side of the matrix . Dotted square underlines inter-community contact enrichments . C6868*: V . cholerae . ( B ) Illustration of the interactions between the 11 largest communities of contigs using a force-directed graph drawing algorithm Force Atlas 2 ( Jacomy et al . , 2014 ) . Each node corresponds to a contig ( or a chunk of a contig ) and each link represents at least one meta3C interaction . The colors correspond to the communities identified by the Louvain algorithm and described in A . ( C ) i: For three communities , the contigs were mapped against the closest reference genomes identified ( Exiguobacterium sp . AT1b , A . veronii B565 , V . cholerae N16961 , plasmids pBM700 and pBM200 ) . To illustrate the specificity of the approach , each community was mapped against each of those genomes ( the color code is the same as in A and the order from the outer circle to the inner circle is indicated in the middle ) . ii: Similarly , the three Bacilli communities highlighted in color were mapped against two plasmid sequences . ( D ) Genomic contact map of an unknown species ( most likely A . veronii or a close relative ) generated by mapping the reads present in the community 478 against the genome of A . veronii ( bin size = 10 kb ) . The corresponding 3D structure is indicated next to the contact map . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 02310 . 7554/eLife . 03318 . 024Figure 3—figure supplement 1 . MG-RAST analysis of the 11 largest meta3C communities . The similarity between the sequences present in these communities and those of known species was to annotate them . ( A ) Homogeneity regarding class-level annotations; ( B ) homogeneity regarding family-level taxonomy . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 02410 . 7554/eLife . 03318 . 025Animation 12 . 3D reconstruction of a genome closely related to A . veronii . Blue and black beads: Ter and Ori regions , according to the reference genome of A . veronii . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 025
Our application of meta3C to controlled mixes of bacteria and eukaryotes revealed new 3D genome organizations for four bacterial and several yeast species and showed that this approach can also be used to generate de novo communities of contigs corresponding to individual species in complex metagenomic mixtures ( a flowchart of this approach is presented in Figure 4 ) . 10 . 7554/eLife . 03318 . 026Figure 4 . Flowchart representing the computational analysis steps of a meta3C experiment . First , the reads from the sequenced meta3C library are assembled into contigs . The meta3C contact information between the contigs is then used to generate a network of the contacts of all contigs against each other . This contact network is searched for so-called ‘communities’ ( by analogy with social network analysis ) using the Louvain algorithm . The significant communities can be annotated by NCBI and correspond principally to sequences from individual species . Ultimately , the contact information can be used to reorder the DNA segments within each community and thus generate the 3D contact map of the corresponding species . In this process contigs can be reassembled by software such as GRAAL , thereby decreasing the percentage of chimeric fragments in the assembly and improving its continuity . DNA segments originating from other species are put aside automatically during this process given their lack of 3C contacts with the rest of the genome under reassembly . DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 026 The average organization of yeast genomes was found to differ radically from those of bacterial nucleoids , most likely because of contrasted replication and division processes and timings . Most of the organization in Saccharomycetes appears driven by the clustering of the centromeres at the spindle pole body; this clustering remains the prominent structural feature throughout most of the cell cycle ( i . e . , G1 + S phase + G2 ) during exponential growth , with mitosis representing only a fraction of the cycle . In bacteria , in contrary , the strong overall organizational features are the activation of a unique replication origin per chromosome , the ability to initiate multiple replication forks , and the fast division cycle . In addition , important topological constraints are exerted on bacterial circular chromosomes around the Ter regions , and our observation of significant contacts at these positions was probably linked to these phenomena . More imaging and 3C-like analyses will be required to understand the precise choreography of chromosome segregation in these different microorganisms . Taking advantage of chromatin conformation capture data to address genomic questions is a dynamic field: while this paper was under review , two studies were released that also aimed at exploiting the physical contacts between DNA molecules to deconvolve genomes from controlled mixes of microorganisms ( Beitel et al . , 2014; Burton et al . , 2014 ) . Although these analyses open interesting perspectives , the meta3C approach presented in this work differs in several aspects . First , both studies used HiC to scafold genomes from contigs obtained either from simulated assemblies ( Beitel et al . , 2014 ) or from independent experiments ( Burton et al . , 2014 ) . Our approach conveniently uses a single meta3C library and a blind analysis for generating contigs , binning them , scaffolding them , and revealing the 3D structure of the corresponding species . Such approach based on a single experiment may require more sequencing depth than an approach combining multiple libraries ( HiC + shotgun + mate pair , for instance ) , but the exact trade-off remains to be determined and depends on the specifics of the experiment , the sequencing technology , and on the amount of starting material available . Besides , our blind analysis allowed us to delineate communities without prior knowledge of the number of species present in the mix; this contrasts with the approach of Burton et al . ( 2014 ) , which apparently requires such prior knowledge . Similarly , we generate de novo chromosome scaffolds without any assumptions regarding the number of chromosomes present in the mixture , a realistic approach when it comes to exploring environmental samples or complete the assembly of complex genomes . Furthermore , our experience with the analysis of chromosome organization in both bacteria and yeast species emphasized the importance of the initial steps for the success of such experiment . The adequacy between the cross-linking step , which depends on the incubation time and the concentration of the fixating agent , and the restriction step and choice of the restriction enzyme , is essential for the recovery of long-distance cis contacts that improve de novo genome scaffolding and , importantly , reveal the 3D structure . Optimizing the cross-linking conditions allows new insights into the diversity of 3D chromosome organization of several species . Last but not least , the meta3C approach remains to be applied on a truly natural ( not enriched ) sample , such as a gut or wine microbiome . Based on our experience , we envision that , this experiment should include several ( two at least ) enzymes recognizing sites with various GC percentages , in order for both GC-rich and GC-poor genomes to be appropriately represented in the meta3C library . Doing the experiment with multiple enzymes would also allow taking into account GC content ( in addition to average coverage ) to improve the binning of contigs into communities . Identifying contigs presenting largely divergent characteristics compared to their neighbors and redistributing them to their most likely communities would also lead to improved assemblies for each species ( see for instance Albertsen et al . , 2013 ) . Overall , quantifying physical contacts between chromosomes provides an objective and convenient principle to segregate the genomes of sympatric species and , from there , to explore the biological diversity of complex ecosystems .
Meta3C protocols were adapted from 3C protocols ( notably , Dekker et al . , 2002; Oza et al . , 2009 ) . The strains used for the meta3C bacterial library were B . subtilis BS168 ( Burkholder and Giles , 1947 ) , E . coli HB101 ( Boyer and Roulland-Dussoix , 1969 ) , and V . cholerae MV127 ( Val et al . , 2012 ) . For each strain , 100 ml of LB were inoculated with 106 cells/ml and incubated at 37°C until a final concentration of about 2 × 107 cells/ml . Cells from the different species were then mixed and cross-linked with fresh formaldehyde for 30 min ( 3% final concentration; Sigma Aldrich , Saint Louis , Missouri ) at room temperature ( RT ) followed by 30 min at 4°C . Formaldehyde was quenched with a final concentration of 0 . 25 M glycine for 5 min at RT followed by 15 min at 4°C . Fixed cells were collected by centrifugation , frozen on dry ice , and stored at −80°C until use . Frozen pellets of 3 × 109 cells were thawed on ice and resuspended in a final volume of 650 µl 1× TE pH 8 before adding 4 µl of Ready-Lyse lysozyme ( 35 U/µl; Tebu Bio , France ) , followed by incubation at RT for 20 min . SDS was added to a final concentration of 0 . 5% followed by 10 min RT incubation . 50 µl of lysed cells were put in eight tubes containing 450 µl of digestion mix ( 1× NEBuffer 1 [New England Biolabs , Ipswich , Massachusetts] , 1% Triton X-100 , and 100U HpaII enzyme [NEB; C^CGG] ) . The chromatin was then digested for 3 hr at 37°C , split into four aliquots , and diluted with 8 ml ligation buffer ( 1× ligation buffer NEB without ATP , 1 mM ATP , 0 . 1 mg/ml BSA , 125 units of T4 DNA ligase [5 U/µl − Weiss Units − Thermo Fisher Scientific , Waltham , Massachusetts] ) . Ligation was performed at 16°C for 4 hr followed by a de-cross-linking step consisting of an overnight ( ON ) incubation at 65°C in the presence of 250 µg/ml proteinase K in 6 . 2 mM EDTA . DNA was then precipitated with 800 µl of 3 M sodium-acetate ( pH 5 . 2 ) and 8 ml iso-propanol . After 1 hr at −80°C , DNA was pelleted by centrifugation . Pellets were suspended in 500 µl 1× TE buffer and the RNA degraded with a final concentration of 0 . 03 mg/ml RNAse for 1 hr at 37°C . DNA was transferred into 2 ml centrifuge tubes , extracted twice with 500 µl phenol–chloroform pH 8 . 0 , precipitated , washed with 1 ml cold ethanol ( 70% ) , and diluted in 30 µl 1× TE buffer . All tubes were pooled and the resulting 3C library was quantified on gel using the program QuantityOne ( Bio-Rad , Richmond , California ) . The strains used for the meta3C yeast library were Y . lipolytica CLIB122 , L . kluyveri CBS3082 , Candida lusitaniae ATCC42720 , C . albicans SC5T314 , Kluyveromyces lactis CLIB210 , S . bayanus 623-6C , Kluyveromyces thermotolerans CBS6340 , Saccharomyces cerevisiae BY4741 , N . castellii CBS 4309 , Candida glabrata CBS138 , and Debaryomyces hansenii CBS767 . All strains were grown at 30°C in 50 ml BMW medium until reaching 1 × 107 cells/ml ( Thompson et al . , 2013 ) . The cultures were then mixed and cross-linked for 30 min with fresh formaldehyde ( 3% ) . The formaldehyde was quenched with a final concentration of 0 . 25 M glycine for 5 min at RT followed by 15 min at 4°C . Fixed cells were pooled as aliquots of 3 × 109 cells , collected by centrifugation , frozen on dry ice , and stored at −80°C . Aliquots were thawed on ice and resuspended in 6 ml of 1× DpnII buffer ( NEB ) . The cells were then split into four tubes and lysed using a Precellys grinder ( 3 cycles: 6700 rpm − 3 × 20 s ON/60 s OFF; Bertin Technologies , France ) and VK05 beads . Lysed cells were pooled and their volume was adjusted to 6 ml with 1× DpnII buffer . SDS was added to a final concentration of 0 . 3% and the solution was split into twelve 2 ml tubes ( Eppendorf–DNA LoBind , Eppendorf , Germany ) and incubated for 20 min at 65°C followed by 30 min at 37°C under agitation . As a next step , 6 µL of 10× restriction enzyme buffer and 50 µL of Triton X-100 20% were added to each tube , mixed carefully , and incubated at 37°C for another 30 min under agitation . The chromatin was digested for 3 hr with 50 units of restriction enzyme under agitation ( DpnII: G^ATC , NEB ) . Following incubation , 100 units of restriction enzyme were added and the incubation was extended overnight . The digested chromatin was pooled into four equal reactions and the samples were then processed as described above . The river sediments ( 300 g ) were incubated for one night in 500 ml of LB at 30°C . The next morning , the culture was filtrated on Whatman paper ( with a size cut-off of 15 µm ) and 20 mg of wet material were resuspended in 100 ml of fresh LB , then treated with fresh formaldehyde ( 5% final concentration ) for 30 min at RT followed by 30 min at 4°C . The formaldehyde was quenched with glycine ( 0 . 4 M final ) for 5 min at RT followed by 15 min at 4°C . Fixed cells were collected by centrifugation , frozen on dry ice , and conserved at −80°C until use . Frozen pellets were slowly thawed on ice and resuspended in 800 µl of TE 1× . Cells were then lysed using a Precellys grinder ( 3 cycles: 6700 rpm − 3 × 20 s ON/60 s OFF ) and VK05 beads . About 600 µl of lysed cells were recovered , to which SDS was added at a 0 . 5% final concentration before incubation at RT for 10 min . 50 µl of lysed cells were put in eight tubes containing 450 µl of digestion mix ( 50 µl tampon NEB 2 10× , 50 µl Triton X-100 10% , 10 µl of HaeIII enzyme [GG^CC] − 10 U/µl NEB , 340 µl H2O ) . Chromatin was then digested during 3 hr at 37°C under agitation . The digested chromatin was pooled into four equal reactions and the samples were processed as described above . Aliquots of 5 µg of each 3C library were dissolved in water ( final volume 130 µl ) and sheared using a Covaris S220 instrument ( duty cycle 5 , intensity 5 , 200 cycles per burst , 4 cycles of 60 s each; Covaris Ltd . , Woburn , Massachusetts ) . The sheared DNA was purified on QIAquick columns and processed using a commercial kit ( Paired-End DNA sample Prep Kit—Illumina—PE-930-1001; Illumina , San Diego , California ) . The DNA was ligated to custom-made versions of the Illumina PE adapters ( see Table 1 ) for 3 hr at room temperature in a final volume of 30 µl ( 20 µl of DNA [around 8 µg] , 3 µl of ligation buffer 10× [NEB] , 3 µl of T4 DNA ligase [400 U/µl from NEB] , and 4 µl of 10 µM adapter solutions ) . Tubes were then incubated at 65°C for 20 min . 10 . 7554/eLife . 03318 . 027Table 1 . List of the custom-made Illumina adapters used in this studyDOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 027OligosSequenceLibraryMM70GTANNNNNNAGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGMix of 11 yeastsMM71ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNNTACTMM76CAGNNNNNNAGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGMix of 3 bacteriaMM77ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNNCTGTMM70GTANNNNNNAGATCGGAAGAGCGGTTCAGCAGGAATGCCGAGRiver sedimentMM71ACACTCTTTCCCTACACGACGCTCTTCCGATCTNNNNNNTACT DNA fragments ranging in size from 400–800 pb were purified using a PippinPrep apparatus ( SAGE Science , Beverly , Massachusetts ) . For each library , test PCR reactions were performed to determine the optimal number of PCR cycles and a large-scale PCR ( eight reactions ) was then set-up with the number of PCR cycles determined previously . The PCR products were finally purified using Qiagen MinElute columns ( Qiagen , Netherlands ) and paired-end ( PE ) sequenced on an Illumina platform ( HiSeq2000; PE 2 × 100 ) . The raw data from each 3C experiment were processed as follow: first , reads were demultiplexed using the small tag present at the beginning of each sequence ( contained in the custom-made adapters ) . Then , PCR duplicates were collapsed using the six Ns present on each adapter ( Table 1 ) . Reads from the raw data used in the present study were aligned using Bowtie 2 in its most sensitive mode ( Langmead and Salzberg , 2012 ) . We used an iterative alignment procedure similar to Imakaev et al . ( 2012 ) , that is , only the first 20 base pairs of the read were initially mapped then the length of the read was progressively increased until the mapping became unambiguous ( with a mapping quality superior to 40 ) . Paired reads were aligned independently . Indexes were built in one step and included the genome sequences of all the different organisms . Each mapped read was assigned to a restriction fragment . The matrices were then binned into units of 10 or 200 fragments , resulting in 4292 × 4292 or 2229 × 2229 matrices for the mixtures of three bacteria and 11 yeasts , respectively . Matrices were normalized using the sequential component normalization procedure ( SCN ) described in Cournac et al . ( 2012 ) , similar to the iterative normalization procedure described in Imakaev et al . ( 2012 ) . The SCN procedure ensures that the sum over the column and lines of the matrix equals 1 , which reduces the biases inherent to the protocol . Full resolution contact maps are available for bacteria , yeasts , and the environmental sample on the Dryad Digital Repository: http://dx . doi . org/10 . 5061/dryad . gv595 ( Marbouty et al . , 2014 ) . In order to build the 3D structures of the different genomes from the chromosomal contact maps we used the algorithm described in Lesne et al . ( 2014 ) . Briefly , we first converted the normalized contact matrix into an adjacency graph in which each node represented a genomic region and each link had a weight corresponding to the inverse of the number of contacts detected between the two corresponding nodes in the meta3C experiment . We then converted this graph into a distance matrix using the Floyd–Warshall algorithm . This algorithm computes the distance between each pair of genomic regions by determining the shortest distance on the graph between the two corresponding nodes . We finally converted this distance matrix into a 3D structure using distance geometry theorems as described for molecular structures in Havel et al . ( 1983 ) . All this procedure was implemented in Matlab . Reads containing undetermined bases were removed before the assembly step to retain only good-quality reads . De novo assemblies were then performed using the program IDBA-UB ( Peng et al . , 2012 ) with the pre-correction option and default parameters . Chromosome plugs were prepared as described previously ( Koszul et al . , 2004 ) , using lysozyme instead of zymolyase ( 1% agarose gels , 0 . 25× TBE buffer at pH8 . 3 ) . PFGE was performed in a Rotaphor R23 tank ( Biometra , Germany ) using the following program: 12°C , 5 V/cm for 65 hr , angle 110° , pulse ramps 200 to 80 V . Southern blot hybridization was performed using probes derived from PCR products obtained with the primers listed in Table 2 . 10 . 7554/eLife . 03318 . 028Table 2 . List of the oligonucleotides used to PCR amplify probes for the Southern blot of the PFGE ( Figure 1—figure supplement 2 ) DOI: http://dx . doi . org/10 . 7554/eLife . 03318 . 028OligosSequenceCoordinatesProbeMeta1CATCCCGTGAGAAATAATGGTCG2 , 256 , 273 ( MG1655 ) Genomic probeMeta2TGTGCATCCCGTCACAAATTC2 , 257 , 545 ( MG1655 ) Meta3TTGAGCTTATCAAAGTCGTCGGAG323 , 607 ( MG1655 ) Duplication probeMeta4TGATGTGAACTAACGCAGGAAC324 , 796 ( MG1655 ) Meta5TTTACCTCTGATACTGGCTCTGG79 , 024 ( plasmid F ) Plasmid F probeMeta6ACGTGGCATATTCATGCAGAC80 , 159 ( plasmid F ) To group the different contigs into communities reflecting the different genomes present in the sequenced mixtures , we adopted an approach based on graph theory . Among several community-detection algorithms , we found that the Louvain method ( Blondel et al . , 2008 ) generated the best reconstructions of the controlled mixes of bacteria and yeast species . Before applying the algorithm , contigs longer than 2 . 5 kb were divided into equal-sized chunks corresponding to several nodes in the graph ( since large contigs exhibit more contact than smaller ones , they are more prone to clustering when running the Louvain algorithm ) . We used the Louvain method with default parameters , except for the mix of three bacteria ( r=10 for three communities ) . For bacterial mix , 10% of the contigs of the smallest size were not attributed to a community with these parameters ( representing ∼15% of the total reads ) . For yeast , few contigs were left aside , representing in total ∼2% of the number of reads . For the river sediment experiment , ∼15% of the contigs were left aside at the binning step . The contigs in each bin resulting from the 3D binning were characterized using BLAST to determine the dominant species . The program GRAAL ( for Genome ( Re ) -Assembly Assessing Likelihood from 3D ) aims at improving incomplete genome assemblies through the probabilistic exploitation of the physical contacts endured by chromosomes within a cellular compartment ( Marie-Nelly et al . , 2014a ) . The program only needs two datasets for initialization: a set of contigs and a 3C/HiC dataset . Upon initialization and depending on the depth of the HiC dataset , the software splits the contigs into smaller pieces/bins encompassing at least 2 restriction fragments . It then iteratively ( over 1000s of steps ) searches through a broad range of structures generated from reordering these small bins for genome structures more likely to be true given the 3D data . Importantly , at each step , a bin is tested for a variety of ‘structural variations’ with respect to its most likely neighbors , including duplications , deletions , fusions , inversion , etc . The likelihood of each of these new genome structures is then computed in light of the contact data , and one of those structures is sampled for the next iteration ( during which the position of a new bin is tested ) . Each bin is tested several times throughout the entire process , and step by step the genome structure originally made of thousands of small independent bins converges towards a structure reflecting the best solution given the 3D contact data . The entire program and full source code are freely available online here: https://github . com/koszullab/GRAAL as well as on the website of RK laboratory . The description of the program and its application are presented in Marie-Nelly et al . ( 2014a ) . We used GRAAL to reassemble the 2 , 060 contigs ( N50 = 16 , 764 bp ) contained in the S . bayanus community identified by the Louvain algorithm . The resulting scaffolding showed a large improvement for all assembly parameters . The N50 length now reached 190 , 514 bp , and 77% of the total length of the assembly was assembled into regions larger than 50 kb ( as compared to 3% before running the software ) . The result was even more spectacular for N . castellii ( probably due to its higher coverage in the meta3C library ) : after running GRAAL on the 999 contigs ( N50 = 27 , 452 bp ) in the community delineated by the Louvain algorithm , N50 length reached 792 , 652 bp , and 96% of the assembled data was in scaffolds larger than 50 kb ( with 11 scaffolds covering 95% of the 10 chromosomes ) . For general visualization of the meta3C assembly data we used force-directed graph-drawing algorithms . This class of algorithms positions the nodes of a graph by assigning forces among the set of edges according to weighted interactions between the nodes . In our case , the 3C contacts dictated the strength of interactions between nodes . These layouts allowed us to visualize conveniently large clusters of nodes that were subsequently confirmed by the use of the Louvain algorithm . All graphs were visualized using the network software Gephi ( Bastian et al . , 2009; Martin et al . , 2011 ) . Contigs longer than 2 . 5 kbp were divided into equal-sized chunks corresponding to several nodes in the graph ( to ‘normalize’ the appearance of the clusters , since otherwise every contig , whatever its size , would be represented by a single point ) . For visualizing the meta3C bacterial assembly we used the Force Atlas 2 algorithm ( Jacomy et al . , 2014 ) . This algorithm assigns spring-like attractive forces and repulsive forces ( like those between electrically charged particles ) to the set of edges and the set of nodes: in the equilibrium state , discrete clusters are obtained . The entire set of bins was then color-labeled , with each color corresponding to a community detected by the Louvain algorithm ( revealing a strong correlation with the clusters obtained using the visualization approach ) . For the circular representation and comparison of the sequences present in the communities against known bacterial genomes , we used the CGView server ( Grant and Stothard , 2008 ) . | Microbial communities play vital roles in the environment and sustain animal and plant life . Marine microbes are part of the ocean's food chain; soil microbes support the turnover of major nutrients and facilitate plant growth; and the microbial communities residing in the human gut support digestion and the immune system , among other roles . These communities are very complex systems , often containing 1000s of different species engaged in co-dependent relationships , and are therefore very difficult to study . The entire DNA sequence of an organism constitutes its genome , and much of this genetic information is stored in large structures called chromosomes . Examining the genome of a species can provide important clues about its lifestyle and how it evolved . To do this , DNA is extracted from cells and is then usually cut into smaller fragments , amplified , and sequenced . The small stretches of sequence obtained , called reads , are finally assembled , yielding ideally the complete genome of the organism under study . Metagenomics attempts to interpret the combined genome of all the different species in a microbial community and has been instrumental in deciphering how the different species interact with each other . Metagenomics involves sequencing stretches of the community's DNA and matching these pieces to individual species to ultimately assemble whole genomes . While this may be a relatively straightforward task for communities that contain only a handful of members , the metagenomes derived from complex microbial communities are huge , fragmented , and incomplete . This often makes it very difficult or even nearly impossible to match the inferred DNA stretches to individual species . A method called chromosome conformation capture ( or ‘3C’ for short ) can reveal the physical contacts between different regions of a chromosome and between the different chromosomes of a cell . How often each of these chromosomal contacts occurs provides a kind of physical signature to each genome and each individual chromosome within it . Marbouty et al . took advantage of these interactions to develop a technique that combines metagenomics and chromosome conformation capture—called meta3C—that can analyze the DNA of many different species mixed together . Testing meta3C on artificial mixtures of a few species of yeast or bacteria showed that meta3C can separate the genomes of the different species without any prior knowledge of the composition of the mix . In a single experiment , meta3C can identify individual chromosomes , match each of them to its species of origin , and reveal the three-dimensional structure of each genome in the mix . Further tests showed that meta3C can also interpret more complex communities where the number and types of the species present are not known . Meta3C holds great promise for understanding how microbial communities work and how the genomes of the species within a community are organized . However , further developments of the technique will be required to investigate communities as diverse as those present in most natural environments . | [
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] | 2014 | Metagenomic chromosome conformation capture (meta3C) unveils the diversity of chromosome organization in microorganisms |
Social behaviour is regulated by activity of host-associated microbiota across multiple species . However , the molecular mechanisms mediating this relationship remain elusive . We therefore determined the dynamic , stimulus-dependent transcriptional regulation of germ-free ( GF ) and GF mice colonised post weaning ( exGF ) in the amygdala , a brain region critically involved in regulating social interaction . In GF mice the dynamic response seen in controls was attenuated and replaced by a marked increase in expression of splicing factors and alternative exon usage in GF mice upon stimulation , which was even more pronounced in exGF mice . In conclusion , we demonstrate a molecular basis for how the host microbiome is crucial for a normal behavioural response during social interaction . Our data further suggest that social behaviour is correlated with the gene-expression response in the amygdala , established during neurodevelopment as a result of host-microbe interactions . Our findings may help toward understanding neurodevelopmental events leading to social behaviour dysregulation , such as those found in autism spectrum disorders ( ASDs ) .
The tight association that animals have with the trillions of microbes that colonise them is the result of a long evolutionary history . Although we only very recently started to understand the intimate relationship between microbes and host physiology , including brain function , it is now well accepted that host neurodevelopment , brain function and behaviour are regulated by presence and activity of the host-associated microbiota ( Collins et al . , 2012; Cryan and Dinan , 2012; Foster et al . , 2017; Lyte , 2013; Mayer et al . , 2014a; Sampson and Mazmanian , 2015; Sharon et al . , 2016 ) . One of the most recurrent and evolutionary conserved behaviours observed to be influenced by the microbiota , both during a host’s lifetime as well as on evolutionary time scales , is host social behaviour ( Arentsen et al . , 2015; Crumeyrolle-Arias et al . , 2014; de Theije et al . , 2014; Desbonnet et al . , 2014; Ezenwa et al . , 2012; Gacias et al . , 2016; Kwong et al . , 2017; Leclaire et al . , 2017; Lewin-Epstein et al . , 2017; Montiel-Castro et al . , 2013; Montiel-Castro et al . , 2014; Parashar and Udayabanu , 2016; Sharon et al . , 2010; Snyder-Mackler et al . , 2016; Stilling et al . , 2014a; Theis et al . , 2013; Tung et al . , 2015 ) . Importantly , a growing body of data in healthy volunteers and patient populations is emerging , indicating microbial influences also translate to human emotional behaviours ( Tillisch et al . , 2013 ) and have been suggested to play a role in neurodevelopmental disorders such as autism spectrum disorders ( ASDs ) ( Stilling et al . , 2014a; Hsiao et al . , 2013; Kang et al . , 2017; Mayer et al . , 2014b; Strati et al . , 2017 ) and schizophrenia ( Dinan et al . , 2014 ) . Just very recently , it was shown in two independent mouse models of autism that the microbiota is necessary for expression of autistic-like symptoms in these models ( Golubeva et al . , 2017; Kim et al . , 2017 ) . It is , however , still largely unclear how and where the microbiota influence brain function and which mechanisms mediate changes in behaviour . While the amygdala is critically involved in anxiety and fear-related behaviours and memory , it is also a well-established key emotional brain centre for evaluating and responding to social stimuli in humans and other mammals ( Allsop et al . , 2014; Amaral , 2003; Kliemann et al . , 2012; Noonan et al . , 2014; Phelps and LeDoux , 2005; Sabatini et al . , 2007; Sallet et al . , 2011 ) . As such , neuropsychiatric disorders characterised by social deficits ( including autism spectrum and anxiety disorders ) are associated with structural and functional changes in the amygdala ( Amaral and Corbett , 2003; Baron-Cohen et al . , 2000; Monk et al . , 2010; Schultz , 2005 ) . Evidence for a role of the microbiota in regulating amygdala function is emerging ( Tillisch et al . , 2013; Hoban et al . , 2018; Stilling et al . , 2015; Luczynski et al . , 2016a; Hoban et al . , 2017 ) but this has been largely descriptive . Germ-free ( GF ) mice , lacking microbial colonisation throughout development , are a well-established and essential tool spearheading the characterisation of microbiota-host interactions in regulating development and physiological and behavioural parameters in the host ( Desbonnet et al . , 2014; Hsiao et al . , 2013; Bäckhed et al . , 2007; Bercik et al . , 2011; Clarke et al . , 2013; Diaz Heijtz et al . , 2011; McVey Neufeld et al . , 2013; Neufeld et al . , 2011; Ridaura et al . , 2013; Luczynski et al . , 2016 ) . By colonising GF mice at weaning age ( exGF ) , developmental effects can be distinguished from dynamic , reversible effects of a functional microbiota . Previous studies , focussed on the ability of post-weaning colonisation of formerly GF rodents to reverse behavioural deficits , have produced mixed results with some phenotypes being reversible while others seemed to be developmentally programmed ( see ( Stilling et al . , 2014b ) for review ) . Thus , the mechanistic underpinnings of behavioural changes in GF mice and how they are controlled by early colonisation during development are still elusive . We here provide evidence that the microbiota is a critical regulator of social interaction-induced gene expression in the amygdala . Using an unbiased , genome-wide approach to determine gene expression in the amygdala by paired-end , stranded , ribodepleted RNA-sequencing together with a comprehensive downstream analysis pipeline , we studied alterations in the amygdala transcriptome in response to a social interaction stimulus . We find a unique transcriptional response in GF mice that involves upregulation of the splicing machinery , which is able to partially compensate for impairments in neuronal plasticity signalling during social interaction in these animals .
Mice lacking any interaction with microorganisms throughout development have a range of behavioural phenotypes ( Luczynski et al . , 2016 ) , including memory impairments and altered anxiety behaviour ( Hoban et al . , 2018; Gareau et al . , 2011 ) . Here , we subjected conventional mice ( CON-SI ) , germ-free mice ( GF-SI ) , and germ-free mice colonized after weaning ( exGF-SI ) to a social stimulus ( the three-chamber social interaction test ( 3CSIT ) , based on Nadler et al . , 2004 ) and measured the time during which the test mice interacted with a conventional conspecific male mouse or a non-social object ( Figure 1A , experimental design and workflow ) . As previously reported ( Desbonnet et al . , 2014; Buffington et al . , 2016 ) , on average the group of GF-SI mice showed significantly decreased interaction with a conspecific compared with controls and colonized animals , while interaction with the non-social object was similar among the three groups ( Figure 1B–C ) . Notably , we found high inter-individual variability in the GF-SI group for the time interacting with the conspecific , ranging from approximately control levels ( >300 s ) to as little as 60 s ( Figure 1B ) . However , the distribution passed the D'Agostino and Pearson omnibus normality test , the Shapiro-Wilk normality test and the KS normality test , all at alpha = 0 . 05 . Time spent with the non-social object was similarly variable in all three groups ( Figure 1C ) . Since we have previously shown that social interactions in the mouse 3CSIT behavioural paradigm reliably activates stimulus-dependent genes expression in the amygdala ( Stilling et al . , 2015 ) , we hypothesised that we would observe changes in gene expression in this brain region as a function of colonisation status and , especially , social experience . Thus we sought to identify potential molecular pathways involved in mediating microbiome-to-brain signalling by systematically comparing gene expression patterns in the amygdala of mice that were exposed to social environmental stimulation ( social interaction , SI ) mice . To this end , we retrieved RNA from naïve animals and 1 hr after the social experience for CON , GF and exGF animals and performed a highly comprehensive type of RNA-sequencing , using paired-end RNA libraries that retains information on which DNA strand was transcribed and includes also non-polyadenylated RNA species . This way our analysis incorporated gene expression changes at baseline and in response to stimulus-induced transcription of both , mRNAs and long non-coding RNAs ( lncRNAs ) that lack a poly-A tail . In a first step , we performed analysis of differential gene expression on all meaningful pairwise comparisons ( Table 1 ) . When comparing the number of differentially expressed genes ( DEGs , both up- and downregulated ) between naïve animals of different colonisation status , we find the highest number of DEGs in the CON-GF comparison ( Figure 2A , Supplementary file 1 ) , as we have previously reported in an independent study ( Stilling et al . , 2015 ) . Interestingly , when comparing naïve vs . stimulated animals the response in terms of number of DEGs was strongest in GF animals by far , providing evidence for the amygdalar transcriptomes to diverge between the three groups upon environmental stimulation by social interaction . Transcriptional divergence driven by neuronal activity is further supported by the number of DEGs when comparing the SI groups among each other , as these numbers are substantially ( 4–9 times ) higher than DEG numbers for naïve comparisons ( Figure 2A ) . Increasing stringency of the analysis ( log2 ( fold-change ) > |±0 . 5| ) had little relative effect on these results ( Figure 2—figure supplement 1A ) . Next we looked for overlapping DEGs between pairwise comparisons . Because meaningful Venn diagrams are limited to four or five comparisons , to identify groups of genes that are regulated in multiple comparisons , we plotted the presence or absence for each of the 4522 non-redundant genes found in any of the comparisons ( Figure 2B ) . As expected , there were multiple overlapping clusters between SI comparisons , demonstrating a core cluster of genes that are induced 1 hr after social novelty , independent of colonisation status . However , multiple clusters of genes showed differential regulation only in a particular group , suggesting differences in the transcriptomic response of the individual groups . We identified a cluster of genes that was differentially regulated in both GF naïve animals and upon social interaction stimulation in conventional animals ( Figure 2C ) . This overlap was highly significant and made up large proportions of all genes up- or downregulated in naive GF animals compared to CON controls ( 38% and 26% , respectively ) . This cluster was not differentially regulated in GF animals upon stimulation by social interaction ( Figure 2D ) . In fact , there was a counter directional overlap ( e . g . genes upregulated in CON vs . GF but downregulated in GF upon stimulation ) . Together , these data suggest that several genes typically induced in the amygdala of conventional controls upon social interaction are already elevated in GF mice , pointing towards amygdalar hyperactivity in these animals ( Stilling et al . , 2015 ) . To identify pathways and biological functions that are induced in the amygdala by social interaction under the different colonisation conditions , we next analysed the dataset for functional enrichment . To this end , we tested upregulated genes in each group for enrichment of biological functions using the gene ontology ( GO ) database and compared resulting p-values ( Figure 2E , Supplementary file 2 ) . We found a wide-range of biological functions to be enriched in the three conditions . While overall the groups differed substantially , we identified one cluster that showed good agreement between comparisons . For all three groups significant enrichment was found for processes such as protein modification and folding as well as regulation of gene expression . This is in good agreement with the fact that there is a core cluster of genes overlapping ( see Figure 2B ) . However , large differences were seen in processes that are associated with intracellular signalling such as protein phosphorylation and dephosphorylation , which was enriched in the CON-SI comparison , but not in GF-SI or exGF-SI . The second striking difference was dramatic enrichment of processes associated with RNA splicing , most prominently in the GF-SI but also the exGF-SI group ( Figure 2E , Supplementary file 3 ) . Using gene-set enrichment analysis ( GSEA ) , which does not rely on cut-offs for p-value or fold-change , we find similar differences in functional enrichment ( Supplementary file 2 ) . We also analysed the dataset for enrichment of genes associated with specific cellular pathways using the KEGG pathways database . As expected , in the CON-SI group the MAP kinase ( MAPK ) signalling pathway was the dominant pathway enriched due to social interaction treatment ( Figure 3A , Supplementary file 2 ) . This pathway , which is well established to be induced upon neuronal activity , was also enriched in GF-SI and exGI-SI groups , albeit to a much lesser degree . In fact , under more stringent analysis parameters enrichment failed to reach significance in these groups ( Figure 2—figure supplement 1A ) . For the GF-SI group we found a highly significant enrichment of genes associated with the spliceosome that was also significantly enriched , to a lesser extent , in the exGF-SI group and almost completely absent from the CON-SI group ( Figure 3A ) . To elucidate the differences in gene regulation in response to social interaction between CON and GF mice we analysed genes exclusively induced in either of the groups for further functional enrichment . In line with GO Term and KEGG pathway analysis we found that DEGs exclusively upregulated in CON-SI mice , were highly enriched in genes involved in intracellular signalling , especially the MAPK pathway , while DEGs exclusively upregulated in GF-SI mice were strongly enriched in genes associated with RNA processing , that is splicing ( Figure 3B , Supplementary file 2 ) . The core of genes that are induced independent of colonisation status is characterised by enrichment of genes involved in protein folding and , to a lesser degree , both , spliceosome and MAPK signalling pathway . Genes falling into these two KEGG categories made up relatively large proportions of all DEGs in the CON-SI and the GF-SI group , respectively ( Figure 3C , Supplementary file 2 ) . Genes associated with the GO Term ‘RNA processing’ made up 4 . 4% of all DEGs in the GF-SI group ( 111/2510 ) , which represents 15% of all mouse genes in this category ( 111/741 ) . Comparing response-induced fold changes of these 111 genes between colonisation statuses , further highlighted that expression of this group of genes is highly distinctive in GF-SI animals ( Figure 3D ) . This unique pattern was also evident when we plotted a comparison of these genes , where the individual gene identity was maintained across groups and where mean expression values for the remaining 630 , not statistically significant mouse genes in the ‘RNA processing’ GO category for each group were included ( Figure 2—figure supplement 1B ) . Stimulus-induced upregulation of a number of well-established immediate early genes , that show rapid and reproducible upregulation in response to neuronal activity , including activity induced by environmental novelty in the hippocampus , was also seen in the amygdala of conventional animals after social interaction ( Figure 2—figure supplement 1C ) . In line with reduced activation of the MAPK pathway in GF-SI mice , we find several of these genes , including Egr3 , Fos , and Ier5 not to be upregulated in this group , possibly due to elevated expression levels at baseline ( Supplementary file 1 ) . Together these data suggest that GF animals show a unique response signature toward environmental stimulation by social interaction . The transcriptional response is characterised by a marked upregulation of the genes involved in RNA processing and the splicing machinery , accompanied by an arrested upregulation of typical response genes , especially in those involved in MAPK signalling . Analysis of differential gene expression between naïve GF animals and controls also revealed that this lack of induction may be due to upregulation of several genes involved in these processes already at baseline . All analyses on GF animals colonised with a conventional microbiota at weaning ( exGF ) presented so far show results with features of both , CON and GF groups . Since ‘RNA processing’ and spliceosome-associated pathways were highly enriched in the amygdala of GF animals and also exGF animals , we next analysed our dataset for functional consequences of this upregulation by searching for alternative splicing events , that is differentials spliced genes ( DSGs ) . Under naïve conditions alternative splicing was highest when comparing CON and GF animals ( Figure 4A , Supplementary file 4 ) . These genes were highly enriched in several functional categories involved in neuronal function , such as long-term potentiation ( padj = 2 . 3e-6 ) and synaptic transmission ( padj = 1 . 2e-10 ) ( Supplementary file 5 ) . Similar enrichment , although to a lesser degree was found when comparing GF and exGF animals , while no significant functional enrichment was found for the relatively few DSGs in the CON vs . exGF comparison . High congruence in alternative splicing between CON and exGF mice was also evident from the high degree of overlap between DSGs that distinguished both groups from the GF group ( Figure 4B ) . Together these data are in agreement with phenotypic similarities between CON and exGF animals , that is reversibility of the GF phenotype by colonisation at weaning . In line with diverging transcriptomes between groups due to differential gene expression after stimulation by social novelty , the transcriptional landscapes changed substantially as a result of alternative splicing . While conventionally colonised controls exposed to social interaction showed comparatively little alternative exon usage , we found a dramatically increased number of DSGs in the GF-SI group , and noticed an even higher number of SI-induced DSGs in exGF animals . This was surprising since enrichment of splicing-associated genes among DEGs was highest in the GF-SI comparison , albeit significantly present also in the exGF-SI group . As with the previous finding for DEGs , DSGs of the three comparisons also shared a ‘core’ set of genes that were alternatively spliced in response to social interaction , independent of colonisation status ( Figure 4C ) . This core was slightly enriched in genes associated with functional categories that are involved in neuronal function such as long-term potentiation ( padj=1 . 5e-02 ) and synaptic transmission ( padj=1 . 9e-4 ) . In fact , the high degree of overlap between all three groups together with the high number of DSGs in GF-SI and exGF-SI groups suggests that , in addition to the conventional response , these two groups show supplementary cellular responses towards activation of the amygdala by social interaction . Interestingly , this additional response is even more pronounced in the exGF-SI group and is characterised by a high number of DSGs highly enriched in genes involved in mainly four principal functional categories: splicing/RNA processing , protein turnover , neuronal functions , and intracellular signalling pathways ( Figure 4D , Supplementary file 6 ) . The fact that a highly enriched proportion of the alternatively spliced genes themselves were also members of the splicing machinery suggests a self-regulating mechanism of gene expression in these two groups . Given the high behavioural variability in active social interaction time specifically in the GF-SI group , together with the divergence of the transcriptional landscape in the amygdala one hour after exposure , we hypothesised that gene expression may be correlated with behavioural performance in individual mice . To test this hypothesis , we ranked all expressed genes in our dataset by correlation ( Pearson correlation coefficient ) between expression level and time spent in active social interaction ( Supplementary file 7 ) . In order to identify functions that are associated with positively or negatively correlated genes we used the Gene Set Enrichment Analysis ( GSEA ) algorithm that assigns one or multiple functions to each gene in the list based on the GO Term or KEGG Pathway databases ( Subramanian et al . , 2005 ) . The algorithm then calculates an enrichment score for each function based on the rank of genes associated with this function ( Figure 5B , C ) . Excitingly , genes whose expression was positively correlated with social interaction time were significantly associated with splicing , along with protein turnover ( Figure 5B–D ) . There was no significant gene set enrichment for negative correlations between sociability behaviour and gene expression . These data suggest direct involvement of splicing and protein turnover pathways in the amygdala with behavioural performance . This result was confirmed , when we compared gene expression of the six germ-free animals with the highest social behaviour performance ( GF-SIhigh ) with those six animals with lowest social behaviour performance ( GF-SIlow ) . We found three genes that were significantly higher expressed in GF-SIhigh animals , namely DnaJb6 ( encoding a brain-enriched heat-shock family protein with chaperone function ) , Cribp ( encoding a stimulus-inducible , brain-enriched , nuclear RNA-binding protein involved in mRNA stabilization ) , and D030028A08Rik ( a long-noncoding RNA with yet unknown function ) . These three genes also show a statistically significant correlation with behavioural performance in the GF-SI group ( Figure 5E ) .
The amygdala is a key node in the emotional processing network , responsible for processing social stimuli and fear-related cues ( Allsop et al . , 2014; Amaral , 2003; Kliemann et al . , 2012; Noonan et al . , 2014; Phelps and LeDoux , 2005; Sabatini et al . , 2007; Sallet et al . , 2011; Sliwa and Freiwald , 2017 ) . A rapidly developing literature increasingly implicates the microbiome in host brain function and behaviour , especially in these emotion processing networks ( Crumeyrolle-Arias et al . , 2014; Luczynski et al . , 2016a; Clarke et al . , 2013; Diaz Heijtz et al . , 2011; Neufeld et al . , 2011; Arseneault-Bréard et al . , 2012; Bercik et al . , 2010; Gilbert et al . , 2013 ) . Social behaviour appears to be among the behaviours most intimately connected to a functional microbiome ( Arentsen et al . , 2015; Desbonnet et al . , 2014; Gacias et al . , 2016; Lewin-Epstein et al . , 2017; Theis et al . , 2013; Tung et al . , 2015; Hsiao et al . , 2013; Buffington et al . , 2016; Arentsen et al . , 2017; Koch and Schmid-Hempel , 2011 ) . However , the mechanistic underpinnings of this influence are only beginning to be resolved . Here we show , for what is to our knowledge the first time , that absence of the microbiome results in dysregulation of unique transcriptional-response pathways in the amygdala . In line with previous reports , we show that microbes are necessary for development of appropriate sociability behaviour ( Arentsen et al . , 2015; Desbonnet et al . , 2014; Buffington et al . , 2016 ) . In agreement with these studies we show reduced sociability behaviour of GF mice in the three-chamber social interaction task , rescued by post-weaning colonisation with a conventional microbiome . However , we do not observe a lack of preference of the conspecific mouse over an inanimate novel object in GF mice . Interestingly , while on average the group of GF mice spent significantly less time interacting with a conspecific than conventionally raised animals , some individual mice of this group performed at control level . This finding suggests , that the underlying networks controlling sociability behaviour are subject to a dynamic regulation , possibly associated with differences in intracellular and extracellular neuronal signalling pathways due to subtle differences accumulating during individual development . However , the fact that exGF mice , colonised with a conventional microbiota at weaning age , spent an intermediate amount of time in social interaction with more control-like variability within the group , argues that this development is highly susceptible to influence by symbiotic signals from the microbiota . Also in several analyses of gene expression regulation in response to social interaction exGF mice resemble an intermediate phenotype , bearing features of both CON and GF mice . As such , exGF mice show high enrichment of genes involved in ‘regulation of gene expression’ ( most prominently enriched among CON-SI upregulated genes ) as well as ‘RNA processing’ ( most prominently enriched among GF-SI upregulated genes ) . This intermediate gene expression phenotype is also evident from the visualized functional enrichment using colour-coded functional GO-Terms ( Figure 2E ) . To characterize real transcriptome-wide changes in gene expression , in this study we used stranded , ribodepleted as opposed to poly-A enriched libraries for RNA-sequencing . In result , we describe dynamic regulation of several previously undescribed pathways in response to environmental stimulation . As such , we see regulation of RNA-processing non-coding RNAs , several of which are found in subnuclear Cajal bodies , that are particularly prominent in neurons - especially , when transcriptionally active - and are crucially involved in splicing regulation ( Wang et al . , 2016; Lafarga et al . , 2017 ) . Our RNA-seq experiment thus offers exclusive and comprehensive insight into gene regulation in response to a social stimulus in the amygdala . Interestingly , induction of gene expression in this brain region shows similarities with the hippocampal transcriptional response to environmental novelty ( Stilling et al . , 2014c ) . This overlap is very likely due to a ubiquitous , though highly specific transcriptional response in neurons towards neuronal activity , which includes induction of several well established immediate early genes such as Fos or Arc , the MAP-K pathway , and neurotrophic signalling via Bdnf . Moreover , we find upregulation of complement components , which have lately been established to be necessary for synaptic rearrangements and plasticity upon neuronal activity ( Schafer et al . , 2012; Stephan et al . , 2012; Stevens et al . , 2007 ) . Interestingly , innate immune system genes together with neuronal activity-dependent genes have recently been shown to be dysregulated in autism ( Gupta et al . , 2014 ) . Induction of complement genes upon social interaction was not seen in GF mice , possibly due to upregulation of C1q already under naïve conditions as compared to CON mice . Indeed , we find that a highly significant share of genes upregulated upon social interaction stimulation in CON mice is already upregulated in naïve GF mice . In perfect agreement with previous reports ( Hoban et al . , 2018; Stilling et al . , 2015 ) , this finding suggests baseline hyperactivity of neurons in the amygdala in GF mice . Together , these findings confirm that the expected response of the control group serves as a positive control and provide internal validation that the methodology of this study is able to detect relevant changes in transcriptional regulation between groups . Our data further shows that altered splicing activity is a normal process in neurons of the amygdala in response to social interaction in conventionally raised mice . Indeed , it is now well established , that neuronal activity induces alternative splicing patterns ( Ding et al . , 2017; Hermey et al . , 2017; Iijima et al . , 2016; Schor et al . , 2009 ) , a dysfunction of which has been associated with changes seen in autism and autistic-like behaviour in mice ( Quesnel-Vallières et al . , 2016 ) . Surprisingly , the regulation of genes involved in splicing , including Cajal body-associated genes , as well as alternative splicing activity is extremely exaggerated in GF mice , which possibly reflects a compensatory mechanism for already elevated activity-induced signalling at baseline . The finding that increased splicing activity is even more amplified in exGF mice , which display a largely normalized behavioural response , together with a positive correlation between expression of splicing-associated genes and behavioural performance suggests that in fact upregulation of the splicing machinery supports an adequate amygdalar response towards a social interaction environmental stimulus . In fact , previous research shows that post-weaning colonization ( exGF ) does not rescue impairments in social cognition , seen in GF mice ( Desbonnet et al . , 2014 ) . This is likely a reflection of an incomplete rescue of the molecular underpinnings of social behaviour investigated here . Although beyond the scope of the current study and technically challenging , future investigations should focus on untangling the network and pathways that drive the observed RNA processing changes and derive molecular consequences from altered mRNA/protein amino acid sequences . In summary , our data is fully congruent with and offers a molecular basis for previous data on alterations in social cognition ( Desbonnet et al . , 2014 ) , amygdala volume and dendrite complexity ( Luczynski et al . , 2016a ) , and increased transcription of activity-associated gene expression in the amygdala ( Hoban et al . , 2018; Stilling et al . , 2015 ) in microbiome-deficient mice . We here show for what is to our knowledge the first time that the microbiota is necessary for regulation of core biological processes on the molecular and cellular level in the brain , which makes a strong case for a causal involvement of the microbiota in the molecular mechanisms leading to the observed impairments in sociability behaviour and the aetiology of neurodevelopmental diseases , which opens the possibility for new therapeutic strategies .
Male F1-generation offspring from germ-free ( GF ) and conventionally-raised ( CON ) Swiss Webster breeding pairs previously obtained from Taconic ( Germantown , New York , USA ) were used in all experiments as previously described ( Desbonnet et al . , 2014; Stilling et al . , 2015; Clarke et al . , 2013; O'Tuathaigh et al . , 2007 ) . GF mice were housed in groups of two-four per cage in flexible-film gnotobiotic isolators at a 12 hr light/dark cycle . Ex-germ-free ( exGF ) mice were removed from the GF unit after weaning on postnatal day p21 , and housed on CON-used bedding next to CON mice in the standard animal unit to allow colonization of microbes present in the facility environment . CON mice were similarly housed two–five per cage under controlled conditions ( temperature 20–21°C , 55–60% humidity ) on the same 12 hr light/dark cycle . All groups received the same autoclaved , pelleted diet ( Special Diet Services , product code 801010 ) . Age at tissue extraction for all groups and experiments was 10 weeks . The 3CSIT was performed as described previously ( Desbonnet et al . , 2014; Stilling et al . , 2015 ) . In brief , mice were habituated to the test room for half an hour and then habituated to a white plastic arena ( 40 × 20×20 cm ) , divided into three chambers by separators with small circular openings and lined with fresh bedding , for 10 min . The left and right chamber contained empty wire-mesh cages . These were then used to display an age- and sex-matched conventionally-raised mouse ( chamber 1 ) or a mouse-sized and –coloured ( white ) non-social object during the test phase ( porcelain egg cup ) . Exploration of the three chambers by the test mouse was recorded on video for 10 min and time spent in active interaction with the conspecific or object was measured by an experimenter , blinded to colonization status of the test mice . Group size for behaviour was n = 12 for GF-SI mice and n = 11 per group for CON-SI and exGF-SI groups , after removing outliers . The amygdala from the left brain hemisphere was rapidly dissected on ice from fresh brain tissue as adapted from ( Zapala et al . , 2005 ) , stored in RNAlater RNA Stabilization Reagent ( Qiagen , Netherlands ) at 4°C for 24 hr and then transferred to −80°C . Total RNA was extracted using the mirVanaTM miRNA Isolation kit ( Ambion/life technologies ) and DNase treated ( Turbo DNA-free , Ambion/life technologies ) according to the manufacturers recommendations For each group ( CON , GF , exGF , CON-SI , GF-SI , exGF-SI ) , 8–12 animals were used ( see Table 1 for details ) . RNA concentration and quality were determined using a Nanodrop 1000 ( Thermo Scientific ) and a Bioanalyzer ( Agilent ) was used to measure RNA integrity . After this , for all naïve groups equal amounts of RNA from two animals were then pooled to yield four samples per group . Therefore , for naïve groups , each sample ( technical replicate ) analysed by RNA-seq represents the average of two biological replicates . Ribodepletion and library preparation was performed by Vertis Biotechnology ( Freising , Germany ) . Sequencing as well as Fastq-file generation was done by Beckman Coulter Genomics service ( Danvers , MA , USA ) . Stranded , paired-end reads of 2 × 100 bp were produced on an Illumina HiSeq2500 sequencer . Details on RNA sample quality and sequencing quality control are given in Supplementary file 8 . To validate differential expression results by the RNAseq pipeline , we performed qRT-PCR analysis of all individual RNA samples for the six groups for 18 selected genes ( Figure 2 - figure supplement 2 ) . 61% of comparisons showed matching results between the two methods . qRT-PCR was performed as previously described ( Stilling et al . , 2015 ) . In brief , qRT-PCR of 3 technical replicates was done for each biological sample on a LightCycler 480 system ( Roche LifeScience ) and analysed using the ΔΔCt – method . Two-way ANOVA with multiple-testing correction ( Tukey post-hoc for effect of colonisation status; Sidak post-hoc for effect of social interaction stimulation ) , was used to test for statistical significance between groups for each gene . Significance level was: padj <0 . 05 . Graphpad Prism ( v6 . 0h , RRID:SCR_002798 ) was used for statistical hypothesis testing of behavioural analysis ( Figure 1 ) and expression of RNA processing genes ( Figure 3D ) . Before hypothesis testing on behavioural data , normal distribution was confirmed by D'Agostino and Pearson omnibus normality , Shapiro-Wilk normality , and KS normality tests ( p<0 . 05 for all three tests ) . Sample size of n = 12 was based on a power calculation with an expected effect size of f = 0 . 4 , a significance level of α = 0 . 05 and a power of 0 . 8 . A Grubbs test for outliers ( p<0 . 05 ) identified one outlier each in CON-SI and exGF-SI groups . One-way ANOVA with Tukey's multiple comparisons post-hoc test was run to compare social interaction between the groups . For hypothesis testing of expression of RNA processing genes non-parametric Friedman testing was performed with Dunn’s multiple comparison post-hoc test to compare expression levels between groups . Statistical significance for overlaps of differentially expressed genes in pairwise comparisons ( Figure 2C , D ) was computed using a publicly available web service ( http://nemates . org/MA/progs/overlap_stats . html ) , which is based on the hypergeometric distribution and Fischer’s exact test . Heatmaps for functional enrichment ( Figure 2E , Figure 4D ) : FDR-adjusted p-values were calculated for all enriched ( padj <0 . 1 ) biological-function GO Terms using DAVID Bioinformatic Resources ( v6 . 7 ) ( see above ) and log-transformed ( -log10 ) and colour-coded using Microsoft Excel ( v15 ) . Pearson correlation coefficient for correlation between behavioural performance and gene expression level in individual mice was calculated using Microsoft Excel ( v15 ) . | In our bodies , there are at least as many microbial cells as human cells . These microbes , known collectively as the microbiome , influence the activity of our brain and also our behaviour . Studies in species from insects to primates have shown that the microbiome affects social behaviour in particular . For example , germ-free mice , which grow up in a sterile environment and thus have no bacteria in or on their bodies , are less sociable than normal mice . For animals to show behaviours such as social interaction , cells in specific regions of the brain must change the activity of their genes . These brain regions include the amygdala , which is part of the brain’s emotion processing network , and also contributes to fear and anxiety responses . Stilling et al . set out to determine whether gene activity in the amygdala during social interaction differs between germ-free mice and those with a normal microbiome . Stilling et al . placed each mouse into a box with three chambers . One chamber contained an unfamiliar mouse while another contained an inanimate object . Germ-free mice were less sociable and spent less time than control animals interacting with the unfamiliar mouse . Before entering either test chamber , the germ-free animals showed signs of excessive activity in the amygdala . During social interaction , they displayed a strikingly different pattern of gene activity in this brain region compared to controls . In particular , they had increased levels of a process called alternative splicing . This process enables cells to produce many different proteins from a single gene . These results reveal one of the steps leading from absence of bacteria during brain development to reduced sociability in adulthood in mice . Increases in gene activity in the amygdala may provide clues to the processes underlying reduced sociability in people with autism spectrum disorders . This new study thus deepens our understanding of the link between the microbiome and brain health . | [
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] | 2018 | Social interaction-induced activation of RNA splicing in the amygdala of microbiome-deficient mice |
Epithelial cells that lose attachment to the extracellular matrix undergo a specialized form of apoptosis called anoikis . Here , using large-scale RNA interference ( RNAi ) screening , we find that KDM3A , a histone H3 lysine 9 ( H3K9 ) mono- and di-demethylase , plays a pivotal role in anoikis induction . In attached breast epithelial cells , KDM3A expression is maintained at low levels by integrin signaling . Following detachment , integrin signaling is decreased resulting in increased KDM3A expression . RNAi-mediated knockdown of KDM3A substantially reduces apoptosis following detachment and , conversely , ectopic expression of KDM3A induces cell death in attached cells . We find that KDM3A promotes anoikis through transcriptional activation of BNIP3 and BNIP3L , which encode pro-apoptotic proteins . Using mouse models of breast cancer metastasis we show that knockdown of Kdm3a enhances metastatic potential . Finally , we find defective KDM3A expression in human breast cancer cell lines and tumors . Collectively , our results reveal a novel transcriptional regulatory program that mediates anoikis .
Epithelial cells that lose attachment to the extracellular matrix ( ECM ) , or attach to an inappropriate ECM , undergo a specialized form of apoptosis called anoikis . Anoikis has an important role in preventing oncogenesis , particularly metastasis , by eliminating cells that lack proper ECM cues ( Simpson et al . , 2008; Zhu et al . , 2001 ) . Anoikis also functions to prevent the invasion of tumor cells into the luminal space , which is a hallmark of epithelial tumors ( Debnath et al . , 2002 ) . In general , epithelial-derived cancers , such as breast cancer , develop resistance to anoikis ( reviewed in Schwartz , 1997 ) . Several signaling pathways have been shown to regulate anoikis ( reviewed in Paoli et al . , 2013 ) . In particular , anoikis is suppressed by integrin signaling , which functions through focal adhesion kinase ( FAK ) , an activator of the RAF/MEK/ERK pathway ( King et al . , 1997 ) . FAK signaling is active in attached cells and is inactive following detachment ( Frisch et al . , 1996 ) . Anoikis is also suppressed by integrin-mediated , ligand independent activation of the epidermal growth factor receptor ( EGFR ) signaling pathway ( Moro et al . , 1998 ) , which , like FAK , also stimulates RAF/MEK/ERK activity . These cell signaling pathways have been found to regulate the levels of BIM ( also called BCL2L11 ) and BMF , two pro-apoptotic members of the BCL2 family of apoptosis regulators previously shown to contribute to anoikis ( Reginato et al . , 2003; Schmelzle et al . , 2007 ) . However , depletion of BIM or BMF diminishes but does not completely prevent anoikis ( Reginato et al . , 2003; Schmelzle et al . , 2007 ) , suggesting the existence of other factors and regulatory pathways that can promote anoikis . Moreover , the basis of anoikis resistance remains to be determined and to date has not been linked to alterations in expression or activity of BIM or BMF .
To investigate the possibility that there are additional factors and regulatory pathways that promote anoikis , we performed a large-scale RNA interference ( RNAi ) screen for genes whose loss of expression confer anoikis resistance . The screen was performed in MCF10A cells , an immortalized but non-transformed human breast epithelial cell line that has been frequently used to study anoikis ( see , for example , Huang et al . , 2010; Reginato et al . , 2003; Schmelzle et al . , 2007; Taube et al . , 2006 ) . A genome-wide human small hairpin RNA ( shRNA ) library comprising ~62 , 400 shRNAs directed against ~28 , 000 genes ( Silva et al . , 2003; Silva et al . , 2005 ) was divided into 10 pools , which were packaged into retroviral particles and used to stably transduce MCF10A cells . Following selection , the cells were divided into two populations , one of which was plated on poly-2-hydroxyethylmethacrylate ( HEMA ) -coated plates for 10 days to inhibit cell attachment to matrix , and another that was cultured attached to matrix for 10 days as a control ( Figure 1A ) . Surviving cells were selected and shRNAs identified by deep sequencing . Bioinformatic analysis of the two populations identified 26 shRNAs whose abundance was significantly enriched >500-fold following detachment ( Figure 1—source data 1 ) ; such shRNAs presumably confer upon MCF10A cells a selective advantage by protecting them from undergoing anoikis . 10 . 7554/eLife . 16844 . 003Figure 1 . Identification of KDM3A as an anoikis effector in breast cancer epithelial cells . ( A ) Schematic of the design of the large-scale RNAi screen to identify anoikis effectors . ( B ) Cell death , monitored by annexin V staining , in MCF10A cells expressing a non-silencing ( NS ) shRNA and cultured attached to the matrix , or in detached cells ( cultured in suspension for 96 hr ) expressing a NS shRNA or one of five candidate shRNAs . Error bars indicate SD . P value comparisons are made to the detached , NS shRNA control . **p<0 . 01 . ( C ) Crystal violet staining of MCF10A cells expressing vector , KDM3A or the catalytically-inactive KDM3A ( H1120G/D1122N ) mutant . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 00310 . 7554/eLife . 16844 . 004Figure 1—source data 1 . List of 26 shRNAs , and the target genes , whose abundance was significantly enriched >500-fold following detachment of MCF10A cells . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 00410 . 7554/eLife . 16844 . 005Figure 1—source data 2 . Source data for Figure 1B . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 00510 . 7554/eLife . 16844 . 006Figure 1—figure supplement 1 . FACS analysis . Representative FACS plots corresponding to Figure 1B . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 00610 . 7554/eLife . 16844 . 007Figure 1—figure supplement 2 . Confirmation of the results of Figure 1B using a second , unrelated shRNA . ( A ) Cell death , monitored by annexin V staining , in MCF10A cells expressing a non-silencing ( NS ) shRNA and cultured attached to the matrix , or in detached cells ( cultured in suspension for 96 hr ) expressing a NS shRNA or one of five candidate shRNAs unrelated to those used in Figure 1B . Error bars indicate SD . **p<0 . 01 . ( B ) Representative FACS plots corresponding to ( A ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 00710 . 7554/eLife . 16844 . 008Figure 1—figure supplement 3 . Analysis of BIM and candidate shRNA knockdown efficiencies . qRT-PCR analysis monitoring knockdown efficiencies of BIM and two unrelated shRNAs directed against the five candidate genes in MCF10A cells . Error bars indicate SD . *p<0 . 05; **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 00810 . 7554/eLife . 16844 . 009Figure 1—figure supplement 4 . Confirmation of increased levels of KDM3A upon ectopic expression . Immunoblot analysis monitoring levels of KDM3A in MCF10A cells expressing vector , KDM3A or KDM3A ( H1120G/D1122N ) . The results confirm increased expression of KDM3A in cells transfected with KDM3A-expressing plasmids . α-tubulin ( TUBA ) was monitored as a loading control . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 009 To validate candidates isolated from the primary screen , we selected the top 20 most highly enriched shRNAs and analyzed them in an independent assay for their ability to confer resistance to anoikis . Briefly , MCF10A cells were transduced with a single shRNA , detached from matrix for 96 hr , and analysed for cell death by annexin V staining . As expected , knockdown of BIM , a positive control , decreased cell death following detachment compared to the control non-silencing ( NS ) shRNA ( Figure 1B and Figure 1—figure supplement 1 ) . Of the 20 candidate shRNAs tested , five reduced the level of detachment-induced apoptosis compared to the NS shRNA , indicating they conferred anoikis resistance ( Figure 1B and Figure 1—figure supplement 1 ) . Similar results were obtained using a second , unrelated shRNA directed against the same target gene ( Figure 1—figure supplement 2 ) . Quantitative RT-PCR ( qRT-PCR ) confirmed in all cases that expression of the target gene was decreased in the knockdown cell line ( Figure 1—figure supplement 3 ) . One of the top scoring validated candidates was KDM3A ( Figure 1—source data 1 ) , a histone demethylase that specifically demethylates mono-methylated ( me1 ) and di-methylated ( me2 ) histone H3 lysine 9 ( H3K9 ) ( Yamane et al . , 2006 ) . H3K9 methylation is a transcriptional repressive mark , and the identification of KDM3A raised the intriguing possibility that induction of anoikis involves transcriptional activation of specific genes through H3K9me1/2 demethylation . Therefore , our subsequent experiments focused on investigating the role of KDM3A in anoikis . We asked whether ectopic expression of KDM3A was sufficient to promote cell death in attached cells . MCF10A cells were transduced with a retrovirus expressing wild-type KDM3A , a catalytically inactive KDM3A mutant [KDM3A ( H1120G/D1122N ) ] ( Beyer et al . , 2008 ) or , as a control , empty vector ( Figure 1—figure supplement 4 ) , and then treated with puromycin for 10 days at which time viability was assessed by crystal violet staining . The results of Figure 1C show that ectopic expression of wild-type KDM3A but not KDM3A ( H1120G/D1122N ) greatly reduced MCF10A cell viability . Collectively , the results of Figure 1 demonstrate that KDM3A is necessary and sufficient for efficient induction of anoikis in breast epithelial cells . We next examined the relationship between KDM3A expression and induction of anoikis . The immunoblot of Figure 2A shows that KDM3A protein levels were very low in attached MCF10A cells , but robustly increased in a time-dependent manner following detachment . The qRT-PCR analysis of Figure 2B shows that an increase in KDM3A expression following detachment was also detected at the mRNA level . 10 . 7554/eLife . 16844 . 010Figure 2 . Detachment and loss of integrin and growth factor receptor signaling induces KDM3A expression . ( A ) Immunoblot monitoring KDM3A levels in attached MCF10A cells , or detached cells cultured in suspension for 4 , 8 or 24 hr . β-actin ( ACTB ) was monitored as a loading control . ( B ) qRT-PCR analysis monitoring KDM3A mRNA levels in attached MCF10A cells , or detached cells cultured in suspension for 24 hr . Error bars indicate SD . **p<0 . 01 . ( C ) Immunoblot monitoring levels of KDM3A and BIMEL in attached MCF10A cells or detached MCF10A cells cultured in suspension for 24 hr and treated in the presence or absence of Matrigel . α-tubulin ( TUBA ) was monitored as a loading control . ( D ) Immunoblot monitoring levels of KDM3A , phosphorylated FAK ( p-FAK ) or total FAK ( t-FAK ) in MCF10A cells treated for 48 hr with 0 , 1 , 5 or 10 µM FAK inhibitor . ( E ) Immunoblot monitoring levels of KDM3A and BIMEL in MCF10A cells expressing either vector , EGFR or MEK2DD and cultured as attached ( A ) or detached ( D ) cells grown in suspension for 24 hr . ( F ) Immunoblot monitoring levels of KDM3A , phosphorylated EGFR ( p-EGFR ) or total EGFR ( t-EGFR ) in MCF10A cells treated for 48 hr with 0 , 1 , 5 or 10 µM gefitinib . ( G ) Immunoblot monitoring levels of KDM3A , phosphorylated ERK1/2 ( p-ERK1/2 ) or total ERK1/2 ( t-ERK1/2 ) in MCF10A cells treated for 48 hr with 0 , 1 , 5 or 10 µM U0126 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 01010 . 7554/eLife . 16844 . 011Figure 2—source data 1 . Source data for Figure 2B . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 01110 . 7554/eLife . 16844 . 012Figure 2—figure supplement 1 . Inhibition of FAK , EGFR , or MEK in MCF10A cells increases KDM3A expression . ( A–C ) qRT-PCR analysis monitoring KDM3A expression in MCF10A cells treated for 48 hr with 0 , 1 , 5 or 10 µM FAK inhibitor ( A ) , gefitinib ( B ) , or U0126 ( C ) . Error bars indicate SD . **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 012 We next sought to understand the basis for the increase in KDM3A levels following detachment . As mentioned above , anoikis is suppressed by integrin signaling , which functions through FAK , a regulator of the RAF/MEK/ERK pathway ( Frisch et al . , 1996; King et al . , 1997 ) . Detachment causes a disruption in integrin–ECM contacts , resulting in a loss of FAK signaling in the detached cells ( Frisch and Francis , 1994; Frisch et al . , 1996 ) , which we observed have elevated KDM3A levels ( see Figures 2A and B ) . We therefore tested whether restoration of integrin signaling in detached cells would block the increase in KDM3A levels . The results of Figure 2C show that the addition of Matrigel basement membrane-like matrix , which restores integrin signaling , to detached cells markedly blocked the elevated levels of the BIM isoform BIMEL , as expected , and KDM3A . Treatment of MCF10A cells with a FAK inhibitor increased the levels of KDM3A protein ( Figure 2D ) and mRNA ( Figure 2—figure supplement 1A ) . Thus , the increase in KDM3A levels upon detachment of MCF10A cells is due , at least in part , to the loss of integrin/FAK signaling . We next analyzed the relationship between the EGFR signaling pathway and KDM3A levels . In the first set of experiments , we ectopically expressed either EGFR or a constitutively active MEK mutant , MEK2 ( S222D/S226D ) ( MEK2DD ) ( Voisin et al . , 2008 ) , both of which have been previously shown to block anoikis in detached cells ( Reginato et al . , 2003 ) . Consistent with these previous results , Figure 2E shows that in detached MCF10A cells , expression of either EGFR or MEK2DD substantially decreased the level of BIMEL ( Reginato et al . , 2003 ) . Expression of either EGFR or MEK2DD also decreased the levels of KDM3A in detached MCF10A cells . Conversely , KDM3A protein levels were increased in attached MCF10A cells treated with the EGFR inhibitor gefitinib ( Barker et al . , 2001; Ward et al . , 1994 ) ( Figure 2F ) or the MEK inhibitor U0126 ( Favata et al . , 1998 ) ( Figure 2G ) . Both gefitinib and U0126 treatment also resulted in increased KDM3A mRNA levels ( Figure 2—figure supplement 1B , C ) . The results described above suggest a model in which following detachment , the resulting increase in KDM3A demethylates H3K9me1/2 to stimulate expression of one or more pro-apoptotic genes . To test this model and identify pro-apoptotic KDM3A target genes , we took a candidate-based approach and analyzed expression of a panel of genes encoding pro-apoptotic BCL2 proteins ( Boyd et al . , 1994; Lomonosova and Chinnadurai , 2008; Matsushima et al . , 1998 ) in attached MCF10A cells and detached cells expressing a NS or KDM3A shRNA . We sought to identify genes whose expression increased following detachment in control but not in KDM3A knockdown cells . We found that expression of the vast majority of genes encoding pro-apoptotic BCL2 proteins were unaffected by detachment in MCF10A cells ( Figure 3A and Figure 3—figure supplement 1 ) . Consistent with previous results ( Reginato et al . , 2003; Schmelzle et al . , 2007 ) , expression of BIM and BMF were increased upon detachment . However , knockdown of KDM3A did not decrease expression of either BIM or BMF . By contrast , following detachment , expression of BNIP3 and BNIP3L increased , and were the only genes whose expression was diminished more than 2-fold by KDM3A knockdown ( Figure 3A and Figure 3—figure supplement 1 ) . We therefore performed a series of experiments to determine whether BNIP3 and BNIP3L are critical KDM3A target genes that mediate anoikis . 10 . 7554/eLife . 16844 . 013Figure 3 . KDM3A induces anoikis by transcriptionally activating BNIP3 and BNIP3L . ( A ) qRT-PCR analysis monitoring expression of pro-apoptotic BCL2 genes in detached MCF10A cells grown in suspension for 24 hr and expressing a NS or KDM3A shRNA . The expression of each gene is shown relative to that obtained in attached cells expressing a NS shRNA , which was set to 1 . P value comparisons for each gene are made to the NS shRNA control . Genes whose expression is decreased >2-fold upon KDM3A knockdown are indicated in red . ( B ) Immunoblot analysis monitoring levels of BNIP3 and BNIP3L in attached MCF10A cells , and detached cells following growth in suspension for 4 , 8 or 24 hr . ( C ) ChIP monitoring binding of KDM3A on the promoters of BNIP3 and BNIP3L or a negative control region ( NCR ) in attached MCF10A cells or detached cells grown in suspension for 24 hr . P value comparisons for each region are made to the attached control . ( D ) ChIP monitoring the levels of H3K9me2 on the promoters of BNIP3 and BNIP3L or a negative control region in attached MCF10A cells or detached cells expressing a NS or KDM3A shRNA and grown in suspension for 24 hr . P value comparisons for each region are made to the detached , NS shRNA control . ( E ) Cell death , monitored by annexin V staining , in MCF10A cells expressing a NS , BNIP3 or BNIP3L shRNA . ( F ) Crystal violet staining of MCF10A cells expressing vector , BNIP3 , BNIP3L or both BNIP3 and BNIP3L . ( G ) Model . Error bars indicate SD . *p<0 . 05; **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 01310 . 7554/eLife . 16844 . 014Figure 3—source data 1 . Source data for Figure 3A , C , D and E . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 01410 . 7554/eLife . 16844 . 015Figure 3—figure supplement 1 . Confirmation of the results of Figure 3A using a second , unrelated KDM3A shRNA . qRT-PCR analysis monitoring expression of BCL2 pro-apoptotic genes in detached MCF10A cells expressing a NS or a second , unrelated KDM3A shRNA to that used in Figure 3A . The expression of each gene is shown relative to that obtained in attached cells , which was set to 1 . Error bars indicate SD . *p<0 . 05; **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 01510 . 7554/eLife . 16844 . 016Figure 3—figure supplement 2 . The level of H3K9me1 on the BNIP3 and BNIP3L promoters is diminished following detachment , which is counteracted by knockdown of KDM3A . ChIP monitoring the levels of H3K9me1 on the promoters of BNIP3 and BNIP3L or a negative control region ( NCR ) in attached MCF10A cells or detached cells expressing a NS or KDM3A shRNA and grown in suspension for 24 hr . P value comparisons for each region are made to the detached , NS shRNA control . Error bars indicate SD . *p<0 . 05; **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 01610 . 7554/eLife . 16844 . 017Figure 3—figure supplement 3 . Overexpression of KDM3A , but not KDM3A ( H1120G/D1122N ) , in attached MCF10A cells results in decreased levels of H3K9me1 and H3K9me2 on the BNIP3 and BNIP3L promoters and increased expression of BNIP3 and BNIP3L . ( A ) ChIP monitoring the levels of H3K9me1 , H3K9me2 and KDM3A on the promoters of BNIP3 and BNIP3L or a negative control region ( NCR ) in attached MCF10A cells expressing empty vector , wild-type KDM3A or KDM3A ( H1120G/D1122N ) . The increased occupancy of KDM3A ( H1120G/D1122N ) on the BNIP3 and BNIP3L promoters is not unexpected because the mutations are in the catalytic domain and should not affect DNA binding . ( B ) qRT-PCR analysis monitoring expression of BNIP3 , BNIP3L or KDM3A in attached MCF10A cells expressing empty vector , wild-type KDM3A or KDM3A ( H1120G/D1122N ) . Error bars indicate SD . *p<0 . 05; **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 01710 . 7554/eLife . 16844 . 018Figure 3—figure supplement 4 . Analysis of BNIP3 and BNIP3L shRNA knockdown efficiencies . qRT-PCR analysis monitoring knockdown efficiency of two unrelated BNIP3 and BNIP3L shRNAs in MCF10A cells . Error bars indicate SD . **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 01810 . 7554/eLife . 16844 . 019Figure 3—figure supplement 5 . Confirmation of the results of Figure 3E using a second , unrelated shRNA . ( A ) Cell death , monitored by annexin V staining , in MCF10A cells expressing a non-silencing ( NS ) shRNA or BNIP or BNIP3L shRNA unrelated to that used in Figure 3E . Error bars indicate SD . *p<0 . 05; **p<0 . 01 . ( B ) Representative FACS plots corresponding to Figure 3E and ( A ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 01910 . 7554/eLife . 16844 . 020Figure 3—figure supplement 6 . Confirmation of increased levels of BNIP3 and BNIP3L upon ectopic expression . Immunoblot analysis monitoring levels of BNIP3 or BNIP3L in MCF10A cells expressing vector , BNIP3 or BNIP3L . The results confirm increased expression of the proteins . α-tubulin ( TUBA ) was monitored as a loading control . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 020 In the first set of experiments we analyzed BNIP3 and BNIP3L protein levels during anoikis induction . The immunoblot of Figure 3B shows that BNIP3 and BNIP3L levels were very low in attached cells and substantially increased following detachment , with a time course similar to that of detachment-induced KDM3A expression ( see Figure 2A ) . The chromatin immunoprecipitation ( ChIP ) experiment of Figure 3C shows that KDM3A was bound to the BNIP3 and BNIP3L promoters in detached but not attached cells . Moreover , the levels of H3K9me2 ( Figure 3D ) and H3K9me1 ( Figure 3—figure supplement 2 ) on the BNIP3 and BNIP3L promoters were greatly diminished following detachment , which was counteracted by knockdown of KDM3A . Conversely , overexpression of KDM3A but not KDM3A ( H1120G/D1122N ) in attached MCF10A cells resulted in decreased levels of H3K9me1 and H3K9me2 on the BNIP3 and BNIP3L promoters and increased expression of BNIP3 and BNIP3L ( Figure 3—figure supplement 3 ) . Finally , knockdown of BNIP3 or BNIP3L ( Figure 3—figure supplement 4 ) resulted in decreased apoptosis following detachment ( Figure 3E and Figure 3—figure supplement 5 ) . To further establish the pro-apoptotic role of BNIP3 and BNIP3L in MCF10A cells , we ectopically expressed BNIP3 , BNIP3L or both in attached cells ( Figure 3—figure supplement 6 ) . Figure 3F shows that moderate cell death was observed upon ectopic expression of either BNIP3 or BNIP3L , but substantial cell death occurred in cells ectopically expressing both BNIP3 and BNIP3L . Collectively , these results establish BNIP3 and BNIP3L as critical KDM3A target genes that mediate anoikis ( Figure 3G ) . We considered the possibility that decreased KDM3A expression may contribute to anoikis resistance in breast cancer cells and performed a series of experiments to test this idea . We first analyzed a panel of human breast cancer cell lines ( BT549 , MDA-MB-231 , MCF7 , SUM149 and T47D ) comparing , as a control , anoikis-sensitive MCF10A cells . As expected , detachment-induced apoptosis was significantly diminished in breast cancer cell lines compared to MCF10A cells , indicative of anoikis resistance ( Figure 4A and Figure 4—figure supplement 1 ) . Moreover , following detachment of the breast cancer cell lines , induction of KDM3A at both the protein ( Figure 4B ) and mRNA ( Figure 4C ) levels was much lower than that observed in MCF10A cells . However , ectopic expression of KDM3A was sufficient to induce apoptosis in each of the five breast cancer cell lines ( Figure 4D ) . Collectively , these results indicate that anoikis-resistance of human breast cancer cells is due , at least in part , to inefficient induction of KDM3A following detachment . 10 . 7554/eLife . 16844 . 021Figure 4 . KDM3A prevents metastasis and its expression is defective in human breast cancer cell lines and tumors . ( A ) Cell death , monitored by annexin V staining , in MCF10A cells and a panel of human breast cancer cell lines cultured as attached cells or detached following growth in suspension for 96 hr . Error bars indicate SD . P value comparisons for each breast cancer cell line are made to the detached MCF10A sample . ( B ) Immunoblot analysis monitoring KDM3A levels in MCF10A cells and a panel of human breast cancer cell lines cultured as attached ( A ) cells or detached ( D ) following growth in suspension for 24 hr . All images for the KDM3A antibody were cropped from the same blot and thus were processed and exposed in the same manner , as were images for the TUBA loading control . ( C ) qRT-PCR analysis monitoring KDM3A expression in MCF10A cells and a panel of human breast cancer cell lines cultured as attached cells or detached following growth in suspension for 24 hr . Error bars indicate SD . P value comparisons for each breast cancer cell line are made to the detached MCF10A sample . ( D ) Crystal violet staining of human breast cancer cells expressing vector , KDM3A or KDM3A ( H1120G/D1122N ) . ( E ) qRT-PCR analysis monitoring KDM3A expression in normal breast epithelial cells and human breast tumors . TN , triple negative [estrogen receptor-negative ( ER- ) , human epidermal growth factor receptor 2-negative ( HER2- ) and progesterone receptor-negative ( PR- ) ] . Error bars indicate SD . The differences in KDM3A expression between subtypes are not statistically significant . ( F ) Mouse pulmonary survival assay . ( Left ) Representative plates showing colony formation of CLS1 cells expressing a NS or Kdm3a shRNA that had been isolated from mouse lungs following tail vein injection . ( Right ) Quantification of colony formation ( n = 4 mice per shRNA ) . Error bars indicate SD . ( G ) Live animal imaging monitoring lung tumor metastasis in mice following injection of 67NR cells expressing a NS or Kdm3a shRNA ( n = 3 mice per group ) . ( H ) Primary tumor growth in mice injected with 4T07 cells expressing a NS ( n = 7 ) or Kdm3a ( n = 8 ) shRNA . Error bars indicate SEM . The differences in primary tumor growth between groups are not statistically significant . ( I ) Metastatic burden . Number of metastatic lesions per lung in mice injected with 4T07 cells expressing a NS ( n = 7 ) or Kdm3a ( n = 8 ) shRNA . Error bars indicate SEM . **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 02110 . 7554/eLife . 16844 . 022Figure 4—source data 1 . Source data for Figure 4A , C , E , F , H and I . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 02210 . 7554/eLife . 16844 . 023Figure 4—figure supplement 1 . FACS analysis . Representative FACS plots corresponding to Figure 4A . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 02310 . 7554/eLife . 16844 . 024Figure 4—figure supplement 2 . Oncomine analysis of KDM3A expression in breast cancer . The Oncomine Cancer Profiling database was queried to access Finak ( A ) , Sorlie ( B ) , Zhao ( C ) and The Cancer Genome Atlas ( TCGA ) ( D ) breast cancer data sets . The results reveal that KDM3A is significantly under-expressed in breast carcinoma relative to normal tissue . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 02410 . 7554/eLife . 16844 . 025Figure 4—figure supplement 3 . Analysis of basal KDM3A expression in human breast cancer cell lines . qRT-PCR analysis of KDM3A expression in MCF10A cells and a panel of human breast cancer cell lines cultured as attached cells . The results were normalized to that obtained in MCF10A cells , which was set to 1 . The results show that basal KDM3A expression levels were diminished in four of five human breast cancer cell lines analyzed . Error bars indicate SD . *p<0 . 05; **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 02510 . 7554/eLife . 16844 . 026Figure 4—figure supplement 4 . Analysis of Kdm3a shRNA knockdown efficiency in mouse CLS1 cells . qRT-PCR analysis monitoring knockdown efficiency of Kdm3a in CLS1 cells . Error bars indicate SD . **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 02610 . 7554/eLife . 16844 . 027Figure 4—figure supplement 5 . Analysis of Kdm3a expression in a mouse breast cancer carcinoma progression series . qRT-PCR analysis of Kdm3a expression in 67NR , 4T07 , and 4T1 cells . Error bars indicate SD . **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 02710 . 7554/eLife . 16844 . 028Figure 4—figure supplement 6 . Analysis of Kdm3a shRNA knockdown efficiency in mouse 67NR cells . qRT-PCR analysis monitoring knockdown efficiency of Kdm3a in 67NR cells . Error bars indicate SD . **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 02810 . 7554/eLife . 16844 . 029Figure 4—figure supplement 7 . Analysis of Kdm3a shRNA knockdown efficiency in mouse 4T07 cells . qRT-PCR analysis monitoring knockdown efficiency of two unrelated Kdm3a shRNAs in 4T07 cells . Error bars indicate SEM . *p<0 . 05 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 02910 . 7554/eLife . 16844 . 030Figure 4—figure supplement 8 . Confirmation of the results of Figure 4H using a second , unrelated shRNA . Primary tumor growth in mice injected with 4T07 cells expressing a NS ( n = 7 ) or Kdm3a ( n = 9 ) shRNA unrelated to that used in Figure 4H . Error bars indicate SEM . The differences in primary tumor growth between groups are not statistically significant . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 03010 . 7554/eLife . 16844 . 031Figure 4—figure supplement 9 . Confirmation of the results of Figure 4I using a second , unrelated shRNA . Number of metastatic lesions per lung in mice injected with 4T07 cells expressing a NS ( n = 7 ) or Kdm3a ( n = 9 ) shRNA unrelated to that used in Figure 4I . Error bars indicate SEM . **p<0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 16844 . 031 We next analyzed KDM3A expression in human breast cancer patient samples . Interrogation of the Oncomine database ( Rhodes et al . , 2007 ) revealed decreased expression levels of KDM3A in several breast cancer datasets ( Figure 4—figure supplement 2 ) . To confirm these in silico results , we analyzed KDM3A expression by qRT-PCR in a series of human breast cancer patient samples . The results of Figure 4E show that compared to normal breast epithelium KDM3A expression was significantly decreased in a high percentage of breast cancers . Likewise , basal KDM3A expression levels were also diminished in most human breast cancer cell lines analyzed ( Figure 4—figure supplement 3 ) . Finally , we performed a series of experiments to determine whether KDM3A affects metastatic potential . We first asked whether depletion of KDM3A would promote anoikis resistance in vivo using a mouse pulmonary survival assay . Briefly , immortalized but non-transformed mouse mammary epithelial CLS1 cells were stably transduced with an NS or Kdm3a shRNA ( Figure 4—figure supplement 4 ) and injected into the tail vein of syngeneic mice . After 2 weeks , the lungs were harvested , dissociated into single cell suspensions , and plated in media containing puromycin to select for cells expressing the shRNA . The surviving colonies were visualized by crystal violet staining and quantified . The results of Figure 4F show that Kdm3a knockdown significantly increased the number of cells that survived in the mouse lung relative to the control NS shRNA . In a second set of experiments , we used a well-characterized mouse breast cancer carcinoma progression series comprising isogenic cell lines with increasing metastatic potential: ( 1 ) non-invasive and non-metastatic 67NR cells , which form primary tumors , ( 2 ) invasive and non-metastatic 4T07 cells , which enter the circulation but fail to establish secondary tumors , and ( 3 ) highly metastatic 4T1 cells , which disseminate widely and colonize distant organ sites ( Aslakson and Miller , 1992 ) . qRT-PCR analysis revealed decreased Kdm3a expression in cell lines with greater metastatic potential ( Figure 4—figure supplement 5 ) . We expressed either a control NS shRNA or a Kdm3a shRNA in 67NR cells containing a luciferase reporter gene ( Figure 4—figure supplement 6 ) . Cells were injected into the tail veins of three syngeneic mice and pulmonary metastases were visualized by live animal imaging after 5 weeks . The results of Figure 4G show , as expected , that control 67NR cells failed to form pulmonary metastases in any of the three mice analyzed . By contrast , Kdm3a knockdown 67NR cells formed substantial pulmonary metastases in all three mice . Finally , in a more stringent metastasis experiment , control and Kdm3a knockdown 4T07 cells ( Figure 4—figure supplement 7 ) , a non-metastatic mouse breast cancer cell line , were injected in the mammary fat pad of ten syngeneic mice . After 22 days the primary tumors were surgically removed and 8 weeks post-injection the animals were sacrificed and pulmonary tumors quantified . The growth of primary tumors formed by NS or Kdm3a knockdown cells was similar ( Figure 4H and Figure 4—figure supplement 8 ) . However , Kdm3a knockdown cells caused significantly increased metastatic burden in the lungs compared to control 4T07 cells ( Figure 4I and Figure 4—figure supplement 9 ) . Consistent with our results , knockdown of Bnip3 has also been shown to cause increased metastasis in similar in vivo experiments ( Manka et al . , 2005 ) . Collectively , these results show that KDM3A functions to prevent metastasis . Based on the results presented above , we propose a model of anoikis induction that is illustrated in Figure 3G and discussed below . Following detachment of non-transformed cells , integrin signaling is decreased leading to transcriptional induction of KDM3A . The increased levels of KDM3A results in its recruitment to the pro-apoptotic genes BNIP3 and BNIP3L , where it promotes demethylation of inhibitory H3K9me1/2 marks and transcriptional activation of the two genes , resulting in anoikis induction . Consistent with this model , previous studies have shown that hypoxia results in transcriptional activation of KDM3A , BNIP3 and BNIP3L ( Beyer et al . , 2008; Sowter et al . , 2001 ) . We have found that in anoikis-resistant human breast cancer cell lines and tumors , KDM3A expression is defective , highlighting the importance of this pathway in promoting anoikis . Collectively , our results reveal a novel transcriptional regulatory program that mediates anoikis in non-transformed cells and is disabled during cancer development . As described above , previous studies have shown that BIM and BMF are also effectors of anoikis ( Reginato et al . , 2003; Schmelzle et al . , 2007 ) . However , we have found that unlike BNIP3 and BNIP3L , BIM and BMF are not regulated by KDM3A . Thus , our results reveal that anoikis is promoted by multiple non-redundant pathways , which may help prevent the development of anoikis resistance .
T47D , MDA-MB-231 , BT549 and CLS1 cells were obtained from ATCC ( Manassas , VA ) and grown as recommended by the supplier . MCF7 cells ( National Cancer Institute , Bethesda , MD ) were maintained in DMEM ( GE Healthcare Life Sciences , Marlborough , MA ) supplemented with 1X nonessential amino acids ( NEAA; Thermo Scientific , Waltham , MA ) and 10% fetal bovine serum ( FBS; Atlanta Biologics , Norcross , GA ) . MCF10A cells ( ATCC ) were maintained in DMEM/F12 ( GE Healthcare Life Sciences ) supplemented with 5% donor horse serum ( Thermo Scientific ) , 20 ng/ml epidermal growth factor ( Peprotech , Rocky Hill , NJ ) , 10 µg/ml insulin ( Life Technologies , Grand Island , NY ) , 1 ng/ml cholera toxin ( Sigma-Aldrich , St . Louis , MO ) , 100 µg/ml hydrocortisone ( Sigma-Aldrich ) , 50 U/ml penicillin ( Thermo Scientific ) , and 50 µg/ml streptomycin ( Invitrogen , Grand Island , NY ) . SUM149 cells were obtained from Dr . Donald Hnatowich ( University of Massachusetts Medical School , Worcester , MA ) and grown in RPMI ( Invitrogen ) supplemented with 10% FBS , 0 . 01% insulin , 50 U/ml penicillin , and 50 µg/ml streptomycin . 67NR and 4T07 cells were obtained from Dr . Fred Miller ( Wayne State University School of Medicine , Detroit , MI ) and were grown in high glucose DMEM ( GE Healthcare Life Sciences ) supplemented with 10% FBS , 50 U/ml penicillin , and 50 µg/ml streptomycin . Cell lines used in this study have not been authenticated for identity . KDM3A and KDM3A ( H1120G/D1122N ) were PCR amplified from pCMV-JMJD1A and pCMV-JMJD1A ( H1120G/D1122N ) , respectively , obtained from Dr . Peter Staller ( Biotech Research and Innovation Centre , University of Copenhagen , Denmark ) , using primers ( forward , 5’-CTCGAGCCGTTAAGGTTTGCCAAAAC-3’ and reverse , 5’-ATCGTTAACAGGGAGATTAAGGTTTGCCA-3’ ) engineered with XhoI and HpaI restriction sites and then cloned into pMSCVpuro ( ClonTech Laboratories , Inc . , Mountain View , CA ) . BNIP3L was PCR amplified from Bnip3L pcDNA3 . 1 ( plasmid #17467 , Addgene , Cambridge , MA ) using primers ( forward , 5’-AATCTCGAGCATGTCGTCCCACCTAGT-3’ and reverse 5’-ATCGAATTCTTAATAGGTGCTGGCAGAGG-3’ ) engineered with XhoI and EcoRI restriction sites and cloned into pMSCVhygro ( ClonTech Laboratories , Inc . ) . BNIP3 was PCR amplified from MGC Human BNIP3 cDNA ( Dharmacon , Marlborough , MA ) using primers ( forward , 5’-AATCTCGAGCATGTCGCAGAACGGAGCG-3’ and reverse 5’-ATCGAATTCACTAAATTAGGAACGCAGCAT-3’ ) engineered with XhoI and EcoRI restriction sites and cloned into pMSCVpuro . MCF10A cells stably expressing pMSCVpuro-JMJD1A , pMSCVpuro-JMJD1A-H1120G/D1122N , pMSCVpuro-BNIP3 , pMSCVhygro-BNIP3L , pMSCVpuro-empty , pMSCVhygro-empty , pBABE-MEK2DD ( obtained from Dr . Sylvain Meloche , Université de Montréal ) , pBABE-EGFR ( Addgene ) , or pBABE-empty ( Addgene ) were generated by retroviral transduction as described previously ( Santra et al . , 2009 ) . Twelve days after puromycin or hygromycin selection , cells were stained with 0 . 5% crystal violet . The human shRNAmir pSM2 library ( Open Biosystems/Thermo Scientific , Pittsburgh , PA ) was obtained through the University of Massachusetts Medical School RNAi Core Facility ( Worcester , MA ) . Retroviral pools were generated and used to transduce MCF10A cells as described previously ( Gazin et al . , 2007 ) . Following puromycin selection , transduced cells were divided into two populations: one was plated on poly-HEMA-coated tissue culture plates ( plates were coated with poly-HEMA ( 20 mg/ml ) ( Sigma-Aldrich ) , dried at room temperature overnight , and washed with phosphate buffered saline ( PBS ) before use ) and grown for 10 days , and the other was grown for 10 days under normal tissue culture conditions . Cells that survived 10 days in suspension ( a time point at which >95% of cells transduced with the control NS shRNA were killed ) were seeded under normal tissue culture conditions to expand the population . shRNAs present in the surviving suspension population and the attached population were identified by deep sequencing at the University of Massachusetts Medical School Deep Sequencing Core Facility ( Worcester , MA ) . The frequency of individual shRNAs in each sample was determined as described previously ( Xie et al . , 2012 ) . The raw sequencing data have been uploaded to NCBI Gene Expression Omnibus and are accessible through GEO Series accession number GSE80144 . For stable shRNA knockdowns , 1 × 105 cells were seeded in a six-well plate to 50% confluency and subsequently transduced with 200 µl lentiviral particles expressing shRNAs ( obtained from Open Biosystems/Thermo Scientific through the UMMS RNAi Core Facility , listed in Supplementary file 1 ) in a total volume of 1 ml of appropriate media supplemented with 6 µg/ml polybrene ( Sigma-Aldrich ) . Media was replaced after overnight incubation to remove the polybrene , and viral particles and cells were subjected to puromycin selection ( 2 µg/ml ) for 3 days . Total RNA was isolated and reverse transcription was performed as described ( Gazin et al . , 2007 ) , followed by qRT-PCR using Power SYBR Green PCR Master Mix ( Applied Biosystems , Grand Island , NY ) . RPL41 or GAPDH were used as internal reference genes for normalization . See Supplementary file 2 for primer sequences . Each sample was analyzed three independent times and the results from one representative experiment , with technical triplicates or quadruplicates , are shown . Cells were placed in suspension in normal growth media in the presence of 0 . 5% methyl cellulose ( Sigma Aldrich ) ( to avoid clumping of cells ) on poly-HEMA-coated tissue culture plates . All anoikis assays were done at a cell density of 3 × 105 cells/ml . Control cells were cultured under normal cell culture conditions . Cell death was measured by staining the cells with FITC-conjugated Annexin-V ( ApoAlert , ClonTech ) according to the manufacturer’s instructions followed by analysis by flow cytometry ( Flow Cytometry Core Facility , University of Massachusetts Medical School ) at the indicated times . To restore integrin signaling in suspension , media was supplemented with 5% growth-factor-reduced Matrigel ( BD Biosciences , San Diego , CA ) . Each sample was analyzed in biological triplicate . Cell extracts were prepared by lysis in Laemmli buffer in the presence of protease inhibitor cocktail ( Roche , Indianapolis , IN ) . The following commercial antibodies were used: beta-ACTIN ( Sigma-Aldrich ) ; BNIP3 , BNIP3L , KDM3A , H3K9me2 ( all from Abcam , Cambridge , MA ) ; cleaved Caspase 3 , BIM , phospho-ERK1/2 , total ERK1/2 , phospho-EGFR , total EGFR , phospho-FAK ( all from Cell Signaling Technology , Danvers , MA ) ; total FAK ( Millipore , Billerica , MA ) ; and α-tubulin ( TUBA; Sigma-Aldrich ) . Cells were treated with dimethyl sulfoxide ( DMSO ) , 1 , 5 or 10 µM U0126 ( Cell Signaling Technology ) , gefitinib ( Santa Cruz Biotechnology , Inc . , Dallas , TX ) , or FAK inhibitor 14 ( CAS 4506-66-5 , Santa Cruz Biotechnology , Inc . ) for 48 hr prior to preparation of cell extracts or total RNA isolation , as described above . ChIP assays were performed as previously described ( Gazin et al . , 2007 ) using antibodies against KDM3A and H3K9me2 ( both from Abcam ) and H3K9me1 ( Epigentek ) . ChIP products were analyzed by qPCR ( see Supplementary file 2 for promoter-specific primer sequences ) . Samples were quantified as percentage of input , and then normalized to an irrelevant region in the genome ( ~3 . 2 kb upstream from the transcription start site of GCLC ) . Fold enrichment was calculated by setting the IgG control IP sample to a value of 1 . Each ChIP experiment was performed three independent times and the results from one representative experiment , with technical duplicates , are shown . This study was approved by the institutional review boards at the University of Massachusetts Medical School ( UMMS ) and the Mayo Clinic . Total RNA from 24 breast cancer patient samples were obtained from Fergus Couch ( Mayo Clinic , Rochester , MN ) and total RNA from five normal breast samples were obtained from the University of Massachusetts Medical School Tissue and Tumor Bank Facility . KDM3A expression was measured by qRT-PCR in technical triplicates of each patient sample . Statistical analysis ( unequal variance t-test ) was performed using R , a system for statistical computation and graphics ( Ihaka and Gentleman , 1996 ) . The Oncomine Cancer Profiling Database ( Compendia Bioscience , Ann Arbor , MI ) was queried using the cancer type Breast Cancer and a threshold p-value of 0 . 05 to access Finak ( Finak et al . , 2008 ) , Sorlie ( Sorlie et al . , 2001 ) , Zhao ( Zhao et al . , 2004 ) and TCGA ( TCGA , 2011 ) datasets . Histograms depicting KDM3A gene expression in each sample , and the p value for the comparison of KDM3A expression between the groups , were obtained directly through the Oncomine software . All animal protocols were approved by the Institution Animal Care and Use Committee ( IACUC ) . Animal sample sizes were selected based on precedent established from previous publications . CLS1 cells were stably transduced with either a NS or Kdm3a shRNA and selected with 2 µg/ml puromycin for 5 days . Stably transduced CLS1 cells ( 2 × 105 ) were injected into the tail vein of 4–6 week old female BALB/c mice ( Taconic Biosciences ) ( n = 4 mice per shRNA ) . Two weeks post injection the lungs were harvested , dissociated into single cell suspension , and plated onto tissue culture plates . Transduced CLS1 cells were selected for by treating the dissociated lung cells with 2 µg/ml puromycin . Surviving colonies were stained with crystal violet and quantified by counting . All experiments were performed in accordance with the Institutional Animal Care and Use Committee ( IACUC ) guidelines . 67NR cells were transduced with a NS or Kdm3a shRNA and selected with 2 µg/ml puromycin for 5 days . Stably transduced 67NR cells ( 2 × 105 ) were injected into the tail vein of 6–8 week old female BALB/c mice ( n = 3 mice per shRNA ) . Five weeks post injection , mice were given an intraperitoneal injection of D-Luciferin ( 100 mg/kg ) ( Gold Biotechnology , St . Louis , MO ) and imaged on the Xenogen IVIS-100 ( Caliper Life Sciences ) . Images were taken with Living Image software . All experiments were performed in accordance with the Institutional Animal Care and Use Committee ( IACUC ) guidelines . Female BALB/c mice ( 4–6 weeks ) were purchased from Charles River Laboratories ( Shrewsbury , MA ) . The mice were housed in facilities managed by the McGill University Animal Resources Centre ( Montreal , Canada ) , and all animal experiments were conducted under a McGill University–approved Animal Use Protocol in accordance with guidelines established by the Canadian Council on Animal Care . Spontaneous metastasis studies were carried out as previously described ( Tabaries et al . , 2011 ) . Briefly , 4T07 cells expressing a NS or Kdm3a shRNA were first tested for mycoplasma contamination and found to be negative . Cells were then harvested from subconfluent plates , washed once with PBS , and resuspended ( 5 × 103 cells ) in 50 µl of a 50:50 solution of Matrigel ( BD Biosciences ) and PBS . This cell suspension was injected into the right abdominal mammary fat pad of BALB/c mice ( n = 10 mice per shRNA ) and measurements were taken beginning on day 7 post-injection . Animals that did not develop a primary tumor were excluded from the study . Tumor volumes were calculated using the following formula: πLW2/6 , where L is the length and W is the width of the tumor . Tumors were surgically removed , using a cautery unit , once they reached a volume around 500 mm3 , approximately 3 weeks post injection . Lungs were collected 8 weeks post-injection . Tumor burden in the lungs was quantified from four H&E stained step sections ( 200 µm/step ) . The number of lesions per section were counted using Imagescope software ( Aperio , Vista , CA ) . All quantitative data were collected from experiments performed in at least triplicate , and expressed as mean ± standard deviation , with the exception of Figure 4H and I , which are expressed as mean ± SEM . Differences between groups were assayed using two-tailed Student’s t test , except where noted above . Significant differences were considered when P<0 . 05 . | Epithelial cells line the inside of blood vessels , intestines and other organs throughout the body . Any epithelial cells that become detached from their natural surroundings die by a process called anoikis ( a Greek word meaning “being without a home” ) . This process has an important role in preventing cancer from spreading around the body because it eliminates cells that are not in their proper environment . However , some cancers that start from epithelial cells , such as breast cancer , develop resistance to anoikis . Gaining a better understanding of the cellular factors that regulate anoikis , and how resistance develops , may reveal new drug targets for the treatment of breast cancer . Previous studies found proteins called BIM and BMF promote anoikis by inducing cell suicide . However , it is possible that other factors can also promote this process in different ways . Pedanou et al . performed a large-scale genetic screen in human breast epithelial cells and identified several new factors that promote anoikis . Inside our cells , DNA is packaged around proteins called histones , which can influence whether a gene is switched on or off . One of the factors Pedanou et al . identified is a protein called KDM3A that can remove small chemical groups ( known as methyl groups ) from histones – a process that is known to switch on genes . Further experiments show that epithelial cells in their natural surroundings only produce low levels of KDM3A , but that the levels of this protein increase if these cells become detached . This promotes anoikis by activating two genes called BNIP3 and BNIP3L that induce cell suicide . However , KDM3A levels are low in human breast cancers , which suggests that these cancers become resistant to anoikis by preventing increases in KDM3A production . Using a mouse model of breast cancer , Pedanou et al . found that switching off KDM3A in cancer cells increases their ability to move around the body . Collectively , these findings reveal a new mechanism that triggers anoikis in normal breast epithelial cells and is disabled during breast cancer development . Future challenges are to identify factors that directly regulate the production of KDM3A , and to understand how these factors are manipulated in breast cancer cells to cause anoikis resistance . | [
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] | 2016 | The histone H3K9 demethylase KDM3A promotes anoikis by transcriptionally activating pro-apoptotic genes BNIP3 and BNIP3L |
DNA damage accumulates with age ( Lombard et al . , 2005 ) . However , whether and how robust DNA repair machinery promotes longevity is elusive . Here , we demonstrate that ATM-centered DNA damage response ( DDR ) progressively declines with senescence and age , while low dose of chloroquine ( CQ ) activates ATM , promotes DNA damage clearance , rescues age-related metabolic shift , and prolongs replicative lifespan . Molecularly , ATM phosphorylates SIRT6 deacetylase and thus prevents MDM2-mediated ubiquitination and proteasomal degradation . Extra copies of Sirt6 extend lifespan in Atm-/- mice , with restored metabolic homeostasis . Moreover , the treatment with CQ remarkably extends lifespan of Caenorhabditis elegans , but not the ATM-1 mutants . In a progeria mouse model with low DNA repair capacity , long-term administration of CQ ameliorates premature aging features and extends lifespan . Thus , our data highlights a pro-longevity role of ATM , for the first time establishing direct causal links between robust DNA repair machinery and longevity , and providing therapeutic strategy for progeria and age-related metabolic diseases .
A variety of metabolic insults can generate DNA lesions in mammalian cells , which , if incorrectly repaired , can lead to somatic mutations and cell transformation ( Vijg , 2014 ) . If unrepaired , such lesions can accumulate and constantly activate the DNA damage response ( DDR ) , a unique feature and mechanism of senescence ( Halliwell and Whiteman , 2004; Tanaka et al . , 2006 ) . Ataxia telangiectasia mutated ( ATM ) , a serine/threonine protein kinase , is one of the key regulators of DDR ( Guleria and Chandna , 2016 ) . Upon DNA damage , self-activated ATM phosphorylates downstream transducers and effectors , promoting DNA repair ( Bakkenist and Kastan , 2003; Paull , 2015 ) . H2AX is a well-documented phosphorylation target of ATM; phosphorylated H2AX at S139 ( γH2AX ) is widely recognized as a hallmark of DNA damage ( Burma et al . , 2001 ) . Accompanied by decline in DNA repair function , γH2AX-enriched DNA damage foci accumulate in senescent cells and in tissues from aged animals ( Maslov and Vijg , 2009 ) , supporting causal links between defective DDR and aging . In human fibroblasts , a dramatic decline of homologous recombination ( HR ) efficiency , attributable to defective recruitment of Rad51 , has been observed ( Li et al . , 2016 ) . Similar defects in HR also have been observed in Hutchinson-Gilford progeria syndrome ( HGPS ) , which is predominantly caused by a LMNA C1024T mutation ( Liu et al . , 2005 ) . In addition to DNA damage accumulation , inherited loss-of-function mutations in essential components of DNA repair machinery accelerate aging in humans and mice ( Hoeijmakers , 2009b ) . Patients suffering from ataxia telangiectasia ( A-T ) develop prominent aging features in their second decades ( Boder and Sedgwick , 1958; Shiloh and Lederman , 2017 ) . Werner syndrome , Bloom’s syndrome and Rothmund-Thomson syndrome are all progeria syndromes caused by mutations of genes that directly regulate DNA repair ( Balajee et al . , 1999; Cooper et al . , 2000; Lebel et al . , 1999; Li and Comai , 2000 ) . Homozygous disruption of Atm in mice causes many premature aging features of A-T , such as growth retardation , infertility , neurodegeneration , immunodeficiency and cancer predisposition ( Barlow et al . , 1996 ) . Mouse models deficient in DNA repair factors , including DNA-PKcs , Ku70 , Ku80 , DNA ligase IV , Artemis or Ercc1 etc . , phenocopy premature aging features ( Hasty , 2005; Hoeijmakers , 2009a ) , supporting the suggestion that defects in DNA repair accelerate aging . However , whether and how robust DNA repair machinery promotes longevity is poorly understood . Metabolic disturbance is another antagonistic hallmark of aging ( López-Otín et al . , 2013 ) . Although DNA repair deficiency is implicated in aging and age-related diseases including metabolic disorders ( López-Otín et al . , 2016; Shimizu et al . , 2014 ) , the mechanistic link between decreased DNA repair machinery and metabolic reprogramming during aging is poorly understood . Notably , in response to oxidative stress , ATM phosphorylates Hsp27 , shifting glucose metabolism from glycolysis to the pentose phosphate pathway ( PPP ) ( Cosentino et al . , 2011; Krüger and Ralser , 2011 ) . Inactivating ATM enhances glucose and glutamine consumption by inhibiting P53 and upregulating c-MYC ( Aird et al . , 2015 ) . However , the role of ATM in age-onset metabolic disturbances is as yet unclear . Here , we identified a progressive decline in ATM-centered DNA repair machinery during aging , along with shunted glucose metabolism to glycolysis . DNA damage-free activation of ATM by chloroquine ( CQ ) promotes DNA damage clearance , rescues age-related metabolic shift , and alleviates cellular senescence . Mechanistically , ATM phosphorylates and stabilizes pro-longevity protein SIRT6 . Extra copies of Sirt6 attenuate metabolic abnormality and extend lifespan in Atm-/- mice . Importantly , long-term treatment of CQ restores metabolic reprogramming and extends the lifespan of nematodes and a progeria mouse model .
In searching for genes/pathways that drive senescence , we employed human primary endothelial cells , which underwent replicative senescence at passage 21 , with increased p21 expression and β-galactosidase activity ( Figure 1—figure supplement 1a–b ) . By RNAseq analysis , a gradual decline of ATM-centered DNA repair machinery was identified ( Figure 1—figure supplement 1c–e ) . Western blotting analysis confirmed progressively downregulated protein levels of ATM and its downstream target NBS1 and RAP80 in senescent human skin fibroblasts ( HSFs ) ( Figure 1a ) . Mouse embryonic fibroblasts ( MEFs ) with limited growth capacity and senescent phenotypes when cultured in vitro ( Parrinello et al . , 2003; Samper et al . , 2003; Sherr and DePinho , 2000 ) , and brain tissues from aged mice also showed progressive decline of ATM , NBS1 , and RAP80 ( Figure 1b–c ) . Concomitantly , upregulation of γH2AX , indicating accumulated DNA damage , and an increase in p16Ink4a were observed in senescent HSFs , MEFs , and aged brain tissues ( Figure 1a–c ) . Knocking down ATM via shRNA accelerated senescence in HSFs , evidenced by increased β-galactosidase activity ( Figure 1d–e ) , enlarged morphology ( Figure 1—figure supplement 2a ) , accumulated γH2AX ( Figure 1f ) , and reduced cell proliferation ( Figure 1—figure supplement 2b ) . These data indicate that ATM decline retards DDR and drives senescence . Other than DNA damage , ATM is activated by chloroquine ( CQ ) , an antimalarial drug that modulates chromatin confirmation ( Bakkenist and Kastan , 2003 ) . We confirmed that a low dose of CQ increased the level of pS1981 auto-phosphorylation of ATM but not γH2AX ( Figure 1—figure supplement 2c ) . We then investigated whether activating ATM by CQ can ameliorate senescence . As shown , the CQ treatment activated ATM ( pS1981 ) , promoted clearance of DNA damage ( γH2AX ) , and inhibited apoptosis ( cleaved Casp3 ) in HSFs ( Figure 1g ) . Also , the CQ treatment suppressed β-galactosidase activity , which was abrogated if ATM was knocked down ( Figure 1h–i ) . Importantly , CQ treatment extended the replicative lifespan of HSFs ( Figure 1j ) . Likewise , CQ treatment activated Atm , cleared up accumulated DNA damage , suppressed β-galactosidase activity ( Figure 1k and Figure 1—figure supplement 2d–e ) , and prolonged replicative lifespan in MEFs ( Figure 1l ) . Although both 10 μM and 1 μM of CQ activated ATM , a dose-dependent toxicity assay showed that 1 μM is suitable for long-term treatment ( Figure 1—figure supplement 2f–g ) . Of note , ATM KD or low dose of CQ applied in this study had little effect on basal autophagic activity ( Figure 1f , j and Figure 1—figure supplement 2g ) . Collectively , CQ activates ATM to alleviate replicative senescence . A-T patients lacking functional ATM display features of premature aging , accompanied by insulin resistance and glucose intolerance ( Bar et al . , 1978; Espach et al . , 2015 ) . Senescent cells exhibit impaired mitochondrial respiration , but enhanced glycolysis producing more lactate ( Hagen et al . , 1997; Lenaz et al . , 2000 ) . As such , we wondered whether ATM decline triggers an age-associated metabolic shift . Levels of glycolytic genes LDHB and PDK1 were dramatically increased in senescent MEFs and HSFs ( Figure 2a and Figure 2—figure supplement 1a ) , and in liver tissues from Atm-/- mice ( Figure 2—figure supplement 1b ) . Significantly , activating ATM via CQ suppressed senescence-associated glycolysis ( Figure 2a and Figure 2—figure supplement 1a ) . Similarly , the inhibitory effect on glycolysis was diminished when ATM was depleted in HepG2 cells ( Figure 2b ) . These data suggest a role for ATM in inhibiting glycolysis . To examine how ATM regulates glycolysis , we performed RNA-Seq in Atm-/- MEF cells , and revealed a significant upregulation of glycolytic pathways ( Figure 2c , and Figure 2—source data 1 ) . Specific genes were validated by q-PCR ( Figure 2—figure supplement 1c ) . As p53 is critical in glycolysis ( Kruiswijk et al . , 2015; Schwartzenberg-Bar-Yoseph et al . , 2004 ) , we further analyzed metabolomics of Atm-/- and control MEFs in p53 null background . As shown , the metabolic profile exhibited a clear shift , i . e . mitochondrial electron transport chain and intermediates of TCA cycle were reduced , while intermediates of glycolysis were elevated ( Figure 2d , Figure 2—figure supplement 1d–e and Figure 2—source data 2 ) . The data suggest that ATM deficiency enhances anaerobic glycolysis in a p53-independent manner . Sirt6 deacylase is able to shunt energy metabolism away from anaerobic glycolysis to the TCA cycle via H3K9ac-mediated local chromatin remodeling ( Sebastián et al . , 2012; Zhong et al . , 2010 ) . We noted that the level of H3K9ac was enhanced in cells depleted ATM ( Figure 2e ) . Re-expressed ATM in A-T cells suppressed H3K9ac level ( Figure 2f ) . ChIP analysis showed that H3K9ac was enriched at the promoter regions of glycolytic genes in Atm-/- cells ( Figure 2g ) , where the relative occupancy of SIRT6 was abolished ( Figure 2h ) . Consistent with increased H3K9ac , SIRT6 protein level was dramatically downregulated in Atm-/- mouse livers , and ATM-deficient HepG2 , U2OS and HEK293 cells ( Figure 2—figure supplement 1f–i ) . In contrast , protein levels of other sirtuins were not much affected in ATM KO HEK293 cells ( Figure 2i ) , and mRNA levels of all sirtuins remained unchanged ( Figure 2—figure supplement 1j ) . Moreover , transcriptomic analysis and q-PCR data illustrated that Sirt6 depletion upregulated a similar cluster of genes essential for glycolysis ( Figure 2—figure supplement 2a–b and Figure 2—source data 3 ) . More importantly , the hyper-activated glycolytic pathway caused by ATM deficiency was completely restored by ectopic SIRT6 in HepG2 cells ( Figure 2—figure supplement 2c ) . The CQ treatment upregulated SIRT6 level and reduced H3K9ac level , especially at the regulatory regions of glycolytic genes ( Figure 2—figure supplement 2d–e ) . Knocking down SIRT6 abolished the inhibitory effect of CQ on glycolysis ( Figure 2b ) . Additionally , ATM depletion in HEK293 cells , HSFs , and MEFs , significantly downregulated SIRT6 protein level , with little effect on SIRT1 or SIRT7 ( Figure 2i and Figure 2—figure supplement 2f–g ) . Thus , these data suggest that ATM decline triggers an age-associated metabolic shift via SIRT6-mediated chromatin remodeling . Other than metabolic abnormality , depleting Sirt6 leads to premature aging features and shortened lifespan ( Mostoslavsky et al . , 2006a ) , whereas extra copies of Sirt6 promote longevity in male mice ( Kanfi et al . , 2012 ) . Given that Sirt6 was destabilized in Atm null mice , we wondered whether the Sirt6 transgene could rescue premature aging phenotypes and shortened lifespan in Atm-/- mice . To this end , we generated Sirt6 transgenic mice by microinjection , and bred them with Atm-/- mice . The overexpression of Sirt6 was demonstrated by western blotting ( Figure 2—figure supplement 2h ) . Significantly , ectopic Sirt6 restored the elevation of serum lactate , and extended lifespan of Atm-/- mice of both genders ( Figure 2j and Figure 2—figure supplement 2i ) . Importantly , Atm-/-;Sirt6-tg mice exhibited improved glucose tolerance and decreased insulin resistance ( Figure 2k–l ) . Given that little difference was observed in glucose metabolism between young wild-type ( WT ) and Sirt6-transgenic mice ( Kanfi et al . , 2012 ) , these data suggest a contributory role of the Atm-Sirt6 axis in the age-associated metabolic reprogramming . Next , we examined how ATM regulates SIRT6 . Significantly , overexpression of ATM increased SIRT6 level , but this was abolished when ATM was S1981A-mutated to block dimeric ATM dissociation ( Bakkenist and Kastan , 2003; Berkovich et al . , 2007 ) ( Figure 3a ) . Moreover , in addition to CQ , hypotonic buffer ( 20 mM NaCl ) , low glucose ( LG ) , DNA-damaging agent camptothecin ( CPT ) , and doxorubicin ( Dox ) all activated ATM and concomitantly increased SIRT6 protein level ( Figure 3—figure supplement 1a–c ) , which was abrogated in ATM-depleted cells ( Figure 3—figure supplement 1b–c ) . These data implicate a direct regulation of SIRT6 stability by ATM kinase activity . To confirm this , we first performed co-immunoprecipitation ( Co-IP ) in cells transfected with various FLAG-sirtuins . Interestingly , ATM was predominantly associated with SIRT6 among seven sirtuins ( Figure 3b ) . The interaction was further confirmed at both ectopic and endogenous levels ( Figure 3c and Figure 3—figure supplement 1d ) . Immunofluorescence microscopy showed co-localization of SIRT6 and ATM protein in the nucleus ( Figure 3d ) . A domain mapping experiment indicated that the C-terminal domain was required for SIRT6 binding to ATM ( Figure 3—figure supplement 1e ) . To determine whether ATM physically binds to SIRT6 , 10 consecutive recombinant GST-ATM proteins were obtained and the binding to purified His-SIRT6 was analyzed . As shown ( Figure 3e ) , His-SIRT6 bound predominantly to GST-ATM-4 ( residues 770–1102 ) and relatively weakly to GST-ATM-1 ( residues 1–250 ) ; both belong to the N-terminal HEAT repeat domain of ATM . We next examined whether ATM phosphorylates SIRT6 . Firstly , we found that CQ or CPT treatment significantly enhanced the binding of SIRT6 to ATM ( Figure 3f and Figure 3—figure supplement 1f ) , whereas the S1981A mutant blocked such association ( Figure 3—figure supplement 1g ) . ATM preferentially phosphorylates the S/T-Q motif . In the presence of CPT , an increased p-S/TQ level of SIRT6 was identified ( Figure 3g ) . Of note , lambda protein phosphatase ( λPP ) diminished the p-S/TQ level of SIRT6 ( Figure 3—figure supplement 1h ) . Likewise , the p-S/TQ level of SIRT6 was elevated in cells treated with low glucose , which activates ATM by ROS generation ( Assaily et al . , 2011; Sarre et al . , 2012 ) ( Figure 3—figure supplement 1i ) . Moreover , ectopic ATM significantly increased the p-S/TQ level of SIRT6 , but this was abolished in the case of S1981A mutant ( Figure 3h ) . Consistently , a pronounced reduction of p-S/TQ level of SIRT6 was observed in cells lacking ATM or treated with KU55933 , a selective and specific ATM kinase inhibitor ( Berkovich et al . , 2007; Hickson et al . , 2004 ) ( Figure 3i–j ) . The decrease in p-S/TQ level was primarily attributable to loss of ATM , as it was restored by ectopic FLAG-ATM in a dose-dependent manner ( Figure 3—figure supplement 1j ) . Indeed , SIRT6 has one evolutionarily conserved S112Q113 motif ( Figure 3k ) . We therefore constructed S112A and S112D mutants , which resemble hypo- and hyper-phosphorylated SIRT6 respectively . As shown , these mutations almost abolished the pS/T-Q level of FLAG-SIRT6 ( Figure 3l ) . The in vitro kinase assay showed that ATM could phosphorylate GST-SIRT6 , but not S112A ( Figure 3—figure supplement 1k ) . Furthermore , compared with SIRT6 S112A , ectopic S112D exhibited a much higher inhibitory effect on glycolytic gene expression in sh-SIRT6 HepG2 cells ( Figure 3m and Figure 3—figure supplement 1l ) , and enhanced chromatin association of SIRT6 ( Figure 3n ) . Collectively , the data suggest that ATM directly phosphorylates SIRT6 at Serine 112 . We next examined whether ATM is involved in regulating SIRT6 protein stability . Notably , compared with WT or vehicle control , the degradation rate of ectopic and endogenous SIRT6 was largely increased in ATM KO HEK293 cells , Atm-/- MEFs , and cells incubated with KU55933 in the presence of cycloheximide ( CHX ) ( Figure 4a–b and Figure 4—figure supplement 1a–c ) . Recently , MDM2 was demonstrated to ubiquitinate SIRT6 and promote its proteasomal degradation ( Thirumurthi et al . , 2014 ) . We therefore examined the polyubiquitination level of SIRT6 . As shown , the ubiquitination level of FLAG-SIRT6 in ATM KO cells was significantly elevated compared with WT ( Figure 4—figure supplement 1d ) . While S112A mutant markedly enhanced the polyubiquitination level of SIRT6 , S112D had little effect ( Figure 4—figure supplement 1e ) . Moreover , S112A accelerated SIRT6 degradation , whereas S112D retarded it ( Figure 4c–d ) , indicating that the Ser112 phosphorylation by ATM regulates SIRT6 ubiquitination and thus protein stability . Indeed , ectopic MDM2 enhanced the polyubiquitination level of FLAG-SIRT6 ( Figure 4—figure supplement 1f ) . In the case of ATM depleted or SIRT6 S112A mutant , the binding capacity of SIRT6 to MDM2 was enhanced ( Figure 4e and Figure 4—figure supplement 1g ) . In searching for key residues that are polyubiquitinated by MDM2 , we identified two clusters of lysine residues , i . e . K143/145 and K346/349 , which are conserved across species . We then generated KR mutations of these residues , and found that K346/349R remarkably reduced the polyubiquitination level of SIRT6 ( Figure 4—figure supplement 1h ) . Individual KR mutation showed that K346R significantly blocked MDM2-mediated ubiquitination and degradation of SIRT6 , whereas K349R hardly affected it ( Figure 4f–g ) . More importantly , K346R restored the increased ubiquitination and accelerated protein degradation of SIRT6 S112A ( Figure 4—figure supplement 1i–j ) . Collectively , these data indicate that K346 is subject to MDM2-mediated ubiquitination , which is inhibited by ATM-mediated S112 phosphorylation . The cellular data suggest a pro-longevity function of ATM . We then tested it at organismal level . We employed Caenorhabditis elegans , which have a short lifespan of approximate 30 days . Nematodes deficient for atm-1 , an orthologue of mammalian ATM , and WTs were exposed to various doses of CQ ( see Materials and methods ) . Significantly , the period treatment with CQ ( 1 . 0 µM ) extended the median lifespan ( ~14% ) of C . elegans ( Figure 5a ) . The lifespan-extending effect was abolished in atm-1 KO ( Figure 5b ) or in SIRT6 homolog sir-2 . 4 KD nematodes ( Figure 5—figure supplement 1a–b ) . The data suggest that CQ promotes longevity in an ATM- and SIRT6- dependent manner . We further examined the beneficial effect of CQ in a HGPS model , i . e . Zmpste24-/- mice , which has a shortened lifespan of 4–6 months ( Pendás et al . , 2002 ) and impaired ATM-mediated DNA repair signaling ( Liu et al . , 2013a ) . We found that the level of Atm was dramatically reduced in Zmpste24-/- MEFs and tissues ( Figure 5c and Figure 5—figure supplement 1c ) . Significantly , CQ treatment activated Atm , stabilized Sirt6 , decreased the accumulated DNA damage , inhibited glycolysis , and alleviated senescence in Zmpste24-/- cells ( Figure 5d–e and Figure 5—figure supplement 1d–e ) . The CQ treatment also delayed body weight decline , increased running endurance , and prolonged lifespan in Zmpste24-/- mice ( Figure 5f–h ) , but had no significant effect on the lifespan of Atm-/- mice ( Figure 5—figure supplement 1f ) . Physiologically aged mice frequently develop aging-associated metabolic disorders , with high glucose and lactate ( Houtkooper et al . , 2011 ) . Given that ATM declines with age , and activation of ATM by CQ inhibits glycolysis in senescent cells and Zmpste24-/- mice , we intraperitoneally administrated 12-month-old ‘old’ male mice with low-dose CQ ( 3 . 5 mg/kg ) twice a week . Remarkably , compared with the saline-treated group , CQ treatment inhibited glycolysis , lowered serum lactate level , and attenuated body weight decline ( Figure 5i and Figure 5—figure supplement 1g–h ) , implicating potential benefits of CQ in physiologically aged mice . Generally , these data demonstrate a lifespan-extending benefit of ATM activation by CQ .
DNA damage accumulates with age , and defective DDR and DNA repair accelerates aging . However , whether boosting DNA repair machinery promotes healthiness and longevity is still unknown . DNA damage stimulates DDR , but if persistent , it instead leads to senescence . Therefore , if enhancing DDR efficacy possibly promotes longevity , it must be DNA damage free . The antimalarial drug CQ can intercalate into the internucleosomal regions of chromatin , unwind DNA helical twist , and thus activate ATM without causing any DNA damage ( Bakkenist and Kastan , 2003; Krajewski , 1995 ) . We demonstrate that long-term treatment with CQ activates ATM , improves DNA repair , restores age-related metabolic shift , alleviates cellular senescence , and extends lifespan of nematodes and Zmpste24 null mice . Mechanistically , ATM phosphorylates the longevity gene SIRT6 ( Tasselli et al . , 2017 ) , and prevents MDM2-mediated ubiquitination and proteasomal degradation of SIRT6 . To our knowledge , this is the first study to establish direct causal links between robust DNA repair machinery and longevity . In support of this notion , DNA repair efficacy has been shown to be enhanced in long-lived naked mole rat ( MacRae et al . , 2015 ) , and human longevity has been shown to be associated with single nucleotide polymorphisms ( SNPs ) in DNA repair genes/pathways ( Debrabant et al . , 2014; Soerensen et al . , 2012 ) . Interestingly , the heterozygous rather than homozygous status of a SNP , albeit both enhance the transcription of ATM , is associated with longevity in Chinese and Italian populations ( Chen et al . , 2010; Piaceri et al . , 2013 ) . Therefore , in future study , it would be worthwhile evaluating whether Atm can promote longevity in model organisms , and , if so , how many extra copies are required . Accumulation of DNA damage and metabolic disturbance are common denominators of aging ( López-Otín et al . , 2013; Moskalev et al . , 2013 ) . Metabolic reprogramming from the TCA cycle to glycolysis is prominent in both physiological and pathological aging ( Feng et al . , 2016; Shimizu et al . , 2014 ) . Why senescent cells become glycolytic is poorly understood . The crosstalk between cellular metabolism and DDR is not well elucidated . Upon genotoxic stress , ATM represses the rapamycin-sensitive mammalian target of the rapamycin ( mTORC1 ) pathway ( Alexander et al . , 2010 ) , but activates the pentose phosphate pathway ( PPP ) ( Cosentino et al . , 2011 ) , suggesting that cell metabolism may be key downstream of DDR signaling . Moreover , it is recognized that deficiency in DNA repair machinery such as ATM , WRN , and Ercc1 , accelerates aging and causes severe metabolic disorders ( Garinis et al . , 2008; White and Vijg , 2016 ) . In this study , we showed that boosting ATM activity using a low dose of CQ enhances genomic stability , attenuates age-onset metabolic reprogramming , alleviates senescence , and extends lifespan in mice . The data demonstrate for the first time that enhanced DNA repair machinery ( ATM-SIRT6 axis ) promotes longevity . Considering that ATM and SIRT6 function not only in maintenance of genome integrity but also as homeostatic protein modifiers , the pro-longevity role of ATM is most likely benefited from enhanced DNA repair and metabolic homeostasis , but it is difficult to determine which is more important . Recently , Bohr’s group identified that increased consumption of NAD+ by an early DDR factor poly ( ADP-ribose ) polymerase ( PARP1 ) , owing to accumulated DNA damage , accelerated aging in Atm mutant mice ( Fang et al . , 2016 ) . NAD+ serves as a cofactor of sirtuins , including SIRT1 and SIRT6 . Therefore , this work establishes a linear causal link between deficient DDR , DNA damage accumulation , consumption of NAD+ , decline in sirtuin activity , and aging . Moreover , administration of nicotinamide mononucleotide or nicotinamide riboside ameliorates age-related function decline and extends lifespan in mice ( Mills et al . , 2016; Zhang et al . , 2016 ) . Here , we found that ATM decline during aging causes DNA damage accumulation and enhances glycolysis , both of which consume most of the NAD+ , providing an explanation for the low NAD+ level in Atm-/- mice and physiologically aged mice . Closely resembling normal aging , HGPS has attracted numerous efforts in understanding of molecular mechanisms and developing therapeutic strategies ( De Sandre-Giovannoli et al . , 2003; Eriksson et al . , 2003 ) . We and others have found that HGPS and Zmpste24 null cells undergo premature senescence because of defective chromatin remodeling ( Ghosh et al . , 2015; Krishnan et al . , 2011; Liu et al . , 2013a; Liu et al . , 2013b ) , delayed DDR , and impaired DNA repair ( Liu et al . , 2005; Liu et al . , 2013b; Varela et al . , 2005 ) . Specifically , Atm-Kap-1 signaling is compromised ( Liu et al . , 2013a ) , and SIRT6 protein level and deacetylase activity are reduced in progeria cells ( Endisha et al . , 2015; Ghosh et al . , 2015 ) . Here we showed that Atm is significantly downregulated , which explains the reduced SIRT6 , delayed DDR , and metabolic shift in progeria cells and mice . It would be interesting to investigate whether ectopic Atm or Sirt6 could rescue progeroid features in these mice . Nevertheless , the activation of ATM via CQ remarkably improves glucose homeostasis , DNA damage clearance , and running endurance , and extends lifespan in progeria mice . It would be worthwhile evaluating the pro-longevity benefits of CQ in physiological aging . CQ is an FDA-approved and clinically used medicine for treatment of malaria ( 2015 ) . Via activation of ATM , long-term treatment of CQ protects against atherosclerosis , improves insulin sensitivity , and rescues glucose tolerance in type 2 diabetes ( T2D ) ( Emami et al . , 1999; Razani et al . , 2010; Schneider et al . , 2006 ) . The lysosomotropic property of CQ also makes it a potent inhibitor of autophagy ( Yang et al . , 2013 ) . The application of CQ for antimalarial treatment ( 500 mg/week , maximum 0 . 8 µM in plasma ) and for cancer therapy ( 100–500 mg/day ) ( Kimura et al . , 2013 ) is attributed to its inhibitory action on autophagy . Of note , CQ also the attenuates inflammatory response by inhibiting autophagy ( Szatmári-Tóth et al . , 2016; Whelan et al . , 2017; Wu et al . , 2018 ) , requiring a high dosage of 50 mg/kg for mice . In the current study , we used a low dose of CQ to activate ATM , i . e . 1–10 μM for cell line and 3 . 5 mg/kg twice a week for mice ( Schneider et al . , 2006 ) . The results showed that at such low doses , CQ has no toxicity and little effect on basal autophagic activity . Moreover , a low dose of CQ prolongs lifespan in progeroid mice , but exhibits little effect on Atm KO background , supporting an ATM-dependent pro-longevity function of CQ . Unfortunately , we could not test the CQ effect in Sirt6-/- mice with only 1-month lifespan ( Mostoslavsky et al . , 2006b ) . Here , we addressed the pro-longevity benefits of CQ-activated ATM , most likely attributable to improved DNA repair and glucose metabolism . Given that ATM also displays anti-inflammatory function ( Erttmann et al . , 2017; Shoelson , 2006 ) , we could not rule out an anti-inflammatory effect in lifespan extension observed in CQ-treated mice . In conclusion , our data establish direct causal links between robust DNA repair machinery and longevity . In line with DNA damage theory of aging , we propose that DNA damage activates DDR; however , its constant activation causes senescence; defective ATM-SIRT6 axis underlies premature aging , exemplified by HGPS and A-T mouse models , which are rescued by treatment of CQ and Sirt6 transgene , respectively; in physiological aging , DNA damage-free activation of ATM by CQ stabilizes SIRT6 , thus promoting longevity in nematodes and most likely also in mice ( Figure 5—figure supplement 2 ) . Our findings provide a novel therapeutic strategy for HGPS , and could facilitate clinical trials of CQ as an effective treatment for age-related diseases .
Zmpste24-/- mice and Atm-/- mice have been described previously ( Barlow et al . , 1996; Pendás et al . , 2002 ) . Sirt6-transgenic mice ( Sirt6-tg ) of C57BL/6J background were constructed by injecting cloned mSirt6 cDNA with CAG promoter into fertilized eggs . Primers for genotyping of Sirt6 transgenic allele were as follows: forward: 5’-CTGGTTATTGTGCTGTCTCATCAT-3’; reverse: 5’-CCGTCTACGTTCTGGCTGAC-3’ . Atm-/- mice were crossed to Sirt6-tg mice to get Atm-/-;Sirt6-tg mice . Chloroquine ( CQ ) experiments were conducted as described ( Schneider et al . , 2006 ) . Briefly , 12-month-old wild-type C57BL/6J male mice , 2-month-old Zmpste24-/- , and Atm-/- male mice were administered with CQ ( Sigma , St . Louis , MO ) in 0 . 9% saline twice per week at 7 mg/kg body weight , and the control group was treated with saline alone . At least 8 weeks after treatment of CQ , mice were subjected to functional tests . Body weight and lifespan was recorded . The survival rate was analyzed using the Kaplan–Meier method and statistical comparison was performed using the Log-rank Test . Mice were housed and handled in the laboratory animal research center of Shenzhen University . All experiments were performed in accordance with the guidelines of the Institutional Animal Care and Use Committee ( IACUC ) . The protocols were approved by the Animal Welfare and Research Ethics Committee of Shenzhen University ( Approval ID: 201412023 ) . C . elegans nematode survival assay was performed according to standard protocols ( Kenyon et al . , 1993 ) . Briefly , wild-type and atm-1 null nematodes ( 100 to 150 per group ) synchronized to prefertile young adult stage were exposed to NGM plates containing the indicated dosage of CQ . After 1-day incubation , animals were transferred to fresh incubation plates without CQ for another 2 days . This procedure was repeated every 3 days . Nematodes that showed no response to gentle stimulation were recorded as dead . The survival data were analyzed using the Kaplan–Meier method and statistical comparison was performed using the Log-rank Test . HEK293 ( CRL-1573 ) , HepG2 ( HB-8065 ) , and U2OS ( HTB-96 ) cells were purchased from ATCC . Human skin fibroblasts HSFs ( F2-S ) and primary MEFs were prepared as described previously ( Liu et al . , 2005 ) . Immortalized Atm-/-; p53-/- and Sirt6-/- MEFs were provided as a kind gift from Dr . Yosef Shiloh ( Tel Aviv University , Israel ) and Dr . Raul Mostoslavsky ( Massachusetts General Hospital Cancer center , USA ) , respectively . These cell lines were authenticated by short tandem repeat ( STR ) profile analysis and genotyping , and were mycoplasma free . Cells were cultured in Gibco DMEM ( Life Technologies , USA ) with 10% fetal bovine serum ( FBS ) , 100 U/ml penicillin and streptomycin ( P/S ) at 37°C in 5% CO2 and atmospheric oxygen conditions . For CQ experiments , cells were maintained in the medium containing 1 μM chloroquine for 12 hr , and then grown in new fresh medium for 48 hr . Human Flag-SIRT6 , pcDNA3 . 1 Flag-ATM , Flag-ATM S1981A , and pcDNA3 human MDM2 were all purchased from Addgene ( Cambridge , MA ) . Flag-SIRT6 with amino acid substitution mutations ( S112A , S112D , K346R/K349R ) were generated by PCR-based mutagenesis using pcDNA3-Flag-SIRT6 as a template and a QuikChange II site-directed mutagenesis kit ( Agilent Technologies ) , following the manufacturer’s instructions . Primer sequences for amino acid mutations of SIRT6 were as follows: SIRT6 S112A: ( forward ) 5'-cgtccacgttctgggcgaccaggaagcgga-3' , ( reverse ) 5’-tccgcttcctggtcgcccagaacgtggacg-3’; SIRT6 S112D: ( forward ) 5'-ccgtccacgttctggtcgaccaggaagcggag-3' , ( reverse ) 5'-ctccgcttcctggtcgaccagaacgtggacgg-3'; SIRT6 K346R: ( forward ) 5'-ggccttcacccttctggggggtctgtg-3' , ( reverse ) 5'-cacagaccccccagaagggtgaaggcc-3'; SIRT6 K349R: ( forward ) 5'-gccttggccctcacccttttggggggt-3' , ( reverse ) 5'-accccccaaaagggtgagggccaaggc-3 . HA-tagged human SIRT6 plasmid was amplified from the respective cDNAs and constructed into pKH3-HA vector . To express four truncated forms of SIRT6 protein , HA-SIRT6 plasmid as a template was constructed by PCR-based deletion . For whole cell protein extraction , cells were suspended in five volumes of suspension buffer ( 20 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1 mM DTT , protease inhibitor cocktail ) , and then five volumes of 2X SDS loading buffer were added and incubated at 98°C for 6 min . Mice tissues were homogenized with 1 ml of ice-cold tissue lysis buffer ( 25 mM TrisHCl , pH 7 . 5 , 10 mM Na3VO4 , 100 mM NaF , 50 mM Na4P2O7 , 5 mM EGTA , 5 mM EDTA , 0 . 5% SDS , 1% NP-40 , protease inhibitor cocktail ) . After homogenization and sonication , lysates were centrifuged at 16 , 000 g for 15 min . The clean supernatant was carefully transferred to new tubes . Protein concentrations were determined using a bicinchoninic acid ( BCA ) assay method ( Pierce , Rockford , IL ) and were normalized with lysis buffer for each sample . Samples were denatured in 1X SDS loading buffer by boiling at 98°C for 6 min . Proteins were separated by loading to SDS-polyacrylamide gels , and then were transferred to PVDF membrane ( Millipore ) . The protein levels were determined by immunoblotting using respective antibodies . The ImageJ program was used for densitometric analysis of immunoblotting , and the quantification results were normalized to the loading control . Rabbit anti-SIRT6 ( ab62739 ) , ATM ( ab78 ) , SIRT1 ( ab12193 ) , γH2AX ( ab81299 ) , RAP80 ( ab52893 ) , Kap-1 ( ab10484 ) , and p-KAP-1 ( Ser824 , ab70369 ) antibodies were obtained from Abcam ( Cambridge , UK ) . Anti-lamin A/C ( sc-20681 ) , p21 ( sc-6246 ) , MDM2 ( sc-965 ) , and P53 ( sc-6243 ) antibodies were purchased from Santa Cruz Biotechnology . Rabbit anti-γH2AX ( 05–636 ) , p-ATM ( Ser1981 ) ( 05–740 ) , histone H3 ( 07–690 ) , anti-H3K56ac ( 07–677 ) , and H3K9ac ( 07–352 ) antibodies were sourced from EMD Millipore . Mouse anti-p-ATM ( Ser1981 ) ( #5883 ) , p-S/TQ ( #9607 ) , ubiquitin ( #3936 ) , and cleaved caspase-3 ( #9661 ) antibodies were purchased from Cell Signaling Technology ( Beverly , MA ) . Mouse anti- HA , Flag , rabbit anti-LC3B , and P62 antibodies were obtained from Sigma-Aldrich . Anti-Nbs1 ( NB100-143 ) antibody was purchased from Novus Biologicals . Mouse anti-actin , tubulin antibodies were obtained from Beyotime . Anti-pS112 SIRT6 monoclonal antibodies were prepared by Abmart generated from a specific phosphorylated peptide ( peptide sequence CLRFVSPQNV ) . HEK293 cells ( WT and ATM-deficient cells ) were transfected with Flag-SIRT6 alone or together with Mdm2 . 48 hr later , the cells were treated with 50 μg/ml of cycloheximide ( CHX , Sigma-Aldrich ) , a translation inhibitor . For endogenous SIRT6 protein degradation assay , ATM wild-type and null MEFs were grown in 6 cm plates , and were treated with 50 mg/ml CHX for indicated time points . Cells were collected and the protein levels were determined by western blotting , the subsequent quantification was performed with ImageJ software . In vivo ubiquitination assay was performed by transfecting HEK293 cells in 6 cm dishes with 1 μg Myc-ubiquitin , 2 μg Flag-SIRT6 or its mutations , and/or 1 μg MDM2 vector . 48 hr after transfection , cells were lysed in the buffer ( 25 mM Tris-HCl pH 8 . 0 , 250 mM NaCl , 10 mM Na3VO4 , 1 mM EDTA , 10% glycerol , protease inhibitor cocktail , and 0 . 1 mM phenylmethylsulphonyl fluoride ) , and then incubated with Flag-M2 beads ( Sigma-Aldrich ) overnight at 4°C . Beads were washed with lysis buffer three times , bound proteins were eluted by adding 1 . 5 × SDS loading buffer . The ubiquitin levels were analyzed by immunoblotting . HEK293T cells were transfected with 10 μg of FLAG-ATM and then treated with CPT . Activated ATM was immune-purified from the cell extracts with FLAG beads ( Sigma , M8823 ) . GST-SIRT6 or the S112A mutant was purified from bacteria . Kinase reactions were initiated by incubating purified ATM with GST-SIRT6 in the kinase buffer with or without 1 mM ATP for 120 min at 30°C . After reaction , proteins were blocked by SDS loading buffer . The membrane was then subjected to western blotting with antibodies against p-S/TQ . Cells under indicated treatments were totally lysed in lysis buffer containing 20 mM HEPES , pH 7 . 5 , 150 mM NaCl , 10 mM Na3VO4 , 10% glycerol , 2 mM EDTA , protease inhibitor cocktail , and 0 . 1 mM phenylmethylsulphonyl fluoride . After sonication and centrifugation , the supernatant was collected and incubated with H3K9ac ( Millipore , 2 μg/sample ) overnight at 4°C with a gentle rotation . Protein A/G agarose ( Pierce , 10 μl/sample ) were added to the tubes and rotated at 4°C for 2 hr . Beads were precipitated by centrifugation at 1000 g for 15 s and washed three times with cold lysis buffer . The pellet was resuspended in 1 . 5 × SDS loading buffer and incubated at 98°C for 6 min . The supernatants were collected and used for western blotting . A series of GST fusion proteins of truncated ATM , which together spanned the full length of ATM , were constructed into pGEX4T-3 vector . For GST pull-down , bacterially expressed 6 × His tagged SIRT6 was separately incubated with various GST-ATM fragments in a buffer of 150 mM NaCl , 20 mM Tris-HCl [pH 7 . 5] , 5 mM MgCl2 , 0 . 2 mM EDTA , 10% glycerol , 0 . 2% NP-40 , and protease inhibitors ( Roche Complete ) . GST-fusion proteins were then precipitated by adding Glutathione Sepharose fast flow ( GE Healthcare ) . After washing twice with TEN buffer ( 0 . 5% Nonidet P-40 , 20 mM Tris-HCl [pH 7 . 4] , 0 . 1 mM EDTA , and 300 mM NaCl ) , glutathione agarose beads were analyzed by western blotting and coomassie staining . Briefly , cells were transfected with small interfering RNAs ( siRNAs ) for 48 hr using Lipofectamine 3000 ( Invitrogen , USA ) according to the manufacturer’s instructions . The siRNAs targeting human ATM , SIRT6 , and HDM2 were purchased ( GenePharma , China ) with sequences as follows , si-ATM#1: 5’-AAUGUCUUUGAGUAGUAUGUU-3’ ( Zhou et al . , 2003 ) ; Si-ATM#2: 5’-AAGCACCAGUCCAGUAUUGGC-3’ ( Zhang et al . , 2005 ) ; si-SIRT6#1: 5’-AAGAAUGUGCCAAGUGUAAGA-3’; si-SIRT6#2: 5’-CCGGCTCTGCACCGTGGCTAA-3’; si-HDM2#1: 5’-AACGCCACAAATCTGATAGTA-3’; si-HDM2#2: 5’-AATGCCTCAATTCACATAGAT-3’ . A scrambled siRNA sequence was used as control . Lentiviral shRNA constructs were generated in a pGLVH1 backbone ( GenePharma , China ) , and virus was produced in HEK293 cells . To deplete ATM in HSF cells and SIRT6 in HepG2 cells , lentiviral infection was performed in the presence of 5 μg/ml polybrene . Two days later , the infected HSF cells or HepG2 cells were selected with 2 μg/ml puromycin . To downregulate sir-2 . 4 expression , the NL2099 worms were exposed to incubation plates containing HT115 bacteria with sir-2 . 4 RNAi vector . Gene mutagenesis by the CRISPR/Cas9 system was conducted as described ( Ran et al . , 2013 ) . The following gRNAs targeting human ATM , SIRT6 were constructed in pX459 vector ( Addgene , #48139 ) . sgATM F: 5’-CACCGATATGTGTTACGATGCCTTA-3’ , R: 5’- AAACTAAGGCATCGTAACACATATC-3’ . HEK293 cells were transfected with pX459 or pX459-gRNA using Lipofetamine 3000 Transfection Reagent according to the manufacturer's instructions . After 2-day culture , cells were selected with 2 μg/ml puromycin , six colonies were picked and grown to establish stable cell lines . The targeted mutations were identified by western blotting , and PCR-based sequencing . EdU incorporation assays were conducted in HSF cells to estimate cell proliferation using the Click-iT EdU Alexa Fluor 488 Kit ( Invitrogen , USA ) . HSF cells , infected by the respective lentiviruses containing shNC and shATM , were cultured in a six-well plate containing the coverslips in the presence of 10 μM EdU for 12 hr . Cells were fixed in 3 . 7% formaldehyde followed by 0 . 5% Triton X-100 permeabilization , and then stained with Alexa Fluor picolyl azide . Five random views were captured to calculate the positive staining rate for each group . Cell population doublings were monitored using a Coulter Counter . SA-β-galactosidase assay in primary cells was performed using Senescence β-galactosidase staining Kit ( #9860 , CST ) according to the manufacturer's instructions . Five views were captured randomly to calculate the positive staining rate for each group . Total RNA was extracted from cells or mouse tissues using Trizol reagent RNAiso Plus ( TaKaRa , Japan ) following the phenol–chloroform extraction method . Purified total RNA was used to obtain cDNA using PrimeScript RT Master Mix ( Takara , Japan ) following this method: 37°C for 30 min , and 85°C for 5 s . The gene expression was analyzed with the CFX Connected Real-Time PCR Detection System ( BioRad ) with SYBR Ex Taq Premixes ( Takara , Japan ) . Gene expression levels were normalized to actin . Mice were fasted overnight ( 6 p . m . to 9 a . m . ) , and D-glucose ( 2 . 5 g/kg body weight ) was administrated intraperitoneally . Blood glucose levels were determined from tail vein blood using a glucometer ( Onetouch ultravue , Johnson , USA ) at 0 , 30 , 60 , 90 , and 120 min after D-glucose injection . Mice were fasted for 6 hr ( 8 a . m . to 2 p . m . ) , and recombinant human insulin ( 0 . 75 U/kg body weight ) was administered intraperitoneally . Blood glucose levels were determined in tail vein blood using a glucometer ( Onetouch ultravue , Johnson ) at 0 , 30 , 60 , 90 , and 120 min after insulin injection . Mouse serum was five-fold diluted , and lactate concentration was determined with the Lactate Colorimetric Assay Kit ( BioVision ) . Zmpste24-/- mice were treated for 8 weeks with chloroquine or saline before running on a Rota-Rod Treadmill ( YLS-4C , Jinan Yiyan Scientific Research Company , Shandong , China ) to test the effect of chloroquine on fatigue resistance . Mice were placed on the rotating lane , and the speed was gradually increased to 10 r/min . When mice were exhausted and safely dropped from the rotating lane , the time latency to fall was automatically recorded . Wild-type and ATM KO cells were grown in normal medium for 24 hr , and methanol-fixed cell pellets were analyzed by a two liquid chromatography-tandem mass spectrometry ( LC-MS ) method as described ( Luo et al . , 2007 ) . The cells were fixed using 4% paraformaldehyde at room temperature for 15 min , permeabilized by 0 . 5% Triton X-100 at room temperature for 10 min , blocked using 10% FBS/PBS , and then incubated with primary antibodies diluted in PBS containing 2% BSA overnight at 4°C . The primary antibodies were detected using an Alexa-488-conjugated anti-mouse secondary antibody ( Invitrogen ) . The nuclei were stained using DAPI in anti-fade mounting medium . Images were captured using a Zeiss LSM880 confocal/multiphoton microscope . Cells were fixed in 1% formaldehyde for 10 min at room temperature . The crosslinking reaction was quenched with 0 . 125 M glycine . After washing with PBS , cells were lysed with lysis buffer ( 50 mM Tris·HCl pH 8 . 0 , 2 mM EDTA , 15 mM NaCl , 1% SDS , 0 . 5% deoxycholate , protease inhibitor cocktail , 1 mM PMSF ) , followed by sonication and centrifugation . The supernatant was collected and precleared in dilution buffer ( 50 mM Tris-HCl pH 8 . 0 , 2 mM EDTA , 150 mM NaCl , 1% Triton X-100 ) with protein A/G Sepharose and pre-treated salmon DNA . The precleared samples were incubated overnight with H3K9ac antibody ( 2 μg/sample , Millipore ) or appropriate control IgGs ( Santa Cruz ) , and protein A/G Sepharose ( Invitrogen ) . After washing sequentially with a series of buffers , the beads were heated at 65°C to reverse the crosslink . DNA fragments were purified and analyzed . Real-time PCR was performed with primers as described ( Zhong et al . , 2010 ) : LDHB-ChIP-5’: AGAGAGAGCGCTTCGCATAG LDHB-ChIP-3’: GGCTGGATGAGACAAAGAGC ALDOC-ChIP-5’: AAGTGGGGCACTGTTAGGTG ALDOC-ChIP-3’: GTTGGGGATTAAGCCTGGTT PFKM-ChIP-5’: TTAAGACAAAGCCTGGCACA PFKM-ChIP-3’: CAACCACAGCAATTGACCAC LDHA-ChIP-5’: AGGGGGTGTGTGAAAACAAG LDHA-ChIP-3’: ATGGCTTGCCAGCTTACATC LDHA-ChIP-1Kb-5’: TGCAAGACAAGTGTCCCTGT LDHA-ChIP-1Kb-3’: GAGGGAATGAAGCTCACAGC Statistical analyses were conducted using two-tailed Student's t-test between two groups . All data are presented as mean ± S . D . or mean ± S . E . M . as indicated , and a p value < 0 . 05 was considered statistically significant . | As cells live and divide , their genetic material gets damaged . The DNA damage response is a network of proteins that monitor , recognize and fix the damage , which is also called DNA lesions . For example , an enzyme called ATM senses when DNA is broken and then begins a process that will get it repaired , while another enzyme known as SIRT6 participates in the actual mending process . When organisms get older , the repair machinery becomes less efficient , and the number of DNA lesions and errors increases . The accumulation of DNA damage may cause the ‘symptoms’ of old age – from cancer , to wrinkles and the slowing down of the body’s chemical processes . In fact , individuals with defective ATMs ( who thus struggle to repair their DNA ) age abnormally fast; conversely , SIRT6 promotes longevity . If declining repair mechanisms cause aging , would boosting the DNA damage response slow down this process ? Chloroquine is a drug used to combat malaria , but it can also enhance the activity of ATM without damaging DNA . Qian , Liu et al . show that chloroquine helps cells remove broken DNA and keep dividing for longer . In animals , the drug increases the lifespan of worms and prolongs the lives of mice who have mutations that make them age quicker . Qian , Liu et al . also demonstrate that ATM works by chemically altering the pro-longevity enzyme SIRT6 . These changes make SIRT6 more stable , and keep it safe from cellular processes that destroy it . In addition , mice that are genetically engineered to lack ATM can survive for longer if they also produce extra SIRT6 . These experiments show that enhancing the DNA damage response can slow down aging , thus linking the DNA repair machinery to longevity . Progeria is a group of rare genetic conditions with inefficient DNA repair; people with progeria age fast and die young . The results by Qian , Liu et al . , if confirmed in humans , could provide a new way of treating these diseases . | [
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] | 2018 | Boosting ATM activity alleviates aging and extends lifespan in a mouse model of progeria |
Sumoylation during genotoxic stress regulates the composition of DNA repair complexes . The yeast metalloprotease Wss1 clears chromatin-bound sumoylated proteins . Wss1 and its mammalian analog , DVC1/Spartan , belong to minigluzincins family of proteases . Wss1 proteolytic activity is regulated by a cysteine switch mechanism activated by chemical stress and/or DNA binding . Wss1 is required for cell survival following UV irradiation , the smt3-331 mutation and Camptothecin-induced formation of covalent topoisomerase 1 complexes ( Top1cc ) . Wss1 forms a SUMO-specific ternary complex with the AAA ATPase Cdc48 and an adaptor , Doa1 . Upon DNA damage Wss1/Cdc48/Doa1 is recruited to sumoylated targets and catalyzes SUMO chain extension through a newly recognized SUMO ligase activity . Activation of Wss1 results in metalloprotease self-cleavage and proteolysis of associated proteins . In cells lacking Tdp1 , clearance of topoisomerase covalent complexes becomes SUMO and Wss1-dependent . Upon genotoxic stress , Wss1 is vacuolar , suggesting a link between genotoxic stress and autophagy involving the Doa1 adapter .
Maintenance of genome integrity is essential for cell survival and faithful transfer of genetic information . Eukaryotic cells evolved a DNA damage response ( DDR ) to sense , evaluate , and repair chromosome and DNA lesions or to direct cells for destruction when the harm is irreparable ( Ciccia and Elledge , 2010 ) . This dynamic process is under precise spatiotemporal control , requiring posttranslational modifications ( PTM ) ; phosphorylation , ubiquitylation , and sumoylation , to name a few ( Polo and Jackson , 2011 ) . Compared to phosphorylation , bulky modifications with ubiquitin ( Ub ) , and SUMO provide additional scaffolding for regulation of composition , function , and stability of protein assemblies ( Jackson and Durocher , 2013 ) . Both modifiers are conjugated to target proteins by the concerted action of activating enzymes ( E1 ) , conjugating enzymes ( E2 ) and ligases ( E3 ) and both form chains through repeated cycles of self-conjugation ( Kerscher et al . , 2006 ) . The effects of modification depend on its position on the target protein , chain length , and type of lysine ( K ) linkage . Conjugation is reversible; specific proteases ensure regeneration of the modifier pool and regulate the PTM by reversing the modification , or remodeling the chains ( Eletr and Wilkinson , 2014 ) . Ub and SUMO are involved in the displacement of proteins from chromatin ( Meyer et al . , 2012; Jackson and Durocher , 2013; Vaz et al . , 2013 ) . Assembly of repair complexes during genotoxic stress requires clearing the damaged and stalled chromatin components from the site of DNA damage . During or at the completion of repair , the assembled repair complexes must be disassembled . Ub-dependent protein degradation by the 26S proteasome is a major mechanism of protein clearing , involving Cdc48-promoted disassembly of chromatin-bound complexes ( Meyer et al . , 2012; Vaz et al . , 2013 ) . Cdc48 , an AAA-ATPase chaperone long-thought to be Ub-specific , also interacts with sumoylated substrates via one of its multiple cofactors , Ufd1 ( Nie et al . , 2012 ) . This interaction is required to protect cells from Topoisomerase ( Top1 ) -induced DNA damage . The Cdc48/Ufd1/Npl4 complex is also targeted to sumoylated Rad52 and negatively regulates its interaction with Rad51 , inhibiting excessive recombination ( Bergink et al . , 2013 ) . Thus , Cdc48 plays a central role in DDR , acting as a Ub and SUMO-dependent segregase assembling and disassembling macromolecular complexes . Other DDR components such as SUMO-targeted Ub ligases or STUbLs ( Sriramachandran and Dohmen , 2014 ) exhibit dual specificity . STUbLs bind to sumoylated and poly-sumoylated proteins via SUMO-interacting motifs ( SIMs ) and catalyze their ubiquitylation . Sumoylated–ubiquitylated substrates may then be degraded by the proteasome . Cdc48 may also function parallel to STUbLs in another partially redundant pathway since SUMO binding by Cdc48 becomes essential in cells with STUbL mutations ( Nie et al . , 2012 ) . Wss1 may also be required for clearance of SUMO conjugates in the absence of functional STUbL ( Mullen et al . , 2010 , 2011 ) . Wss1 , Weak Suppressor of smt3-331 , is a suppressor of the temperature-sensitive ( ts ) phenotype produced by an L26S SUMO mutation ( Biggins et al . , 2001 ) and is implicated in the response to genotoxic stress through unknown mechanisms ( O'Neill , 2004 ) . Bioinformatics suggested that Wss1 belongs to a new family of metalloproteases ( Iyer et al . , 2004 ) . Recent work demonstrated that it is a DNA-dependent protease involved in repair of DNA–protein crosslinks ( Stingele et al . , 2014 ) . These authors also showed that Wss1 is important for cell survival upon topoisomerase 1 ( Top1 ) -inflicted DNA damage . Notably , this function of Wss1 is assisted by Cdc48 . Here , we show that Wss1 is a SUMO-specific Cdc48 cofactor involved in clearing high molecular weight SUMO conjugates ( HMW-SUMO ) . Important novel observations demonstrate that Wss1 binds SUMO and has both SUMO ligase and latent metalloprotease activities; is regulated by a cysteine switch mechanism that can be activated by chemical stress and/or DNA binding; forms a ternary complex with Cdc48 and Doa1 that exhibits specificity for SUMO and promotes binding of HMW-SUMO to Cdc48; is an inactive metalloprotease under normal conditions and promotes poly-sumoylation of proteins with which it interacts , including Cdc48; is accumulated within nuclear foci and its metalloprotease activity is activated upon genotoxic stress; is accumulated in the vacuole during stress , suggesting an intriguing link between SUMO-dependent DDR and vacuolar degradation . These studies confirm and extend those of Stingele et al . and suggest a unique model of environmental switching of Wss1 from a ligase to a protease in response to DNA damage .
Wss1 is a DDR component important for survival upon UV irradiation ( O'Neill , 2004 ) . Wss1 , along with other proteins of WLM-family ( Wss1-Like Metalloproteases ) , were predicted to be proteases ( Iyer et al . , 2004 ) and while this article was in preparation , Stingele et al . ( 2014 ) reported that Wss1 is a DNA-activated metalloprotease that processes DNA-protein crosslinks . Neither they , nor we , can confirm the SUMO- or Ub-isopeptidase activity previously observed ( Mullen et al . , 2010 ) , and it seems plausible that it resulted from co-purified deubiquitinating enzymes . Our structural analysis suggests that WLM domains share similar structural organization with minigluzincin proteases ( Lopez-Pelegrin et al . , 2013 ) and SprT enzymes ( Figure 1—figure supplement 2 , see below ) . Recombinant Wss1 produced in bacteria is largely insoluble . When purified under native conditions , soluble protein shows SUMO ligase activity ( see below ) while protease activity ( Figure 2 ) required denaturation and refolding of inclusion bodies . Stingele et al . ( 2014 ) reported that the protease activity of Wss1 is activated in vitro by any DNA longer than 16 bp . We too see activation by DNA , albeit only when ssDNA is present . We assume that differences in the refolding conditions lead to these subtle differences in the activity of recombinant protein . However , it is important to note that Wss1 protease activity is normally latent , but activated by genotoxic stress in cells . Our principal insight into the Wss1 mechanism comes from the finding that the protease is regulated by a cysteine-switch mechanism . We demonstrated that Wss1 proteolysis can be activated by thiol-modifying reagents such as thiram and APMA as well as by ssDNA ( Figure 2 and Figure 2—figure supplement 2 ) . In addition , polySUMO binding enhances the proteolysis of recombinant protein ( Figure 3 ) and SIM2 is required for both Wss1 and Cdc48 cleavage under conditions of stress . Thus , binding to DNA is not absolutely required for Wss1 proteolytic activity . Moreover , our observations that Wss1 can process itself , sumoylated proteins and Cdc48 demonstrate that Wss1 substrates are not limited to DNA-bound proteins , as previously suggested by Stingele et al . ( 2014 ) . DNA ( Figure 2 ) and SUMO chains ( Figure 3 ) may play a cofactor role by inducing Wss1 oligomerization and bringing together the protease and its substrates ( Figure 2—figure supplement 4 ) . Our data in vitro and in vivo strongly suggest that polysumoylated proteins are the substrates for Wss1 . Although the exact mechanism of how Wss1 processes SUMO conjugates remains unclear , one possible scenario may be the activation of metalloprotease and cleavage within a hinge region of the polysumoylated proteins . Alternatively , our finding that Wss1 binds to SUMO-PA , self-cleaves at C-terminus and produces a ladder of proteolytic fragments upon activation by ssDNA may suggest that it functions as a SUMO-targeted carboxypeptidase . Even more surprising was our finding that Wss1 promotes SUMO chain formation in vitro and in unstressed cells , acting as a SUMO ligase . Wss1 shows a preference for binding polysumoylated ligands ( Figure 1D ) , sumoylates itself and associated cellular proteins , changes the global sumoylation pattern , and partially restores SUMO-conjugates in a siz1Δ strain . Sumoylation depended on tandem SIMs at the C-terminus of Wss1 and is probably the result of juxtaposed SUMO binding sites that bring SUMO thiol ester in proximity to an acceptor SUMO . Interestingly , among other known non-canonical SUMO E3-ligases , including RanBP2 , polycomb Pc2 , topors , Slx5 , and Rad18 , all , except RanBP2 , catalyze sumoylation by SIM-dependent mechanism ( Ii et al . , 2007; Perry et al . , 2008; Merrill et al . , 2010; Parker and Ulrich , 2014 ) . Furthermore , the oligomerization of Wss1 may also play an important role in SUMO ligase activity ( Figure 4D and Figure 4—figure supplement 2 ) . The extension of a SUMO-chain by Wss1 may signal assembly of Wss1 complexes required for substrate processing , as well as target these complexes to specific cellular locations . Notably , the sumoylation of Cdc48 was shown to induce its translocation from nucleus to membranous cytoplasmic structures ( Makhnevych et al . , 2009 ) . Finally , the role of SUMO binding is controversial . Stingele et al . ( 2014 ) suggested that SUMO binding is dispensable for Wss1 function although they did not demonstrate Wss1 proteolysis in the cell . We show that Wss1-dependent proteolysis is activated under conditions that damage DNA and is SUMO dependent . Although we too observed that Wss1-ΔSIM2 could suppress CPT sensitivity of wss1Δ tdp1Δ cells , competent SUMO binding is required for the clearing of HMW-SUMO conjugates as well as Wss1 targeting to tdp1Δ-induced foci . Moreover , because cellular Wss1-ΔSIM2 remained stable under various DNA-damaging conditions , it seems that SUMO binding is essential for Wss1 auto-proteolysis as well . The partial effects seen with SIM mutants may be masked by the fact that , as we have shown , the WLM domain also contributes to SUMO binding . We find that full-length Wss1 interacts with Cdc48 ATPase ( Figure 5 , Figure 5—figure supplement 2 , Figure 5—figure supplement 3 , and Supplementary file 1 ) , supporting a role for Wss1 in removing sumoylated proteins from the chromatin . Wss1 contains VIM and SHP Cdc48-binding motifs , conserved within the WLM family . Moreover , the incidence of other Cdc48-relevant interaction domains in WLM members indicates that these proteins might also be Cdc48 partners ( Iyer et al . , 2004 ) . Our structural analysis demonstrated that both VIM and SHP motifs contributed to Cdc48 binding , suggesting a bipartite interaction mechanism , a characteristic mode of binding of common Cdc48 adaptors ( Bruderer et al . , 2004; Yeung et al . , 2008 ) . We also found that Wss1 specifically binds another Cdc48 cofactor , Doa1 , and forms a ternary 1:1:1 Cdc48/Wss1/Doa1 complex . Although Doa1 and Cdc48 have been considered to be Ub specific ( Mullally et al . , 2006 ) , we demonstrated here that Wss1 redirects the Doa1/Cdc48 complex to SUMO substrates . Another Cdc48 complex , Cdc48/Ufd1/Npl4 , binds mixed Ub-SUMO substrates and assists STUbL in SUMO-dependent Top1cc repair ( Nie et al . , 2012 ) . Existing data suggest that Wss1 and STUbL may share common substrates and act in the same DDR pathway ( Figure 4—figure supplement 1 and Mullen et al . , 2011 ) . Indeed , their response to various genotoxic threats places Wss1 and STUbL in the same ‘nucleotide-excision-repair’ ( NER ) epistasis group . We identified NER proteins by MS suggesting physical association of Wss1 and NER machinery ( Figure 5—figure supplement 1 and Supplementary file 1 ) . Both Wss1 and STUbL are involved in Top1cc repair in the absence of Tdp1 , pathways shown to be NER-dependent ( Figure 9 and Liu et al . , 2002; Vance and Wilson , 2002; Heideker et al . , 2011; Stingele et al . , 2014 ) . Although the function of Wss1 and STUbL in NER is unclear , it may be required to reduce the size of NER substrates ( Stingele et al . , 2014 ) . NER cannot process DNA adducts containing proteins larger than 16 kDa ( Nakano et al . , 2007 , 2009 ) , while the size of Top1 protein exceeds 90 kDa . Wss1 and STUbL may also act by removing stalled sumoylated NER enzymes in addition to NER substrates . Recent data demonstrate that DNA damage induces extensive sumoylation of multiple NER components ( Cremona et al . , 2012; Psakhye and Jentsch , 2012 ) . Although it was suggested that SUMO assists the assembly of repair complexes , in some cases sumoylation promotes protein dissociation and turnover ( Hardeland et al . , 2002; Fernandez-Miranda et al . , 2010; Sarangi et al . , 2014 ) . Wss1 and STUbL pathways may direct substrates to different downstream degradation events , orchestrated by different Cdc48 complexes . The major degradative machineries of the cell are the proteasome and the vacuole/lysosome , Cdc48 being implicated in both ( Meyer et al . , 2012 ) . Chromatin-associated proteolysis involving STUbL pathways has been linked mostly to the Cdc48/Ufd1/Npl4 complex and the proteasome ( Meyer et al . , 2012; Vaz et al . , 2013; Sriramachandran and Dohmen , 2014 ) . Alternatively , autophagy is implicated in Wss1-mediated processes; a third of our best-rated MS hits were proteins associated with vesicle transport and the vacuole , indicating a possible role of Cdc48/Wss1/Doa1 complex in vacuolar degradation . Doa1 has been implicated in regulation of two main vacuolar pathways , autophagy and vesicle sorting ( Ren et al . , 2008; Ossareh-Nazari et al . , 2010 ) . We have demonstrated that various genotoxic conditions induce accumulation of Wss1 within the vacuole . Moreover , under genotoxic stress Wss1 was found associated with autophagic vesicle-like structures in the cytoplasm and on the vacuolar membrane . Because autophagy is one of the DDR activated pathways ( Robert et al . , 2011; Dotiwala et al . , 2013; Vessoni et al . , 2013 ) , our data suggest an intriguing possibility that Cdc48/Wss1/Doa1 mediates degradation of sumoylated chromatin substrates via specific autophagic mechanisms . Our data suggest a model for the SUMO-dependent extraction of proteins from chromatin ( Figure 11 ) . DNA lesions induce exposure of ssDNA and sumoylation of nearby chromatin-bound proteins ( Psakhye and Jentsch , 2012 ) . Thus , exposure of ssDNA and/or initial sumoylation of proteins at the sites of DNA damage could provide binding sites for Wss1 . Its intrinsic ligase activity could then sumoylate other proteins , extending SUMO chains , become proteolytically activated . Wss1 would also recruit Doa1/Cdc48 to disassemble proteins from the damage site ( s ) and target the complexes to the vacuole . The proteolytic activation of Wss1 both down regulates Wss1 mediated action and fragments other proteins to induce their dissociation . Thus , we propose that autophagy processes poor proteasome substrates formed as a result of DNA damage , that is , protein–protein and protein–nucleic acid crosslinks , irreversibly trapped enzymes and abnormal nucleic acid intermediates ( Figure 11 ) . Alternatively , or in addition , STUBL could be recruited to ubiquitinate chromosomal lesions that have not been repaired and recruit Doa1/Ufd1/Npl4 to chaperone ubiquitinated substrates to the proteasome . 10 . 7554/eLife . 06763 . 033Figure 11 . SUMO-dependent extraction of proteins from the chromatin . ( A ) ssDNA-activated SUMO E3 ligase sumoylates DNA-bound protein and induces its dissociation . ( B ) Delay in dissociation results in SUMO chain formation through multiple rounds of protein sumoylation . Subsequent ubiqutylation by STUbL promotes Cdc48/Npl4/Ufd1 loading , protein extraction and degradation via proteasome . ( C ) When the extraction is compromised ( e . g . , covalent protein–DNA adduct ) , the protein is processed by Cdc48/Wss1/Doa1 complex . Wss1 is targeted to sumoylated protein via its SIMs and promotes extension of SUMO chain that in return could further stimulate Wss1 accumulation and oligomerization at the site of DNA damage ( Wss1 foci ) . Binding to ssDNA and oligomerization triggers metalloprotease activity of Wss1 and initiates substrate processing . The process is assisted by Cdc48 and Doa1 and finally ends in the vacuole . DOI: http://dx . doi . org/10 . 7554/eLife . 06763 . 033 Wss1 shows structural and functional similarity to DVC1/Spartan ( Figure 1—figure supplement 1C and Figure 1—figure supplement 2 ) , a protein from higher eukaryotes that brings Cdc48 to stalled replication forks ( Centore et al . , 2012; Davis et al . , 2012; Mosbech et al . , 2012 ) . Like Wss1 , DVC1/Spartan protects cells from UV damage , probably by removing promiscuous TLS-polymerases from the chromatin . DVC1/Spartan has a putative metalloprotease domain SprT appended to a SHP motif , a PCNA-interacting PIP box and a Ub-binding Zn-finger domain UBZ4 . No proteolytic activity has been observed for the proteins of the SprT family , yet the putative protease active site is essential for DVC1/Spartan function ( Kim et al . , 2013 ) . Mutations in DVC1/Spartan SprT domain result in genomic instability ( Lessel et al . , 2014; Maskey et al . , 2014 ) and have recently been linked to a new progeroid syndrome with early onset of hepatocellular carcinoma ( Lessel et al . , 2014 ) . Our analysis suggests that the structure of SprT is very similar to the WLM domain ( Figure 1—figure supplement 1C and Figure 1—figure supplement 2 ) . Moreover , DVC1/Spartan has several cysteine residues , one of which , conserved C205 , seems not to be involved in disulfide bonds and is a potential candidate for protease regulation by a cysteine-switch mechanism . Thus , DVC1/Spartan may also be a functional protease that assists Cdc48 in removing chromatin components upon DNA damage stress . While Wss1 is targeted to SUMO , DVC1/Spartan binds PCNA and Ub , reflecting probably the differences in DDR regulation in higher and lower eukaryotes . Notably , in DVC1/Spartan from Caenorhabditis elegans and other species PIP box is replaced by SIM upstream of UBZ4 domain , while some WLM proteins have both SIM and NZF ( Figure 1—figure supplement 1C ) . This suggests that these proteins could have dual Ub-SUMO binding specificity and that in higher eukaryotes the SIM function is substituted by PIP motif . Overall , this work confirms and extends the elegant findings of Stingele et al . ( 2014 ) . It reveals the regulation of Wss1 activity by a cysteine-switch mechanism , demonstrates a ternary complex of Wss1 with Cdc48 and Doa1 that exhibits specificity for SUMO , and promotes binding of HMW-SUMO to Cdc48 , describes for the first time the SUMO ligase activity of Wss1 , and demonstrates that Wss1 moves from nuclear foci to the vacuole during stress , reinforcing numerous intriguing links between SUMO-dependent DDR and vacuolar degradation . Finally , Wss1 proteolytic activity may be downregulated by auto-proteolysis to terminate the stress-activated proteolysis by Wss1 .
Strain construction , cell growth , yeast transformation , spotting assay followed standard protocols ( Guthrie and Fink , 2002 ) . Yeast knockout and TAP-tagged strains were obtained from Open Biosystems ( GE Healthcare Dharmacon Inc . , France ) . Other strains are listed in Supplementary file 3 . Cell irradiation with UV light was performed with calibrated 254 nm UVC germicidal lamp . Cell treatment with 0–0 . 2 μg/ml 4-Nitroquinoline N-oxide ( 4-NQO , Sigma–Aldrich , France ) was performed in log-phase cultures for 3 hr before analysis . For experiments with proteasome inhibitor MG132 cells were grown on synthetic medium containing L-proline instead of ammonium sulfate as the sole nitrogen source and were permeabilized with sodium dodecyl sulfate ( SDS , 0 . 003% ) before addition of the inhibitor ( 75 μM final concentration for 3 hr ) ( Liu et al . , 2007 ) . Genotoxic treatment and colony-forming assays were performed as described previously ( Liu et al . , 2002 ) . Plasmid vectors used for expression of recombinant proteins are listed in Supplementary file 4 . Soluble HA-Wss1 proteins were produced in ArcticExpress ( DE3 ) cells ( Stratagene , Agilent Technologies , Santa Clara , CA , USA ) . Induction was performed in 1 l culture with 50 μM IPTG at 12°C overnight . Cells were collected , washed with PBS , resuspended in 10 ml of lysis buffer ( LyB , 150 mM NaCl , 2 μM ZnCl2 , 1% Triton X-100 , 50 mM Tris pH 7 . 4 ) supplemented with protease inhibitor cocktail w/o EDTA ( Pierce , Thermo Fisher Scientific , France ) , and disrupted by sonication . All protein preparation and manipulation procedures were performed on ice or at 4°C , unless otherwise indicated . Cell lysate was cleared by centrifugation ( 25 , 000×g , 0 . 5 hr , Avanti J-26XP Beckman Coulter ) . The supernatant was filtered through sterile 0 . 2-μm filter and added to 200 μl of pre-equilibrated anti-HA affinity beads ( Sigma–Aldrich , France ) . After incubation ( with permanent rotation ) for 1 hr in a cold room , beads were collected , washed three times with 2 ml LyB , and once with 2 ml of elution buffer ( ElB , 150 mM NaCl , 0 . 1% Triton X-100 , 50 mM Tris pH 7 . 4 ) supplemented with 10% glycerol . The HA-Wss1 proteins were eluted by 1 mg/ml HA peptide ( Anaspec , Fremont , CA , USA ) in ElB-10% glycerol , aliquoted , and stored at −80°C . For soluble MBP-Wss1 fusion proteins similar procedure was applied except that the expression was done in BL21pLysS ( DE3 ) cells ( Life Technologies , France ) , and induction was performed with 100 μM IPTG at 20°C overnight . The protein was purified on amylose resin ( New England Biolabs , Ipswich , MA , USA ) and eluted with 10 mM maltose in ElB-10% glycerol . For preparation of Wss1-his6 proteins from inclusion bodies , the expression was induced in 1 l of BL21 ( DE3 ) ( Life Technologies ) culture with 1 mM IPTG for 4 hr at 37°C . Cells were collected , washed with PBS , resuspended in 20 ml of suspension buffer ( SuB , 50 mM NaCl , 1% Tx-100 , 5 mM β-metrcaptoethanol and 20 mM HEPES , pH 7 . 5 ) supplemented with 1 mM PMSF and protease inhibitor cocktail w/o EDTA ( Pierce ) and disrupted by sonication . Protein inclusion bodies were collected by centrifugation ( 25 , 000×g , 0 . 5 hr ) and washed with brief sonication in SuB two more times . The pellet was resuspended in 20 ml bind buffer ( BiB , 7 M urea , 250 mM NaCl , 5 mM β-metrcaptoethanol , and 20 mM HEPES , pH 7 . 5 ) supplemented with 2% SDS ( per liter of growth ) and shaken at 37°C to solubilize the inclusion bodies . The protein containing solution was centrifuged at room temperature ( 25 , 000×g , 0 . 5 hr ) . The supernatant was diluted fourfold with BiB to reduce SDS concentration to 0 . 5% , loaded on Ni-NTA resin ( 2 ml Ni-NTA Superflow , Qiagen , France ) . The column was washed with 20 ml of BiB and the protein was eluted with BiB supplemented with 200 mM imidazole . The purified Wss1 was refolded by stepwise dialysis using 3 kD cut-off membrane ( Spectra/Por , Spectrum Laboratories Inc . , Piscataway , NJ , USA ) by gradually decreasing the concentration of urea in BiB from 7 M to 1 . 5 M . The BiB was also supplemented with 2 mM CaCl2 , 5 μM ZnCl2 , and 0 . 1% Triton X-100 . The final dialysis step was done in 150 mM NaCl , 0 . 1% Triton X-100 , and 20 mM HEPES , pH 7 . 5 buffer with 10% glycerol . The Wss1 proteins were aliquoted and stored at −80°C . The recombinant HA-Wss1 protein and HA-Wss1-AQA mutant were prepared similarly except that the Ni-NTA purification step was omitted . When examining the effect of various additives on Wss1 refolding and activity , all molecules , except SDS ( 0 . 1% final concentration ) were added directly into protein solution before dialysis: heparin ( 200 μg/ml sodium salt , Sigma–Aldrich ) , plasmid DNA ( 100 μg/ml pMAL-c2 ) , and ssDNA ( 100 μg/ml M13mp18 single-stranded DNA , New England Biolabs ) . N-terminal protein sequencing was performed by automated Edman degradation ( LF 3400; Beckman Instruments ) . All other recombinant proteins were prepared following previously published protocols ( Supplementary file 4 ) . SUMO- and Ub-phosphoric acids were synthesized from Smt3 ( 1–97 ) -MESNa and Ub ( 1–75 ) -MESNa , which were produced as previously reported ( Wilkinson et al . , 2005 ) and lyophilized . For reaction , 5 mg of MESNa thiol ester was solubilized in 0 . 5 M solution of aminomethylphosphonic acid solution ( AMPA-Na , pH 8 . 5 ) containing 1 mM N-hydroxysuccinimide and incubated overnight at 37°C . The reaction was analyzed by following the loss of protein thiol ester with concomitant appearance of protein phosphonate by high-performance liquid chromatography ( HPLC , Waters ) . After completion , the reaction was dialyzed against 50 mM HCl , and lyophilized . The resulted SUMO-PA and Ub-PA were ∼80% pure ( HPLC ) and were used without further purification . 10 . 7554/eLife . 06763 . 044Figure 12 . SUMO-PA and Ub-PA synthesis . DOI:http://dx . doi . org/10 . 7554/eLife . 06763 . 044 Commercial affinity matrices anti-HA and anti-Myc affinity gels ( Sigma–Aldrich ) , IgG- and glutathione- Sepharose ( GE Healthcare Life Sciences , France ) , amylose resin ( New England Biolabs ) and Ni-NTA Superflow ( Qiagen ) were used for immunoprecipitation and pull-down assays . Other affinity beads were prepared with SUMO ( PA ) , Ub ( PA ) , and MBP-tagged recombinant proteins by using CNBr-activated Sepharose 4B ( GE Healthcare Life Sciences ) according to the manufacturer's protocol . The binding assays were performed in 150 mM NaCl , 50 mM Tris pH 7 . 4 and 0 . 1% or 1% ( high stringency buffer ) of Triton X-100 . In vitro interaction assays were performed with 10–100 μg/ml of recombinant proteins for 1 hr with permanent rotation at 4°C . After incubation the beads were washed with the same buffer and bound proteins were eluted with 2× Laemmli buffer , unless otherwise indicated . In vitro sumoylation reactions were performed as described ( Johnson and Gupta , 2001 ) in 150 mM NaCl , 10 mM MgCl2 , 0 . 1 mM DTT , 20 μg/ml bovine serum albumin ( Fraction V , Sigma–Aldrich ) , and 20 mM HEPES ( pH 7 . 5 ) . The reaction mix contained 50 μg/ml SUMO or reductively methylated SUMO ( prepared as described , White and Rayment , 1993 ) , 2 μg/ml Uba2/Aos1 , 10 μg/ml Ubc9 , 4 mM ATP and either 1 μg/ml or 10 μg/ml of purified HA-Wss1 . For auto- and Cdc48- sumoylation assays 10 μg/ml of HA-Wss1 and 20 μg/ml of Cdc48 and Doa1 was used . The reactions were conducted for 0 . 5–2 hr at 30°C . SUMO chains for Wss1 binding and proteolysis assays were prepared similarly with 100–200 μg/ml SUMO , 5 μg/ml Uba2/Aos1 , 10 μg/ml Ubc9 , 5 mM ATP , and 10 μg/ml of truncated Siz2 ligase ( Takahashi et al . , 2003 ) incubated at 30°C overnight and dialyzed against 150 mM NaCl , 50 mM Tris pH 7 . 4 , and 0 . 05% of Triton X-100 . In vitro proteolysis reactions were performed in 150 mM NaCl , 50 mM Tris pH 7 . 4 , and 0 . 05% of Triton X-100 . The reactions were conducted in a PCR block at 25°C . The reaction mix contained 50–200 μg/ml of refolded recombinant HA-Wss1 protein . To activate Wss1 protease , 0 . 5 mM thiram ( dimethylcarbamothioylsulfanyl N , N-dimethylcarbamodithioate , 100 mM stock solution in DMSO ) , 1 mM APMA ( 4-aminophenylmercuric acetate , 200 mM stock solution in DMSO ) , and 2 . 5 μM DNA oligonucleotide ( mbpTop1d , CGTCATCAGAAGACAACTCATGATTAACTTTGGAAGC ATCAGCAATAGTCGAGCTCGAATTAGTCTGCG , 100 μM stock solution in water ) . Wss1 substrates ( SUMO chains , His6-Ub-SUMO-HA ) were used at 100–200 μg/ml concentrations . Fluorescence anisotropy measurements with SUMO1-FP substrate were performed on a MOS-450 spectrometer ( BioLogic , Inc . ) in a 150 ml quartz cuvette at 25°C essentially as described ( Geurink et al . , 2012 ) . The antibodies used for western blotting were anti- SUMO ( Smt3 y-84 , Santa-Cruz , Dallas , TX , USA ) , anti-Ub ( clone 6C1 , Sigma–Aldrich ) , anti-HA ( clone 16B12 , Covance , France ) , anti-MBP ( clone MBP-17 , Sigma–Aldrich ) , anti-Flag ( FLAG M2 , Sigma–Aldrich ) , anti-cMyc ( Sigma–Aldrich ) , anti-Cdc48 ( VCP antibody , Cell Signaling , Danvers , MA , USA ) , anti-GST ( clone 8–326 , Pierce ) , anti-TAP ( Pierce ) , anti-tubulin ( Sigma ) , anti-histone H3 ( ab46765 , Abcam , France ) secondary anti-rabbit HPR-coupled ( Pierce ) , and secondary anti-mouse HPR-coupled ( Pierce ) . Whole cell lysate ( WCL ) was prepared by heating yeast cells in WCL-buffer ( 100 mM NaOH , 2% SDS , 50 mM EDTA , and 2% β-mercaptoethanol ) as described ( von der Haar , 2007 ) . The isolation of chromatin fraction was performed as described ( Verma et al . , 2011 ) . The chromatin proteins were then solubilized by heating in WCL-buffer . Protein concentration was measured by Nanodrop and 80 μg of protein per lane of gel were generally used for western blot analysis . NuPAGE 3–8% Tris-acetate and 4–12% ( and 4–20% ) Bis-Tris precast polyacrylamide gels were used ( Life Technologies ) . To study the effect of Wss1 expression on STUbL substrates , SUMO conjugates were isolated from EJY251-11b cells containing p315-PGAL-HFSMT3 plasmid ( Johnson et al . , 1997 ) . Cells were transformed with HA-Wss1 constructs ( pYEPGAP-URA3 ) and grown at 30°C in selective media containing L-proline as the sole nitrogen source and 2% galactose/1% raffinose as a carbon source . Cells were permeabilized with 0 . 003% SDS before treatment with 75 μM MG132 for 3 hr . Cells were harvested and lysed in hot WCL-buffer as described above . The WCLs were extracted by methanol-chloroform-water ( 4:1:3 ) , proteins were precipitated with methanol and briefly dried ( von der Haar , 2007 ) . The resulted pellet was solubilized in load buffer ( LoB , 6 M urea , 10 mM NaCl , 50 mM Tris , pH 7 . 4 ) , loaded onto Ni-NTA Superflow beads , rinsed with LoB , and eluted in LoB supplemented with 200 mM imidazole or directly by 2× Laemmli buffer . The eluted proteins were analyzed by western blotting . To identify Wss1 partner proteins , we used a strain expressing Wss1-Myc13 from endogenous loci . Cell lysates were prepared from 2 l of log-phase WSS1-MYC13 culture and parental strain as a control . Cells were collected , frozen at −80°C , and cell lysate was prepared essentially as described ( Puig et al . , 2001 ) except that the dialysis step was omitted . The lysate was incubated with 250 μl of anti-c-Myc agarose affinity gel ( Sigma–Aldrich ) for 1 hr at 4°C; the gel was washed with cell lysis buffer ( CLB ) , and the bound proteins were eluted with 0 . 1 M ammonium hydroxide and lyophilized . For MS analysis the samples were solubilized in 1× Laemmli buffer and separated by gel electrophoresis . To identify Doa1 partner proteins , cells lysates were prepared from 4 l of log-phase BY4742 culture and pre-cleared by passing through 2 ml of Ni-NTA Superflow gel ( Qiagen ) . To one half of the lysate 1 mg of purified recombinant HF-Doa1 was added , and the mixture was passed through 1 ml of Ni-NTA Superflow gel . Another half of the lysate was treated the same way but without HF-Doa1 and serves a control . The bound proteins were eluted with 1× Laemmli buffer and were cleaned by running a very short 9% SDS gel ( ∼3 mm long ) followed by staining and extensive wash . In all samples the gel was excised into slices and digested by trypsin ( Shevchenko et al . , 1996 ) . The resulting peptides were dissolved in a loading buffer ( 6% acetic acid , 0 . 005% heptafluorobutyric acid and 5% acetonitrile ) and analyzed by reverse phase liquid chromatography coupled with tandem MS ( LC-MS/MS ) ( Peng and Gygi , 2001 ) on an LTQ-Orbitrap hybrid mass spectrometer ( Thermo Electron , San Jose , CA , USA ) . The analysis of all MS/MS spectra was performed using the SEQUEST algorithm ( version 27 ) ( Eng et al . , 1994 ) and a composite target/decoy database . While the target proteins were derived from Saccharomyces cerevisiae sequences and known contaminant proteins , such as porcine trypsin and human keratins , the decoy components contained the randomized sequences of all target proteins ( Peng et al . , 2003 ) . The database search was processed without enzyme restriction and with mass tolerance of 3 . 05 Da for precursor ion . Modifications were permitted to allow for the detection of the following ( mass shift shown in Daltons ) oxidized methionine ( +15 . 9949 Da ) and acrylamide-cysteine adduct ( +71 . 0371 Da ) . The results of SEQUEST were filtered according to XCorr and Cn to obtain a false-positive rate of 1% in peptide identification . The Wss1 VIM ( 209–219 ) peptide was synthesized in Anaspec . RMN data were acquired at 278 K on an Agilent VNMRS 800 MHz spectrometer equipped with a triple-resonance HCN cold probe and pulsed field gradients . The 1H frequencies assignment was achieved with the combined use of 1H-1H- DQF-COSY , TOCSY ( mixing time 80 ms ) and NOESY ( mixing time: 250 ms ) spectra . CYANA 2 . 1 was used to calculate the structure of Wss1-VIM peptide ( Guntert et al . , 1997 ) , using NOE restraints measured from the 1H-1H NOESY spectrum . From the observation of NOE cross peaks characteristic of alpha helical conformation daN ( i , i+2 ) daN ( i , i+3 ) and dNN ( i , i+2 ) ( Wuthrich , 1986 ) and the chemical shift analysis of the Ha chemical shifts of the peptide using the program TALOS ( Shen et al . , 2009 ) showing helical propensities for all residues , we applied PHI and PSI dihedral angles restraints corresponding to canonical helix values along the peptide backbone . The automatically assigned NOEs were calibrated within CYANA according to their intensities . After seven rounds of calculation ( 10 , 000 steps per round ) , 120 cross-peak NOE assignments were used in the final calculation . The 10 lowest energy conformations of the peptide have no constraint violations and show a backbone root-mean-square deviation of 0 . 2 ± 0 . 1 Å and a heavy atom root- mean-square deviation of 0 . 8 ± 0 . 2 Å . Conserved regions in WLM proteins were identified using BLOCKS Database looking for the proteins documented in the Prosite Database ( http://blocks . fhcrc . org/help ) . Consensus secondary structure prediction was performed on SYMPRED web server ( http://www . ibi . vu . nl/programs/sympredwww/ ) . 3D structure prediction and modeling of WLM and SprT proteins were performed using the Phyre2 server ( Kelley and Sternberg , 2009; http://www . sbg . bio . ic . ac . uk/phyre2/html/page . cgi ? id=index ) . The PatchDock web server was used to perform the docking runs ( Schneidman-Duhovny et al . , 2005; http://bioinfo3d . cs . tau . ac . il/PatchDock/ ) . Protein interaction network ( medium confidence ) was constructed using STRING ( Search Tool for the Retrieval of Interacting Genes/Proteins: http://string-db . org/ ) , and analyzed in GO terms with Cytoscape_v2 . 8 . 3 . To isolate Wss1-interacting proteins , lysates were prepared from cells transformed with HA-Wss1 constructs treated or not with 0 . 2 μg/ml of 4-NQO . Cells from an A600 ≅ 15 culture were harvested by centrifugation , washed in water , and processed by glass bead beating ( Disruptor Genie , Scientific Industries ) for 5 min at 4°C in 350 μl CLB ( 150 mM NaCl , 50 mM Tris pH 7 . 4 , 0 . 5% Triton X-100 ) supplemented with 30 mM NEM and protease inhibitor cocktail w/o EDTA . The sample was centrifuged at 20 , 000×g for 15 min at 4°C , the supernatant was collected and the pellet was extracted two more times as described above . The supernatant fractions were pooled and added to 50 ml of anti-HA beads pre-equilibrated with CLB . The beads were rotated for 1 hr at 4°C , washed 3× 500 μl CLB , and bond proteins were eluted either with 100 μl of 2× Laemmli buffer w/o reducing agent ( denaturing conditions ) or with 100 μl of 1 mg/ml HA peptide in CLB ( native complexes ) . To study Wss1 proteolysis the native complexes were incubated in CLB either with or without a protease inhibitor ( 1 mM PMSF , 2 mM OPA , or 30 μM MG132 ) , and the reaction was quenched with 2× Laemmli buffer and analyzed by western blotting . Similar protocol was used for pull-down assays with CNBr-crosslinked MBP-Wss1 beads and IgG beads . Elution of protein complexes from IgG beads by cleavage of TAP-tag with TEV protease was performed in CLB buffer supplemented with 0 . 5 mM EDTA and 1 mM DTT ( Puig et al . , 2001 ) . Recombinant TEV protease was added to the beads suspension ( 50 µg/ml final concentration ) , and the beads were rotated 2 hr at 20°C . The eluate was recovered after centrifugation and analyzed by western blotting . Standard live yeast cell microscopy techniques were used ( Guthrie and Fink , 2002 ) . Cells were grown to log-phase on glucose- or galactose-containing medium before harvesting . For staining of vacuolar membranes , cells were incubated for 0 . 5 hr with 1 µg/ml FM4-64 ( Life Technologies ) with shaking at 30°C . Cells then were resuspended in fresh medium lacking the dye and were allowed to grow for 1 hr at 30°C . For DNA staining , cells were incubated for 0 . 5 hr with 2 . 5 µg/ml of DAPI ( Sigma–Aldrich ) before observation . The expression of Wss1-GFP under control of inducible MET25 promoter ( pUG35 and pUG36 vectors ) was regulated by varying methionine concentration in the growth medium from 10 mg/l ( low level of expression ) to 0 mg/l ( high level of expression ) . Cells were analyzed in fresh medium on concanavalin A ( Sigma–Aldrich ) -coated slides using AxioImager Z1 Zeiss microscope . Live cell cytofluorimetry was performed with Becton Dickinson LSR II cytofluorimeter . | DNA repair is essential for cell survival . Every time DNA is damaged , several protein complexes sense the damage and act to repair it . These complexes need to be carefully regulated . One way this is achieved is by the addition of molecular tags that change the activity of these complexes . Sumoylation is one such modification , which involves the addition of a bulky molecular tag called SUMO . Sumoylation during DNA damage is known to regulate the precise assembly and activity of the repair complexes . This modification is reversible and when the DNA repair is completed , the SUMO tags are removed and the repair complexes are disassembled . A protein called Cdc48 was known to work together with other molecules to clear SUMO-modified complexes from the DNA after the repair is complete . But it was unclear how this occurred and what roles other proteins played in the process . Balakirev et al . now analyze the detailed workings of another protein called Wss1 and how it contributes to SUMO processing in yeast cells . The experiments show that Wss1 helps to remove the SUMO-modified complexes from the DNA by forming a complex with Cdc48 and the Cdc48-adaptor protein Doa1 . Wss1 is a protease , an enzyme that can break down proteins , but it is inactive under the normal conditions inside a cell . Wss1 is found in the cell's nucleus ( which contains most of the cell's DNA ) until it senses DNA damage , which it does by recognizing damage-specific forms of DNA ( such as single stranded DNA ) and the SUMO tag . Balakirev et al . found that Wss1 binds to the site of DNA damage and lengthens the SUMO tag . This indicates that Wss1 can also act as a ligase—an enzyme that helps to assemble polymeric SUMO . The polymeric SUMO in turn leads to the accumulation of more Wss1 and activates its protease activity . The protease cleaves the associated proteins in the repair complex , thus helping to extract the SUMO-modified proteins from the DNA . DNA damage also results in the transfer of Wss1 into a compartment inside the cell , called a vacuole . This suggests that autophagy—a mechanism used by cells to break down damaged cellular components—is one way that proteins are removed from the nucleus . Together , Balakirev et al . 's findings reveal a previously unknown role for Wss1 and introduce us to another level of control in the DNA damage response . The next challenges will be to identify specific cellular components involved in transporting Wss1 to the vacuole and to examine whether this mechanism is conserved with a human version of Wss1 , the Spartan/DVC1 protein . | [
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] | 2015 | Wss1 metalloprotease partners with Cdc48/Doa1 in processing genotoxic SUMO conjugates |
The CRISPR-associated endonuclease Cas9 from Streptococcus pyogenes ( SpyCas9 ) , along with a programmable single-guide RNA ( sgRNA ) , has been exploited as a significant genome-editing tool . Despite the recent advances in determining the SpyCas9 structures and DNA cleavage mechanism , the cleavage-competent conformation of the catalytic HNH nuclease domain of SpyCas9 remains largely elusive and debatable . By integrating computational and experimental approaches , we unveiled and validated the activated Cas9-sgRNA-DNA ternary complex in which the HNH domain is neatly poised for cleaving the target DNA strand . In this catalysis model , the HNH employs the catalytic triad of D839-H840-N863 for cleavage catalysis , rather than previously implicated D839-H840-D861 , D837-D839-H840 , or D839-H840-D861-N863 . Our study contributes critical information to defining the catalytic conformation of the HNH domain and advances the knowledge about the conformational activation underlying Cas9-mediated DNA cleavage .
The clustered regularly interspaced short palindromic repeats ( CRISPR ) -associated endonuclease Cas9 from Streptococcus pyogenes ( SpyCas9 ) has become a gene-editing tool that holds an immense promise for the development of novel therapeutic approaches for human diseases ( Cong et al . , 2013; Jinek et al . , 2012; Knott and Doudna , 2018; Mali et al . , 2013 ) . Two , magnesium ( Mg2+ ) ion-dependent , nuclease domains ( i . e . HNH and RuvC domains ) in Cas9 cleave the target DNA strand ( tDNA ) complementary to the guide region of a dual single-guide RNA ( sgRNA ) and the non-target DNA strand ( ntDNA ) , respectively ( Gasiunas et al . , 2012; Jinek et al . , 2012 ) . The conformational and mechanistic knowledge of Cas9 activation to achieve DNA cleavage is essential for the rational engineering of Cas9 to possibly ensure minimal off-target effects while retaining high gene-editing efficiency ( Chen et al . , 2017a; Kleinstiver et al . , 2016; Slaymaker et al . , 2016; Sternberg et al . , 2015 ) . Several structures of the SpyCas9-sgRNA-DNA ternary complex to depict the HNH nuclease domain in a ‘cleavage-competent’ state have been reported ( Huai et al . , 2017; Palermo et al . , 2018; Palermo et al . , 2017; Zuo and Liu , 2017 ) . Notably , the amino acid residue D861 in the HNH domain of SpyCas9 pointed towards the catalytic center in the absolute majority of resolved crystal structures ( Anders et al . , 2016; Anders et al . , 2014; Dong et al . , 2017; Hirano et al . , 2016; Jiang et al . , 2015; Jiang et al . , 2016; Jinek et al . , 2014; Liu et al . , 2019; Nishimasu et al . , 2014; Olieric et al . , 2016; Yang and Patel , 2017 ) , molecular dynamic simulation models ( Palermo et al . , 2018; Zuo and Liu , 2017 ) , and cryo-electron microscopy ( cryo-EM ) structures ( Huai et al . , 2017; Jiang et al . , 2019; Shin et al . , 2017 ) ( Figure 1a–1d ) . Despite lacking experimental evidence , it is generally believed that D861 directly participates in Mg2+ chelation and tDNA cleavage ( Huai et al . , 2017; Palermo et al . , 2018; Palermo et al . , 2017; Zuo and Liu , 2017 ) . An in silico model ( Yoon et al . , 2019 ) that was recently reported also suggested that D861 and N863 are potentially involved in chelating the Mg2+ ion at the catalytic center of the HNH domain , although this discovery also remained untested in an experimental setting . In the diverse homologous structures of DNA/RNA nucleases from other species ( Yang , 2008; Yang , 2011 ) , however , the residues spatially equivalent to the D861 of SpyCas9 are conserved as an asparagine . The substitution ( N62D ) of the corresponding asparagine in the active center of bacteriophage T4 Endonuclease VII ( T4 Endo VII ) has been shown to abrogate its DNA cleavage activity ( Biertümpfel et al . , 2007 ) . These observations motivated us to examine the potential role of D861 in the HNH domain of Cas9 . We mutated D861 to alanine and tested the activity of the D861A variant using an experimental approach based on Cas9-mediated disruption of the egfp gene in EGFP-expressing human cells . Unexpectedly , this Cas9 variant exhibited DNA-cleavage activity level similar to that of the wild-type protein ( Figure 1e and Figure 1—figure supplement 1 ) . To further validate our finding , we performed in vitro cleavage assays using either plasmid or oligo DNA as a substrate and observed that the D861 variant retained similar activity as the wild type , given enough reaction time ( Figure 1—figure supplement 2 and Figure 1—figure supplement 3 ) . Our experiments thus demonstrate that D861 is not critical for HNH domain-catalyzed tDNA cleavage , unlike what would be expected from the reported Cas9 complex structures ( Anders et al . , 2014; Huai et al . , 2017; Jiang et al . , 2016; Palermo et al . , 2018; Palermo et al . , 2017; Zuo and Liu , 2017 ) . In other words , the previously reported structures of the HNH domain of DNA-bound Cas9 ( Huai et al . , 2017; Palermo et al . , 2018; Palermo et al . , 2017; Zuo and Liu , 2017 ) potentially represent a conformation that is incompetent for tDNA cleavage . Hence , we refer to this cleavage-incompetent conformation with an inward-facing D861 as ‘psuedoactive state’ hereafter . This report aims to unmask the catalytic state of the HNH nuclease domain in Cas9 and explore the underlying mechanism of activation .
Overall , our study has delineated a molecular framework underlying the catalytic conformation of the HNH nuclease domain of SpyCas9 . The findings presented here advance our knowledge of conformational activation that enables Cas9-mediated DNA cleavage , set an important foundation for future studies to further understand the structure-function relationships of Cas9 , and facilitate the rational design of Cas9 variants in the future .
Human embryonic kidney 293T ( HEK293T ) cells ( ATCC , Manassas , VA ) as a subclone of the HEK293 cell line were cultured in DMEM ( Thermo Fisher Scientific , Carlsbad , CA ) containing 10% fetal bovine serum ( FBS; Thermo Fisher Scientific , Carlsbad , CA ) at 37°C . All cells were periodically tested using the MycoAlert mycoplasma detection kit ( Lonza , Walkersville , MD ) and free of mycoplasma . The HEK293T cells were used to established EGFP-expressing cells by the lentivirus-mediated transduction of pLenti-CMV-GFP-Puro expression plasmid ( Addgene , Cambridge , MA ) into the cells followed by the selection of single-cell clones that stably express EGPF and fluorescence green ( Campeau et al . , 2009 ) . The stable clone A2 was used in this study . For testing Cas9-mediated editing of the egfp gene in the HEK293T-EGFP ( A2 ) cells , an EGFP-targeting sgRNA sequence ( EGFP sgRNA1: 5’GGGCGAGGAGCTGTTCACCG3’ ) was cloned into a lentiCRISPR plasmid ( Addgene , Cambridge , MA ) and resulted in a construct of a one-vector system for co-expression of sgRNA and wild-type SpyCas9 ( Addgene , Cambridge , MA ) ( Shalem et al . , 2014 ) . The site-directed mutagenesis was performed to specifically introduce mutations into the cas9 gene open reading frame ( ORF ) in the expression construct to generate the expression vectors of different Cas9 variants with the EGFP sgRNA sequence . After mutagenesis , the DNA sequencing of each expression construct was performed to confirm the mutations of the Cas9 gene ORF . HEK293T-EGFP ( A2 ) cells transduced with the Cas9 and sgRNA expression constructs were selected using 5 ug/ml puromycin for two weeks prior to the downstream analysis to determine the editing efficiencies of different Cas9 variants . The general procedure for immunoblotting was described in previously published reports ( Wang et al . , 2008; Zolekar et al . , 2018 ) . The primary antibody against SpyCas9 ( catalog# ab191468 ) was obtained from Abcam ( Cambridge , MA ) . HRP-conjugated secondary antibodies were from Jackson ImmunoResearch Laboratories ( West Grove , PA ) . For determining fluorescence intensity and quantifying the percentages of EGFP fluorescence-positive cells in cell samples , samples ( ~5×105 cells per sample ) harvested and resuspended in phosphate-buffered saline ( PBS ) were analyzed using a SH800Z cell sorter ( Sony Biotechnology , San Jose , CA ) . The protein variants , SpyCas9D861A and SpyCas9N863A were produced by sequence independent cloning method ( SLIC ) using SpyCas9WT template plasmid ( Addgene: pMJ806 ) ( Jinek et al . , 2012 ) and mutagenic primers ( Supplementary file 2 ) ( Scholz et al . , 2013 ) . Sequence confirmed clones were transformed into Escherichia coli Rosetta strain 2 ( DE3 ) for protein expression . Overexpression and protein purification were carried out using previously published protocols ( Babu et al . , 2019; Jinek et al . , 2012 ) . The template for in vitro transcription of sgRNA ( 98-nucleotide long ) contained a 20 nt long spacer as previously described ( Babu et al . , 2019; Nishimasu et al . , 2014 ) . The protocols used for in vitro transcription and sgRNA annealing were as reported previously ( Babu et al . , 2019 ) . For creating target DNA , a 30 nt long protospacer flanked by a PAM ( GGG ) was introduced into pUC19 ( Babu et al . , 2019 ) . The proteins were diluted in 20 mM HEPES pH 7 . 5 , 150 mM KCl , and 2 mM TCEP , and the cleavage assays were carried out in a final volume of 10 μL . The reaction mix contained 20 mM Tris-HCl pH 7 . 5 , 100 mM KCl , 5 mM MgCl2 , 5% ( v/v ) glycerol , 0 . 5 mM TCEP , 100 ng of substrate plasmid , 50 nM SpyCas9 , and 60 nM sgRNA ( protein and RNA at a ratio of 1:1 . 2 molar ) . The reaction mixture was incubated at 37°C and stopped at different time points ( 15 s , 30 s , 1 min , 2 . 5 min , 5 min , 7 . 5 min , 10 min , 15 min , 30 min , 45 min , and 60 min ) by the addition of 50 mM EDTA and 1% SDS . The reaction products were resolved on 1% agarose gel and products were visualized by ethidium bromide staining . The gel was imaged using a BioRad ChemiDoc MP apparatus . To quantify the cleavage activities , each gel image was analyzed using the ImageJ software ( Schneider et al . , 2012 ) . The bands of nicked ( N ) , linear ( L ) , and supercoiled ( SC ) DNA were quantified and designated as IN , IL , and ISC respectively . The nicked , linear and total activity ( TA ) was calculated using the following equations: ( 1 ) Nicked ( % ) =[ININ+IL+ISC]×100 ( 2 ) Linear ( % ) =[ILIN+IL+ISC]×100 ( 3 ) TA ( % ) =[IN+ILIN+IL+ISC]×100 For each reported data point , average values were obtained from a minimum of three replications that were performed using proteins produced from two independent preparations to account for variations in active protein fraction between different preparations . Standard deviation ( SD ) and standard error of mean ( SEM ) were calculated based on the number of replications using the following equations: ( 4 ) SD=∑R-RAV2/n-1 ( 5 ) SEM=SD/nwhere R is a data value from each replication , RAV is average of data values of all the replications , and n is the number of replications ( a total of three for each protein variant ) . Two separate oligo DNA strands used for the radioactive assay were ordered from Integrated DNA technology ( IDT , Supplementary file 2 ) . Target ( T ) and non-target ( NT ) strands were mixed at equimolar concentration in the annealing buffer ( 30 mM HEPES pH 7 . 5 , 100 mM potassium acetate ) and heated at 95°C for 2 min and allowed for slow cooling . The annealed duplex oligo DNA was 5’ end labeled with 32P ( γ−32P ATP purchased from PerkinElmer ) using T4 polynucleotide kinase ( New England Biolabs ) . The labeled oligo DNA was purified using BioSpin column P-30 ( BioRad ) . The reaction buffer contained 20 mM Tris-HCl pH 7 . 5 , 100 mM KCl , 10 mM MgCl2 , 5% ( v/v ) glycerol , 0 . 5 mM TCEP . Approximately 5 nM of labeled oligo duplex was incubated with 250 nM SpyCas9 and 300 nM sgRNA ( protein and RNA at a ratio of 1:1 . 2 molar ) at 37°C and stopped at different time points ( 15 min , 30 min , and 60 min ) using EDTA at 10 mM final concentration . Then the samples were treated with proteinase K ( New England Biolabs ) for 15 min at 50° C to remove SpyCas9 . This was followed by addition of equal volume of loading dye ( 2X concentration is 20 mM EDTA , 95% formamide , 2% SDS , and 0 . 025% bromophenol blue ) . The reaction samples were resolved on a 16% poly-acrylamide gel containing 20% formamide and 6 . 4 M urea . The bands were visualized by phosphor imaging with Typhoon FLA 7000 system ( GE life sciences ) . Three independent replications were performed using proteins from two independent preparations . The initial coordinates of the apo-state SpyCas9 were taken from the Protein Data Bank ( PDB ) under accession number 4CMP ( solvated at 2 . 6 Å resolution ) ( Jinek et al . , 2014 ) . This X-ray structure contains two Cas9 monomers , and the molecule B was considered for modeling here ( Figure 1—figure supplement 4a ) . The disordered regions were built up with the tool SWISS-MODEL ( Waterhouse et al . , 2018 ) and the missing heavy atoms and hydrogens were added by using the leap program within AMBERTOOL16 ( Salomon-Ferrer et al . , 2013 ) . The complete structure was then solvated in a cubic water box with a minimal thickness of 13 . 5 Å from each edge , leading to a periodic boundary box of 138 × 153×126 Å3 . The system was neutralized by Na+ , and additional NaCl was added to generate a physiological ionic strength of 150 mM . The resulting simulation box contains ~230 , 000 atoms . The above system was simulated by the CUDA-accelerated version of AMBER16 pmemd engine ( pmemd . cuda; Salomon-Ferrer et al . , 2013 ) using the amber force field ff14SBonlysc for protein , the TIP3P model for water ( Jorgensen et al . , 1983 ) , and the Joung-Cheatham parameter sets for monovalent ions ( Joung and Cheatham , 2008 ) . The non-bonded interactions were truncated at 10 Å , and the long-range electrostatics were calculated through the particle mesh Ewald ( PME ) summation method ( Darden et al . , 1993 ) , with a grid spacing of 1 Å . The bonds involving hydrogens were constrained via the SHAKE algorithm ( Miyamoto and Kollman , 1992 ) , allowing use of 2-fs time step of integration . After thorough energy minimization , the system underwent slow heating over 50 ps from 0 K to the target 310 . 15 K in the isothermal-isochoric ( NVT ) ensemble , followed by a 20-ns equilibration in the isothermal-isobaric ( NPT ) condition . The protein backbone atoms were restrained in the heating and equilibration stages . Finally , the production run was performed under the NPT ensemble without restraints , extending up to 100 ns . The pressure was controlled at 1 . 013 bar via the Monte Carlo barostat , and the temperature was maintained at 310 . 15 K through the Langevin thermostat implemented in AMBER16 . The final structural snapshot from the above simulation was then extracted , and a Mg2+ ion was placed at its HNH domain active center to set up the Mg2+-bound system ( Figure 1—figure supplement 4 ) by reference to the AnaCas9 crystal structure bound with a Mg2+ ion ( Figure 1—figure supplement 5a ) ( Jinek et al . , 2014 ) . Also , extra Mg2+ ions were introduced into the system to obtain a physiological concentration of 5 mM . The parameter set developed by Li et al . ( Li et al . , 2013b ) was selected for Mg2+ . In the equilibration stage , the distances between the Mg2+ and the coordinating oxygens on D839 and N863 was restrained to 2 . 1 Å ( i . e . , the experimental ion-oxygen distance; Zheng et al . , 2008 ) . The production run without restrains was extended to 50 ns . The starting structure of the pseudoactive Cas9 complex ( Figure 1d , right panel ) was obtained from our recent work ( Zuo and Liu , 2017 ) , which was derived by employing the unbiased , brute-force MD simulations on the crystal structure of Cas9-sgRNA-DNA ( PDB code: 5F9R ) that was captured in the pre-cleavage state ( Jiang et al . , 2016 ) . The structural model has been validated by different experiments , yet the Mg2+ ion at the HNH domain catalytic center appeared to lose one critical coordination bond with the leaving group O3’ of the scissile phosphate ( Zuo and Liu , 2017 ) ( Figure 2—figure supplement 7 ) , as compared to the homologous T4 Endo VII structure complexed with a DNA junction ( Biertümpfel et al . , 2007 ) ( Figure 2c ) . We reasoned that this issue may be due to the deficiency with the simple point-charge Mg2+ model used . Most recently , we systematically evaluated the performance of all four types of non-bonded Mg2+ ion models in terms of maintaining a challenging metal center configuration in a nuclease system ( Nowotny et al . , 2005 ) . Our benchmark calculations demonstrated that the multisite models based a 12-6-4 Lennard-Jones potential ( Li and Merz , 2014; Liao et al . , 2017 ) , which take charge-induced dipole effects into account , are the only ones that are capable of reproducing the experimental coordination patterns ( Zuo and Liu , 2018a ) . Accordingly , the 12-6-4 type multisite model ( Jorgensen et al . , 1983 ) ( here the midC4 set ) was considered for the Cas9 complex simulation , along with the TIP4PEw model for water , the Joung-Cheatham parameter sets for monovalent ions ( Joung and Cheatham , 2008 ) , and the amber force fields ff14SBonlysc , ff99bsc0_chiOL3 , ff99bsc0_OL15 for protein , RNA and DNA , respectively . Basically , the complex system was set up and simulated following the above protocol for the apo-Cas9 systems . The generated simulation box is approximately 109 × 145×166 Å3 sized , containing ~282 , 000 atoms . With different random seed numbers , two parallel simulations were carried out by using the latest AMBER18 ( Salomon-Ferrer et al . , 2013 ) that enables GPU calculations of the 12-6-4 ion potential . The simulation length was set to 200 ns each . The initial model for the active Cas9 complex was constructed by replacing the α-helical segment of the ββα-Me motif in the optimized pseudoactive Cas9 complex ( Figure 1a ) with the corresponding part in the Mg2+-bound apo-Cas9 structure ( Figure 1—figure supplement 4c ) . The pseudoactive Cas9 complex structure was taken from the above production simulation near 100 ns ( i . e . about half of the simulation time ) . The Mg2+-bound apo-Cas9 structure from the simulation trajectory was selected based on the observation of reasonable bonding with the connecting residues and minimal steric clashes after replacement of the α-helical segment . After sufficient energy minimization , the structural model was subjected to multi-stage equilibration: an initial 20-ns relaxation of the α-helical segment and surrounding residues , an another 20-ns equilibration with the inter-atomic distances within the metal center retrained relative to the T4 Endo VII system ( Biertümpfel et al . , 2007 ) , followed by an additional 20-ns equilibration with the restraints gradually released . Subsequently , two independent replicas were performed ( 250 ns/run ) under the same simulation conditions set for the pseudoactive system above . Additional MD simulations were performed to investigate the relative stability of the two conformational states ( i . e . N863-IN and N863-OUT ) of the α structure element containing N863 and D861 . The starting coordinates were taken from the respective structure models above , and only the HNH domain of Cas9 ( residues 781 to 905 ) was included in our simulation to enhance the sampling efficiency . Each isolated HNH domain in the two states was immersed in a truncated octahedral water box , with a minimal thickness of 14 . 5 Å . The ionic centration was set to 100 mM by adding an appropriate number of K+ and Cl- ions in the aqueous solution . The amber force field ff14SBonlysc and the TIP3P model were used for describing the protein and the water molecules , respectively , and the parameter sets for the monovalent ion were derived from the work by Joung and Cheatham ( 2008 ) . For each system , five independent simulations were performed under the NPT ensemble with different initial velocities , using a timestep of 2-ps . Each replica was extended to ~10 us , yielding a total of ~50 us of sampling for each system . The semiempirical DFTB3 QM/MM simulations were further implemented to improve the reliability of our MD-derived structure models . DFTB3 is the third-order variant of density functional theory tight binding ( DFTB ) that is formulated in a DFT framework ( Gaus et al . , 2012 ) . According to the extensive benchmark calculations , DFTB3 in its current form is most reliably for structural properties , including for fairly complex bimetallic motifs in diverse metalloenzymes ( particularly the phosphoryl-transfer enzymes ) ( Gaus et al . , 2012; Lu et al . , 2015; Lu et al . , 2016; Roston et al . , 2018 ) . The QM region includes the catalytic Mg2+ ion , the protein residues that coordinate the metal ion ( i . e . , D839 and D861/N863 ) , the general base H840 , part of the scissile phosphate and nearby atoms on the target strand , and the water molecules surrounding the metal ions , H840 , and the scissile phosphate ( cf . Figure 2a and d ) . Only the side chains of protein and the backbone of DNA are kept in the QM region , and link atoms are added between the Cα and Cβ atoms for the amino acids or between the C4’ and C5’ atoms for the nucleotides . The partitioning results in a total of 72 and 75 QM atoms for the pseudoactive and active Cas9 models , respectively . The dummy complex for the Mg2+ ions employed in pure MD simulations is changed back to the realistic single-atom form . The MM part of the protein and nucleic acids are described using the same AMBER force fields as mentioned above , and the water molecules are described with the TIP3P model . After the stages of energy minimization and slow heating , each system was subjected to two parallel 1 , 000-ps QM/MM simulations performed with the AMBER program . The free energies of the Cas9-nucleic acid complex conformers were estimated through the end-point Molecular Mechanics-Generalized Born Surface Area ( MM-GBSA ) approach ( Miller et al . , 2012 ) . Compared to the alternative Molecular mechanics-Poisson Boltzmann Surface Area ( MM-PBSA ) , MM-GBSA has been proven to be give comparable or even better accuracy in ranking ligand binding affinities as well as in calculating the relative stability of multiple conformations of a biomolecular system , though MM-PBSA is physically more rigorous ( Li et al . , 2013a; Zuo and Liu , 2016a; Zuo et al . , 2018b ) . For each state , the MM-GBSA calculations were performed over an ensemble of 2000 snapshots extracted from the last 50 ns of the simulation trajectories using the program MMPBSA . py in AmberTools16 . The pairwise GB model of Hawkins , Cramer , and Truhlar ( GBHCT ) ( Hawkins et al . , 1995; Hawkins et al . , 1996 ) was used , with the parameters described by Tsui and Case ( 2000 ) . The default values of the surface tension and the offset to correct the non-polar contribution to the solvation free energy were adopted and the salt concentration was set to 150 mM . Following our recent works ( Zuo and Liu , 2016a; Zuo et al . , 2018b ) , the two water molecules closest to the Mg2+ ion at the HNH domain active center were retained as part of the complex , considering the importance of the interfacial water for binding . The entropic contribution was not taken into account due to high computational demand and potential convergence problem , yet omission of this term does not qualitatively affect the results as previously suggested ( Hou et al . , 2011; Li et al . , 2013a; Zuo et al . , 2018b; Zuo et al . , 2016b ) . | The DNA inside human cells provides instructions for all of the processes that happen inside the body . Errors in the DNA may lead to cancer , sickle cell disease , cystic fibrosis , Huntington’s disease , or other genetic disorders . Medical researchers are exploring whether it is possible to replace or repair the faulty DNA ( an approach known as gene therapy ) to reduce the symptoms , or even cure individuals , of these conditions . Over the last ten years , a new technology known as CRISPR-Cas9 gene editing has proved to be a reliable and efficient way to make small and precise changes to DNA in living cells . First , an enzyme called Cas9 searches for a segment of target DNA segment that matches a template molecule the enzyme carries . Cas9 then cuts the target DNA , which is repaired to match a new customized DNA sequence: this changes the genetic information of the cell . The Cas9 protein is made of a succession of building blocks called amino acids that create long chains which then fold to form the final three-dimensional shape of the enzyme . A region of Cas9 known as the HNH domain is responsible for cutting the target DNA . However , it remains unclear exactly which amino acids within this domain work together to sever the DNA . Here , Zuo et al . combined computational and experimental approaches to reveal the three-dimensional structure of the Cas9 enzyme when the HNH domain is poised to cut the target DNA . The findings were used to generate a computational model of Cas9 and this model predicted that the HNH domain relies on a group of three amino acids known collectively as D839-H840-N863 to cleave DNA strands . This knowledge is useful to understand exactly how Cas9 modifies genetic information . Ultimately , this may help to improve CRISPR-Cas9 technology so it could be safely used in geneediting therapies . | [
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] | 2019 | Structural and functional insights into the bona fide catalytic state of Streptococcus pyogenes Cas9 HNH nuclease domain |
Episodic memory retrieval of events at a specific place and time is effective for future planning . Sequential reactivation of the hippocampal place cells along familiar paths while the animal pauses is well suited to such a memory retrieval process . It is , however , unknown whether this awake replay represents events occurring along the path . Using a subtask switching protocol in which the animal experienced three subtasks as ‘what’ information in a maze , I here show that the replay represents a trial type , consisting of path and subtask , in terms of neuronal firing timings and rates . The actual trial type to be rewarded could only be reliably predicted from replays that occurred at the decision point . This trial-type representation implies that not only ‘where and when’ but also ‘what’ information is contained in the replay . This result supports the view that awake replay is an episodic-like memory retrieval process .
One of the most important aspects of episodic memory is the mental retrieval of personal recollections of what happened “where” and “when” , referred to as “mental time travel” ( Tulving , 1983 ) . Episodic memory retrieval is important not only for remembering recent events but also for imagining future ones ( Hassabis et al . , 2007 ) . The hippocampus is critically involved in episodic memory retrieval , and within this structure reside place cells—principal cells exhibiting place-specific firing patterns in particular locations . The presence of such cells has led to the hypothesis that the hippocampus is the locus of the “where” element of episodic memory ( O'Keefe and Dostrovsky , 1971 ) . Following on from this seminal finding , more recent studies have demonstrated that hippocampal place cell activity simultaneously represents a broad range of aspects of memory , including odors and their match/non-match status in a particular location , called the “place field” , as well as elapsed time ( Otto and Eichenbaum , 1992; Sakurai , 1996; Wood et al . , 1999; Pastalkova et al . , 2008; MacDonald et al . , 2011 ) . In the place field , both firing rate and location can change independently and are affected by external factors , such as spatial environment and task demand , and internal factors such as prospective and retrospective memory ( Markus et al . , 1995; Frank et al . , 2000; Wood et al . , 2000; Anderson and Jeffery , 2003; Ferbinteanu and Shapiro , 2003; Smith and Mizumori , 2006 ) . During maze running , place cell activity sequences in rodents convey information on both familiar paths and accompanying subtasks ( Allen et al . , 2012; Takahashi , 2013 ) . Within sharp wave/ripples ( SWRs ) in local field potentials ( LFPs ) during slow-wave sleep or periods of awake immobility , internally generated place cell activity sequences are often reactivated in a temporally compressed manner ( Wilson and McNaughton , 1994; Foster and Wilson , 2006; O'Neill et al . , 2006; Diba and Buzsáki , 2007; Karlsson and Frank , 2009; Gupta et al . , 2010 ) . During brief periods of immobility , pivotal paths to a remembered goal can be predicted , regardless of the location in which the SWRs have previously occurred ( Pfeiffer and Foster , 2013 ) . Such “replay” is thus considered a neuronal substrate for retrieving memories on familiar paths ( Carr et al . , 2011 ) . However , previous studies investigating the content of replays have primarily focused on the geography and timeline of the animals' running path as experienced in the environment . It is therefore unclear whether the replay conveys information on events ( i . e . , “what” information ) occurring along the reactivated path . I recently recorded the ensemble activity of place cells in the hippocampal CA1 of rats made to continuously navigate four journeys in a figure-eight maze under three intermittently switching subtasks within a single session: visually guided discrimination ( VD ) , non-delayed spatial alternation ( NA ) , and delayed spatial alternation ( DA ) ( Takahashi , 2013 ) . This task design allowed for comparison of hippocampal neuronal activity between and within different journeys and subtasks . I found that while the animal is running in the maze , differences between individual journeys and between subtasks in the maze are independently encoded in the firing locations and rates of place cells , respectively . Furthermore , all trial types ( i . e . , journey type and subtask ) experienced in the maze could be decoded from the ensemble activity of place cells . Since the rats ran along similar spatial paths while performing different subtasks throughout this task , differences between subtasks can be interpreted as non-spatial “what” information . Provided that the mechanisms are preserved in temporally compressed replays that occur during brief periods of immobility , the subtask as “what” information occurring along the path in the maze might be examined in the replay . Building on this previous study ( Takahashi , 2013 ) , I accordingly investigated whether “what” information is contained in the replay of place cell activity sequences during the brief immobility periods that occur while the animal is engaging in the task .
I tested whether the methods used for the subsequent replay analyses could predict running path and trial type from the place cell activity sequences recorded during running . A total of 1084 principal cells in the dorsal hippocampal CA1 were recorded using an array of 10 extracellular dodecatrodes ( Takahashi and Sakurai , 2005 ) in four well-trained rats ( individuals that had achieved an overall task performance of >90% for a week ) , while they were performing the task ( Table 1 ) . Because running speed , head direction , and physical position can influence place cell firing ( McNaughton et al . , 1983; Wiener et al . , 1989 ) , only 556 place cells were examined in the following analyses . These cells , which had spatial information >0 . 3 bits/spike , fired at significantly different rates during different trial types ( i . e . , journey type and subtask ) , even when running speed , head direction , and x and y coordinates were taken into account ( Analysis of covariance ( ANCOVA ) with these covariates , p < 0 . 05 ) ( Wood et al . , 2000 ) . The rats were exposed to each of the subtasks intermittently at least twice throughout the entire session , but my previous study suggested that place fields are not remapped between trials of the same type , regardless of differences in levels of exposure to the trials ( Takahashi , 2013 ) . I therefore combined place cell activity from all trials of the same type , irrespective of the level of exposure . 10 . 7554/eLife . 08105 . 005Table 1 . Behavioral and electrophysiological measurements and estimation accuracy of position and trial-type during runningDOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 005RAT numberIIIIIIIVTotalNumber of erroneous laps091415Number of correct laps142142139139562Task performance ( % ) 100%94 . 0%99 . 3%97 . 2%97 . 4%Number of principal cells4121932022771084Number of place cells ( which meet the defined criteria ) 23897110111556Number of interneurons2415101261Unit isolation quality ( isolation distance , mean ± SEM ) 45 . 4 ± 7 . 923 . 3 ± 1 . 633 . 6 ± 2 . 120 . 6 ± 0 . 9Accuracy of position estimation during running ( overall median ) 6 . 0 cm6 . 5 cm13 . 1 cm13 . 5 cmAccuracy of trial-type prediction during running ( overall mean ) 89 . 5%77 . 6%74 . 3%75 . 0% Differences between individual journeys are encoded in the timing of firing across place cells while the animal is running ( Figure 1—figure supplement 1A , B ) ( Takahashi , 2013 ) . For each trial type , the rats' paths could be accurately decoded from the place cell activity sequences ( Figure 1C , top ) using a memoryless Bayesian decoder ( Figure 1C , middle; median error: 6 . 0–13 . 5 cm ) . Since similar paths could be decoded from the place cell activity sequences during trial types with the same journey but different subtasks ( e . g . , VDLR , NALR , and DALR ) , the Bayesian decoder cannot per se identify trial types . As my previous study reported ( Takahashi , 2013 ) , differences between subtasks are encoded in firing rates across place cells . Therefore , using a prediction method based on these firing rates ( Allen et al . , 2012 ) , I predicted trial type from the firing rate pattern across place cells in conjunction with the decoded path . The bottom portion of Figure 1C shows trial types predicted from rat I in a single trial . In the visually guided VDRR and VDLL trials , some mismatches occurred because the rats could not accurately predict the trial type until they reached the junction . The predictions were most accurate for the spatial alternation trials . Confusion matrices between predicted and actual trial types for each rat during the entire session ( Figure 1D ) show that a similar pattern was observed for all rats ( overall mean , 74 . 3–84 . 5% ( leave-one-out estimate ) , chance = 12 . 5%; Table 1 ) . The Bayesian decoder and the prediction method together are sufficient for interpreting the representation of path and trial type in place cell activity sequences . Before examining the replays , I investigated the rats’ behavior during the brief periods of immobility , when the replays occurred and the animals' behavior suggested that they were actively gathering information ( Benjamini et al . , 2011 ) . Specifically , I assessed the directions in which the rats' heads were pointing at these times . In correct trials , the rats' heads pointed towards a goal in the opposite direction from the respective start zone ( Watson–Williams test , P: 1 . 1 × 10−16 , n = 588 ( left ) , 1305 ( right ) ; Rayleigh test; P: left , 1 . 1 × 10−56 , right , 4 . 9 × 10−324 ) and stem ( Watson–Williams test , P: 1 . 1 × 10−16 , n = 257 ( left ) , 395 ( right ) ; Rayleigh test; P: left , 1 . 7 × 10−235 , right , 5 . 2 × 10−125 ) ( Figure 2 ) . This suggests that the rats sampled a memory-guided goal in preparation for switching to either the NA or the DA subtask . In this experiment , head direction is therefore considered a behavioral sign indicating expected future choice . In erroneous trials , however , head direction was ambiguous only in the maze stem ( Figure 2C , D; Watson–Williams test , ( 2C ) P: 4 . 4 × 10−16 , n = 29 ( left ) , 30 ( right ) , ( 2D ) P: 0 . 29 , n = 26 ( left ) , 10 ( right ) ; Rayleigh test , P: ( 2C ) left , 3 . 1 × 10−5 , right , 8 . 2 × 10−18 , ( 2D ) left , 2 . 0 × 10−14 , right , 5 . 1 × 10−7 ) . Considering that the barrier in the maze stem would have been an indication to the rats of the ongoing DA subtask , this suggests that the spatial memory demand in preparation for NA/DA subtasks decreased within the maze stem . In addition , prior to making erroneous choices , the rats did not make a decision at the behavioral level within the maze stem . 10 . 7554/eLife . 08105 . 006Figure 2 . Rats' head direction during replays and replay location . ( A , B ) Rose diagrams of rats' head directions in replays occurring during left-to-right and right-to-left journeys in the start zone ( A ) and within the maze stem ( B ) in correct trials . ( C , D ) as for ( A , B ) but for erroneous trials . Occurrence locations are organized in two columns . In each column , green and red dotted lines enclose left-to-right and right-to-left journeys , respectively . ( E , F ) Physical locations where the replays occurred ( green dots: left-to-right journeys; red dots: right-to-left journeys ) . Note that circular medians ( red bars ) were oriented toward a memory-guided goal ( i . e . , a goal opposite to the current location ) , except in replays occurring in the maze stem during erroneous trials . The head directions during delay periods in the DA subtask within the maze stem were excluded . ***: p < 0 . 0001 , n . s: p > 0 . 05 . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 006 I examined path representation in the temporally compressed replay of place cell activity sequences during the brief periods of immobility in the awake behavioral state . Some reactivations of place cell activity sequences during SWRs were related to the rats' upcoming path on the task . For instance , consider the spike raster plots of nine replays that occurred in the start zone , central stem , or junction ( Figure 3 ) . To arrange the sequences in terms of the cells' place fields along the journey , I ordered the cells by the latency of their peak firing rates during the corresponding trial type . The cell number decreases from the start zone to the goal . Sequential reactivation of the place cell activity during the replays shown in the raster plots began where the rat paused ( blue circle in the trial-type illustration ) and moved forward toward a goal . 10 . 7554/eLife . 08105 . 007Figure 3 . Decoding path and trial-type prediction during periods of awake immobility . Graphs are arranged in three columns according to the location at which the replay occurred ( A: start zone; B: central maze stem; C: junction ) . Within each column is one subcolumn for each trial type . Each subcolumn consists of a raster plot of spiking activity of the place cells ( top , left ) , the corresponding firing rates ( top , right ) , the posterior probability of decoded paths ( middle ) , and predicted trial types ( bottom ) for representative candidate replays during periods of immobility . The scale bars indicate 10ms . Values are indicated by color bars ( middle , right ) . In the decoded paths ( middle ) , the upper junctions in the maze are enclosed by two green dotted lines , and the rat's physical location when the replay occurred is indicated by a solid blue circle . In the trial-type prediction ( bottom ) , red bars indicate the most frequently predicted trial type . Red and green labels indicate whether the most frequently predicted and actual trial types matched or not , respectively . The replays depicted an upcoming path to a memory-guided goal irrespective of where the replay occurred . In fact , the path representation ended at a memory-guided , opposite goal to the visually guided goal in the VDRR ( green arrow ) . During replays that occurred at the junction in DARL and VDRL trials ( rightmost , black arrows ) , the spike raster plots ( top , left ) and the decoded paths ( middle ) showed similar patterns . However , the firing rates ( top , right ) for DARL were greater than those for VDRL . Trial types predicted from the replays occurring at the junction ( C , bottom ) using the prediction method based on firing rates were accurate on the whole . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 00710 . 7554/eLife . 08105 . 008Figure 3—figure supplement 1 . Procedure for Bayesian decoding of locations and trial-type prediction . ( A ) Candidate replays were defined as periods when the smoothed ( Gaussian kernel; SD: 10 ms ) population activity ( middle ) was higher than the mean ( black dotted line ) and the peak was above the defined threshold ( mean + 3SD , green dotted line ) . During candidate replays , the local field potential ( LFP ) ( top ) clearly showed SWRs ( Table 2 ) . Location and trial type were estimated based on the spiking activity ( bottom , spike raster ) enclosed by the red lines in the replay . ( B ) The memoryless Bayesian decoder was used to decode the posterior probability of the linearized location from spiking activity and journey-specific place maps ( C ) for every time window . ( D ) The trial-type prediction method based on firing rate was used to predict trial type from spiking activity ( A , bottom ) in conjunction with decoded locations as a maximum a posteriori probability for every time window ( B ) , and the trial-type-specific firing probability map ( E ) . The trial-type representation for each replay was defined as the most frequently predicted trial type across all time windows in that replay ( red bar ) . Red labels indicate that predicted and actual trial types matched . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 00810 . 7554/eLife . 08105 . 009Figure 3—figure supplement 2 . LFP theta power decrease in the replay . The average LFP power in the theta band ( 4–12 Hz ) during running ( speed > 5 cm/s ) , and immobility ( speed < 2 cm/s ) , and for 100–200 ms immediately before and after each replay throughout the entire session . ( Kruskal–Wallis test , Tukey's post hoc multiple-comparison; ***: p < 0 . 0001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 00910 . 7554/eLife . 08105 . 010Figure 3—figure supplement 3 . Estimated compression rate of firing during replays . Histogram of the compression rate of firing during replays , estimated across all rats as the average interspike interval during candidate replays divided by that during the entire session excluding the candidate replays . The red line indicates the median ( 9 . 0 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 010 To test whether the replays consistently related to upcoming behavior , I used the Bayesian decoder at a 10-fold compressed scale ( Davidson et al . , 2009; Pfeiffer and Foster , 2013 ) in the replay analyses . To avoid confusing replay and sequential activation during movement-related phase precession , population bursts of spiking activity during periods of awake immobility , when rats were moving less than 2 cm/s , were identified as candidate replays ( Figure 3—figure supplement 1A ) . Of a total of 2820 candidates , 552 were identified as statistically significant replays with a continuous path , whose length and duration satisfied several criteria ( Davidson et al . , 2009; Pfeiffer and Foster , 2013 ) ( see ‘Materials and methods’; Figure 3—figure supplement 1; Table 2 ) . LFPs during the path replays were largely coincident with the SWRs ( 99 . 8%; Table 2 ) , and their theta power ( 4–12 Hz ) was significantly smaller than during running ( Figure 3—figure supplement 2 ) . This confirms that the path replays identified here were similar to those reported previously ( Foster and Wilson , 2006; Diba and Buzsáki , 2007; Davidson et al . , 2009; Pfeiffer and Foster , 2013 ) and did not include low-speed replay events associated with theta oscillation ( Johnson and Redish , 2007; Jezek et al . , 2011 ) . 10 . 7554/eLife . 08105 . 011Table 2 . Replay statisticsDOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 011Correct trialsCandidate-startCandidate-stemNumber1893810Replay typePath-startEpisodic-startPath-stemEpisodic-centerEpisodic-junctionNumber345711901717Percent confirmed18 . 2%20 . 6%23 . 5%21 . 5%15 . 3%Percent SWR coincident100%100%99 . 5%98 . 7%100%Erroneous trialsCandidate-startCandidate-stemNumber5958Replay typePath-startEpisodic-startPath-stemEpisodic-centerEpisodic-junctionNumber91802Percent confirmed15 . 3%12 . 5%13 . 8%0%25 . 0%Percent SWR coincident100%100%100%–100%SWR , sharp wave/ripple . To examine the spatial specificity of path representation in the replays , I analyzed the posterior probability of locations decoded from path replays by the Bayesian decoder . The middle portions of Figure 3 show nine representative decoded paths from the spiking activity shown in the raster plots during candidate replays . As the spike raster plots suggest , the decoded paths also began where the rats paused and moved forward toward a goal in the corresponding trial type . In addition , despite the fact that the actual goal in the VDRR trial was on the right side , the decoded path ended on the left side ( green arrow ) , suggesting that the replay represents an upcoming path to a memory-guided goal ( i . e . , opposite to the previous goal ) . Across all replays , the posterior probability of decoded locations was spatially concentrated around the vicinity of the goal ( Figure 4A ) . To statistically compare the strength of path representation in the replays between selected regions , path representation strength was defined as the sum total of the posterior probability . Path representation in the vicinity of both goals was significantly greater than in the remainder of the maze area ( Figure 4B; Wilcoxon rank-sum test , p = 3 . 3 × 10−106 , n = 552 ) . As awake replay often begins at the animal's current location ( Foster and Wilson , 2006; Diba and Buzsáki , 2007 ) and is enhanced by rewarding outcomes ( Singer and Frank , 2009 ) , the replay may be initiated at the goal . Although reward expectation increases occupancy time and place-field representation around the goals , such initiation bias was not correlated with either occupancy time ( Figure 4—figure supplement 1A , C ) or spatial distribution of place-field representation ( Figure 4—figure supplement 1B , D ) . This suggests that the path replays were not a simple consequence of either reward expectation or place-specific firings in the location where they occurred ( Gupta et al . , 2010; Carr et al . , 2011 ) . Rather , they may represent paths on a memory-guided journey towards an upcoming goal ( Pfeiffer and Foster , 2013 ) , or on the immediately preceding journey ( O'Neill et al . , 2006; Karlsson and Frank , 2009 ) . 10 . 7554/eLife . 08105 . 012Figure 4 . Spatial tendency of candidate replays . ( A ) Sum of posterior probabilities of decoded locations for the candidate replays throughout the entire session averaged across all rats . The dotted line indicates the location of the decision point , and the red line indicates the location of the goal . The green shaded area shows the defined goal vicinity . ( B ) Path representation , that is , the sum total of the posterior probabilities of decoded locations , within the goal vicinity and in the remaining area . Red lines indicate SD . ***: p < 0 . 0001 . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 01210 . 7554/eLife . 08105 . 013Figure 4—figure supplement 1 . The relationship between occupancy time / place-field distribution and path replay representation . ( A , B ) Average normalized posterior probability of decoded locations as a function of ( A ) average normalized occupancy time which the rat spent , and ( B ) average normalized place-field representation ( i . e . , firing rate ) , at the locations decoded by the Bayesian decoder for the path-start replays that occurred in the start zone . To examine whether the initiation bias could be correlated with either occupancy time or spatial distribution of place-field representation , replays were divided into two types: those for which the average normalized posterior probability of decoded locations was greater than the overall mean + SD ( i . e . , replays showing relatively higher probability; green dots ) and the rest ( blue dots ) . Black dashed lines indicate the best-fit lines ( linear least squares ) for the blue dots . Pearson's r: ( A ) −0 . 012 ( p = 0 . 91 ) , ( B ) 0 . 27 ( p = 0 . 010 ) . ( C , D ) , as for ( A , B ) , but for the path-stem replays that occurred within the maze stem . Pearson's r: ( C ) 0 . 087 ( p = 0 . 41 ) , ( D ) 0 . 27 ( p = 0 . 0098 ) . Replays showing relatively higher probability ( green dots ) did not lie along the best-fit lines . Thus , neither occupancy time nor place-field representation could account for the posterior probability of decoded locations for either the path-start or the path-stem replays . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 013 To test this hypothesis , I examined the representation of future and past journeys in the replays . The path replays predominantly occurred in the start zone and in the maze stem ( Figure 2E , F ) . Unlike the start zones , the maze stem was common to all journeys . There were therefore two types of path replays: path-start and path-stem . I centered the posterior probability of decoded locations on the start position and rotated it according to the direction of the memory-guided goal . As expected from the initiation bias , the rats' current location was strongly represented in the posterior probability of decoded locations in path-start replays ( Figure 5A ) . To statistically test the difference between future and past journeys , I defined future and past regions as those from the start position to memory-guided and previous goals , respectively . It is worth noting that the memory-guided goal was always opposite to the previous goal even in visually guided trials ( VDLL , VDRR ) , because the task design did not allow the rats to identify the subtask until they reached the junction ( see ‘Materials and methods’ ) . Path representation ( i . e . , the sum total of the posterior probabilities of decoded locations ) of future regions was significantly greater than that of past regions in the correct spatial alternation trials ( Figure 5B; Wilcoxon signed-rank test , p = 0 . 0037 , n = 345 ) . Similarly , future regions were strongly and statistically significantly represented in the correct visually guided trials ( Figure 5C , D; p = 0 . 0078 , n = 52 ) . In erroneous trials , path representation of future regions was slightly greater than that of past regions ( Figure 5E ) , but the difference was not significant ( Figure 5F; p = 0 . 73 , n = 9 ) . However , due to the physically different start zones of different journeys , these characteristics may be affected by the place-specificity of the locations in which the replays occurred . 10 . 7554/eLife . 08105 . 014Figure 5 . Decoded paths in path replays . ( A , C , E ) Upper panels show the posterior probability of decoded locations for path replays that occurred in the vicinity of the start position in the spatial alternation trials ( VDLR , VDRL , NALR , NARL , DALR , DARL ) ( A ) , the visually guided trials ( VDLL , VDRR ) ( C ) , and erroneous trials ( E ) , centered on the start position and rotated according to the direction of the memory-guided goal . The replays were ordered by the location showing the maximum posterior probability . Values are indicated by color bars ( right ) . Lower panels show that the posterior probability of the decoded location averaged across all path replays . Distance was centered on the start position and rotated according to the direction of the memory-guided goal . The shaded area indicates SD , green dots indicate the rat's physical location , and red lines indicate the start position . The schematic diagrams of the maze on the right show the start position ( black filled circles ) and rotation direction ( solid line: towards the memory-guided goal; dotted line: towards the previous goal ) . Note that in the visually guided trials ( VDLL , VDRR ) , the rats could get rewards at the previous goal but were always under spatial memory demand due to the task design , so the choice to go toward the memory-guided goal ( i . e . , the goal opposite to the previous goal ) was defined as an erroneous response . ( B , D , F ) Path representation ( the sum total of posterior probabilities of decoded locations ) averaged across all path replays within future and past regions , defined from the start position to memory-guided/previous goals , for the trials shown in A , C , and E . Red bars indicate SD . ( G–L ) as for ( A–F ) , but for path replays occurring within the stem . Distance was centered on the rat's physical location at the time of occurrence , rotated according to the direction of the memory-guided goal , and scaled according to the distance from the rat's physical location to a memory-guided goal . Green lines indicate the rat's physical location , and red lines indicate the memory-guided/previous goals . Future and past regions were defined based on the rat's physical location and the memory-guided/previous goals , respectively . *: p < 0 . 05 , **: p < 0 . 01 , ***: p < 0 . 001 , n . s . : p > 0 . 05 . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 01410 . 7554/eLife . 08105 . 015Figure 5—figure supplement 1 . Decoded paths with the highest a posteriori probability in path replays . ( A–L ) As for Figure 5 , but for occupancy of the decoded locations with the highest a posteriori probability for path replays . Wilcoxon signed-rank test , P: ( B ) , 0 . 0022; ( D ) , 0 . 0084; ( F ) , 0 . 89; ( H ) , 3 . 1 × 10−8; ( J ) , 0 . 0085; ( L ) , 0 . 0078 . **: p < 0 . 01 , ***: p < 0 . 001 , n . s . : p > 0 . 05 . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 015 To compare the representation of past and future journeys in the path-stem replays , I centered the posterior probability of decoded locations on the rat's physical location at the time of occurrence , rotated it according to the direction of the memory-guided goal , and scaled it according to the distance to the memory-guided goal . Path representation was similar to that in the replays that occurred in the start zones ( Figure 5G , I; Figure 5H: p = 9 . 8 × 10−8 , n = 190; Figure 5J: p = 0 . 034 , n = 38 ) . Surprisingly , however , path representation in path-stem replays on journeys toward an erroneous goal ( Figure 5K ) was significantly different from that on journeys toward a correct goal ( Figure 5L; p = 0 . 0078 , n = 8 ) , despite the fact that the rats sampled both goal sides evenly . Similar to the findings of a previous study ( Pfeiffer and Foster , 2013 ) , this suggests that in the maze stem , where spatial memory retrieval is a prerequisite for predicting a future goal , path representation in the replay is directly linked to the animal's future path . The exact path encoded in the replay may be decoded from the locations showing the highest a posteriori probability . I therefore reanalyzed the path replays using point estimates of locations based on a posteriori probability . Similar to the posterior probability analyses , the decoded locations in the replays tended to represent an upcoming journey to a memory-guided goal ( Figure 5—figure supplement 1 ) . To corroborate the results from the Bayesian decoder , I furthermore applied a simple spatial reconstruction algorithm , based on place fields during running , to the path replays . To estimate path representation in the replay , the place maps of the maze in neurons participating in the replay were simply summed up in response to the number of spikes ( see ‘Materials and methods’ ) . Similarly to the results obtained using the Bayesian decoder , the path representation estimated using the simple spatial reconstruction algorithm also largely predicted an upcoming path to a memory-guided goal ( Figure 6 ) . A previous study ( Pfeiffer and Foster , 2013 ) reported that the awake replays represent an upcoming path to a memory-guided goal , but did not examine erroneous behaviors . My findings strongly support this view , with the additional finding that the upcoming path encoded in the awake replay can be linked to the animal's future actions . 10 . 7554/eLife . 08105 . 016Figure 6 . Estimated place preference in the path replays . ( A , C , E ) Upper panels show the place preference estimated using the simple spatial reconstruction algorithm for path replays that occurred in the vicinity of the start position in the spatial alternation trials ( VDLR , VDRL , NALR , NARL , DALR , DARL ) ( A ) , the visually guided trials ( VDLL , VDRR ) ( C ) , and erroneous trials ( E ) , centered on the start position and rotated according to the direction of the memory-guided goal . The replays were ordered by the location showing the maximum place preference . Values are indicated by color bars ( right ) . Lower panels show that the place preference averaged across all path replays . Distance was centered on the start position and rotated according to the direction of the memory-guided goal . The shaded area indicates SD , green dots indicate the rat’s physical location , and red lines indicate the start position . The schematic diagrams of the maze on the right show the start position ( black filled circles ) and rotation direction ( solid line: towards the memory-guided goal; dotted line: towards the previous goal ) . Note that in the visually guided trials ( VDLL , VDRR ) , the rats could get rewards at the previous goal but were always under spatial memory demand due to the task design , so the choice to go toward the memory-guided goal ( i . e . the goal opposite to the previous goal ) was defined as an erroneous response . ( B , D , F ) Path representation ( the sum total of place preferences ) averaged across all path replays within future and past regions , defined from the start position to memory-guided/previous goals , for the trials shown in A , C , and E . Red bars indicate SD . ( G–L ) as for ( A–F ) , but for path replays occurring within the stem . Distance was centered on the rat’s physical location at the time of occurrence , rotated according to the direction of the memory-guided goal , and scaled according to the distance from the rat’s physical location to a memory-guided goal . Green lines indicate the rat’s physical location , and red lines indicate the memory-guided/previous goals . Future and past regions were defined based on the rat’s physical location and the memory-guided/previous goals , respectively . Wilcoxon signed-rank test , *: P<0 . 05 , **: P<0 . 01 , ***: P<0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 016 Although hippocampal place cell activity per se represents non-spatial information as well as spatial information ( Wood et al . , 1999 ) , the question of whether replays include non-spatial information remains to be addressed . I next examined whether place cell activity during replays can convey information not only on the path but also on the subtask occurring along it , similarly to place cell activity occurring during running . I observed that the firing rate of place cells participating in replays during different subtasks was dramatically different between subtasks even if the replay depicted a similar decoded path . For instance , consider the firing rate histograms for participating neurons in replays occurring in the start zone , central stem , or junction ( Figure 3 ) . As expected from the timing of temporally compressed firings across place cells , the maximum firing rates of place cells during replays ( ∼80 Hz ) were about 10 times faster than typical maximum firing rates during running ( ∼8 Hz ) . To estimate the exact temporal compression rate of place cell firing during replays , I divided the average interspike interval during candidate replays by that during the entire rest of the session . The median of the estimated compression rate was 9 . 0 ( Figure 3—figure supplement 3 ) , suggesting that similarly to the timing of firing , place cell firing rates were also temporally compressed during replays . During replays occurring at the junction , the raster plots of spiking activity in DARL and VDRL trials recorded from rat II showed a similar activity sequence ( Figure 3C , rightmost , arrows ) . The replays also depicted a similar decoded path . However , the firing rates during DARL trials were substantially greater than during VDRL trials , suggesting that the firing rates of neurons participating in the replay may contain information on the differences between subtasks . To test whether subtask information is encoded in the firing rates in the replay , I predicted trial types based on the firing rates of a group of cells recorded during path replays , using the prediction method in conjunction with the decoded locations ( see ‘Materials and methods’; Figure 3—figure supplement 1 ) . It was necessary to ensure that the replays used predicted a trial type with a probability greater than that dictated by chance . Accordingly , of the total 552 path replays , 108 were classified as episodic replays that satisfied criteria concerning the most frequently predicted trial type and its predicted probability sum ( see ‘Materials and methods’; Table 2 ) . As expected , only the replays that occurred in the junction ( Figure 3C ) appeared to predict the actual trial type correctly . To examine this further , I investigated preference for subtask ( i . e . , VD , NA , DA subtasks ) . I examined whether there was any preference in the replays for particular rewarded subtasks . As expected , based on a previous study suggesting that awake SWRs support spatial working memory and not reference memory ( Jadhav et al . , 2012 ) , the occurrence rate of overall episodic replays across all actual subtasks to be rewarded was significantly biased toward the DA subtask , which required spatial working memory ( Figure 7A; Chi-square goodness-of-fit test , post hoc multiple binomial tests corrected by the Benjamini and Hochberg procedure , VD vs DA: p = 8 . 6 × 10−5 , NA vs DA: p = 9 . 9 × 10−4 ) . The null hypothesis tested was that the occurrence rates of predicted subtasks were equal to the rates expected based on the ratio of subtasks experienced throughout the entire session ( 60 laps VD: 40 laps NA: 40 laps DA ) . 10 . 7554/eLife . 08105 . 017Figure 7 . Subtask preference and relationship between actual and predicted trial types . ( A ) The number of episodic replays across all rats as a function of actual subtasks to be rewarded . Red lines indicate the estimated numbers calculated by multiplying the total number of replays by the ratio of repeatedly experienced subtasks throughout the entire session ( 60: 40: 40; null hypothesis: the replays represent all subtasks evenly ) . ( B , C ) The number of episodic-start ( B ) and episodic-stem ( C ) replays across all rats as a function of actual subtasks . Red lines as in ( A ) . ( D ) The number of episodic replays across all rats as a function of subtasks predicted from the replay . **: p < 0 . 001 , ***: p < 0 . 0001 . ( E–H ) The number of episodic replays across all rats normalized across predicted trial types of matches between actual and predicted trial types ( for spatial alternation trials only ) in episodic-start ( E ) , episodic-stem ( F ) , episodic-center ( G ) , and episodic-junction ( H ) replays . Blue in the schematic diagrams to the right indicates the regions in which the replays occurred in the maze . Note that whereas VDRL and NALR trials were frequently predicted from the episodic replays in the central stem when the actual trial type was VDRL ( G ) , such partial matching may be accidental . In contrast , in the junction ( H ) , the trial types showing the largest predicted value matched the actual trial types well for NALR , VDRL , NARL , and DARL trials . The overall matching score across all trial types is significantly different from random matching ( z test , p = 3 . 7 × 10−4 ) , suggesting that episodic replays can accurately represent actual trial types , but only in the junction . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 01710 . 7554/eLife . 08105 . 018Figure 7—figure supplement 1 . Subtask preference in replays occurring in the start zone , and time spent pausing . ( A–C ) The number of path-start ( A ) and candidate ( B ) replays and SWR events ( C ) occurring in the start zone , across all rats , as a function of actual subtasks to be rewarded . Red lines as for Figure 7A . ***: p < 0 . 001 ( Chi-square goodness-of-fit test , post hoc multiple binomial tests corrected by the Benjamini and Hochberg procedure ) . Note that the bias towards the DA subtask can be seen throughout all categories of replay events . ( D–E ) Time spent pausing ( running speed < 2 cm/s ) in the start zone ( D ) and maze stem ( E ) across all rats , as a function of actual subtasks ( red lines: median; blue boxes: first and third quartiles; bars: minimum and maximum values ) . The rats spent more time pausing during the DA subtask in both the start zone and the maze stem . Kruskal–Wallis test , Tukey's post hoc multiple-comparison , ***: p < 0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 01810 . 7554/eLife . 08105 . 019Figure 7—figure supplement 2 . Relationship between actual and predicted trial types using the simple spatial reconstruction algorithm . The number of matches between actual and predicted trial types ( for all rats but spatial alternation trials only ) , normalized across predicted trial types , in episodic-center ( A ) and episodic-junction ( B ) replays . Location was estimated using the simple spatial reconstruction algorithm instead of the Bayesian decoder . Blue in the schematic diagrams to the right indicates the regions in which the replays occurred . In the junction ( B ) , the predicted trial types with the largest predicted value fit the actual trial types well for VDLR , VDRL , NARL , and DARL trials . The overall matching score across all trial types was significantly different from random matching ( z test , p = 0 . 0078 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 01910 . 7554/eLife . 08105 . 020Figure 7—figure supplement 3 . Trial-type prediction on the time-window basis . The topmost illustration shows the actual trial type . The graphs show the average prediction probability from the episodic replays , on the time-window basis , as a function of predicted trial type . Red bars indicate the highest average probability . Red and green labels indicate whether the predicted trial type with the highest probability matched the actual trial type . Post hoc Tukey HSD test , *: p < 0 . 05 , **: p < 0 . 01 , ***: p < 0 . 001 . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 020 Does this bias depend on where the replay occurred ? To address this question , episodic replays were divided into two types according to the occurrence location of the replay: episodic-start and episodic-stem replays . This was done because the head direction analyses suggested that spatial memory demand at the behavioral level was lower within the maze stem . The partitioning showed that the subtask bias occurred in episodic-start replays ( Figure 7B; VD vs DA: p = 3 . 7 × 10−9; NA vs DA: p = 5 . 1 × 10−7 ) , but not in episodic-stem replays ( Figure 7C ) . Similarly , in the path-start replays , all candidates and SWR events showed such a bias ( Figure 7—figure supplement 1A–C ) . This bias may be accounted for by occupancy time while the rats paused . However , since the rats specifically spent more time pausing in the DA subtask in both the start zones and the maze stem ( Figure 7—figure supplement 1D , E ) , the bias cannot be explained by the time spent pausing . Combined with behavioral signs of decreased spatial memory demand within the maze stem , these results suggest that the occurrence rate of replays is enhanced by upcoming spatial working memory demand . In contrast , the subtasks predicted from the episodic replays did not show such a bias , and instead the prediction rates reflected the ratio of subtasks experienced throughout the entire session ( Figure 7D ) . This suggests that the enhancement is not specifically due to the reactivated DA subtask , but instead due to the reactivation of all previously experienced subtasks . Examination of trial type , composed of journey type and subtask , should provide further understanding of subtask preference . Even if the replay can represent an upcoming path , disambiguation of the subtasks is still required for identifying the actual trial type . I therefore investigated the relationship between the actual trial type to be rewarded and trial-type representation in the episodic replays . Since the path representation suggested that the replay encodes an upcoming path to a memory-guided goal , I only examined the memory-guided trial types ( i . e . , VDLR , VDRL , NALR , NARL , DALR , and DARL ) . Trial-type representation in the episodic replays did not match the actual trial type in the start zones ( Figure 7E; Choen's κ = 0 . 017; z test , p = 0 . 83 , n = 71 ) , but did match it in the stem ( Figure 7F; Choen's κ = 0 . 31; p = 8 . 4 × 10−3 , n = 34 ) . This discrepancy may be the result of the extent of spatial working memory demand at the location where the replay occurs . To further examine trial-type preference , I divided the episodic-stem replays into two types: episodic-center and episodic-junction replays . Trial-type representation matched actual trial type moderately well in replays at the junction ( i . e . , the decision point ) where the rats could evidently identify the actual trial type based on the visual cue ( Figure 7H; Choen's κ = 0 . 58; p = 3 . 7 × 10−4 , n = 17 ) , but not at the center of the stem ( Figure 7G; Choen's κ = 0 . 022; p = 0 . 93 , n = 17 ) . To corroborate these results , the Bayesian decoder in the trial-type prediction method was replaced by the simple spatial reconstruction algorithm . Similarly , trial types predicted using this method matched actual trial types moderately well only in replays at the junction ( Figure 7—figure supplement 2 ) . To further check whether the predicted and actual trial types were coincidently matched , the probability of trial-type prediction across all replays occurring at the junction was analyzed on a time-window basis . There was a significant effect of the probability of trial-type prediction for five trial types ( NALR , DALR , VDRL , NARL , and DARL ) in episodic replays ( ANOVA , p < 0 . 01; Figure 7—figure supplement 3 ) . The predicted trial types showing the highest probability matched actual trial types for NALR , VDRL , NARL , and DARL trials . Post hoc comparisons using the Tukey honest significant difference ( HSD ) test indicated that the predicted trial types with the highest probability were significantly different from most of the others , suggesting that the matches were not coincidental . At the junction , only a single visual cue was present in the visually guided trials . To test whether the trial type encoded in these replays was therefore just a representation of the available sensory cues , I examined whether the difference in the number of visual cues showing ( one or both ) could be used to distinguish VD from the other subtasks encoded in replays at the junction . The number of visual cues showing did not account for VD representation , even at the junction ( Figure 8A; χ2 test , p = 0 . 20 ) . The barrier that appeared at the center of the stem only in DA trials may also represent a sensory cue , which would distinguish NA from DA trials . However , the subtask predicted in the center of the stem did not match actual NA and DA trials ( Figure 8B; χ2 test , p = 0 . 26 ) . This suggests that trial-type representation in the replays is more than just a representation of external cues , and that it is internally generated . I therefore conclude that the internally generated replay represents the actual trial type—not only the upcoming path but also the actual subtask to be rewarded—when the trial type can be identified , but otherwise it evenly represents all trial types thus far experienced on the reactivated path . 10 . 7554/eLife . 08105 . 021Figure 8 . The relationship between predicted subtask and sensory cue . ( A ) The number of episodic-junction replays with matches between the predicted subtask and the configuration of visual cues , across all rats . ( B ) The number of episodic-center replays with matches between the predicted memory-guided subtask and the presence of a barrier at the center of the maze , across all rats . DOI: http://dx . doi . org/10 . 7554/eLife . 08105 . 021
My investigations into whether the subtask and path experienced during task performance are represented in replays of place cell activity sequences associated with SWRs during brief immobility periods , produced the following findings . Firstly , the replay can represent non-spatial information on subtask as well as spatial information concerning the path . Secondly , whereas the path in the replay is encoded in temporally compressed firing timings across place cells , the accompanying subtask is encoded in their temporally compressed firing rates . Thirdly , the replay is enhanced by spatial working memory demand . Finally , the replay can represent the actual trial type accurately when the trial type can be identified , but otherwise it evenly represents all trial types previously experienced along the reactivated path . Place cell activity can encode not only spatial information about past , current , and future places ( Frank et al . , 2000; Wood et al . , 2000; Ferbinteanu and Shapiro , 2003 ) but also non-spatial information including task demand ( Markus et al . , 1995; Anderson and Jeffery , 2003; Smith and Mizumori , 2006 ) , odors and their match/non-match status ( Otto and Eichenbaum , 1992; Sakurai , 1996; Wood et al . , 1999 ) , and elapsed time ( Pastalkova et al . , 2008; MacDonald et al . , 2011 ) . The sequential activation of place cells reflects distinct episodic-like memories ( Pastalkova et al . , 2008 ) ; in fact , reactivation of the firing rates of single neurons in the human hippocampus represents specific episodes during free recall ( Gelbard-Sagiv et al . , 2008 ) . However , such episodic-like memory traces are present across time-scales similar to those during the actual experience , and are thus distinct from the rapid , temporally compressed replays reported here . Although numerous previous reports have concluded that place cell activity sequences are reactivated in a temporally compressed manner in the awake state ( Foster and Wilson , 2006; O'Neill et al . , 2006; Diba and Buzsáki , 2007; Singer and Frank , 2009; Gupta et al . , 2010 ) as well as during slow-wave sleep ( Wilson and McNaughton , 1994 ) , they have shown only that replays encode familiar paths . Until now , the possibility that replays simultaneously represent both spatial and non-spatial information has been purely conjectural . It is well known that spatial information is encoded in temporally compressed firing timings across place cells in replays . I have now shown that replays also encode non-spatial information in temporally compressed firing rates , revealing that global and rate remapping mechanisms during running ( Leutgeb et al . , 2005; Takahashi , 2013 ) are preserved in the temporally compressed replays that occur during brief periods of immobility . Since awake replay is more common in a novel environment ( Cheng and Frank , 2008 ) , a few previous studies ( O'Neill et al . , 2006 ) have speculated that the content of replays reflects the total of previous experience . Contradicting with this hypothesis , growing evidence suggests that replays are not a simple function of experience ( Gupta et al . , 2010 ) , but rather that reward outcomes enhance the reactivation of experiences ( Singer and Frank , 2009 ) and that replays can depict future paths to remembered goals ( Pfeiffer and Foster , 2013 ) . In addition , awake replays often begin at the animal's current location ( Diba and Buzsáki , 2007 ) . The relationship between replay content and experience therefore does not seem to be straightforward . Some previous reports suggest that awake replay is similar to vicarious trial-and-error ( VTE ) events ( Hu and Amsel , 1995; Johnson and Redish , 2007 ) , because it can represent possible future paths even in completely unfamiliar places ( Gupta et al . , 2010 ) . Although VTE events are reported to occur outside of SWRs , a subsequent study suggested that awake replay associated with SWRs is seen frequently at the choice point in a similar task , where animals must make a memory-guided decision between two journeys ( Karlsson and Frank , 2009 ) . Consistent with activity observed in VTE events , these awake replays extend out to the left or right arms of the track , as would be expected if the animal were playing out possible upcoming choices . Frank et al . therefore proposed a unified interpretation in which replays provide information on possible upcoming paths to downstream brain structures such as the prefrontal cortex and nucleus accumbens , which can assess the value of different paths and make a decision about future actions ( Carr et al . , 2011 ) . In this study , I trained rats to perform a similar task , but path representation in these replays was directly linked to the animal's future path . This apparent disparity may reflect the proficiency of the task in previous studies . In contrast to previous VTE studies ( Johnson and Redish , 2007; Karlsson and Frank , 2009; Gupta et al . , 2010 ) , well-trained rats in the present study could accurately predict a goal-directed path at the behavioral level even at the decision point . However , the task design did not allow prediction of the subtask until the animal had entered the decision point . This suggests that not only the path but also the subtask representation in the replay is similar to VTE events . By extending the unified interpretation to include non-spatial memory in the retrieved memory , the fact that replays in the present study reflected all previously experienced trial types ( except at the decision point ) suggests that replays prepare the brain for unforeseen changes , which would allow continuous switching between subtasks . I also found that replays were enhanced by spatial working memory demand . Since the initiation of an awake replay is often related to external spatial input at the animal's location ( Karlsson and Frank , 2009 ) , this enhancement of replays suggests that the internal demand of the hippocampal-dependent spatial working memory is another factor . Considering that this is similar to a cued memory retrieval process , my findings strongly support the hypothesis that awake replay plays a key role in episodic-like memory retrieval , which contributes to memory-based navigational planning and decision making ( Carr et al . , 2011 ) . The sequential activation of place cell activity associated with SWRs can also represent paths where the animal has never experienced ( Dragoi and Tonegawa , 2011; Dragoi and Tonegawa 2013a; 2013b; Ólafsdóttir et al . , 2015 ) . As a result , a fairly large proportion of events representing different and multiple experiences can spontaneously occur , even in naive animals . I speculate that the reason why paths and subtasks were encoded in temporally compressed firing timings and rates , respectively , in the replays may be to arrange such multiple preplayed episodes into a single replay event to easily recall and imagine an upcoming episode based on prior experience . In the present study , the rats were sequentially trained in the VD , NA , and DA subtasks . Together with the preplay mechanism , this training protocol might have helped to recruit a consistent path representation across the three subtasks . Until now , previous studies investigating the content of preplays have primarily focused on the geography and timeline of the animals' running . Non-spatial information in the preplay may provide further insight into memory encoding and retrieval . This study has shown that non-spatial subtask information occurring along temporally ordered places , as experienced by the animal , could be predicted by the sequential reactivation of place cells occurring while the animal paused during engagement in the task . I therefore speculate that awake replay is linked to the typical abilities of episodic memory: mental time travel and foreseeing future situations . This provides novel insight into debates on animals' capacities to use the faculties provided by episodic memory ( Allen and Fortin , 2013 ) .
Four male Wistar rats implanted with a custom-made microdrive were housed individually in cages where the light was maintained in a 12-hr light–dark cycle . The tests were performed during the light phase . The weight of all rats was kept at 80% of free-feeding body weight . All procedures were approved by the Doshisha University and Kyoto Sangyo University Institutional Animal Care and Use Committees . The rats were initially trained to run along the left/right O-shaped track unidirectionally for reward signals ( medial forebrain bundle ( MFB ) stimulation ) in the start zone . After the rats were running smoothly along both left and right O-shaped tracks , they were trained in the figure-eight maze ( overall: 100 × 140 cm , 20-cm height; path: 20-cm width; Figure 1A ) to perform the VD subtask for a reward . In the VD subtask , one of the two visual cues ( LED-lights at the right and left corners ) was illuminated randomly; the visual cue at the decision point of the maze ( Figure 1A , decision point ) indicated which direction to turn to receive the reward . The rats were trained until they achieved at least 80% correct decisions for >20 laps . Next , they were trained to perform the NA subtask for >20 laps . In this case , both LEDs were illuminated , so the rats could not rely on the visual cue . Instead , they had to choose the direction opposite to the previous goal . Once they had achieved at least 80% correct decisions for >20 laps , they were trained to perform the DA subtask for 20 laps . This was almost identical to the NA subtask except that it included a delay period . This was enforced by a barrier appeared for 5 s , 20 cm ahead of the entrance to the maze stem ( Figure 1A , red dotted line ) . The rats paused reliably , facing the forward direction , in front of the barrier , and achieved a 90% correct decision rate from the beginning of this task . The detailed training protocols are described in a previous study ( Takahashi , 2013 ) . To prevent the rats from receiving any unintended distal room cues , the task was performed under dim light . They did not receive the visual cue until they reached the decision point because the height of the walls of the maze ( 20 cm ) was much higher than the level of the rat's eyes ( ∼5 cm ) and they did not stand up during the experiment . The sole signal for subtask switching was therefore LEDs that were visible only at the decision point . Under isoflurane anesthesia , stimulation electrodes were inserted into the MFB in the right lateral hypothalamus ( AP 2 . 5 , ML 1 . 0 , DV 9 . 5 ) . A custom-made microdrive with 10 independently movable dodecatrodes was then fixed to the skull above the left hippocampus ( AP 3 . 8 , ML 3 . 0 , DV 0 . 5 ) . A week after surgery , the electrodes were individually lowered into the pyramidal cell layer of the hippocampal CA1 . The extracellular signals were unity-gain buffered , filtered ( 600 Hz–6 kHz ) , amplified ( gain = 5000 ) , and continuously sampled at 25 kHz . One channel of each electrode was duplicated for detecting LFPs and was filtered ( 0 . 1 Hz–6 kHz ) and amplified ( gain = 500 ) . The LFP recordings were further band-pass filtered ( 150–250 Hz ) using the Hilbert transform . Data recorded during the MFB stimulation were removed prior to analysis . Local peaks in the power of this filtered signal of magnitude >mean + 3SD were identified and extracted as periods . The boundaries for each period were defined as the point at which the amplitude crossed the mean . Periods during which running speed was <2 cm/s were identified as SWR events . For theta-wave detection , the z-score power of a band-pass filtered ( 4–12 Hz ) signal was calculated . After spike sorting as previously described ( Takahashi et al . , 2003; Takahashi and Sakurai , 2009; Takahashi , 2013 ) , putative principal cells were distinguished from putative fast-spiking cells based on spike width ( 0 . 4 ms ) and average firing rate ( 5 Hz ) . Cells whose firing rate was <0 . 1 Hz were excluded . Only the putative principal cells were used in the analyses described below . Spikes during delay periods in the DA subtask , when hippocampal activity was not place-specific ( Pastalkova et al . , 2008 ) , were excluded . The trajectory of the rats was linearized for each trial by projecting the actual trajectory onto a predefined idealized journey using nearest-neighbor Delaunay triangulation . Spatial bins had a resolution of approximately 2 cm . A one-dimensional map of the place field for each place cell was then constructed for each trial type in the standard manner ( Takahashi , 2013 ) ( smoothed with a Gaussian kernel of 3-cm width ) . The place map was constructed from spikes only when running speed was >5 cm/s so that spikes generated while the rats paused in front of the barrier wall in the DA subtask were excluded . A memoryless Bayesian decoder ( Zhang et al . , 1998 ) was used to decode the rats' locations on the basis of place cell activity . Firstly , the probability of a rat's location given place cell firings within a time window was estimated as follows:Prob ( Pos|spikes ) = ( ∏i=1Nfi ( Pos ) ni ) exp−τ∑i=1Nfi ( Pos ) where fi and ni represent the place map and the number of spikes of the i-th place cell within the time window , respectively , N indicates the total number of place cells , and τ represents the duration of the time window . The probability within each time window was normalized for every location as follows ( Pfeiffer and Foster , 2013 ) :nProb ( Pos|spikes ) =Prob ( Posk|spikes ) /∑k=1MProb ( Posk|spikes ) where Prob ( Posk|spikes ) represents the probability at the k-th location bin within the time window , and M represents the total number of location bins . Time windows for task performance and replays were set at 250 ms and 20 ms , respectively . Provided that nProb ( Posk|spikes ) showed unimodality ( Hartigan's dip test , p < 0 . 05 ) , a point estimation of the location on the journeys was made based on this value , using maximum likelihood estimation . A simple spatial reconstruction algorithm was used to virtually reconstruct the place preference of replays from place cell activity while the animal briefly paused . The place preference of replays , Rp , was estimated as follows:Rp=∑i=1Nnifiwhere fi and ni represent the place map and the number of spikes of the i-th place cell firing in the replay , respectively , and N indicates the total number of place cells . For trial-type prediction , the location encoded in the replay was estimated based on the location where Rp was maximized . Firstly , candidate replays were selected as follows ( as described in Figure 3—figure supplement 1A ) . A candidate replay was defined as a period where the smoothed ( Gaussian kernel; SD: 10 ms ) population activity was greater than the mean and the peak was above the defined threshold ( mean + 3SD ) . To minimize ambiguous detections , periods were decreased based on the following criteria . Time windows were reduced as much as possible such that at least 10% of all detected cells were detected within the time window , each cell included at least two spikes , and the duration was at least 30 ms . Secondly , for each period , the rat's location was decoded using the Bayesian decoder , for 20-ms time windows advanced in 5-ms increments . Periods were concatenated when the distance between neighboring decoded locations was less than 25 cm . Thirdly , concatenated periods whose sequence of decoded locations covered a total distance greater than that covered during four time windows were classified as candidate replays . Fourthly , two significance tests were conducted for every candidate , using a Monte Carlo method with two different random shuffling modes: cell identity and cell place field ( 5000 times each ) . Finally , candidates with p < 0 . 05 for both shuffle modes were defined as path replays . Trial type was predicted for every time window , using mean firing rate as a point of reference ( Allen et al . , 2012 ) . Firstly , a binary population vector , C , was constructed . This vector could exist in one of two states , high or low . The state was determined on the basis of 200-ms time windows throughout the entire session , based on whether the cell's firing rate in each time window was higher or lower than its mean firing rate for the whole session . Eight population reference vectors , R , were constructed to store the probability of the place cells being in the high or low state in each of the eight trial types , for every possible linearized location . The probability of obtaining the vector C within each time window was computed as follows:Prob ( C|t ) =∏i=1NProbi ( c|t ) where Probi ( c|t ) represents the probability , taken from vector R , that the i-th place cell is in state c during the t-th trial type , at location p , decoded from place cell activity within the corresponding time window using the Bayesian decoder or the simple spatial reconstruction algorithm . For each time window , the trial type with the highest value for Prob ( C|t ) was recorded as predicted for that time window . For each path replay , trial type was predicted for each time window . The representative trial type for each path replay was defined as the most frequently predicted trial type across all time windows in that replay . Significance tests were conducted using a Monte Carlo method with two random shuffling modes: cell identity and decoded location ( 5000 times each ) . The path replays with p < 0 . 05 for both shuffle modes were defined as episodic replays . All analyses were performed using custom-made programs based on Matlab ( v8 . 3; MathWorks ) and R ( R Development Core Team , 2013 ) functions . After the rats were sacrificed by pentobarbital sodium overdose and perfused with formalin , their brains were cut coronally at 30 μm and stained with cresyl violet . The location of the tip of each electrode was estimated . | Place cells are neurons that respond to a particular location in the physical world . For example , as a rat runs around a maze , some place cells will become active when the rat reaches one corner . When the rat moves on towards a different corner , other place cells activate instead . The real-time activity of these place cells helps the rat to work out where it is in the maze . This activity also contains information about what the rat is doing . In addition to their real-time activity , place cells also help previous events to be ‘replayed’ mentally , which is important for making decisions . Previous studies have shown that when a rat pauses during a task , place cell replays allow it to mentally map out the route it needs to take . However , it is less clear whether these replays also provide information about what the rat needs to do . Takahashi gave rats a number of tasks to perform inside a figure of eight maze . In one of these tasks , the rat had to assess which one of two lights was lit up , and run towards it . In the other two tasks , the rat had to remember the direction it took on the previous occasion , and go in the opposite direction . During these tasks , the rat would occasionally pause to replay information about the task . Takahashi recorded what the rats' place cells were doing during these pauses , and found that the place cell replays contained information about both the path the rat needed to take ( ‘where and when’ information ) , and which task it needed to carry out ( ‘what’ information ) . This suggests that replays are important for the ability to recall information about specific events , which is known as episodic memory . Takahashi's results may therefore also help us to learn more about this “mental time travel” and human conditions that damage episodic memory , such as Alzheimer's disease . | [
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] | 2015 | Episodic-like memory trace in awake replay of hippocampal place cell activity sequences |
Across species , aging is associated with an increased ability to choose delayed over immediate gratification . These experiments used young and aged rats to test the role of the basolateral amygdala ( BLA ) in intertemporal decision making . An optogenetic approach was used to inactivate the BLA in young and aged rats at discrete time points during choices between levers that yielded a small , immediate vs . a large , delayed food reward . BLA inactivation just prior to decisions attenuated impulsive choice in both young and aged rats . In contrast , inactivation during receipt of the small , immediate reward increased impulsive choice in young rats but had no effect in aged rats . BLA inactivation during the delay or intertrial interval had no effect at either age . These data demonstrate that the BLA plays multiple , temporally distinct roles during intertemporal choice , and show that the contribution of BLA to choice behavior changes across the lifespan .
Intertemporal choice refers to decisions between rewards that differ with respect to both their magnitude and how far in the future they will arrive . Biases in intertemporal choice , whether manifesting as extreme impulsivity or patience , strongly associate with psychiatric disease . For example , enhanced preference for smaller , immediate rewards ( greater impulsive choice ) is a hallmark of attention deficit hyperactivity disorder and substance use disorders ( Bickel et al . , 2014; Hamilton et al . , 2015; Patros et al . , 2016 ) , whereas pronounced preference for delayed gratification is characteristic of the eating disorder anorexia nervosa ( Steinglass et al . , 2012; Kaye et al . , 2013; Decker et al . , 2015 ) . Independent of psychopathology , intertemporal choice both associates with life outcomes and changes across the lifespan ( Denburg et al . , 2007; Boyle et al . , 2012; Beas et al . , 2015; Hess et al . , 2015 ) . Contrary to economic models predicting that older individuals should account for reduced time on the horizon in making intertemporal choices , healthy older adults actually exhibit a marked increase in preference for delayed outcomes ( Green et al . , 1996; Green et al . , 1999; Jimura et al . , 2011; Löckenhoff et al . , 2011; Mata et al . , 2011; Samanez-Larkin et al . , 2011; Eppinger et al . , 2012 ) . Although this pattern of choice behavior is sometimes characterized as ‘wisdom’ , increased preference for delayed over immediate rewards may also be maladaptive . For example , biases toward delayed gratification in older adults could contribute to inappropriately conservative financial strategies that forgo expenditures necessary to maintain quality of life . The neural circuits underlying age-associated changes in intertemporal choice remain poorly understood . Relevant to elucidating this circuitry is the fact that intertemporal choice is a multicomponent process that involves a series of temporally distinct cognitive operations ( Rangel et al . , 2008; Fobbs and Mizumori , 2017 ) . Specifically , most decisions begin with representations of past choices , as well as some idea of the outcomes associated with each choice option . These representations are weighted by one’s motivation to obtain the choice outcomes at the time of the decision . A second phase of decision making occurs after a choice is made and involves evaluating the outcome to determine the degree to which it matches its predicted value . Feedback from this evaluative process can be used to adjust representations of the options to guide future choices . Both deliberation before a choice and outcome evaluation after a choice are supported by a network of brain structures that mediate reward processing , prospection , planning , prediction error , and value computations ( Peters and Büchel , 2011; Orsini et al . , 2015a; Bailey et al . , 2016; Fobbs and Mizumori , 2017 ) . The basolateral amygdala ( BLA ) , which forms associations between cues or actions and their outcomes ( Johansen et al . , 2012; Wassum and Izquierdo , 2015 ) , plays a central role in decision making and has been specifically implicated in both deliberative and evaluative processing ( Schoenbaum et al . , 1998; Schoenbaum et al . , 1999; Ghods-Sharifi et al . , 2009; Peters and Büchel , 2011; Zuo et al . , 2012; Grabenhorst et al . , 2012; Zangemeister et al . , 2016; Orsini et al . , 2017 ) . The BLA also undergoes structural and functional alterations with advanced age , and BLA neural activity during intertemporal decision making is attenuated in aged rats ( Lolova and Davidoff , 1991; Rubinow and Juraska , 2009; Rubinow et al . , 2009; Roesch et al . , 2012; Burke et al . , 2014; Prager et al . , 2016; Samson et al . , 2017 ) . It remains unclear , however , how age-associated changes in BLA recruitment actually influence intertemporal choice . Optogenetic tools have been employed previously to define temporally-specific roles of BLA during deliberation and outcome evaluation in young rats performing a decision-making task involving risk of punishment ( Orsini et al . , 2017 ) . Specifically , BLA inactivation at discrete timepoints in the decision process shifted choice behavior in opposite directions , highlighting multiple roles for BLA information processing in risky decision making . The present study used a similar optogenetic approach to define the roles of BLA neural activity in intertemporal choice ( Figure 1 ) and to further determine if the roles of BLA change across the lifespan .
Virally-transduced neurons were identified by mCherry expression and targeted for whole-cell patch clamp recordings using a combination of epifluorescence and differential interference contrast microscopy . Virally-transduced BLA neurons examined in slices from young and aged animals did not differ with respect to input resistance ( Young: 122 . 6 ± 20 . 7 MΩ , n = 26 cells; Aged: 120 . 2 ± 11 . 6 MΩ , n = 28 cells; t ( 52 ) =0 . 101 , p=0 . 920 ) , whole cell capacitance ( Young: 139 . 7 ± 7 . 54 pF , n = 26 cells; Aged: 138 . 8 ± 6 . 91 pF , n = 28 cells; t ( 52 ) =0 . 081 , p=0 . 936 ) , or current required to maintain the membrane potential at −70 mV ( Young: −104 . 34 ± 15 . 9 pA; n = 26 cells; Aged: −116 . 0 ± 13 . 3 pA , n = 28 cells; t ( 52 ) =0 . 566 , p=0 . 574 ) . Young and aged neurons filled with biocytin and visualized with 2-photon mediated epifluorescence microscopy were multipolar and had substantial dendritic branching , consistent with the morphology of BLA principal neurons ( Figure 2A , E ) . Light pulses ( 1 s duration ) produced similar outward currents in young and aged virally-transduced BLA neurons , as observed in voltage clamp ( young: 78 . 8 ± 10 . 6 pA , n = 26 cells , aged: 89 . 1 ± 8 . 4 pA , n = 28 cells , t ( 52 ) =0 . 764 , p=0 . 448 , Figure 2B , F ) . Activation of eNpHR3 . 0 in this manner was consistently sufficient to silence both young and aged neurons when firing under a moderate ( ~50–200 pA ) load ( Figure 2C , G ) . Additional experiments were conducted to confirm that age differences did not emerge with longer light pulses ( 4 s ) that matched or exceeded durations used in the in vivo experiments . A 4 s light-induced activation of eNpHR3 . 0 produced similar outward currents in young and aged cells ( young: 55 . 2 ± 11 . 9 pA , n = 16 cells; aged: 55 . 9 ± 6 . 8 pA , n = 16 cells , t ( 30 ) =0 . 050 , p=0 . 961 , Figure 2D , H ) . Finally , because long term activation of eNpHR3 . 0 plausibly could alter chloride gradients across the cell membrane , evidence for rebound excitation after 4 s light pulses was evaluated in both young and aged neurons . Overall , voltage clamp experiments measured in the first 500 msec after the light pulse revealed similar mean currents in young and aged neurons ( young: 0 . 6 ± 1 . 6 pA , n = 16 cells , aged: 4 . 1 ± 2 . 1 pA , n = 16 cells , t ( 30 ) =1 . 351 , p=0 . 187 ) . Similarly , only 1 of 61 neurons examined in current clamp ( across both ages and light durations ) that were silent before exposure to light fired any action potentials within 1 s of cessation of the light pulse . Overall , these data indicate that light-induced activation of eNpHR3 . 0 produces robust inhibition of virally-transduced BLA principal neurons , and that these effects do not significantly vary with age . The data further demonstrate that rebound excitation after eNpHR3 . 0 activation in BLA is minimal and unlikely to be functionally impactful . Expression of mCherry was used to confirm viral transduction in the BLA of rats used in behavioral studies that were injected with either AAV5-CamKIIα-eNpHR3 . 0-mCherry ( AAV-eNpHR3 . 0 , black circles in Figure 3 ) or AA5-CamKIIα-mCherry alone ( AAV-control , white circles in Figure 3 ) . Cannula placements were centered in the BLA , and the brain volumes virally transduced by AAV-eNpHR3 . 0 and AAV-control ( calculated from the atlas of Paxinos and Watson , 2005 ) were comparable in young and aged rats . Previous work shows that aged rats display attenuated discounting of delayed rewards ( Simon et al . , 2010; Hernandez et al . , 2017 ) . Therefore , prior to inactivation sessions , delays were adjusted on an individual basis to ensure that all rats’ choice performance was within the same parametric space ( Figure 4A ) . This approach allowed a comparable range of effects from BLA inactivation to be observed in both young and aged rats , without concern for ceiling or floor effects . Figure 4B shows the actual delays used in the second and third blocks to achieve roughly 66% and 33% choice of the large reward , respectively , plotted as a function of age . A two-factor ANOVA ( age × delay block ) comparing the actual delays indicated the expected main effect of block ( F ( 2 , 26 ) =18 . 685 , p<0 . 001 , ηp2=0 . 606 , 1-β=0 . 930 ) , as well as a main effect of age ( F ( 1 , 13 ) =6 . 402 , p=0 . 025 , ηp2=0 . 330 , 1-β=0 . 648 ) and an age × delay block interaction ( F ( 2 , 26 ) =6 . 913 , p=0 . 004 , ηp2=0 . 347 , 1-β=0 . 891 ) . Post hoc analyses comparing the actual delays of young and aged rats in blocks 2 and 3 indicated that aged rats required longer delays than young to achieve comparable preference for large vs . small rewards ( Block 2: t ( 13 ) =-2 . 234 , p=0 . 044 , Cohen’s d = 1 . 114 , 1-β=0 . 480; Block 3: t ( 13 ) =-2 . 660 , p=0 . 020 , Cohen’s d = 1 . 328 , 1-β=0 . 625 ) . Consistent with this analysis , aged rats in comparison to young rats had a greater indifference point ( the delay at which rats showed equivalent preference for large and small rewards; t ( 13 ) = −2 . 168 , p=0 . 049 , Cohen’s d = 1 . 080 , 1-β=0 . 457; Figure 4C ) . Inactivation of the BLA during the deliberation epoch ( n = 8 young and n = 7 aged ) significantly increased choice of the large reward to the same extent in young and aged rats , particularly at long delays ( Figure 5A ) . A three-factor ANOVA ( laser condition × age × delay block ) indicated a main effect of laser condition ( F ( 1 , 13 ) =103 . 507 , p<0 . 001 , ηp2=0 . 888 , 1-β=1 . 000 ) but no main effect of age ( F ( 1 , 13 ) = 0 . 089 , p=0 . 770 ) nor an age ×laser condition interaction ( F ( 1 , 13 ) =1 . 838 , p=0 . 198 ) . A reliable main effect of delay block was observed ( F ( 2 , 26 ) =112 . 005 , p<0 . 001 , ηp2=0 . 896 , 1-β=1 . 000 ) , as was as an interaction between laser condition and delay block ( F ( 2 , 26 ) =38 . 369 , p<0 . 001 , ηp2=0 . 747 , 1-β=1 . 000 ) . Follow-up analyses , conducted to further explore the laser condition × delay block interaction , compared the effects of inactivation at each block . This analysis indicated that BLA inactivation significantly increased choice of the large reward in blocks 2 ( t ( 14 ) =-6 . 494 , p<0 . 001 , Cohen’s d = 1 . 724 , 1-β=0 . 995 ) and 3 ( t ( 14 ) =-9 . 434 , p<0 . 001 , Cohen’s d = 2 . 228 , 1-β=1 . 000 ) , but not in block 1in which rats of both ages strongly preferred the large reward , even under control conditions ( t ( 14 ) =-0 . 323 , p=0 . 751 ) . In direct contrast to the effects of BLA inactivation during deliberation , BLA inactivation during the small reward epoch ( n = 6 young and n = 6 aged ) significantly decreased choice of the large reward only in young rats ( Figure 5C ) . A three-factor ANOVA ( laser condition × age × delay block ) indicated main effects of laser condition ( F ( 1 , 10 ) =5 . 131 , p=0 . 047 , ηp2=0 . 339 , 1-β=0 . 534 ) and delay block ( F ( 2 , 20 ) =248 . 854 , p<0 . 001 , ηp2=0 . 961 , 1-β=1 . 000 ) , but no interaction between laser condition and delay block ( F ( 2 , 20 ) = 1 . 317 , p=0 . 290 ) . Notably , although there was no main effect of age ( F ( 1 , 10 ) =0 . 941 , p=0 . 355 ) , the effects of BLA inactivation during small reward delivery did reliably interact with age ( laser condition × age: F ( 1 , 10 ) =7 . 127 , p=0 . 024 , ηp2=0 . 416 , 1-β=0 . 673 ) . To better define the nature of this interaction , follow-up analyses using two-factor ANOVAs ( laser condition × delay block ) were performed on choice behavior separately in young and aged rats . BLA inactivation significantly decreased choice of the large reward in young rats ( main effect of laser condition: F ( 1 , 5 ) =18 . 226 , p=0 . 008 , ηp2=0 . 785 , 1-β=0 . 922 , main effect of delay block: F ( 2 , 10 ) =173 . 588 , p<0 . 001 , ηp2=0 . 972 , 1-β=1 . 000; laser condition × delay block: F ( 2 , 10 ) =3 . 829 , p=0 . 058 ) but not in aged rats ( main effect of laser condition: F ( 1 , 5 ) =0 . 061 , p=0 . 814; main effect of delay block: F ( 2 , 10 ) =93 . 015 , p<0 . 001 , ηp2=0 . 949 , 1-β=1 . 000; laser condition × delay block: F ( 2 , 10 ) =0 . 185 , p=0 . 834 ) . Because different delays to large reward delivery were required to achieve comparable levels of choice preference in young and aged rats , it is possible that the absence of BLA inactivation effects in aged rats was due to the different delay durations employed rather than age differences per se . To address this possibility , an additional analysis was conducted in which aged rats were sub-grouped into those with delays comparable to young ( ‘aged delay-matched’ ) and those with delays that exceeded young ( ‘aged delay-unmatched’ ) . A multi-factor ANOVA was conducted with age subgroup ( two levels: aged delay-matched and aged delay-unmatched ) as the between-subjects factor and laser condition ( two levels: laser on or off ) and block ( three levels: blocks 1 , 2 , and 3 ) as within-subjects factors . Critically , this analysis revealed no choice difference between aged delay-matched and aged delay-unmatched subgroups when the BLA was inactivated during the small reward delivery ( main effect of sub-group: F ( 1 , 4 ) =0 . 180 , p=0 . 694; main effect of laser condition: F ( 1 , 4 ) =0 . 050 , p=0 . 834; main effect of block: F ( 2 , 8 ) =80 . 518 , p<0 . 001 , ηp2=0 . 953 , 1-β=1 . 000; laser condition × sub-group: F ( 1 , 4 ) =0 . 082 , p=0 . 789; laser condition × block: F ( 2 , 8 ) =0 . 167 , p=0 . 849; sub-group × block: F ( 2 , 8 ) =0 . 328 , p=0 . 729; laser condition × sub group × block: F ( 2 , 8 ) =0 . 515 , p=0 . 616 ) . These results indicate that it is unlikely that the different delay durations contributed to the age difference in the role of the BLA during small reward delivery . The data above show that BLA inactivation during the deliberation and small reward epochs altered choice behavior in different directions ( i . e . , BLA inactivation during deliberation increased choice of the large reward in both young and aged rats , whereas BLA inactivation during small reward delivery decreased choice of the large , delayed reward in young rats but had no effect in aged rats ) . A trial-by-trial analysis was conducted on these data to determine the effects of BLA inactivation on two distinct behavioral strategies that could mediate these shifts in choice preference . Specifically , during the deliberation epoch , this analysis determined the degree to which BLA inactivation influenced rats to ‘shift’ to the large reward option following a choice of the small reward on the previous trial , versus ‘stay’ with the large reward option following choice of the large reward on the previous trial . In the small reward epoch , the analysis assessed the degree to which BLA inactivation influenced rats to ‘shift’ to the small reward following choice of the large reward on the previous trial , versus ‘stay’ with the small reward following choice of the small reward on the previous trial . As shown in Figure 5B , the percentage of trials during deliberation epoch inactivation on which a large reward choice was followed by a second large reward choice ( large-stay ) did not differ as a function of laser condition or age ( main effect of laser condition: F ( 1 , 13 ) =2 . 563 , p=0 . 605; main effect of age: F ( 1 , 13 ) =0 . 282 , p=0 . 605; laser condition × age: F ( 1 , 13 ) =0 . 153 , p=0 . 702 ) . In contrast , a similar analysis conducted on the percentage of trials on which a choice of the small reward was followed by choice of the large reward ( small-shift ) revealed a main effect of laser condition but no effect of age ( main effect of laser condition: F ( 1 , 13 ) =40 . 051 , p<0 . 001 , ηp2=0 . 755 , 1-β=1 . 000; main effect of age: F ( 1 , 13 ) =0 . 425 , p=0 . 526; laser condition × age interaction: F ( 1 , 13 ) =0 . 003 , p=0 . 954 ) . Planned paired-samples t-tests showed that a significant increase in shifting after a choice of the small reward was evident in both young ( t ( 7 ) =4 . 095 , p=0 . 005 , Cohen’s d = 1 . 802 , 1-β=0 . 917 ) and aged ( t ( 6 ) =5 . 342 , p=0 . 002 , Cohen’s d = 2 . 442 , 1-β=0 . 987 ) rats . This finding indicates that the effects on choice behavior of BLA inactivation during deliberation result from rats shifting choices toward the large reward following a choice of the small reward . Applying a parallel analysis to sessions in which inactivation took place during the small reward epoch yielded a different pattern of results ( Figure 5D ) . BLA inactivation during the small reward epoch significantly increased the percentage of trials on which a small reward choice was followed by a second small reward choice ( small-stay; main effect of laser condition: F ( 1 , 10 ) =6 . 026 , p=0 . 034 , ηp2=0 . 376 , 1-β=0 . 601; main effect of age: F ( 1 , 10 ) =2 . 421 , p=0 . 151; laser condition × age interaction: F ( 1 , 10 ) =3 . 519 , p=0 . 090 ) . Planned paired-samples t-tests showed that young rats were more likely to repeat the small reward choice on subsequent trials ( small-stay; ( t ( 5 ) =3 . 593 , p=0 . 016 , Cohen’s d = 1 . 694 , 1-β=0 . 754 ) but this pattern was not observed in aged rats ( t ( 5 ) =0 . 363 , p=0 . 732 ) . In contrast , neither BLA inactivation nor age influenced the percentage of trials on which a choice of the large reward was followed by a choice of the small reward ( large-shift; main effect of laser condition: F ( 1 , 10 ) =1 . 120 , p=0 . 315; main effect of age: F ( 1 , 10 ) =0 . 105 , p=0 . 753; laser condition × age: F ( 1 , 10 ) =0 . 086 , p=0 . 775 ) . Other task measures were compared between BLA inactivation and baseline conditions in both deliberation and small reward epochs using a mixed-factor ANOVA , with age as the between-subjects factor and laser condition as the within-subjects factor . As shown in Table 1 , the number of trials completed in a session did not differ as a function of laser condition or age in either the deliberation or small reward outcome epochs ( Fs <3 . 431 , ps >0 . 094 ) . Similarly , as shown in Table 2 , latency to press either the small or large reward lever did not differ as a function of laser condition or age in either epoch ( Fs <4 . 149 , ps >0 . 069 ) . See Tables 1 and 2 for full statistical results of these analyses . Choosing the large reward lever resulted in a variable delay period that was followed by large ( three food pellets ) reward delivery . The effects of BLA inactivation during the delay and large reward delivery epochs were initially tested in separate sessions ( n = 6 young and n = 6 aged ) . Subsequently , the effects of BLA inactivation across both the delay and large reward epochs were tested in a subset of these rats ( n = 3 young and n = 3 aged ) . To confirm the temporal specificity of the BLA inactivation effects , rats ( n = 6 young , n = 6 aged ) were tested while the BLA was inactivated during the intertrial interval ( ITI ) . Although the expected main effect of delay block was observed ( F ( 2 , 20 ) =116 . 459 , p<0 . 001 , ηp2=0 . 921 , 1-β=1 . 000 ) , BLA inactivation during the ITI did not alter choice performance compared to baseline in young or aged rats ( main effect of laser condition: F ( 1 , 10 ) =0 . 082 , p=0 . 780; main effect of age: F ( 1 , 10 ) =0 . 042 , p=0 . 842; laser condition × age: F ( 1 , 10 ) =0 . 298 , p=0 . 597; laser condition × delay block: F ( 2 , 20 ) =0 . 344 , p=0 . 713; age × delay block: F ( 2 , 20 ) =0 . 216 , p=0 . 808; laser condition × age × delay block: F ( 2 , 20 ) =0 . 198 , p=0 . 822; Figure 7 ) . To control for non-specific effects of light delivery and viral transduction ( e . g . , changes in tissue temperature and off-target transduction effects ) , the effects of light delivery in rats virally transduced with a control virus that did not contain the eNpHR3 . 0 gene were tested during behavioral epochs in which BLA inactivation influenced choice behavior ( i . e . , deliberation: n = 4 young and n = 4 aged rats; and small reward: n = 4 young rats ) .
A large literature supports a role for BLA in assigning and updating the value of stimuli and events ( Hatfield et al . , 1996; Málková et al . , 1997; Baxter et al . , 2000; Baxter and Murray , 2002; Shiflett and Balleine , 2010; Izquierdo et al . , 2013; Parkes and Balleine , 2013; Wassum and Izquierdo , 2015 ) . With respect to decision making , the evaluative process mediated by BLA after an outcome has been received appears to involve acquisition and/or integration of information about the negative properties of that outcome . For example , previous work from our laboratory using a risky decision-making task demonstrated that BLA inactivation during receipt of a large , punished reward increased subsequent choice of this option over a small but safe reward ( Orsini et al . , 2017 ) . Given this previous finding , it was somewhat surprising that in the current study , BLA inactivation during the large reward following the delay and/or the delay interval itself had no effect on choice behavior . These data demonstrate that the aversive properties of delays that bias choice behavior toward immediate options are not critically mediated by BLA . Importantly , however , BLA inactivation during the small reward epoch did reliably bias young rats toward choices of the small , immediate reward . This bias , which mimics that produced by BLA lesions ( Winstanley et al . , 2004; Churchwell et al . , 2009 ) , indicates that BLA is specifically important for evaluating and integrating the aversive properties that make the small reward less attractive than the large . Indeed , the trial-by-trial analysis showed that BLA inactivation rendered rats more likely to ‘stay’ with choices of the small reward after selection of that option on the previous trial , as though the negative feedback about that small reward ‘not being good enough’ had been attenuated . While these data are consistent with the idea that the BLA processes information about the aversive properties of outcomes in order to bias future behavior toward more favorable options ( Ghods-Sharifi et al . , 2009; Orsini et al . , 2017 ) , they also suggest that final integration of the values of both reward magnitude and delay occurs outside of the BLA . Given that working memory appears to contribute to the ability to delay gratification during intertemporal choice ( Shamosh et al . , 2008; Bobova et al . , 2009; Shimp et al . , 2015; Hernandez et al . , 2017 ) , it is likely that brain regions such as the hippocampus and prefrontal cortex mediate at least some components of information processing during the delay period . As such , one might predict that temporally-selective inactivation of these structures while waiting for a large reward ( i . e . , the delay interval ) would influence future choice of that delayed option ( Churchwell et al . , 2009; Mariano et al . , 2009; Abela and Chudasama , 2013; Sonntag et al . , 2014; Yates et al . , 2014 ) . It should also be noted that BLA might be more critically involved in integrating information about delays and reward magnitude under other intertemporal choice conditions , such as in the presence of a cue that predicts large reward delivery during the delay ( Zeeb et al . , 2010 ) . Future work applying temporally-discrete inactivation approaches to other brain regions implicated in intertemporal decision making ( e . g . , prefrontal cortex and hippocampus ) will help to more fully elucidate their unique and/or shared contributions with BLA to choice behavior . In contrast to results obtained during outcome evaluation , BLA inactivation during deliberation in young rats increased choice of the large , delayed reward . This shift toward less impulsive choice is opposite of that produced by inactivation methods such as lesions , which inhibit the BLA throughout the entire decision process ( Winstanley et al . , 2004; Churchwell et al . , 2009 ) . The prior study from our lab investigating risky decision making also showed that BLA inactivation during the deliberation epoch produced effects on choice behavior that were opposite those produced by neurotoxic lesions ( Orsini et al . , 2015b; Orsini et al . , 2017 ) . Specifically , during choices between small , safe and large , risky rewards , BLA lesions increase risky choice whereas selective optogenetic BLA inactivation during deliberation attenuates risky choice . Together , these data indicate a critical role for BLA during deliberation , which is normally overshadowed by its role in outcome evaluation . Importantly , although the risky decision-making and intertemporal choice tasks are analogous in design , performance on the two tasks is orthogonal and involves dissociable neural mechanisms ( Floresco et al . , 2008; Churchwell et al . , 2009; Simon et al . , 2011; Mitchell et al . , 2014; Orsini et al . , 2015a; Orsini et al . , 2018; Bailey et al . , 2016 ) . Thus , the similar pattern of results observed with BLA manipulations suggests a common role for this structure in multiple forms of decision making . Specifically , BLA activity during deliberation under normal conditions may be important for ascribing incentive salience to the choice options , signaling their motivational value , or how much they are ‘wanted’ , in the moment of the decision . Consequently , choice may be biased toward the more immediate reward in the intertemporal choice task , and toward the larger , albeit riskier , reward in the risky choice task . When this signal is removed ( e . g . , during BLA inactivation ) , rats are more likely to wait in the intertemporal choice task and less likely to risk punishment in the risky choice task to obtain the large reward . Indeed , the trial-by-trial analysis in the current study shows that BLA inactivation during deliberation renders rats more likely to shift their choices to the delayed reward following a choice of the immediate reward , as though the incentive properties driving the immediate choice have been attenuated . This interpretation agrees with evidence from other behavioral contexts . For example , an intact BLA is necessary for the potentiating influence of reward-predictive cues on instrumental responding for reward ( Everitt et al . , 2003 ) , as well as for maintaining effortful choices of preferred options ( Hart and Izquierdo , 2017 ) . Across species , aging is accompanied by an increased ability to delay gratification ( Green et al . , 1994; Green et al . , 1999; Simon et al . , 2010; Jimura et al . , 2011; Löckenhoff et al . , 2011; Samanez-Larkin et al . , 2011; Eppinger et al . , 2012; Hernandez et al . , 2017 ) . Previous work from our labs showed that relative to young rats , aged rats display greater preference for large , delayed over small , immediate rewards in a ‘fixed delays , block design’ intertemporal choice task . This difference is not readily attributable to age-related deficits in cognitive flexibility , working memory , or food motivation , nor is it attributable to impairments in reward or temporal discrimination ( Simon et al . , 2010; Hernandez et al . , 2017 ) . The present study replicated these prior findings using a task variant in which the fixed delays/block design employed in our previous work was maintained , but the delays to large reward delivery were adjusted on an individual basis to obtain equivalent levels of choice preference in young and aged rats . Under these conditions , aged rats required longer delays to achieve levels of choice preference comparable to young , consistent with the idea that delays are less effective at discounting reward value in aged compared to young rats . Inactivation of BLA during the deliberation epoch decreased impulsive choice in both young and aged rats . In contrast , BLA inactivation during the small reward epoch increased impulsive choice in young rats but had no effect in aged rats . Importantly , this lack of effect in aged rats is unlikely to be attributable to age-related impairments in viral transduction or optogenetic efficacy . Histological reconstruction showed comparable BLA viral transduction in young and aged rats ( Figure 3 ) . Moreover , in vitro electrophysiological experiments showed that halorhodopsin-transduced BLA neurons in both age groups were silenced to a similar degree in response to light pulses ( Figure 2 ) . Most importantly , inactivation during the deliberation epoch in the same aged rats used to test the effects of inactivation during the small reward epoch produced effects on behavior that were as robust as those in young rats ( Figure 5A ) . These latter data provide direct , in vivo verification that the absence of behavioral effects following BLA inactivation during the small reward epoch in aged rats cannot be ascribed to attenuated halorhodopsin efficacy . It is possible that aged rats’ bias toward the large , delayed reward could have resulted in insufficient parametric space in which to observe optogenetically-induced shifts in choice behavior . The intertemporal choice task was explicitly designed to address this possibility by adjusting the delays to equate baseline choice preference in young and aged rats ( Figure 4A ) . Additional analyses were conducted to ensure that the age difference in the delay to large reward delivery that resulted from this design did not itself influence the role of BLA in intertemporal choice . Specifically , aged rats were divided into subgroups based on whether their delays to large reward delivery matched or exceeded the range of young . The effects of BLA inactivation were identical between these aged subgroups , and in particular , BLA inactivation during the small reward epoch had no effect in either subgroup . These data indicate that the different delay durations experienced by young and aged rats do not account for the age differences in the role of BLA in intertemporal choice . The distinct effects of BLA inactivation in young and aged rats could suggest that aged subjects fundamentally make decisions differently than young , relying less heavily on evaluation of choice outcomes ( Löckenhoff et al . , 2011; Mather et al . , 2012; Samanez-Larkin and Knutson , 2015; Pachur et al . , 2017 ) . For example , whereas young adults readily incorporate new information to guide decisions , older adults tend to rely more heavily on previously-learned , ‘crystalized’ knowledge for decision making ( Horn , 1982; Mather , 2006; Mata et al . , 2011 ) . If the cognitive structure of the decision process differs , the absence of BLA inactivation effects during outcome evaluation in aged rats may not necessarily reflect BLA dysfunction . For example , the increased reliance of older adults on crystalized knowledge for decision making has been attributed in part to vulnerability in neural structures such as hippocampus , which compromises encoding of new information and prospective memory ( Del Missier , 2015; Shadlen and Shohamy , 2016; Hu et al . , 2017; Levin et al . , 2017 ) . Moreover , there is substantial evidence in aged humans and rodents for recruitment of brain circuits that are distinct from those engaged by young subjects during complex cognitive operations , even when performance is equated ( Antonenko and Flöel , 2014; Lighthall et al . , 2014; Tomás Pereira et al . , 2015; Wang et al . , 2015 ) . The cause-effect relationships in the influences of aging on neural versus cognitive ‘restructuring’ are difficult to disentangle . It is certainly possible , however , that the fact that aged rats don’t use the BLA for outcome evaluation during intertemporal decision making reflects neural dysfunction and/or compensation associated with other brain regions . While the current findings do not necessarily reflect age-associated neurobiological impairments within the BLA , prior evidence does suggest that this region is susceptible to age-associated changes ( Rubinow and Juraska , 2009; Rubinow et al . , 2009; Roesch et al . , 2012; Prager et al . , 2016 ) . Indeed , although BLA neuron number remains relatively stable with age , baseline firing rate of BLA neurons in vivo is reduced in aged rats ( Almaguer et al . , 2002; Roesch et al . , 2012 ) , and a recent electrophysiological recording study reported enhanced β-power in BLA of aged rats during reward evaluation in a probabilistic decision-making task ( Samson et al . , 2017 ) . Projections from the BLA to the nucleus accumbens ( NAc ) may be particularly relevant for reward outcome evaluation . Pharmacological disconnection of the BLA and NAc impairs discrimination between a devalued vs . a non-devalued food reward ( Shiflett and Balleine , 2010 ) , and optogenetic inactivation of BLA terminals in NAc during outcome evaluation increases preference for the ‘risky’ option in a probabilistic decision-making task ( Bercovici et al . , 2018 ) . These results support the idea that BLA projections to NAc are responsible for mediating negative feedback regarding choice outcomes . Notably , Eppinger et al . ( 2013 ) showed blunted activity in ventral striatum during reward prediction errors in older adults performing a learning task . Together with the current study , these findings suggest that the BLA-NAc circuit is disengaged during decision making in older adults . Unlike BLA inactivation during outcome evaluation , inactivation during deliberation in both young and aged rats mimicked the attenuated impulsive choice observed in aging ( Figure 4C; Simon et al . , 2010; Hernandez et al . , 2017 ) . This effect is only observed using temporally-discrete optogenetic inhibition during deliberation , and not during outcome evaluation or with experimental methods such as lesions or pharmacological inactivation that inhibit the BLA across all stages of the decision process ( Winstanley et al . , 2004; Churchwell et al . , 2009 ) . Such data suggest that in young rats , activity in BLA circuits involved in outcome evaluation may be the primary driver of choice behavior . The failure to engage such outcome evaluation circuits in aging , however , may ‘unmask’ the contributions of BLA during deliberation . According to this hypothesis , structural or functional changes in BLA that occur with aging would thus exert their influence on intertemporal choice through a putative ‘deliberation circuit’ , perhaps involving BLA projections to prefrontal cortex ( PFC; Burgos-Robles et al . , 2017 ) . The BLA contains co-distributed neurons that send distinct efferent projections to PFC and NAc ( Pérez-Jaranay and Vives , 1991; Ambroggi et al . , 2008; Shiflett and Balleine , 2010; Dilgen et al . , 2013; McGarry and Carter , 2017 ) . These distinct populations of BLA efferents may subserve unique roles in intertemporal choice , and further , may be differentially susceptible to aging . In other words , attenuated impulsive choice in aged rats might reflect a failure to engage a BLA-NAc ‘outcome evaluation’ circuit in combination with a hypoactive BLA-PFC ‘deliberation’ circuit that mimics the effect of BLA inactivation during deliberation . Future experiments applying circuit-based optogenetic approaches to the study of decision making in aging should be helpful for further elucidating the neurobiological substrates of age-associated alterations in intertemporal choice . The current experiments demonstrate several unique roles for BLA activity in intertemporal choice . First , these data demonstrate a novel role for BLA in promoting impulsive choices when deciding whether to delay gratification . Moreover , these experiments defined a second role for BLA in attenuating impulsive choices , by providing negative feedback about the inadequacy of small vs large reward . Notably , this latter role in negative feedback does not extend to the aversive properties of the delay , indicating that the integrated valuation of reward and costs occurs outside of the BLA . Finally , the current experiments demonstrate that these temporally distinct roles of BLA in decision making change in aging . Specifically , aged rats do not appear to use BLA in any form of outcome evaluation . Moreover , the effects of age on intertemporal choice are mimicked by inactivation of BLA during deliberation . These findings suggest complex effects of aging within and/or outside BLA , which may uniquely impact distinct BLA efferent circuits . This study is among the first to apply optogenetic techniques to the study of cognitive aging . The findings offer unique insights into how BLA mediates intertemporal choice , and show that optogenetic approaches can be used to complement and extend our understanding of how changes in neural activity guide behavioral alterations in aged subjects .
Young ( 6 months old , n = 24 ) and aged ( 24 months old , n = 19 male Fischer 344 x Brown Norway F1 hybrid ( FBN ) rats were obtained from the National Institute on Aging colony ( Charles River Laboratories ) and individually housed in the Association for Assessment and Accreditation of Laboratory Animal Care International-accredited vivarium facility in the McKnight Brain Institute building at the University of Florida in accordance with the rules and regulations of the University of Florida Institutional Animal Care and Use Committee and National Institutes of Health guidelines . The facility was maintained at a consistent temperature of 25° with a 12 hr light/dark cycle ( lights on at 0600 ) and free access to food and water except as otherwise noted . Rats were acclimated in this facility and handled for at least one week prior to initiation of any procedures . A subset of rats completed only some of the behavioral epochs due to lost headcaps and premature death , and some rats were excluded entirely for misplaced injections . Only the final numbers of rats included in each analysis are provided below . Surgical procedures were performed as in our previous work ( Orsini et al . , 2017 ) . Rats were anesthetized with isofluorane gas ( 1–5% in O2 ) and secured in a stereotaxic frame ( David Kopf ) . An incision along the midline over the skull was made and the skin was retracted . Bilateral burr holes were drilled above the BLA and five additional burr holes were drilled to fit stainless steel anchoring screws . Bilateral guide cannulae ( 22-gauge , Plastics One ) were implanted to target the BLA ( anteroposterior ( AP ) : −3 . 25 mm from bregma , mediolateral ( ML ) : ±4 . 95 mm from bregma , dorsoventral ( DV ) : −7 . 3 mm from the skull surface ) and secured to the skull using dental cement . A total of 0 . 6 µL of a 3 . 5 × 1012 vg/ml titer solution ( University of North Carolina Vector Core ) containing AAV5 packaged with either halorhodopsin ( CamKIIα-eNpHR3 . 0-mCherry , n = 8 young and n = 7 aged rats ) or mCherry alone ( CamKIIα-mCherry , n = 4 young and n = 4 aged rats ) was delivered through the implanted cannulae at a rate of 0 . 6 µL per min . Stainless steel obdurators were placed into the cannulae to minimize the risk of infection . Immediately after surgery , rats received subcutaneous injections of buprenorphine ( 1 mg/kg ) and meloxicam ( 2 mg/kg ) . Buprenorphine was also administered 24 hr post-operation , and meloxicam 48–72 hr post-operation . A topical ointment was applied as needed to facilitate wound healing . Sutures were removed after 10–14 days and rats recovered for at least 2 weeks before food restriction and behavioral testing began . For in vitro electrophysiological verification of functional halorhodopsin ( eNpHR3 . 0 ) , young ( n = 4 ) and aged ( n = 3 ) rats underwent surgery as described above except that neither guide cannulae nor skull screws were implanted . Following a 3–4 week survival time , rats were deeply anesthetized via i . p . injection of ketamine ( 75–100 mg/kg ) and xylazine ( 5–10 mg/kg ) . The brain was rapidly cooled via transcardial perfusion with cold oxygenated sucrose-laden artificial cerebrospinal fluid ( ACSF ) containing ( in mM ) : 205 sucrose , 10 dextrose , 1 MgSO4 , 2 KCl , 1 . 25 NaH2PO4 , 1 CaCl2 , and 25 NaHCO3 . Rats were then decapitated , brains extracted and coronal slices ( 300 µm ) prepared using a Leica VT 1000 s vibratome . Slices were incubated for 30 min at 37°C in oxygenated low-calcium ACSF containing ( in mM ) : 124 NaCl , 10 dextrose , 3 MgSO4 , 2 . 5 KCl , 1 . 23 NaH2PO4 , 1 CaCl2 , and 25 NaHCO3 , after which they were transferred to room temperature for a minimum of 30 min prior to experimentation . During recording experiments , slices were bathed in ACSF containing ( in mM ) : 125 NaCl , 11 dextrose , 1 . 5 MgSO4 , 3 KCl , 1 . 2 NaH2PO4 , 2 . 4 CaCl2 , and 25 NaHCO3 , maintained at 28–30°C . The pipette ( internal ) solution contained ( in mM ) : 125 K-gluconate , 10 phosphocreatine , 1 MgCl2 , 10 HEPES , 0 . 1 EGTA , 2 Na2ATP , 0 . 25 Na3GTP , and 5 biocytin , adjusted to pH 7 . 25 and 295 mOsm with KOH . BLA neurons were visualized using a combination of IR-DIC and epifluorescence microscopy using an Olympus BX51WI microscope and a TTL-controlled light source ( X-Cite 110 LED light source , XF102-2 filter set , Omega Optical , excitation 540–580 nm , emission 615–695 nm , also used for in vitro activation of eNpHR3 . 0 for 1 or 4 s ) . Patch pipettes were prepared with a Flaming/Brown type pipette puller ( Sutter Instrument , P-97 ) from 1 . 5 mm/0 . 8 mm borosilicate glass capillaries ( Sutter Instruments ) and pulled to an open tip resistance of 4–7 MΩ using internal solution and ACSF noted above . Electrophysiological recordings were performed using a Mutliclamp 700B amplifier and Digidata 1440A digitizer ( Axon Instruments/Molecular Devices ) . Electrophysiological data were collected at 20 kHz and low-pass filtered at 2 kHz . At the conclusion of experiments , a subset of slices was transferred to 10% formalin ( 4°C , 24 hr ) to allow for post hoc histological analysis . Slices were washed in PBS , permeabilized in PBS containing 0 . 1% Triton-X , and incubated in streptavidin conjugate with fluorophore ( 1:1000 , 594 nm , ThermoFisher S32356 ) . Slices were then mounted onto slides and coverslipped using VECTASHIELD . Morphological reconstruction was achieved by creating an all-in-focus maximum intensity projection of a Z-series acquired with a two-photon laser scanning epifluoresence microscope ( 810 nm excitation ) . After completion of behavioral testing , rats were administered a lethal dose of Euthasol ( sodium pentobarbital and phenytoin solution; Virbac , Fort Worth , TX , USA ) and perfused transcardially with a 4°C solution of 0 . 1M phosphate buffered saline ( PBS ) , followed by 4% ( w/v ) paraformaldehyde in 0 . 1M PBS . Brains were removed and post-fixed for 24 hr then transferred to a 20% ( w/v ) sucrose solution in 0 . 1M PBS for 72 hr ( all chemicals purchased from Fisher Scientific , Hampton , NH , USA ) . Brains were sectioned at 35 µm using a cryostat maintained at −20 ˚C . Sections were rinsed in 0 . 1M TBS and incubated in blocking solution consisting of 3% normal goat serum , 0 . 3% Triton-X-100 in 0 . 1M TBS for 1 hr at room temperature . Sections were then incubated with rabbit anti-mCherry antibody ( ab167453 , Abcam , Cambridge , MA , USA ) diluted in blocking solution at a dilution of 1:1000 ( 72 hr , 4°C ) . Following primary incubation , sections were rinsed in 0 . 1M TBS and incubated in blocking solution containing the secondary antibody ( donkey anti-rabbit conjugated to AlexaFluor-488 , 1:300 ) for 2 hr at room temperature . After rinsing in 0 . 1M TBS , sections were mounted on electrostatic glass slides and coverslipped using Prolong Gold containing DAPI ( Thermo Fisher Scientific , Waltham , MA , USA ) . Slides were sealed with clear nail polish and sections were visualized at 20X using an Axio Imager 2 microscopy system ( Carl Zeiss Microscopy , LLC , Thornwood , NY , USA ) to assess mCherry expression in BLA neurons . Cannula placements and mCherry expression were mapped onto plates adapted from the rat brain atlas of Paxinos and Watson ( 2005 ) . | One marshmallow now or two in 15 minutes ? That was the choice offered to young children in a classic psychology experiment known as the Stanford marshmallow test . Children who chose to wait went on to do better at school and to show healthier body weights in later life than those who ate the single marshmallow . A brain region called the basolateral amygdala ( BLA ) helps individuals choose between rewards that differ both in size and in when they will be available . Studies in people and in rodents show that the ability to wait for a larger reward – to delay gratification – increases with age . But whether changes in BLA activity contribute to this change was not known . Choosing between a small reward now versus a larger one later involves several steps . Before a choice , individuals use their previous experience to compare the value of the immediate and the delayed rewards . How they feel at the time can bias this judgment . Someone who is hungry , for example , will assign greater value to receiving a single marshmallow now than someone who feels full . After making their choice , the individual then decides whether the reward they received was better or worse than they expected . This information helps them adjust their expectations for next time . Hernandez et al . set out to examine how the BLA contributes to these different parts of the decision . Young and old rats were given a choice between a small food reward now or a larger reward after a delay . Hernandez et al . used optogenetic tools to temporarily inactivate the BLA either before or after the rats made their choice , and found that the role of the BLA varies across the lifespan . Inactivating the BLA before the choice made both young and old rats more likely to wait for the larger reward . By contrast , inactivating the BLA after a choice made young rats less likely to wait next time round , but had no effect in the older rats . Changes in BLA activity with aging may thus make it easier to delay gratification in later life . But while the willingness of older adults to forego short-term rewards for long-term gain is often viewed as ‘wisdom’ , such behavior can also be problematic . A pensioner who decides not to spend some of their savings on heating , for example , may be needlessly reducing their quality of life . Moreover , extreme impulsivity and extreme patience both feature in psychiatric disorders . The former may drive addiction , while the latter is a hallmark of anorexia . Identifying the mechanisms that underlie the ability to delay gratification may therefore help to promote effective decision-making in aging and psychiatric disorders . | [
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] | 2019 | Optogenetic dissection of basolateral amygdala contributions to intertemporal choice in young and aged rats |
The Apelin receptor ( Aplnr ) is essential for heart development , controlling the early migration of cardiac progenitors . Here we demonstrate that in zebrafish Aplnr modulates Nodal/TGFβ signaling , a key pathway essential for mesendoderm induction and migration . Loss of Aplnr function leads to a reduction in Nodal target gene expression whereas activation of Aplnr by a non-peptide agonist increases the expression of these same targets . Furthermore , loss of Aplnr results in a delay in the expression of the cardiogenic transcription factors mespaa/ab . Elevating Nodal levels in aplnra/b morphant and double mutant embryos is sufficient to rescue cardiac differentiation defects . We demonstrate that loss of Aplnr attenuates the activity of a point source of Nodal ligands Squint and Cyclops in a non-cell autonomous manner . Our results favour a model in which Aplnr is required to fine-tune Nodal output , acting as a specific rheostat for the Nodal/TGFβ pathway during the earliest stages of cardiogenesis .
During gastrulation , complex cell movements occur which result in the localization of progenitor populations to discrete embryonic regions for subsequent organogenesis . Loss of Apelin receptor ( Aplnr ) function in zebrafish , as manifested in the recessive grinch mutant , results in a decrease or absence of cardiogenesis , and affects expression of the earliest known cardiac mesoderm markers ( Scott et al . , 2007; Zeng et al . , 2007 ) . The role of Aplnr in the proper formation of the heart appears to be conserved in vertebrates . In mice , Aplnr ( also known as Apj and Agtrl1 ) is expressed in the gastrulating mesoderm , with Aplnr mutant mice exhibiting incompletely penetrant cardiovascular malformations including thinning of the myocardium , ventricular septation defects , an enlarged right ventricle and improper heart looping ( Kang et al . , 2013 ) . In vitro , overexpression of Aplnr in mouse embryonic stem cells results in enhanced cardiac differentiation of embryoid bodies , while Aplnr inhibition leads to impaired cardiac differentiation ( D'Aniello et al . , 2013; D'Aniello et al . , 2009 ) . While a role for Aplnr signaling in the earliest events of cardiac development is evident , how Aplnr functions in this context remains unclear . In zebrafish , Aplnr has been implicated in the movement of cardiac progenitors during gastrulation to the anterior lateral plate mesoderm ( ALPM ) , the site of heart development , with a delay in anterior migration of presumed cardiac progenitors during gastrulation ( Paskaradevan and Scott , 2012 ) . These early effects on gastrulation movements suggest an early function for Aplnr in cardiac development , well before expression of cardiac mesoderm genes , such as Nkx2 . 5 , is initiated ( Scott et al . , 2007; Zeng et al . , 2007; Paskaradevan and Scott , 2012; Pauli et al . , 2014; Chng et al . , 2013 ) . Interestingly , the requirement for Aplnr in cardiac development appears to be primarily non-cell autonomous , which is to say that Aplnr is not required in the cells that will form the heart per se but rather in surrounding cells ( Paskaradevan and Scott , 2012 ) . The genetic deletion of aplnrb or its endogenous early ligand elabela ( also known as apela or toddler ) causes gastrulation movement defects with aberrant cardiac and endoderm development in zebrafish ( Paskaradevan and Scott , 2012; Pauli et al . , 2014; Chng et al . , 2013 ) . This , together with the ability of overexpressed Aplnr to rescue cardiac differentiation in Cripto-null mouse embryonic stem cells ( D'Aniello et al . , 2009 ) , indicate a strong functional link between Aplnr and Nodal signaling for proper cardiac specification and differentiation . In this study we report that Aplnr directly modulates Nodal/TGFβ signaling during gastrulation , a key pathway essential for mesendoderm induction and migration ( Carmany-Rampey and Schier , 2001; Dougan et al . , 2003 ) . Several lines of evidence show that levels of Nodal activity are attenuated in aplnr mutants . Loss-of-function of Aplnr leads to a reduction in Nodal target gene expression , whereas activation of Aplnr signaling increases the expression of these same targets . By elevating Nodal levels in aplnr mutant/morphant embryos , we are able to restore cardiac differentiation . We find that loss of Aplnr attenuates the activity of a point source of the Nodal ligands Squint ( Sqt , Ndr1 ) and Cyclops ( Cyc , Ndr2 ) and that the Aplnr regulates Nodal signaling in a cell non-autonomous fashion . We propose a model in which the Aplnr fine-tunes Nodal activity during the onset of gastrulation to initiate the migration of lateral margin cells and proper heart formation . Aplnr may therefore act as a rheostat for the Nodal/TGFβ pathway .
The zebrafish genome harbours two paralogues ( aplnra and aplnrb ) of the human APLNR gene . Only aplnrb , for which the first mutant was aptly named grinch ( aplnrbs608 , p . Trp90Leu ) , is known to be involved in early cardiogenesis ( Scott et al . , 2007; Zeng et al . , 2007 ) . In order to assess the contribution of aplnra to the process of gastrulation and heart development , we knocked it out using custom TALEN pairs targeted to its unique exon on chromosome 8 ( Figure 1A ) . The resulting null allele , which we named max ( the compliant dog companion of the Grinch ) , encodes a truncated 17-amino acid protein resulting from an early frameshift . The aplnramax allele ( p . Thr16TrpfsX2 ) deletes 95% of Aplnra including its seven transmembrane domains ( Figure 1A ) . Present at sub-Mendelian ratios , approximately 15% of mutant larvae from heterozygous aplnramax intercrosses showed pericardial edema ( Figure 1B–C ) . As with aplnrb mutants ( Scott et al . , 2007; Pauli et al . , 2014; Chng et al . , 2013 ) , sox17-positive endodermal progenitors at 8 hr post-fertilization ( hpf ) and myl7-positive cardiomyocytes at 1 day post-fertilization ( dpf ) were significantly reduced in numbers and intensity in aplnramax fish ( Figure 1—figure supplement 1A–D ) . Note that in this current study a novel aplnrbhu4145 ( p . W54X ) allele is being used . An independent allele , aplnrains , resulting from a viral insertion was obtained from Znomics ( Figure 1A ) . Homozygous aplnrains embryos recapitulated the phenotype of aplnramax and aplnrbhu4145 with similar pericardial edema ( Figure 1D ) , reduced nkx2 . 5-positive cardiac mesoderm at the 15-somite stage ( Figure 1F–H ) and reduced myl7-positive cells at 2 dpf ( Figure 1J–K ) . The number and spread of sox17-positive cells was significantly reduced in homozygous aplnrains when compared to wildtype ( WT ) and was not significantly different from aplnrbhu4145 single mutants ( Figure 1M–O and Figure 1—figure supplement 1E–F ) . These aplnra mutant phenotypes suggest redundant functions for aplnra and aplnrb . 10 . 7554/eLife . 13758 . 003Figure 1 . aplnra mutant embryos display defects in endoderm and heart formation . ( A ) Schematic detailing the aplnramax and aplnrains alleles . TM indicates the transmembrane domain . ( B–E ) Gross morphology of aplnramax , aplnrains and aplnrains; aplnrbhu4145 mutant embryos compared to WT ( wild type ) at 48 hpf ( hours post-fertilization ) . ( F–I ) nkx2 . 5 expression at the 15 somite stage in WT , aplnrbhu4145 , aplnrains , and aplnrains; aplnrbhu4145 mutant embryos . Dorsal view with anterior to the top . ( J–L ) In situ hybridization showing expression of myl7 at 48 hpf in aplnrains and aplnrains; aplnrbhu4145 embryos compared to WT when viewed from the anterior . ( M–P ) Comparison of sox17 expression at 8 hpf between WT , aplnrains , aplnrbhu4145 and aplnrains; aplnrbhu4145 mutant embryos . Dorsal views are shown with a lateral view in inset panels . DOI: http://dx . doi . org/10 . 7554/eLife . 13758 . 00310 . 7554/eLife . 13758 . 004Figure 1—figure supplement 1 . aplnra and aplnra; aplnrb double mutant characterization . ( A–D ) Wholemount RNA in situ hybridization ( WISH ) showing expression of sox17 at 8 hpf and myl7 at 24 hpf in aplnramax embryos compared to WT . ( E–F ) Quantification of the number and spread of sox17-positive cells in WT , aplnrains , aplnrbhu4145 and aplnrains; aplnrbhu4145 embryos at 8 hpf . Data are represented as means ± SEM . *p<0 . 05 , **p<0 . 01 , n . s . = not significant unpaired two-tailed t-test . DOI: http://dx . doi . org/10 . 7554/eLife . 13758 . 00410 . 7554/eLife . 13758 . 005Figure 1—figure supplement 2 . aplnra; aplnrb double mutants display defects in endodermal organ development . ( A–I ) WISH showing expression of foxa1 ( A–C ) , foxa2 ( D–F ) and foxa3 ( G–I ) at 48 hpf in WT and aplnrains; aplnrbhu4145 homozygous mutant embryos . P indicates the pancreatic bud , L indicates the liver bud and the arrow indicates the most anterior pharyngeal endoderm . For double mutants , two representative images are shown for each probe ( n=7 for each marker ) . Embryos are visualized ventrally . DOI: http://dx . doi . org/10 . 7554/eLife . 13758 . 005 Double aplnrbhu4145; aplnrains mutants were generated to evaluate functional redundancy for these two paralogues in early development . Double mutant embryos exhibited normal morphology at 2 dpf with pericardial edema ( Figure 1E ) . In contrast to aplnra or aplnrb single mutants , which usually possess significant cardiac tissue at 2 dpf , double mutant embryos exhibited either complete absence of or an extremely small heart ( Figure 1L ) . In addition , double aplnra; aplnrb mutant embryos exhibited a further reduction in both the spread and number of sox17 expressing cells when compared to the aplnrains or aplnrbhu4145 single mutants ( Figure 1M–P and Figure 1—figure supplement 1E–F ) . nkx2 . 5 expression was negligible in double mutants suggesting a near-complete absence of early cardiac progenitors ( Figure 1I ) . It should be noted that the double mutant phenotype faithfully phenocopies that seen with the injection of aplnra/b morpholinos ( MOs ) both at the morphological and molecular levels as seen by the expression of these three diagnostic markers sox17 , nkx2 . 5 and myl7 ( Paskaradevan and Scott , 2012; Chng et al . , 2013 ) . Given the substantial reduction in the number of endodermal progenitors during gastrulation in aplnra; aplnrb double mutants , we investigated the subsequent effects on development of the endodermal-derived organs . The morphology of the gut tube was examined by performing wholemount RNA in situ hybridization ( WISH ) for foxa1 , foxa2 and foxa3 at 48 hpf . While the pharyngeal endoderm appeared to be primarily intact , the most anterior population of these cells appeared to be either dramatically reduced or absent in double mutants ( Figure 1—figure supplement 2A–I ) . Furthermore , the liver and pancreatic buds were consistently found to be smaller or absent , with misorientation of the pancreatic bud evident in some embryos . Taken together , this data suggests that Aplnra is required for both proper endoderm differentiation and cardiac development and that Aplnra and Aplnrb have redundant roles in these early processes . To gain insight into how Aplnr signaling regulates early cardiac development , we pursued a gene expression profiling approach . Comparative microarray analysis at 50% epiboly ( 5 hpf ) of cDNA from WT and double aplnra/b morphants ( injected with MOs ) embryos revealed a reduction in a set of genes known to be downstream of the Nodal signaling pathway . Previous work has identified 72 Nodal-regulated genes in zebrafish at 6 hpf ( Bennett et al . , 2007 ) . Remarkably nearly one-third ( 23 out of 72 ) of these genes were down-regulated in aplnra/b morphants ( Figure 2A ) . The estimated probability of observing such a large overlap by chance is very small ( 8 . 6 × 10-17 by hypergeometric distribution ) , suggesting that this overlap might be biologically significant , i . e . that Nodal signaling is decreased in the absence of Aplnr function . Conversely , using Gene Set Enrichment Analysis ( GSEA ) ( Subramanian et al . , 2005 ) , we found that the genes downregulated in aplnra/b morphants were significantly enriched for genes upregulated in zebrafish sphere stage embryos injected with sqt mRNA , which encodes for one of the activating Nodal ligands Squint ( Nelson et al . , 2014 ) , further substantiating our hypothesis that Aplnra/b promotes Nodal signaling ( Figure 2—figure supplement 1A ) . Given the known role of Nodal signaling in induction and migration of the mesendoderm ( Carmany-Rampey and Schier , 2001; Feldman et al . , 2000; Gritsman et al . , 1999 ) , we surmised that Aplnr might work upstream , or in parallel , to the Nodal pathway . To more directly assess Nodal signaling levels in the embryo , we injected a Nodal/TGFβ luciferase reporter construct into WT , aplnra/b MOs , oep MO and lefty1/2 MOs injected embryos . At 30% epiboly ( 4 . 7 hpf ) , embryos injected with MOs against the Nodal pathway antagonist lefty1/2 had higher levels of Nodal/TGFβ luciferase reporter activity , while those injected with a MO against the essential Nodal co-receptor oep ( also known as cripto or tdgf1 ) had lower levels ( Figure 2B ) . Consistently , aplnra/b morphant embryos exhibited significantly lower levels of Nodal/TGFβ reporter activity compared to WT , indicating a reduction in Nodal signaling in these embryos , in agreement with our microarray results . We next sought to confirm attenuated Nodal signaling following aplnra/b knockdown by means of chemical inhibition . Embryos were incubated from the sphere stage ( 4hpf ) onwards with increasing concentrations of SB431542 , which acts as a dedicated Alk4/5/7 antagonist . Phenotypes were scored according to the severity of cyclopia , a hallmark feature of Nodal impairment in zebrafish ( Gritsman et al . , 1999; Rebagliati et al . , 1998 ) , at 2 dpf ( Figure 2C ) . Following multiple independent tests ( N=4 ) aplnra/b morphants were found to be significantly more sensitive to SB431542 treatment than were WT embryos ( Figure 2D–E ) , suggesting reduced ( but not absent ) Nodal signaling levels when Aplnr is lost . 10 . 7554/eLife . 13758 . 006Figure 2 . Aplnr deficient embryos exhibit a reduction in Nodal signaling . ( A ) List and Venn diagram of 23 Nodal target genes found to be down-regulated in a microarray of aplnra/b morphant embryos compared to WT at 50% epiboly ( 5 . 25 hpf ) . ( B ) Relative luciferase activity regulated by the Activin response element ( ARE ) in WT , aplnra/b morpholino ( MO ) , oep MO and lefty1/2 MO injected embryos at 30% epiboly ( 4 . 7 hpf ) . Data are represented as means ± SEM . *p<0 . 05 unpaired two-tailed t-test . ( C–E ) Phenotypic characterization of WT ( D ) and aplnra/b morphant embryos ( E ) when treated with the indicated concentration of the Alk4/5/7 inhibitor SB431542 from the sphere stage ( 4 hpf ) onwards . ( F–S ) Visualization of the expression of the canonical nodal target genes gsc , flh and sox32 in WT ( F , J , N ) , aplnra/b MOs injected ( G , K , O ) , aplnrains; aplnrbhu4145 double mutant ( H , L , P ) and aplnrb RNA injected treated with the Aplnr agonist ML233 ( I , M , Q ) embryos at 8 hpf . Embryos are viewed from the dorsal side . Quantification of the number and spread of sox32 expressing cells ( R , S ) . Data are represented as means ± SEM . *p<0 . 05 , **p<0 . 01 unpaired two-tailed t-test . DOI: http://dx . doi . org/10 . 7554/eLife . 13758 . 00610 . 7554/eLife . 13758 . 007Figure 2—figure supplement 1 . Loss of aplnr affects Nodal target gene expression . ( A ) Gene set enrichment analysis ( GSEA ) of genes downregulated in aplnra/b morphants when compared against genes upregulated in zebrafish sphere stage ( 4 hpf ) embryos injected with sqt mRNA . ( B–G ) WISH showing expression of lefty1 at 4 hpf ( B , E ) and 5 . 5 hpf ( C , F ) and lefty2 at 5 . 5 hpf ( D , G ) in aplnra/b morphant embryos when compared to WT from an animal pole ( top of embryo ) view . DOI: http://dx . doi . org/10 . 7554/eLife . 13758 . 00710 . 7554/eLife . 13758 . 008Figure 2—figure supplement 2 . Aplnr activation enhances Nodal target gene expression . ( A–I ) Expression of the canonical Nodal target genes gsc , flh and sox32 in WT ( A–C ) , aplnrb RNA injected embryos ( D–F ) and embryos treated with the Aplnr agonist ML233 ( G–I ) at 8 hpf . Embryos are viewed from the dorsal side with anterior to the top . ( J–O ) Expression of the canonical Nodal target genes gsc , flh and sox32 in aplnra/b morphant embryos with or without ML233 at 8 hpf . Embryos are viewed from the dorsal side with anterior to the top . ( P–U ) Expression of the canonical Nodal target genes gsc , flh and sox32 in oep morphant embryos with or without the injection of aplnrb RNA and addition of ML233 at 8 hpf . Embryos are viewed from the dorsal side with anterior to the top . DOI: http://dx . doi . org/10 . 7554/eLife . 13758 . 008 We next used WISH to assess the expression of direct downstream targets of Nodal in double aplnra/b morphant embryos . The canonical Nodal target genes floating head ( flh ) , goosecoid ( gsc ) and sox32 ( Gritsman et al . , 1999; Chen and Schier , 2001 ) all showed reduced expression in aplnra/b morphant embryos at 8 hpf relative to WT embryos ( Figure 2F–G , J–K , N–O ) . Analysis of sox32 expression , which marks endodermal precursors , revealed both a reduced number of endoderm cells and a decreased extent of endodermal migration ( quantified in Figure 2R–S ) , consistent with previous analysis of aplnrb mutants ( Pauli et al . , 2014; Chng et al . , 2013 ) . Down-regulation of all three genes was also documented in aplnra; aplnrb double mutant embryos ( Figure 2H , L , P ) . Further analysis of additional Nodal target genes lefty1 and lefty2 also revealed a decrease in expression before and at the beginning of gastrulation ( Figure 2—figure supplement 1B–G ) . We next examined if the ectopic activation of Aplnr could be sufficient to increase the expression of Nodal target genes . Overexpression of the ligands of Aplnr , Elabela and Apelin , each result in phenotypes similar to Aplnr loss-of-function , possibly as a consequence of ligand-mediated receptor internalization and signal desensitisation ( Scott et al . , 2007; Zeng et al . , 2007; Paskaradevan and Scott , 2012; Pauli et al . , 2014; Chng et al . , 2013 ) . To bypass this limitation , we instead used ML233 , a non-peptide small molecule agonist of Aplnr signaling ( Khan et al . , 2011 ) . Treating embryos injected with 150 pg of aplnrb RNA with 2 . 5 μM of ML233 resulted in a significant increase in the expression of the three Nodal targets gsc , flh and sox32 relative to WT ( Figure 2I , M , Q ) and increased both the number of endoderm cells and the extent of migration to a more anterior position ( Figure 2R–S ) . Overexpression of aplnrb , or ML233 treatment alone , resulted in increased flh and gsc expression , whereas sox32 expression and endoderm migration was largely unaffected ( Figure 2—figure supplement 2A–I ) . ML233 had no effect on gsc , flh or sox32 expression in aplnra/b morphants , indicating that the action of ML233 is Aplnr-dependent ( Figure 2—figure supplement 2J–O ) . Moreover , overexpression of aplnrb in oep-depleted embryos was not sufficient to induce expression of gsc , flh or sox32 , even in the presence of ML233 ( Figure 2—figure supplement 2P–U ) . This argues against a scenario where Aplnr signaling is acting in parallel to Nodal signaling . Taken all together , these data suggest that Aplnr signaling is sufficient to boost endogenous levels of Nodal signaling at gastrulation stages . As Nodal signaling is reduced in aplnr mutant embryos , we reasoned that increasing Nodal may ameliorate or rescue cardiogenesis in the absence of Aplnr function . To test this hypothesis , we took two complementary approaches . We first elevated the levels of endogenous Nodal signaling by injecting a MO against lefty1 , a direct negative feedback Nodal antagonist ( Feldman et al . , 2002 ) . lefty1 MO was injected into embryos bearing two different aplnrb mutant alleles , s608/grinch ( p . W90L ) and hu4145 ( p . W54X ) , which exhibit a small heart ( Figure 3A–C ) . While the penetrance of the heart phenotype varied within each clutch , lefty1 MO treatment was capable of rescuing cardiogenesis in both mutants , with nearly all embryos showing rescue of the small heart phenotype ( Figure 3D , note that 25% of embryos in a given cross would be homozygous null mutants , data for 2 independent clutches per mutant is shown ) . One caveat of this approach is that aplnra/b gene expression is regulated by Nodal signaling ( D'Aniello et al . , 2009; Pauli et al . , 2014 ) . It is therefore conceivable that elevating Nodal levels in aplnrb single mutants provides rescue simply by elevating aplnra gene expression . To address this issue , lefty1 MO was injected into embryos generated from an in-cross of aplnra; aplnrb heterozygous parents . Embryos were evaluated for heart formation by WISH for myl7 gene expression and subsequently genotyped . Strikingly , over 60% of aplnra/b double mutants exhibited proper cardiac formation when injected with lefty1 MOs , which was not observed in un-injected mutant siblings ( Figure 3E–H ) . This suggests that elevated Nodal signaling is capable of rescuing the Aplnr cardiac phenotype , even in the complete absence of Aplnr function . 10 . 7554/eLife . 13758 . 009Figure 3 . Elevation of Nodal signaling in aplnr mutant/morphant embryos rescues cardiogenesis . ( AC ) myl7 WISH showing a representative heart phenotype at 48 hpf in a WT embryo ( A ) and two different aplnrb mutant alleles; hu4145 ( B ) and s608/grinch ( C ) . Anterior is oriented towards the left . ( D ) Quantification of the number of embryos with a small heart at 48 hpf from individual clutches of embryos in which half were injected with lefty1 MO . Clutches were obtained from crosses of two different aplnrb heterozygous mutants ( hu4145 and s608/grinch as indicated ) . ( E–H ) Classification of heart phenotype in aplnrains; aplnrbhu4145 double mutant embryos at 48 hpf when injected with lefty1 MO as compared to un-injected embryos . Severity of cardiac phenotypes was scored based on myl7 WISH ( H ) . ( I ) Schematic displaying the transplantation of injected donor cells into the margin of host embryos . Contribution of transplanted cells to the heart is scored based on expression of the myl7:EGFP transgene in donor cells . ( J–N” ) Margin transplants of WT or tar* ( activated Nodal receptor ) overexpressing myl7:EGFP cells into WT or aplnra/b morphant embryos at 48 hpf . Arrow indicates the heart . Embryos are displayed from a lateral view with the anterior of the embryo towards the right . Data are represented as means ± SEM . *p<0 . 05 , n . s . = not significant , Tukey’s Multiple Comparison test following significant ( p<0 . 05 ) one way ANOVA . ( O–-R ) Gross morphology and myl7 expression at 24 hpf in WT ( O ) , embryos injected with a sub-optimal dose of oep MOs ( P ) , aplnra/b morphant embryos ( Q ) and aplnra/b/oep morphant embryos ( R ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13758 . 009 As a complementary approach , we attempted to specifically elevate the levels of Nodal signaling within lateral margin cells and see if this rescued cardiac contribution . To perform this experiment , donor cells from myl7:EGFP transgenic embryos either injected or uninjected with taram-a* ( tar* ) RNA encoding a hyper-activated Nodal receptor ( Renucci et al . , 1996 ) were transplanted to the margin of aplnra/b morphant hosts ( Figure 3I ) . As we have previously shown ( Scott et al . , 2007; Paskaradevan and Scott , 2012 ) , WT cells placed at the margin of aplnra/b morphant hosts contributed to the myocardium at an appreciably reduced frequency ( 7 . 8% , N=3 , n=157 ) as compared to when WT hosts were used ( Figure 3J ) . In contrast , tar* overexpressing cells , when transplanted to the margin of aplnra/b morphant embryos , contributed to the myocardium at a much higher frequency ( 20 . 7% , N=3 , n=164 ) with no significant difference when compared to transplantation of WT cells into WT hosts ( 22 . 5% , N=4 , n=234 ) , suggesting a near complete rescue ( Figure 3J–N” ) . It should be noted that transplantation of WT tar* expressing cells into the margin of WT embryos did not increase the contribution of donor cells to the heart ( 22% , N=4 , n=213 ) . These experiments further argue that the heart defects observed in aplnr deficient embryos are suppressed if Nodal signaling is increased . To confirm that the absence of Aplnr results in lower Nodal activity , which consequently reduces/eliminates cardiogenesis , we further compromised Nodal signaling by partially depleting the embryo of Oep , the obligate Nodal co-receptor ( Gritsman et al . , 1999 ) . In conditions where oep MO injections induced cardia bifida ( Figure 3O–P ) , we observed that triple aplnra/aplnrb/oep-depleted embryos displayed more severe defects , including cyclopia , and were completely devoid of myl7 expression at 1 dpf ( Figure 3Q–R ) . Collectively these results argue that Aplnra/b directly promotes Nodal signaling to ensure proper heart formation , with lower levels of Nodal signal being received by presumptive cardiac progenitors in the absence of Aplnr function . We next assessed how a reduction in Nodal signaling may cause a delay in mesendodermal ingression during gastrulation . Previous work has shown that Nodal target genes become activated depending on the dose and/or associated time of exposure to the Nodal ligand ( Hagos and Dougan , 2007 ) . We hypothesized that Aplnr may be required to boost the Nodal signal in order to activate the expression of genes required for ingression at the right time . A particularly interesting category of genes that were down-regulated in the double aplnra/b morphant microarray was the mesp family of transcription factors ( Figure 4—source data 1 ) . In mice , Mesp1/2 have been shown to regulate the migration of mesoderm through the primitive streak during gastrulation and are essential for cardiac formation ( Kitajima et al . , 2000 ) . Treatment of 4 . 5–55-5 . 25 hpf WT embryos with Nodal inhibitor SB505124 completely abrogated mespaa and mespab expression ( Figure 4A and Figure 4—figure supplement 1A ) . In addition , animal cap transplants of sqt-overexpressing cells induced mespaa/mespab expression , demonstrating that mesp genes are bona fide Nodal targets ( Figure 4B and Figure 4—figure supplement 1B ) . By WISH , we confirmed that both mespaa and mespab are expressed around the margin during gastrulation ( Cutty et al . , 2012; Sawada et al . , 2000 ) and that their expression is dramatically decreased in aplnra/b morphant embryos at 50% epiboly ( 5 . 25 hpf , Figure 4C–D and G–H ) . By carefully examining the expression pattern of mespaa/ab throughout development in WT and aplnra/b morphants , we found that at shield stage ( 6 hpf , 45 min after 50% epiboly ) expression of both mespaa and mespab appeared to largely recover in aplnra/b morphant embryos ( Figure 4E–F and I–J ) . Embryos at both stages were stage matched based on morphology , arguing against a general developmental delay as the cause of this phenotype . This demonstrates that Aplnr is required for timely activation of mesp genes and that the loss of Aplnr results in a delay in activation , rather than a general attenuation of Nodal target gene expression . 10 . 7554/eLife . 13758 . 010Figure 4 . Loss of aplnr results in a delay in mesp gene expression and the attenuation of Sqt and Cyc activity in a non-cell autonomous manner by aplnr . ( A ) Animal view of mespaa expression at 50% epiboly ( 5 . 25 hpf ) in embryos treated with 10 μM of SB505124 from 4–5 . 25 hpf . Animal cap view with dorsal to the bottom . ( B ) Animal view of mespaa expression at 50% epiboly ( 5 . 25 hpf ) in embryos in which cells expressing 4 pg of sqt RNA were transplanted into the animal cap . Animal cap view with dorsal to the bottom . ( C–J ) Expression of mespaa and mespab at 50% epiboly ( 5 . 3 hpf ) ( C , D , G , H ) and the shield stage ( 6 hpf ) ( E , F , I , J ) in WT and aplnra/b morphant embryos when examined by WISH . Embryos are viewed from the animal pole with dorsal at the bottom . ( K ) Animal cap transplant of GFP expressing control cells detected by WISH . ( L–T ) Animal cap transplants of sqt or cyc overexpressing cells into WT ( L–O ) or aplnra/b morphant embryos ( P–S ) at 5 . 5 hpf . gsc and ntl expression is displayed in blue and gfp expressing donor cells are marked in brown . Both donor cells and hosts are of the same background ( WT into WT or morphant into morphant ) . Embryos are viewed from the animal pole with dorsal at the bottom . Data are represented as means ± SEM . ( U–X ) Animal cap transplants of cells expressing high levels of sqt RNA at 5 . 5 hpf . ntl expression is visualized in blue and gfp expressing donor cells are brown . Four different combinations of donor/host cells were examined , WT into WT ( donor into host ) ( U ) , aplnra/b morphant into WT ( V ) , WT into aplnra/b morphant ( W ) and aplnra/b morphant into morphant ( X ) . Donor cells are circled in white . Embryos are viewed from the animal pole with dorsal at the bottom . DOI: http://dx . doi . org/10 . 7554/eLife . 13758 . 01010 . 7554/eLife . 13758 . 011Figure 4—source data 1 . Microarray data for mesp family genes on aplnra/b morphant array . Fold change for all mesp family probes present on the microarray . Fold change is comparing WT to aplnra/b morphant embryos from normalized data from four biological replicates . DOI: http://dx . doi . org/10 . 7554/eLife . 13758 . 01110 . 7554/eLife . 13758 . 012Figure 4—figure supplement 1 . mespaa and mespab are Nodal target genes and Nodal ligand expression is not affected in aplnra/b morphant embryos . ( A ) Animal view of mespab expression at 50% epiboly ( 5 . 25 hpf ) in embryos treated with 10 μM of SB505124 from 4–5 . 25 hpf . Animal cap view with dorsal to the bottom . ( B ) Animal view of mespab expression at 50% epiboly ( 5 . 25 hpf ) in embryos in which cells expressing 4 pg of sqt RNA were transplanted into the animal cap . Animal cap view with dorsal to the bottom . ( C–N ) Lateral view of the Nodal ligands sqt ( C–H ) and cyc ( I–N ) expression at sphere ( 4 hpf ) , 30% epiboly ( 4 . 7 hpf ) and 50% epiboly ( 5 . 25 hpf ) in WT and aplnra/b morphant embryos . DOI: http://dx . doi . org/10 . 7554/eLife . 13758 . 01210 . 7554/eLife . 13758 . 013Figure 4—figure supplement 2 . Aplnr is required to enhance Nodal signaling for proper cardiac development . ( A–B ) Model for the Nodal-mediated nature of the Aplnr phenotype . In WT embryos , Aplnr is required for the appropriate Nodal threshold to be reached in order to initiate the expression of the downstream transcriptional program to drive the ingression of cardiac progenitors and their migration towards the anterior of the embryo . Threshold refers to the integrated level of Nodal signaling that a cell is required to receive in order to activate this program . In aplnr mutant embryos it takes a longer period of time in order for this threshold to be reached and results in a delay in gene expression and ingression of the mesendoderm . As a result cardiac progenitors are unable to migrate all the way to the heart forming regions . Red circle denotes the location of cardiac progenitors in the embryo . ( C–D ) Model for the autonomy of Aplnr function in the context of regulating Nodal signaling . Aplnr appears to be required not in Nodal secreting cells or the cardiac progenitor cells receiving the signal , but instead in the surrounding environment . This role is important for the activation of the transcriptional program required for development of cardiac progenitor cells ( CPCs ) . In the absence of Aplnr , the Nodal signal received by the cardiac progenitor is diminished ( demonstrated by a reduction in the colour of the green Nodal ligands ) . This may reflect improper ligand processing or activity . DOI: http://dx . doi . org/10 . 7554/eLife . 13758 . 013 We next sought to determine if Aplnr might directly act on the Nodal signaling pathway . An examination by WISH revealed that in aplnra/b morphants cyc and sqt expression appeared increased at 4 hpf , and normal at 4 . 7–5 . 3 hpf ( Figure 4—figure supplement 1C–N ) , suggesting that attenuated Nodal signaling was not a consequence of reduced ligand expression . To address possible effects of Aplnr on Nodal signal transduction , we took advantage of a previously developed zebrafish Nodal point source assay ( Chen and Schier , 2001 ) . Nodal overexpressing cells were transplanted into the animal cap of host embryos and the ability to induce target genes was subsequently evaluated ( Figure 4K ) . While in WT ( WT host and donor ) embryos 4 pg of sqt RNA was found to be sufficient to induce gsc expression , this dose was not sufficient to achieve gsc induction in aplnra/b morphant ( host and donor ) embryos ( Figure 4L , P and T ) . To evaluate if this was a complete loss of Sqt activity or simply a reduction , we looked at the ability to induce the low threshold Nodal target gene ntl ( Chen and Schier , 2001 ) . In contrast to gsc , ntl expression was induced at a high frequency in both WT and morphant embryos , demonstrating that the loss of Aplnr resulted in an attenuation but not a complete loss of Sqt activity ( Figure 4M , Q and T ) . Similarly , 20 pg of cyc RNA was less effective at inducing both gsc and ntl in aplnra/b morphant embryos , suggesting that Aplnra/b regulates the activity of both Nodal ligands ( Figure 4N–O , R–S and T ) . To investigate whether Aplnr is required cell autonomously for its effect on Nodal signaling a further series of transplantation experiments were conducted . For these experiments , a higher dose of sqt RNA ( 40 pg ) was injected in donor embryos , which when transplanted into recipient embryos induced a ring of ntl expression in the host cells surrounding the donor cells ( Figure 4U , donor cells are encircled ) . While both WT and aplnra/b morphant donor cells induced a strong ring of ntl expression when transplanted into WT host embryos , a significantly smaller expression domain was induced when these cells were transplanted into aplnra/b morphant host embryos ( Figure 4V–X ) . This suggests that Aplnra/b activity is not strictly required in Nodal secreting cells for proper Nodal signaling .
The link between the Aplnr defect and Nodal signaling fits well into the context of previously published literature . Nodal signaling establishes the mesendoderm and a loss of Nodal signaling or its downstream transcriptional effectors results in a heartless phenotype ( Feldman et al . , 2000; Kunwar et al . , 2003 ) . Furthermore , cells lacking the functional Nodal co-receptor oep are unable to internalize during gastrulation and cannot contribute to the mesoderm or endoderm ( Carmany-Rampey and Schier , 2001 ) . These cells stay at the margin and continue to move towards the vegetal pole . Likewise , aplnra/b morphant cells display delayed ingression kinetics that do not support proper cardiac development ( Paskaradevan and Scott , 2012 ) . However , unlike the loss of Oep , loss of Aplnr results in a partial and not total loss of Nodal activity , and mesendodermal ingression is evident , albeit at a later time . This also provides a plausible explanation for the incomplete penetrance of the cardiac phenotype observed in aplnra and aplnrb mutant embryos ( Scott et al . , 2007; Chng et al . , 2013 ) . In mouse embryonic stem cells , graded Nodal signaling over 18 hr regulates differentiation to mesendodermal fates , with very subtle ( two-fold ) changes in levels of phospho-Smads having profound effects ( Lee et al . , 2011 ) . As gastrulation proceeds far more quickly in zebrafish than it does in mice , this may also explain why profound cardiogenesis defects are not frequently seen in Aplnr/Apj mutant mice ( Kang et al . , 2013 ) . Our report of the targeted genetic knockout of zebrafish aplnra supports the notion that both paralogues fulfil a common role during cardiogenesis . In contrast to previous work with aplnra MOs ( Nornes et al . , 2009 ) , we find that aplnra mutants do not have epiboly defects , but rather only share features of the aplnrbgrinch/hu4145 cardiac and endoderm phenotypes . As loss of either aplnra or aplnrb can have effects on cardiac progenitor specification , we hypothesize that both act in concert to modulate Nodal signaling , with loss of either potentially resulting in a sufficient decrease to impinge on cardiogenesis . It has been previously demonstrated that distinct Nodal target genes require different Nodal activity thresholds for activation ( Chen and Schier , 2001; Lee et al . , 2011 ) . The level of Nodal signal that a cell perceives depends on both the concentration and duration of the signal ( Hagos and Dougan , 2007; Dubrulle et al . , 2015 ) . In aplnr mutants , given the reduction in Nodal signaling , marginal cells likely require a longer exposure to Nodal ligands before a certain threshold is reached to induce migration and mesp expression . This may explain why endodermal progenitors do eventually migrate and why mesp expression recovers in aplnr morphant embryos . In support of this hypothesis , mesp expression in aplnr morphant embryos at 50% epiboly appears to be retained in the most marginal blastomeres , consistent with the fact that these blastomeres are closest to the source of Nodal ligand . The basic helix-loop-helix Mesp transcription factor family has been shown to regulate the migration of mesoderm through the primitive streak in mice , and play key roles in cardiac development in several contexts ( Kitajima et al . , 2000; Saga et al . , 1999; Satou et al . , 2004; Bondue et al . , 2008 ) . This provides a molecular mechanism for how reduced levels of Nodal may translate into a delay of cell movement during gastrulation . However , we do not believe that defects in mesp expression can fully account for the aplnr cardiac phenotype . In our hands , mespaa overexpression was not sufficient to rescue cardiogenesis in aplnr morphants or mutants ( A . R . D . and I . C . S . , unpublished results ) . On the other hand , finer temporal expression of mesp expression may be required for proper cardiac specification . The cellular autonomy of Aplnr function in cardiac progenitor development has been an area of confusion , notably as both cell autonomous ( Scott et al . , 2007; Zeng et al . , 2007 ) and non-autonomous ( Paskaradevan and Scott , 2012 ) roles have been documented . Our results clearly show that Aplnr is not absolutely required in cells expressing Nodal ligands for signaling activity ( Figures 4U–X ) , arguing against a model where Aplnr affects ligand secretion . Given that our previous experiments suggest that the Aplnr is also not required in cardiac progenitors themselves , we do not favour a model where Aplnr is required cell autonomously for reception or readout of the Nodal signal . It is possible that rather than playing a strictly cell autonomous ( in cardiac progenitors ) or non-autonomous ( in Nodal signal sending cells ) role , a threshold level of Aplnr activity is required in cells surrounding cardiac progenitors to ensure that proper levels of Nodal signaling can take place ( Figure 4—figure supplement 2C–D ) . This model helps explain previous conflicting results , in which aplnra/b morphant donor cells typically have a reduced , but not completely absent , ability to develop as cardiomyocytes ( Scott et al . , 2007; Paskaradevan and Scott , 2012 ) . It may be that in cases where a larger donor clone lacking Aplnr function is assayed , many cells inside the clone ( encompassing both cardiac progenitors and other cells ) do not receive the proper Nodal signal for cardiogenesis . Aplnr signaling may act , for example , to regulate Nodal ligand processing or activity , which has been shown to occur extracellularly ( Beck et al . , 2002 ) . The interpretation of transplant experiments may therefore be confounded by the size of donor tissue . The role of a “community effect” in amplifying the Nodal signal to drive collective epithelial-to-mesenchymal transition during gastrulation has recently been described ( Voiculescu et al . , 2014 ) . As Aplnr is both activated by Nodal ( at the level of gene expression ) and in turn potentiates Nodal signaling , this may provide a feed-forward mechanism to help achieve maximal Nodal signaling for proper gastrulation in a timely manner . Mechanistically , how Aplnr activity impinges on the Nodal pathway remains to be determined . Signaling cascades downstream of Aplnr , both G protein-dependent and -independent , have been described ( reviewed in ( Chapman et al . , 2014; O'Carroll et al . , 2013 ) . Which of these are required for Aplnr function in cardiac development , or if a new pathway is involved , remains to be elucidated . How signaling at the level of the Aplnr happens in the context of early gastrulation also remains unknown . Numerous studies have described roles for the classical Apelin/Aplnr hormone GPCR ( G-protein Coupled Receptor ) signaling pair in adult physiology , however in the context of early heart development Apelin does not appear to be the correct Aplnr ligand ( Scott et al . , 2007; Chng et al . , 2013; Ashley et al . , 2005; Kuba et al . , 2007; Szokodi et al . , 2002 ) . This has been confirmed by the recent discovery of a second small endogenous peptide ligand for Aplnr , Elabela ( www . elabela . com ) , whose mutation also results in loss of cardiac differentiation in zebrafish ( Pauli et al . , 2014; Chng et al . , 2013 ) and mice ( unpublished results L . H and B . R ) . How these two ligands may fit into the Aplnr regulation of Nodal remains an intriguing area for future investigation . In conclusion , we find that Aplnr is required to enhance Nodal signaling in order to activate genes required for proper cell movement and consequently cardiac development at the right time . This work opens several lines of future investigation on the early events required for the movement of the mesendoderm during gastrulation and early cardiac progenitor development . The levels and timing of key signaling pathways such as Nodal/TGFβ are essential to developmental output , as can be measured during differentiation of pluripotent stem cells in culture ( Kattman et al . , 2011 ) . Similar mechanisms to that described here for Aplnr signaling may therefore remain to be discovered for other major developmental pathways . As to why lateral populations are specifically affected in aplnra/b mutants and not dorsal ones , where Nodal signaling is particularly prevalent , we speculate that the levels of Nodal modulated by Aplnr will not have as large a consequence in a high Nodal signaling environment like the shield/dorsal aspect of the embryo . Furthermore , only aplnra and not aplnrb is expressed in the dorsal part of the embryo ( Tucker et al . , 2007 ) . Further , given that Aplnr signaling has been shown to regulate multiple aspects of adult physiology ( reviewed in ( Chapman et al . , 2014; O'Carroll et al . , 2013 ) , the role of this novel signaling mechanism and the potential functions of Elabela and/or Apelin in the context of physiological homeostasis and disease ( Ho et al . , 2015; Murza et al . , 2016 ) are areas of great interest .
Zebrafish were housed and handled as per Canadian Council on Animal Care and Hospital for Sick Children Laboratory Animal Services guidelines . Zebrafish embryos were raised at 28 degrees Celsius according to standard techniques ( Westerfield and Book , 1993 ) . The Tg ( myl7:EGFP ) twu34 line and aplnrbs608 ( grinch , p . W90L ) mutants have been previously described ( Scott et al . , 2007; Chng et al . , 2013; Huang et al . , 2003 ) . aplnrbhu4145 ( p . W54X ) fish were a gift from Stefan Schulte-Merker . In the aplnrbhu4145allele a STOP codon is introduced at the 54th amino acid , resulting in a severely truncated protein with no predicted function . A loss-of-function aplnramax mutant line was generated with TALEN pairs purchased from ToolGen ( South Korea ) . The TALEN-binding sites are as follows: 5’ TACACCGAGACATACGATTA 3’ and 5’ TCACACCCAGAGTCATTATA 3’ . An additional aplnrainsmutant allele was purchased from Znomics , Inc . ( Portland , OR ) . aplnraZM00177433Tg has a ( c . 886_887insTg ( ZM ) ) retroviral insertion ( Amsterdam and Hopkins , 2006 ) that disrupts the ORF of the single coding exon . Imaging was performed using a Leica DFC320 camera on a Leica M205FA stereomicroscope . In the aplnrbhu4145 mutant allele a premature stop codon has been induced into the coding sequence at amino acid position 54 . Primers used for genotyping are used to amplify a 215 bp product which when cut with AciI yields fragments of 140 bp , 52 bp and 25 bp , the mutant allele will not be cut with AciI . Forward primer: CATCTTCATCCTGGGACTCACTG Reverse primer: AGCACCACATAGCTGCTGATCTT . For genotyping the allele of aplnrb grinch the same primers were used as for the hu4145 allele , but the resultant PCR product was instead cut with EaeI , generating a 141 bp of the 215 bp product in the mutant allele . Genotyping the aplnraZM00177433Tg allele was performed using the following primers to detect the presence of the insertion: Forward primer: ACCCTGGAAACATCTGATGGTTC; Reverse primer: AACGGATTGAGGCAGCTGTTGAC . To determine the presence of the WT aplnra allele the following forward primer is used instead: Forward primer: CTCGGGTTTCTTCTGCCTTTCCT . Genotyping of the aplnramaxallele was performed using the following primers to detect the absence or presence of the deletion . Forward Primer: CGCTTCAGCTTCCAGTGAG; Reverse Primer: ATGTTGACCAGCACCACGTA . To determine for the presence of the WT aplnra allele the following forward primer was added: Forward Primer: CACCGAGACATACGATTACTACG . To determine for the presence of the aplnramax allele the following forward primer was added: Forward Primer: CACCGAGACATACGATTACTACTG . An Agilent zebrafish microarray ( V3: 026437 ) was used to compare the gene expression profile of WT vs aplnra/b morphant embryos . 4 replicates were performed and for each experiment 20 embryos were collected at 50% epiboly and total RNA was prepared using the RNAqueous kit ( Ambion , Waltham , MA ) . Microarray results were analyzed using Genespring v11 . 0 . 1 ( Agilent Technologies , Inc . , Santa Clara , CA ) . As recommended by the manufacturer , the data was normalized using Agilent’s Spatial Detrending Lowess normalization . All data analysis was performed on log2-transformed data . Standard single factor t-tests were used followed by ranking with fold changes . After normalization and averaging the four chips , the data was filtered to remove the probes that showed no signal in order to avoid confounding effects on subsequent analysis ( probes below the 20th percentile of the distribution of intensities were removed ) . MIAME-compliant microarray data was submitted to GEO ( accession #GSE58683 ) . GSEA was performed with default settings using aplnra/b MO-downregulated genes as a custom gene set for comparison against the sqt overexpression dataset from #GSE51890 . Embryos were injected at the one cell stage according to standard procedures . Translation blocking MOs against aplnra ( 5’ – cggtgtattccggcgttggctccat – 3’ ) and aplnrb ( 5’ - agagaagttgtttgtcatgtgctc – 3’ ) have been previously described ( Scott et al . , 2007 ) . aplnr morphant embryos were co-injected with 0 . 5 ng of aplnrb MOs and 1ng of aplnra MOs . The translation blocking MOs against lefty1 ( 5’ – cgcggactgaagtcatcttttcaag – 3’ ) has been previously described ( Feldman et al . , 2002 ) . lefty1 morphant embryos was injected with 6 ng of MOs per embryo . Translation blocking MOs against oep ( 5’ - gccaataaactccaaaacaactcga – 3’ ) has been previously described ( Feldman and Stemple , 2001 ) , with 2 . 5 ng injected per embryo . In vitro transcribed RNA was prepared using the mMessage Machine Kit ( Ambion ) and purified using the MegaClear kit ( Ambion ) . 0 . 5 pg of tar* RNA was injected per donor embryo ( Renucci et al . , 1996 ) . WISH was carried out using DIG labelled antisense probes as previously described ( Thisse and Thisse , 2008 ) . Double ISH was performed against a fluorescein-labelled gfp S65C probe using previously established protocols ( Zhou et al . , 2011 ) . Fluorescein-labelled probes were detected using INT/BCIP and DIG labelled probes with NBT/BCIP . Probes for mespaa , mespab , sqt and cyc were prepared from templates containing full length coding sequences . Probes for myl7 , nkx2 . 5 , gsc , sox17 , sox32 , foxa1 , foxa2 , foxa3 and flh have been previously described ( Kikuchi et al . , 2001; Schulte-Merker et al . , 1994; Talbot et al . , 1995; Chen and Fishman , 1996; Alexander et al . , 1999; Yelon et al . , 1999; Inohaya et al . , 1997; Akimenko et al . , 1994; Odenthal and Nüsslein-Volhard , 1998 ) . ImageJ analysis software was used to document sox17 and sox32 cell numbers and spread . Transplantation was performed as previously described ( Scott et al . , 2007 ) . Donor embryos were injected with 5% tetramethylrhodamine dextran ( 10 , 000 MW , Molecular Probes , Waltham , MA ) as a lineage tracer . Transplants were performed by placing 10–20 cells into the margin or animal cap of a host embryo at the sphere stage ( 4 hpf ) . For cyc/sqt animal cap transplants 200 pg of gfp S65C RNA was co-injected into donor embryos . Double ISH was performed to visualize donor cells . Nodal inhibition was performed by treating embryos with either SB505124 ( 10 uM ) or SB431542 ( Sigma , St . Louis , MO ) in egg water/0 . 1% DMSO . The APLNR agonist ML233 was obtained from Glixx Laboratories Southborough , MA ) . Embryos were treated with 2 . 5 μM ML233 in egg water/1% DMSO from the sphere stage onwards . Embryos were injected with 90 pg of ARE3-luc ( Huang et al . , 1995 ) , which contains three copies of the Activin responsive element ( ARE ) , together with 10 pg of CMV-pRL vector ( Promega , Madison , WI ) at the 1-cell stage . At 30% epiboly , three groups of 20 embryos were lysed with passive lysis buffer ( Promega ) at room temperature for 20 min . The firefly luciferase activity , normalized to that of Renilla luciferase , was measured using the Dual luciferase assay system ( Promega ) . | In one of the first events that happens as an embryo develops , cells become the different stem cell populations that form the body’s organs . So what makes a cell become one stem cell type rather than another ? In the case of the heart , the first important event is the activity of a signaling pathway called the Nodal/TGFβ pathway . Nodal signaling can drive cells to become many different stem cell types depending on its level of activity . Many different levels of regulation fine-tune Nodal signaling to produce these activity thresholds . Zebrafish that have a mutation in the gene that encodes a protein called the Apelin receptor have no heart . The loss of this receptor interferes with how heart stem cells ( called cardiac progenitors ) are made and how they move to where heart development occurs . Deshwar et al . have now studied mutant zebrafish in order to investigate how the Apelin receptor influences early heart development . This revealed that Nodal signaling levels are slightly lower in the mutant zebrafish embryos than in normal fish at the time when Nodal activity induces cardiac progenitors to form . When Nodal activity is experimentally boosted in zebrafish that lack the Apelin receptor , they become able to develop hearts . Deshwar et al . also found that the Apelin receptor does not work in cells that produce or receive Nodal signals . This suggests that the Apelin receptor modulates Nodal signaling levels by acting in cells that lie between the cells that release Nodal signals and the cardiac progenitors . An important question for future work to address is how this modulation works . As Nodal is a key determinant of many cell types in developing embryos , learning how Apelin receptors regulate its activity could help researchers to derive specific cell types from cultured stem cells for use in regenerative medicine . | [
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NG2 cells , oligodendrocyte progenitors , receive a major synaptic input from interneurons in the developing neocortex . It is presumed that these precursors integrate cortical networks where they act as sensors of neuronal activity . We show that NG2 cells of the developing somatosensory cortex form a transient and structured synaptic network with interneurons that follows its own rules of connectivity . Fast-spiking interneurons , highly connected to NG2 cells , target proximal subcellular domains containing GABAA receptors with γ2 subunits . Conversely , non-fast-spiking interneurons , poorly connected with these progenitors , target distal sites lacking this subunit . In the network , interneuron-NG2 cell connectivity maps exhibit a local spatial arrangement reflecting innervation only by the nearest interneurons . This microcircuit architecture shows a connectivity peak at PN10 , coinciding with a switch to massive oligodendrocyte differentiation . Hence , GABAergic innervation of NG2 cells is temporally and spatially regulated from the subcellular to the network level in coordination with the onset of oligodendrogenesis .
The discovery of bona fide synapses formed on non-neuronal NG2-expressing cells ( Bergles et al . , 2000 ) , the progenitors of myelinating oligodendrocytes , has challenged the dogma that synapses are a unique feature of neurons in the central nervous system . Since then , the existence of functional synapses between neurons and NG2 cells is recognized as a major physiological feature of these cells throughout the brain ( Maldonado and Angulo , 2014 ) . In the somatosensory cortex , NG2 cells receive a major synaptic input from local GABAergic interneurons that disappears after the second postnatal ( PN ) week ( Vélez-Fort et al . , 2010; Balia et al . , 2015 ) . Cortical NG2 cells are , therefore , believed to be transiently embedded in GABAergic microcircuits at a period known to undergo oligodendrocyte differentiation in the neocortex ( Baracskay et al . , 2002 ) . However , while the connectivity patterns between neocortical interneurons and their neuronal partners begin to be elucidated ( Fino and Yuste , 2011; Pfeffer et al . , 2013 ) , the rules governing the GABAergic innervation of NG2 cells in the network are elusive . Cortical GABAergic interneurons are one of the most heterogeneous populations of neurons in the brain ( Cauli et al . , 1997; Petilla Interneuron Nomenclature Group et al . , 2008 ) . Their diversity has been a matter of intense investigation for several decades and is known to impact synaptic signaling and computational capacities of neuronal networks ( Klausberger and Somogyi , 2008; Fishell and Rudy , 2011 ) . Different types of interneurons target specific subcellular compartments of their postsynaptic neuron . Such compartmentalization creates a specific distribution of channels , receptors , and signaling mechanisms and allows for an effective regulation of synaptic integration , plasticity , and spiking ( Huang et al . , 2007 ) . For instance , it has been observed that the localization of different GABAA receptors ( GABAARs ) in neocortical pyramidal neurons is input-specific since presynaptic parvalbumin ( PV ) -positive , fast-spiking cells innervate proximal postsynaptic sites with GABAARs-containing α1 subunits , whereas bitufted interneurons contact postsynaptic sites with GABAARs-containing α5 subunits ( Ali and Thomson , 2008 ) . At a higher level , the connectivity patterns of neocortical interneurons in the network also appear to be highly specific ( Pfeffer et al . , 2013 ) . PV-positive interneurons strongly inhibit one another but provide little inhibition to other subtypes of interneurons , whereas somatostatin-positive interneurons strongly inhibit all other interneurons but are poorly interconnected with each other ( Pfeffer et al . , 2013 ) . Despite the existence of specific connectivity patterns among interneurons , this heterogeneous cell population carves out unspecific and dense connections with pyramidal cells ( Fino and Yuste , 2011; Packer et al . , 2014 ) . Hence , the connectivity of interneurons cannot be generalized and categorized in a simple way . Is interneuron-NG2 cell connectivity governed by any specific rule ? Our knowledge of NG2 cell synaptic physiology and connectivity is still very limited because it derives exclusively from studies based on spontaneous synaptic activity or on averaged synaptic currents generated by the stimulation of unidentified neurons . No information exists on the identity of presynaptic inputs impinging on NG2 cells , the dynamics of their individual synapses , and their microcircuit architectures . Here , we investigate the properties of unitary interneuron-NG2 cell connections during the critical period of NG2 cell differentiation in the somatosensory cortex of Slc32a1-Venus;Cspg4-DsRed transgenic mice ( hereafter called VGAT-Venus;NG2-DsRed mice ) . By combining immunohistochemistry , paired recordings , and holographic photolysis for circuit mapping , our results reveal that interneuron-NG2 cell connections in the developing neocortex form a transient and organized local network that is functional only during the most critical days of cortical oligodendrogenesis . A local microcircuit architecture with interneuron-NG2 cell intersomatic distances never exceeding 70 µm is accompanied with a specific subcellular arrangement of inputs from fast-spiking interneurons ( FSIs ) and from non-fast-spiking interneurons ( NFSIs ) . These two classes of interneurons target different segregated postsynaptic domains containing distinct GABAARs . In conclusion , these progenitors form their own structured network with interneurons whose properties are temporally and spatially regulated in concordance with the onset of oligodendrocyte differentiation process .
To test whether NG2 cells are wired by interneurons in a specific manner , we searched for presynaptic and postsynaptic principles governing individual interneuron-NG2 cell synapses . We performed paired recordings between layer V Venus+ interneurons and DsRed+ NG2 cells in acute somatosensory cortical slices of VGAT-Venus;NG2-DsRed mice from PN8 to PN13 ( Figure 1A—figure supplement 1A ) . Paired recordings allowed us to characterize the action potential firing behavior of the interneuron , the characteristic conductance profile of the NG2 cell , and the specific synaptic properties of the connection ( Figure 1A—figure supplement 1B–G; see ‘Materials and methods’ ) . In 38 out of 147 pairs , action currents elicited in presynaptic interneurons induced inward postsynaptic currents ( PSCs ) sensitive to the GABAAR antagonist SR95531 in NG2 cells recorded with a CsCl-based intracellular solution ( Figure 1A—figure supplement 1D ) . All unitary connections displayed currents with small amplitudes and showed paired-pulse depression without recovery within 250 ms ( Figure 1A—figure supplement 1D–G ) . FSIs and NFSIs can be distinguished by their firing properties even if they have not attained maturity at this developmental stage ( Daw et al . , 2007 ) . To investigate the identity of recorded interneurons in all tested pairs , we analyzed nineteen different electrophysiological parameters in current-clamp mode ( Figure 1B , D; Table 1 ) . FSIs were primarily distinguished from NFSIs by their narrow action potential waveforms with profound after-hyperpolarizations ( AHPs ) , a negligible spike broadening and spike amplitude reduction during trains ( Figure 1B , D , E ) ( Cauli et al . , 1997; Daw et al . , 2007 ) . Other seven parameters were also statistically different between FSIs and NFSIs and clearly separated these neurons in two distinct groups ( Table 1 ) . The identity of FSIs was further confirmed by the expression of PV in biocytin-labeled interneurons , a reliable marker for this cell class that was absent in NFSIs ( Figure 1C—figure supplement 2 ) . As expected for these two classes of interneurons , FSIs appeared as a relatively homogeneous population with restricted distributions of the main discriminative parameters , whereas NFSIs encompassed different subtypes as revealed by the large distribution of their electrophysiological values ( Figure 1E—figure supplement 3A ) ( Cauli et al . , 1997 ) . 10 . 7554/eLife . 06953 . 003Figure 1 . FSIs are highly connected to NG2 cells . ( A ) Paired recording between a presynaptic fast-spiking interneuron ( FSI ) and a NG2 cell . An action current evoked in the presynaptic interneuron ( upper trace ) elicits PSCs recorded in the NG2 cell ( bottom trace; average of 200 traces ) . ( B , D ) Current-clamp recordings of the FSI recorded in A ( B ) , and a non-fast-spiking interneurons ( NFSI ) ( D ) connected to a NG2 cell during injections of −150 pA and 200 pA . Note differences on spike properties between the two cells ( insets , right ) . ( C ) The connected FSI was loaded with biocytin and was immunoreactive for PV ( stacks of 17 Z-sections; each 2 µm ) . ( E ) 3D plot of the three major electrophysiological parameters distinguishing connected FSIs ( gray ) from NFSIs ( black ) . ( F ) Histograms showing the fraction of Venus+ interneurons that are PV+ ( n = 6 mice ) . ( G ) Percentages of tested and connected FSI ( gray ) and NFSI ( black ) with respect to all tested interneurons . ( H ) Percentages of connected and unconnected FSIs ( gray ) and NFSIs ( black ) with respect to each group of interneurons separately . ( I ) Connection probability of FSI and NFSI as a function of three postnatal stages ( 45 , 44 , and 44 tested pairs at PN8-9 , PN10-11 , and P12-13 , respectively ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 00310 . 7554/eLife . 06953 . 004Figure 1—figure supplement 1 . Paired recordings in double VGAT-Venus;NG2-DsRed transgenic mouse . ( A ) Confocal images of the VGAT-Venus;NG2-DsRed transgenic mouse in which interneurons and NG2 cells were identified by the expression of the fluorescent proteins Venus ( green ) and DsRed ( red ) , respectively ( stacks of 15 Z-sections; each 0 . 5 µm ) . ( B–D ) Paired recording between a presynaptic Venus+ interneuron and a DsRed+ NG2 cell . Current-clamp recording of the interneuron recorded with a KGlu-based intracellular solution during injections of −150 pA and 200 pA ( B ) and voltage-clamp recording of the NG2 cell held at −70 mV during voltage steps from +40 mV to −120 mV ( C ) . Note the presence of INa+ ( C , inset ) . Two action currents evoked in presynaptic interneurons elicited PSCs recorded in NG2 cells ( D , black trace; average of 100 traces ) that are completely abolished by the GABAA receptor antagonist SR95531 ( 5 µM; gray trace; average of 100 traces ) . ( E , F ) Distribution of mean current amplitudes ( E ) and paired-pulse ratios ( F , PPR ) for 38 connected pairs . ( G ) Interneuron-NG2 cell connections do not show any recovery from depression within at least 250-ms interstimulus interval ( n = 5–10 per interstimulus interval ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 00410 . 7554/eLife . 06953 . 005Figure 1—figure supplement 2 . PV marker is expressed in electrophysiologically identified FSIs , but not in NFSIs . ( A , B ) Current-clamp recordings of a FSI ( A ) and a NFSI ( B ) during current injections ( left ) . Note the differences on spike properties between the two cells ( insets , right ) . Phase plots ( ΔV/Δt vs potential; right ) illustrate differences on spiking pattern for the same discharge ( Daw et al . , 2007 ) . During a train discharge , action potentials are relatively homogeneous for FSIs , but they change drastically for NFSIs as revealed by the variance of successive loops in the phase plot . ( C , D ) PV immunostaining of biocytin-loaded FSI ( C; n = 12 ) and NFSI ( D; n = 7 ) . Note the lack of PV labeling for NFSI ( stacks of 27 and 17 Z sections , respectively , each 0 . 5 µm ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 00510 . 7554/eLife . 06953 . 006Figure 1—figure supplement 3 . Cumulative distributions for the three main electrophysiological parameters used to differentiate FSIs from NFSIs . ( A , B ) . Comparison of cumulative distributions for AHP ( left ) , spike duration increase ( middle ) , and spike-amplitude reduction ( right ) between FSIs ( gray ) and NFSIs ( black ) for 132 interneurons ( A ) and for 38 connected interneurons to NG2 cells ( B ) . Note the restricted distributions of these parameters for FSIs compared to NFSIs for both tested and connected neurons . No significant differences were observed between tested and connected FSIs as well as between tested and connected NFSIs ( p > 0 . 05 ) . Altogether , these results support the idea that FSIs constitute a homogeneous population , whereas NFSIs , connected or not to NG2 cells , form a heterogeneous group of cells that comprises different subtypes of interneurons . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 00610 . 7554/eLife . 06953 . 007Table 1 . Electrophysiological properties of Venus+ FSI and NFSIDOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 007ParameterFSI ( n = 27 ) NFSI ( n = 105 ) pComparisonFtotal ( Hz ) 28 . 32 ± 2 . 7324 . 18 ± 0 . 93NS–Finit ( Hz ) 37 . 12 ± 3 . 8945 . 29 ± 2 . 3NS–F200 ( Hz ) 29 . 55 ± 2 . 7324 . 48 ± 0 . 89NS–Ffinal ( Hz ) 26 . 36 ± 2 . 7221 . 11 ± 0 . 83NS–Early accommodation14 . 14 ± 3 . 6139 . 33 ± 1 . 85<0 . 0001FSI < NFSILate accommodation11 . 43 ± 3 . 018 . 87 ± 0 . 68NS–Threshold ( mV ) −35 . 68 ± 0 . 64−36 . 31 ± 0 . 47NS–First spike amplitude ( mV ) 70 . 73 ± 2 . 3370 . 61 ± 1 . 09NS–Second spike amplitude ( mV ) 70 . 55 ± 2 . 0865 . 51 ± 1 . 3NS–Spike amplitude reduction−0 . 24 ± 0 . 947 . 59 ± 1 . 14<0 . 0001FSI < NFSIFirst spike duration ( ms ) 1 . 37 ± 0 . 12 . 41 ± 0 . 1<0 . 0001FSI < NFSISecond spike duration ( ms ) 1 . 46 ± 0 . 113 . 83 ± 0 . 22<0 . 0001FSI < NFSISpike duration increase6 . 79 ± 0 . 7555 . 06 ± 5 . 92<0 . 0001FSI < NFSIAHP ( mV ) −15 . 89 ± 0 . 61−8 . 43 ± 0 . 4<0 . 0001FSI > NFSIAHP width ( ms ) 25 . 57 ± 1 . 6121 . 53 ± 1 . 05NS–Peak to AHP trough ( ms ) 7 . 18 ± 0 . 9213 . 85 ± 0 . 88<0 . 0001FSI < NFSIAP-depolarizing slope ( mV/ms ) 207 . 34 ± 12 . 86140 . 45 ± 5 . 63<0 . 0001FSI > NFSIAP-hyperpolarizing slope ( mV/ms ) −67 . 38 ± 4 . 35−32 . 81 ± 1 . 36<0 . 0001FSI > NFSIRin ( MΩ ) 208 . 22 ± 17 . 97399 . 96 ± 19 . 75<0 . 0001FSI < NFSIFor the identification of FSI and NFSI by firing properties , we first analyzed spike-frequencies in Venus+ interneurons during suprathreshold pulses in current clamp configuration from −70 mV ( 200 pA , 800–1000 ms ) . Firing frequency was calculated for the entire pulse as the number of spikes divided by the pulse duration ( Ftotal ) . Three instantaneous discharge frequencies were also calculated: ( 1 ) between the first pair of spikes ( Finitial ) ; ( 2 ) at 200 ms from the beginning of the pulse ( F200 ) ; and ( 3 ) at the end of the pulse ( Ffinal ) . These values were used to quantify both early and late accommodations in accordance with the following formulas: ( Finitial − F200/Finitial ) and ( F200 − Ffinal/Finitial ) , respectively . We also dissected the spike morphology from action potentials elicited by 80-ms suprathreshold pulses from −70 mV ( 150–200 pA ) . From these recordings , the spike threshold corresponded to the voltage at which the derivative of the AP ( dV/dt ) experienced a twofold increase . The amplitudes , the first and the second AP ( A1 and A2 ) , were calculated from the threshold to peak . Their duration ( D1 and D2 ) corresponded to the full-width at half maximum ( FWHM ) from a Gaussian fit of the depolarized face of the AP immediately after the threshold . Both amplitude reduction and duration increase were calculated by the formulas A1 − A2/A1 and D2 − D1/D1 , respectively . The amplitude of the after-hyperpolarization ( AHP ) was calculated as the difference between the threshold and the peak of the fast hyperpolarization . We also estimated the AHP width as the FWHM and the latency of AP peak to AHP trough . We extracted the positive and negative peaks from the derivative of the AP waveform to quantify the maximal speed excursion of the membrane voltage during both depolarizing and hyperpolarizing faces of the AP . Finally , the input resistance ( Rin ) was measured in current clamp by applying hyperpolarizing pulses from −60 mV ( −200 pA ) . The three major parameters to differentiate FSI from NFSI appeared in bold . Note that seven other parameters also easily differentiate these interneurons . NS: no significant difference . The amount of tested FSIs and NFSIs was in agreement with those of PV+/Venus+ and PV−/Venus+interneurons in the transgenic mouse , respectively , with NFSIs being more abundant ( Figure 1F , G ) . However , the proportion of connected FSIs was high compared to the FSI abundance in tested pairs ( Figure 1G ) or within the population of tested FSIs ( Figure 1H ) . Conversely , the proportion of connected NFSIs was low compared to the NFSI abundance in tested pairs ( Figure 1G ) or within the population of tested NFSIs ( Figure 1H ) . These data suggest that connection probabilities of FSIs and NFSIs are different . To determine whether differences in the connection probabilities for FSIs ( p = 0 . 43 ) and NFSIs ( p = 0 . 21 ) did not arise by chance , we modeled each of the two data sets as observations from two binomial distributions . The estimated connection probabilities at 90% confidence intervals for FSIs and NFSIs were 0 . 30–0 . 57 and 0 . 15–0 . 27 , respectively ( Brown et al . , 2001; Supplementary file 1 ) . If the two populations have the same connection probabilities , we expect non-overlapping intervals in at most 1% of the cases ( 0 . 1 × 0 . 1 ) . No overlap was observed between these two intervals , indicating that FSIs and NFSIs have different connection probabilities ( p < 0 . 01 ) . We obtained a similar result using a chi-square test ( Pearson's chi-square of 6 . 93; p < 0 . 01 ) . In another hand , it is noteworthy that no significant differences of main discriminative electrophysiological parameters were found between tested and connected NFSIs , indicating that NFSIs innervating NG2 cells constitute a heterogeneous population of interneurons as observed in the tested population ( Figure 1—figure supplement 3A , B ) . Altogether , these results indicate that NFSIs are by far more abundant and diverse but poorly connected to NG2 cells , whereas FSIs are less abundant but highly connected to NG2 cells in the developing GABAergic network . Finally , we previously demonstrated that NG2 cells receive a transient GABAergic synaptic input from interneurons that disappears after the second PN week ( Vélez-Fort et al . , 2010; Balia et al . , 2015 ) . We , thus , tested how this transient connectivity occurs for FSI and NFSI separately . Figure 1I illustrates the connection probability of these interneurons at three different postnatal stages . A transient peak of connectivity occurred at PN10-11 for both FSI and NFSI ( Figure 1I ) , indicating that the connectivity of these two classes of interneurons is not differently affected by the stage of development . We next investigated whether FSI-NG2 cell connections could be distinguished from NFSI-NG2 cell connections by their synaptic properties . Paired-pulse ratios , PSC1 and PSC2 amplitudes with or without failures , coefficients of variation , and probabilities of response were not significantly different between FSI-NG2 cell and NFSI-NG2 cell connections , suggesting that presynaptic mechanisms of GABA release did not participate in the selectivity for presynaptic inputs ( Figure 2—figure supplement 1A–E ) . However , we observed a significant difference in current kinetics with rise and decay times faster for unitary FSI-NG2 cell connections ( Figure 2A , B ) . These kinetic differences could result from two main causes: ( 1 ) FSI-NG2 cell and NFSI-NG2 cell synapses are distributed at different subcellular locations with FSI-NG2 cell conductances at proximal contact sites being properly clamped during whole-cell recordings , and NFSI-NG2 cell conductances at more distal contacts being electrotonically filtered; ( 2 ) the subunit composition of postsynaptic GABAARs is different according to the presynaptic identity of the interneuron ( Pearce , 1993; Ali and Thomson , 2008 ) . It is noteworthy that no correlation was observed for rise and decay times with respect to the postnatal day for either FSI-NG2 cell or NFSI-NG2 cell connections ( p > 0 . 05 ) . 10 . 7554/eLife . 06953 . 008Figure 2 . Specific subcellular distribution of FSI and NFSI synaptic contacts and postsynaptic GABAARs on NG2 cells . ( A ) Different kinetics of unitary postsynaptic NG2 cell currents depends on the identity of the presynaptic interneuron . Superimposed postsynaptic currents evoked in a FSI-NG2 cell connection ( gray ) and a NFSI-NG2 cell connection ( black ) . Note the faster rise ( inset , right ) and decay times for the FSI-NG2 cell connection ( t10–90 = 0 . 91 ms and τ = 12 . 7 ms for the FSI-NG2 cell connection and 2 . 25 ms and 23 . 2 ms for the NFSI-NG2 cell connection ) . Only traces showing a response in the first presynaptic action current were averaged . ( B ) Histogram comparing rise ( left ) and decay times ( right ) for different FSI-NG2 cell and NFSI-NG2 cell connections . ( C ) 3D reconstruction of a biocytin-loaded NG2 cell ( gray ) , VGAT ( red ) , and PV ( green ) labeling at PN10 ( see original immunostainings in Figure 2—figure supplement 2A ) . VGAT+/PV+ puncta were localized at proximal branches and soma ( yellow arrows ) , whereas VGAT+/PV− puncta were mainly localized at distal branches ( white arrowheads ) . Only soma and two main branches of the NG2 cell are shown . Inset illustrates a VGAT+/PV+ puncta . ( D ) Percentage of PV+ and PV− puncta in somata and branches of NG2 cells ( n = 4 cells; 6–8 branches per cell ) . ( E ) Cumulative distributions of PV+ and PV− puncta in respect to their distance from the soma . Note the restricted distribution for PV+ puncta . **p < 0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 00810 . 7554/eLife . 06953 . 009Figure 2—figure supplement 1 . Properties of unitary synaptic interneuron-NG2 cell connections . ( A–E ) Comparison of synaptic properties between FSI-NG2 cell and NFSI-NG2 cell connections . Note that no differences exist between these two types of connections though some differences exist when comparing PSC1 ( 1 ) and PSC2 ( 2 ) within each type of connection . ( F–J ) Comparison of synaptic properties between connections showing single- and double-vesicular release sites . Note that the PPR ( F ) and mean amplitudes with failures ( G ) of PSC1 ( 1 ) and PSC2 ( 2 ) were different between these two types of connections , whereas rise ( H ) and decay ( I ) times and the coefficient of variation ( J ) were similar . NS: not significant; *p < 0 . 05 , **p < 0 . 001 . For readability , NS tests were indicated only in panels B–D and G . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 00910 . 7554/eLife . 06953 . 010Figure 2—figure supplement 2 . Homogeneous distribution of VGAT+/PV+ puncta around NG2 cells . ( A ) Original immunostainings against VGAT ( red ) and PV ( green ) around the biocytin-labeled NG2 cell ( gray ) shown in Figure 2C . Note the accurate match of colocalized puncta between these images and 3D reconstructions in Figure 2C . ( B ) Distribution of all VGAT+ ( red ) and VGAT+/PV+ ( green ) puncta around a biocytin-labeled NG2 cell ( left ) . Top and lateral views illustrate how the density of puncta was calculated in defined volumes from the center of the field ( right , dashed lines; see ‘Materials and methods’ ) . ( C ) Distribution of the density of all VGAT+ ( red ) and VGAT+/PV+ ( green ) puncta for each volume in respect to the center of the field . Note the homogeneous distribution of both all VGAT+ and VGAT+/PV+ puncta , confirming that FSI inputs are not unequally located around the soma of the NG2 cell . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 010 To test for the first possibility , we analyzed the distribution of FSI- and NFSI-GABAergic synaptic contacts on NG2 cells by using 3D confocal reconstructions of the vesicular GABA transporter ( VGAT ) and PV immunolabeling on biocytin-loaded NG2 cells at PN10 , that is , at the peak of connectivity ( Figure 2C–E—figure supplement 2A ) . We considered that VGAT+/PV+ and VGAT+/PV− puncta corresponded to FSI and NFSI contacts , respectively . VGAT+/PV+ puncta were more abundant than VGAT+/PV− on the soma , whereas their proportion was relatively similar in NG2 cell branches ( Figure 2C–D ) . Furthermore , the distribution of puncta was significantly closer to the soma for VGAT+/PV+ than for VGAT+/PV− puncta ( Figure 2E ) with a mean distance in branches of 12 . 8 ± 1 . 2 µm and 22 . 5 ± 1 . 3 µm , respectively ( p < 0 . 0001 excluding puncta on the soma ) . To rule out that the specific segregation of inputs was caused by their uneven distribution around recorded NG2 cells rather than a specific subcellular targeting , we analyzed the distribution of puncta surrounding these cells ( Figure 2—figure supplement 2B; see ‘Materials and methods’ ) . VGAT+/PV+ puncta were homogeneously distributed in the space at the vicinity of analyzed NG2 cells ( Figure 2—figure supplement 2B–C ) . These results indicate that FSIs contact preferentially NG2 cell somata and proximal branches , whereas NFSIs mainly contact distal branches . This is consistent with the faster current kinetics observed for unitary FSI-NG2 cell connections and demonstrates a differential distribution of presynaptic inputs on NG2 cells , according to the interneuron identity . In interneuron–neuron connections , the subunit composition of postsynaptic GABAARs can change according to both the location of the receptors in the somato-dendritic compartment and the identity of the presynaptic neuron ( Huang et al . , 2007; Ali and Thomson , 2008 ) . Differences on postsynaptic GABAAR subunit composition in NG2 cells may also account for input specificity . We recently showed that GABAARs of NG2 cells in the second PN week had a variable and complex subunit composition with around 40% of cortical NG2 cells expressing mRNAs for the GABAARs containing γ2 subunits ( Balia et al . , 2015 ) . In agreement with transcript expression , the effect of the positive modulator diazepam ( DZP; a benzodiazepine acting on receptors containing the γ2 subunit ) on extracellularly evoked currents is also very variable ( Passlick et al . , 2013; Balia et al . , 2015 ) . To confirm the presence of γ2-subunit protein at postsynaptic sites of NG2 cells , we performed triple immunostainings against γ2 , VGAT ( presynaptic marker ) , and NG2 ( a marker of NG2 cell membranes ) in NG2-DsRed mice at PN10 ( Figure 3A ) . We observed numerous VGAT+/γ2+ and VGAT+/γ2− puncta on NG2+ cell membranes of the soma and branches as previously observed for PV/VGAT+ in biocytin-loaded cells . In addition , VGAT+/γ2+ synaptic puncta on neurons can be clearly distinguished from those present on NG2 cells at this developmental stage ( Figure 3A ) , corroborating the presence of VGAT+ puncta on these progenitors . 10 . 7554/eLife . 06953 . 011Figure 3 . Expression of γ2 subunit of GABAARs at FSI-NG2 cell synapses . ( A ) Confocal images of VGAT+ ( red ) and γ2+ ( green ) puncta on NG2+ membranes ( gray ) of a NG2 cell at PN10 ( objective 63×; stack of 8 Z sections , each 0 . 32 µm ) . As expected from previous data ( Balia et al . , 2015 ) , numerous VGAT+/γ2+ ( yellow arrows ) and VGAT+/γ2− ( white arrowheads ) puncta on NG2+ soma and branches were observed . Note that neuronal VGAT+/γ2+ puncta are clearly distinguished from those present on NG2 cells at this developmental stage ( white arrow ) . ( B ) DZP effect on PSC amplitudes in two NG2 cells connected , respectively , to a FSI ( top ) and a NFSI ( bottom ) . ( C ) Histogram comparing DZP effect on PSCs evoked by FSIs and NFSIs . The number of tested cells is indicated on top of histogram bars . **p < 0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 011 To evaluate whether the presynaptic identity is associated to the presence of γ2 subunits in GABAARs at postsynaptic sites , we bath-applied DZP ( 10 µM ) during paired recordings of mice from PN9 to PN13 . This benzodiazepine significantly increased the amplitude of unitary synaptic currents in connected FSI-NG2 cell pairs without modifying the paired-pulse ratio ( PPR ) , whereas it did not affect currents of unitary NFSI-NG2 cell connections ( Figure 3B–C; PPR of 0 . 55 ± 0 . 08 and 0 . 38 ± 0 . 05 with and without DZP for FSIs and 0 . 46 ± 0 . 13 and 0 . 54 ± 0 . 14 with and without DZP for NFSIs , respectively , p > 0 . 05 ) . Hence , FSIs preferentially targeted proximal postsynaptic sites of NG2 cells containing GABAARs with γ2 subunits , whereas NFSIs favored more distal sites lacking this subunit . Overall , the analyses of current kinetics , 3D reconstructions of PV/VGAT+ puncta , and pharmacology of γ2-containing GABAARs demonstrated that FSIs and NFSIs target distinct subcellular segregated domains of NG2 cells , containing different GABAARs . A highly specific spatial arrangement of synaptic inputs , thus , exists at the subcellular level in NG2 cells . The extracellular stimulation of neuronal fibers induces postsynaptic currents in NG2 cells that can reach hundreds of pA in the somatosensory cortex ( Maldonado et al . , 2013 ) . However , current amplitudes of unitary interneuron-NG2 cell connections are very small compared to extracellularly evoked currents , suggesting that a single NG2 cell is densely connected by many interneurons in the network , but that a single interneuron contacts a glial progenitor through a restricted number of release sites . To determine whether interneuron-NG2 cell connections consisted of single- or multiple-release sites , we examined the response probabilities by using paired-pulse stimulations of presynaptic neurons ( Angulo et al . , 1999 ) . In 8 out of 18 connections , quantal analysis revealed no statistical differences between cumulative distributions of PSC1 and PSC2 amplitudes , excluding failures , although the response probabilities were significantly higher for PSC1 ( Figure 4A , B , E , F; see ‘Materials and methods’ ) . This implies that PSCs result from the release of only one quantum of transmitters , and therefore , a single vesicle was released per connection ( Stevens and Wang , 1995 ) . The mean amplitude of PSCs without failures corresponded to a mean quantal size of −7 . 71 ± 0 . 71 pA in our recording conditions . These interneurons , thus , innervate NG2 cells probably through one release site , independent of the presynaptic interneuron identity and the postnatal day ( 5 FSIs and 3 NFSIs displayed single vesicular release at postnatal days from PN8 to PN13 ) . 10 . 7554/eLife . 06953 . 012Figure 4 . One or two release sites per interneuron-NG2 cell connection . ( A , C ) Unitary connections with single ( A ) or double ( C ) vesicular release . Mean ( middle traces ) and individual ( bottom traces ) PSCs recorded in NG2 cells in response to two action currents evoked in presynaptic interneurons ( top traces ) . ( B , D ) Cumulative distributions of PSC1 and PSC2 without failures for connections shown in A and C . Insets show superimposed mean PSC1 and PSC2 without failures . ( E , F ) Histograms of amplitudes without failures ( E ) and response probabilities ( F ) of PSC1 and PSC2 for connections with single and double vesicular release . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 012 For the other 10 pairs , quantal analysis showed statistical differences between cumulative amplitude distributions and response probabilities of PSC1 and PSC2 , indicating that more than one vesicle were released per connection ( Figure 4C–F ) . Interestingly , amplitudes and probabilities of response were around twice for PSC1 and the same for PSC2 when compared to those of PSC1 resulting from a single vesicular release ( Figure 4E–F; Figure 2—figure supplement 1F–J ) . Thereby , these connections exhibited double vesicular release and probably displayed double release sites . In addition to the specific spatial distribution of GABAergic inputs impinging on NG2 cells , interneurons established a point-to-point communication with these progenitors through single- or double-release sites . This restricted innervation contrasts with the ability of interneurons to innervate other neurons through multiple release sites at the same PN stage ( Pangratz-Fuehrer and Hestrin , 2011 ) . The highly organized interneuron-NG2 cell connectivity at the subcellular level suggests that NG2 cells are specifically wired by interneurons inside functional neural assemblies , probably forming a specific network . To test this hypothesis , we investigated the arrangement of functional interneuron-NG2 cell microcircuits in layer V from PN8 and PN11 and compared it with that of interneuron–pyramidal cell microcircuits . To establish GABAergic connectivity maps of NG2 cells and pyramidal neurons , we exploited the flexibility and high-spatial precision of holographic photolysis ( Lutz et al . , 2008; Zahid et al . , 2010 ) ( Figure 5—figure supplement 1A–B ) . By generating precise light patterning in real time , this optical method enables the photolysis of caged neurotransmitters such as MNI-glutamate to photostimulate neurons ( Lutz et al . , 2008; Zahid et al . , 2010 ) . This cage compound is one of the most efficient in terms of release of glutamate by light and of stability at physiological pH and temperature ( Matsuzaki et al . , 2001 ) . However , as many other cages , it has a blocking effect on GABAARs in brain slices ( Fino et al . , 2009 ) ( Figure 5—figure supplement 1C ) . For this reason , we first searched for an appropriate concentration of MNI-glutamate for which the effect on GABAARs of NG2 cells is minimal while triggering efficiently action potentials in interneurons at a single-cell resolution . We found that a concentration of 50 µM MNI-glutamate fulfilled these two prerequisites ( Figure 5—figure supplement 1C , D and Figure 5—figure supplement 2 ) . To build GABAergic connectivity maps of recorded cells , interneurons were sequentially photostimulated in the excitation field using a 5-µm light spots , while patched cells were recorded with CsMeS-based intracellular solution at 0 mV , the reversal potential of their ionotropic glutamatergic receptors ( Figure 5A–D ) . Outward GABAAR-mediated PSCs , sensitive to SR95531 , were induced in recorded cells by photostimulation of nearby interneurons ( Figure 5E ) . We considered as connected pairs those showing both PSCs detected in averaged traces with a threshold of 2 times the standard deviation of the noise and an increased occurrence probability of individual PSCs within 100 ms after photostimulation when visualized in raster plots ( Figure 5A–D ) . This time window corresponds to the latency after photostimulation required to trigger an action potential in interneurons ( Figure 5—figure supplement 3A–B ) . Indeed , action potential generation with light through the activation of glutamate receptors cannot be precisely controlled as with patch-clamp recordings . In most cases , action potential generation of interneurons was delayed with respect to the photostimulation time and displayed a spike jitter ( Figure 5—figure supplement 3A–B ) . As a consequence , there is also a variable latency and jitter in photo-evoked PSCs during mapping experiments ( Figure 5B , D , insets ) . Nevertheless , to confirm irrefutably the monosynaptic nature of the connections , the photo-activated interneuron inducing PSCs in a postsynaptic cell was patched with a second pipette to test its connectivity with paired recordings ( Figure 5—figure supplement 3D , E ) . Three out of three-tested pairs were truly connected with the postsynaptic cell . 10 . 7554/eLife . 06953 . 013Figure 5 . Spatial selectivity of holographic photostimulation to detect unitary interneuron-NG2 cell connections . ( A , C ) Excitation fields ( dashed circles ) in epifluorescent images of Venus+ interneurons . Recorded NG2 cell ( A ) and pyramidal neuron ( C ) are in the center ( + , non-visible ) . A 3-ms photostimulation of an interneuron ( A; spot 1 ) induces unitary PSCs in a NG2 cell held at 0 mV and recorded with a CsMeS-based intracellular solution ( top trace; average of 28 traces ) . The spatial selectivity of this connection is confirmed by displacing the illumination spot near the targeted soma ( spot 2; bottom trace; average of 12 traces ) . ( B ) Raster plot of GABAergic synaptic events from the recorded NG2 cell illustrated in A . Each tick corresponds to a PSC . Note that random and sparse spontaneous synaptic currents are observed 2 s before and after interneuron photostimulation ( red vertical line ) , whereas photo-evoked synaptic events reproducibly occur within 100 ms after the photostimulation . Photo-evoked events disappear when the 5-µm spot is moved to spot 2 ( gray box ) . Note that in some single traces more than one postsynaptic event was elicited upon photo-stimulation ( insets right ) and that the average response in A displays two peaks . In these examples , the targeted presynaptic interneuron probably elicited more than one action potential . ( C ) A 3-ms photostimulation of an interneuron ( C; spot 1 ) does not induce unitary synaptic currents in a pyramidal neuron held at 0 mV and recorded with a CsMeS-based intracellular solution ( top trace ) . The excitation time was increased to test for a possible false negative connection . An increase in the excitation time of the interneuron to 3 . 3 ms induces unitary PSCs in the pyramidal neuron ( spot 1; middle trace; average of 11 traces ) that disappear when the spot is displaced 5 µm apart , confirming the photostimulation selectivity ( spot 2; bottom trace; average of 26 traces ) . ( D ) Raster plot of GABAergic synaptic events from the recorded pyramidal neuron illustrated in C . Note that random spontaneous synaptic currents are observed 2 s before and after interneuron photostimulation ( 3 . 3 ms; red vertical line ) , whereas synaptic events reproducibly occur within 100 ms after photostimulation ( inset right ) . These events disappear when the 5-µm spot is moved to position 2 ( gray box ) . Failures of response were rarely observed in pyramidal neurons . ( E ) Averaged unitary PSC photo-induced in a recorded NG2 cell ( middle trace; average of 9 traces ) and completely abolished by 5 µM SR95531 ( bottom trace; average of 13 traces ) . ( F ) Probability of encountering false negative and false positive connections . Unspecific connections were discriminated by changing the pulse duration of the laser and the position of the spot as in A and C . ( G , H ) Connection probabilities for all tested cells ( G ) and for cells showing at least one connection ( H ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 01310 . 7554/eLife . 06953 . 014Figure 5—figure supplement 1 . Optical set-up and effect of MNI-glutamate on GABAA receptor-mediated currents in NG2 cells . ( A ) Optical set-up for holographic photolysis . The focal length of the lenses are f1 = 350 mm ( L1 ) and f2 = 180 mm ( L2; see ‘Materials and methods’ for details ) . ( B ) Phase hologram ( left ) used to create a 5-µm spot ( right ) . This illumination spot used in the present study was visualized by exciting a thin layer of rhodamine ( right ) . Scale bar: 5 µm . ( C ) GABA ( 75 µM ) was prepared in the extracellular solution and applied by pressure from a patch pipette ( 250 ms , each 10–15 s; 0 . 52 bars ) . The application pipette was positioned at 50–100 µm from cells recorded in CsCl-based intracellular solution . Inward currents were elicited by local applications of GABA at a holding potential of −90 mV in control conditions and in the presence of MNI-glutamate . Note the amplitude reduction of elicited currents in 200 µM , but not in 50 µM of MNI-glutamate . ( D ) Plots of current amplitudes elicited in different NG2 cells in control conditions ( ctl ) and in the presence of MNI-glutamate ( MNI-Glu ) . Mean amplitudes are shown in black ( *p < 0 . 05 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 01410 . 7554/eLife . 06953 . 015Figure 5—figure supplement 2 . Spatial selectivity of holographic photostimulation of targeted Venus+ interneurons . ( A ) DIC image of a simultaneous patch-clamp recording of two photostimulated Venus+ interneurons ( left; fluorescence not shown ) . Note the close proximity of the two cell somata . Current-clamp recordings obtained for each recorded interneuron in response to current injections ( right ) . ( B , C ) Simultaneous current-clamp recordings in response to single-cell photostimulation of either interneuron 1 ( B ) or interneuron 2 ( C ) . Note that the laser pulse elicits a reproducible action potential in the target interneuron ( top traces ) , but not in the nearby interneuron ( bottom traces ) . ( D ) Histogram of the percentage of interneurons for which the photostimulation was selective when the illumination spot was moved onto the soma of the other interneuron . Note that the selectivity is attained in 76% of the cases . It is noteworthy that simultaneous recordings were performed between interneurons located at different planes and at very short intersomatic distances ( mean intersomatic distance measured between the center of somata: 21 . 8 ± 2 . 0 µm , n = 17 simultaneous recordings ) . In addition , the time pulse used to test the spatial selectivity to generate action potentials in simultaneous recordings of two interneurons was always at least the minimal time needed to elicit an action potential in the nearby non-photostimulated interneuron . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 01510 . 7554/eLife . 06953 . 016Figure 5—figure supplement 3 . Monosynaptic connections with holographic photolysis . ( A ) Current-clamp recordings of a Venus+ interneuron held at −60 mV in response to single-cell photostimulation . Note that a 3-ms laser pulse elicits an action potential in the target interneuron with a delay of several tens of ms and a jitter in the spike peak ( inset ) . ( B ) Histogram of latencies between the beginning of the laser pulse and the peak of the photo-evoked action potential for recorded interneurons . Note that this latency can reach 100 ms . ( C ) Histogram of minimal excitation time needed to photo-evoke a single action potential in different Venus+ interneurons . Most interneurons are photo-activated by less than 3-ms pulses . ( D ) Excitation field ( dashed circles ) in epifluorescent images of Venus+ interneurons . A 3-ms photostimulation of an interneuron ( spot 1 ) induces unitary synaptic currents in a recorded pyramidal neuron ( + , non-visible ) held at 0 mV and recorded with a CsMeS-based intracellular solution ( top traces ) . The spatial selectivity of this connection is confirmed by displacing the illumination spot near the targeted soma ( spot 2; bottom trace ) . ( E ) The connection in c was further confirmed to be monosynaptic by patching the interneuron with a second patch pipette . Two action currents evoked in the presynaptic interneurons ( upper trace ) elicits PSCs recorded in the postsynaptic neuron ( bottom trace ) . This experiment was reproduced for 3 out of 3 postsynaptic cells . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 016 Although most interneurons were photo-activated by less than 3-ms pulses , some others required a pulse duration up to 7 ms to reach their action potential threshold ( Figure 5—figure supplement 3C ) . In order to take into account different action potential thresholds , we systematically increased or decreased the duration of the laser pulse for each interneuron . Furthermore , to ensure the spatial selectivity of the system , we displaced the spot away from the soma , resulting in the disappearance of the response ( Figure 5A–D—figure supplement 3D ) . This allows us to reveal false positive and negative connections , respectively ( Figure 5A , C , F ) . Only in rare cases , the unambiguous discrimination of connected interneurons was not possible , and thus , these targeted interneurons were not considered in cell maps ( 2 . 8% of all tested interneurons ) . The online evaluation of each photostimulated interneuron allowed us to set reliable maps of connectivity for recorded cells . As expected for connections with a limited number of release sites , the amplitudes of PSCs induced in NG2 cells by photostimulation were smaller than those in pyramidal neurons ( Figures 5A , C , 6B–E ) . Either in the sample of tested cells ( Figure 5G ) or in maps showing at least one connection ( Figure 5H ) , the probability to find a connected pair was lower for NG2 cells than for pyramidal neurons . Interestingly , connectivity maps of NG2 cells involved very local microcircuits with connections at interneuron-NG2 cell intersomatic distances never exceeding 70 µm and cumulative distributions of intersomatic distances significantly different for connected and unconnected interneurons ( Figure 6G ) . Moreover , the connection probability highly decreased after intersomatic distances of 50 µm ( Figure 6G ) . On the contrary , connectivity maps of pyramidal cells were relatively homogeneous within 100 μm with similar cumulative distributions of intersomatic distances between connected and unconnected cells in agreement with previous reports ( Fino and Yuste , 2011 ) ( Figure 6H ) . The local architecture of interneuron-NG2 cell connections , thus , did not result from the impossibility to detect connections over 70 µm with holographic photolysis . These findings reveal that interneuron-NG2 cell microcircuits are arranged according to a specific connectivity pattern that follows a very local microarchitecture . 10 . 7554/eLife . 06953 . 017Figure 6 . GABAergic connectivity maps of NG2 cells and pyramidal neurons . ( A , D ) Excitation fields ( dashed circles ) in epifluorescent images of Venus+ interneurons . Recorded NG2 cell ( A ) and pyramidal neuron ( D ) are in the center ( + , non-visible ) . ( B , E ) Photostimulation of interneurons ( 1 and 2 ) in A and D induces unitary PSCs in a recorded NG2 cell ( B ) and pyramidal neuron ( E ) . ( C , F ) Connectivity maps within 1 . 05 × 106 µm3 volume of cells in A and D showing connected ( red ) and unconnected ( black ) interneurons . ( G , H ) Cumulative distribution of connected and unconnected interneurons ( left ) and distribution of connection probabilities in respect to intersomatic distances ( right ) between interneurons and either NG2 cells ( n = 13 ) or pyramidal neurons ( n = 11 ) displaying at least one connection . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 017 Our results demonstrate that GABAergic innervation of NG2 cells is spatially organized following a specific arrangement of inputs at the subcellular and network levels during the second postnatal week , that is , a critical period for NG2 cell differentiation . Indeed , the production of premyelinating oligodendrocyte in the cerebral cortex starts during the first postnatal week and reaches a peak at PN14 when compared to PN21 ( Trapp et al . , 1997; Baracskay et al . , 2002 ) . We investigated , therefore , whether GABAergic innervation of NG2 cells during this period occurs in conjunction with the active phase of differentiation of these progenitors . For these , we analyzed the connection probability of paired recordings on a daily basis , from PN8 to PN13 , and compared it with the NG2 cell differentiation process in layer V . We observed that the connection probability reached a peak at PN10 , when we found 44% of connected pairs , and then decreased ( Figure 7A ) , confirming the transient GABAergic innervation of NG2 cells ( Vélez-Fort et al . , 2010 ) . This time course of the connectivity is specific for NG2 cells since the probability of interneuron-to-neuron connection is known to increase with postnatal cortical development ( Pangratz-Fuehrer and Hestrin , 2011; Yang et al . , 2012 ) . 10 . 7554/eLife . 06953 . 018Figure 7 . Time course of interneuron-NG2 cell connectivity and NG2 cell differentiation during the second PN week . ( A ) Connection probability for interneuron-NG2 cell pairs and CC1+/Olig2+ cell density as a function of postnatal days ( 19–25 pairs tested per day ) . Note that the peak of NG2 cell connection probability at PN10 precedes a large increase in CC1+/Olig2+ differentiated oligodendrocytes at PN11 . *p < 0 . 05; **p < 0 . 01 , ***p < 0 . 001 . ( B–F ) CC1+/Olig2+ and CC1−/Olig2+ cells in layer V at different PN days ( stacks of 10 Z-sections; each 1 µm ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 01810 . 7554/eLife . 06953 . 019Figure 7—figure supplement 1 . Na+ current density and frequency of spontaneous activity of NG2 cells correlates at PN10 , but not at PN8 and PN13 . ( A–C ) Plot of synaptic current frequencies against Na+ current densities at PN8 ( A ) , PN10 ( B ) , and PN13 ( C ) . There is a significant correlation only at PN10 . DOI: http://dx . doi . org/10 . 7554/eLife . 06953 . 019 To establish whether the high degree of synaptic connectivity at PN10 correlates with the cortical NG2 cell-differentiation process , we performed immunostainings against CC1 , a specific marker of differentiated oligodendrocytes , and Olig2 , a specific marker of the oligodendrocyte lineage , in NG2-DsRed mice from PN9 to PN13 ( Figure 7B–F; see ‘Materials and methods’ ) . The density of CC1+/Olig2+ cells was low at PN9 and PN10 and significantly increased from PN11 ( Figure 7A–F ) . Therefore , the peak of synaptic connectivity of cortical NG2 cells at PN10 coincides with a switch to a massive NG2 cell differentiation occurring between PN10 and PN11 in cortical layer V ( Figure 7A ) . To further analyze whether the transient GABAergic connectivity of NG2 cells and the switch to oligodendrocyte differentiation were related processes , we tested whether the Na+ current density of NG2 cells correlates with the frequency of spontaneous synaptic events at different postnatal days . Indeed , it has been shown that both the amplitude of Na+ currents and the synaptic current frequency decrease when these progenitors undergo differentiation ( De Biase et al . , 2010; Kukley et al . , 2010 ) . As expected from paired recordings and previous studies ( Vélez-Fort et al . , 2010; Balia et al . , 2015 ) , the frequency of spontaneous synaptic activity increases from PN8 to PN10 and then decreases at PN13 ( 0 . 12 ± 0 . 02 Hz , 0 . 24 ± 0 . 05 Hz , 0 . 12 ± 0 . 02 Hz , respectively; p < 0 . 05 for PN10 ) . However , no statistical differences were observed either on the averaged amplitudes of spontaneous synaptic currents or Na+ current densities between PN8 , PN10 , and PN13 ( amplitude of synaptic currents: −10 . 45 ± 1 . 04 pA , −12 . 75 ± 1 . 27 pA , −12 . 07 ± 0 . 90 pA , respectively; Na+ current densities: 22 . 59 ± 2 . 99 pA/pF , 23 . 46 ± 3 . 56 pA/pF , 26 . 10 ± 3 . 72 pA/pF , respectively; p > 0 . 05 ) . Interestingly , while no correlation was observed between the Na+ current density and the frequency of spontaneous synaptic events at PN8 and PN13 when connectivity is low , a positive correlation was observed at PN10 when connectivity is high and the switch to differentiation starts ( Figure 7—figure supplement 1 ) . These data suggest that interneurons build up a large number of functional synapses in more immature NG2 cells at the onset of massive oligodendrocyte differentiation ( PN10 ) and then , GABAergic synapses disassemble . The transient NG2 cell-synaptic connectivity is thus not only spatially , but also temporally regulated in coordination with the active differentiation phase of these progenitors in the second PN week .
Our findings uncover both spatially and temporally structured interneuron-NG2 cell connections , from the subcellular level to the cortical network during a critical period of NG2 cell differentiation . We demonstrate that NG2 cells form a transient and highly organized network with interneurons that is characterized by a high-connection probability at PN10 , a specific distribution of synaptic inputs on cell bodies and branches , a restricted number of contacts per interneuron , and very local connectivity maps . GABAergic innervation of NG2 cells , thus , appears as a finely regulated process that follows its own logic , which cannot be inferred from previous studies on NG2 cells and on classical neuronal synapses . In the neocortex , different classes of interneurons , guided by genetically determined mechanisms , are known to target distinct subcellular domains of pyramidal neurons , allowing a differential compartmentalized signal processing ( Huang et al . , 2007 ) . We demonstrate that input-specific projections also exist for interneuron-NG2 cell connections . NG2 cells compartmentalize FSI- and NFSI-input regions as revealed by distinct anatomical locations of PV+ and PV− contacts and by a different distribution of GABAARs with or without γ2 subunits at postsynaptic sites . FSIs and NFSIs impinging on these glial progenitors , thus , probably encode distinct information that regulates different cellular NG2 cell processes . Located at proximal sites and somata , FSI synapses could regulate NG2 cell differentiation by controlling gene expression , whereas distal NFSI synapses could affect motility or migration . Indeed , it has been suggested that GABA probably promotes NG2 cell migration through the activation of GABAA receptors ( Tong et al . , 2009 ) . NG2 cells receive inputs from both FSIs and NFSIs , but FSIs are proportionally more connected to these progenitors , suggesting that they play a major role in controlling NG2 cell activity during early stages of postnatal development . Compared to adult animals , immature FSIs possess different electrophysiological properties , such as high-input resistance , relatively low-membrane time constants , and a certain degree of spike-frequency adaptation , and are thus unlikely to perform the same function that in mature circuits ( Okaty et al . , 2009 ) . In fact , FSIs probably do not contribute to coordinating cortical neuronal activity in the perinatal period since FSI-pyramidal cell connections are detected only from PN5 ( Pangratz-Fuehrer and Hestrin , 2011 ) . FSIs start to influence cortical activity only during the second postnatal week ( Pangratz-Fuehrer and Hestrin , 2011 ) , and as shown here , it is also at this period that they constitute a significant , transient presynaptic input to NG2 cells . Synaptic communication between FSIs and NG2 cells could , therefore , play a role in the maturation of FSIs in the developing neuronal network . Although local cortical interneurons are believed to lack myelin because they possess relatively short axons that do not project outside the cortex , Somogyi et al . ( 1983 ) demonstrated the presence of myelin enwrapping the axon of basket cells , most probably FSIs , in cats ( Somogyi et al . , 1983 ) . Since myelination is an activity-dependent process ( Zalc and Fields , 2000 ) , an interesting possibility would be that the restricted point-to-point GABA release at unitary interneuron-NG2 cell connections encodes a signal that triggers NG2 cell differentiation and myelination of the presynaptic interneuronal axon , preferentially that of FSIs . Myelination of neocortical FSIs is likely to occur since myelin is important for the organization of Kv1 channels ( Rasband et al . , 1998 ) , and these channels have been proposed to mediate the variability in FSI-firing patterns ( Golomb et al . , 2007 ) . In addition , axonal signals are essential to regulate oligodendrocyte production and survival ( Barres et al . , 1992; Trapp et al . , 1997 ) , and interneuronal axons transiently contact NG2 cells during active-cortical oligodendrogenesis . Many one-photon uncaging systems have been used to study the synaptic connectivity patterns in brain regions , but their low-spatial resolution does not allow for photo-stimulating neurons at a single-cell level ( Dantzker and Callaway , 2000; Shepherd et al . , 2003; Yoshimura et al . , 2005 ) . On the contrary , we show that one-photon holographic photolysis is a suitable alternative tool to elicit action potentials in neurons at single-cell resolution and find unitary connections . Using this technique , we demonstrate that interneuron-NG2 cell connections display a very local arrangement in the network . This difference cannot be explained exclusively by the small volume occupied by NG2 cells . Indeed , axons of interneurons that ramify extensively and travel long did not innervate NG2 cells over 70 µm . In fact , the connectivity of NG2 cells already decreases at interneuron-NG2 cell intersomatic distances of 50 µm ( Figure 6G ) . It has been reported that no specificity exists between the spatial profiles of interneuron-pyramidal cell connectivity maps , and thus , that pyramidal cells do not form specific networks in the neocortex ( Fino and Yuste , 2011; Packer et al . , 2014 ) . In contrast , interneuron-NG2 cell connectivity maps follow a local spatial arrangement , reflecting a focal control of NG2 cell activity by interneurons . This microarchitecture supposes a close relationship between the proximal part of interneuronal axons and NG2 cells . The existence of a local network formed by interneurons and NG2 cells , embedded within the developing neuronal network , implies the involvement of selective molecular and cellular mechanisms ensuring this local connectivity . These mechanisms remain to be elucidated . In the developing postnatal brain , the proper maturation of interneuron–neuron microcircuits requires the interactions between intrinsic genetic programs and neuronal activity ( Cossart , 2011 ) . We demonstrate that the emergence of properly organized GABAergic neuronal microcircuits is not only confined to neurons , but also includes a non-neuronal cell type . Interestingly , after the peak of synaptic connectivity at PN10 , the decrease of GABAergic synaptic innervation of NG2 cells is accompanied by other relevant physiological changes in these progenitors: ( 1 ) there is a decrease in the amplitude of GABAAR-mediated miniature events ( Balia et al . , 2015 ) ; ( 2 ) Kir channels start to be upregulated ( Kressin et al . , 1995 ) ; and ( 3 ) the first wave of oligodendrocytes arising from Nkx2 . 1-expressing precursors of MGE and the anterior entopeduncular area is eliminated ( Kessaris et al . , 2006 ) . An intriguing question for future research is whether cortical NG2 cell development onto oligodendrocytes occurs under the control of interneuronal activity in the developing neuronal network . The GABAergic synaptic input probably does not control per se cortical oligodendrocyte production , which is a protracted process that occurs during several weeks , even after the loss of functional synapses . However , the coincidence between the peak of connectivity ( PN10 ) and the switch to a massive NG2 cell differentiation from PN10 raises the possibility that the interneuron-NG2 cell network sets precisely the onset of oligodendrogenesis occurring in deep layers of the neocortex during the second postnatal week . This would explain why a transient and structured NG2 cell connectivity is necessary .
All experiments followed European Union and institutional guidelines for the care and use of laboratory animals . Acute parasagittal slices ( 300 µm ) of the barrel cortex with an angle of 10° to the sagittal plane were obtained from a double VGAT-Venus;NG2-DsRed transgenic mouse ( Ziskin et al . , 2007; Wang et al . , 2009 ) , as previously described ( Vélez-Fort et al . , 2010 ) . Excitation light to visualize Venus and DsRed fluorescent proteins was provided by Optoled Light Sources ( Blue and Green Optoleds; Cairn Research , UK ) , and images were collected with an iXon+ 14-bit digital camera ( Andor Technology , UK ) through an Olympus BX51 microscope equipped with a 40× fluorescent water-immersion objective . Excitation and emission wavelengths were obtained by using , respectively , 470- and 525-nm filters for Venus and 560- and 620-nm filters for DsRed . The Imaging Workbench 6 . 0 software ( Indec Biosystems , USA ) was utilized to acquire and store images for off-line analysis . Patch-clamp recordings were performed at RT or 33°C using an extracellular solution containing ( in mM ) : 126 NaCl , 2 . 5 KCl , 1 . 25 NaH2PO4 , 26 NaHCO3 , 20 glucose , 5 pyruvate , 3 CaCl2 , and 1 MgCl2 ( 95% O2 , 5% CO2 ) . NG2 cells were recorded with different intracellular solutions according to the experiment and containing ( in mM ) : either 130 CsCl or 125 CsCH3SO3H ( CsMeS ) , 5 4-aminopyridine , 10 tetraethylammonium chloride , 0 . 2 EGTA , 0 . 5 CaCl2 , 2 MgCl2 , 10 HEPES , 2 Na2-ATP , 0 . 2 Na-GTP , and 10 Na2-phosphocreatine ( pH ≈ 7 . 3 ) . Presynaptic interneurons were recorded with an intracellular solution containing ( in mM ) : 130 K-gluconate ( KGlu ) , 10 GABA , 0 . 1 EGTA , 0 . 5 CaCl2 , 2 MgCl2 , 10 HEPES , 2 Na2-ATP , 0 . 2 Na-GTP , and 10 Na2-phosphocreatine ( pH ≈ 7 . 3 ) . Potentials were corrected for a junction potential of −10 mV when using CsMeS and KGlu-based intracellular solutions . Whole-cell recordings were obtained using Multiclamp 700B , filtered at 4 kHz , and digitized at 20 kHz . Digitized data were analyzed off-line using pClamp10 . 1 software ( Molecular Devices ) , Neuromatic package ( http://www . neuromatic . thinkrandom . com/ ) and Spacan ( http://www . spacan . net ) within IGOR Pro 6 . 0 environment ( Wavemetrics , USA ) . Na+ current densities and frequency of spontaneous synaptic activity were analyzed as we previously described ( Vélez-Fort et al . , 2010; Balia et al . , 2015 ) . Paired recordings were performed between a Venus+ interneuron and a DsRed+ NG2 cell both held at −70 mV with two patch pipettes . To test for a functional connection , paired-pulse stimulation was applied to the interneuron in voltage-clamp mode to elicit action currents at 8-s intervals ( 1 ms , 80 mV pulse; 50 ms paired-pulse interval ) . This protocol allows for a precise timing of action potential generation in interneurons . We considered as a unitary connection , those pairs showing averaged PSCs in NG2 cells larger than 2 times the standard deviation of the noise . To evaluate the recovery from depression , we applied two test pulses using interstimulus intervals ranging from 10 ms to 250 ms . Paired-pulse ratios were calculated as PSC2/PSC1 . Quantal analyses were performed on 18 out of 38 connections for which 100 or more traces were recorded , and individual PSCs in single traces could be differentiated from the noise using a detection threshold of 2 times the standard deviation . The holographic setup was adapted to the Olympus microscope as previously described ( Figure 5—figure supplement 1A ) ( Lutz et al . , 2008; Zahid et al . , 2010 ) . Briefly , a 405-nm diode CW-laser ( CUBE 405-100 , Coherent ) was used for uncaging experiments . The output beam was expanded ( 6× ) to match the input window of a LCOS-SLM ( X10468-01 , Hamamatsu ) , which operates in reflection mode . The device was controlled by a custom-designed software described in Lutz et al . ( 2008 ) that calculated the corresponding phase hologram and addressed the pattern to the LCOS-SLM , given a target intensity distribution at the focal plane of the microscope objective . The SLM plane was imaged at the back aperture of the microscope objective through a telescope ( L1 , f1 = 350 mm; L2 , f2 = 180 mm ) . The undiffracted component ( zero-order spot ) was masked at the focal plane of L1 using a coverslip with a black dot . Acute slices transferred into the recording chamber were perfused with the extracellular solution at 2–3 ml/min using a recycling bubbled system ( 10 ml ) that allows for the continuous perfusion of the caged MNI-glutamate ( 50 µM ) . Selective photostimulation of interneurons during patch-clamp recordings of either NG2 cells or pyramidal neurons was obtained with 5-µm illumination spots during 3–8 ms and a laser power of ∼12 mW under the objective . Patched cells were recorded with CsMeS-based intracellular solution and held at 0 mV to minimize the direct photo-activation of their glutamatergic receptors in the excitation field . The protocol consisted in using an initial photostimulation of 3 ms that photo-evoked single or few action potentials in most targeted interneurons ( Figure 5—figure supplement 3C ) . False negative and false positive connections were discerned by changing the laser time pulse and by moving the illumination spot outside the soma of the targeted interneuron ( Figure 5A–D , F ) . We considered as photo-induced unitary PSCs those that: ( 1 ) showed an increased occurrence probability of individual PSCs within 100 ms after photostimulation when visualized in raster plots ( this time window corresponded to the time needed for interneurons to spike after the photostimulation , Figure 5—figure supplement 3B ) ; and ( 2 ) were detected in averaged traces with a threshold of 2 times the standard deviation of the noise . For CC1 and Olig2 immunostainings , NG2-DsRed mice of the same litters were perfused intracardially with phosphate buffer saline ( PBS ) alone followed by 0 . 15 M phosphate buffer , pH 7 . 4 ( PB ) containing 4% paraformaldehyde at PN9–PN13 ( n = 6 litters and 5–7 animals per age ) . Brains were removed and placed in a 4% paraformaldehyde solution overnight . Then , brain slices ( 50 µm ) were prepared in PBS ice-cold solution ( 4°C ) , permeabilized with 0 . 2% triton X-100 and 4% Normal Goat Serum ( NGS ) for 1 hr , and incubated one night with antibodies diluted in a 0 . 2% triton X-100 solution and 2% NGS . For VGAT , γ2 , and NG2 triple immunostainings , animals were similarly perfused , and brains placed in 4% paraformaldehyde solution for 1 hr . Then , brain slices ( 100 µm ) were prepared in PBS ice-cold solution ( 4°C ) , permeabilized with 1% triton X-100 and 4% NGS for 1 hr , and incubated three nights with antibodies diluted in a 0 . 2% triton X-100 solution and 2% NGS . Double immunostainings were performed by combining rabbit anti-Olig2 ( 1:400; ref . AB9610 , Millipore ) with mouse monoclonal anti-CC1 ( 1:100; ref . OP80 , Calbiochem ) antibodies . Puncta were immunostained with guinea pig anti-VGAT ( 1:500; ref . 131 004 , Synaptic Systems ) and mouse anti-γ2 ( 1:500; ref . 224011; Synaptic Systems ) and NG2 cells with rabbit anti-NG2 ( 1:400; ref AB5320 , Millipore ) . All primary antibodies were washed 3 times in PBS and incubated in secondary antibodies coupled to DyLight-405 , Alexa-488 , or Alexa-633 for 2 hr at room temperature ( 1:500; ref . 106-475-003 , Jackson ImmunoResearch and ref . A11029 and A21071 , Life Technologies , respectively ) . Interneurons and NG2 cells were recorded with intracellular solutions containing 5 . 4 mM biocytin . Slices containing injected cells were fixed overnight in 4% paraformaldehyde at 4°C . For identification of FSIs , interneurons were immunostained with rabbit anti-PV antibody ( 1:2000; ref . PV-25 , Swant ) in VGAT-Venus;NG2-DsRed mice . Biocytin was revealed with Cy-5 conjugated streptavidin ( ref . 016-170-084 , Jackson Immuno- Research ) during incubation with the secondary antibody . Negative controls for immunostainings were performed by omitting all primary antibodies or by incubating a primary antibody with a secondary antibody against an omitted primary antibody . Optical sections of confocal images were sequentially acquired using a 10× or 63× oil objectives ( NA = 1 . 4 ) with the LSM-710 software ( Zeiss ) . Images were processed and analyzed using ImageJ and Imaris softwares . For counting layer V Olig2+/CC1+ cells , we analyzed 270 × 270 µm of 10–20 Z-sections ( each 1 µm ) . For counting layer V PV+ interneurons , we analyzed 225 × 225 µm of 55 Z-sections ( each 0 . 5 µm ) from PV immunostainings of barrel cortex of Venus+ mice . Co-localization of biocytin-loaded NG2 cells and VGAT+/PV puncta as well as NG2+ cells and VGAT+/γ2+ was assessed on 125 × 125 µm 60 Z-sections ( each 0 . 32 µm ) . 3D surfaces were created for all channels after applying a median filter to reduce noise . First , we quantified the number of VGAT+ puncta per NG2 cell somata and branches . For this , we extracted the fluorescent profiles for VGAT puncta and biocytin-loaded NG2 cells by tracing a line crossing both the puncta and the biocytin-loaded region . We considered as a contacting puncta those showing more than 23% overlapping of fluorescent profiles . Finally , we checked whether or not those VGAT+ puncta on NG2 cells colocalized with PV . Countings were performed for 4 biocytin-loaded NG2 cells and on 6–8 branches per NG2 cell . Similar colocalization parameters were applied for VGAT+/γ2+ on NG2+ cells ( n = 8 cells from two mice ) . To determine the spatial distribution of all VGAT+ and VGAT+/PV+ puncta around biocytin-loaded NG2 cells , we first obtained the 3D coordinates for all puncta ( Imaris tools ) . Then , puncta densities ( number of puncta per µm3 ) were calculated in increasing eccentric volumes from the center of the stack where the NG2 cell soma was located . Each volume corresponded to a sphere to which spherical caps were removed to remain inside the stack ( Figure 2—figure supplement 2B ) . The first volume was calculated from a sphere of 5-µm radius around the center of the field . The next volume was calculated from a sphere of 10-µm radius to which we subtracted the previous smaller volume . We repeated this volume calculation until we attained the x , y axis boundaries of the field . For each calculated volume , we determined all VGAT+ and VGAT+/PV+ puncta densities . Data are expressed as mean ± SEM . The nonparametric Mann–Whitney U test for independent samples was used to determine statistical differences between different pairs . When comparisons within single pairs were required , the Wilcoxon signed-rank test for related samples was used ( GraphPad InStat software version 3 . 06 ) . Cumulative distributions were compared using Kolmogorov–Smirnov test . Multiple group comparisons were done using one-way Kruskal–Wallis test followed by a Dunn's multiple comparison post-hoc test . Binomial distributions and confidence intervals ( Wilson interval; Brown et al . , 2001 ) for connection probabilities of FSIs and NFSIs were obtained using a custom routine in Python kindly provided by Christophe Pouzat ( Supplementary file 1 ) . Correlations were tested with a Pearson r test . | Neurons are outnumbered in the brain by cells called glial cells . The brain contains various types of glial cells that perform a range of different jobs , including the supply of nutrients and the removal of dead neurons . The role of glial cells called oligodendrocytes is to produce a material called myelin: this is an electrical insulator that , when wrapped around a neuron , increases the speed at which electrical impulses can travel through the nervous system . Neurons communicate with one another through specialized junctions called synapses , and at one time it was thought that only neurons could form synapses in the brain . However , this view had to be revised when researchers discovered synapses between neurons and glial cells called NG2 cells , which go on to become oligodendrocytes . These neuron-NG2 cell synapses have a lot in common with neuron–neuron synapses , but much less is known about them . Orduz , Maldonado et al . have now examined these synapses in unprecedented detail by analyzing individual synapses between a type of neuron called an interneuron and an NG2 cell in mice aged only a few weeks . Interneurons can be divided into two major classes based on how quickly they fire , and Orduz , Maldonado et al . show that both types of interneuron form synapses with NG2 cells . However , these two types of interneuron establish synapses on different parts of the NG2 cell , and these synapses involve different receptor proteins . Together , the synapses give rise to a local interneuron-NG2 cell network that reaches a peak of activity roughly two weeks after birth , after which the network is disassembled . This period of peak activity is accompanied by a sudden increase in the maturation of NG2 cells into oligodendrocytes . Further experiments are needed to test the possibility that activity in the interneuron-NG2 cell network acts as the trigger for the NG2 cells to turn into oligodendrocytes , which then supply myelin for the developing brain . | [
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Toll-dependent patterning of the dorsoventral axis in Drosophila represents one of the best understood gene regulatory networks . However , its evolutionary origin has remained elusive . Outside the insects Toll is not known for a patterning function , but rather for a role in pathogen defense . Here , we show that in the milkweed bug Oncopeltus fasciatus , whose lineage split from Drosophila's more than 350 million years ago , Toll is only required to polarize a dynamic BMP signaling network . A theoretical model reveals that this network has self-regulatory properties and that shallow Toll signaling gradients are sufficient to initiate axis formation . Such gradients can account for the experimentally observed twinning of insect embryos upon egg fragmentation and might have evolved from a state of uniform Toll activity associated with protecting insect eggs against pathogens .
In the fly Drosophila melanogaster , the Toll pathway has essential functions both for innate immunity and for dorsoventral ( DV ) axis formation ( Leulier and Lemaitre , 2008; Stein and Stevens , 2014 ) . While Toll's immune function is broadly conserved in animals ranging from hydra to humans , its role in axis formation appears to be an evolutionary novelty of insects ( Leulier and Lemaitre , 2008; Franzenburg et al . , 2012; Gilmore and Wolenski , 2012 ) . Other animals do not employ Toll but rather use BMP signaling to establish their DV axis ( De Robertis , 2008 ) . BMP signaling also plays a crucial , but spatially restricted role in Drosophila DV patterning ( O'Connor et al . , 2006 ) . This suggests that Toll signaling was recruited into an ancestral BMP-based patterning network during evolution of the insect lineage . So far molecular studies of DV patterning in insects have been largely restricted to the most speciose supraorder , Holometabola , the insects with complete metamorphosis ( Lynch and Roth , 2011 ) . However , already within the Holometabola , a clear evolutionary trend was observed: the more basally branching lineages show an increased reliance on BMP signaling while the importance of Toll signaling is reduced ( Figure 1 ) . In Drosophila , both the polarity and pattern of the DV axis depend on a stable long range gradient of Toll signaling that promotes the graded nuclear uptake of the NF-κB transcription factor Dorsal ( Reeves and Stathopoulos , 2009 ) . NF-κB/Dorsal acts in a concentration-dependent manner to activate or repress genes required for DV cell fate specification ( Figure 1 ) . The ventral cell fates of the mesoderm , mesectoderm and neuroectoderm directly depend on NF-κB/Dorsal target genes . The dorsal cell fates ( non-neurogenic ectoderm and extraembryonic amnioserosa ) are determined in a more indirect way by Toll signaling restricting and polarizing an opposing BMP signaling gradient ( O'Connor et al . , 2006; Hong et al . , 2008 ) . The gene regulatory network ( GNR ) controlled by NF-κB/Dorsal has been extensively characterized . It encompasses 60–70 target genes which fall into six classes according to their enhancer structure ( Hong et al . , 2008 ) . The sensitivity of these enhancers to different NF-κB/Dorsal concentrations is fine-tuned by ubiquitously distributed activators and repressors ( Garcia and Stathopoulos , 2011; Ozdemir et al . , 2014 ) . 10 . 7554/eLife . 05502 . 003Figure 1 . The evolution of Toll's role in dorsoventral ( DV ) patterning in insects . In holometabolous insects Toll signaling is activated by ventral eggshell cues and forms an activity gradient ( red ) that is essential at the very least for specifying the ventral-most cells on the DV axis , giving rise to the mesoderm ( brown ) , by activating the gene twist ( twi ) ( black arrow ) . In the fly Drosophila Toll signaling not only determines the mesoderm , but also the neuroectoderm ( yellow ) and restricts BMP signaling to the dorsal side through several parallel mechanisms , including the activation of the BMP inhibitor short gastrulation ( sog ) ( black arrow ) and repression of the major BMP ligand decapentaplegic ( dpp ) ( black T-bar ) ( Hong et al . , 2008; Reeves and Stathopoulos , 2009 ) . On the dorsal side a BMP gradient ( blue ) is established ( gray arrow and T-bar indicate BMP ligand production and inhibition , respectively ) that specifies non-neurogenic ectoderm ( blue ) and extraembryonic tissue ( green ) ( O'Connor et al . , 2006 ) . Toll signaling is dynamic in Tribolium and polarizes BMP signaling only by activating sog ( Nunes da Fonseca et al . , 2008 ) . BMP signaling in turn has an increased role in ectodermal patterning compared to flies ( van der Zee et al . , 2006 ) . In contrast to both Drosophila and Tribolium Toll signaling in the wasp Nasonia appears to be restricted to a narrow ventral region where it is only transiently active . Here , Toll signaling is required to induce mesodermal and mesectodermal fates . But the size of the mesodermal region as well as the fate and position of all other regions along the DV axis are determined by a BMP signaling gradient emanating from the dorsal side by an unknown ( Toll-independent ) mechanism ( black T-bar indicates repression of twi ) ( Özüak et al . , 2014a , 2014b ) . Thus , in the holometabolous insects BMP signaling gets increasingly more important towards basally branching groups , while Toll's role is diminished , but remains essential for ventral-most cell fates . Here we provide evidence that the bug Oncopeltus , representing the Hemiptera within the sister group of Holometabola ( Paraneoptera ) , uses Toll signaling only as spatial cue ( dashed black arrow ) to polarize a dynamic BMP signaling network that establishes a gradient responsible for patterning the cell fates along the DV axis . The key regulatory element of this network is the transcriptional repression of sog by BMP signaling . A reaction-diffusion model which incorporates this regulatory element shows that the formation of stable BMP gradients requires only weakly polarized Toll signaling ( Box 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 003 All major components of the BMP signaling network are controlled by NF-κB/Dorsal: one of the BMP ligands , the BMP2/4 homolog decapentaplegic ( dpp ) , and the extracellular protease tolloid ( tld ) are repressed by NF-κB/Dorsal and thus confined to the dorsal side of the embryo while an extracellular BMP inhibitor , the chordin homolog short gastrulation ( sog ) , and a transcriptional repressor of BMP target genes ( brinker , brk ) are activated by NF-κB/Dorsal at the ventral side ( Jazwinska et al . , 1999; O'Connor et al . , 2006; Hong et al . , 2008; Rushlow and Shvartsman , 2012 ) . The ventral-to-dorsal transport of Sog and Sog-BMP complexes and their dorsal cleavage by Tld leads to a BMP signaling gradient with peak levels at the dorsal side . Thus , Toll signaling via NF-κB/Dorsal not only provides precise spatial information for the ventral half of the axis , but indirectly also determines the patterning of the dorsal half . An independent maternal input at the dorsal side of Drosophila embryos has been discussed , but it apparently plays only a minor role ( Araujo and Bier , 2000 ) . In contrast to Drosophila , Toll signaling in the beetle Tribolium castaneum is highly dynamic due to positive and negative feedback of Toll pathway components ( Nunes da Fonseca et al . , 2008 ) . These dynamics lead to a temporally shifting NF-κB/Dorsal gradient which refines and disappears before the major DV patterning genes have established stable expression domains . This suggests that NF-κB/Dorsal concentration thresholds play a less direct role in specifying these domains ( Chen et al . , 2000 ) . In addition , NF-κB/Dorsal does not act as a repressor of BMP signaling components or as an activator of brk ( Nunes da Fonseca et al . , 2010 ) . Consequently , the establishment of the BMP gradient entirely relies on ventral ( NF-κB/Dorsal dependent ) activation of sog . The BMP gradient in turn is required for all the polarity of the ectoderm ( van der Zee et al . , 2006 ) . Thus , in Tribolium the direct role of Toll signaling is largely restricted to mesoderm and mesectoderm ( Figure 1 ) . Finally , in the wasp Nasonia , representing the basal-most branch ( Hymenoptera ) of the Holometabola , Toll signaling appears to be active only in a narrow domain along the ventral midline , where it is required to induce ventral-most cell fates ( Özüak et al . , 2014b ) . However , the borders of the ventrally expressed genes are not defined by thresholds of Toll signaling , but rather by repressive BMP signaling . Thus , in Nasonia BMP signaling specifies gene expression domains along the entire DV axis ( Figure 1 ) . In this respect the DV system in Nasonia is similar to the ancestral type of DV axis formation in bilaterian animals . However , a closer look at the mechanisms of gradient formation reveals that the Nasonia system is highly derived even when compared to Drosophila . Functional studies show that the BMP gradient of Nasonia is established from a maternal source along the dorsal midline independent from ventral Toll signaling ( Özüak et al . , 2014b ) . Indeed , the Nasonia genome lacks a sog homolog and no ventrally expressed BMP inhibitor was identified ( Özüak et al . , 2014a ) . The establishment of BMP signaling gradients by an opposing inhibitor gradient of Chordin/Sog is however , one of the most conserved aspects of DV axis formation in Bilateria and is even preserved in flies ( De Robertis , 2008 ) . Moreover , given the fact that Nasonia also uses Toll for mesoderm/mesectoderm induction , it establishes its DV axis in a bipolar manner employing independent signaling sources along the ventral and dorsal midline of the egg . Bipolar DV axis formation has so far not been described in any other system . Despite all the variability found so far in Holometabola there are two common themes . ( 1 ) In more basal lineages BMP signaling is responsible for functions that are performed by Toll signaling in more derived lineages . ( 2 ) The ventral-most regions of the DV axis , giving rise to the mesoderm and mesectoderm , remain strictly dependent on Toll signaling . By studying a representative of insects with incomplete metamorphosis ( Hemimetabola ) we asked whether this situation is characteristic for all insects or whether a further reduction of the DV patterning function of Toll can be observed , allowing us to analyze how it originated . To this end we investigated DV patterning in the milkweed bug , Oncopeltus fasciatus , representing the order Hemiptera , within the sister group ( Paraneoptera ) to the Holometabola ( Liu and Kaufman , 2009 ) . We provide evidence that in Oncopeltus Toll is indeed no longer essential for mesoderm formation since repression of BMP signaling suffices to induce mesoderm . Like in other systems inhibition of BMP signaling is accomplished by sog . However , the transcriptional regulation of sog in Oncopeltus is more dynamic than in the other well-studied systems . It combines uniform Toll-independent activation with ventral enhancement by Toll and repression by BMP . We build a theoretical model based on the experimental findings and show that the BMP/sog pathway in Oncopeltus exhibits self-organized patterning ( Box 1 ) . Specifically , the interplay of BMP-dependent sog repression and Sog-dependent BMP transport generates a Turing instability ( Turing , 1952 ) . Toll's role in this system seems to be reduced to providing a trigger that enhances Sog activity above a certain threshold to initiate the patterning process . However , this patterning mechanism differs from the well-studied activator-inhibitor models ( Gierer and Meinhardt , 1972 ) ; while sog is inhibited by BMP , there is no activator in our model .
To mark different DV regions of Oncopeltus blastoderm embryos we chose twist ( twi ) , a ventrally expressed marker for the mesoderm ( Thisse et al . , 1988 ) ( Figure 2A lateral view ) , single minded ( sim ) , a mesectodermal marker ( Thomas et al . , 1988 ) expressed in lateral stripes bordering the mesoderm and in a ventral-anterior domain ( Figure 2B lateral view ) , and short gastrulation ( sog ) , the insect homolog of the BMP antagonist Chordin ( Francois et al . , 1994 ) , which is expressed in a ventral domain slightly broader than that of twi ( Figure 2C lateral view ) . 10 . 7554/eLife . 05502 . 004Figure 2 . Knockdown ( KD ) of BMP signaling components results in completely ventralized ( dpp- , tld-RNAi ) or completely dorsalized ( sog- , tsg-RNAi ) embryos . Expression of twi ( A , E , I , M , Q ) , sim ( B , F , J , N , R ) and sog ( C , G , K , O , S ) in wild type ( wt ) embryos ( A–C ) , dpp-RNAi embryos ( E–G ) , sog-RNAi embryos ( I–K ) , tsg-RNAi embryos ( M–O ) and tld-RNAi embryos ( Q–S ) monitored by whole mount in situ hybridization ( ISH ) . The view is lateral with the dorsal side pointing up ( A–C ) , ventral ( K ) , or not determined as the expression is DV-symmetric ( E–G , I , J , M–O , Q–S ) . Embryos are at the blastoderm stage ( ∼26–32 hpf: A , C , E–G , I–K , M , O , Q , S ) , or at the beginning of anatrepsis ( posterior invagination of the embryo , ∼33–37 hpf ) ( B ) . Scale bar ( A ) corresponds to 200 µm . For phenotype frequencies and confirmation of KD see Figure 2—figure supplement 2 and Figure 5—figure supplement 1 . ( D , H , L , P , T ) Simulations of the reaction diffusion system described in Box 1 on a two-dimensional cylinder ( Figure 10 ) . Depicted is one half of the cylinder surface stretching from the dorsal ( D ) to the ventral ( V ) midline . Blue: sog expression ( η ) . Gray: BMP concentration ( b ) . ( D ) In wt sog expression is confined to a ventral stripe . ( H ) Loss of BMP ( b = 0 ) leads to uniform derepression of sog . ( L ) Loss of sog ( s = 0 ) leads to uniformly high levels of BMP . ( P ) Loss of Tsg was modeled by assuming that no Sog-BMP complexes are formed ( k+ = 0 ) . This results in high BMP signaling throughout the embryo . ( T ) Loss of Tld was modeled by reducing the degradation constant of Sog ( αs ) by 90% . As Sog-BMP complexes are not degraded , BMP is not released , causing uniform derepression of sog . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 00410 . 7554/eLife . 05502 . 005Figure 2—figure supplement 1 . Expression of BMP signaling components during blastoderm . Anterior is to the left . DV orientation cannot be determined at this stage . The expression of dpp and tsg cannot be detected by ISH during blastoderm stages between 26 to 28 hpf . gbb and tld show uniform expression along the embryonic circumference . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 00510 . 7554/eLife . 05502 . 006Figure 2—figure supplement 2 . Phenotype frequencies after parental RNAi . The KD of dpp and tld by parental RNAi caused partial or complete ventralization . No wt embryos were observed . Completely ventralized embryos show a uniform expansion of twi , sog , or the ventral-anterior sim domain ( analyzed by ISH ) and lack detectable pMAD staining ( analyzed by immunostaining ) . In partially ventralized embryos marker gene expansion was not complete and residual pMAD staining could be detected . Each marker was analyzed separately . The KD of sog and tsg caused partial or complete dorsalization . Completely dorsalized embryos lack twi , sog or sim ( except for the terminal domain ) and exhibit uniform levels of nuclear localized pMAD levels . Partially dorsalized embryos show residual twi , sog or sim expression and their nuclear pMAD levels are not completely uniform . No wt-like patterns were observed . The table shows % of complete ventralization or complete dorsalization . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 006 None of the known dorsally expressed genes from the Holometabola showed specific dorsal expression in early Oncopeltus embryos . This includes both the BMP signaling components ( O'Connor et al . , 2006 ) decapentalplegic ( dpp ) , glass bottom boat ( gbb ) , tolloid ( tld ) and twisted gastrulation ( tsg ) ( Figure 2—figure supplement 1 ) as well as target genes potentially activated by BMP signaling like zerknüllt , pannier , dorsocross and iroquois ( Panfilio et al . , 2006; Nunes da Fonseca et al . , 2010; Buchta et al . , 2013 ) ( data not shown ) . In the absence of dorsal marker genes we monitored the distribution of phosphorylated Mad ( pMAD ) , the activated form of the transcription factor downstream of BMP signaling ( Dorfman and Shilo , 2001; van der Zee et al . , 2006 ) . In early embryos pMAD accumulates more strongly in nuclei of dorsal than of ventral cells ( Figure 3A , F; nuclear density can be used to distinguish dorsal and ventral regions of the embryos , Figure 3—figure supplement 1 ) . Over time this asymmetry is enhanced . At the beginning of gastrulation , high levels of pMAD are restricted to the dorsal 30% of the egg circumference with sharp lateral borders ( Figure 3—figure supplement 1 ) . Within the domain of high nuclear concentrations the pMAD distribution is flat; i . e . , it lacks the sharp peak along the dorsal midline which has been observed in Drosophila and Nasonia and is also less graded than the pMAD profile of Tribolium ( Dorfman and Shilo , 2001; van der Zee et al . , 2006; Özüak et al . , 2014b ) . 10 . 7554/eLife . 05502 . 007Figure 3 . BMP signaling activity is uniformly abolished or expanded in ventralized or dorsalized phenotypes , respectively . pMAD distribution in blastoderm stage ( 26–32 hr post fertilization , hpf ) wt ( A , A′ , F , F′ ) , dpp-RNAi ( B , B′ , G , G′ ) , sog-RNAi ( C , C′ , H , H′ ) , tsg-RNAi ( D , D′ , I , I′ ) and Toll1-RNAi ( E , E′ , J , J′ ) embryos . For each embryo a ventral and a dorsal view , or views from opposite sides if DV polarity is lacking ( B–G , D-J ) are shown . Magnified surface views to the right of each embryo ( x' ) reveal the presence or absence of pMAD in individual nuclei . The scale bar ( A , A′ ) corresponds to 50 µm . For identifying the polarity of the DV axis and for BMP signaling activity during later development see Figure 3—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 00710 . 7554/eLife . 05502 . 008Figure 3—figure supplement 1 . Nuclear density and late pMAD distribution identify the dorsal side of Oncopeltus blastoderm embryos . Top: the embryo was stained for pMAD and nucleic acids ( SYTOX ) to visualize the distribution of nuclei . Differences in nuclear density can be used to detect the polarity of the DV axis . At the dorsal side nuclear density is higher than at the ventral side . pMAD nuclear accumulation is found in regions of higher nuclear density , indicating that pMAD can be used as a marker for the dorsal region . Bottom: pMAD distribution at mid-blastoderm ( ∼26–28 hpf ) , late blastoderm ( ∼29–32 hpf ) and at the beginning of the posterior invagination ( anatrepsis ) ( ∼33–37 hpf ) . For each embryo a dorsal , lateral and ventral view is shown . The pMAD distribution is progressively confined to the dorsal side . At the beginning of anatrepsis pMAD is restricted to a dorsal domain comprising approximately 30% of the embryonic circumference . The scale bar corresponds to 200 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 008 Having established these four markers of distinct DV domains , we first analyzed the role of BMP signaling in Oncopeltus . Nuclear pMAD accumulation was largely abolished by knockdown ( KD ) , via parental RNAi targeting the ortholog of Drosophila BMP ligand dpp ( O'Connor et al . , 2006 ) . This result confirms the specificity of our pMAD staining and demonstrates that the KD leads to a severe reduction of BMP signaling in the early embryo ( Figure 3B , G ) . Strikingly , this reduction of BMP signaling results in a massive expansion of twi and sog expression around the entire embryonic circumference ( Figure 2E , G , for phenotype frequencies see Figure 2—figure supplement 2 ) . The loss of lateral expression of sim ( Figure 2F ) shows that all fates dorsal to the mesoderm are lacking , indicating that the embryo is completely ventralized . Thus , BMP signaling is required in Oncopeltus to restrict the ventral-most , mesodermal cell fate . Absence of BMP signaling leads to a complete loss of DV polarity , which is not recovered during later stages of development ( Figure 4D–F ) , a phenotype so far not known from other insects where BMP signaling either has no influence on the mesoderm ( van der Zee et al . , 2006; Lynch and Roth , 2011 ) or only partially suppresses mesodermal cell fates ( Özüak et al . , 2014b ) . 10 . 7554/eLife . 05502 . 009Figure 4 . Late phenotypes of dpp , sog and Toll1 KD embryos . Expression of msh ( top row ) , sim ( center row ) , and twi ( bottom row ) in wt ( A–C ) , dpp-RNAi ( D–F ) , sog-RNAi ( G–I ) and Toll1-RNAi ( J–L ) embryos monitored by ISH . The anterior of the embryo is on the left . Embryos are at the germ band stage ( ∼40–48 hpf ) . msh: in wt germ band stage embryos msh is expressed in the dorsal-most part of the CNS and in the mesoderm of the limb buds ( dorsal-lateral view ) . dpp-RNAi germ band embryos lack msh expression except for an anterior domain . sog- or Toll1-pRNAi embryos have a tube-like appearance lacking mesoderm and limb buds . Along these tubes msh is either not expressed or it is expressed at uniform levels around the entire circumference . This indicates that the ectoderm of sog- and Toll1 KD embryos is dorsalized either at the level of the dorsal non-neurogenic or the dorsal-most neurogenic ectoderm . sim: in wt germ band stage embryos sim is expressed along the ventral midline ( ventral-lateral view ) . Upon dpp- , sog- or Toll1-pRNAi , sim expression is lacking except for a ring of expression at the posterior tip of the growth zone in sog-RNAi and Toll1-RNAi embryos . This indicates that the ventral neuroectoderm is lost in these KD embryos . twi: in germ band stage embryos twi is expressed in the invaginated mesoderm , which forms initially a cord within the embryo ( lateral view ) . In dpp-RNAi embryos twi is expressed in the entire germ band indicating complete mesodermalization . In sog-and Toll1-RNAi embryos twi is not expressed . This , in addition to the loss of sim expression , indicates that sog and Toll1 KD embryos consistently lack ventral cell fates along their entire AP axis . Scale bar corresponds to 200 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 009 Given the striking expansion of the ventral-most cell fate upon loss of BMP signaling in Oncopeltus , we wondered how ectopic BMP signaling would affect DV patterning . For this purpose we knocked down sog and twisted gastrulation ( tsg ) . Sog ( Chordin ) is known from Drosophila and several vertebrates to inhibit BMP signaling via ligand sequestering ( Little and Mullins , 2006; O'Connor et al . , 2006 ) . For Tsg both anti- and pro-BMP functions have been observed ( Wang and Ferguson , 2005; Little and Mullins , 2006; Nunes da Fonseca et al . , 2010; Özüak et al . , 2014b ) . In Oncopeltus , the KD of these two genes causes elevated levels of pMAD at the ventral side and frequently leads to a uniform distribution of pMAD around the embryonic circumference , indicating that the asymmetry of BMP signaling depends on sog and tsg ( Figure 3C , D , H , I ) . The levels of pMAD around the entire circumference are similar to the levels found at the dorsal side of wild type ( wt ) embryos . The KD embryos show a complete loss of the mesoderm and mesectoderm , as demonstrated by the loss or strong reduction of twi , sog and sim expression ( Figure 2I–K , M–O; for phenotype frequencies see Figure 2—figure supplement 2 ) . This further indicates dorsalization and a lack of DV polarity when BMP signaling is uninhibited . During later development , DV polarity is not recovered: gastrulation and all subsequent morphogenetic movements lack DV asymmetry ( Figure 4G–I ) . Thus , in Oncopeltus , in contrast to all other insects analyzed so far ( Lynch and Roth , 2011 ) , BMP signaling has to be suppressed ventrally by Sog ( in conjunction with Tsg ) to allow polarization of the DV axis and specification of ventral cells . The essential role of Sog is supported by the consequences of a depletion of Tolloid ( Tld ) , which is known to cleave and inactivate Sog and thereby to release bound BMP ligands ( O'Connor et al . , 2006 ) . As with the dpp KD , tld KD leads to a complete ventralization of the embryo , indicating that BMP ligands are largely ( or completely ) sequestered in inhibitory Sog-BMP complexes in the absence of Tld ( Figure 2Q–S ) . Besides the complete loss of embryonic DV polarity , the KD phenotypes reveal an interesting regulatory feature of the BMP network in Oncopeltus . sog expression is expanded or suppressed by reducing ( dpp KD ) or expanding ( tsg KD ) BMP activity , respectively , demonstrating that BMP signaling negatively regulates its own antagonist in Oncopeltus . This was never observed in holometabolous insects , where sog expression either exclusively depends on Toll signaling lacking feedback control by BMP ( Drosophila and Tribolium ) ( Jazwinska et al . , 1999; van der Zee et al . , 2006 ) or is absent ( Nasonia ) ( Özüak et al . , 2014b ) . However , in spiders ( Akiyama-Oda and Oda , 2006 ) , vertebrates ( De Robertis and Kuroda , 2004 ) , and sea anemones ( Saina et al . , 2009 ) the sog homolog chordin is directly or indirectly repressed by BMP signaling , indicating that the BMP network of Oncopeltus exhibits a regulatory property that is ancestral for animals . As all cell fates along the DV axis are affected by BMP signaling , we wondered whether Toll signaling is even required for DV patterning in Oncopeltus . A recent study in another hemipteran , Rhodnius prolixus has provided evidence that Toll signaling plays a role in DV patterning ( Berni et al . , 2014 ) . In Oncopeltus , KD of the Toll1 ortholog resulted in loss of twi and sim expression ( Figure 5E , F ) and sog expression was completely lacking in 38% of the mid and late blastoderm stage embryos ( Figure 5G; for phenotype frequencies and confirmation of KD see Figure 5—figure supplement 1 ) . As expected , this leads to high uniform levels of pMAD around the entire embryo circumference ( Figure 3E , J ) . Expression analysis of germ band stage embryos confirms that the Toll1 KD embryos are dorsalized and lack all DV polarity ( Figure 4J–L ) . KD of other downstream components of Toll signaling ( Myd88 , Pelle , Tube-like kinase ) leads to identical phenotypes ( data not shown ) . Two homologs of NF-κB/Dorsal ( Of-dl1 and Of-dl2 ) , the transcription factor acting downstream of Toll signaling ( Stein and Stevens , 2014 ) , were identified . KD of both caused a loss of ventral gene expression , albeit to varying degrees , indicating at least partially redundant functions ( shown for dl1: Figure 5H–J; Figure 5—figure supplement 1 ) . Taken together , interfering with Toll signaling leads to dorsalized phenotypes , which closely resemble those produced by KD of sog and tsg . 10 . 7554/eLife . 05502 . 010Figure 5 . BMP signaling is epistatic to Toll signaling in Oncopeltus . Expression of twi ( A , E , H , L ) , sim ( B , F , I , M ) , sog ( C , G , J , N , P , R , T , V ) in wt embryos ( A–C , R , T , V ) , Toll1-RNAi embryos ( E–G , P ) , dl1-RNAi embryos ( H–J ) and Toll1-dpp-RNAi embryos ( L–N ) monitored by ISH . The view is ventral ( A–C , J , T , V ) , or not determined as the expression is DV symmetric ( E–G , H , I , L–N , P , R ) . Embryos are at the blastoderm stage ( A–C , E–G , H–J , L–N: 26–32 hpf; P–V see figure labels ) . Green arrowheads mark the anterior border of sim expression . The scale bar ( A ) corresponds to 200 µm . For phenotype frequencies and confirmation of KD see Figure 5—figure supplement 1 . ( D , K , O , Q , S , U , W ) Simulations of the reaction diffusion system described in Box 1 on a two-dimensional cylinder ( Figure 10 ) . Depicted is the ventral part of the cylinder . Blue: sog expression level ( η ) . Gray: BMP concentration ( b ) . ( D ) wt: sog expression is confined to a ventral stripe . ( K ) Upon loss of active NF-κB/Dorsal ( d = 0 ) due to either KD of Toll1 or KD of dl1 , early activation of sog ( P ) is insufficient to initiate patterning resulting in uniformly high BMP signaling . ( O ) Upon simultaneous loss of Dorsal ( d = 0 ) and BMP ( b = 0 ) sog activation is possible despite lack of NF-κB/Dorsal; however , activation is uniform . ( Q ) sog activation at early stages in the absence of Toll signaling ( d = 0 ) . This reflects ηo , NF-κB/Dorsal-independent sog activation ( Box 1 ) . ( S , U , W ) Developmental progression of sog activation ( η ) during blastoderm stages . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 01010 . 7554/eLife . 05502 . 011Figure 5—figure supplement 1 . Phenotype frequencies and transcript levels after RNAi . Top: the KD of Toll and dl1 caused partial or complete dorsalization . Completely dorsalized embryos lack twi , sog or sim ( except for the terminal domain ) expression and exhibit uniform levels of nuclear localized pMAD levels . Partially dorsalized embryos show residual twi , sog or sim expression and their nuclear pMAD levels are not completely uniform . No wt-like patterns were observed . The table shows % of complete ventralization or complete dorsalization . The double KD of dpp plus Toll by parental RNAi caused complete ventralization . Completely ventralized embryos show a uniform expansion of twi , sog , or the ventral-anterior sim domain ( analyzed by ISH ) . Each marker was analyzed separately . Bottom: expression of actin 5C , dpp and/or Toll1 in Toll1-RNAi , dpp-RNAi , Toll1-dpp-RNAi compared to wt ( left column ) detected by gel electrophoresis after semi-qPCR . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 011 However , loss of Toll signaling also has consequences not observed by manipulating the BMP pathway . This becomes apparent by looking at marker genes expressed in head anlagen like muscle-specific homeobox ( msh ) . In wt blastoderm embryos msh is expressed in a stripe with a sharp anterior border and a posterior border positioned at approximately 60% egg length ( 0% is the posterior pole ) . After Toll1 and dl1 KD the posterior msh border is shifted anteriorly , typically to 80% egg length , and the stripe expands towards the anterior tip of the embryo ( Figure 6B , C ) . This does not occur in dorsalized embryos after tsg KD ( Figure 6D ) . Using other markers , AP shifts have also not been seen in ventralized embryos after dpp and tld KD ( see anterior sim stripe in Figure 2F , R ) . We therefore assume that Toll , unlike BMP signaling , is not only dedicated to DV patterning in Oncopeltus , but also contributes to specifying the AP axis . A role for Toll in positioning the embryo along the AP axis has recently been suggested for the hemipteran Rhodnius ( Berni et al . , 2014 ) . 10 . 7554/eLife . 05502 . 012Figure 6 . Toll signaling affects AP patterning . Expression of msh is monitored by ISH in blastoderm embryos . The view is lateral ( A ) , or not determined as the expression is DV symmetric ( B–D ) . The red arrowheads mark the posterior border of msh expression which is positioned at approximately 60% egg length ( 0% posterior pole ) in wt ( A ) and tsg KD ( D ) embryos . In Toll1 and dl1 KD embryos , the msh domain expands to the anterior tip of the embryo and its posterior border is shifted anteriorly ( to approximately 80% egg length ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 012 Since twi and sog are completely dependent on Toll signaling for their activation in all studied holometabolous insects , we hypothesized that the expansion of twi and sog in dpp KD was due to a corresponding expansion of Toll signaling in the absence of BMP-dependent repression . To test this hypothesis , we produced embryos simultaneously lacking Toll and BMP signaling . To our surprise the Toll1 dpp double KD embryos showed uniform twi and sog expression along the embryonic circumference ( Figure 5L , N; Figure 5—figure supplement 1 ) , the same as the dpp single KD ( Figure 2E , G ) . However , in contrast to the single KD of dpp , the double KD embryos also show an expansion and/or shift of the sog , twi and ( anterior ) sim domains towards the anterior pole ( compare Figure 2E–G and Figure 5L–N ) . This is likely due to the additional role of Toll signaling in anterior patterning and allows for an unambiguous distinction between double and single KD embryos ( additional confirmation by RT-PCR , Figure 5—figure supplement 1 ) . Our results suggest that DV patterning genes that require Toll signaling for expression in other insects can be activated in the absence of Toll signaling in Oncopeltus . As these genes are repressed by elevated BMP activity their state of expression seems to be mainly controlled by different levels of BMP signaling . This leads to the crucial question: What then is Toll's role within the DV patterning system of Oncopeltus if Toll is neither strictly required to activate ventral genes nor to prevent their repression ? To address this question we carefully studied the dynamics of sog expression in wt and Toll1 KD embryos . Interestingly , sog transcription is activated ubiquitously in early blastoderm embryos ( Figure 5R ) . Only later is sog expression enhanced at the ventral side , while weak sog expression is still seen dorsally ( Figure 5T , V ) . Finally , during mid-blastoderm ( 25–28 hpf ) the typical sog expression domain is established , with high levels in the ventral 40% and no detectable expression in the dorsal 60% of the germ rudiment ( Figures 5C , 2C ) . Early Toll1 KD embryos show uniform expression of sog ( Figure 5P ) , which disappears during later stages ( Figure 5G ) . Thus , Toll is not required to initiate sog expression , but rather to enhance its expression ventrally . A weakly asymmetric Toll gradient might suffice to fulfill this function . To get a first impression of the shape of the Toll signaling gradient in Oncopeltus , we analyzed the expression of cactus ( cact ) genes encoding the insect I-κB homologs which bind to NF-κB/Dorsal and prevent nuclear transport ( Bergmann et al . , 1996 ) . The transcriptional activation of I-κB genes by Toll signaling appears to be an ancestral negative feedback loop essential for attenuating the innate immune response triggered by Toll ( Hoffmann et al . , 2002 ) . During Drosophila and Tribolium DV patterning cact is an early target gene of Toll signaling expressed in regions of high nuclear NF-κB/Dorsal concentrations ( Sandmann et al . , 2007; Nunes da Fonseca et al . , 2008 ) . The same appears to apply to Nasonia where cact expression is restricted to a narrow stripe straddling the ventral midline indicating a highly refined pattern of Toll activity ( Buchta et al . , 2013; Özüak et al . , 2014b ) . The Oncopeltus genome harbors six cact paralogs , four of which are expressed during blastoderm stages ( Vargas Jentzsch et al . , 2015 ) ( and data not shown ) . While cact1 , 2 and 4 show only weakly asymmetric expression ( Figure 7A–C , and data not shown ) , cact3 is expressed in a broad ventral domain encompassing 60–80% of the embryonic circumference with graded borders toward the dorsal side ( Figure 7E–G , I , J ) . The expression of cact3 does not refine into a more narrow domain , but remains broad during later blastoderm stages . Toll1 KD embryos lack ( or show reduced ) cact1 and cact3 expression , confirming the regulatory link known from other insects ( Figure 7D , H ) . These observations support the notion that in Oncopeltus , Toll signaling is transiently active almost along the entire DV axis and forms a shallow gradient with lower levels in the dorsal half . 10 . 7554/eLife . 05502 . 013Figure 7 . Expression of cact1 and cact3 . Expression of cact1 ( A–D ) and cact3 ( E–H ) are monitored by ISH with embryos at early to late blastoderm stages ( 20–32 hpf ) . ( A′–D′ , E′ , H′ ) SYTOX Green staining shows nuclear density to determine developmental stage . ( A , B ) cact1 expression is initiated evenly . ( C ) With proceeding development cact1 expression vanishes from the dorsal side . ( D ) 20% of Toll1 KD embryos lack cact1 expression . The remainder show reduced expression compared to wt . ( E ) cact3 expression is initiated uniformly along the DV axis between 20% and 60% egg length . ( F , G ) In older blastoderm stages cact3 is expressed in a broad domain encompassing 60–80% of the egg circumference . ( H ) 47% of Toll1 KD embryos lack cact3 expression . The remainder show reduced expression compared to wt . ( I , J ) Double ISH for cact3 ( blue ) and sog ( red ) confirms that cact3 is expressed ventrally and that its domain expands more dorsally than the sog domain . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 013 In sum , our empirical observations lead to the following model for DV patterning in Oncopeltus ( Figure 1 , Box 1 ) . We posit that during early blastoderm stages , weak uniform BMP signaling is balanced by the uniform Toll-independent production of the BMP inhibitor Sog . A shallow Toll signaling gradient breaks this symmetry by enhancing sog expression at the ventral side . This leads both to ventral suppression of BMP signaling , and to a flux of BMP-Sog complexes to the dorsal side . Subsequently , the Tld-dependent cleavage of Sog releases BMP and hence increases BMP signaling . Since BMP signaling represses sog expression , the asymmetry initiated by Toll is dynamically enhanced . To investigate the dynamics of the Sog/BMP system we constructed a minimal reaction-diffusion model as in previous work in Drosophila ( Eldar et al . , 2002 ) ( Box 1 , ‘Materials and methods’ ) . In this model the rate of sog expression combines NF-κB/Dorsal-independent and NF-κB/Dorsal-dependent activation with repression of sog by BMP . Parameter settings were selected such that NF-κB/Dorsal-dependent sog activation is necessary in order to initiate patterning ( Figures 8 , 9 and Table 1 ) . This mode of sog activation tightly links DV axis formation to egg polarity via Toll signaling ( Stein and Stevens , 2014 ) and provides stability against random fluctuations ( Box 1 , ‘Materials and methods’ , Figure 9 ) . 10 . 7554/eLife . 05502 . 017Figure 8 . Dynamics of pattern formation . Each plot shows the concentration of a particular protein species in space ( x running from 0 to lx along the front of the plot parameterizing the circumference of the cylinder ) and time ( running towards the back ) . Left: the concentration of NF-κB/Dorsal shows a broad Gaussian profile that decays to zero with time . Center: starting from a uniform distribution a region of high Sog concentrations forms where the initial distribution of NF-κB/Dorsal had its maximum . Right: BMP is depleted where Sog levels are high . Initial conditions are b ( x , t = 0 ) = 0 . 32 , s ( x , t = 0 ) = 0 . 01 , c ( x , t = 0 ) = 0 . 14 , d ( x , t = 0 ) = Doexp{−12 ( 2/lx ) 2 ( x−2/lx ) 2} . Throughout the text Do=0 . 3 , except in the twinning figure ( Box 1 ) , where Do=1 was used to ensure a sufficient amount of NF-κB/Dorsal in both halves of the embryo . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 01710 . 7554/eLife . 05502 . 018Figure 9 . Pattern formation from different initial levels of NF-κB/Dorsal . The initial concentration gradient of NF-κB/Dorsal is shown on top ( gray ) . Initial amplitudes of NF-κB/Dorsal are Do=0 . 15 , 0 . 3 , 1 from left to right , the dashed line indicates the threshold NF-κB/Dorsal concentration required for patterning . Below , steady-state levels of free BMP ( red ) and free Sog are shown ( blue , rescaled to facilitate plotting on the same plot as BMP ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 01810 . 7554/eLife . 05502 . 019Table 1 . Model parameters . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 019η2 . 8 × 10−4η12 × 10−3b00 . 2d01ηb4 × 10−5αs2 x 10−3αb5 × 10−5αc2 × 10−4K+5k−5 × 10−5Ds1 . 5 × 10−13Db7 . 8 × 10−13Dc2 . 5 × 10−9lx0 . 0017Units are arbitrary but are suggested to be seconds for time and meters for length . Two-dimensional simulations on a cylinder representing the trunk region of the ellipsoid embryo show that the model robustly replicates the formation of stripe-like sog expression domains ( Figure 2D; Figure 5D , W; Figure 10 ) . Moreover , the model correctly recovers the steady state of sog expression and BMP distribution in KD embryos ( Figure 2H , L , P , T; Figure 5K , O ) , including the dynamics of sog expression in Toll1 KD ( Figure 5Q , K ) , as well as wt embryos ( Figure 5S , U , W ) . 10 . 7554/eLife . 05502 . 020Figure 10 . Pattern formation in two dimensions . Starting from a distribution of NF-κB/Dorsal with a broad maximum running in parallel to the cylinder's axis ( bottom , shown in green ) , a stripe of high Sog concentration develops ( top , Sog shown in blue , BMP shown in red ) . The figures show concentrations at times 0 , 1000 , 2000 , 3000 , 4000 from left to right . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 020 Simulations also reveal that even weakly polarized NF-κB/Dorsal gradients result in sharp BMP signaling profiles ( Box 1 , Figure 9 ) . The final patterning output is robust with regard to variation in width of the NF-κB/Dorsal gradient along the AP axis ( Figure 11 ) . Likewise , raising the NF-κB/Dorsal concentration above the critical threshold for sog activation along the entire DV axis had no impact on the patterning output ( Figure 9 ) . 10 . 7554/eLife . 05502 . 021Figure 11 . Independence from the stripe of initial conditions ( same data as Figure 10 ) . ( top ) The initial distribution of NF-κB/Dorsal from Figure 10 varies along the cylinder's axis ( y-direction of this contour plot , with the x-direction describing the circumference ) in both standard deviation and amplitude by about 10%; d ( x , t=0 ) = exp{−522 ( 1+0 . 1sin ( πy/ly ) ) 2 ( x−2/lx ) 2} ( 1+0 . 1sin ( πy/ly ) ) , and decays over time ( time points 0 , 1000 , 2000 , 3000 , 4000 shown from left to right ) . ( bottom ) The resulting distribution of Sog ( and correspondingly of BMP ) becomes uniform along the cylinder axis . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 021 Such weakly polarized , broad NF-κB/Dorsal distributions in conjunction with the proposed dynamic BMP signaling system can explain embryonic twinning induced by egg fragmentation in another hemipteran species , the leaf hopper Euscelis plebejus ( Sander , 1971 ) . In the course of his experiments , Sander produced dorsal and ventral egg fragments during early development ( preblastoderm ) using a guillotine which completely separated the two egg halves . Complete germ band embryos developed within each half ( Box 1 ) . The model proposed here can reproduce such regulative behavior . A split along the DV axis prior to the initiation of patterning prevents diffusion from the ventral to the dorsal half . Thus , BMP acting as a long-range inhibitor of sog expression cannot travel from the ventral to the dorsal half to suppress sog . Since the NF-κB/Dorsal gradient extends to the dorsal half , sog can be activated dorsally and initiates a second round of patterning . Consequently , sog domains and BMP gradients are produced independently in each half despite the NF-κB/Dorsal gradient itself not having been altered ( Box 1 ) . The sizes of the sog and BMP domains are adjusted to the dimensions of the egg halves implying that the patterning process shows almost perfect scaling . Furthermore , the predicted orientation of the embryos with ventral sides pointing to the dorsal egg pole ( after axis inversion through anatrepsis , [Panfilio , 2008] ) corresponds to the most frequently observed experimental outcome ( Sander , 1971 ) . Thus , our model represents a minimal BMP/Sog ( Chordin ) system that exhibits self-organized DV patterning and explains a striking result from classical insect embryology . The only requirement for the NF-κB/Dorsal gradient is that it extends into the dorsal half of the embryo . The expression of cact suggests that this condition is fulfilled in Oncopeltus . Unfortunately , the mechanical properties of Oncopeltus eggs prevents egg fragmentation to directly investigate the potential for twinning .
In Drosophila and Tribolium , the expression of sog is dependent on Toll signaling ( van der Zee et al . , 2006; Liberman and Stathopoulos , 2009; Nunes da Fonseca et al . , 2010 ) . sog can be neither activated in absence of Toll ( Stathopoulos and Levine , 2004 ) nor can it be repressed by ectopic BMP signaling ( Jazwinska et al . , 1999 ) . However , a binding site for transcription factors acting downstream of BMP signaling ( Schnurri-Mad-Medea sites ) has recently been identified within the proximal enhancer of Drosophila sog ( Ozdemir et al . , 2014 ) . Its functional significance is not known , but it might represent an evolutionary relict of the regulatory logic we have observed in Oncopeltus in which inhibitory BMP signaling is essential for defining the sog expression domain . A negative feedback of BMP on sog/chordin expression is familiar from many animal phyla . It has even been found in sea anemones , predating the emergence of the bilaterian body plan ( Saina et al . , 2009; Genikhovich et al . , 2015 ) . For both hemichordates and basal chordates , evidence was provided that BMP suppresses chordin , directly or indirectly ( Lowe et al . , 2006; Yu et al . , 2007 ) . This applies also to spiders , which however have evolved a special strategy of DV axis formation that is radically different from most other animal phyla . In spiders the migration of the BMP expressing cumulus cells towards a symmetric ring of chordin expression breaks DV symmetry ( Akiyama-Oda and Oda , 2006 ) . Thus , spiders polarize the DV axis not by localizing inhibitor expression , but rather by localizing BMP . Among the well-studied DV patterning systems , Oncopeltus can be best compared to vertebrates . In the zebrafish and the frog the negative regulation of chordin by BMP signaling is an important feature to explain normal patterning and axis duplication ( twinning ) after transplantations ( Oelgeschläger et al . , 2003; De Robertis , 2009; Langdon and Mullins , 2011; Xue et al . , 2014 ) . However , the networks involved in size regulation of the embryonic axis are more complex and require many additional components such as ADMP ( antidorsalizing morphogenetic protein ) , a BMP-type ligand co-expressed with Chordin , and Sizzled , an antagonist of Tld that is co-expressed with BMP . ADMP and Sizzled have been implicated in scaling both experimentally and by modeling approaches ( Reversade and De Robertis , 2005; Ben-Zvi et al . , 2008; Inomata et al . , 2013 ) . No homologs of these genes were found in the Oncopeltus genome and transcriptomes ( Ewen-Campen et al . , 2011 ) ( Vargas Jentzsch et al . , 2015 ) , and appropriate scaling occurred in our theoretical simulations of twinning , without the need to invoke such additional modulators . Thus , the Oncopeltus system is surprisingly simple and may represent a minimal network able to support self-organized patterning . Our theoretical model emerged from modifications of equations that have been used to describe the formation of peak levels of BMP signaling along the dorsal midline in Drosophila ( Eldar et al . , 2002 ) . The BMP signaling peak in Drosophila forms within a domain of uniform dpp and tld expression and depends on both the diffusion of Sog-BMP complexes towards the dorsal side ( from the ventral source region of sog ) and on the degradation of Sog by Tld to release active BMP ligands dorsally . The Drosophila system is static , that is , the regions of ventral sog expression and the abutting regions of dpp and tld are fixed by the NF-κB/Dorsal gradient . The early dynamics of the system are restricted to reaction diffusion processes . In Oncopeltus , on the other hand , the transcriptional feedback of BMP signaling on sog expression creates a situation in which the size of the sog expression domain itself is an outcome of the system dynamics and becomes largely independent from NF-κB/Dorsal . In Drosophila and Tribolium the transport of BMP by Sog leads to dorsal BMP signaling levels that are higher than the signaling levels in the absence of Sog ( Dorfman and Shilo , 2001; van der Zee et al . , 2006 ) . This seems not to be the case in Oncopeltus , as the uniformly high BMP levels seen in sog or tsg KD embryos match the levels found on the dorsal side of control embryos ( Figure 3 ) . Furthermore , the BMP profile during normal development is flat , with a narrow and steep transition between uniform BMP signaling dorsally and the absence of BMP signaling ventrally . In our simulations the sog and BMP profiles are also flat , indicating that the negative feedback of BMP signaling on sog expression might prohibit the formation of sharp peak levels of BMP signaling ( Box 1 , Figures 8 , 9 ) . Similarly , in vertebrates Chordin-mediated BMP transport does not markedly enhance BMP signaling levels , as chordin KD does not lead to lateralization , but rather to ventralization of the embryo ( Schulte-Merker et al . , 1997; Oelgeschläger et al . , 2003 ) . An unusual feature of the Oncopeltus system is the strong anti-BMP function of Tsg . In Drosophila Tsg has a mild pro-BMP function that is independent of Sog ( Wang and Ferguson , 2005 ) . In Tribolium , Tsg is essential for all BMP activity in a Sog-independent manner ( Nunes da Fonseca et al . , 2010 ) . The same holds true for Nasonia , which lacks Sog ( Özüak et al . , 2014b ) , suggesting that a Sog-independent pro-BMP function of Tsg might be ancestral for insects . To our surprise we observed the opposite in Oncopeltus , where the strong anti-BMP function of tsg is responsible for the similarity of the sog and tsg KD phenotypes ( Figures 2 , 3 ) . Thus , Sog can exert its inhibitory effect on BMP only in the presence of Tsg . It will be interesting to find out whether this strong anti-BMP function of Tsg has a particular significance in a system where sog is repressed by BMP signaling . We assume that patterning in Oncopeltus is initiated when NF-κB/Dorsal-dependent enhancement of sog expression at the ventral side exceeds a certain threshold . This conclusion is based on the analysis of embryos with incomplete KD of sog ( Figures 2K , 5J ) . Such embryos frequently show an asymmetric pMAD distribution although they lack ventral gene ( twi , sim ) expression ( Figure 2—figure supplement 2 ) . Thus , ventral BMP signaling has to decrease below a certain threshold to enable normal DV pattern formation . This may provide the system with robustness against fluctuating BMP signaling levels . In our theoretical analysis , the homogeneous steady state of Sog and BMP undergoes a Turing instability when NF-κB/Dorsal-dependent activation of sog reaches a threshold ( Figure 9 ) . Then , rapid diffusion of BMP-Sog complexes and derepression of sog upon removal of BMP from the ventral side lead to the formation of a stripe with high expression of sog . The position of the stripe is determined by the initial NF-κB/Dorsal polarity cue . In all insects studied so far the specification of mesodermal and mesectodermal cell fates requires Toll signaling . In Drosophila and Tribolium , the shape of the NF-κB/Dorsal gradient directly or indirectly determines the width of the mesodermal domain ( Chen et al . , 2000; Hong et al . , 2008; Nunes da Fonseca et al . , 2008 ) . In Nasonia all ventrally expressed genes ( e . g . , twi ) are first turned on in a narrow stripe straddling the ventral midline ( Özüak et al . , 2014b ) . This region also shows high levels of cact expression indicating high activity of Toll signaling . Subsequently , cact expression disappears , however , the expression of ventral genes expands . The size of their final domains is determined by repressive BMP signaling from the dorsal side , since KD of BMP leads to progressive expansion of ventral gene expression resulting in a massive , albeit nonuniform expansion of the mesoderm . Thus , in Nasonia BMP effects all subdivisions of the DV axis ( Özüak et al . , 2014b ) . However , the expansion of ventral genes remains dependent on their prior activation by Toll , as Toll bmp double KD embryos lack the expression of ventral genes like twi . In Oncopeltus dpp KD embryos , twi is completely derepressed , that is , its expression is uniform around the embryonic circumference and the developing embryos are fully mesodermalized ( Figures 2E , 4F ) . This phenotype does not result from a progressive expansion of a narrow domain as in Nasonia . Most importantly , the same phenotype is observed in Toll dpp double KD embryos , with the exception that twi expression in addition expands anteriorly due to an AP function of Toll signaling ( Figure 5L ) . These data suggest that Toll signaling in Oncopeltus is no longer strictly required to activate ventral genes . As a consequence , all cell fate decisions along the DV axis of Oncopeltus ultimately depend on different levels of BMP signaling . As pointed out previously , the early pattern of BMP signaling in Oncopeltus seems to be very simple with a plateau of high signaling at the dorsal side and a broad domain lacking BMP signaling ventrally ( Figure 3—figure supplement 1 ) . Accordingly , the early DV fate map of Oncopeltus has apparently only few subdivisions . Although we have thus far not identified genes expressed specifically on the dorsal side , we expect such genes to have broad expression domains encompassing 30–50% of the embryonic circumference . Likewise , the ventral twi and early sim domains are broad ( data not shown ) . sim refines to lateral stripes bordering twi during later blastoderm stages . None of the columnar genes ( vnd , ind , msh ) show stripe-like expression in lateral regions of the blastoderm as in holometabolous insects ( von Ohlen and Doe , 2000; Wheeler et al . , 2005; Buchta et al . , 2013 ) . We therefore expect that the early BMP gradient provides little patterning information on either the dorsal or ventral side , and that further refinement occurs progressively during and after gastrulation . This refinement also includes large-scale morphogenetic movements . For example , the narrow twi domain of germ band embryos ( Figure 4C ) results from massive convergent extension during anatrepsis ( posterior invagination of the embryo and the amnion into the yolk [Panfilio , 2008] ) . Several lines of evidence suggest that the Toll gradient acts differently in Oncopeltus compared to the known holometabolous insects: ( i ) BMP is epistatic to Toll , ( ii ) ventrally expressed genes can be readily suppressed by increased BMP signaling , ( iii ) the repression of sog by BMP makes the distribution of BMP signaling largely independent from that of Toll signaling . Although we have not demonstrated the latter point experimentally , modeling shows that adding the repression of sog by BMP to the well known reaction diffusion system ( Eldar et al . , 2002 ) leads to a decoupling of input ( Toll signaling ) and output ( BMP signaling ) patterns ( Figures 9 , 11 ) . Toll remains essential at the ventral side to initiate patterning , and therefore , it would be highly interesting to monitor Toll activity by looking at the NF-κB/Dorsal distribution . In absence of functional antibodies we used the expression of the early Toll target gene cact as a proxy for Toll signaling . In all known holometabolous insects cact is activated by the NF-κB/Dorsal gradient . This applies even to Drosophila as demonstrated with the help of an enhancer reporter construct which shows twist-like expression ( Sandmann et al . , 2007 ) . However , due to maternal loading of cact , its zygotic expression apparently has little functional relevance in Drosophila ( Roth et al . , 1991 ) . In Tribolium , cact is only zygotically expressed and tightly follows the shifting gradient of NF-κB/Dorsal ( Nunes da Fonseca et al . , 2008 ) . Finally , in Nasonia , cact expression is restricted to a narrow stripe straddling the ventral midline where the expression of all other Toll signaling-dependent ventral genes is initiated ( Özüak et al . , 2014b ) . Although the NF-κB/Dorsal distribution is not known from Nasonia , a cluster of NF-κB/Dorsal binding sites in the vicinity of the cact transcript suggests a direct regulatory input by Toll signaling . By analogy we assume that the broad , weakly graded expression of cact in Oncopeltus reflects a flat Toll signaling gradient which extends from the ventral to the dorsal half of the embryo ( Figure 7 ) . The upstream cascade , which leads to Toll activation in Drosophila ( Stein and Stevens , 2014 ) appears to be largely conserved in Oncopeltus ( data not shown ) . Preliminary data suggest that the asymmetry of Toll signaling originates from asymmetric eggshell cues that are established during oogenesis . By activating Toll signaling in a broad gradient , these eggshell cues would provide global polarity to the embryo , which is essential for establishing bilateral symmetry . Such a strong geometric influence does not exclude , but rather enables , certain forms of self-organized patterning . Classical insect embryology had described many instances of partial or complete twinning after experimental interference with patterning along the DV axis of the egg ( Sander , 1976 ) . These experiments were not restricted to hemimetabolous insects , but included examples from beetles as well as butterflies . The most famous set of experiments was conducted by Sander with Euscelis , a leaf hopper which ( like Oncopeltus ) belongs to the Hemiptera ( Sander , 1971 ) . Sander produced not only left and right , but also dorsal and ventral egg fragments and was able to recover apparently complete germ rudiments from all fragments . These findings could not previously be explained on the basis of the fairly deterministic mechanism of DV axis formation known from Drosophila . The mechanism we have discovered in Oncopeltus can , in principle , account for this type of axis duplication ( Box 1 ) . As long as Toll activity is globally provided so that sog can also be activated in the dorsal half of the egg , a new round of patterning can be initiated dorsally . A prerequisite for pattern re-initiation is a diffusion barrier , which prevents the transport of ( inhibitory ) BMP molecules from the ventral to the dorsal side . The guillotine-like mechanism with which Sander separated the egg fragments provided such a barrier . In summary , the data presented here not only provide a potential explanation for experiments from classical insect embryology , they also suggest a scenario of how the elaborate morphogen function of Toll signaling found in Drosophila could have originated and evolved ( Figure 1 ) . Ancestrally , Toll signaling might have only provided a polarizing function for a self-organizing BMP system responsible for patterning the entire DV axis . Within certain lineages ( e . g . , flies , beetles and wasps ) Toll signaling became more important in directly specifying cell fates along the axis , gradually replacing ancestral BMP functions . Our data might also help to explain the transition from the ancestral immune function of Toll found in most metazoan lineages ( Gilmore and Wolenski , 2012 ) to its unique role in DV patterning restricted to insects . Recent findings show that insect eggs are immune competent ( Jacobs and van der Zee , 2013; Jacobs et al . , 2014 ) . We suggest that this also applied to ancestral insects and that furnishing their eggs with a Toll-mediated pathogen defense system was crucial for early insects to adopt a terrestrial life style . The activation of Toll signaling by eggshell cues might have been fairly uniform throughout the embryo . Subsequently , only a mild polarization of Toll signaling together with weak transcriptional inputs on sog were sufficient to initiate the co-option of Toll signaling for DV patterning .
Putative O . fasciatus homologs of Toll and BMP signaling components were found by a local blast against a maternal and embryonic transcriptome ( Ewen-Campen et al . , 2011 ) using the NCBI blast+ toolkit and BioEdit software , or by degenerate PCR followed by RACE PCR using the SMARTER RACE kit ( Clontech , France ) to extend the sequence information . Specific primers of all candidates were designed for sequencing , cloning and to confirm the homology with Drosophila and Tribolium ( Supplementary file 1 ) . Phylogenetic and molecular evolutionary analyses were then conducted using MEGA version 5 ( Tamura et al . , 2011 ) or phylogeny . fr ( Dereeper et al . , 2008 ) . All Oncopeltus gene sequences have been submitted to GenBank . To knock down gene function , gene-specific double-stranded RNA ( dsRNA ) ( 0 . 1–8 µg/µl ) for parental RNAi was prepared as previously described and injected into virgin females ( Nunes da Fonseca et al . , 2008 ) . After injection , embryos were collected , fixed and stored in methanol at −20°C as previously described ( Liu and Kaufman , 2004 ) for further phenotype analysis . Total RNA from a single cohort of staged embryos was homogenized and extracted by TRIzol reagent ( Life Technologies , Germany ) with DNase treatment , and cDNA was synthesized with the VILO Kit ( Invitrogen , Germany ) , following manufacturers' protocols . Gene expression analysis using semi-quantitative RT-PCR was performed using gene-specific primers , with an annealing temperature of 60°C , and 30 thermocycles . Detection of gene expression was performed by in situ hybridization ( ISH ) with digoxigenin-labeled probes as previously described ( Liu and Kaufman , 2004 ) . The double ISH was performed with digoxigenin ( DIG ) and biotin labeled probes hybridized simultaneously followed by incubation with anti-Biotin-AP ( 1:5000 , Roche , Germany ) . After the first round of staining the anti-Biotin-AP antibody was inactivated by treating the embryos with 0 . 1 M Glycine-HCl , pH = 2 . 2 , 0 . 1% Tween20 for 10 min , followed by washing , blocking and incubation with the second AP antibody ( anti-DIG-AP , 1:5000 , Roche , Germany ) . For color reactions we used the HNPP Fluorescent Detection Set ( Roche , Germany ) and NBT/BCIP . Immunostaining was performed using anti-Phospho-Smad1/5 ( 41D10 ) rabbit antibodies ( Cell Signaling , Germany ) with 1:30 dilution . We introduced the TSA plus DNP system ( Perkin Elmer , Waltham , MA ) to amplify the signal before DAB detection ( or DAB with nickel ammonium sulfate ) . We consider only one BMP ligand and neglect the fact that BMPs are secreted as homodimers or heterodimers ( Shimmi et al . , 2005 ) , although we have experimental evidence for a second BMP ligand in Oncopeltus ( Figure 2—figure supplement 1 ) . This is in agreement with other recent theoretical models for BMP gradient formation ( Mizutani et al . , 2005; Shimmi et al . , 2005; Ben-Zvi et al . , 2008; Umulis et al . , 2010; Peluso et al . , 2011; Inomata et al . , 2013 ) and reflects the notion that additional ligands might contribute to increased robustness ( Shimmi et al . , 2005 ) , but have no essential impact on the mechanisms of pattern formation . The binding of the BMPs to their receptors is not part of our model although we are aware of receptor-mediated degradation affecting the mean free path of the ligand ( Mizutani et al . , 2005 ) . The model also neglects the binding of Tsg to Sog-BMP complexes and does not explicitly mention Tolloid ( Tld ) , the enzyme that cleaves Sog . This can be justified by the fact that in blastoderm embryos tsg transcripts are not detectable ( by ISH ) suggesting very weak uniform expression and that tld is evenly expressed around the embryonic circumference ( Figure 2—figure supplement 1 ) . Similar assumptions have been made in a recent model for BMP signaling in Xenopus ( Inomata et al . , 2013 ) . Our minimal model has similarity to one originally suggested by Eldar et al . ( 2002 ) ( Meinhardt and Roth , 2002 ) with the crucial difference that sog expression itself is controlled by BMP signaling . The NF-κB/Dorsal gradient in our model plays a similar role as the source density in the Gierer-Meinhardt model , which was used to explain regulative behavior in hydra ( Gierer and Meinhardt , 1972 ) . In fact the mechanism of pattern formation bears some similarity with the local activation-lateral inhibition mechanism proposed by Gierer and Meinhardt ( 1972 ) : BMP inhibits Sog production through transcriptional repression . However , this is the sole regulatory interaction in our model . Local self-activation arises from the fast transport of BMP within Sog-BMP complexes , which moves the repressor for sog from regions of high sog expression ( increasing sog expression in those regions ) to regions of low sog expression ( decreasing sog expression there ) . To describe the repression of sog by BMP we use a Michaelis–Menten model; the rate of sog expression is proportional to 1/ ( 1 + b/b0 ) where b is the concentration of BMP and b0 is the concentration of BMP reducing the expression of sog by a factor of two . The constant of proportionality depends on the NF-κB/Dorsal concentration d , which enhances sog expression . In the absence of BMP , the sog expression rate ( η0 + η1 d/d0 ) / ( 1 + d/d0 ) extrapolates between η0 at d = 0 and η1 > η0 at high levels of d . d0 denotes the concentration where the intermediate transcription rate ( η0 + η1 ) /2 is reached . Combining the repression of sog by BMP with its enhancement by NF-κB/Dorsal we obtain the transcription rate of sog as a function of the concentrations of BMP and NF-κB/Dorsal as , ( 1 ) ηs ( b , d ) = η0 + η1 d/d0 ( 1+b/b0 ) ( 1+d/d0 ) . This model of expression regulation can also be derived from a thermodynamic model of two transcription factors ( a repressor and an activator ) independently binding to a regulatory region . We model the geometry of the embryo as a cylinder with circumference lx . When taking all concentrations to be constant along the axis of the cylinder , one obtains an effectively one-dimensional model . This assumption will be examined below , where the dynamics on the surface of the cylinder are considered . In the one-dimensional case , the concentrations s of Sog , b of BMP , c of the Sog-BMP complex , and d of the transcription factor NF-κB/Dorsal depend on time and on the variable x∈[0 , lx] running along the circumference of the cylinder , with x = 0 denoting the ventral side and x = lx/2 the dorsal side . We now formulate a reaction-diffusion model for the concentrations s ( x , t ) of Sog , b ( x , t ) of BMP , c ( x , t ) of the Sog-BMP complex , and d ( x , t ) of the transcription factor NF-κB/Dorsal . Under the processes described above , these concentrations evolve as , ∂ts =Ds∇2s+ ηs ( b , d ) − k+sb+k−c−αss , ( 2 ) ∂tb =Db∇2b+ ηb− k+sb+k−c+αsc−αbb , ∂tc =Dc∇2c+ k+sb−k−c−αsc , ∂td =−αdd . In one dimension ∇2=∂2/∂x2 , D with appropriate subscript denotes the diffusion constants , analogously η the rates of gene expression , α the degradation rates , and k+ and k− the binding and unbinding rates of Sog and BMP . Table 1 gives the parameters used here . We neglect degradation of BMP in the complex , although our results do not depend on this . It turns out that many of these parameters can be changed over at least one order of magnitude without affecting pattern formation ( Table 2 ) . 10 . 7554/eLife . 05502 . 022Table 2 . Range of model parameter values where a single stripe is formedDOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 022ηs¯6 × 10−4–1 . 4 × 10−3b00 . 1–0 . 5ηb8 × 10−6–10−4αs1 . 6 × 10−3–1 . 2 × 10−2αb0–10−4K+0 . 05–200k−0–0 . 5Ds0–10−9Db0–10−11Dc7 × 10−10–10−6Each parameter is varied keeping the other parameters fixed at the values specified in Table 1 . One exception is the parameters η0 and η1 , which affect pattern formation jointly through the parameter ηs¯ ( d ) ≡ η0+ η1d/d01+d/d0 which is set to 1 . 2 × 10−3 ( except in the first line , where this parameter itself is varied ) . Writing the sog expression rate as ηs ( b , d ) ≡ ηs¯ ( d ) / ( 1+b/b0 ) with ηs¯ ( d ) ≡η0+ η1d/d01+d/d0 , we see that the NF-κB/Dorsal concentration d controls the difference in sog expression between high and low levels of BMP . We will show below that pattern formation crucially depends on ( i ) a sufficiently high diffusion rate of the Sog-BMP complex and ( ii ) repression of sog expression by BMP . The homogeneous state ( uniform concentrations of BMP , Sog , and the Sog-BMP complex ) can be stable at low levels of NF-κB/Dorsal , but becomes unstable ( via the mechanism above ) once a certain critical NF-κB/Dorsal level is reached . Once a stripe has formed , however , it can persist even in the absence of NF-κB/Dorsal ( Figure 8 ) . We now explore the dynamics of pattern formation in this model starting from different concentrations of the transcription factor NF-κB/Dorsal ( shown in gray ) , resulting in steady-state concentrations of Sog and BMP shown blue and red , respectively . Starting from small levels of NF-κB/Dorsal , a steady state with uniformly low level of Sog arises ( Figure 9 , left ) . A threshold amount of NF-κB/Dorsal is required initially to form a stripe of high Sog concentration ( Figure 9 , center ) . As a result , small fluctuations in the NF-κB/Dorsal concentration thus do not lead to the formation of a stripe . The size of this stripe does not change if the initial amount of NF-κB/Dorsal is increased ( Figure 9 , right ) . This NF-κB/Dorsal-induced instability of the homogeneous state is also behind the twinning phenomenon ( Box 1 ) : if the level of NF-κB/Dorsal exceeds the critical threshold everywhere in the system , a stripe of Sog centered on the maximum of NF-κB/Dorsal forms . Elsewhere , sog is repressed by BMP and transported away from the high-Sog stripe via the Sog-BMP complex . If the system is divided into two separate halves , this transport is interrupted , but the NF-κB/Dorsal level is still above the critical threshold everywhere . Now there are two maxima of NF-κB/Dorsal ( one in each of the two halves ) , and a stripe of Sog forms at each of them . We perform a linear stability analysis to determine when a spatially homogeneous steady-state solution is instable against small sinusoidal spatial oscillations . For now , we consider a spatially uniform NF-κB/Dorsal concentration d as a parameter that can be tuned to take on different values . Then the reaction-diffusion Equations ( 2 ) become , ∂ts =Ds∇2s+ fs ( s ( x , t ) , b ( x , t ) , c ( x , t ) ) , ( 3 ) ∂tb =Db∇2b+fb ( s ( x , t ) , b ( x , t ) , c ( x , t ) ) , ∂tc =Dc∇2c+ fc ( s ( x , t ) , b ( x , t ) , c ( x , t ) ) , with the shorthands , fs ( s , b , c ) =ηs¯ ( d ) / ( 1+b/b0 ) − k+sb+k−c−αss , ( 4 ) fb ( s , b , c ) = ηb− k+sb+k−c+αsc−αbb , fc ( s , b , c ) = k+sb−k−c−αsc . We now consider a spatially homogeneous fixed point of Equation ( 3 ) defined by s ( x , t ) = s ¯ , b ( x , t ) =b¯ , c ( x , t ) = c¯ with 0= fs ( s¯ , b¯ , c¯ ) = fb ( s¯ , b¯ , c¯ ) =fc ( s¯ , b¯ , c¯ ) . In the vicinity of such a fixed point the reaction-diffusion dynamics can be written as , ( 5 ) ∂t ( s ( x , t ) b ( x , t ) c ( x , t ) ) =D∂2∂x2 ( s ( x , t ) b ( x , t ) c ( x , t ) ) +A ( s ( x , t ) −s¯b ( x , t ) −b¯c ( x , t ) −c¯ ) , where A is a matrix of partial derivatives evaluated at the fixed point , ( 6 ) A=\left ( ∂fs∂s|0∂fs∂b|0∂fs∂c|0∂fb∂s|0∂fb∂b|0∂fb∂c|0∂fc∂s|0∂fc∂b|0∂fc∂c|0\right ) and D is the diagonal matrix , ( 7 ) D= ( DsDsDc ) . The standard ansatz to check stability of the fixed point against small spatial oscillations proceeds by adding a sinusoidal term to the fixed point ( Turing , 1952 ) . ( 8 ) ( s ( x , t ) b ( x , t ) c ( x , t ) ) = ( s¯b¯c¯ ) +ewt+ikx ( δsδbδc ) . Inserting this ansatz into Equation ( 5 ) gives the eigenvalue equation , ( 9 ) w ( δsδbδc ) = ( A−Dk2 ) ( δsδbδc ) . The fixed point is stable against small spatial sinusoidal perturbations in one spatial dimension if and only if for all values of the wave vector k compatible with the cylindrical geometry all eigenvalues w of the matrix A − Dk2 have a negative real part . Figure 12 shows the largest eigenvalue of A − Dk2 for the wavenumber k = 2π/lx , that is , the smallest non-zero wavenumber compatible with the circular geometry , showing how the homogeneous steady state becomes unstable for sufficiently large values of Dc and d . 10 . 7554/eLife . 05502 . 023Figure 12 . Stability of the homogeneous fixed point . This contour plot shows the largest eigenvalue w of A − Dk2 for k = 2π/lx . The thick line separates the parameters leading to a stable homogeneous fixed point ( w < 0 ) from an instable homogeneous fixed point ( w > 0 ) . ( left ) w is plotted as a function of the diffusion constant of the Sog-BMP complex and the rate of sog expression at zero BMP , ηs¯ . ( right ) The same data are plotted against log ( d ) using ηs¯ ( d ) ≡η0+ η1d/d01+d/d0 . The homogeneous fixed point becomes unstable for sufficiently large values of the diffusion constant of the complex Dc and the concentration of NF-κB/Dorsal d . The remaining parameters are as given in Table 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 05502 . 023 We now explore the dynamics of the model constructed above on a two-dimensional cylinder . The surface of the cylinder is described by a variable y ∈ [0 , ly=0 . 002] running along its axis , and x∈[0 , lx] running along the circumference . The equations of motion follow from Equation ( 2 ) with ∇2=∂2/∂x2+∂2/∂y2 and open boundary conditions at the two ends of the cylinder . We use the same parameters as in the one-dimensional case ( Table 1 ) . Starting from an initial distribution of NF-κB/Dorsal along the ventral side of the cylinder , a stripe of Sog forms along the cylinder ( Figure 10 ) . All concentrations turn out to be independent of y . We find this also holds in the steady state if the initial distribution of NF-κB/Dorsal varies in the y-direction . This becomes more apparent in Figure 11 , where the initial distribution of NF-κB/Dorsal from Figure 10 is shown in a contour plot , alongside the steady-state concentration of Sog . Thus , our model produces stripes of constant width ( sog expression ) , which are centered on the ventral midline defined by NF-κB/Dorsal . This is a remarkable feature as it had been difficult to produce striped patterns centered on the midline with local activation-lateral inhibition or substrate depletion mechanisms ( Meinhardt , 2004 ) . | How an animal develops from a fertilized egg has fascinated scientists for decades . As such , much effort has gone into answering the related question: what makes the belly ( or underside ) of an animal develop differently from its back ? Like almost all other biological processes , the development of an embryo is controlled by interactions between different molecules within cells and tissues . Some of these molecules promote the activity of others; some have the opposite effect; and together these molecules and their interactions form ‘signaling networks’ . One such network , which involves a protein called BMP , is needed to establish the belly-to-back axis of nearly all animals . However , insects are a unique exception . Most insects ( including flies , beetles and wasps ) use a different signaling network to control their development from their belly to their back , one that involves a protein called Toll instead . This is unexpected because , in other animals , Toll proteins are best known for their role in the immune system; and it remains unclear how Toll signaling came to be involved in insect development . Now , Sachs , Chen et al . have studied an insect—called the milkweed bug—that is unlike most insects in that it does not have a larval stage ( i . e . , a maggot or a caterpillar ) in its life-cycle . This characteristic makes the milkweed bug more similar to the ancestor of all insects , and thus makes it an excellent model to study how the Toll protein took over from BMP in insect development . First , Sachs , Chen et al . experimentally reduced BMP signaling in milkweed bug embryos . This caused the embryos to develop features all around their bodies that are normally only associated with the animal's underside . In other insects , the development of these so-called ‘ventral’ features is typically controlled by Toll signaling; but in the milkweed bug this activity instead depends on a protein called Sog . Indeed , when Sachs , Chen et al . experimentally reduced both BMP and Toll signaling , the effect was the same as having reduced only BMP signaling , implying that Toll is not needed . Instead , Toll increased the level of the Sog protein up to a particular threshold . Above this threshold , Sog and BMP control each other to set out the animal's body plan . As insects evolved , it seems likely that Toll transitioned from being a trigger of BMP signaling to an important controller of insect development in its own right . But why was Toll put in the egg in the first place ? It is possible that Toll was required to protect the eggs of early insects from attack by bacteria and fungi . Future work will now test this assumption and aim to explain how and why the Toll protein changed its role—from immunity to development—during evolution . | [
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] | 2015 | Dynamic BMP signaling polarized by Toll patterns the dorsoventral axis in a hemimetabolous insect |
Ageing is associated with physical decline and the development of age-related diseases such as metabolic disorders and cancer . Few conditions are known that attenuate the adverse effects of ageing , including calorie restriction ( CR ) and reduced signalling through the mechanistic target of rapamycin complex 1 ( mTORC1 ) pathway . Synthesis of the metabolic transcription factor C/EBPβ-LIP is stimulated by mTORC1 , which critically depends on a short upstream open reading frame ( uORF ) in the Cebpb-mRNA . Here , we describe that reduced C/EBPβ-LIP expression due to genetic ablation of the uORF delays the development of age-associated phenotypes in mice . Moreover , female C/EBPβΔuORF mice display an extended lifespan . Since LIP levels increase upon aging in wild type mice , our data reveal an important role for C/EBPβ in the aging process and suggest that restriction of LIP expression sustains health and fitness . Thus , therapeutic strategies targeting C/EBPβ-LIP may offer new possibilities to treat age-related diseases and to prolong healthspan .
Delaying the occurrence of age related-diseases and frailty ( disabilities ) and thus prolonging healthspan , would substantially increase the quality of life of the ageing population and could help to reduce healthcare costs . Calorie restriction ( CR ) or pharmacological inhibition of the mTORC1 pathway by rapamycin are considered as potential effective interventions to delay aging and to increase healthspan in different species ( Kaeberlein et al . , 2015 ) . However , for humans CR is a difficult practice to maintain and may have pleiotropic effects depending on genetic constitution , environmental factors and stage of life . Likewise , the long-term use of rapamycin is limited by the risk of side effects , including disturbed glucose homeostasis , impaired wound healing , gastrointestinal discomfort and others ( Augustine et al . , 2007; de Oliveira et al . , 2011; Lamming et al . , 2012; Wilkinson et al . , 2012 ) . Therefore , there is a need to investigate alternative targets that are part of the CR/mTORC1 pathway that can be manipulated to reach similar beneficial effects . Our work suggests that the transcription factor C/EBPβ may provide such a target . C/EBPβ regulates the expression of metabolic genes in liver and adipose tissue ( Desvergne et al . , 2006; Roesler , 2001 ) . From its mRNA , three protein isoforms are synthesized through the usage of different translation initiation sites: two isoforms acting as transcriptional activators , liver-enriched activator protein ( LAP ) −1 and −2 , and a transcriptional inhibitory isoform called liver-enriched inhibitory protein ( LIP ) ( Descombes and Schibler , 1991 ) . We showed earlier that translation into LIP depends on a cis-regulatory uORF ( Figure 1A ) and is stimulated by mTORC1 signalling ( Calkhoven et al . , 2000; Jundt et al . , 2005; Zidek et al . , 2015 ) . Pharmacological or CR-induced inhibition of mTORC1 in mice selectively reduces LIP-protein synthesis and thereby increases the LAP/LIP ratio in different tissues ( Zidek et al . , 2015 ) . Experimental reduction of LIP expression by genetic ablation of the uORF in C/EBPβΔuORF knockin mice is associated with a CR-type improved metabolic profile , including enhanced fatty acid oxidation and reduction of steatosis , improved insulin sensitivity and glucose tolerance , and higher adiponectin levels . Notably , these metabolic improvements are achieved without reducing calorie intake ( Albert and Hall , 2015; Zidek et al . , 2015 ) . Because of the similarities between the C/EBPβΔuORF mutation and CR , we investigated lifespan and age-associated phenotypes in C/EBPβΔuORF mice . Here , we show that the C/EBPβΔuORF mutation is associated with an increase in lifespan and reduced tumour incidence in female mice . In addition , we show an improvement in a broad spectrum of age-associated phenotypes to varying degrees in males and females .
Others showed that LIP levels increase during aging in liver and white adipose tissue ( WAT ) ( Hsieh et al . , 1998; Karagiannides et al . , 2001; Timchenko et al . , 2006 ) . Similarly , in our cohorts of wt C57BL/6J mice LIP levels are significantly higher in livers of old ( 20–22 months ) versus young ( 5 months ) mice , resulting in a decrease in the LAP/LIP ratio during ageing ( Figure 1B , C and Figure 1—figure supplement 1A ) . In contrast , in C/EBPβΔuORF mice LIP levels are low and stay low in old mice . LAP levels in C/EBPβΔuORF males and to a lesser extent in females are increased , which is probably due to additional initiation events at the LAP-AUG by ribosomes that normally would have initiated at the uORF ( Calkhoven et al . , 2000 ) . The Cebpb-mRNA levels are comparable at different ages and in the different genotypes ( Figure 1D , E ) . Similarly , LIP expression is higher in white adipose tissue ( WAT ) of old female mice ( WAT from males is not available ) ( Figure 1—figure supplement 1B ) . Since translation into LIP is stimulated by mTORC1 through phosphorylation of 4E-binding protein ( 4E-BP ) ( Zidek et al . , 2015 ) , we reasoned that the higher LIP levels in aged livers and WAT might correlate with increased mTORC1 signalling with age . While the analysis of mTORC1-downstream phosphorylation of 4E-BP1 and p70 ribosomal protein S6 kinase 1 ( S6K1 ) did not reveal a significant difference between young versus old or wt versus C/EBPβΔuORF mice in liver ( Figure 1—figure supplement 1C , D ) , 4E-BP1 phosphorylation was significantly higher in old compared to young WAT samples from both wt and C/EBPβΔuORF females ( Figure 1—figure supplement 1E , F ) . In contrast phosphorylation of ribosomal S6 protein in WAT was not significantly altered upon ageing . Thus , LIP levels increase with age and this increase is dependent on the uORF in the Cebpb-mRNA and seems to correlate with mTORC1/4E-BP1 signalling in WAT but not in the liver . We hypothesised that the C/EBPβΔuORF mutation may have positive effects on healthspan and lifespan based on the CR-like metabolic improvements in C/EBPβΔuORF mice ( Zidek et al . , 2015 ) . A lifespan experiment was set up comparing C/EBPβΔuORF mice with wt littermates ( C57BL/6J ) in cohorts of 50 mice of each genotype and gender . The survival curves revealed an increase in median survival of 20 . 6% ( difference in overall survival p=0 . 0014 log-rank test , n = 50 ) for the female C/EBPβΔuORF mice compared to wt littermates ( Figure 2A ) . From the 10% longest-lived females , nine out of ten were C/EBPβΔuORF mice ( Supplementary file 1 ) , showing that the maximum lifespan of C/EBPβΔuORF females is significantly increased ( p=0 . 0157 Fisher’s exact test ) . If maximum lifespan is determined by the mean survival of the longest-lived 10% of each cohort , C/EBPβΔuORF females show an increase of 9 . 14% ( p-value=0 . 00105 Student’s t-test . ) . For the male cohort , we observed a modest increase in median survival of 5 . 2% , however , the overall survival was not significantly increased ( p=0 . 4647 log-rank test , n = 50 ) ( Figure 2B ) . The increase in median survival of the combined cohort of C/EBPβΔuORF mice ( males and females ) was 10 . 5% ( with a significant increase in overall survival p=0 . 0323 log-rank test , n = 100 ) ( Figure 2—figure supplement 1A and supplementary file 1 ) . The observed median survival for wt females ( 623 days ) is lower than what most other labs have reported for C57BL/6J females . We reasoned that this was due to a high incidence of ulcerative dermatitis ( UD ) we observed particularly in our female cohort ( females: 19 mice or 38% for wt and 26 mice or 52% for C/EBPβΔuORF; males: 15 mice or 30% for wt and 10 mice or 20% for C/EBPβΔuORF ) . UD is a common and spontaneous condition in mice with a C57BL/6J background that progress to a severity that euthanasia is inevitable ( Hampton et al . , 2012 ) . Therefore , survival curves were also calculated separately for UD-free mice and for mice that were euthanized because of serious UD ( Figure 2C–F , Figure 2—figure supplement 1B , C and supplementary file 1 for complete overview ) . These data show that median lifespan of UD-free wt females is in a more normal range ( 740 days ) and that the C/EBPβΔuORF mutation results in a significant increase of median survival specifically in females irrespective of the condition of UD . Moreover , the median survival of the C/EBPβΔuORF UD-free females ( 860 . 5 days ) is higher compared to both wt females and wt males ( 829 days ) . The survival curves show an increase in early mortality for the male C/EBPβΔuORF mice in the complete and UD-free cohorts ( Figure 2B , D ) . For these cohorts , we performed a daily chi-square test to access differences between wt and C/EBPβΔuORF males on each day of the lifespan and found a significant ( p<0 . 05 ) reduction in survival only for the UD-free C/EBPβΔuORF males spanning the period 582–637 days , including four mortalities ( Figure 2—figure supplement 1D , E ) . Taken together , these data show that a significant lifespan extension can be concluded only for female C/EBPβΔuORF mice . Aging is the most important risk factor for development of cancer . A reduction in cancer incidence is recurrently observed upon CR , rapamycin-treatment or manipulation of other pathways that increase longevity in several animal models ( Anisimov et al . , 2011; Colman et al . , 2009; Komarova et al . , 2012; Mattison et al . , 2012; Neff et al . , 2013; Serrano , 2016; Weindruch and Walford , 1982 ) . Mice in the lifespan cohorts that died or were sacrificed according to humane endpoint criteria underwent necropsy and tumours were analysed by a board certified veterinary pathologists of the Dutch Molecular Pathology Centre ( DMPC ) . The incidence of neoplasms was markedly reduced in female C/EBPβΔuORF mice compared to female wt mice ( 68% - > 45 , 8% , p=0 . 025 Fisher’s exact test ) ( Figure 3A ) . Furthermore , tumours were detected on necropsy at a higher age in female C/EBPβΔuORF mice compared to wt mice indicating a delay in tumour development ( Figure 3C ) . The increase in median survival of the tumour bearing C/EBPβΔuORF females was 25 . 49% compared to that of tumour bearing wt females ( p=0 . 0217 log-rank test ) ( Figure 3—figure supplement 1A ) . Also the tumour load ( number of different tumour types per mouse ) and the tumour spread ( total number of differently located tumours per mouse irrespective of the tumour type ) were lower in female C/EBPβΔuORF mice ( Figure 3—figure supplement 1B ) . For males no significant reduction in tumour incidence was detected in C/EBPβΔuORF mice ( Figure 3B , D ) . The survival of tumour bearing mice and the tumour load was similar in wt and C/EBPβΔuORF males , while the tumour spread seems to be even slightly increased in C/EBPβΔuORF male mice ( Figure 3—figure supplement 1C , D ) . The main tumour types found in female mice were lymphoma , hepatocellular carcinoma and histiocytic sarcoma . The occurrence of all three types was reduced in C/EBPβΔuORF females ( Supplementary file 2 ) . For other tumour types , the single numbers are too small to make a clear statement about a change in frequency . In male mice , hepatocellular carcinoma and histiocytic sarcoma were the most frequent tumour types observed . Although the overall tumour incidence was similar in C/EBPβΔuORF and wt males , the frequency of hepatocellular carcinoma was reduced in the C/EBPβΔuORF males ( Supplementary file 2 ) . Apart from the reduced tumour incidence and the increase in survival of tumour-bearing C/EBPβΔuORF females , also the survival of tumour-free female C/EBPβΔuORF mice was significantly extended by 25 . 13% ( p=0 . 0467 log-rank test ) compared to wt tumour-free females ( Figure 3—figure supplement 1E ) . This suggests that both the tumour incidence and additional unrelated factors contribute to the increased survival of C/EBPβΔuORF females . The observed increase in median lifespan of tumour-free C/EBPβΔuORF males of 19 . 71% does not correlate with a statistically significant increase in the overall survival ( p=0 . 4647 log-rank test ) ( Figure 3—figure supplement 1F ) . However , the survival curve points to a possible health improvement in the median phase of the male lifespan . Taken together , the C/EBPβΔuORF mutation in mice restricting the expression of LIP results in a significant lifespan extension and decreased tumour incidence in females but not in males . Typically , CR-mediated , genetic or pharmacological suppression of mTORC1 signalling is accompanied by the attenuation of an age-associated decline of health parameters ( Johnson et al . , 2013 ) . We examined the selected health parameters of body weight and composition , glucose tolerance , naïve/memory T-cell ratio , motor coordination and muscle strength in separate ageing cohorts of young ( 3–5 months ) and old ( 18–20 months for females and 20–22 months for males ) mice . In addition , we compared the histological appearance of selected tissues ( liver , muscle , pancreas , skin , spleen and bone ) between old ( 20/22 months ) wt and C/EBPβΔuORF mice . Body weight was significantly increased in all old mice ( Figure 4A , B ) . The increase for the old female C/EBPβΔuORF mice was significantly smaller compared to old wt littermates , while for the males there was no significant difference between the genotypes ( Figure 4A , B ) . The slightly lower body weight for the young C/EBPβΔuORF males was also observed in our previous study ( Zidek et al . , 2015 ) . A similar pattern was observed regarding the fat content that was measured by abdominal computed tomography ( CT ) analysis ( Figure 4C , D and Figure 4—figure supplement 1C ) . The volumes of total fat increased strongly in old mice both in visceral and subcutaneous fat depots ( Figure 4—figure supplement 1A , B ) . Old female C/EBPβΔuORF mice accumulated significantly less fat in the visceral and subcutaneous fat depots than wt females , while there was no difference for male mice ( Figure 4—figure supplement 1A , B ) . The lean body mass was slightly lower in old female C/EBPβΔuORF mice and increased in male wt mice compared to young mice ( Figure 4—figure supplement 1A , B ) . Thus , female C/EBPβΔuORF mice gain less fat upon aging similar to mice under CR or upon prolonged rapamycin treatment ( Fang et al . , 2013 ) . In contrast , although male C/EBPβΔuORF mice had a lower body weight and subcutaneous fat content at a young age compared to wt mice they were not able to maintain this difference during the aging process , which correlates with the lack in lifespan extension . In addition , we found an increase in mRNA expression of the macrophage marker Cd68 as a measure for age-related macrophage infiltration in visceral WAT of old mice , which was attenuated in female but not in male C/EBPβΔuORF mice ( Figure 4—figure supplement 1D ) . Impaired glucose tolerance is a hallmark of the aging process , which is improved by CR ( Barzilai et al . , 1998; Mitchell et al . , 2016 ) . The intraperitoneal glucose tolerance test ( IPGTT ) showed that glucose clearance , calculated as the area under the curve ( AUC ) , is significantly less efficient in old wt compared to young wt mice ( Figure 4E , F ) . Old C/EBPβΔuORF females and males perform significantly better in the IPGTT test than old wt littermates , which is reflected by the lower AUC value . Therefore , the C/EBPβΔuORF mutation protects against age-related decline of glucose tolerance in males and females . The ageing associated increase in memory/naïve T-cell ratio is a robust indicator for the progression of the immunological ageing progress . At a young age naïve T cells predominate and memory T cells are relatively scarce . Upon ageing the naïve T cell population is strongly reduced with a concomitant increase in the memory T cell population , resulting in an increased ratio of memory to naïve T cells ( Hakim et al . , 2004 ) . The ratio of memory ( Cd44high ) to naïve ( Cd44low/Cd62Lhigh ) cytotoxic T ( Cd8+ ) cells or memory ( Cd44high ) to naïve ( Cd44low/Cd62Lhigh ) helper T ( Cd4+ ) cells was analysed by flow cytometric analysis . Both increased upon aging in the blood of males and females of both genotypes ( Figure 5A–D ) . However , in C/EBPβΔuORF mice of both genders , this increase was significantly attenuated compared to wt mice ( Figure 5A–D and Figure 5—figure supplement 1A–D ) . These data suggest that the C/EBPβΔuORF mutation preserves a more juvenile immunological phenotype during ageing . Aging is associated with a significant decline in motor coordination and muscle strength ( Barreto et al . , 2010; Demontis et al . , 2013 ) . In the rotarod test , the time is measured that mice endure on a turning and accelerating rod as an indication for their motor-coordination . As expected , rotarod performance decreased with age both for wt female and male mice ( Figure 6A ) . Remarkably , rotarod performance was completely preserved in old C/EBPβΔuORF females but not in C/EBPβΔuORF males . In the beam walking test , the required crossing time and number of paw slips of mice traversing a narrow beam are measured . Old mice needed more time to cross the beam reflecting loss of motor coordination upon ageing ( Figure 6B ) . The aging-associated increase of the crossing time was less severe in C/EBPβΔuORF males and females , although statistically significant only in males ( Figure 6B ) . Nevertheless , the strong increase in the number of paw slips in old wt mice is almost completely attenuated in C/EBPβΔuORF males and females ( Figure 6C ) . Note that the number of paw slips by young C/EBPβΔuORF males is already significantly lower compared to young wt males . During the wire hang test , the time is measured that mice endure hanging from an elevated wire which serves as an indication for limb skeletal muscle strength ( Brooks and Dunnett , 2009 ) . Similar to the rotarod test , the decline in wire hang performance that is seen in old wt mice is completely restored for the female but not for the male C/EBPβΔuORF mice ( Figure 6D ) . Taken together , these data demonstrate that the decline in motor coordination and muscle strength is less severe and partly abrogated in female C/EBPβΔuORF mice . The results for the old male C/EBPβΔuORF mice are not that clear since they show an improved performance only in the beam walking test . One possible explanation is that only the beam walking test measures purely motor coordination skills whereas the results from the rotarod and wire hang tests are influenced in addition by muscle strength and endurance . Old C/EBPβΔuORF males thus might have maintained their motor coordination upon ageing but still suffer from an ageing-dependent loss of muscle strength . By histological examination of different tissues , we observed a reduction in some age-related alterations in C/EBPβΔuORF mice compared to old wt controls ( Supplementary file 3 ) . We observed a reduced severity of hepatocellular vacuolation and cytoplasmic nuclear inclusions in male C/EBPβΔuORF mice; in the pancreas both male and female C/EBPβΔuORF mice showed a reduced occurrence and severity of islet cell hyperplasia; in skeletal muscle the number of regenerating muscle fibres was higher in male C/EBPβΔuORF mice; the incidence of dermal inflammation was lower in female C/EBPβΔuORF mice . Unexpectedly , a slightly increased level of inflammation was detected in the livers of female C/EBPβΔuORF mice . The incidence of other potential age-related pathologies like focal acinar cell atrophy and inflammation in the pancreas , liver polyploidy , spleen lymphoid hyperplasia and extramedullary haematopoiesis , intramuscular adipose tissue infiltration , subcutaneous fat atrophy and bone density were not significantly altered between old wt and C/EBPβΔuORF mice . We found slightly reduced plasma IGF-1 levels in old C/EBPβΔuORF females compared to old wt females ( Supplementary file 3 ) . A reduction in circulating IGF-1 levels was also found in mice under CR and is believed to be an important mediator of health- and lifespan extending effects of CR ( Breese et al . , 1991; Mitchell et al . , 2016 ) . Taken together , our data show that multiple , but not all , ageing-associated alterations are attenuated in C/EBPβΔuORF mice , and to different extends in males and females . Finally , we performed a comparative transcriptome analysis from livers of 5 and 20 months old wt and C/EBPβΔuORF female mice ( de Jong and Guryev , 2018a; de Jong and Guryev , 2018b; Müller et al . , 2018 ) . A principal component analysis revealed that there was a clear effect of the genotype on gene expression only in the old mice suggesting that the differences in gene expression between wt and C/EBPβΔuORF mice are aging dependent ( Figure 7—figure supplement 1 ) . This is supported by the finding that in young mice only 42 genes were differentially regulated between wt and C/EBPβΔuORF mice ( FDR < 0 . 01; 24 genes upregulated and 18 genes down-regulated in C/EBPβΔuORF mice compared to wt mice ) while in old mice we found 152 differentially regulated genes ( FDR < 0 . 01; 127 genes upregulated and 25 genes downregulated in C/EBPβΔuORF mice compared to wt mice ) . Gene ontology ( GO ) analysis using the David database ( Huang et al . , 2009 ) of the genes upregulated in old C/EBPβΔuORF mice in comparison to old wt mice revealed GO terms including ‘External side of plasma membrane’ , ‘Positive regulation of T-cell proliferation’ , and ‘immune response’ ( see Supplementary file 4 for the complete list of GO-terms ) whereas the GO-terms: ‘Acute phase’ and ‘Extracellular space’ were significantly downregulated ( Supplementary file 5 ) . Despite the improved metabolic phenotype of C/EBPβΔuORF mice ( Zidek et al . , 2015 ) , the analysis did not reveal GO-terms related to metabolism . We reasoned that metabolic genes might not be detected as differentially regulated because they are subject of expression heterogeneity in old mice . Comparison between the coefficient of variation of individual transcripts between young and old mice revealed that inter-individual variation of gene expression increases with age in both genotypes ( Figure 7A , B ) supporting earlier observations made by others ( White et al . , 2015 ) . Direct comparison between old wt and C/EBPβΔuORF mice showed that this effect is less pronounced in C/EBPβΔuORF mice ( Figure 7C ) . KEGG ( Kyoto Encyclopedia of Genes and Genomes ) pathway and GO-term enrichment analysis of the highly variably expressed genes in the aged livers revealed that in wt mice particularly metabolic genes related to fatty acid metabolism and oxidative phosphorylation were affected which was not observed in C/EBPβΔuORF mice ( Figure 7D and supplementary file 6 and 7 ) . In addition , genes whose de-regulation is connected to ageing-associated diseases like non-alcoholic fatty liver disease , Alzheimer’s disease , Parkinson’s disease , Huntington’s disease and cancer were affected by high inter-individual variation in expression levels in old wt but not in old C/EBPβΔuORF mice ( Figure 7D ) . On the other hand , genes involved in cell cycle , transcription and RNA biology showed higher inter-individual variation in old C/EBPβΔuORF mice compared to wt controls ( Supplementary file 7 ) . These findings suggest that expression control of metabolic genes and genes involved in ageing-associated diseases stays more robust upon aging in C/EBPβΔuORF mice .
Taken together , here we show that loss-of-function mutation of a single cis-regulatory element - the uORF - in the Cebpb-mRNA , which prevents the translation into the transcription factor C/EBPβ-LIP , results in a remarkable juvenile phenotype in aged mice including lower cancer incidence , lower body weight and body fat , better glucose tolerance , lower memory/naïve T cell ratios , and better maintenance of motor coordination . However , we observed clear differences between males and females , with only females showing improvements for cancer incidence , body weight , fat content , Rotarod- and wire hang test performance . In addition , a significant lifespan extension was only observed for the female C/EBPβΔuORF mice . We do not know what causes the female specific lifespan extension . C/EBP transcription factors are known for their crosstalk with hormone receptors , including estrogen , progesterone and glucocorticoid receptors ( Calkhoven et al . , 1997; Chang et al . , 2005; Grøntved et al . , 2013; Rotinen et al . , 2009; Seagroves et al . , 2000; Siersbæk et al . , 2012; Zhang et al . , 2010 ) . Therefore , obvious differences in hormone receptor regulation between males and females may determine the outcome of shifts in LAP/LIP ratios . Notably , the C/EBPβΔuORF mutation in males results in higher LAP expression in the liver and therefore 1 . 5 fold higher LAP/LIP ratios compared to females ( Figure 1B , C ) . Possibly , higher LAP levels in males have some adverse effects on health and lifespan , which may neutralize the beneficial effects of LIP deficiency . In line with this assumption is that the C/EBPβΔuORF males show an increase in early deaths ( Figure 2B , D ) that is significant in UD-free males ( Figure 2—figure supplement 1E ) and is mainly due to early cancer development ( Figure 3D ) . A similar scenario has been described for short-term treatment with a high dose of rapamycin that failed to extend the lifespan of female mice due to frequent development of aggressive haematological cancers ( Bitto et al . , 2016 ) . The sex-dependent differences we found are intriguing in the light of studies investigating the lifespan extending effects of CR , rapamycin , and mutations in the mTORC1 pathway . For example , CR by 20% has a greater lifespan extending effect in female C57BL/6J or DBA/2J mice compared to males ( Mitchell et al . , 2016 ) . In addition , moderate overexpression of the mTORC1-upstream inhibitor TSC1 or deletion of the mTORC1-downstream S6K1 results in lifespan extension only in females ( Selman et al . , 2009; Zhang et al . , 2017 ) . Notably , downregulation of LIP under low mTORC1 signalling is dependent on 4E-BP1/2 function and not on inhibition of S6K1 ( Zidek et al . , 2015 ) . Thus , the bias towards female lifespan extension upon reduced mTORC1 signalling seems to be a common feature irrespective of whether the S6K1 or 4E-BP branch is affected . Mutations affecting both mTORC1 and mTORC2 show ambiguous effects; lifespan extension is limited to females in mice heterozygous for mTOR and its cofactor mammalian lethal with Sec 13 protein 8 ( mLST8 ) ( Lamming et al . , 2012 ) , while in a mTOR-hypomorphic mouse model lifespan extension is observed in both males and females ( Wu et al . , 2013 ) . Similarly , inhibition of mTORC1 with rapamycin results in either a gender biased or a general lifespan extension depending on the study design and rapamycin concentration used . For example , treatment of genetically heterogeneous mice as well as C57BL/6J or C57BL/6Nia mice with a low dose of rapamycin ( from 4 . 7 to 14 ppm ) for different time periods has lifespan extending effects that are stronger in females than in males ( Fok et al . , 2014; Harrison et al . , 2009; Miller et al . , 2011; Miller et al . , 2014; Zhang et al . , 2014 ) . In contrast , treatment with higher concentrations of rapamycin ( 42 ppm ) results in a further increase in lifespan and almost completely alleviates the difference between the sexes ( Miller et al . , 2014 ) . However , injection of an even higher rapamycin dose ( 8 mg/kg/day , corresponding to 378 ppm dietary rapamycin ) extended lifespan only in males and not in females with serious side effects in females as mentioned above ( Bitto et al . , 2016 ) . These data indicate that rapamycin treatment with low and probably sub-optimal doses creates differences between sexes ( Kaeberlein , 2014 ) . Although the mechanisms behind these sex-dependent differences are not known , our study suggests that mTORC1-LIP regulation may be involved . Possibly , lifespan-extending pathways downstream of mTORC1 are differentially affected by different rapamycin concentrations , and in a gender dependent way . Providing LIP expression is downregulated by low concentrations of rapamycin the female-biased effect on lifespan might be determined predominantly by low LIP levels as well as by the regulation of other highly sensitive targets like for example S6K1 that similarly shows female-specific effects ( Selman et al . , 2009 ) . At higher rapamycin doses , additional pathways might be engaged from which both males and females benefit . Finally , at too high rapamycin concentrations additional adverse ( gender specific ) effects might counteract the beneficial effects of rapamycin . Therefore , further research on both positive and negative events downstream of mTORC1 is required to be able to tailor treatment and to minimalize side effects . Also in mouse strains with alterations in other pathways like the somatotropic axis lifespan extension is often , but not always , more pronounced in females ( Brown-Borg , 2009 ) . Examples of somatotropic-related female biased lifespan extension are Ames dwarf mice that are deficient in growth hormone ( GH ) and prolactin production ( Brown-Borg et al . , 1996 ) and insulin-like growth factor 1 ( IGF-1 ) receptor heterozygous mice ( Holzenberger et al . , 2003 ) . Also in these mouse models the reason for the female-biased lifespan extension is not known . What contributes to the extended lifespan in the female C/EBPβΔuORF mice ? Our data indicate that reduced tumour incidence is involved . In line with this is that knockin mice with elevated LIP levels show an increased tumour incidence upon ageing that goes along with reduced survival compared to wt controls ( Bégay et al . , 2015 ) . LIP overexpression can stimulate cell proliferation , migration and transformation in vitro and high LIP levels have been detected in different human tumour tissues ( Anand et al . , 2014; Arnal-Estapé et al . , 2010; Calkhoven et al . , 2000; Haas et al . , 2010; Jundt et al . , 2005; Park et al . , 2013; Raught et al . , 1996; Zahnow et al . , 1997 ) . Together , these studies suggest an oncogenic role of LIP and that the reduction of LIP in the C/EBPβΔuORF mice counteracts tumour development at least partially by cell intrinsic mechanisms . Although the incidence of certain tumours like hepatocellular carcinoma is similarly reduced in male C/EBPβΔuORF mice ( Supplementary file 2 ) the overall tumour incidence was not different in comparison to the wt males , again indicating gender specific effects of the C/EBPβΔuORF mutation . Besides tumour development other parameters contribute to the lifespan extension in female C/EBPβΔuORF mice as revealed by the survival curves of the tumour-free female mice ( Figure 3—figure supplement 1E ) . Notably , the ageing-associated increase in body weight and body fat was attenuated in female but not in male C/EBPβΔuORF mice although at younger age also C/EBPβΔuORF males show a reduced body weight and fat content ( Figure 4 ) . Our earlier data showed that food intake is not reduced in the C/EBPβΔuORF mice ( Zidek et al . , 2015 ) suggesting that the increase in fat catabolism and other features like the observed higher physical activity cause leanness of the C/EBPβΔuORF mice ( Zidek et al . , 2015 ) . In accordance with the difference in fat content , we observed a reduction in macrophage infiltration in white adipose tissue from female but not from male C/EBPβΔuORF mice ( Figure 4—figure supplement 1D ) . Inflammation of the visceral adipose tissue is a common feature of the ageing process and is believed to contribute to insulin resistance and other ageing-associated diseases ( Mau and Yung , 2018 ) . Therefore , reduced inflammation in adipose tissues could contribute to the extended health and lifespan of the female C/EBPβΔuORF mice . Global liver transcriptome analysis revealed an increase in the inter-individual variation of gene expression between individuals from the same genotype . However , there is less variation between old C/EBPβΔuORF females than between old wt females . A similar increase in the inter-individual variation of gene expression was also identified by others ( Cellerino and Ori , 2017; White et al . , 2015 ) and might reflect different ageing rates within the same group of individuals . Intriguingly , the inter-individual variation in specific pathways and gene groups is different for C/EBPβΔuORF compared to wt mice . Particularly genes connected to metabolic pathways and to ageing-associated diseases showed high expression heterogeneity in old wt but not in old C/EBPβΔuORF females . Whether the increased inter-individual variation of metabolic transcripts in old wt mice is a direct effect of the observed increase of the inhibitory-acting LIP isoform or is due to unknown secondary effects has to be clarified in future studies . It is however conceivable that increased transcriptional robustness in the old C/EBPβΔuORF mice contributes to the extension in health- and lifespan of the female C/EBPβΔuORF mice . Transcriptome and gene ontology ( GO ) enrichment analysis in liver revealed some involved mechanisms that could contribute to the youthful and long-lived phenotype of the C/EBPβΔuORF females . We found reduced expression of acute phase response genes in livers from old C/EBPβΔuORF females . Acute phase response genes are associated with inflammation and their expression in the liver increases upon ageing ( Lee et al . , 2012 ) . Moreover , expression of acute phase response genes is inhibited by CR or treatment with the CR-mimetic metformin ( Martin-Montalvo et al . , 2013 ) suggesting similar protective mechanisms . In addition , we observed the upregulation of several genes connected to lymphocyte biology in the C/EBPβΔuORF livers . This fits to the increase in lymphoplasmatic inflammation in the liver of old female C/EBPβΔuORF mice ( Supplementary file 3 ) . It is generally believed that ageing associated lymphocyte infiltration rather promotes the ageing process by increasing inflammatory signals ( Singh et al . , 2008 ) that abrogate glucose homeostasis . Nevertheless , recently this view was challenged by showing that hepatic inflammation , involving the activation of IKKβ , can also be beneficial for maintaining glucose homeostasis ( Liu et al . , 2016 ) . Furthermore , infiltrating lymphocytes can also contribute to the removal of senescent or pro-tumorigenic cells , thereby acting protective ( Kang et al . , 2011 ) . Further research is required to find out whether in the case of the female C/EBPβΔuORF mice lymphocyte infiltration in the liver has adverse or beneficial effects . We showed earlier that a cis-regulatory uORF in the Cebpb-mRNA leader sequence is required for translation into LIP , which is stimulated by mTORC1-4E-BP1 signalling ( Calkhoven et al . , 1994; Calkhoven et al . , 2000; Wethmar et al . , 2010; Zidek et al . , 2015 ) . Intriguingly , other uORF-dependent translation events are known to be involved in lifespan regulation . In yeast , translation of the GCN4-mRNA into the GCN4 transcription factor - a basic leucine zipper ( bZIP ) domain transcription factor like C/EBPβ - is controlled by four uORFs ( Hinnebusch , 2005 ) . Phosphorylation of the alpha subunit of the eukaryotic initiation factor 2 ( eIF2α ) by the GCN2 kinase in response to amino acid deprivation or upon other stressors results in global inhibition of translation initiation while GCN4 translation is stimulated due to the skipping of inhibitory uORFs . GCN4 activates genes involved in amino acid biosynthesis and stress response to alleviate nutrient stress ( Hinnebusch , 2005 ) . GCN4 expression is elevated under different conditions that extend either replicative or chronological lifespan in yeast like glucose restriction , inhibition of TOR signalling , depletion of 60S ribosomal subunits or deletion of the arginine transporter canavanine resistance 1 ( CAN1 ) gene and was shown to be at least partially required for the lifespan extending effects of these interventions ( Beaupere et al . , 2017; Cherkasova and Hinnebusch , 2003; Kubota et al . , 2003; Martín-Marcos et al . , 2007; Steffen et al . , 2008; Valenzuela et al . , 2001; Yang et al . , 2000 ) . Furthermore , the overexpression of GCN4 is sufficient to extend replicative lifespan in yeast suggesting that GCN4 is a major player in the regulation of yeast lifespan ( Mittal et al . , 2017 ) . In mammals expression of the GCN4 ortholog ATF4 is similarly upregulated in response to stress-induced eIF2α-phosphorylation through skipping of inhibitory uORFs in the Atf4-mRNA ( Vattem and Wek , 2004 ) . Although an involvement of ATF4 in lifespan regulation in mammals has not been addressed so far , increased expression of ATF4 was found in in livers of long-lived mouse models and upon treatments that extend lifespan and in fibroblasts from slow-ageing Snell dwarf and Pappa KO mice ( Li et al . , 2014; Li and Miller , 2015 ) . In the fibroblasts , increased ATF4 expression was accompanied by an increased stress resistance indicating that ATF4 might play a role also for mammalian lifespan . Notably , C/EBPβ and ATF4 pathways are integrated through heterodimers that bind to composite binding sites ( Fawcett et al . , 1999 ) suggesting that C/EBPβ-ATF4 dimers are involved in health and lifespan regulation in mammals with C/EBPβ-LAP working together with ATF4 in gene activation while C/EBPβ-LIP probably counteracting it . In yeast the deletion of 60S ribosomal subunits was shown to result in a general reduction of occupancy of uORFs indicating uORF skipping although an effect on translation efficiency of the main reading frame was not observed for most of the mRNAs ( Mittal et al . , 2017 ) . Still there might be a subset of uORF containing mRNAs that might be coregulated under low 60S availability and/or other conditions that result in lifespan extension and mediate the lifespan extending effects . In this respect it is intriguing that uORF-mediated translation into the C/EBPβ-LIP isoform is reduced upon knockdown or mutation of the Shwachman-Bodian-Diamond Syndrome ( SBDS ) protein that is required for 60S ribosomal subunit maturation ( In et al . , 2016 ) . Thus , uORF-mediated translation regulation could be a more general mechanism adjusting gene expression during stress response that might play an important role in lifespan extension . In summary , reduced signalling through the mTORC1 pathway is thought to mediate many of the beneficial effects of CR or rapamycin treatment ( Johnson et al . , 2013 ) , and both conditions restrict mTORC1-controlled translation into LIP ( Calkhoven et al . , 2000; Zidek et al . , 2015 ) . These and other studies firmly place LIP function downstream of mTORC1 at the nexus of nutrient signalling and metabolic gene regulation ( Figure 8 ) . However , upon ageing , LIP expression increases ( the LAP/LIP ratio decreases ) in the liver and WAT whereas significant changes in mTORC1/4E-BP1 signalling were only detected in WAT ( Figure 1—figure supplement 1C–F ) . Possibly , in the liver other pathways play a role in age-related upregulation of LIP as has been described for the RNA-binding protein CUGBP1 ( Karagiannides et al . , 2001; Timchenko et al . , 2006 ) . Experimental reduction of the transcription factor C/EBPβ-LIP in mice recapitulates many of the effects of CR or treatment with rapamycin , including the reduced cancer incidence and the generally more pronounced extension of lifespan in females . We have developed a high-throughput screening strategy that allows for discovery of small molecular compounds that suppress the translation into LIP ( Zaini et al . , 2017 ) . The identification of such compounds or conditions that reduce LIP translation may reveal new ways of CR-mimetic-based therapeutic strategies beyond those using mTORC1 inhibition .
C/EBPβΔuORF mice described in ( Wethmar et al . , 2010 ) were back-crossed for 12 generations into the C57BL/6J genetic background . Mice were kept at a standard 12 hr light/dark cycle at 22°C in individually ventilated cages ( IVC ) in a specific-pathogen-free ( SPF ) animal facility on a standard mouse diet ( Harlan Teklad 2916 ) . Mice of the ageing cohort were analysed between 3 and 5 months of age ( young ) and between 18 and 20 months ( old females ) or between 20 and 22 months ( old males ) and were derived from the same breeding pairs as mice used in the lifespan experiment . The body weight of the ageing cohorts was determined before the start of the experimental analysis . All of the animals were handled according to approved institutional animal care and use committee ( IACUC ) protocols of the Thüringer Landesamt für Verbraucherschutz ( #03-005/13 ) and University of Groningen ( #6996A ) . C/EBPβΔuORF and wt littermates ( 50 mice from each genotype and gender ) derived from mating between heterozygous males and females were subjected to a lifespan experiment . Mice were housed in groups with maximum five female mice or four male mice per cage ( separated in genotypes and genders ) and did not participate in other experiments . Mice were checked daily and the lifespan of every mouse ( days ) was recorded . Mice were euthanized when the condition of the animal was judged as moribund and/or to be incompatible with continued survival due to severe discomfort based on the independent assessment of experienced animal caretakers . All mice that were found dead or were euthanized underwent necropsy with a few exceptions when the grade of decomposition of dead animals prevented further examination ( number of mice without necropsy: n = 0 for wt females; n = 2 for C/EBPβΔuORF females; n = 3 for wt males and n = 5 for C/EBPβΔuORF males . Survival curves were calculated with the Kaplan-Meier method . Statistical significance was determined by the log-rank test using GraphPath Prism 7 software . Maximum lifespan was determined by the number of mice for each genotype that were within the 10% longest-lived mice of the combined ( wt and C/EBPβΔuORF ) cohorts . Statistical significance of observed differences was calculated with Fisher’s exact test . In addition , the mean lifespan ( ± SEM ) of the 10% longest lived mice within one genotype was compared to the mean lifespan of the 10% longest lived mice of the other genotype , and the statistical significance was calculated with the Student’s T-test . Suspected tumour tissue found during necropsy of the lifespan cohorts was fixed in 4% paraformaldehyde and Haematoxylin and Eosin stained tissue slices were analysed by experienced board-certified veterinary pathologists of the Dutch Molecular Pathology Centre ( Utrecht University ) to diagnose the tumour type . Tumour incidence was calculated as percentage of mice with pathologically confirmed tumours in respect to all mice from the same cohort that underwent necropsy . Tumour occurrence was defined as the time of death of an animal in which a pathologically confirmed tumour was found . Tumour load was defined as number of different tumour types found in the same mouse and tumour spread was defined as number of different organs harbouring a tumour within the same mouse irrespective of the tumour type with the exception that in those cases in which different tumour types were found in the same organ a number >1 was rated . Rotarod test: Mice were habituated to the test situation by placing them on a rotarod ( Ugo Basile ) with constant rotation ( 5 rpm ) for 5 min at two consecutive days with two trials per mouse per day separated by an interval of 30 min . In the test phase , two trials per mouse were performed with accelerating rotation ( 2–50 rpm within 4 min ) with a maximum trial duration of 5 min in which the time was measured until mice fell off the rod . Beam walking test: Mice were trained by using a beam of 3 cm width and 100 cm in length at two consecutive days ( one trial per mouse per day ) . At the test day , mice had to pass a 1 cm wide beam , 100 cm in length and beam crossing time and number off paw slips upon crossing was measured during three trials per mouse that were separated by an interval of 20 min . To determine the number of mistakes the number of paw slips per trial was counted upon examination of recorded videos . Wire Hang test: To measure limb grip strength mice were placed with their four limbs at a grid with wire diameter of 1 mm at 20 cm over the layer of bedding material , and the hanging time was measured until mice loosened their grip and fell down . Three trials of maximal 60 s per mouse were performed that were separated by an interval of 30 min . The body composition was measured using an Aloka LaTheta Laboratory Computed Tomograph LCT-100A ( Zinsser Analytic ) as described in ( Zidek et al . , 2015 ) . Percentage body fat was calculated in relation to the sum of lean mass and fat mass . Bones of the hind legs were freed from soft tissue and fixed in 4% paraformaldehyde . For determination of the bone volume , trabecular thickness , trabecular number and trabecular separation femurs were analysed by micro CT ( Skyscan 1176 , Bruker ) equipped with an X-ray tube ( 50 kV/500μA ) . The resolution was 9 μm , rotation step was set at 1°C , and a 0 . 5 mm aluminium filter was used . For reconstruction of femora , the region of interest was defined 0 . 45 mm ( for trabecular bone ) or 4 . 05 mm ( for cortical bone ) apart from the distal growth plate into the diaphysis spanning 2 . 7 mm ( for trabecular bone ) or 1 . 8 mm ( for cortical bone ) . Trabecular bone volume/tissue volume ( % ) , trabecular number per μm , trabecular thickness ( μm ) and trabecular separation ( intertrabecular distance , μm ) was determined according to guidelines by ASBMR Histomorphometry Nomenclature Committee ( Dempster et al . , 2013 ) . The intraperitoneal ( i . p . ) glucose tolerance test ( IPGTT ) was performed as described in ( Zidek et al . , 2015 ) . Mice without initial increase in blood glucose concentration were excluded from the analysis . Blood cells from 300 μl blood were incubated in RBC-Lysis buffer ( Biolegend ) to lyse the red blood cells . Remaining cells were washed and incubated with a cocktail of fluorochrome-conjugated antibodies ( Cd4-PE-Cy7 ( #552775 ) and Cd62L-FITC ( #561917 ) from BD Pharmingen; Cd3e-PE ( #12–0031 ) , Cd8a-eFluor 450 ( #48–0081 ) and Cd44-APC ( #17–0441 ) from eBioscience . ) , incubated with propidium iodide for the detection of dead cells and analysed using the FACSCanto II analyser ( BD Biosciences ) . The following T cell subsets were quantified: Cd3+ , Cd8+ , Cd44high cytotoxic memory T cells; Cd3+ , Cd8+ , Cd44low , Cd62Lhigh cytotoxic naïve T cells , Cd3+ , Cd4+ , Cd44high helper memory T cells and Cd3+ , Cd4+ , Cd44low , Cd62Lhigh helper naïve T cells . Tissue pieces were fixed with 4% paraformaldehyde and embedded in paraffin . Sections were stained with Haematoxylin and Eosin ( H and E ) and ageing-related pathologies or tumour types were analysed by experienced board-certified veterinary pathologists of the Dutch Molecular Pathology Centre ( Utrecht University ) . Semi-quantification of muscle regeneration was done by counting the number of myofibers with a row of internalized nuclei ( >4 ) for five 200x fields . Other ageing-associated lesions were scored subjectively , and the severity of the lesions was graded on a scale between 0 and 3 with 0 = absent; 1 = mild; 2 = moderate and 3 = severe . Mouse liver and WAT tissue was homogenized on ice with a glass douncer in RIPA buffer ( 150 mM NaCl , 1% NP40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS , 50 mM TRIS pH 8 . 0 supplemented with protease and phosphatase inhibitors ) . Liver extracts were sonicated immediately , WAT extracts were incubated for 1 hr on ice , centrifuged for 15 min at 4°C after which the lipid layer was carefully removed using a cotton bud and then sonicated . Equal amounts of total protein were separated by SDS-PAGE , transferred to a PVDF membrane and incubated with the following antibodies: C/EBPβ ( E299 ) from Abcam , β-actin ( ab16039 ) from Abcam or ( # 69100 , clone C4 ) from MP Biomedicals; 4E-BP1 ( C-19 ) from Santa Cruz; phospho-p70S6K ( Thr389 ) ( 108D2 ) , p70S6K ( #9202 ) , phospho-S6 ribosomal protein ( Ser235/236 ) ( 2F9 ) , S6 ribosomal protein ( 54D2 ) , and phospho-4E-BP1 ( Thr 37/46 ) ( #9459 ) from Cell Signaling Technology and HRP-linked anti rabbit or mouse IgG from GE Healthcare and HRP-linked anti goat IgG from Santa Cruz . Lightning Plus ECL reagent ( Perkin Elmer ) was used for detection and for re-probing membranes were incubated in Restore Western Blot Stripping buffer ( Thermo Fisher ) . The detection and quantification of protein bands was performed with the Image Quant LAS 4000 Mini Imager ( GE Healthcare ) using the supplied software . Mouse liver or visceral fat tissue was homogenized on ice with a motor driven pellet pestle ( Kontes ) in the presence of QIAzol reagent ( QIAGEN ) and total RNA was isolated as described in ( Zidek et al . , 2015 ) . cDNA synthesis was performed from 1 μg of total RNA with the Transcriptor First Strand cDNA Synthesis Kit ( Roche ) using random hexamer primers . qRT-was performed with the LightCycler 480 SYBR Green I Master mix ( Roche ) using the following primers: Actb ( β-actin ) : 5’-AGA GGG AAA TCG TGC GTG AC-3' and 5'-CAA TAG TGA TGA CCT GGC CGT-3’; Cebpb: 5’-CTG CGG GGT TGT TGA TGT-3’ and 5’-ATG CTC GAA ACG GAA AAG GT-3’; Cd68: 5’-GCC CAC CAC CAC CAG TCA CG-3’ and 5’- GTG GTC CAG GGT GAG GGC CA-3’ . Plasma was prepared as described in ( Zidek et al . , 2015 ) and the IGF-1 specific ELISA was performed according to the instructions of the manufacturer ( BioCat ) . Liver tissue from young ( 5 months ) and old ( 20 months ) wt and C/EBPβΔuORF mice ( from six individuals per group ) was homogenized on ice with a motor-driven pellet pestle ( Kontes ) in the presence of QIAzol reagent ( Qiagen ) , and total RNA was isolated as described in ( Zidek et al . , 2015 ) . Preparation of the sequencing libraries was performed using the TruSeq Sample Preparation V2 Kit ( Illumina ) according to the manufacturer’s instructions . High-throughput single-end sequencing ( 65 bp ) of the libraries was performed with an Illumina HiSeq 2500 instrument . Reads were aligned and quantified using STAR 2 . 5 . 2b ( Dobin et al . , 2013 ) against primary assembly GRCm38 using Ensembl gene build 86 ( http://www . ensembl . org ) . Genes with average expression level below one fragment per million ( FPM ) were excluded from the analysis . A generalized linear model was used to identify differential gene expression using EdgeR package ( McCarthy et al . , 2012; Robinson et al . , 2010 ) . The library normalization was left at the standard setting ( trimmed mean of M-values , TMM ) . The resulting p-values were corrected for multiple testing using the Benjamini-Hochberg procedure . Data visualization , calculation of CV ( coefficient of variation ) and statistical tests were conducted using custom R scripts ( Processed data and R script available at http://www . genomes . nl/CEBPB_delta_uORF/ [de Jong and Guryev , 2018b] or https://github . com/Vityay/CEBPB_delta_uORF [ ( de Jong and Guryev , 2018a]; copy archived at https://github . com/elifesciences-publications/CEBPB_delta_uORF ) . Gene ontology ( GO ) analysis was performed using the DAVID database version 6 . 8 ( Huang et al . , 2009 ) with default DAVID database setting with medium stringency and Mus musculus background . KEGG pathway analysis was performed using gProfiler tool ( Reimand et al . , 2016 ) . For dataset see ( Müller et al . , 2018 ) . Biological replication is indicated ( n = x ) . All graphs show average ± standard error of the mean ( s . e . m . ) . The unpaired , two-tailed Student’s t-Test was used to calculate statistical significance of results with *p<0 . 05; **p<0 . 01; ***p<0 . 001 . Significance of the differences in survival curves was analysed using the log-rank test using Prism7 ( GraphPad Software ) and significance of the difference in maximum lifespan ( number of mice from one cohort within the 10% longest lived mice calculated from the combined cohort ) and tumour incidence was calculated using the Fisher’s exact test with *p<0 . 05 . Daily Chi-square test calculations were carried out to examine the significance of parts of the survival curves . | The risks of major diseases including type II diabetes , cancer and Alzheimer’s are linked to the biological process of ageing . By finding ways to slow ageing , we can help more people to live longer healthier lives while avoiding these illnesses . Placing some animals on a diet that contains only two-thirds as many calories as they would normally eat can improve their fitness during old age and delay the onset of many age-related problems . It is unrealistic to expect people to control their diet to this extent , yet there may be other ways to bring about the same effects . Calorie restriction affects the activity of many different genes; for example , it causes a gene that produces a protein known as Liver-enriched Inhibitory Protein ( LIP for short ) to shut down . LIP controls the activity of many genes involved in metabolism , so it could be a key target for drugs to control ageing . Müller , Zidek et al . used mice that are unable to produce LIP to study this protein’s effect on ageing . The life expectancy of female mice lacking LIP increased by up to 20% . These mice were leaner , fitter , more resistant to cancer , had stronger immune systems and controlled their blood sugar levels better than normal mice . Male mice that lacked LIP did not live longer but did experience some ageing-related benefits . Genetic analysis also showed that gene activity particularly of metabolic genes is more robust in old female LIP-deficient mice and thus more similar to young control mice than old control mice . The results presented by Müller , Zidek et al . suggest that targeting the activity of the LIP gene could help to slow the ageing process . It is not yet clear whether shutting off LIP has similar beneficial effects in humans . Further research is also needed to investigate why female mice gain more benefits from a lack of LIP than males do . | [
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] | 2018 | Reduced expression of C/EBPβ-LIP extends health and lifespan in mice |
Recent evidence suggests that capillary pericytes are contractile and play a crucial role in the regulation of microcirculation . However , failure to detect components of the contractile apparatus in capillary pericytes , most notably α-smooth muscle actin ( α-SMA ) , has questioned these findings . Using strategies that allow rapid filamentous-actin ( F-actin ) fixation ( i . e . snap freeze fixation with methanol at −20°C ) or prevent F-actin depolymerization ( i . e . with F-actin stabilizing agents ) , we demonstrate that pericytes on mouse retinal capillaries , including those in intermediate and deeper plexus , express α-SMA . Junctional pericytes were more frequently α-SMA-positive relative to pericytes on linear capillary segments . Intravitreal administration of short interfering RNA ( α-SMA-siRNA ) suppressed α-SMA expression preferentially in high order branch capillary pericytes , confirming the existence of a smaller pool of α-SMA in distal capillary pericytes that is quickly lost by depolymerization . We conclude that capillary pericytes do express α-SMA , which rapidly depolymerizes during tissue fixation thus evading detection by immunolabeling .
When Rouget first discovered pericytes in 1873 , he observed that these cells might serve as contractile entities to regulate microcirculatory blood flow because of their structure and position on microvessels ( Rouget , 1873 ) . However , this idea was later challenged based on findings that microcirculatory blood flow is largely regulated by upstream arterioles rich in α-smooth muscle actin ( α-SMA ) , the contractile protein present in vascular smooth muscle cells . A number of studies in the past three decades reported that most capillary pericytes in the central nervous system contained little or no α-SMA , whereas α-SMA was detected in pericytes located on pre-capillary arterioles and post-capillary venules ( Bandopadhyay et al . , 2001; Nehls and Drenckhahn , 1991; Kur et al . , 2012; Kornfield and Newman , 2014; Armulik et al . , 2011 ) . In contrast , recent in vitro and in vivo functional studies have demonstrated that a subgroup of capillary pericytes are contractile in the brain and retina , and have the capacity to regulate the microcirculation by contracting and relaxing in response to physiological ( i . e . neurovascular coupling ) as well as pharmacological stimuli ( Kornfield and Newman , 2014; Hall et al . , 2014; Peppiatt et al . , 2006; Fernández-Klett et al . , 2010 ) . Importantly , Hall et al . showed that capillaries covered by pericytes dilate before arte-rioles in response to neuronal stimulus in situ ( Hall et al . , 2014 ) . Recently , an increase in astrocytic calcium was shown to mediate brain capillary changes in response to metabolic demand ( Mishra et al . , 2016 ) . Similarly , Biesecker et al . showed that calcium signaling in Müller cell endfeet was sufficient to evoke capillary dilation in the retina ( Biesecker et al . , 2016 ) . Kisler et al . reported that transgenic mice with a decreased number of pericytes had deficient neurovascular coupling and reduced oxygen delivery to the brain ( Kisler et al . , 2017 ) , providing additional evidence for the importance of pericytes in blood flow regulation . Collectively , these experiments strongly suggest the presence of a bona fide contractile machinery in capillary pericytes . However , a recent paper dismisses the idea of pericytes being contractile by redefining α-SMA-expressing pericytes as smooth muscle cells ( Hill et al . , 2015 ) . To address this paradox between functional and histological findings , we hypothesized that small amounts of α-SMA in capillary pericytes may be rapidly depolymerized during transcardial perfusion and fixation thus evading detection by immunolabeling . Consistent with this idea , α-SMA in pericytes has been detected by electron microscopy in which small amounts of α-SMA can be identified ( Toribatake et al . , 1997; Le Beux and Willemot , 1978; Ho , 1985; Nakano et al . , 2000 ) or by in vitro studies in which fixation is more rapidly achieved ( Herman and D'Amore , 1985 ) . Here , we show that when filamentous-actin ( F-actin ) depolymerization was prevented by F-actin stabilizing agents or by snap fixation , we detected α-SMA in a much larger fraction of microvascular pericytes , including capillary pericytes placed on the intermediate and deeper retinal vascular beds .
To elucidate the current controversy on the presence of α-SMA in capillary pericytes and to test the hypothesis that low α-SMA immunoreactivity in pericytes could stem from a preparation artifact ( Figure 1 ) , we first examined F-actin protein expression in capillary pericytes using fluorescently-tagged phalloidin . Intriguingly , we found substantial F-actin in pericyte processes surrounding capillaries ( Figure 1—figure supplement 1A–D ) . Encouraged by this observation , we tested several fixation methods to enhance α-SMA detection . Snap freeze fixation of retinas with methanol at −20°C , yielded almost twice as many α-SMA-immunopositive microvessels compared to paraformaldehyde ( PFA ) fixation ( Methanol: 441 ± 28 vessels , PFA: 254 ± 63 vessels , p=0 . 023; ANOVA ) ( Supplementary file 1; Figures 1A–B , 2A , C–D and 3A–C ) . The number of α-SMA-labeled microvessels significantly increased in fourth order capillaries ( Methanol: 144 ± 19 vessels , PFA: 60 ± 28 vessels , p=0 . 003; ANOVA ) ( Figures 1A–B , 2A , C–D and 3A–C ) . Ethanol fixation did not improve α-SMA immunostaining ( Figure 1—figure supplement 2A–B ) , suggesting that the effect of methanol was likely due to a faster fixation and not the result of a nonspecific response ( e . g . protein denaturation ) . No immunoreactivity was observed in negative controls when the anti-α-SMA primary antibody was omitted ( Figure 2—figure supplement 1A–B ) . In addition to their typical bump-on-a-log appearance detected by lectin , the α-SMA-positive pericytes on capillaries were identified by NG2 or PDGRFβ co-immunolabeling ( Figure 2B–C ) , or by their red fluorescence in NG2-DsRed mice , a transgenic line that allows selective visualization of pericytes ( Figure 2D ) . To assess the potential contribution of γ-actin cross-reactivity to α-SMA immunolabeling ( Grant et al . , 2017 ) , especially under fixation conditions that might perturb the equilibrium between different actin isoforms , we used a specific antibody against γ-actin after snap freeze fixation of retinas with methanol . Most of the γ-actin immunostaining ran longitudinally , parallel to the capillary and pericyte plasma membrane , unlike the circular α-SMA outlining the pericyte somata and processes around the capillaries ( Figure 4A–G ) . We did not detect any γ-actin immunostaining in pericytes on capillaries after the 4th branch . We did not observe a redistribution of the immunostaining patterns of the two actin isoforms when F-actin depolymerization was inhibited with phalloidin ( Figure 4D–G ) . Phalloidin or jasplakinolide binding to F-actin prevents depolymerization and fixes F-actin in the polymerized state ( Auinger and Small , 2008; Cooper , 1987; Lee et al . , 2010 ) , thus we reasoned that this strategy might enhance detection of α-SMA in retinal capillary pericytes . To test this , retinas were treated with phalloidin or jasplakinolide followed by fixation with methanol . We found that jasplakinolide or phalloidin treatment ( Figures 1C , 2B , 3A–D and 5A–C ) significantly increased the number of α-SMA-labeling on capillaries of 5th and 6th order compared to PFA fixation ( 5th order: jasplakinolide = 225 ± 28 vessels , phalloidin = 80 ± 19 vessels , PFA = 15 ± 7 vessels; 6th order: jasplakinolide = 123 ± 30 vessels , phalloidin = 23 ± 9 vessels , PFA = 0 ± 0 vessels , p=0 . 0001 , ANOVA ) ( Figure 3A ) . Besides this treatment revealed additional α-SMA-immunolabeling on 7th order capillaries ( Figures 2B and 5C ) compared to methanol or PFA fixation ( jasplakinolide: 31 ± 12 vessels , phalloidin: 20 ± 6 vessels , methanol: 0 ± 0 vessels; PFA: 0 ± 0 vessels , p=0 . 002; ANOVA ) ( Figure 3A ) . α-SMA-positive pericytes identified by methanol fixation or phalloidin treatment also expressed NG2 and their number was significantly higher compared to PFA-fixed retinas ( PFA: 509 ± 30 vessels , methanol: 883 ± 56 vessels , phalloidin: 890 ± 138 vessels , p=0 . 035 , ANOVA ) ( Figure 3B ) . Administration of phalloidin combined with methanol fixation also confirmed abundant α-SMA expression in capillary pericytes of the intermediate ( PFA: 97 ± 23 pericytes , methanol: 424 ± 72 pericytes , phalloidin: 509 ± 79 pericytes , jasplakinolide: 497 ± 99 pericytes , p=0 . 03; ANOVA ) and deeper ( PFA: 4 ± 2 . 7 pericytes , methanol: 119 ± 27 pericytes , phalloidin: 260 ± 30 pericytes , jasplakinolide: 359 ± 96 pericytes , p=0 . 01; ANOVA ) retinal plexus ( Figures 2A–D and 3C–D , Figure 2—video 1 ) . The vast majority of the α-SMA-positive capillary pericytes could only be visualized after methanol fixation at −20°C ( Figures 2A , C–D and 3C–D; Figure 2—video 1 ) or upon in vivo administration of jasplakinolide or phalloidin ( Figures 1C , 2B , 3A and C–D ) . Of interest , we found that pericytes at the junction of two capillaries ( i . e . junctional pericytes ) were more frequently α-SMA-positive and exhibited a characteristic circular staining pattern wrapping microvessels . In contrast , pericytes on the linear segment of the capillary , which displayed a helical strand-like staining pattern as reported by other groups ( Nehls and Drenckhahn , 1991; Hartmann et al . , 2015; Sims , 1986 ) , were less often α-SMA-positive . To further confirm α-SMA expression in pericytes , we sought to selectively reduce α-SMA expression using short interfering RNA ( siRNA ) . α-SMA-siRNA significantly suppressed α-SMA expression in capillary pericytes 48 hr after intravitreal administration , while α-SMA expression in pericytes on upstream capillary branches and arterioles was less affected ( Figure 6A–F ) . These results are in agreement with the idea that a small pool of α-SMA in capillary pericytes is quickly lost by depolymerization , hence making its histological detection difficult relative to α-SMA-rich pericytes on pre-capillary arterioles and vascular smooth muscle cells .
In this report , we demonstrate that about 50% of NG2-positive pericytes on high order retinal capillaries ( i . e . >5th ) located in the intermediate and deeper retinal vascular plexus express α-SMA ( Figure 3D ) . Previous reports relied on α-SMA immunohistochemistry involving the slow transcardial infusion of formaldehyde or PFA fixatives ( Thavarajah et al . , 2012 ) , thus lack of or weak α-SMA detection was likely due to rapid F-actin depolymerization ( Huber et al . , 2013 ) leading to the disruption of the antibody-binding sites ( Dudnakova et al . , 2010 ) . Although antigen retrieval on PFA-fixed retinas revealed some α-SMA labeling in retinal microvessels , this signal was modest and only found in a few capillaries . Here , we show that faster tissue fixation with cold methanol strikingly increased the detection of α-SMA-positive pericytes . It is possible that the detection of the minute pool of α-SMA in small soma and processes of pericytes does not only depend on the fixation method , but might also be difficult to visualize in transgenic mice due to dispersion of the limited amount of membrane-bound reporter protein over the relatively large surface area of the pericyte membrane . Moreover , incomplete ( mosaic-like ) fluorescent protein expression after tamoxifen injection in inducible transgenic mice , in which Cre recombinase expression is driven by the α-SMA promoter , can also account for the failure to visualize low levels of α-SMA labeling at capillary pericytes ( Hill et al . , 2015; Hartmann et al . , 2015 ) . Importantly , we demonstrate that inhibition of α-SMA depolymerization in the intact eye using phalloidin or jasplakinolide , two F-actin stabilizing reagents with different pharmacological effects on F-actin , substantially increased visualization of α-SMA in retinal capillary pericytes , particularly those in the deeper retinal plexus . The ratio of α-SMA-positive pericytes was relatively lower to that found in upstream microvessels , consistent with previous reports showing less net O2 delivery from distal capillaries relative to proximal ones ( Sakadžić et al . , 2014 ) . Interestingly , knocking down α-SMA expression led to the disappearance of α-SMA immunostaining mainly in distal capillary pericytes , suggesting that the small pool of α-SMA in capillary pericytes is less stable than in vascular smooth muscle cells . Based on these findings , we conclude that a large population of pericytes , notably those at branching points of retinal microvessels , have the capacity to express α-SMA , which likely mediates their contraction . It has been suggested that γ-actin might contribute to pericyte contractility in cortical microvessels ( Grant et al . , 2017 ) . However , our results do not support this hypothesis since we did not detect γ-actin-positive capillary pericytes on distal order branches after preventing F-actin depolymerization . The latter finding also indicates that the increase in α-SMA-positivity after phalloidin or jasplakinolide was not caused by cross-reactivity between the antibodies against γ- and α-isoforms or by accumulation of an excess amount of F-actin that might increase the immunostaining of all isoforms . Unlike peripheral tissues where the blood flow changes homogeneously , the density of pericytes is high in the CNS and retina , where the blood flow demand varies considerably between neighboring cell layers or groups ( Kornfield and Newman , 2014; Armulik et al . , 2011; Schallek et al . , 2011 ) . In vitro studies and recent in vivo brain studies have provided a growing body of evidence that capillary pericytes contract or dilate in response to vasoactive mediators ( Peppiatt et al . , 2006; Fernández-Klett et al . , 2010; Puro , 2007 ) . This blood flow regulation with fine spatial resolution may be essential for tissues with high functional specialization such as the brain and retina . The retinal capillary dilation in response to light stimulus is reportedly layer-specific ( Kornfield and Newman , 2014 ) . In line with our findings showing clear α-SMA expression in capillary pericytes of the intermediate plexus , the latter study reported robust capillary dilation in this region , but failed to detect α-SMA expression most likely due to the use of PFA-based fixation ( Kornfield and Newman , 2014 ) . A recent study using transgenic mice expressing fluorescent proteins driven by the NG2 or α-SMA promoters also confirmed the contractile capacity of microvascular pericytes in the cortex in vivo , however , it proposed a radical redefinition by classifying the NG2- and α-SMA-expressing ( contractile ) cells as smooth muscle cells , rather than pericytes , as they would have conventionally been named under the original Zimmermann definition used since 1923 ( Hill et al . , 2015 ) . The existence of at least three sub-classes of pericytes and transitional forms from smooth muscle cells ( Zimmermann , 1923 ) has been a matter of confusion , emphasizing the need for an unambiguous definition of pericyte sub-classes and their corresponding specialized functions . In conclusion , we identify key components of the contractile machinery in a large population of pericytes in the healthy retina . The identification of α-SMA in capillary pericytes may contribute to clarify the current paradox between functional and histological studies , and expand our understanding of the mechanisms regulating blood flow at the single-capillary level in neurodegenerative conditions including stroke , retinal ischemia , diabetic retinopathy and Alzheimer’s disease .
Seventy three Swiss albino ( 21–35 g ) , eleven NG2-DsRed ( Schallek et al . , 2013 ) mice were housed under diurnal lighting conditions ( 12 hr darkness and 12 hr light ) and fed ad libitum . The number of animals used in each experiment is indicated in the corresponding legend and the Table 1 , Supplementary file 1 . Animal housing , care , and application of experimental procedures were all carried out in accordance with the institutional guidelines and approved by the Hacettepe University Animal Experiments Local Ethics Committee ( 2012/63 ) , committee guidelines on animal resources at the University of Rochester ( Rochester , New York ) , and the guidelines of the Canadian Council on Animal Care and the Centre de Recherche du Centre Hospitalier de l’Université de Montréal ( CRCHUM , Montreal , Quebec , Canada ) . Eyes were collected , fixed for 1 hr in 4% PFA at room temperature , and the retinas prepared as flattened whole-mounts by making four radial cuts ( Alarcón-Martínez et al . , 2010 ) . Whole retinas were labeled with lectin ( 20 μg/ml in PBS containing 0 . 5% Triton X-100 ( PBST ) , Vector Laboratories , Burlingame , CA ) or antibodies against neural glial antigen-2 ( NG2 ) ( Cspg4 ) ( Millipore , Burllington , MA ) and platelet-derived growth factor receptor beta ( PDGRFβ ) ( Pdgrfb ) ( Abcam , Cambridge , UK ) . Secondary antibody was anti-rabbit IgG conjugated to Cy2 ( Jackson ImmunoResearch , West Grove , PA ) . Briefly , retinas were permeabilized by freezing and thawing in PBST ( −80°C for 15 min , room temperature for 15 min ) , washed 3 times for 10 min , and incubated in 2% PBST at 4°C overnight . The retinas were washed in PBST 3 times for 5 min , incubated in blocking solution ( 10% fetal bovine serum or normal goat serum in PBST ) for 1 hr at room temperature , and then , incubated with each primary antibody diluted in blocking solution ( 5 μg/ml ) at 4°C overnight . The following day , samples were washed in PBST 3 times for 5 min and incubated with secondary antibody diluted in blocking solution ( 3 μg/ml ) for 4 hr at room temperature . We mounted retinas , vitreal side up , on slides and covered them with anti-fade reagent containing Hoechst-33258 to label cellular nuclei ( Molecular Probes , Eugene , OR ) . Retinas were imaged under a light microscope ( 400x , Eclipse E600 , Nikon Instruments Inc . , Melville , NY ) equipped with a manually controlled specimen stage for X , Y , and Z-axis , a color camera ( model DXM1200 , Nikon Instruments Inc . ) , a fluorescent light source ( HB-10104AF , Nikon Instruments Inc . ) , and an image analysis software ( NIS-Elements , Version 3 . 22 , Nikon Instruments Inc . ) . Confocal images of the stained sections were obtained with a Zeiss LSM-510 confocal laser-scanning microscope equipped with a diode laser 488 nm and 561 nm source for fluorescence illumination , and a Leica TCS SP8 DLS ( Leica , Wetzlar , Germany ) confocal laser-scanning microscope , with a X- , Y- , and Z-movement controller , and a high-resolution PMT ( Zeiss , Oberkochen , Germany ) and HyD ( Leica ) detectors . Panoramic pictures of retina were generated by tiling individual images ( 20x ) . Samples were visualized with an Apotome fluorescent microscope ( Apotome 2 , Zeiss ) that allowed optical sectioning and was equipped with an automatic controlled specimen stage for X , Y , and Z-axis , a color camera ( Axiocam 509 mono , Zeiss ) , a fluorescent LED source ( X-cite 120LEDmini , Excelitas , Waltham , MA ) , and an image analysis software ( Zen , Zeiss ) for image acquisition . After sacrificing the animals , eyes were fixed in PFA at room temperature or −20°C in 100%-methanol for 1 hr . Retinas were collected and permeabilized as described above . Tissue was blocked in 10% normal goat serum in PBST at room temperature . Since anti-α-SMA antibodies are commonly generated in mice ( Arnold et al . , 2012; Taylor et al . , 2010; Cao et al . , 2010 ) to avoid non-specific binding to mouse epitopes of the tissue , first we incubated primary antibody against α-smooth muscle actin ( α-SMA ) ( Acta2 ) ( Sigma , San Louis , MO ) separately with monofragments of the secondary antibody ( Jackson Immunoresearch , West Grove , PA ) for 2 hr ( goat anti-mouse , for one retina: 2 µg of primary antibody with 1 . 5 µg of secondary antibody in 10 µl PBS ) . Then , we blocked the potential unbound monofragments by adding 200 µl of 10% normal mouse serum in PBS . Then , tissue was blocked ( 10% normal goat serum in PBS ) , and by incubation in the customized primary and secondary antibody mixture overnight at 4°C . Retinas were washed and mounted for visualization as described below . To prevent actin depolymerization , F-actin was fixed in vivo by 2 µl intravitreal injection of fluorescence phalloidin ( 200 U/ml , Biotium , Freemont , CA ) , non-fluorescent phalloidin oleate ( 5 µg/µl , Millipore , USA ) , or Jasplakinolide ( 10 µM , Abcam , UK ) . Two hrs later , animals were sacrificed and the eyes were collected and fixed in −20°C methanol for 1 hr . Retinas were harvested and subjected to the α-SMA immunostaining protocol described above . Under 200x magnification , we assigned an order number to each vessel segment before branching , starting from arterioles to capillaries , and the total number and the order of α-SMA-positive vessels was determined for each experimental condition . Under 400x magnification and for each fixation method , the number of α-SMA-positive microvessels in each retinal plexus and the total number of α-SMA+/NG2+ pericytes were quantified using a stereological approach . Thus , we analyzed an average of 140 disectors ( field of view: 400 × 300 µm along Z-axis ) per retina ( same area between animal cohorts ) . The number of α-SMA-positive pericytes in each retinal plexus and the total number of α-SMA+/NG2+ pericytes were calculated using the fractionator equation as follows: total number of elements = Σ quantified elements/ssf X asf X tsf , where ssf is the section-sampling fraction ( ssf = number of sections sampled/total sections ) , asf is the area-sampling fraction ( asf = [a ( frame ) ]/area x-y step between disectors ) , and tsf is the thick-sampling fraction ( tsf = frame height/section thickness ) ( Leal-Campanario et al . , 2017 ) . A custom-designed , in vivo specific , HPLC purified α-SMA-siRNA ( siRNA directed against Acta2 ) and a scrambled silencer select negative control siRNA were purchased ( 4457308 and 4404020 , respectively , Ambion LifeTech , Carlsbad , CA ) . This α-SMA-siRNA was previously characterized in wound healing experiments in the murine liver ( Rockey et al . , 2013 ) . Each siRNA was injected into the vitreous using a 34-gauge Hamilton syringe ( 0 . 5 mg/ml siRNAs , total volume: 3 µl ) . Prior to injection , siRNAs were mixed with a transfection reagent to facilitate cell entry in vivo . Briefly , a transfection mixture composed of 3 µl In vivo-jetPEI ( PolyPlus transfection , 201–10G , Illkirch-Graffenstaden , France ) and 12 . 5 µl of 10% glucose in 9 . 5 µl of nuclease free water was added to the nucleic acid mixture ( 3 . 76 µl from 25 µg siRNA , 12 . 5 µl of 10% Glucose in 8 . 74 µl of nuclease free water ) , and incubated for 15 min at room temperature . Transfection mixture was prepared fresh before each knockdown experiment . Forty-eight hrs after intraocular siRNA delivery , mice were sacrificed . To check whether or not siRNAs were taken up by the cells , a fluorescent dye conjugated siRNA ( BLOCK-iTTM Alexa Fluor 555 Fluorescent control; Thermo Fisher Scientific , Waltham , MA ) was mixed with In vivo-jetPEI and delivered to retina as described above . Quantitative RT-PCR siRNA-treated retinas were removed precisely under sterile conditions to eliminate RNase contamination . The samples were stored in RNAlater solution ( Qiagen , Hilden , Germany , 76104 ) at −20°C until RNA isolation . RNAs were extracted with RNeasy Mini Kit ( Qiagen , 74104 ) according to instructions . Five hundred ng of total RNA for each sample was used in cDNA synthesis . cDNA synthesis was performed with random hexamer primers with RevertAid First Strand cDNA Synthesis Kit ( Thermo Fisher Scientific , K1621 ) according to instructions . cDNAs were stored at −20°C . Quantitative RT-PCR was performed with Taqman probe-based technology . Taqman gene expression master mix ( ABI , Foster city , CA , 4369016 ) , FAM-MGB labeled Taqman probes for mouse α-SMA gene ( Assay ID: Mm00725412_s1 ) and mouse GAPDH gene ( Assay ID: Mm9999991_g1 ) were used . PCR was carried out in triplicates in ABI OneStep Q RT-PCR machine ( ABI ) . Thermal cyclic conditions were as follows: 50°C for 2 min , 95°C for 10 min followed by 40 cycles of 95°C 15 s , 60°C for 1 min . The relative expression values were calculated with ΔΔCt method . α-SMA expression in scrambled-siRNA delivered retinas ( n = 3 ) was normalized to one fold expression and then α-SMA expression in α-SMA-siRNA delivered retinas ( n = 3 ) was compared according to control siRNA group . Western blot analysis siRNA-treated retinas were removed and protein homogenates were isolated in the presence of a proteinase and phosphatase inhibitor cocktail containing RIPA buffer . Protein concentration was determined by Pierce BCA protein assay kit ( Thermo Fisher Scientific , 23225 ) , and 40 μg protein per well was loaded to NuPAGE 4–12% Bis-Tris Protein Gels ( Thermo Fisher Scientific , NP0321BOX ) . Proteins were run and transferred to PVDF membranes , followed by incubation in blocking solution containing 5% BSA solution in TBS containing 0 . 5% tween-20 ( TBST ) overnight at 4°C . Blots were incubated with primary α-SMA antibody ( 2 µg/ml , Sigma , A2547-100UL ) diluted in blocking solution for overnight at 4°C then with secondary goat anti-mouse HRP conjugated IgG ( 0 . 08 µg/ml , Santa Cruz Biotechnology , Dallas , TX , sc-516102 ) for 1 hr at room temperature . For loading control , blots were stripped with mild stripping buffer and blocked with 5% fat free milk powder solution in TBST for 1 hr at room temperature , incubated with primary β-tubulin III antibody ( 0 . 08 µg/ml , Sigma , USA , T2200 ) at 4°C for 20 min then with secondary goat anti-rabbit HRP conjugated IgG ( 0 . 05 µg/ml , Santa Cruz Biotechnology , sc-2357 ) for 30 min at room temperature . Bound antibodies were detected with SuperSignal West Femto Maximum Sensitivity Substrate Kit ( Thermo Fisher Scientific , 34095 ) . Densitometric analyses were performed with ImageJ software . After extraction of whole-mount retinas , they were immunostained for α-SMA . Microvessels exhibiting continues α-SMA positivity in the superficial layer of whole mount retinas were counted based on capillary order . Counts were normalized compared to the α-SMA positive microvessel counts from scrambled-siRNA delivered retinas . All values are provided as the mean ±standard error of the mean ( SEM ) . We evaluated all cohorts with normality ( Shapiro-Wilk test ) and variance ( F-test ) tests . For multiple comparisons of values of the stereological quantifications , we used Analysis of Variance ( ANOVA ) followed by Dunnett’s or Tukey’s test where appropriate . p≤0 . 05 was considered significant . For Western blot and qRT-PCR , two-tailed Student’s t-test was applied for statistical analysis . To analyze the α-SMA expression relative to capillary order , we used the specific non-parametric Jonckheere-terpstra test for trend analysis . | Blood vessels in animals’ bodies are highly organized . The large blood vessels from the heart branch to smaller vessels that are spread throughout the tissues . The smallest vessels , the capillaries , allow oxygen and nutrients to pass from the blood to nearby cells in tissues . Some capillaries , including those at the back of the eye ( in the retina ) and those in the brain , change their diameter in response to activity in the nervous system . This allows more or less oxygen and nutrients to be delivered to match these tissues’ demands . However , unlike for larger blood vessels , how capillaries constrict or dilate is debated . While large vessels are encircled by smooth muscle cells , capillaries are instead surrounded by muscle-like cells called pericytes , and some scientists have suggested that it is these cells that contract to narrow the diameter of a capillary or relax to widen it . However , other researchers have questioned this explanation . This is mostly because several laboratories could not detect the proteins that would be needed for contraction within these pericytes – the most notable of which is a protein called α-smooth muscle actin ( or α-SMA for short ) . Alarcon-Martinez , Yilmaz-Ozcan et al . hypothesized that the way samples are usually prepared for analysis was causing the α-SMA to be degraded before it could be detected . To test this hypothesis , they used different methods to fix and preserve capillaries and pericytes in samples taken from the retinas of mice . When the tissue samples were immediately frozen with ice-cold methanol instead of a more standard formaldehyde solution , α-SMA could be detected at much higher levels in the capillary pericytes . Treating samples with a toxin called phalloidin , which stabilizes filaments of actin , also made α-SMA more readily visible . When α-SMA was experimentally depleted from the mouse retinas , the capillary pericytes were more affected than the larger blood vessels . This finding supports the idea that the pericytes contain , and rely upon , only a small amount of α-SMA . Finding α-SMA in capillary pericytes may explain how these small blood vessels can change their diameter . Future experiments will clarify how these pericytes regulate blood flow at the level of individual capillaries , and may give insights into conditions such as stroke , which is caused by reduced blood flow to the brain . | [
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] | 2018 | Capillary pericytes express α-smooth muscle actin, which requires prevention of filamentous-actin depolymerization for detection |
The inducible innate immune response to infection requires a concerted process of gene expression that is regulated at multiple levels . Most global analyses of the innate immune response have focused on transcription induced by defined immunostimulatory ligands , such as lipopolysaccharide . However , the response to pathogens involves additional complexity , as pathogens interfere with virtually every step of gene expression . How cells respond to pathogen-mediated disruption of gene expression to nevertheless initiate protective responses remains unclear . We previously discovered that a pathogen-mediated blockade of host protein synthesis provokes the production of specific pro-inflammatory cytokines . It remains unclear how these cytokines are produced despite the global pathogen-induced block of translation . We addressed this question by using parallel RNAseq and ribosome profiling to characterize the response of macrophages to infection with the intracellular bacterial pathogen Legionella pneumophila . Our results reveal that mRNA superinduction is required for the inducible immune response to a bacterial pathogen .
Gene expression is a concerted process that is regulated at multiple steps , including transcription , mRNA degradation , translation , and protein degradation . Most global studies of gene expression have focused on the transcriptional response , but the relative importance of transcription in determining protein levels remains debated ( Li et al . , 2014; Schwanhäusser et al . , 2011; Breker and Schuldiner , 2014; Maier et al . , 2009; Vogel and Marcotte , 2012; de Sousa Abreu et al . , 2009 ) . One recent study analyzed the response of dendritic cells to lipopolysaccharide ( LPS ) and found that changes in mRNA levels accounted for ~90% of observed alterations in protein levels ( Jovanovic et al . , 2015 ) . However , the response to infection with a virulent pathogen is certainly more complicated than the response to a purified immunostimulatory ligand such as LPS . Indeed , pathogens have evolved to disrupt or manipulate almost every cellular process involved in gene expression ( Finlay and McFadden , 2006 ) . An effective innate immune response to infection therefore requires that host cells be able to induce appropriate responses in the face of pathogen manipulation . Inhibition of host protein synthesis is a common strategy used by many viral and bacterial pathogens to disrupt host gene expression ( Mohr and Sonenberg , 2012; Lemaitre and Girardin , 2013 ) . For example , the intracellular bacterial pathogen L . pneumophila uses its Dot/Icm type IV secretion system ( T4SS ) to translocate into host cells several effector proteins that block host protein synthesis , including at least four effectors that target the elongation factor eEF1A ( Lemaitre and Girardin , 2013; Barry et al . , 2013; Belyi et al . , 2008; Fontana et al . , 2011; Shen et al . , 2009 ) . Similarly , the bacterial pathogen Pseudomonas aeruginosa blocks host translation elongation by secretion of exotoxin A ( Lemaitre and Girardin , 2013; Dunbar et al . , 2012; Iglewski et al . , 1977 ) . Interestingly , we previously discovered that host cells respond to protein synthesis inhibition — whether by Legionella , exotoxin A , or by pharmacological agents that block translation initiation or elongation — by initiating a specific host response characterized by production of specific pro-inflammatory cytokines , including interleukin-23 ( Il23a ) , granulocyte macrophage colony-stimulating factor ( Csf2 ) and interleukin-1α ( Il1a ) ( Barry et al . , 2013; Fontana et al . , 2011 ) . The mechanism by which infected host cells are able to produce certain cytokines despite a global ( >90% ) block in protein synthesis remains unclear , but at least two distinct models have been proposed ( Mohr and Sonenberg , 2012; Lemaitre and Girardin , 2013; Barry et al . , 2013; Fontana et al . , 2011; Dunbar et al . , 2012; Fontana and Vance , 2011; McEwan et al . , 2012; Chakrabarti et al . , 2012 ) . One model posits that the block in protein synthesis leads to superinduction of cytokine mRNAs that is sufficient to overcome the partial block in host protein synthesis ( Barry et al . , 2013; Fontana et al . , 2011 ) . Alternatively , it has been proposed that host cells may circumvent the global block in protein synthesis by selective translation of specific cytokine transcripts ( Dunbar et al . , 2012; Asrat et al . , 2014 ) . To determine how host cells mount an inflammatory response when protein synthesis is disrupted , we performed parallel RNAseq and ribosome profiling ( Ingolia et al . , 2012 , 2009 , 2011 ) of Legionella-infected mouse primary bone-marrow-derived macrophages ( BMMs ) . The results reveal the relative contributions of translational regulation and mRNA induction in controlling immune responses to pathogenic L . pneumophila , and support a model in which the majority of gene induction in response to pathogenic infections occurs at the level of mRNA induction . We were able to identify a subset of mRNAs that display higher-than-average ribosome occupancy , but the elevated occupancy of these mRNAs was observed in uninfected cells as well as in cells infected with L . pneumophila . We propose that mRNA superinduction provides a robust mechanism for host cells to initiate a response to infection despite pathogen-mediated disruption of host gene expression .
The relative role of transcription versus translation in mediating the inducible response to an infection with a virulent bacterial pathogen remains unclear . Thus , we performed ribosome profiling ( Ingolia et al . , 2012 , 2009 , 2011 ) and total ( rRNA-depleted ) RNA sequencing of BMMs infected with L . pneumophila . BMMs were infected with a virulent ΔflaA strain , an avirulent T4SS-deficient ΔdotAΔflaA strain , or a Δ7ΔflaA strain that lacks the seven effectors associated with inhibition of host protein synthesis . RNA was isolated at 6 hr post-infection , which was the earliest we could detect significant L . pneumophila-induced translation inhibition without marked cytotoxicity ( data not shown ) . L . pneumophila strains on the ΔflaA background were used to reduce cell cytotoxicity by avoiding the effects of NAIP5/NLRC4 inflammasome activation by flagellin ( Molofsky et al . , 2006; Ren et al . , 2006 ) and we previously showed loss of flagellin does not affect blockade of host translation or the transcriptional induction of inflammatory cytokines ( Barry et al . , 2013 ) . Control experiments demonstrated that ~90% of macrophages were infected with at least one bacterium under our infection conditions ( Figure 1—figure supplement 1A–B ) . Lysates from infected macrophages were split and used to generate ribosome profiling libraries and RNAseq libraries , thereby allowing us to compare directly the mRNA levels and ribosome occupancy of those mRNAs from the same cells . As a confirmation of the quality of the ribosome profiling libraries , ribosome footprints were found to map preferentially to the exonic regions of infection-induced genes ( Figure 1 ) , and showed a strong bias toward 27–28 nucleotide fragment lengths ( Figure 1—figure supplement 2 ) , consistent with the known size of ribosome-protected footprints . In accord with previous studies , induction of ribosome footprints on Gem , Csf2 , and Il23a required the seven-bacterial effectors associated with the block in host protein synthesis , while induction of ribosome footprints corresponding to Il1a and Il1b required the bacterial T4SS ( Figure 1A–F ) . 10 . 7554/eLife . 22707 . 003Figure 1 . Mapping of ribosome profiling reads to the genomic sequence of specific L . pneumophila-induced genes of interest . ( A–F ) Ribosome footprint reads were mapped to the genome and the number of footprints on the mRNAs for Gapdh ( A ) , Csf2 ( B ) , Gem ( C ) , Il23a ( D ) , Il1a ( E ) , and Il1b ( F ) was visualized . Numbers in parentheses show the total read count of ribosome footprints found on the indicated transcript . Bracketed numbers represent read count data range . Gray , uninfected BMMs . Red , ΔflaA-infected BMMs . Green , ΔdotAΔflaA-infected BMMs . Blue , Δ7ΔflaA-infected BMMs . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 00310 . 7554/eLife . 22707 . 004Figure 1—figure supplement 1 . Quanification of L . pneumophila infectivity . ( A–B ) ΔflaA or ΔdotAΔflaA L . pneumophila-infected BMMs were stained to mark extracellular ( blue ) and all bacteria ( red ) . ( A ) Representative image of ΔflaA L . pneumophila-infected BMMs showing extracellular ( blue and red stain , blue arrow ) and intracellular ( red stain only , yellow arrow ) bacteria . ( B ) Individual BMMs ( n = 1375 ) were analyzed for the presence of at least one intracellular ΔflaA or ΔdotAΔflaA L . pneumophila bacterium . Image is the same as in A with yellow circles marking infected cells and blue circles marking uninfected cells . The average combined infectivity in these conditions is ~90% . See supplemental methods for more details on counting . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 00410 . 7554/eLife . 22707 . 005Figure 1—figure supplement 2 . Ribosome profiling libraries show a strong bias in size distribution . The fraction of total reads with a size of 26–34 nucleotides was plotted for each ribosome profiling library used in this study . These graphs clearly show that the ribosome profiling libraries used in this study have a strong bias for 27–28 nucleotide fragments , consistent with the size of the footprint of the ribosome . Columns indicate infection condition . Rows indicate BMM genotype and/or drug treatment . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 005 We first analyzed WT BMMs for T4SS-dependent gene induction , defined as the ratio of normalized read counts in the virulent ΔflaA to avirulent ΔdotAΔflaA L . pneumophila-infected conditions ( see Materials and methods ) . We found that the majority of T4SS-dependent increases in ribosome footprints could be explained at the level of mRNA induction , as there was nearly a perfect linear correlation between the extent of mRNA induction and ribosome footprints for all T4SS-induced genes ( Figure 2A ) . This correlation held for numerous known pathogen-induced mRNAs , including Il23a , Gem , Csf2 , Il6 , Tnf , Cxcl1 , Cxcl2 , Dusp1 , and Dusp2 , as well as for the cytokines Il1a and Il1b that were previously proposed to be preferentially translated ( Asrat et al . , 2014 ) . To confirm that cytokine protein levels correlate with mRNA levels , we infected BMMs with ΔflaA or ΔdotAΔflaA L . pneumophila and measured the levels of 42 cytokines or immune-related proteins in the supernatants or cell lysates of these BMMs at 6 hr , using commercially available bead arrays . Of the cytokines assayed , 18 cytokines/proteins were measured above the limit of detection in lysates , and 22 cytokines/proteins were measured above the limit of detection in supernatants . The T4SS-dependent fold-induction of these protein levels was plotted versus the T4SS-dependent fold-induction of mRNA levels ( Figure 2B–C ) . We observed a robust correlation between the extent of mRNA induction and the extent of protein induction , particularly in lysates ( Figure 2B ) . The correlation seems to apply for the most highly induced proteins/mRNAs ( e . g . IL-10 ( Il10 ) and GM-CSF ( Csf2 ) ) but also for more modestly induced cytokines ( Il1a , Il1b , Cxcl10 ) . The less robust correlation between mRNA levels and protein levels in the cell supernatant ( Figure 2C ) may reflect differing rates of secretion , accumulation in the supernatant over time , re-binding to cell surface receptors , and stability in the supernatant . Taken together , these results suggest that the inducible immune response to L . pneumophila is controlled primarily at the level of mRNA superinduction ( Figure 2 ) . 10 . 7554/eLife . 22707 . 006Figure 2 . mRNA superinduction controls the T4SS-dependent induction of host gene expression in response to L . pneumophila . ( A ) The ratio of ribosome footprint and RNAseq read counts for well-expressed transcripts ( read count ≥100 ) in ΔflaA-infected versus ΔdotAΔflaA-infected B6 BMMs was calculated for each annotated transcript ( open circles ) in the dataset and plotted . ( B–C ) B6 BMMs were infected with ΔflaA or ΔdotAΔflaA L . pneumophila and at 6 hr post-infection proteins were measured in cell lysates ( B ) or supernatants ( C ) by bead array . The T4SS-induction ( ΔflaA/ΔdotAΔflaA ) of protein in supernatants ( B ) or lysates ( C ) and the T4SS-induction of mRNA ( ΔflaA/ΔdotAΔflaA ) was plotted . Proteins were normalized to total protein levels measured by BCA and RNAseq read counts was normalized to transcript length and the sum of their respective mitochondrial protein coding genes . Data are averaged from four ( A ) or two independent experiments ( B–C ) . Orange circle , Il1a . Green circle , Il1b . Blue circle , subset of inducible genes . Grey dotted line , y = x . Blue dotted line , linear regression model . r2 , coefficient of determination . See also Figure 2—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 00610 . 7554/eLife . 22707 . 007Figure 2—source data 1 . Source data from ribosome profiling , RNAseq , and bead array analysis used for Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 007 L . pneumophila uses multiple mechanisms to block host protein synthesis . It has been shown that up to seven bacterial effectors secreted into the host cytosol can block translation ( Barry et al . , 2013; Fontana et al . , 2011 ) . Interestingly , the ∆7 strain that lacks these effectors is still able to partially suppress host protein synthesis by a mechanism that remains to be fully characterized ( Barry et al . , 2013; Fontana et al . , 2011; Ivanov and Roy , 2013 ) . It has been proposed that T4SS-competent L . pneumophila damages host cell membranes , resulting in ubiquitylation-dependent downregulation of mTOR activity and a block in cap-dependent translation ( Ivanov and Roy , 2013 ) . Consistent with its ability to partially suppress protein synthesis , the ∆7 strain still provokes IL-1α production , although its ability to stimulate Il23a and Csf2 expression is diminished ( Barry et al . , 2013; Fontana et al . , 2011 ) . To determine the mechanism of effector-triggered cytokine induction , we performed parallel RNAseq and ribosome profiling of BMMs infected either with ∆flaA or ∆7∆flaA L . pneumophila . As expected , induction of Gem , Il23a , and Csf2 is highly dependent on the seven bacterial effectors , but again , similar to the total T4SS-dependent gene induction ( Figure 2 ) , the seven effector-dependent induction of ribosome footprints on these genes could be explained at the level of mRNA induction ( Figure 3A ) . While the seven effector-dependent induction of the genes Dusp1 , Dusp2 , Cxcl1 , Cxcl2 , Tnf , Il1a , Il1b , and Il6 was low , all changes in ribosome footprint reads could again be explained by changes in mRNA levels ( Figure 3A ) . These data suggest that T4SS-dependent and seven bacterial effector-dependent induction of inflammatory cytokines occurs by the induction of mRNA transcripts rather than through a mechanism of selective ribosome loading of mRNAs . 10 . 7554/eLife . 22707 . 008Figure 3 . Global induction of mRNAs and ribosome footprints in response to L . pneumophila . ( A–B ) Ribosome footprint and RNAseq read counts were sorted for well-expressed transcripts ( read count ≥100 ) and normalized to the sum of their respective mitochondrial protein coding genes . The ratio of ribosome footprint and RNAseq read counts in ( A ) ΔflaA-infected and Δ7ΔflaA-infected B6 BMMs or ( B ) B6 or Myd88–/– BMMs infected with ΔflaA L . pneumophila was calculated for each annotated transcript ( open circles ) in the dataset and plotted . Data are averaged from two independent experiments . Orange circle , Il1a . Green circle , Il1b . Blue circle , subset of inducible genes . Grey dotted line , y = x . Blue dotted line , linear regression model . r2 , coefficient of determination . See also Figure 3—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 00810 . 7554/eLife . 22707 . 009Figure 3—source data 1 . Source data from ribosome profiling and RNAseq analysis used for Figure 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 009 It was previously proposed that specific transcripts , such as Il1a and Il1b , can be preferentially translated via a mechanism that requires signaling through the adaptor protein MyD88 ( Asrat et al . , 2014 ) . Thus , we performed ribosome profiling and RNAseq on WT and Myd88–/– BMMs infected with ΔflaA L . pneumophila . For all MyD88-induced genes , including Il1a and Il1b , we observed a linear correlation between the induction of ribosome footprints and RNAseq reads ( Figure 3B ) . This implies that MyD88-dependent induction of Il1a and Il1b ribosome footprints is controlled primarily at the level of mRNA induction , rather than at the level of selective ribosome loading of the mRNA . A similar pattern was also observed for other MyD88-induced genes , including Cxcl1 , Csf2 , Tnf , and Il6 ( Figure 3B ) . Taken together , our results argue that the ability of host cells to overcome a pathogen-induced block in protein synthesis , and produce inflammatory cytokines such as IL-1α and IL-1β , requires a T4SS- and MyD88-dependent increase in mRNA levels rather than preferential loading of these cytokine mRNAs with ribosomes . The above analyses sought to determine whether T4SS-dependent or MyD88-dependent gene induction was due to increased mRNA levels or increased ribosome loading of mRNAs . However , the analyses did not reveal whether there is differential ribosome occupancy of constitutively expressed ( i . e . non-induced ) mRNAs . We thus analyzed the ratio of ribosome footprint reads to RNAseq reads for all ( induced and non-induced ) transcripts in uninfected BMMs , and in BMMs infected with ΔflaA , ΔdotAΔflaA , or Δ7ΔflaA L . pneumophila . This analysis revealed a wide range of ribosome occupancies across different transcripts ( Figure 4A–D ) . As might be anticipated , many of the mRNAs with the highest ribosome occupancy encoded abundant ‘housekeeping’ proteins , including Acta1 and histone mRNAs ( e . g . Hist1h2ba , H2afj , and Hist3h2ba ) ( Figure 4A; Table 1 ) . Importantly , most mRNAs that exhibit increased ribosome occupancy in uninfected BMMs also exhibit increased ribosome occupancy in ΔflaA , ΔdotAΔflaA , or Δ7ΔflaA L . pneumophila-infected BMMs ( Figure 4B–D; Table 1 ) , implying that the increased ribosome occupancy of these mRNAs is constitutive and not induced in response to infection . A few mRNAs of immunological interest , namely Lyz1 , S100a11 , and Cxcl3 exhibited elevated ribosome occupancy in all infection conditions ( Figure 4B–D; Table 1 ) . In contrast , Ftl1 mRNA exhibited very low ribosome occupancy ( Figure 4A ) , consistent with a previous report showing that Ftl1 translation can be strongly repressed ( Cairo et al . , 1989 ) . Atf4 is another gene known to be regulated at the level of translation ( Pavitt and Ron , 2012 ) , and in ΔflaA and ΔdotAΔflaA L . pneumophila-infected BMMs , Atf4 exhibited low ribosome occupancy ( Figure 4B–C ) . Atf4 was not expressed at high enough levels to be called as detected in uninfected or Δ7ΔflaA L . pneumophila-infected BMMs ( Figure 4A and D ) . Taken together , our results reveal that several mRNAs exhibit constitutive increased or decreased ribosome occupancy , as expected . Despite this , ribosome occupancy of mRNAs was not markedly affected by L . pneumophila infection ( Figure 4A–D ) . 10 . 7554/eLife . 22707 . 010Figure 4 . Ribosome occupancy does not explain the inducible innate immune response to L . pneumophila . ( A–D ) Ribosome footprint and RNAseq read counts were sorted for well-expressed transcripts ( read counts ≥ 100 ) and normalized to CDS length and the sum of their respective mitochondrial protein coding genes . The normalized read counts for ribosome footprints and RNAseq for all well-expressed annotated transcripts were plotted for ( A ) uninfected , ( B ) ΔflaA , ( C ) ΔdotAΔflaA , or ( D ) Δ7ΔflaA L . pneumophila-infected B6 BMMs . Red dots represent transcripts with low translation efficiency . Purple dots represent a number of transcripts common to all conditions that appear to have significantly higher ribosome occupancy . Data are averaged from three ( A ) , four ( B–C ) , or two independent experiments ( D ) . Orange circle , Il1a . Green circle , Il1b . Blue circles , subset of inducible transcripts . Blue dotted line , linear regression model . Grey lines , 99% prediction interval . r2 , coefficient of determination . See also Table 1 and Figure 4—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 01010 . 7554/eLife . 22707 . 011Figure 4—source data 1 . Source data from ribosome profiling and RNAseq analysis used for Figure 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 01110 . 7554/eLife . 22707 . 012Table 1 . Transcripts with ribosome occupancy eight times greater than the condition average . Bolded , transcripts found in all conditions . Orange , transcripts found in three conditions . Purple , transcripts found in two conditions . Data are averaged from two independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 012UninfectedΔflaAΔdotAΔflaAΔ7ΔflaAGeneRiobosome occupancyGeneRiobosome occupancyGeneRiobosome occupancyGeneRiobosome occupancyActa17105 . 62Acta13137 . 40Acta12489 . 70Acta11633 . 44H2-Q73130 . 45Hist1h4f1177 . 90S100a111115 . 36Hist1h4f943 . 87Hist1h4f2195 . 70S100a11870 . 51Hist1h4f1069 . 93Rpl31893 . 93Hist3h2ba1715 . 98Hist1h2aa844 . 87Rpl31873 . 61Hist1h2aa822 . 96H2afj1524 . 09Hist3h2bb-ps699 . 92Hist1h2ba707 . 62Hist3h2ba625 . 90Hist3h2bb-ps1470 . 88Hist1h2ba692 . 19Hist3h2ba670 . 32S100a11565 . 91Lyz11260 . 16H2afj686 . 47Lyz1533 . 22Fus532 . 18Hist1h2ba1174 . 16Cxcl3675 . 23Hist3h2bb-ps524 . 22Hist1h2ba519 . 60Cd521170 . 22H2-T24557 . 88Fus405 . 23Lyz1498 . 70Fus1102 . 13Lyz1551 . 76H2-T24374 . 06H2-Q7371 . 10H2-Q41022 . 17Hist3h2ba509 . 95Gm5803345 . 78Gm5803368 . 22Rpl381004 . 74Fus480 . 25H2-Q7337 . 36H2afj356 . 99Hist2h2ab796 . 60Saa1475 . 25Hist1h4i302 . 55Hist1h4i348 . 34H2-Q6752 . 77Gm5803436 . 48Cxcl3281 . 86Hist1h4k318 . 08S100a11717 . 99Hist1h4i333 . 72Saa1265 . 08Rrbp1306 . 19Gm5803692 . 32Atp5e315 . 00Hist1h4n244 . 74Hist1h4j301 . 73Tmsb10679 . 27Rrbp1308 . 68Rrbp1225 . 85Hist1h4a298 . 67H2-Q10674 . 79Mt1308 . 36Hist1h4j218 . 84Hist1h4h295 . 99Rpl36672 . 43Hist1h4j304 . 87Hist1h4k217 . 89Hist1h4b288 . 98Mt1672 . 29Hist1h4k303 . 19Atp5e217 . 08Hist1h4n272 . 04Hist2h2bb650 . 90Hist1h4h293 . 11H2-Q4215 . 94BC094916259 . 08H2-Q7629 . 56Hist1h4a292 . 51Hist1h4h215 . 51Hist1h4c255 . 20H2-Q7618 . 80Hist1h4b280 . 60H2afj206 . 14Cxcl3252 . 49Atp5e606 . 27Gm11127272 . 59Hist1h4a205 . 64Atp5e241 . 23H2-T24601 . 41Hist1h4n265 . 41Hist1h4b197 . 15Saa1220 . 10Rpl37584 . 88Fkbp1a264 . 22Hist2h2bb191 . 90Myl12b210 . 72H2-T10545 . 54Hist1h4cHist1h4cHist1h4c187 . 03Gm7030206 . 93Hist1h4i529 . 15Gm7030253 . 47Mt1185 . 28Gm11127512 . 74Myl12b247 . 71Cd52184 . 14Uqcrq511 . 93Rps17234 . 41Gm11127183 . 08Emp1494 . 39Cd52231 . 77Hist1h2bj182 . 74Hist1h2bf484 . 53Sh3bgrl181 . 81Gm7030481 . 28Npc2479 . 93Hist1h2bj478 . 33Usmg5477 . 21Hmga2468 . 10 A benefit of ribosome profiling is that it permits the mapping of ribosome footprints with nucleotide resolution . Thus , to characterize the position of ribosomes on mRNAs after infection with L . pneumophila , we generated metagene ribosome footprint profiles from libraries generated from WT BMMs ( Figure 5 ) . Metagene profiles were generated by mapping the inferred A site position of ribosome footprint reads relative to the start ( Figure 5A , C , E , G , I ) or stop ( Figure 5B , D , F , H , J ) codon on a given transcript . Mapped reads were then summed to produce a global view of the distribution of ribosomes across all transcripts in our dataset . As expected from the known stepwise codon-by-codon movement of the ribosome , all metagene plots demonstrated a characteristic three-nucleotide periodicity ( Figure 5 ) . Interestingly , metagene ribosome profiles of uninfected ( Figure 5A–B ) , ΔflaA ( Figure 5C–D ) , ΔdotAΔflaA ( Figure 5E–F ) , or Δ7ΔflaA ( Figure 5G–H ) infected BMMs appeared grossly similar to each other , even though global translation is blocked only in the ΔflaA and Δ7ΔflaA L . pneumophila-infected conditions ( Barry et al . , 2013; Fontana et al . , 2011; Asrat et al . , 2014 ) . This result can be explained by the fact that ribosome metagene profiles do not distinguish whether ribosome footprints arise from stalled or translating ribosomes , unless the stall occurs at a characteristic distance from the start or stop codon . In fact , we did notice a slight increase in the number of ribosomes found at the start site of the transcript in Δ7ΔflaA L . pneumophila-infected BMMs as compared to other conditions ( Figure 5I ) . This may reflect a selective block in translation initiation by this strain ( see below ) . In addition , we noted that in all conditions , ribosomes accumulated at the stop codon , suggesting that , in BMMs , translation termination may be a limiting step in translation ( Figure 5J ) . 10 . 7554/eLife . 22707 . 013Figure 5 . Metagene profiles of L . pneumophila infected BMMs . ( A–J ) Metagene profiles of uninfected ( A–B ) , ΔflaA ( C–D ) , ΔdotAΔflaA ( E–F ) , Δ7ΔflaA ( G–H ) L . pneumophila-infected B6 BMMs and a merge ( I–J ) . Metagene profiles are depicted relative to the translation start ( A , C , E , G , I ) and stop site ( B , D , F , H , J ) . Metagene analyses show peaks at every three nucleotides , corresponding to the codon-to-codon shifts of the ribosome . Data are representative of two independent experiments ( A–J ) . Black line , uninfected . Red line , ΔflaA-infected . Green line , ΔdotAΔflaA-infected . Blue line , Δ7ΔflaA-infected . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 013 To distinguish whether an observed ribosome footprint arises from a stalled or translating ribosome , we performed ribosome run-off experiments . In these experiments , new translation initiation was blocked by the drug harringtonine 120 s prior to cell lysis . Harringtonine inhibits the first rounds of peptide bond formation following ribosome subunit joining and results in accumulation of ribosomes at the translational start site and run-off of elongating ( but not stalled ) ribosomes that have already cleared the start codon ( Ingolia et al . , 2012 , 2011; Huang and Harringtonine , 1975; Tscherne and Pestka , 1975; Fresno et al . , 1977 ) . Importantly , cells experiencing a block in translation elongation will exhibit less ribosome run-off after harringtonine treatment , and an increased number of reads at the 5ʹ end of mRNAs after drug treatment ( Ingolia et al . , 2011 ) , compared to cells in which elongation is not blocked . As expected , uninfected and ΔdotAΔflaA-infected BMMs show an increase in ribosome footprints at the translation start site and a preferential loss of ribosome footprints from the 5ʹ and 3ʹ end of mRNAs , consistent with the expected effects of harringtonine and demonstrating clear ribosome run-off ( Figure 6A–B , E–F , Figure 6—figure supplement 1A–B ) . By contrast , ΔflaA L . pneumophila-infected BMMs treated with harringtonine exhibited little ribosome run-off ( Figure 6C–D , Figure 6—figure supplement 1A–B ) , consistent with the expectation that ΔflaA L . pneumophila blocks host translation elongation . The Δ7ΔflaA L . pneumophila strain , lacking all known bacterial effectors that block host protein synthesis , nevertheless , shuts down host translation ( Barry et al . , 2013 ) , yet we observed clear evidence of run-off of elongating ribosomes from the 5ʹ and 3ʹ end of mRNAs following harringtonine treatment ( Figure 6G–H , Figure 6—figure supplement 1A–B ) . These data suggest that the residual block in host protein synthesis induced by Δ7ΔflaA L . pneumophila is at the level of translation initiation . Similar results can be seen when analyzing longer stretches of coding sequences ( Figure 6—figure supplement 1C–F ) . 10 . 7554/eLife . 22707 . 014Figure 6 . L . pneumophila-induced block of host protein synthesis occurs at the level of translation initiation and elongation . ( A–H ) Metagene profiles of B6 BMMs uninfected ( A–B ) or infected with ΔflaA ( C–D ) , ΔdotAΔflaA ( E–F ) , or Δ7ΔflaA ( G–H ) L . pneumophila in the presence ( solid line ) or absence ( dashed line ) of the drug harringtonine to block translation initiation . Metagene profiles are depicted relative to the translation start ( A , C , E , G ) and stop site ( B , D , F , H ) . Data are representative of two independent experiments ( A–H ) . Solid line , no drug treatment . Dashed line , harringtonine treatment . Black line , uninfected . Red line , ΔflaA-infected . Green line , ΔdotAΔflaA-infected . Blue line , Δ7ΔflaA-infected . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 01410 . 7554/eLife . 22707 . 015Figure 6—figure supplement 1 . L . pneumophila-induced block in host protein synthesis can occur at the level of translation elongation and initiation . ( A–B ) Metagene profile plot around the translation start ( A ) or stop ( B ) site of all harringtonine-treated conditions normalized to mitochondrial read counts of each condition . ( C–F ) Global weighted averages across transcripts were calculated for BMMs left uninfected ( C ) or infected with ΔflaA ( D ) , ΔdotAΔflaA ( E ) , or Δ7ΔflaA ( F ) L . pneumophila . Weighted averages were generated by scaling each transcript’s ribosome occupancy profile according to the average density from codon 250 to codon 349 and then averaging across the entire condition . Transcripts with very low density in the 250–349 codon region ( or shorter than 349 codons ) are excluded from averaging . If the weighted average is less than 1 , this shows that this region has reduced ribosome footprints , while if the weighted average is greater than one this shows that there are more ribosome footprints in this region . Following a brief pulse of cells with harringtonine ( red line ) there is a change in the distribution of ribosomes from the 5ʹ end of the mRNA to the 3ʹ end of the mRNA ( i . e . , as the ribosomes continue to move 5ʹ to 3ʹ; C–F ) as compared to untreated cells ( blue line ) . This can be seen by an increase in the weighted average at the 3ʹ end of mRNAs ( C , E , F ) but the lack of this change shows a block in translation elongation ( D ) . We also expect an accumulation of ribosomes at the ATG following harringtonine treatment , which is also seen as a peak in the weighted average at the start site ( C–F ) . Data are representative of two independent experiments ( A–F ) . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 015 It is important to note that there is a small proportion of uninfected bystander cells assayed in our experiments . However , it is unlikely that these uninfected cells are responsible for the ribosome run-off seen in Δ7ΔflaA L . pneumophila-infected BMMs because the conditions used in these experiments led to most ( ~90% ) cells being infected with L . pneumophila ( Figure 1—figure supplement 1A–B ) . Furthermore , if our infection conditions resulted in large numbers of uninfected cells , then a similar run-off should have been observed in the ∆flaA-infected sample , which it was not . Thus , these results suggest that the seven effectors are required to block translation elongation , and that the residual translation inhibition induced by Δ7ΔflaA L . pneumophila is at the level of translation initiation ( Figure 6 ) . Although the above results demonstrate a global block in translation elongation in ∆flaA-infected cells , it remains possible that specific transcripts escape this block . We therefore analyzed our translation run-off datasets to assess translation elongation on a per-mRNA basis . We plotted the number of ribosome footprint reads for each transcript in paired untreated and harringtonine treated samples ( Figure 7A–D ) . In this analysis , we expect that an mRNA with actively elongating ribosomes would show a reduction in the number of 5ʹ reads in the harringtonine treated sample , as ribosomes will run off the transcript , compared to the untreated sample . In order to best measure run-off elongation and avoid the expected but confounding effects of harringtonine-induced accumulation of footprints at start codons ( which were clearly observed; Figure 6 ) , we excluded the first 25 codons and analyzed ribosome footprint occupancy over the next 300 codons . Consistent with our previous analysis , we find that uninfected , ΔdotAΔflaA , and Δ7ΔflaA-infected BMMs show a clear global signature of ribosome run-off , again suggesting that the block in host protein synthesis induced by Δ7ΔflaA L . pneumophila infection is occurring at the level of translation initiation ( Figure 7A–D ) . Importantly , in ΔflaA-infected BMMs there is no evidence of ribosome run-off , consistent with ΔflaA L . pneumophila inducing a block in host translation elongation ( Figure 7B ) . Interestingly , in all conditions tested , cytokine-related genes fell well within the average of ribosome retention across all transcripts , and if anything , were found to have reduced ribosome run-off compared to a typical gene ( Figure 7A–D ) . A similar trend was seen when we further examined ribosome run-off for specific immune and housekeeping transcripts by plotting the cumulative read counts over the length of the mRNA ( Figure 7—figure supplement 1 ) . These results imply that at this time point , cytokine transcripts are not preferentially translated in response to pathogenic infection , but instead are controlled at the level of mRNA induction ( Figure 7A–D ) . 10 . 7554/eLife . 22707 . 016Figure 7 . Immune-related genes do not have increased translation rates in response to infection with L . pneumophila . ( A–D ) Read counts from paired samples treated with harringtonine or left untreated were plotted for uninfected ( A ) , ΔflaA ( B ) , ΔdotAΔflaA ( C ) , or Δ7ΔflaA-infected ( D ) BMMs showing where cytokine-related transcripts ( pink circles; Csf1 , Csf2 , Cxcl1 , Cxcl2 , Dusp1 , Dusp2 , Ifnb1 , Il10 , Il12b , Il1a , Il1b , Il23a , Il6 , Lyz1 , and Tnf ) and housekeeping transcripts ( blue circles; Gapdh , Rpl31 , Rps17 , and Tuba1a ) fall among all transcripts ( black circles ) . Grey line , y=x . Data shown are representative of two independent experiments . See Figure 7—source data 1 for individual housekeeping and cytokine-related transcripts . Supporting Information Captions . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 01610 . 7554/eLife . 22707 . 017Figure 7—source data 1 . Source data from ribosome profiling analysis used for Figure 7 . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 01710 . 7554/eLife . 22707 . 018Figure 7—figure supplement 1 . Individual mRNAs do not show evidence of preferential translation . Cumulative read counts across the length of individual transcripts were calculated and normalized to the sum of ribosome footprints of mitochondrial transcripts in each respective library . Red = BMMs treated with harringtonine . Black = BMMs untreated . Numbers in parentheses = total read counts . Rows = individual transcript . Columns = infection condition . DOI: http://dx . doi . org/10 . 7554/eLife . 22707 . 018
Inducible gene expression is of central importance for the immune response to infection . A recent study showed that in response to innate immune stimulation with purified LPS , dendritic cells almost entirely control the induction of genes at the level of transcription ( Jovanovic et al . , 2015 ) . However , this conclusion may not apply to cells infected with a virulent pathogen that manipulates gene expression . We thus investigated the relative contributions of mRNA induction and translation during infection with an intracellular bacterial pathogen , L . pneumophila , that blocks host protein synthesis . Pathogen-induced blockade of host protein synthesis has been shown in a number of infection models to be sensed by the host and induce an inflammatory response ( Barry et al . , 2013; Fontana et al . , 2011; Dunbar et al . , 2012; McEwan et al . , 2012; Chakrabarti et al . , 2012; Fontana et al . , 2012 ) . We previously identified IL-1α as a key inflammatory cytokine induced preferentially in response to translation inhibition imposed by L . pneumophila ( Barry et al . , 2013 ) . However , the mechanism by which cytokine proteins are induced despite a pathogen-induced translation blockade remains unclear . We previously provided evidence for a model in which translation inhibition results in a failure to synthesize negative feedback inhibitors of transcription , for example , IκB or A20 ( Fontana et al . , 2011 ) . We proposed this results in a massive and sustained production of cytokine transcripts , termed mRNA superinduction , that is sufficient to overcome the partial ( ~95% ) block in translation and allow for production of cytokine proteins ( Barry et al . , 2013; Fontana et al . , 2011 ) . Another report provided data suggesting that IL-1 production is mediated by MyD88-enhanced protein synthesis , although alternative explanations were also entertained ( Asrat et al . , 2014 ) . A third study proposed that virulent L . pneumophila regulates cap-dependent translation initiation , via manipulation of the mTOR signaling pathway , to regulate the protein levels of highly abundant transcripts in infected macrophages ( Ivanov and Roy , 2013 ) . In our present study , we found that the induction of ribosome footprints by L . pneumophila could be explained by an underlying induction of mRNAs . We did not find evidence for selective ribosome loading of abundant cytokine mRNAs . In addition , ribosome run-off experiments confirmed that cytokine mRNAs are not selectively translated during infection ( Figure 7 ) . Furthermore , we find that the role of MyD88 signaling in gene expression appears to be primarily at the level of mRNA induction and not translational regulation ( Figure 3 ) . Thus , we conclude that preferential translation does not account for the majority of specific gene induction following infection by L . pneumophila . It remains possible that selective translation initiation mechanisms , for example , via uORFs , might also contribute modestly to the inducible immune response to L . pneumophila , but these subtle effects were not evident in our global analysis . In any case , it is difficult to explain how regulation of translation initiation could overcome a downstream pathogen-induced block in translation elongation such as is observed during L . pneumophila infection . It is also possible that post-translational mechanisms , which are not addressable with the ribosomal profiling techniques used here , may regulate protein production by infected cells . Indeed , inflammasome-dependent caspase-1 processing is known to be an important post-translational regulatory mechanism controlling IL-1β production by infected cells ( von Moltke et al . , 2013 ) . Lastly , our data do not specifically address the mechanism of mRNA induction , although our prior work suggested mRNA induction involves new transcription rather than increased mRNA stability ( Fontana et al . , 2011 ) . Although wild-type L . pneumophila blocks translation elongation via translocated effectors , we found that Δ7ΔflaA L . pneumophila lacking the effectors nevertheless blocks protein synthesis at the level of translation initiation ( Figure 6 ) . Thus , in contrast to a previous study that used virus-based translation reporter experiments in L . pneumophila-infected RAW macrophages ( Ivanov and Roy , 2013 ) , we were clearly able to dissociate the L . pneumophila-induced block in host protein synthesis into two components: ( 1 ) an elongation block that required the seven translocated effectors , and ( 2 ) an initiation block that did not require the seven effectors . In addition , our analysis represents an advance over prior studies because we were able to analyze the translation of all endogenous transcripts simultaneously as opposed to measuring translation only of a single exogenous reporter mRNA . Intriguingly , in contrast to the effector-dependent block in translation that we show occurs at the level of elongation , the majority of host-mediated regulation of translation occurs at the level of translation initiation ( Hershey et al . , 2012 ) . Thus , while it is possible that a novel bacterial effector that directly targets translation initiation could explain the residual inhibition of translation by the Δ7ΔflaA L . pneumophila mutant , we favor the hypothesis that the residual block in host protein synthesis may be a result of the host stress response induced by pathogenic infection , consistent with numerous prior studies ( Mohr and Sonenberg , 2012; Lemaitre and Girardin , 2013; Chakrabarti et al . , 2012; Ivanov and Roy , 2013; Janssens et al . , 2014; Tattoli et al . , 2012 ) . Indeed , T4SS-competent L . pneumophila has been suggested to induce membrane damage that inhibits the mTOR pathway and blocks translation initiation ( Ivanov and Roy , 2013 ) . Further studies will be required to identify the bacterial and host pathways required for the residual translation inhibition caused by the Δ7ΔflaA L . pneumophila mutant . The results presented here further support a role for translation inhibition as a signal that the innate immune system uses to recognize and preferentially respond to pathogens ( Fontana et al . , 2011 ) . Our work provides nucleotide-level analysis of the global block in host protein synthesis induced by L . pneumophila , and demonstrates that L . pneumophila infection results in inhibition of host protein synthesis both at the level of translation initiation and elongation . Importantly , our results also provide insights into the molecular mechanisms by which host cells are able to mount a protective immune response despite a pathogen-induced block in protein synthesis . Using ribosome run-off assays in combination with ribosome profiling and RNAseq , we find that mRNA superinduction , rather than selective mRNA translation , is the strategy by which host cells produce inflammatory cytokines in the face of pathogen-mediated translation inhibition . To be effective , the strategy of mRNA superinduction requires that the magnitude of mRNA superinduction exceeds the magnitude of the block in protein synthesis . Indeed , our data suggest this is the case , as we observe >1000 fold induction of certain mRNAs , whereas we previously estimated the block in protein synthesis to be ~95% ( 20-fold ) ( Fontana et al . , 2011 ) . One possible advantage of mRNA superinduction as a strategy for overcoming a pathogen-mediated block of protein synthesis is that it does not require specific translation factors , as was previously proposed might mediate selective mRNA translation in L . pneumophila-infected cells ( Asrat et al . , 2014 ) . In addition , in mammalian cells , selective translation is usually regulated at the level of translation initiation , a strategy that would be easily defeated by pathogens such as L . pneumophila that block the downstream process of translation elongation . Importantly , since numerous viral and bacterial pathogens and toxins interfere with host protein synthesis , we propose that our results may provide general insight into the inducible innate immune response to infection .
These studies were carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health under animal protocol AUP-2014-09-6665 . The protocol was approved by the Animal Care and Use Committee at the University of California , Berkeley . The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus ( Edgar et al . , 2002 ) and are accessible through GEO Series accession number GSE89184 ( https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE89184 ) . Macrophages were derived from the bone marrow of C57BL/6J ( Jackson Laboratory , Bar Harbor , ME , USA ) and Myd88 –/– ( Hou et al . , 2008 ) mice on the B6 background . Macrophages were derived by 8 days of culture in RPMI supplemented with 10% serum , 100 μM streptomycin , 100 U/mL penicillin , 2 mM L-glutamine and 10% supernatant from 3T3-macrophage-colony-stimulating factor cells , with feeding on day 5 . Cells were re-plated in antibiotic free media 24 hr prior to infection with L . pneumophila . All L . pneumophila strains were derived from LP02 , a streptomycin-resistant thymidine auxotroph derived from L . pneumophila LP01 . The ΔdotAΔflaA , ΔflaA , and Δ7ΔflaA strains were generated on the LP02 background and have been described previously ( Barry et al . , 2013; Fontana et al . , 2011; Ren et al . , 2006; Fontana et al . , 2012 ) . Twofold dilutions of L . pneumophila strains used for infections were grown overnight in liquid buffered-yeast-extract culture and , at the time of infection , cultures with an optical density ( 600 nm ) greater than 4 . 0 were selected . BMMs were plated at a density of 1 . 56 × 105 cells per cm2 ( 1 . 5 . x106 cells per well of a six-well plate ) and infected at an MOI of 3 by centrifugation for 10 min at 400 xg . After 1 hr of infection media was changed . All in vitro L . pneumophila infections were performed in the absence of thymidine to prevent bacterial replication which would otherwise differ between the ∆dotA and Dot+ strains . The lack of thymidine can result in a loss of bacterial viability , although we attempted to mitigate this concern by examining host gene expression at a relatively early 6 hr time point . Ribosome profiling experiments were undertaken as previously described ( Ingolia et al . , 2012 ) . BMMs were plated in tissue culture treated six-well plates ( 1 . 5 × 106 BMMs/well ) or 75 cm2 flasks ( 1 . 2 × 107 BMMs per flask ) . At 6 hr post-infection BMMs were lysed by flash freezing and thawed in the presence of lysis buffer ( Ingolia et al . , 2012 ) . When used , harringtonine ( LKT Laboratories , Saint Paul , MN ) was added at a final concentration of 2 μg/mL for 120 s at the end of the 6 hr infection . 100 μg/mL of cycloheximide ( Sigma-Aldrich , St . Louis , MO ) was added to freeze ribosomes after the 120 s harringtonine treatment . Following cycloheximide treatment cells were immediately lysed . Clarified lysates were split and some was used to generate ribosome footprints while some was used to isolate total RNA for RNA sequencing ( described below ) . All RNA and DNA gel extractions were performed overnight as previously described ( Ingolia et al . , 2012 ) . The Ribo-Zero Gold rRNA Removal Kit ( Illumina , San Diego , CA ) was used to remove rRNA from ribosome profiling samples before the dephosphorylation and linker ligation steps ( Ingolia et al . , 2012 ) . Final ribosome profiling libraries were sequenced on a HiSeq2000 System ( Illumina ) with single read 50 ( SR50 ) read lengths by the Vincent J . Coates Genomics Sequencing Laboratory at UC , Berkeley . Clarified lysate was isolated as described above and 300 μL of lysate was mixed with 900 μL of Trizol LS ( Thermo Fisher Scientific , Waltham , MA ) and RNA was isolated following the manufacturer’s guidelines . RNA integrity was measured utilizing the RNA Pico method on the Agilent 2100 Bioanalyzer at the University of California , Berkeley Functional Genomics Laboratory . High-quality RNA with a RNA integrity number ( RIN ) >8 ( Agilent Technologies , Santa Clara , CA ) was submitted to the QB3-Berkeley Functional Genomics Laboratory and single read 100 base pair read length ( SR100 ) sequencing libraries were generated . Libraries were sequenced on a HiSeq2000 System ( Illumina ) by the Vincent J . Coates Genomics Sequencing Laboratory at UC , Berkeley . RNA sequencing reads were preprocessed using tools from the FASTX-Toolkit ( http://hannonlab . cshl . edu/fastx_toolkit/ ) by trimming the linker sequence from the 3ʹ end of each read and in some cases removing 10–15 nucleotides from the 5ʹ of each read to mitigate a region of overrepresented nucleotides . Alignment and differential expression analysis of RNAseq reads were undertaken as previously described ( Trapnell et al . , 2012 ) . Briefly , high quality and preprocessed sequencing reads were aligned using the TopHat splicing-aware short-read alignment program to a library of transcripts derived from the UCSC Known Gene data set , and those with no acceptable transcript alignment were then aligned against the Mus musculus genome ( mm10 ) . Sequences were processed as described previously ( Ingolia et al . , 2012 ) . Sequences were preprocessed by trimming the linker sequence from the 3ʹ end of each sequencing read and removing the first nucleotide from the 5ʹ end of each read . Reads were then aligned to a rRNA reference using the Bowtie short-read alignment program . All sequences aligning the rRNA reference were discarded . All non-rRNA sequencing reads were aligned using the TopHat splicing-aware short-read alignment program to a library of transcripts derived from the UCSC Known Genes data set , and those with no acceptable transcript alignment were then aligned against the mouse genome ( mm10 ) . Perfect-match alignments were extracted , and these files were used for analyses . For most analyses , footprint alignments were assigned to specific A site nucleotides by using the position and total length of each alignment , calibrated from footprints at the beginning and the end of CDSes , as previously described ( Ingolia et al . , 2012 , 2011 ) . Counting of reads was performed as previously described ( Ingolia et al . , 2009 , 2011 ) . Reads were mapped to coding sequences and counted , excluding reads that mapped to the first 15 codons or the last 5 codons of a CDS due to accumulation of ribosomes ( Ingolia et al . , 2011 ) . In order to analyze gene-specific ribosome run-off ( Figure 7A–D ) , we counted reads mapping from codon 26 to codon 325 , that is , a 300-codon window excluding the first 25 codons of a gene . For analyses of ribosome occupancy ( Figure 4 ) , ribosome footprint and mRNAseq read counts were calculated similarly . Read counts were normalized to CDS length , as longer transcripts inherently have increased read counts , generating a read density ( read density = read count ÷ transcript length ) for each gene . Read densities were further normalized to the sum of read counts of 12 mitochondrial protein-coding genes ( see below ) as an estimate of total cells in each condition , allowing for comparison among different conditions and libraries ( Iwasaki et al . , 2016 ) . For each transcript in the dataset , the average raw ribosome footprint read counts for each infection conditions was calculated and transcripts with an average ribosome footprint or RNAseq read count less than 100 were discarded . Additionally , any transcript that had ribosome footprint reads but 0 RNAseq reads was also discarded . Discarded transcripts were defined as undetectable . Two experiments were used to generate two independent libraries consisting of B6 and Myd88–/– BMMs infected with ΔflaA or ΔdotAΔflaA L . pneumophila . For each gene in the dataset , the average raw ribosome footprint read counts for ΔflaA L . pneumophila-infected B6 BMMs were sorted and genes with an average ribosome footprint or RNAseq read count less than 100 were discarded . Additionally , any gene that had ribosome footprint reads but no detectable RNAseq reads in B6 or Myd88–/– BMMs were discarded . The sorted read counts were then normalized to ribosome footprint or RNAseq read counts of 12 mitochondrial protein-coding genes ( see below ) as an estimate of total cells in each condition . MyD88-dependent gene induction was calculated using the equation: MyD88-dependent gene induction = average ( normalized B6 read count ) ÷ average ( normalized Myd88–/– read count ) . Four independent experiments were used to generate four collections of sequencing libraries consisting of B6 BMMs infected with ΔflaA or ΔdotAΔflaA L . pneumophila . For each gene in the dataset , the average raw ribosome footprint read counts for ΔflaA L . pneumophila infected B6 BMMs were sorted and genes with an average ribosome footprint or RNAseq read count less than 100 were discarded . Additionally , any gene that had ribosome footprint reads but no detectable RNAseq reads in ΔflaA or ΔdotAΔflaA L . pneumophila-infected B6 BMMs were discarded . The sorted read counts were then normalized to ribosome footprint or RNAseq read counts of 12 mitochondrial protein-coding genes ( see below ) as an estimate of total cells in each condition . T4SS-dependent gene induction was calculated using the equation: T4SS-dependent gene induction = average ( normalized ΔflaA-infected read count ) ÷ average ( normalized ΔdotAΔflaA-infected read count ) . B6 BMMs were left uninfected or infected with ΔflaA or ΔdotAΔflaA L . pneumophila at an MOI of 3 in duplicate , as described above . Media was changed 1 hr following infection and at 6 hr post-infection supernatants were collected and BMMs washed with PBS . BMMs were lysed in 400 μL mammalian cell PE lysis buffer ( G-Biosciences , St . Louis , MO ) following the manufacturers instructions . Lysates and supernatants were cleared by spinning at 20 , 000 x g for 30 min at 4°C . Cytokine and protein levels were measured using a commercially available cytokine bead array ( Rodent MAP 4 . 0-Mouse Sample Testing , Ampersand Biosciences , Saranac Lake , NY ) and total protein levels were measured by bicinchoninic acid ( BCA ) assay ( Ampersand Biosciences , Saranac Lake , NY ) . Protein and cytokine levels in each infection condition were normalized to total protein levels . Infectivity was confirmed by staining for L . pneumophila ( see below ) . mRNA levels of cytokines were determined by counting ( counting method described above ) previously acquired RNAseq data of B6 BMMs infected with ΔflaA or ΔdotAΔflaA L . pneumophila at an MOI of 3 for 6 hr . RNAseq read counts were normalized to transcript length and the sum of RNAseq read counts of 12 mitochondrial protein-coding genes ( see below ) as an estimate of total cells in each condition ( RNAseq normalization described above ) . T4SS-dependent induction was measured by taking the ratio of protein or mRNA levels in the ΔflaA infected condition to protein or mRNA levels in the ΔdotAΔflaA infected condition: T4SS-dependent induction = average ( normalized ΔflaA mRNA or protein ) divided by average ( normalized ΔdotAΔflaA mRNA or protein ) . T4SS-induction was averaged from two independent experiments and plotted . Two independent experiments were used to generate two collections of sequencing libraries consisting of B6 BMMs infected with ΔflaA or Δ7ΔflaA L . pneumophila . For each gene in the dataset the average raw ribosome footprint read counts for ΔflaA L . pneumophila-infected B6 BMMs were sorted and genes with an average ribosome footprint or RNAseq read count less than 100 were discarded . Additionally , any gene that had ribosome footprint reads but no detectable RNAseq reads in ΔflaA or Δ7ΔflaA L . pneumophila-infected B6 BMMs were discarded . The sorted read counts were then normalized to ribosome footprint or RNAseq read counts of 12 mitochondrial protein-coding genes ( see below ) as an estimate of total cells in each condition . Seven effector-dependent gene induction was calculated using the equation: seven effector-dependent gene induction = average ( normalized ΔflaA-infected read count ) ÷ average ( normalized Δ7ΔflaA-infected read count ) . Metagene profiles were generated as previously described ( Ingolia et al . , 2009 , 2011 ) . These metagene profiles indicate the total number of ribosome footprints whose A site falls at the indicated position relative to the start or stop codon of the coding sequence , and reflect a simple , unweighted sum of the footprint profiles around the beginning and the end of each protein-coding gene . The A site position was estimated for each footprint using a length-dependent offset from the 5ʹ end of the fragment . The distance from this A site position to the start or stop codon of the coding sequence was then computed , taking into account the fact that translation initiation occurs with the second codon in the A site . Cumulative ribosome occupancy profiles ( Figure 7—figure supplement 1 ) were computed by taking the cumulative sum of ribosome footprints mapping to each position in the gene , scaled by the normalization factor derived from mitochondrial translation in that sample . Gene IDNameSize ( bp ) ENSMUST00000082392mt-Nd1299ENSMUST00000082396mt-Nd2326ENSMUST00000082402mt-Co1495ENSMUST00000082405mt-Co2208ENSMUST00000082407mt-Atp848ENSMUST00000082408mt-Atp6207ENSMUST00000082409mt-Co3241ENSMUST00000082411mt-Nd396ENSMUST00000082414mt-Nd4439ENSMUST00000082418mt-Nd5588ENSMUST00000082419mt-Nd6153ENSMUST00000082421mt-Cytb361 WT BMMs were plated on a sterile #1 . 5 coverslip by placing the coverslip in a tissue-culture-treated six-well plates and adding 1 . 5 × 106 BMMs/well in antibiotic-free media 24 hr prior to infection . Twofold dilutions of L . pneumophila strains used for infections were grown overnight in liquid buffered-yeast-extract culture and , at the time of infection , cultures with an optical density ( 600 nm ) greater than 4 . 0 were selected . BMMs were infected at an MOI of 3 by centrifugation for 10 min at 400 xg . Media was changed after one hour of infection . At 6 hr post-infection coverslips were collected , washed in PBS , and placed in fixative solution ( 100 uM sodium periodate , 75 uM Lysine , 2 . 9 uM NaH2PO4 , 3 . 2% sucrose , and 4% paraformaldehyde ) for 1 hr at 37°C . Following fixation BMMs were blocked in 2% goat serum in PBS . To stain extracellular L . pneumophila , blocked BMMs were incubated with a rabbit anti-Legionella antibody ( RRID: AB_231859; Fitzgerald Industries International , North Acton , MA , USA 20-LR45 ) , washed in PBS , and stained with a goat-anti-rabbit IgG secondary antibody conjugated to Cascade Blue ( RRID: AB_2536453; ThermoFisher Scientific , Waltham , MA , USA , C-2764 ) . In some experiments , mammalian cell membrane was labeled with FITC-labeled wheat germ agglutinin ( Sigma-Aldrich , St . Louis , MO , L4895 ) prior to permeabilization . BMMs were permeabilized by dipping coverslips into ice-cold methanol . Permeabilized BMMs were blocked with 2% goat serum and stained with a rabbit anti-Legionella antibody ( Fitzgerald Industries International , North Acton , MA , 20-LR45 ) followed by incubation with a goat-anti-rabbit IgG secondary antibody conjugated to TexasRed ( RRID: AB_2556776; ThermoFisher Scientific , Waltham , MA , T-2767 ) to mark all ( intracellular and extracellular ) L . pneumophila . Coverslips were mounted in vectashield antifade mounting medium ( Vector Laboratories , Burlingame , CA , H-1000 ) and visualized on a Nikon TE2000 inverted microscope . All antibody stains were incubated for 30 min at 37°C and all blocking steps were incubated for 60 min at 37°C . Importantly , the staining method described above results in intracellular bacteria staining positive for TexasRed while extracellular bacteria are double positive for Cascade Blue and TexasRed . Quantification of infectivity was undertaken by two methods using the differential staining of intracellular and extracellular L pneumophila . In experiments where differential contrast ( DIC ) microscopy , Cascade Blue , and TexasRed were visualized counting of intracellular bacteria in BMMs was done by hand using the image analysis software ImageJ ( RRID:SCR_003070; Rasband , W . S . , ImageJ , U . S . National Institutes of Health , Bethesda , Maryland , USA , http://imagej . nih . gov/ij/ , 1997–2016 ) and the Cell Counter plugin ( https://imagej . nih . gov/ij/plugins/cell-counter . html ) . Uninfected BMMs were classified as BMMs that were not associated with L . pneumophila or only associated with extracellular ( Cascade Blue + TexasRed double positive ) bacteria . Infected BMMs were classified as macrophages containing at least one intracellular L . pneumophila ( Texas Red only ) , independent of the number of extracellular bacteria associated with the BMM . In experiments where the cell membrane of BMMs was labeled with FITC-conjugated wheat germ agglutinin along with DIC , Cascade Blue , and TexasRed , analysis of infectivity was undertaken using the imaging software Imaris ( RRID:SCR_007370; Bitplane , Zurich , Switzerland ) . Using Imaris , surfaces of BMMs were drawn on the FITC-conjugated wheat germ agglutinin channel to mark individual BMMs . All extracellular bacteria were removed from analysis by generating a new channel that subtracted the Cascade Blue channel from the TexasRed channel , for example Intracellular Channel = TexasRed Channel – ( Scaling Value x Cascade Blue Channel ) . The scaling value was calculated by measuring the average pixel intensities in each channel for double positive bacteria . As an example , if the TexasRed channel had an average pixel intensity of 350 and the Cascade Blue channel was 3500 then the equation would be: Intracellular Channel = TexasRed Channel – ( 0 . 1 x Cascade Blue Channel ) . The outcome of this calculation is the generation of a channel that removes the TexasRed signal of extracellular bacteria , thus allowing for analysis of bacteria that are only intracellular . Lastly , using the Sortomato utility ( http://open . bitplane . com/tabid/235/Default . aspx ? id=90 ) in Imaris , new cell surfaces were drawn for cells that contained a signal in the new Intracellular channel ( with double positive bacteria removed ) , marking cells infected with an intracellular bacterium . Surfaces were also drawn for cells that did not have a signal in the Intracellular channel , marking uninfected BMMs or BMMs only associated with extracellular L . pneumophila . Results were checked by eye to confirm that all surfaces accurately marked uninfected and infected BMMs; the surfaces generated by Sortomato were used to quantify infectivity . | We are constantly exposed to microbes that are capable of causing disease , but our immune system is generally able to protect us by producing specific proteins that help kill the microbes . In response , many infectious microbes have developed ways to obstruct the immune system of their host . For example , a bacterium called Legionella pneumophila – which can cause serious lung infections – blocks the ability of host immune cells to generate new proteins . To make a new protein , genetic information in the form of DNA is first copied to make molecules called messenger ribonucleic acids ( or mRNAs for short ) . These molecules are then used as templates to make the protein . Despite the fact that L . pneumophila is capable of interfering with this vital process , the host is still able to mount a protective immune response . It was not clear how this is possible . To address this question , Barry et al . studied immune cells from mice that had been infected with L . pneumophila . The experiments show that these immune cells produce large amounts of mRNAs that correspond to proteins needed for the immune response . These mRNAs overwhelm the protein production block imposed by the bacteria , allowing the immune cells to produce these proteins and trigger an immune response . The experiments suggest that , in response to microbes that block the production of proteins , changes in the amount of mRNA in a cell are the strongest indicators of how much protein the cells will be able to produce . These findings shed new light onto how the immune system can overcome interference by microbes to protect the host . | [
"Abstract",
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"Results",
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"Materials",
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"microbiology",
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] | 2017 | Global analysis of gene expression reveals mRNA superinduction is required for the inducible immune response to a bacterial pathogen |
Coding of information in the peripheral olfactory system depends on two fundamental factors: interaction of individual odors with subsets of the odorant receptor repertoire and mode of signaling that an individual receptor-odor interaction elicits , activation or inhibition . We develop a cheminformatics pipeline that predicts receptor–odorant interactions from a large collection of chemical structures ( >240 , 000 ) for receptors that have been tested to a smaller panel of odorants ( ∼100 ) . Using a computational approach , we first identify shared structural features from known ligands of individual receptors . We then use these features to screen in silico new candidate ligands from >240 , 000 potential volatiles for several Odorant receptors ( Ors ) in the Drosophila antenna . Functional experiments from 9 Ors support a high success rate ( ∼71% ) for the screen , resulting in identification of numerous new activators and inhibitors . Such computational prediction of receptor–odor interactions has the potential to enable systems level analysis of olfactory receptor repertoires in organisms .
The peripheral olfactory system is unparalleled in its ability to detect and discriminate amongst an extremely large number of volatile compounds in the environment . To detect this wide variety of volatiles , most organisms have evolved large families of receptor genes that typically encode 7-transmembrane proteins expressed in the olfactory neurons ( Buck and Axel , 1991; Clyne et al . , 1999; de Bruyne and Baker , 2008; Vosshall et al . , 1999; Dahanukar et al . , 2005 ) . Each volatile chemical in the environment is thought to interact with a specific subset of odorant receptors depending upon odor structure and binding sites on the receptor . This precise detection and coding of odors by the peripheral olfactory neurons are subsequently processed , transformed and integrated in the central nervous system to generate specific behavioral responses that are critical for survival such as finding food , finding mates , avoiding predators etc ( van der Goes van Naters and Carlson , 2006 ) . Currently there are two major rate-limiting steps in analysis of peripheral coding in olfaction: a very small proportion of chemical space can be systematically tested for its activity on odorant receptors and a very small fraction of the numerous odorant receptors have been tested for responses ( Araneda et al . , 2000; Hallem et al . , 2004; Hallem and Carlson , 2006; Pelz et al . , 2006; Kreher et al . , 2008; Saito et al . , 2009; Mathew et al . , 2013 ) . The challenges for overcoming the rate-limiting steps are enormous . First , volatile chemical space is immense , more than 2000 odors in the environment have been catalogued from a small fraction of plant sources alone ( Knudsen et al . , 2006 ) . Second , the complete three-dimensional structures of the 7-transmembrane odorant receptor proteins have not yet been determined and modeling of protein–odor interactions and sophisticated virtual screening methods are not yet possible except in rare instances ( Triballeau et al . , 2008 ) . In the decade since the first systematic study of 47 odorants on the Drosophila antenna in 2001 ( de Bruyne et al . , 2001 ) , additional studies have only identified a total of ∼250 novel activating odors ( de Bruyne et al . , 1999; de Bruyne et al . , 2001; Dobritsa et al . , 2003; Goldman et al . , 2005; Hallem et al . , 2004; Hallem and Carlson , 2006; Kreher et al . , 2005 , 2008; Kwon et al . , 2007; Pelz et al . , 2006; Stensmyr et al . , 2003; Turner and Ray , 2009; van Naters and Carlson , 2007; Yao et al . , 2005; Schmuker et al . , 2007 ) , which have been assembled and compared in an online database ( Galizia et al . , 2010 ) . Here we overcome this challenge by designing a chemical-informatics platform that is effective and fast . In order to do so we focused our attention on one of the most comprehensive quantitative data sets available , where measurements of responses of 24 Drosophila odorant receptors to a panel of 109 odorants are known that provides a rich resource for structure-activity type analyses ( Hallem and Carlson , 2006 ) . We devised a method to identify molecular structural properties that are shared amongst the activating odorants for each receptor . We then utilize information about these shared molecular features of active odorants , that are presumably required for binding to a receptor , to perform in silico screens on a chemical space of >240 , 000 chemicals , including a large collection of naturally occurring and biologically important odors , and identify the top 500 hits for each of the odorant receptors ( Ors ) . We then use single-unit electrophysiology to validate a subset of predictions for 9 Ors in vivo and find that our method met an overall success rate of ∼71% in identifying novel ligands . This approach is specific since testing shows a low ( 10% ) rate of finding ligands while using non-predicted odors . This approach allows us to create a computationally predicted peripheral coding map of a large chemical space , which substantially improves our ability to predict and investigate peripheral olfactory coding and provides a powerful tool for the discovery of novel ligands for Ors , some of which may be ecologically important or useful for behavior modification .
Since the structure of receptor protein complexes is not known , we analyzed receptor–odor interactions by applying the ‘similarity property principle’ , which reasons that structurally similar molecules ( e . g . , activating odorants ) are more likely to have similar properties ( Hendrickson , 1991; Martin et al . , 2002 ) . Although this general approach has been useful in the area of pharmaceuticals ( Martin et al . , 2002; Keiser et al . , 2009 ) , receptor–odor analysis presents significant additional challenges . Not only are odorant molecules generally smaller in size than pharmaceuticals ( average MW of known odors ∼threefold less than FDA approved pharmaceuticals [Wishart et al . , 2008] ) and therefore offer fewer structural features for differentiation , they are also detected by the receptors with specificity at extremely low concentrations in the volatile phase ( Hallem and Carlson , 2006; Kreher et al . , 2008 ) . Additionally , odorant receptors are differentially tuned and can sometimes appear not to follow distinct structural rules: odors that look structurally different can strongly activate the same receptor , while odors that appear very similar may have very different levels of activity ( Hallem and Carlson , 2006 ) . For example , while hexanal and γ-octalactone are structurally very different , they both strongly activate Or85b ( Hallem and Carlson , 2006 ) . Alternatively , while hexanal and pentanal are structurally very similar , they have very different activities against Or85b ( Hallem and Carlson , 2006 ) . Similarity in chemical structure can be described and measured quantitatively using multiple approaches , however a single method may not be ideal for every single application ( Maldonado et al . , 2006 ) . In order to test whether non-optimized approaches would be able to identify similarities in shape of known activators we compared four different approaches: Cerius2 ( Accelrys Software Inc ) , Dragon ( Talete ) , Maximum-Common-Substructure ( MCS ) ( Cao et al . , 2008b ) , and atom-pair ( AP ) ( Carhart et al . , 1985; Cao et al . , 2008a ) . Cerius2 and Dragon represent collections of 200 and 3224 molecular descriptors , respectively , that calculates values for a broad range of chemical properties such as molecular weight , functional group counts , and in the case of Dragon , three-dimensional relationships within molecules . The AP method compares shortest path distances between all atom pairs in a molecule . Lastly , MCS identifies the largest two-dimensional substructure that exists between two compounds . Using each of these approaches , we computed distances between 109 odors that had previously been tested against 24 Ors from Drosophila melanogaster ( Hallem and Carlson , 2006 ) . These represent most of the Or genes expressed in the Drosophila antenna ( Hallem and Carlson , 2006 ) . Upon comparison , we find that none of the four methods were vastly superior and that each method varied in the ability to group known activating odorants ‘actives’ close together in distance as measured for each Or using a method called accumulative-percentage-of-actives ( APoA ) ( Chen and Reynolds , 2002 ) ( ‘Materials and methods’ and Figure 1—figure supplement 1 ) and value of the area-under-the-curve ( AUC ) . Ultimately , Dragon and Cerius2 , which utilize a large number of diverse molecular descriptor values to describe each odor structure , performed better than AP or MCS , suggesting that a more diverse set of descriptors is better at explaining Or activity than two-dimensional measures alone ( Figure 1B ) . Atom-Pair and MCS were subsequently ignored from further development . 10 . 7554/eLife . 01120 . 003Figure 1 . A receptor-optimized molecular descriptor approach has strong predictive power to find new ligands . ( A ) Schematic of the cheminfomatics pipeline used to identify novel ligands from a larger chemical space . ( B ) Plot of mean APoA values for 19 Drosophila Ors calculated using various methods including a previously identified set ( Haddad et al . , 2008 ) . ( C ) Receiver-operating-characteristic curve ( ROC ) representing computational validation of ligand predictive ability of the Or-optimization approach . ( D ) Hierarchical cluster analysis of the 109 odorants of the training set using Or-specific optimized descriptor sets to calculate distances in chemical space for odorant receptors with strong activators ( green ) , and odorant receptors with no strong activators ( yellow ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01120 . 00310 . 7554/eLife . 01120 . 004Figure 1—figure supplement 1 . Analysis of APoA curves for individual odor receptors . Plots of the mean APoA values obtained from various molecular descriptor methods demonstrates that optimized descriptor subsets generate highest values . Previous = 32 Dragon descriptors selected in Haddad et al . ( 2008 ) . Molecular descriptor methods were compared using the 109 compounds that were previously tested in ( Hallem and Carlson , 2006 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01120 . 00410 . 7554/eLife . 01120 . 005Figure 1—figure supplement 2 . Pharmacophores of active compounds for individual Ors . Hierarchical cluster identical to Figure 1D . Known odorant activity scale is indicated using independent color gradient scales . Horizontal black bars underneath cluster indicate part of active cluster , a subset of which were used to generate pharmacophores using the Ligand Scout program ( shown underneath each Or in two orientations ) . Yellow = hydrophobic region , red = Hydrogen-bond acceptor , green/red = Hydrogen-bond donor or acceptor depending upon pH . DOI: http://dx . doi . org/10 . 7554/eLife . 01120 . 005 Individual Ors respond to distinct subsets of ligands with some degree of overlap ( Hallem and Carlson , 2006; Kreher et al . , 2008 ) . We reasoned that rather than using entire Dragon or Cerius2 descriptor sets , which likely includes a number of measurements for features irrelevant for ligand-binding to an individual Or , judiciously selecting subsets of molecular descriptors suited to cluster activators for an individual receptor may be more effective at defining an Or-specific chemical space . To test this hypothesis , we used a Sequential-Forward-Selection ( SFS ) method to incrementally create unique optimized descriptor subsets for each Or from an initial combined set of 3424 descriptors from Dragon and Cerius2 ( Whitney , 1971 ) ( ‘Materials and methods’; Figure 1A ) . This optimization-based analysis was performed on the 19 Ors from the dataset with known activating odors , excluding Or82a , since it has but a single known strong activator ( Hallem and Carlson , 2006 ) . Not surprisingly , the composition of the optimized descriptor sets varied greatly between Ors , as on average only 13% of descriptors are shared between Ors ( Table 1; Supplementary file 1A ) . Molecular descriptors can be categorized from 0 to 3 dimensions . Zero-dimensional ( 0-D ) descriptors define features that can be viewed as not directly being shape dependent , such as molecular weight or vapor pressure . On the other end of the scale , three-dimensional ( 3-D ) descriptors define features of molecules in three-dimensional space , such as the distance between two atoms of an odor molecule . Interestingly , we find an overwhelming preference for three-dimensional and two-dimensional descriptors compared to one-dimensional and zero-dimensional descriptors , suggesting that structural shape features are more important for receptor–odor interactions ( Table 1; Supplementary file 1A ) . We find that Or-optimized descriptor sets were far superior at grouping together activating odors from the training set when compared to the non-optimized methods ( Dragon , Cerius2 , MCS , AP ) and a previously identified collection of descriptors that were identified without receptor-specific optimization ( Haddad et al . , 2008 ) ( Figure 1B , Figure 1—figure supplement 1 ) . 10 . 7554/eLife . 01120 . 006Table 1 . Optimized molecular descriptor set compositionsDOI: http://dx . doi . org/10 . 7554/eLife . 01120 . 006Descriptor class type counts for all Ors GETAWAY descriptors75 3D-MoRSE descriptors66 2D autocorrelations44 Edge adjacency indices44 2D binary fingerprints44 Functional group counts43 Atom-centred fragments37 WHIM descriptors36 Topological charge indices24 Atomtypes ( Cerius2 ) 23 Burden eigenvalues23 Molecular properties23 Topological descriptors22 Geometrical descriptors18 2D frequency fingerprints11 RDF descriptors8 Walk and path counts6 Connectivity indices5 Information indices5 Topological ( Cerius2 ) 4 Constitutional descriptors3 Structural ( Cerius2 ) 2 Randic molecular profiles2Optimized descriptor analysis Average descriptor overlap between Ors13% Average number of descriptors per Or29 . 9 Average number 3D descriptors per Or10 . 8 Average number 2D descriptors per Or12 . 2 Average number 1D descriptors per Or6 . 6 Average number 0D descriptors per Or0 . 3Descriptor dimensionality counts Number three dimensional descriptors205 Number two dimensional descriptors232 Number one dimensional descriptors126 Number zero dimensional descriptors5Descriptor Origin Number Dragon descriptors539 Number Cerius descriptors29Breakdowns of the molecular descriptor class type , dimensionality , origin , and average overlap for all optimized molecular descriptors selected for each Or . In order to validate the predictive ability of the Or-optimized method , we performed five independent trials of fivefold cross-validations followed by a Receiver-Operating-Characteristic ( ROC ) analysis , an established computational approach ( Hastie et al . , 2001; Tan et al . , 2006 ) ( ‘Materials and methods’ ) . Briefly , this involved withholding 20% of the 109 previously tested odors for a receptor . Descriptors were optimized using the remaining 80% odors for training , and ligand-predictions were subsequently performed on the 20% of odors that were withheld . This operation was repeated five times for each receptor , each time selecting a different 20% as withheld from the training set . The entire fivefold operation was repeated five times for each receptor and a mean ROC curve representing the prediction accuracy determined . This analysis was possible for 12 Ors which had >6 known ligands that activated >100 spikes/s . The Area-Under-Curve ( AUC ) value ( 0 . 815 ) is very promising and suggests that the Or-optimized descriptor sets are effective at predicting novel ligands ( Figure 1C ) . In addition to performing the fivefold cross-validation , we also clustered the 109 training odors independently for each Or , using distances calculated from the previously determined receptor specific descriptor sets we identified . As expected , we find that activating odorants cluster tightly together for each Or ( Figure 1D ) and activating odors of an Or have shared sub-structures and shared pharmacophore features ( Figure 1—figure supplement 2 ) . In a few cases , such as for Or35a and Or98a , not all the highly activating compounds are clustered , suggesting the possibility of multiple or flexible binding sites , or imperfect selection of descriptors . Four of the Ors ( Or2a , Or23a , Or43a and Or85f ) have few known activators , none of which activate the receptors at >150 spikes/s , however our descriptor optimization approach is still able to cluster each of the few weak activators together ( Figure 1D ) . Since Or-optimized descriptor sets can efficiently group strong activators in chemical space , we used them to rank untested compounds according to their distance from known activators for specific Ors . We assembled a natural odor library , which contains 3197 naturally occurring odors , and a library derived from Pubchem ( Bolton et al . , 2008 ) , which contains >240 , 000 compounds with similar molecular weights and atom type compositions to known volatiles ( ‘Materials and methods’ ) . We then systematically screened both libraries using the optimized descriptor sets of 19 D . melanogaster Ors in silico . We identify the top 500 ( 0 . 2% ) hits from this vast chemical library for each Or , the top ∼100 of which are reported in Supplementary file 1B . To validate our in silico screen , we obtained a large number of untested odorants belonging to the top 500 predicted ligands for nine different Ors ( 141 total interactions tested; ∼11–23/Or ) that were available from commercial sources at high purity and reasonable prices . The nine receptors were selected on the basis of previous functional mapping studies that enable us to unambiguously identify the antennal olfactory receptor neurons ( ORNs ) they are housed in ( Hallem et al . , 2004; Couto et al . , 2005 ) . We systematically tested each predicted receptor–odor combination using single-unit electrophysiology to record from the ORNs to which these 9 Ors have been previously mapped ( Hallem et al . , 2004; Couto et al . , 2005 ) . We find that a majority of the predicted ligands evoked responses from the target ORNs; ∼71% evoked either activation ( >50 spikes/s above the spontaneous activity ) or inhibition ( >50% reduction in spontaneous activity [reverse agonist activity] ) ( Table 2 ) . These cutoffs were selected based on the study from which the training set was obtained and has been used in other studies in the past that use this type of recordings ( Hallem and Carlson , 2006; Kreher et al . , 2008 ) . Interestingly , the mean vapor pressure of activating odors ( 11 . 84 Torr ) is 7 . 5 times higher than of inactive odors ( 1 . 58 Torr ) , raising the possibility that some inactive odors may not be volatilized and delivered at adequate levels to the ORNs . Additionally , we find that ∼13% of the predicted compounds we tested showed an inhibitory effect on baseline activity of the respective neuron ( Table 2 ) . These inhibitors were identified by virtue of structural similarity to known activators suggesting that they may bind to similar sites on the receptor . Thus as an additional benefit our approach may provide a method to identify inhibitors as well . Such inhibitors would not only provide important tools to investigate mechanisms of odorant receptor inhibition but could also be used in blocking specific odor-mediated behaviors . Consistent with our observations three of the receptor–odor interactions had been previously identified independently as well , Or22a ( Pelz et al . , 2006 ) , and Or49b ( Hallem et al . , 2004 ) . The electrophysiological analysis provides the most important validation of our Or-optimized descriptor-based in silico screen . 10 . 7554/eLife . 01120 . 007Table 2 . Predicted receptor–odor interactions validated as highly accurate using electrophysiologyDOI: http://dx . doi . org/10 . 7554/eLife . 01120 . 007ClassificationOr7aOr10aOr22aOr47aOr49bOr59bOr85aOr85bOr98aTotalLigands ( % ) 883186392791928710071Agonists ( >50 spikes/s ) ( % ) 63318133186469709258Agonists ( >100 spikes/s ) ( % ) 3113621194548486737Inverse agonists ( % ) 250569252317813Summary of prediction accuracy percentages obtained by electrophysiology validation . Ligands = Agonists ( ≥50 spikes/s ) + Inverse agonists ( >50% reduction from baseline activity ) . Since we systematically analyzed responses of a large number of new odorants individually , we were able to characterize the odor-response spectra of several antennal ORN classes to these new ligands ( Figure 2A ) . New activators are reported for every receptor , and inhibitors are identified for several . Ligand predictions for 2 of the 3 receptors that do not perform as well are Or10a and Or49b that detect aromatic compounds . Their poor performance is explained by the lack of aromatic ligands in the initial training set ( 13/109 ) odorants . We find that a >85% of the predicted ligands activate odorant receptors Or7a , Or22a , Or59b , Or85a , Or85b , and Or98a ( Figure 2A ) . 10 . 7554/eLife . 01120 . 008Figure 2 . Electrophysiology validates that odorant receptor-optimized molecular descriptors can successfully identify new ligands for Drosophila . Mean increase in response of neurons to 0 . 5-s stimulus of indicated odors ( 10−2 dilution ) predicted for each associated Or . Dashed lines indicate the activator threshold ( 50 spikes/s ) . ΔH: Or85b ( ab3B ) = flies lack expression of Or22a in neighboring neuron , thus all observed neuron activation is unambiguously caused by Or85b . N = 3 , error bars = s . e . m . DOI: http://dx . doi . org/10 . 7554/eLife . 01120 . 008 We rigorously examined the rate of false negative predictions for each Or by systematically testing newly identified ligands of each Or against the other non-target receptors using electrophysiology . Of 504 non-target receptor–odor interactions tested , we found that only 10% evoked a response >50 spikes/s and 3 . 7% evoked a response >100 spikes/s ( Figure 3A ) . This represents a high degree of specificity , especially considering that the Or-optimized descriptor method did not incorporate any additional computational screening to rule out non-target activators . Additionally , when we plot the percentage of odors that validated as activators when tested using electrophysiology ( considering both predicted and non-target receptor–odor interactions ) , we find that activity is strongly related to predicted odor ranking ( Figure 3B ) . Odors which rank closest to known activators for each Or , particularly within the top 500 hits , are far more likely to be activators than odors further away , and there is a drastic drop-off in activating odors present beyond the 1000 rank . We see the same trend if we plot mean activity of odors for the same ranking divisions . Highly ranked odors have a far higher mean activity than distantly ranked odors . 10 . 7554/eLife . 01120 . 009Figure 3 . Predicted receptor–odor interactions are highly specific . ( A ) Plot of activity ( Top ) for electrophysiologically tested receptor-odor interactions . ( Bottom ) Plot indicating locations of predicted receptor-odor combinations ( green ) and same odorants tested in non-target receptor-odor combinations ( gray ) . ( B ) Plot of percentage of activating odors ( >50 spikes/s ) considering all activating or inactive odors ( >0 spikes/s ) across ranking bins for all odors tested using electrophysiology . DOI: http://dx . doi . org/10 . 7554/eLife . 01120 . 009 Since receptor-optimized descriptor sets and the predicted ligand space they define are a function of shared molecular features that a receptor may employ to recognize ligands , we were now in a position to determine how these characteristics correlate with receptor properties such as their known-activity profiles and amino acid sequences . We used hierarchical cluster analysis to create trees that represent the various receptors based on: shared descriptors selected; known activity-based relationships ( Hallem and Carlson , 2006 ) ; degree of overlap of predicted ligands; and amino acid sequence ( Figure 4A; ‘Materials and methods’ ) . We found that the maximum overlap in Or relationships is retained between the descriptor and the activity trees , and the descriptor and the cross activity trees with 11 out of 24 Ors present in subgroups that are common in both cases . However , only two subgroups ( yellow and grey ) are conserved across the three trees . The largest shared overlap existing in the descriptor tree suggests that the Or-optimized descriptors link the known and the predicted receptor–odor interactions and that our analysis may expand upon odorant receptor activity relationships beyond those previously known from the training data . We also found that the phylogenetic tree has fewer relationships conserved with each of the trees , consistent with previous observations ( Hallem et al . , 2004 ) supporting the idea that , while the most conserved amino acid residues in the Ors provide the structure of the tree , they do not correlate strongly with ligand specificity . 10 . 7554/eLife . 01120 . 010Figure 4 . Analysis of receptor–odor relationships and breadth of tuning . ( A ) Hierarchical clusters created from Euclidean distance values between Drosophila Ors calculated using: ( left to right ) shared optimized descriptors; known activity to training set odors ( Hallem and Carlson , 2006 ) ; overlap across top 500 predicted ligands; and Phylogenic tree of receptors ( Hallem and Carlson , 2006 ) . Sub clusters shaded with colors or bars . ( B ) Frequency distribution of compounds from the >240K library within the top 15% distance from highest active plotted to generate predicted breadth of tuning curves . Green arrows indicate relative distance of the furthest known activating compound determined by electrophysiology . DOI: http://dx . doi . org/10 . 7554/eLife . 01120 . 010 Coding of odors in a large volatile space ( >240 , 000 ) by a receptor repertoire is virtually impossible to determine experimentally . However , based on the Or-optimized descriptor sets we computationally derived prediction frequency distributions for each of the Drosophila Ors in this large chemical space ( Figure 4B ) . As expected , we find substantial variation in the distribution frequency of predicted ligands across different receptors . The predicted response profiles support previous observations made with smaller odor panels that the olfactory system can potentially detect thousands of volatile chemicals , many of which the organism may never have encountered in its chemical environment . Plant volatiles constituted a large portion of compounds that are predicted to be ligands for Drosophila Ors . To further analyze odor source representation , we classified odors that belong to top 500 prediction lists according to their source , if known , and find that Ors are not specialized for odors from a single source ( Figure 5A ) . 10 . 7554/eLife . 01120 . 011Figure 5 . Analysis of predicted natural odor sources and cross activation . ( A ) ( Left ) The numbers of compounds present in the collected volatile library according to source . ( Right ) The numbers and sources of predicted ligands for the 19 Drosophila odor receptors/neurons within the top 500 predicted compounds . ( B ) Comparison of plots for percentage of receptors that are: ( top left ) activated by percentage of known odors from training set ( Hallem and Carlson , 2006 ) ; ( bottom left ) predicted to be activated by Natural compound library; ( top right ) predicted to be activated from >240K library; and ( bottom right ) activated by ligands for 10 shared Ors in this study vs activated by comparable actives previously tested ( Hallem and Carlson , 2006 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01120 . 011 To study the ensemble activation patterns of odors predicted across all Ors , we analyzed the across-receptor activation patterns of the 3197 known compounds for nine receptors ( Or7a , 10a , 22a , 47a , 49b , 59b , 85a , 85b , 98a ) . Surprisingly , we find that only 25% of compounds from the ‘natural’ odor library found in the top 500 predictions for each Or are predicted to activate multiple Ors ( Figure 5B , lower left panel ) . If we consider compounds from the Pubchem library in the top 500 predicted activators for each receptor , we observe further reduction in the proportion of across-receptor activating compounds ( Figure 5B , upper right ) . Consistent with this prediction we find that cross-activation by ligands functionally evaluated in this study for nine receptors is lower than that reported previously using ligands of comparable strength for the same nine receptors ( Hallem and Carlson , 2006 ) ( Figure 5B , lower right panel ) . These data suggest that a number of natural odors may be detected by a few receptors , particularly at low concentrations .
A primary element of the olfactory code is information about odor identity , represented by the characteristic interaction of an odor with the ensemble of olfactory receptors in the nose . Here we report an in silico approach to systematically identify ligands from a vast chemical space for a large number of Ors expressed in the antenna of Drosophila . We demonstrate that our predictions are accurate using two different validation approaches—computational validations and functional validation using electrophysiology . There is a strong correlation between ranks of predicted ligands to electrophysiological activity . Obtaining and testing odors using traditional methods is time and cost intensive . Electrophysiology and calcium imaging are consuming processes that require not only a great deal of time to perform , but also the purchase of each odor to be physically tested . Moreover , large plate-based combinatorial chemical libraries , which are commonly implemented in drug discovery in the pharmaceutical industry , are not available for volatile odor libraries at reasonable costs . Since Drosophila is a premier model for understanding neurobiology of olfaction , several laboratories over the last 12 years have together screened ∼250 odors , activities of which have been and compiled into a valuable database that standardizes across studies ( Galizia et al . , 2010 ) . In this study we screen >240 , 000 chemicals and predict >10 , 000 new ligands which represents a substantial expansion of the known peripheral olfactory code for this important model organism and provides a system-level view of odor detection ( Figure 6A ) . 10 . 7554/eLife . 01120 . 012Figure 6 . Predicted odor space and network view of odor coding . ( A ) Expansion of the peripheral olfactory code in this study: large increase in numbers of identified activators and inhibitors . The different sized circles represent the approximate ratio of numbers of previously known ligands ( top circles ) , predicted ligands based on a cutoff of the top 500 predicted compounds per receptor and corrected to the validation success rate ( lower , diffuse circles ) . ( B ) Drosophila receptor–odor network . Each known interaction ( >50 spikes/s ) from this and previous studies ( Hallem and Carlson , 2006 ) is linked by a purple edge . Predicted receptor–odor network ( top 500 hits ) are linked by light-grey edges . All compounds are represented as small black circles and Ors are represented as large colored circles matching the colors used in ( Figure 4A ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01120 . 012 The predicted ligands and prediction method will increase the speed of receptor–odor decoding and allow for interpretation of data at a large scale that is difficult to achieve . This could help answer questions such as breadth of receptor tuning , investigating responses to odorants from natural sources , and evolution of odor coding across a receptor repertoire . Additionally , using chemical informatics , it becomes possible to infer and prioritize for testing the network of odorant receptors that are activated from complex odor blends without the expensive and time consuming process of purchasing and testing all possible odors and receptor combinations ( Figure 6B ) . Interestingly , our attempts to identify molecular descriptors that would differentiate agonists from inverse agonists were not successful with this data set . This could be due to several reasons: an insufficient number of inverse agonists amongst the training odors , or the inverse agonists may act via the same binding sites as agonists and share many of the same structural features of the activating odors making them difficult to distinguish . We feel that this remains an important challenge to be overcome in the future with improved computational approaches or larger odor training sets . A similar , yet much smaller , analysis applied chemical informatics on Drosophila olfactory neuron activities to 47 odorants and screened ligands from 21 untested compounds in Drosophila ( Schmuker et al . , 2007 ) . Although this study had a relatively modest success rate of ∼25% at predicting untested odorants as activators ( by applying the same 50 spikes/s threshold for comparison ) , it also highlighted that structure-based ligand prediction is a viable method for further development . In another interesting analysis a Quantitative Structure Activity Relationship ( QSAR ) model was applied to describe odor-activity for Drosophila Ors . Using cheminformatics , important amino acid residues were identified using information from orthologous Or sequences identifying potential odor-binding regions , which was postulated to be 15 angstroms deep and 6 angstroms wide ( Guo and Kim , 2010 ) . These studies , along with ours , suggests that computational approaches could have great utility in study of sensory receptors . It will also be very interesting to use our method for making ligand predictions for the structurally distinct receptors such as olfactory ionotropic glutamate receptors ( IRs ) , and gustatory receptors ( Grs ) in insects , and olfactory and taste GPCRs in vertebrates . Our approach is conservative and designed to search for novel odors that share structural features from a previously tested odor panel . Odor molecules are limited in size as well , and may offer a limited scaffold such that novel isofunctional chemotype identification may not be as prevalent as has been seen in other examples of scaffold-hopping ( Schneider et al . , 2006 ) . However while compounds that share similar values for the optimized descriptors do have structural similarity for selected parts of the molecule , it does not mean that they are not structurally different in other parts of the molecule . In the future , application of machine learning approaches , such as Support Vector Machines ( SVMs ) to the receptor-optimized molecular descriptor sets , may be useful to further increase the predictive ability . Additionally , we could replace our SFS approach with sequential floating search techniques , which allows for removal , as well as addition , of descriptors in the growing optimized list . Our predictions suggest that a number of odorants at low concentrations may be detected by only a few receptors . In the current model of combinatorial coding emphasis is placed on the notion that combinations of several odorant receptors detect the majority of volatile chemicals , with the exception of pheromones and CO2 . One possible explanation for this disparity could be that our predictions are fundamentally conservative in nature because we focus only on structurally similar ligands and 7-transmembrane heteromeric receptors may also contain additional unexplored binding sites . Another possibility is that previously tested subsets of odors were potentially selected on the basis of strong responses in electroantennograms and behavior assays , which could bias selection of cross-activating odors . In fact , complex fruit odor blends activate fewer Ors than the number activated by individual odorants at comparable concentrations using electrophysiology ( Hallem and Carlson , 2006 ) and Calcium imaging ( Semmelhack and Wang , 2009 ) . The architecture of the olfactory code therefore appears to integrate two different models . On the one hand , most odors are detected by a few Ors from the repertoire , which may enhance the specificity of the olfactory system for detection of a large number of odors . On the other hand , 15–20% of odors are predicted to activate several Ors ( up to 50% ) at the same time , which may serve to aid the olfactory of the system in discriminating between fine concentration changes of important stimuli by having Ors tuned to low and high concentrations such as shown for Or42a and Or42b ( Kreher et al . , 2008 ) . By identifying a large number of new ligands for each odorant receptor , we can also begin to systematically compare the ligand tuning profiles for each in the endogenous neurons vs the ‘empty neuron’ decoder system . If clear differences were identified , it could enable the identification of underlying reasons such as differences in levels of receptor expression in the neurons , or presence of different odorant binding proteins ( OBPs ) in the sensillum lymph . This cheminformatics pipeline can also be applied for system-level analysis of other insects whose receptors and ORNs have been decoded such as mosquitoes ( Carey et al . , 2010 ) , and vertebrates such as mice and humans ( Saito et al . , 2009 ) . The search for novel insect repellents and attractants for species that transmit disease and destroy crops can be greatly assisted by a rational prioritization using such a cheminformatics approach .
We assembled a subset of 3197 volatile compounds from annotated origins including plants ( Knudsen et al . , 2006 ) , insects ( El-Sayed , 2009 ) , humans , and a fragrance collection ( Sigma-Aldrich , 2007 ) that may have additional fruit and floral volatiles ( Zeng et al . , 1991; Cork and Park , 1996; Zeng et al . , 1996; Meijerink et al . , 2000; Curran et al . , 2005; Knudsen et al . , 2006; Gallagher et al . , 2008; Logan et al . , 2008 ) . We also assembled a subset of 241 , 150 odors from Pubchem , which have similar characteristics to known odor molecules . Compounds met a criteria of MW <200 and only being composed of the following atoms ( C , O , N , H , I , Cl , S , F ) . The three-dimensional structures were predicted for compounds through use of the Omega2 software package ( Bostrom et al . , 2003; Hawkins et al . , 2010 ) . The Omega2 software package identified the lowest energy 3D conformer for each compound in our Pubchem and Natural compound libraries were stored for use in molecular descriptor calculation . Commercially available software packages Cerius2 , Accelrys ( 200 idescriptors ) and Dragon , Talete ( 3224 descriptors ) were used to calculate molecular descriptors from three-dimensional molecular structures . Descriptor values were normalized across compounds to standard scores by subtracting the mean value for each descriptor type and dividing by the standard deviation . Molecular descriptors that did not show variation in values across the compounds were removed . Maximum Common Substructures were determined using an existing algorithm ( Cao et al . , 2008b ) . Atom Pairs were computed from the version implemented in ChemmineR ( Cao et al . , 2008a ) . Since we were interested in identifying descriptors which best described activating compounds , we needed to first determine which compounds to classify as ‘active’ based on their electrophysiology activity for the receptor being studied . All of the training odors were clustered using hierarchical clustering by activity individually for each Or . The resulting tree can then be then be used to select the branch containing the majority of activating odors ( >50 spikes/s ) . The activity threshold therefore was set as the lowest spike/s activity of any odor present in the selected branch . A compound-by-compound activity distance matrix was calculated using training odor activity data for each of the Ors ( Hallem and Carlson , 2006 ) . A separate compound-by-compound descriptor distance matrix was calculated using the 3424 descriptor values for training odors calculated by Dragon and Cerius2 . Activating compounds for each Or were identified individually through activity thresholds , as described above . The correlation between the compound-by-compound activity ( CbCA ) and compound-by-compound descriptor distance matrices were compared for each actively classified compound , considering their distances to all other compounds . The goal was to identify molecular descriptors that best correlated with activity . To achieve this we applied a sequential forward selection ( SFS ) approach to identify optimal descriptors for each Or ( Whitney , 1971 ) . The SFS functioned by iteratively building a list of molecular descriptors for a single Or by maximally increasing the correlation between the CbCA and CbCD matrices . In the first iteration the values for each single molecular descriptor were used to create CbCD matrices . The rows corresponding to activating compounds were compared to the same rows of the CbCA matrix by correlation . The descriptor which best described the activity ( results in the highest correlation between descriptor and activity ) was retained . In the second iteration the best single descriptor was combined with all possible descriptors and correlations are calculated again , resulting in a best two-descriptor combination . The process was continued in this fashion to iteratively search for additional descriptors with each iteration aiming to further increases in correlation values . In this manner , the size of the optimized descriptor set increases by one in each iteration , as the best descriptor set from the previous step is combined with all possible descriptors to find the next best descriptor . This process is halted when all possible descriptor additions in an iteration fails to improve the correlation value from the previous step . Molecular descriptors can be selected multiple times for each Or , effectively creating weights for descriptors , as a descriptor that was selected twice will have double the importance when predicting activity of the odor libraries . This whole process is run independently for each Or resulting in unique descriptor sets that are optimized for each Or . The accumulative percentage of actives is calculated for each descriptor set individually as previously described ( Chen and Reynolds , 2002 ) . Compounds are ranked according to their distance from each known activator using the Or-optimized descriptor values as distances , resulting in one set of ranked compound distances from each activating odor . Moving down the list for each of these rankings , ratios are calculated for the number of activating compounds observed divided by the total number of compounds inspected , or the APoA . APoA values are averaged across all activating compound rankings for each receptor , creating a single set of mean values representing the APoA for a single Or and descriptor set . Using this approach , ApoA mean values are calculated for each of the 24 Ors separately for each descriptor set used , including Or-optimized sets , all Dragon descriptors , all Cerius2 descriptors , Atom Pair , and Maximum Common Substructure . The area-under-the-curve ( AUC ) scores were calculated by approximation of the integral under each plotted APoA line . The first 20 descriptors selected by our optimized descriptor selection algorithm for each Or were used to create an identity matrix . Each row representing an Or and column value specifying the presence of absence of a specific descriptor . This matrix was then converted into an Or-by-Or Euclidean distance matrix and clustered using hierarchical clustering and complete linkage . The responses of each of the Ors that had previously been tested against a panel of compounds were converted into an Or-by-Or Euclidean distance matrix ( Hallem and Carlson , 2006 ) . Ors were clustered using hierarchical clustering and complete linkage . Specifically , this was achieved by creating a compound-by-compound distance matrix using the differences in activity between compounds tested on a singe Or . Hierarchical clustering using each Or distance matrix and then identifying the sub cluster which contained the most compounds . Percentages of overlapping predictions within the top 500 predicted compounds were calculated pair-wise for all Ors . Euclidean distances were calculated from the similarity between Ors . Initially , all extreme outliers were removed from the dataset for each Or . On average 5 . 82 compounds were removed for each Or , resulting in a mean dataset reduction of 0 . 0024% . Next , all compounds whose distance was >3 standard deviations from the strongest activating compound were removed to reduce outliers . Distribution frequencies were produced for each Or . All compound distances were converted into a percentage of the most distant compound for each Or . Frequencies of compounds in the top 15% were plotted . The Or-ligand interaction map was developed using Cytoscape ( Shannon et al . , 2003 ) . Each predicted Or-ligand interaction from the top 500 predicted ligands for all of the Ors listed were used to calculate the map . All predicted interactions are labeled in purple . In addition all interactions identified in this study and the previous study ( Hallem and Carlson , 2006 ) were included and labeled in gray . All compounds are represented as small black circles and Ors are represented as large colored circles . Or names are provided on the upper right corner of each Or . We performed five independent fivefold cross-validations . For each independent validation the dataset was divided into five equal sized partitions containing roughly 22 compounds each . During each run , one of the partitions is selected for testing , and the remaining four sets are used for training . The training process is repeated five times with each unique odorant set being used as the test set exactly once . For every training iteration , a unique set of descriptors was calculated from the training compound set . These descriptors were then used to calculate distances of the test set compounds to the closest activating compound , exactly as we use to predict ligands in our ligand discovery pipeline . Once test set compounds have been ranked by distance from closest to furthest to a known activating in the training set , a receiver operating characteristics ( ROC ) analysis is used to analyze the performance of our computational ligand prediction approach . Using ROC we were able to determine our predictive ability for the 12 receptors . This validation could be performed only on receptors for which sufficient training odors had previously been identified . We consider this to consist of at least one very strongly activating known odor ( >150 spikes/s ) and at least five strongly activating odors ( >100 spikes/s ) , thus allowing for at least one activating odor for each of the five test sets in the cross-validation ( DmOr7a , DmOr9a , DmOr10a , DmOr22a , DmOr35a , DmOr43b , DmOr12 , DmOr59b , DmOr67a , DmOr67c , DmOr85b , DmOr98a ) . Test set validations for all 12 Ors were combined and a single ROC curve representing an average across all Ors was plotted ( Figure 1C ) . Extracellular single-sensillum electrophysiology was performed as before ( Dobritsa et al . , 2003; Hallem and Carlson , 2006; de Bruyne et al . , 2001 ) with a few modifications . Diagnostic odorants were used to distinguish individual classes of ORNs in sensilla ( ab1-ab7 ) and therefore unequivocally identify the target Or expressing ORN for testing ( de Bruyne et al . , 2001; Hallem et al . , 2004 ) . 50 μl odor at 10−2 dilution in paraffin oil was applied to cotton wool plugged odor cartridge . Due to variability in temporal kinetics of response across various odors , the counting window was shortened to 250 ms from the start of odor stimulus . | Although our sense of smell is regarded as inferior to that of many other species , we can nevertheless distinguish between roughly 10 , 000 different odors . These are made up of molecules called odorants , each of which activates a specific subset of odorant receptors in the nose . However , much of what we know about this process has come from studying the fruit fly , Drosophila , which detects odors using receptors located mainly on its antennae . The number of potential odorants in nature is vast , and only a tiny fraction of the interactions between odorants and receptors can be physically tested . To address this challenge , Boyle et al . have used a computational approach to study in depth the interactions between a subset of 24 odorant receptors in Drosophila antennae and 109 odorants . After developing a method to identify structural features shared by the odorants that activate each receptor , Boyle et al . used this information to perform a computational ( in silico ) screen of more than 240 , 000 different odorant-like volatile compounds . For each receptor , they compiled a list of the 500 odorants predicted to interact most strongly with it . They then tested their predictions for a subset of the receptors by performing experiments in living flies , and found that roughly 71% of predicted compounds did indeed activate or inhibit their receptors , compared to only 10% of a control sample . In addition to providing new insights into the nature of the interactions between odorants and their receptors , the computational screen devised by Boyle et al . could aid the development of novel insect repellents , or compounds that mask the odors used by disease-causing insects to identify their hosts . It could also be used in the future to develop novel flavors and fragrances . | [
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] | 2013 | Expanding the olfactory code by in silico decoding of odor-receptor chemical space |
Glutaminase ( GLS ) isoenzymes GLS1 and GLS2 are key enzymes for glutamine metabolism . Interestingly , GLS1 and GLS2 display contrasting functions in tumorigenesis with elusive mechanism; GLS1 promotes tumorigenesis , whereas GLS2 exhibits a tumor-suppressive function . In this study , we found that GLS2 but not GLS1 binds to small GTPase Rac1 and inhibits its interaction with Rac1 activators guanine-nucleotide exchange factors , which in turn inhibits Rac1 to suppress cancer metastasis . This function of GLS2 is independent of GLS2 glutaminase activity . Furthermore , decreased GLS2 expression is associated with enhanced metastasis in human cancer . As a p53 target , GLS2 mediates p53’s function in metastasis suppression through inhibiting Rac1 . In summary , our results reveal that GLS2 is a novel negative regulator of Rac1 , and uncover a novel function and mechanism whereby GLS2 suppresses metastasis . Our results also elucidate a novel mechanism that contributes to the contrasting functions of GLS1 and GLS2 in tumorigenesis .
Metabolic changes are a hallmark of cancer cells ( Berkers et al . , 2013; Cairns et al . , 2011; Ward and Thompson , 2012 ) . Increased glutamine metabolism ( glutaminolysis ) has been recognized as a key metabolic change in cancer cells , along with increased aerobic glycolysis ( the Warburg effect ) ( Berkers et al . , 2013; Cairns et al . , 2011; DeBerardinis et al . , 2007; Hensley et al . , 2013; Ward and Thompson , 2012 ) . Glutamine is the most abundant amino acid in human plasma ( Hensley et al . , 2013 ) . Glutamine catabolism starts with the conversion of glutamine to glutamate , which is converted to α-ketoglutarate for further metabolism in the tricarboxylic acid ( TCA ) cycle . Recent studies have shown that increased glutamine metabolism plays a critical role in supporting the high proliferation and survival of cancer cells by providing pools of the TCA cycle intermediates , as well as the biosynthesis of proteins , lipids , and nucleotides ( Berkers et al . , 2013; Cairns et al . , 2011; DeBerardinis et al . , 2007; Hensley et al . , 2013; Ward and Thompson , 2012 ) . Glutaminase ( GLS ) is the initial enzyme in glutamine metabolism , which catalyzes the hydrolysis of glutamine to glutamate in cells . Two genes encode glutaminases in human cells: GLS1 ( also known as kidney-type glutaminase ) , and GLS2 ( also known as liver-type glutaminase ) . GLS1 and GLS2 proteins exhibit a high degree of amino acid sequence similarity , particularly in their glutaminase core domains . While GLS1 and GLS2 both function as glutaminase enzymes in glutamine metabolism , recent studies show that they have very different functions in tumorigenesis . GLS1 is ubiquitously expressed in various tissues , and its expression can be induced by the oncogene MYC ( Gao et al . , 2009 ) . GLS1 is frequently activated and/or overexpressed in various types of cancer , including hepatocellular carcinoma ( HCC ) ( Gao et al . , 2009; Thangavelu et al . , 2012; Wang et al . , 2010; Xiang et al . , 2015 ) . GLS1 has been reported to promote tumorigenesis in different types of cancer , including HCC , which is mainly attributable to its glutaminase activity and role in promoting glutamine metabolism ( Gao et al . , 2009; Thangavelu et al . , 2012; Wang et al . , 2010; Xiang et al . , 2015 ) . By contrast , GLS2 is specifically expressed in only a few tissues , including the liver tissue . Recent studies including ours have shown that GLS2 is a novel target gene of the tumor suppressor p53 . GLS2 is transcriptionally up-regulated by p53 and mediates p53’s regulation of mitochondrial function and anti-oxidant defense in cells ( Hu et al . , 2010; Suzuki et al . , 2010 ) . Considering the critical role of p53 and its pathway in tumor suppression , the identification of GLS2 as a p53 target gene strongly suggests a potentially important role of GLS2 in tumor suppression . Recent studies have shown that , in contrast to the tumorigenic effect of GLS1 , GLS2 displays a tumor suppressive function ( Hu et al . , 2010; Liu et al . , 2014a; Suzuki et al . , 2010 ) . GLS2 expression is frequently reduced in HCC ( Hu et al . , 2010; Liu et al . , 2014a; Suzuki et al . , 2010; Xiang et al . , 2015 ) . Ectopic expression of GLS2 greatly inhibited the growth and colony formation of HCC cells in vitro and the growth of HCC xenograft tumors in vivo ( Hu et al . , 2010; Liu et al . , 2014a; Suzuki et al . , 2010 ) . Given that GLS1 and GLS2 both function as glutaminase enzymes , the mechanisms underlying their contrasting roles in tumorigenesis remain unclear . In this study , immunoprecipitation ( IP ) followed by liquid chromatography-tandem mass spectrometry ( LC/MC-MS ) analysis was employed to screen for potential proteins interacting with GLS2 . The small GTPase Rac1 was identified as a novel binding protein for GLS2 . Rac1 cycles between inactive guanosine 5′-diphosphate ( GDP ) -bound and active guanosine 5'-triphosphate ( GTP ) -bound forms in cells , and regulates a diverse array of cellular events , including actin dynamics . The Rac1 signaling is frequently activated in various types of cancer , in which it plays a critical role in promoting migration , invasion and metastasis of cancer cells ( Bid et al . , 2013; Heasman and Ridley , 2008 ) . We found that GLS2 binds to Rac1 , and inhibits the interaction of Rac1 with its guanine-nucleotide exchange factors ( GEFs ) such as Tiam1 and VAV1 , which would normally activate Rac1 . Thus , GLS2 inhibits Rac1 activity , which in turn inhibits migration , invasion and metastasis of cancer cells . This function of GLS2 requires the C-terminus of GLS2 and is independent of its glutaminase activity . In contrast , GLS1 does not interact with Rac1 to inhibit Rac1 activity , and consequently , cannot inhibit cancer metastasis via this pathway . p53 plays a pivotal role in suppressing cancer metastasis , but its underlying mechanism is not fully understood ( Muller et al . , 2011; Vousden and Prives , 2009 ) . Our results further show that , as a direct downstream target of p53 , GLS2 mediates p53’s function in metastasis suppression through inhibiting the Rac1 signaling . Taken together , our results demonstrated that GLS2 is a novel negative regulator of Rac1 , and plays a critical role in suppression of metastasis through its negative regulation of Rac1 activity . Our results also revealed that GLS2 plays an important role in mediating the function of p53 in suppression of cancer metastasis .
GLS2 was reported to interact with several proteins although the biological functions of these interactions remain unclear ( Boisguerin et al . , 2004; Olalla et al . , 2001 ) . These findings raised the possibility that GLS2 may exert its function in tumor suppression through its interactions with other proteins . Herein , we screened for potential GLS2-interacting proteins in human HCC Huh-1 cells stably transduced with pLPCX-GLS2-Flag retroviral vectors to express GLS2-Flag and control cells transduced with control vectors . Co-IP assays using an anti-Flag antibody followed by LC-MS/MS assays were employed . These assays identified the small GTPase Rac1 as a potential GLS2 interacting protein ( Figure 1A ) . Rac1 is frequently activated or overexpressed in various types of cancer , including HCC , and has been reported to play a critical role in promoting cancer cell migration , invasion and metastasis mainly through its regulation of actin dynamics ( Bid et al . , 2013; Heasman and Ridley , 2008 ) . 10 . 7554/eLife . 10727 . 003Figure 1 . Rac1 is a novel interacting protein for GLS2 . ( A ) The potential GLS2-interacting proteins identified by co-IP followed by LC-MS/MS analysis . Huh-1 cells expressing GLS2-Flag or cells transduced with control vectors were used for co-IP with the anti-Flag antibody followed by LC-MS/MS analysis . The potential GLS2 interacting proteins are listed with the number of peptides identified by LC-MS/MS analysis . ( B ) GLS2-Flag interacted with Myc-Rac1 in cells . Huh-1 cells were transduced with Myc-Rac1 , GLS2-Flag and control vectors as indicated for co-IP assays using the anti-Myc ( left panels ) and anti-Flag antibodies ( right panels ) , respectively . ( C ) GLS1-Flag did not interact with Myc-Rac1 in cells . Huh-1 cells were transduced with Myc-Rac1 and GLS1-Flag vectors for co-IP assays using the anti-Myc ( left panels ) and anti-Flag antibodies ( right panels ) , respectively . ( D ) Endogenous GLS2 but not GLS1 interacted with endogenous Rac1 in Huh-1 and HepG2 cells detected by co-IP assays . ( E ) Schematic representation of vectors expressing Flag-tagged WT or serial deletion mutants of GLS2 . ( F ) The C-terminus of GLS2 , GLS2-C139 , is necessary and sufficient for GLS2 to interact with Rac1 . Huh-1 cells were transduced with WT or different mutant GLS2-Flag vectors listed in Figure 1E together with Myc-Rac1 vectors for co-IP assays . ( G ) The relative glutaminase activities of WT and different mutant GLS2 . Huh-1 and HepG2 cells were transduced with WT and different mutant GLS2 vectors . The relative glutaminase activities in cells transduced with WT GLS2 vectors were designated as 100 . **: p<0 . 001 . Student’s t-test . GLS , glutaminase; IB: immunoblot; IP , immunoprecipitation; LC/MC-MS , liquid chromatography-tandem mass spectrometry; WT , wild-type . DOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 003 The interaction between GLS2 and Rac1 was confirmed by co-IP followed by western blot assays in Huh-1 cells co-transduced with the vectors expressing GLS2-Flag and Myc-Rac1 , respectively . GLS2-Flag was co-precipitated by the anit-Myc antibody , and Myc-Rac1 was co-precipitated by the anti-Flag antibody , indicating that GLS2-Flag interacted with Myc-Rac1 in cells ( Figure 1B ) . In contrast , no interaction was observed between GLS1-Flag and Myc-Rac1 in Huh-1 cells ( Figure 1C ) . The interaction between endogenous Rac1 with endogenous GLS2 but not GLS1 was also observed in Huh-1 and HepG2 cells ( Figure 1D ) . To identify the domain of GLS2 that interacts with Rac1 , three Flag-tagged deletion mutants of GLS2 were constructed , including ΔN163 ( deletion of N-terminal amino acids ( aa ) 1–163 ) , ΔC139 ( deletion of C-terminal aa 464–602 ) , and C139 ( C-terminal aa 464–602 only ) ( Figure 1E ) . Co-IP assays in Huh-1 cells showed that the GLS2-ΔN163 and GLS2-C139 , but not GLS2-ΔC139 , interacted with Myc-Rac1 , indicating that the C-terminus of GLS2 is necessary and sufficient for the interaction between GLS2 and Rac1 ( Figure 1F ) . Since the C-terminus of GLS2 ( GLS2-C139 ) does not contain the glutaminase core domain , which encodes the glutaminase catalytic domain ( Figure 1E ) and hence lacks glutaminase activity ( Figure 1G ) , these results demonstrate that the binding of GLS2 to Rac1 is independent of its glutaminase activity . As a molecular switch , Rac1 cycles between inactive GDP-bound and active GTP-bound forms in cells ( Bid et al . , 2013; Heasman and Ridley , 2008; Raftopoulou and Hall , 2004 ) . It is well known that constitutively active mutant Rac1 ( CA Rac1-G12V ) exists constitutively in the GTP-bound form in cells , whereas the dominant negative Rac1 mutant ( DN Rac1-T17N ) exists constitutively in the GDP-bound form in cells ( Feig , 1999; Ridley et al . , 1992 ) . Rac1-GDP and Rac1-GTP display different conformations and interact with different proteins . For instance , GEFs specifically bind to Rac1-GDP to catalyze the exchange of GDP to GTP and thereby activate Rac1 , whereas GAPs ( GTPase-activating proteins ) specifically bind to Rac1-GTP to hydrolyze GTP and thereby inactivate Rac1 ( Cherfils and Zeghouf , 2013; Vetter and Wittinghofer , 2001 ) . To investigate the biological function of the interaction between GLS2 and Rac1 , Huh-1 cells were co-transfected with GLS2-Flag vectors and CA Myc-Rac1-G12V or DN Myc-Rac1-T17N vectors for co-IP assays . We found that GLS2-Flag preferentially bound to Myc-Rac1-T17N but not Myc-Rac1-G12V ( Figure 2A ) . To confirm this result , lysates from Huh-1 cells co-transduced with GLS2-Flag and Rac1-Myc were pretreated with GDP or GTPγS ( a non-hydrolyzable analog of GTP ) to convert Rac1 in cell lysates into Rac1-GDP or Rac1-GTP form as previously described ( Castillo-Lluva et al . , 2010; Fukata et al . , 2002 ) . Co-IP assays showed that GLS2-Flag preferentially bound to Myc-Rac1 in cell lysates pretreated with GDP but not GTPγS ( Figure 2B ) . These results showed that GLS2 preferentially binds to Rac1-GDP , suggesting that GLS2 is involved in regulating Rac1 activity . 10 . 7554/eLife . 10727 . 004Figure 2 . GLS2 interacts with Rac1-GDP and negatively regulates the Rac1 activity . ( A ) GLS2-Flag preferentially interacted with the DN Myc-Rac1-T17N but not the CA Myc-Rac1-G12V in Huh-1 cells . Cells were transduced with GLS2-Flag vectors together with Rac1-T17N or Rac1-G12V vectors for co-IP assays . ( B ) GLS2-Flag preferentially bound to Rac1-GDP but not Rac1-GTP in cell lysates . Cell lysates from Huh-1 cells transduced with vectors expressing Myc-Rac1 and GLS2-Flag were pretreated with GDP or GTPγS to convert Rac1 into Rac1-GDP or Rac1-GTP form before co-IP assays . ( C ) Ectopic expression of GLS2 inhibited Rac1 activities represented by decreased levels of Rac1-GTP in HCC cells measured by the GST-p21-binding domain of PAK1 pull-down assays . Left panels: Represented results of Rac1 activity analysis in Huh-1 and HepG2 cells . Right panels: relative Rac1-GTP/total Rac1/Actin levels in Huh-1 , HepG2 , Hep3B and Huh-7 cells . Data present mean ± SD ( n=3 ) . *p<0 . 01; Student’s t-test . ( D ) Knockdown of GLS2 by shRNA vectors increased Rac1 activities in HCC cells . Left panels: Represented results of Rac1 activity analysis in Huh-1 and HepG2 cells . Right panels: relative Rac1-GTP/total Rac1 /Actin levels in Huh-1 , HepG2 , Hep3B and Huh-7 cells . Data present mean ± SD ( n=3 ) . *p<0 . 01; #p<0 . 05; Student’s t-test . ( E ) Ectopic expression of GLS2-Flag decreased the levels of p-PAK1 at Ser199/204 in Huh-1 and HepG2 cells . ( F ) Knockdown of GLS2 by shRNA vectors increased the levels of p-PAK1 at Ser199/204 in Huh-1 and HepG2 cells . ( G ) The C-terminus of GLS2 , GLS2-C139 , interacted with DN Myc-Rac1-T17N but not CA Myc-Rac1-G12V in Huh-1 cells detected by co-IP assays . ( H ) The C-terminus of GLS2 , GLS2-C139 , inhibited the Rac1 activity in Huh-1 and HepG2 cells . Left panels: Represented results of Rac1 activity analysis in Huh-1 cells transduced with different GLS2-Flag vectors . Right panels: relative Rac1-GTP/total Rac1/Actin levels in Huh-1 and HepG2 . Data present mean ± SD ( n=3 ) . *p<0 . 01; #p<0 . 05; Student’s t-test . GDP , guanosine 5′-diphosphate; GLS , glutaminase; GTP , guanosine 5'-triphosphate; HCC , hepatocellular carcinoma; IP , immunoprecipitation; shRNA , short hairpin RNA; WT , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 00410 . 7554/eLife . 10727 . 005Figure 2—figure supplement 1 . GLS2 inhibits Rac1 activity in HCC cells . ( A ) Ectopic expression of GLS2-Flag inhibited Rac1 activities represented by decreased Rac1-GTP levels in Hep3B and Huh-7 cells . ( B ) Knockdown of GLS2 by 2 different shRNA vectors in different HCC cells detected by Taqman real-time Polymerase chain reaction assays . The messenger RNA expression of GLS2 was normalized with Actin . Data are presented as mean ± SD ( n=3 ) . **p<0 . 001; Student’s t-test . ( C ) Knockdown of endogenous GLS2 by shRNA enhanced Rac1 activities represented by increased Rac1-GTP levels in Hep3B and Huh-7 cells . GLS2 knockdown was presented in Figure 2—figure supplement 1B . ( D ) Ectopic expression of GLS1 did not clearly affect the Rac1 activity in Huh-1 or HepG2 cells . ( E ) Knockdown of endogenous GLS1 by shRNA vectors did not clearly affect the Rac1 activity in Huh-1 or HepG2 cells . In D and E: data present mean ± SD ( n=3 ) . GLS , glutaminase; GTP , guanosine 5'-triphosphate; HCC , hepatocellular carcinoma; shRNA , short hairpin RNADOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 00510 . 7554/eLife . 10727 . 006Figure 2—figure supplement 2 . The expression of endogenous GLS2 and exogenous GLS2-Flag in HCC cells . ( A ) The expression of endogenous GLS2 protein in HCC cells . To enhance the sensitivity of the anti-GLS2 antibody , endogenous GLS2 in cells was pulled down by IP assays with the anti-GLS2 antibody and detected by western blot assays . ( B ) The expression of exogenous GLS2-Flag protein in HCC cells . The levels of exogenous GLS2-Flag protein in HCC cells transduced with the GLS2-Flag retrovival vector were measured by western-blot assays . ( C ) The relative mRNA levels of GLS2 in HCC cells with or without transduction of the GLS2-Flag vector . ( D ) The relative mRNA levels of GLS1 in HCC cells . In C and D , the mRNA levels of GLS2 and GLS1 in cells were measured by real-time Polymerase chain reaction assays and normalized with Actin . Two normal liver tissues were used as controls . The relative mRNA levels of GLS2 ( C ) and GLS1 ( D ) in Huh-1 cells were designated as 1 , respectively . GLS , glutaminase; HCC , hepatocellular carcinoma; IP , immunoprecipitation; mRNA , messenger RNADOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 006 We further investigated whether GLS2 inhibits the Rac1 activity in different human HCC cell lines , including Huh-1 and HepG2 ( p53 wild type; WT ) , Hep3B ( p53-null ) and Huh-7 cells ( p53 mutant ) . PAK1 ( p21-activated kinase 1 ) is a critical Rac1 effector protein . It has been well established that the p21-binding domain of PAK1 binds specifically to the Rac1-GTP but not Rac1-GDP in cells ( Galic et al . , 2014; Hayashi-Takagi et al . , 2010; Palacios et al . , 2002 ) . Based on this fact , the GST-p21-binding domain of PAK1 pull-down assays have been widely used to measure the levels of Rac1-GTP in cells as a standard method to analyze the Rac1 activity in cells ( Galic et al . , 2014; Hayashi-Takagi et al . , 2010; Palacios et al . , 2002 ) . We found that ectopic expression of GLS2-Flag greatly decreased the levels of Rac1-GTP measured by p21-binding domain of PAK1 pull-down assays in Huh-1 and HepG2 cells ( Figure 2C ) , as well as Hep3B and Huh-7 cells ( Figure 2C and Figure 2—figure supplement 1A ) . In contrast , the expression of GLS2-Flag did not affect the levels of total Rac1 protein in these cells measured by western blot assays ( Figure 2C and Figure 2—figure supplement 1A ) , indicating that GLS2 inhibits Rac1 activity . Consistently , knockdown of GLS2 greatly increased the levels of Rac1-GTP but not total Rac1 in Huh-1 and HepG2 cells ( Figure 2D ) as well as in Hep3B and Huh-7 cells ( Figure 2D and Figure 2—figure supplement 1B , C ) . The endogenous levels of GLS2 in these HCC cells and the levels of GLS2-Flag in HCC cells transduced with GLS2-Flag expression vectors were shown in Figure 2—figure supplement 2A–D . It has been known that Rac1 binds to PAK1 and results in the auto-phosphorylation of PAK1 at multiple sites , including Ser199/204 , leading to PAK1 activation ( Chong et al . , 2001; Heasman and Ridley , 2008 ) . Therefore , we further investigated the effect of GLS2 on Rac1 activity by detecting the Ser199/204 phosphorylation of PAK1 in cells . Ectopic expression of GLS2-Flag in Huh-1 and HepG2 cells greatly reduced Ser199/204 phosphorylation of PAK1 ( Figure 2E ) , whereas GLS2 knockdown enhanced Ser199/204 phosphorylation of PAK1 ( Figure 2F ) , which further indicates that GLS2 inhibits the Rac1 activity in HCC cells . In contrast , ectopic GLS1 expression or GLS1 knockdown did not affect the Rac1 activity in HCC cells ( Figure 2—figure supplement 1D , E ) . Consistent with WT GLS2 , the C-terminus of GLS2 , GLS2-C139 , specifically bound to the DN Rac1-T17N but not CA Rac1-G12V in Huh-1 cells ( Figure 2G ) . Furthermore , ectopic expression of GLS2-C139 greatly inhibited the Rac1 activity in Huh-1 cells ( Figure 2H ) . In contrast , GLS2-ΔC139 , which did not bind to Rac1 ( Figure 1F ) , failed to inhibit the Rac1 activity ( Figure 2H ) . This result indicates that the interaction between GLS2 and Rac1 is critical for GLS2 to inhibit the Rac1 activity . Furthermore , the function of GLS2 in binding to and inhibiting Rac1 is independent of its glutaminase activity since the C-terminus of GLS2 ( GLS2-C139 ) lacks the glutaminase activity ( Figure 1G ) . Collectively , these results revealed that GLS2 is a novel negative regulator of the Rac1 signaling; GLS2 inhibits the Rac1 activity through its interaction with Rac1-GDP , and furthermore , this function of GLS2 requires the C-terminus of GLS2 and is independent of GLS2 glutaminase activity . We further investigated the mechanism by which GLS2 inhibits Rac1 . Rac1 was reported to interact with other proteins through its Switch I & II regions or its C-terminus , which contains the protein transduction domain and GTPase C-termini ( van Hennik et al . , 2003; Vetter and Wittinghofer , 2001 ) ( Figure 3A ) . To examine the domain involved in the interaction of Rac1 with GLS2 , different Myc-tagged deletion mutants of Rac1 were constructed , including the ΔC33 ( deletion of aa 160–192 ) , the ΔN29 ( deletion of aa 1–29 ) , and the ΔSwitch ( deletion of aa 30–74 ) ( Figure 3A ) . Co-IP assays showed that the Rac1-ΔC33 and Rac1-ΔN29 , but not Rac1-ΔSwitch , interacted with GLS2-Flag ( Figure 3B ) , indicating that the Switch I & II regions are necessary for Rac1 to interact with GLS2 . 10 . 7554/eLife . 10727 . 007Figure 3 . GLS2 inhibits the interaction of Rac1-GDP with Tiam1 and VAV1 . ( A ) Schematic representation of Rac1 deletion mutants . Myc-tagged vectors expressing WT Rac1 or serial deletion mutants were constructed . ( B ) GLS2 bound to Rac1 through its Switch I & II regions . Huh-1 cells were transduced with different Myc-Rac1 vectors listed in Figure 3A together with GLS2-Flag vectors for co-IP assays . ( C ) Tiam1 ( upper panels ) and VAV1 ( lower panels ) bound to Rac1 through its Switch I & II regions . Huh-1 cells were transduced with different Myc-Rac1 vectors listed in Figure 3A together with vectors expressing Tiam1-HA or VAV1-HA for co-IP assays . ( D ) Ectopic expression of Tiam1 and VAV1 activated Rac1 in cells . Huh-1 cells were transduced with vectors expressing Tiam1-HA and VAV1-HA , respectively , and Rac1 activity was analyzed . Data are presented as mean ± SD ( n=3 ) . *p<0 . 01; Student’s t-test . ( E , F ) Ectopic expression of GLS2 inhibited the interaction of DN Myc-Rac1-T17N with ectopic Tiam1-HA ( E ) and VAV1-HA ( F ) in cells . Huh-1 cells were transduced with Myc-Rac1-T17N vectors and Tiam1-HA ( E ) or VAV1-HA vectors ( F ) ( 2 μg ) , together with increasing amount of GLS2-Flag vectors ( 1 , 3 , 6 μg ) for co-IP assays . ( G ) Ectopic expression of GLS2 inhibited the interaction of endogenous Rac1 with endogenous Tiam1 and VAV1 in cells . Huh-1 and HepG2 cells were transduced with increasing amount of GLS2-Flag expression vectors ( 1 , 3 , 6 μg ) for co-IP assays . ( H ) Knockdown of endogenous GLS2 by shRNA vectors in Huh-1 and HepG2 cells promoted the interaction of endogenous Rac1 with endogenous Tiam1 and VAV1 as measured by co-IP assays . GLS , glutaminase; GTP , guanosine 5'-triphosphate; IP , immunoprecipitation; shRNA , short hairpin RNA; WT , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 00710 . 7554/eLife . 10727 . 008Figure 3—figure supplement 1 . Tiam1 and VAV1 preferentially bind to Rac1-GDP . ( A ) Tiam1-HA preferentially bound to the DN Myc-Rac1-T17N but not CA Myc-Rac1-G12V . Huh-1 cells were transduced with DN Rac1-T17N or CA Rac1-G12V vectors together with vectors expressing Tiam1-HA for co-IP assays . ( B ) VAV1-HA preferentially bound to the DN Myc-Rac1-T17N but not CA Myc-Rac1-G12V . Huh-1 cells were transduced with DN Myc-Rac1-T17N or CA Myc-Rac1-G12V vectors together with vectors expressing VAV1-HA for co-IP assays . IP , immunoprecipitation; WT , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 008 It has been well-established that Rac1 GEFs can specifically bind to Rac1-GDP through the Switch I & II regions to catalyze the exchange of GDP to GTP to activate Rac1 ( Rossman et al . , 2005; Vetter and Wittinghofer , 2001 ) . Tiam1 and VAV1 are two most common and critical GEFs of Rac1 ( Heo et al . , 2005; Worthylake et al . , 2000 ) . Consistent with previous reports , ectopically expressed Tiam1-HA and VAV1-HA specifically bound to Rac1-GDP ( shown by their preferential interactions with Rac1-T17N but not Rac1-G12V; Figure 3—figure supplement 1A , B ) through the Switch I & II regions ( Figure 3C ) , leading to the activation of Rac1 in Huh-1 cells ( Figure 3D ) . Since both GLS2 and Rac1 GEFs , such as Tiam1 and VAV1 , bind to Rac1-GDP through the Switch I & II regions , this raised the possibility that GLS2 may inhibit Rac1 activity through competing with Rac1 GEFs for the Switch I & II regions of Rac1-GDP . To test this possibility , co-IP assays were performed in Huh-1 cells co-transduced with DN Myc-Rac1-T17N vectors and Tiam1-HA or VAV1-HA vectors , as well as increasing amount of vectors expressing GLS2-Flag . Increasing amount of GLS2-Flag resulted in a progressive reduction of Tiam1-HA or VAV1-HA bound to Myc-Rac1-T17N in cells ( Figure 3E , F ) . Consistently transducing Huh-1 and HepG2 cells with increasing amount of GLS2-Flag vectors resulted in a progressive reduction of endogenous Tiam1 and VAV1 bound to endogenous Rac1 ( Figure 3G ) . Furthermore , knockdown of endogenous GLS2 greatly promoted the interaction of endogenous Tiam1 and VAV-1 with endogenous Rac1 in Huh-1 and HepG2 cells ( Figure 3H ) . These results suggest that GLS2 inhibits the Rac1 activation by interacting with Rac1-GDP to block its interaction with Rac1 GEFs , such as Tiam1 and VAV1 . GLS2 expression is frequently diminished in human HCC ( Hu et al . , 2010; Liu et al . , 2014a; Suzuki et al . , 2010; Xiang et al . , 2015 ) . However , its role in HCC , especially HCC metastasis , is poorly understood . The malignancy and poor prognosis of HCC has been related to the high metastatic characteristic of HCC ( El-Serag and Rudolph , 2007; Tang , 2001 ) . Currently , the mechanism underlying HCC metastasis is not well-understood . Rac1 is frequently activated or overexpressed in various types of cancer , including HCC , which plays a critical role in promoting cancer cell migration , invasion and metastasis ( Bid et al . , 2013; Heasman and Ridley , 2008 ) . As shown in Figure 4—figure supplement 1A–C , ectopic expression of CA Myc-Rac1-G12V greatly promoted migration and invasion of Huh-1 and HepG2 cells as determined by trans-well assays , whereas expression of DN Myc-Rac1-T17N greatly inhibited migration and invasion of these cells . Therefore , our findings that GLS2 binds to and inhibits Rac1 raised the possibility that GLS2 may play an important role in suppressing cancer metastasis . Here , we investigated the effects of GLS2 on the abilities of migration and invasion of different HCC cells , including Huh-1 , HepG2 , Hep3B and Huh7 cells , by using chamber trans-well assays . Cells were seeded into the upper chamber containing serum-free medium without or with matrigel for migration and invasion assays , respectively . Compared with cells transduced with control vectors , ectopic expression of GLS2 by GLS2-Flag retroviral vectors greatly reduced the migration and invasion of above-mentioned different HCC cells ( Figure 4A , B ) . Furthermore , knockdown of GLS2 by short hairpin RNA ( shRNA ) vectors greatly promoted the migration and invasion of these cells ( Figure 4C , D ) . Serum-free medium was used in the upper chamber to minimize the effect of GLS2 on cell proliferation in the trans-well assays . As shown in Figure 4—figure supplement 2 , no significant difference in the viability and number of these cells among different groups was observed after being cultured in serum-free medium for 36 hr at the end of trans-well assays . Contrary to the role of GLS2 in suppressing migration and invasion , ectopic expression of GLS1-Flag significantly promoted the migration and invasion of Huh-1 and HepG2 cells ( Figure 4E ) , whereas knockdown of endogenous GLS1 significantly reduced the migration and invasion of these cells ( Figure 4F ) . 10 . 7554/eLife . 10727 . 009Figure 4 . GLS2 inhibits migration and invasion of HCC cells through negative regulation of Rac1 . ( A , B ) Ectopic GLS2 expression inhibited the migration ( A ) and invasion ( B ) of different HCC cells as determined by trans-well assays . Upper panels: representative images of migrating ( A ) and invading ( B ) Huh-1 cells transduced with control ( con ) or GLS2-Flag vectors . Scale bars: 200 μm . Lower panels: quantification of average number of migrating ( A ) and invading ( B ) cells/field in different HCC cells transduced with control ( con ) or GLS2-Flag vectors . Data present mean ± SD ( n=6 ) . **p<0 . 001; Student’s t-test . ( C , D ) Knockdown of GLS2 by shRNA vectors promoted the migration ( C ) and invasion ( D ) of different HCC cells . Upper panels: representative images of migrating ( C ) and invading ( D ) Huh-1 cells with or without GLS2 knockdown . Scale bars , 200 μm . Data present mean ± SD ( n=6 ) . **p<0 . 001; Student’s t-test . ( E ) Ectopic GLS1 expression promoted the migration ( left ) and invasion ( right ) of Huh-1 and HepG2 cells as determined by trans-well assays . ( F ) Knockdown of GLS1 decreased the migration ( left ) and invasion ( right ) of Huh-1 and HepG2 cells . In E , F , data are presented as mean ± SD ( n=6 ) . **p<0 . 001; Student’s t-test . ( G , H ) Ectopic expression of DN Rac1-T17N largely abolished the promoting effects of GLS2 knockdown on migration ( G ) and invasion ( H ) of different HCC cells as measured by trans-well assays . Cells with knockdown of GLS2 by shRNA vectors were transduced with Rac1-T17N expression vectors for trans-well assays . Data present mean ± SD ( n=6 ) . **p<0 . 001; Student’s t-test . ( I , J ) Ectopic expression of the C-terminus of GLS2 , GLS2-C139 , inhibited migration ( I ) and invasion ( J ) of Huh-1 and HepG2 cells as measured by trans-well assays . Cells were transduced with different GLS2 expression vectors described in Figure 1E for assays . Upper panels in I: representative images of migrating Huh-1 cells . Data present mean ± SD ( n=6 ) . **p<0 . 001; Student’s t-test . GLS , glutaminase; HCC , hepatocellular carcinoma; shRNA , short hairpin RNA; WT , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 00910 . 7554/eLife . 10727 . 010Figure 4—figure supplement 1 . Rac1 promotes the migration and invasion of Huh-1 and HepG2 cells . ( A ) Ectopic expression of CA Myc-Rac1-G12V and DN Myc-Rac1-T17N in Huh-1 and HepG2 cells detected by western-blot assays . Cells were transduced with Myc-Rac1-G12V and Myc-Rac1-T17N vectors for assays . ( B ) Ectopic expression of CA Myc-Rac1-G12V promoted the migration of Huh-1 and HepG2 cells , whereas ectopic expression of DN Myc-Rac1-T17N inhibited the migration of Huh-1 and HepG2 cells . Upper panels: representative images of migrating Huh-1 cells . Scale bars: 200 μm . Lower panel: quantification of average number of migrating cells/field . Data are presented as mean ± SD ( n=6 ) . **p<0 . 001; Student’s t-test . ( C ) Ectopic expression of CA Myc-Rac1-G12V promoted the invasion of Huh-1 and HepG2 cells , whereas ectopic expression of DN Myc-Rac1-T17N inhibited the invasion of Huh-1 and HepG2 cells . Data are presented as mean ± SD ( n=6 ) . **p<0 . 001; Student’s t-test . DOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 01010 . 7554/eLife . 10727 . 011Figure 4—figure supplement 2 . The viability and number of HCC cells with GLS2 overexpression or knockdown after being cultured in serum-free medium for 36 hr . ( A , B ) The viability of HCC cells with GLS2 overexpression ( A ) or knockdown ( B ) cultured in serum-free medium for 36 hr . ( C , D ) The relative number of HCC cells with GLS2 overexpression ( C ) or knockdown ( D ) cultured in serum-free medium for 36 hr . Same number of cells with GLS2 overexpression or knockdown and their control cells were cultured in serum-free medium for 36 hr before the the viability and number of cells was measured in a Vi-CELL Cell Viability Analyzer ( Beckman Coulter , Indianapolis , IN ) . The viability of cells was analyzed by the trypan blue cell exclusion method . GLS , glutaminase; HCC , hepatocellular carcinomaDOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 01110 . 7554/eLife . 10727 . 012Figure 4—figure supplement 3 . Knockdown of endogenous Rac1 largely abolishes the effect of GLS2 on migration and invasion of HCC cells . ( A ) The knockdown of Rac1 by 2 different shRNA vectors in Huh-1 and HepG2 cells measured by western blot assays . ( B ) Knockdown of endogenous Rac1 largely abolished the promoting effects of GLS2 knockdown on migration of Huh-1 and HepG2 cells as measured by trans-well assays . The endogenous Rac1 was knocked down by shRNA vectors in Huh-1 and HepG2 cells with GLS2 knockdown . Left panels: representative images of migrating Huh-1 cells . Scale bars , 200 μm . Right panels: quantification of number of migrating cells/field . Data are presented as mean ± SD ( n=6 ) . **p<0 . 001; Student’s t-test . ( C ) Knockdown of endogenous Rac1 largely abolished the promoting effects of GLS2 knockdown on invasion of Huh-1 and HepG2 cells as measured by trans-well assays . Data are presented as mean ± SD ( n=4 ) . **p<0 . 001; Student’s t-test . ( D ) The viability ( left panel ) and number ( right panel ) of HCC cells with GLS2 and/or Rac1 knockdown cultured in serum-free medium for 36 hr . Same number of cells were cultured in serum-free medium for 36 hr before the the viability and number of cells was measured in a Vi-CELL Cell Viability Analyzer ( Beckman Coulter ) . The viability of cells was analyzed by the trypan blue cell exclusion method . GLS , glutaminase; HCC , hepatocellular carcinoma; shRNA , short hairpin RNA . DOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 012 We further investigated whether GLS2 inhibits migration and invasion of HCC cells through its negative regulation of Rac1 . Ectopic expression of the DN Myc-Rac1-T17N significantly reduced the migration and invasion of the above-mentioned four different HCC cells ( Figure 4G , H ) . Notably , DN Myc-Rac1-T17N largely abolished the promoting effects of GLS2 knockdown on migration and invasion of these cells ( Figure 4G , H ) . Consistently , knockdown of endogenous Rac1 significantly reduced the migration and invasion of Huh-1 and HepG2 cells , and , furthermore , largely abolished the promoting effects of GLS2 knockdown on migration and invasion ( Figure 4—figure supplement 3A–C ) . No significant difference in the viability and number of these cells among different groups was observed after being cultured in serum-free medium for 36 hr at the end of trans-well assays ( Figure 4—figure supplement 3D ) . Consistent with WT GLS2 , ectopic expression of the C-terminus of GLS2 , GLS2-C139 , which interacted with Rac1-GDP and inhibited the Rac1 activity ( Figure 2G , H ) , greatly inhibited the migration and invasion of Huh-1 and HepG2 cells ( Figure 4I , J ) . In contrast , deletion of the C-terminus of GLS2 ( GLS2-ΔC139 ) , which resulted in the loss of GLS2’s ability to interact with Rac1 and inhibit the Rac1 activity ( Figure 2G , H ) , largely abolished the ability of GLS2 to inhibit the migration and invasion of Huh-1 and HepG2 cells ( Figure 4I , J ) . Taken together , these results demonstrate that the negative regulation of the Rac1 activity by GLS2 is crucial for GLS2 to inhibit the migration and invasion of cancer cells , and furthermore , this function of GLS2 requires the C-terminus of GLS2 and is independent of the glutaminase activity of GLS2 . Lung metastasis is the most frequently observed distant metastasis in HCC patients ( Kitano et al . , 2012; Uka et al . , 2007 ) . We investigated the effect of GLS2 on metastasis in vivo by employing the lung metastasis model in mice . Huh-1 and HepG2 cells with ectopic GLS2-Flag expression or GLS2 knockdown and their control cells were transduced with luciferase-expressing lentiviral vectors and injected into BALB/c athymic nude mice via the tail vein . The metastasis of HCC cells to lung was monitored by in vivo bioluminescence imaging . Bioluminescence imaging results showed that ectopic expression of GLS2-Flag in both Huh-1 and HepG2 cells significantly inhibited lung metastasis ( Figure 5A ) . Histological analysis confirmed that mice injected with cells with ectopic GLS2-Flag expression had fewer and smaller metastatic tumors in the lung ( Figure 5B ) . Furthermore , knockdown of endogenous GLS2 led to significantly increased lung metastasis of both Huh-1 and HepG2 cells analyzed by in vivo imaging and histological analysis , respectively ( Figure 5C , D ) . 10 . 7554/eLife . 10727 . 013Figure 5 . GLS2 inhibits lung metastasis of HCC cells in mice , and GLS2 expression is associated with metastasis in human HCC . ( A , B ) Ectopic expression of GLS2 inhibited lung metastasis of Huh-1 and HepG2 cells in nude mice . Huh-1 and HepG2 cells with GLS2 ectopic expression were transduced with lentiviral vectors expressing luciferase for lung metastasis assays . The lung metastasis was analyzed by in vivo bioluminescence imaging ( A ) and histological analysis ( B ) at 7 weeks after inoculation of cells . Left panels in A: representative images of lung metastasis of Huh-1 cells analyzed by in vivo imaging . Right panels in A: quantification of lung photon flux ( photons per second ) . Left panels in B: representative images of hematoxylin and eosin staining of lung metastasis of Huh-1 cells . Right panels in B: The average number of tumors/lung . ( C , D ) Knockdown of endogenous GLS2 by shRNA promoted lung metastasis of Huh-1 and HepG2 cells in nude mice . ( E , F ) Ectopic expression of DN Rac1-T17N largely abolished the promoting effects of GLS2 knockdown on lung metastasis of HCC cells in vivo . Huh-1 and HepG2 cells with stable ectopic Rac1-T17N expression and GLS2 knockdown were used for lung metastasis assays in mice . In A-F , data represent mean ± SD ( n=8 mice/group ) . *p<0 . 01; **p<0 . 001; Student’s t-test . Scale bars , 200 μm . Arrows indicate metastatic tumors . ( G , H ) GLS2 expression is significantly decreased in human HCCs with metastasis compared with HCCs without metastasis . GLS2 mRNA expression in non-metastatic ( without vascular invasion ) and metastatic ( with vascular invasion ) HCCs was obtained from the TCGA ( G ) and GSE6764 ( H ) . p=0 . 0066 in G; p=0 . 0198 in H; Student’s t-test . GLS , glutaminaseDOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 013 We further investigated whether inhibition of Rac1 mediates GLS2’s function in suppression of lung metastasis of HCC cells in vivo . As shown in Figure 5E , F , ectopic expression of the DN Rac1-T17N greatly reduced lung metastasis of Huh-1 and HepG2 cells in nude mice . Notably , The DN Rac1-T17N largely abolished the promoting effects of GLS2 knockdown on lung metastasis of Huh-1 and HepG2 cells . These results together suggest that GLS2 inhibits cancer metastasis through its down-regulation of the Rac1 activity . Our results from cancer cell migration and invasion assays as well as lung metastasis models clearly showed that GLS2 inhibited metastasis of different human HCC cells , which strongly suggests that decreased expression of GLS2 in human HCC could be an important mechanism contributing to the high metastasis of human HCC . To this end , we investigated the association of decreased GLS2 expression with cancer metastasis in human HCC samples . We first queried the The Cancer Genome Atlas ( TCGA ) database to compare GLS2 expression between HCC samples with or without vascular invasion of HCC cells . As shown in Figure 5G , GLS2 expression was significantly lower in HCCs with vascular invasion ( n=57 ) , compared with HCCs without vascular invasion ( n=110 ) ( decreased by 3 . 03-fold; p=0 . 0066 ) . Consistent results were also observed in another cohort from Gene Expression Omnibus ( GEO , GSE6764 ) by using Oncomine , a human genetic dataset analysis tool . GLS2 expression was significantly lower in HCCs with vascular invasion ( n=18 ) , compared with HCCs without vascular invasion ( n=15 ) ( decreased by 4 . 62-fold; p=0 . 0198 ) ( Figure 5H ) . These results indicated that the decreased GLS2 expression is significantly associated with enhanced metastasis in human HCC . p53 plays a critical role in inhibiting cancer metastasis . However , while extensive work has been done on the mechanisms underlying p53-mediated apoptosis , cell cycle arrest and senescence , the mechanism underlying p53’s function in suppressing cancer metastasis is much less well-understood ( Muller et al . , 2011; Vousden and Prives , 2009 ) . Previous reports including ours have shown that as a direct p53 target , GLS2 is up-regulated by p53 in cells under both non-stressed and stressed conditions ( Hu et al . , 2010; Suzuki et al . , 2010 ) . Consistently , p53 knockdown by shRNA greatly reduced the mRNA and protein levels of GLS2 in Huh-1 and HepG2 cells which express WT p53 ( Figure 6A ) . Considering the potent activity of GLS2 in inhibiting cancer cell metastasis , our findings raised the possibility that GLS2 may be an important mediator of p53’s function in suppressing cancer metastasis . 10 . 7554/eLife . 10727 . 014Figure 6 . GLS2 mediates p53’s function in inhibiting migration , invasion and lung metastasis of HCC cells . ( A ) Knockdown of endogenous WT p53 reduced GLS2 expression in Huh-1 and HepG2 cells as measured by western-blot ( left ) and Taqman real-time polymerase chain reaction assays ( right ) , respectively . ( B , C ) Knockdown of p53 promoted the migration ( B ) and invasion ( C ) of Huh-1 and HepG2 cells measured by trans-well assays . ( D , E ) Simultaneous knockdown of GLS2 and p53 by shRNA vectors in Huh-1 and HepG2 cells did not display an addictive promoting effect on the migration ( D ) and invasion ( E ) of cells . In A–E , data represent mean ± SD ( n=6 ) . **p<0 . 001; Student’s t-test . ( F , G ) Simultaneous knockdown of GLS2 and p53 in Huh-1 and HepG2 cells did not display an addictive promoting effect on lung metastasis in vivo . Huh-1 and HepG2 cells with individual knockdown of GLS2 or p53 , or simultaneous knockdown of GLS2 and p53 were used for assays . In F , lung metastasis was analyzed by in vivo bioluminescence imaging at 7 weeks after inoculation of cells . Upper panels: representative images of lung metastasis of Huh-1 cells analyzed by in vivo imaging . Lower panels: quantification of lung photon flux . In G , lung metastasis was analyzed by histological analysis at week 7 . Left panels: hematoxylin and eosin staining of lung metastasis of Huh-1 cells . Scale bars: 200 μm . Right panels: The average number of tumors/lung . Data represent mean ± SD ( n=10 mice/group ) . **p<0 . 001; Student’s t-test . GLS , glutaminase; HCC , hepatocellular carcinoma; shRNA , short hairpin RNA . DOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 01410 . 7554/eLife . 10727 . 015Figure 6—figure supplement 1 . The viability and number of HCC cells with p53 knockdown cultured in serum-free medium for 36 hr . ( A , B ) The viability ( A ) and relative number ( B ) of HCC cells with p53 knockdown by shRNA vectors cultured in serum-free medium for 36 hr . Same number of cells with or without p53 knockdown were cultured in serum-free medium for 36 hr before the the viability and number of cells was measured in a Vi-CELL Cell Viability Analyzer ( Beckman Coulter ) . The viability of cells was analyzed by the trypan blue cell exclusion method . HCC , hepatocellular carcinoma; shRNA , short hairpin RNA;DOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 015 Here , we tested this hypothesis . Knockdown of p53 significantly promoted the migration and invasion of both Huh-1 and HepG2 cells measured by trans-well assays ( Figure 6B , C ) . As shown in Figure 6—figure supplement 1 , no significant difference in the viability and number of these cells among different groups was observed after being cultured in serum-free medium for 36 hr at the end of trans-well assays . Notably , while individual knockdown of GLS2 or p53 dramatically promoted the migration and invasion of Huh-1 and HepG2 cells , simultaneous knockdown of GLS2 and p53 did not display a clear additive effect on the migration and invasion of these cells ( Figure 6D , E ) . Consistently , while individual knockdown of GLS2 or p53 dramatically promoted lung metastasis of Huh-1 and HepG2 cells in mice , simultaneous knockdown of GLS2 and p53 did not display an additive effect on lung metastasis of these cells ( Figure 6F , G ) . These results demonstrate that GLS2 is a novel and important mediator of p53 in suppressing cancer metastasis . It has been reported that p53 inhibits Rac1 activity , but its mechanism remains unclear ( Bosco et al . , 2010; Guo and Zheng , 2004; Muller et al . , 2011 ) . As shown in Figure 7A , B , expression of DN Rac1-T17N greatly abolished the promoting effects of p53 knockdown on migration and invasion of Huh-1 and HepG2 cells . Consistently , Rac1 knockdown greatly abolished the promoting effects of p53 knockdown on migration and invasion of Huh-1 and HepG2 cells ( Figure 7—figure supplement 1A , B ) . These results suggest that Rac1 inhibition is an important mechanism for p53 to inhibit metastasis . 10 . 7554/eLife . 10727 . 016Figure 7 . GLS2 mediates p53’s function in negative regulation of the Rac1 activity . ( A , B ) Ectopic expression of DN Rac1-T17N greatly abolished the promoting effects of p53 knockdown on migration ( A ) and invasion ( B ) of Huh-1 or HepG2 cells as measured by trans-well assays . Data represent mean ± SD ( n=6 in A , B ) . **p<0 . 001; Student’s t-test . ( C , D ) GLS2 mediates p53’s function in negative regulation of Rac1 activity in Huh-1 and HepG2 cells . In C , knockdown of p53 in cells with GLS2 knockdown did not further promote Rac1 activity . In D , GLS2 overexpression largely abolished the promoting effect of p53 knockdown on the Rac1 activity . Left panels: represented results of Rac1 activity analysis in cells transduced with #1 shRNA vectors . Right panel: relative Rac1-GTP/total Rac1/Actin levels in cells transduced with two different shRNA vectors ( #1 and #2 ) . Data represent mean ± SD ( n=3 ) . *p<0 . 01; **p<0 . 001; Student’s t-test . ( E , F ) p53 inhibits the interaction of Rac1 with Tiam1 and VAV1 through GLS2 in Huh-1 and HepG2 cells . In E , knockdown of p53 in cells with GLS2 knockdown did not further promote the interaction of Tiam1 and VAV1 with Rac1 . The knockdown of p53 and GLS2 was shown in Figure 7C . Two shRNA vectors against p53 and GLS2 , respectively , were used , and very similar results were observed . In F , GLS2 overexpression largely abolished the promoting effect of p53 knockdown on the interaction of Tiam1 and VAV1 with Rac1 . ( G ) Proposed model for the negative regulation of Rac1 activity and cancer metastasis by GLS2 and p53 . GDP , guanosine 5′-diphosphate; GLS , glutaminase; GTP , guanosine 5'-triphosphate; shRNA , short hairpin RNA . DOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 01610 . 7554/eLife . 10727 . 017Figure 7—figure supplement 1 . Knockdown of endogenous Rac1 greatly abolished the effects of p53 on migration and invasion of HCC cells . ( A , B ) Knockdown of endogenous Rac1 greatly abolished the promoting effects of p53 knockdown on migration ( A ) and invasion ( B ) of Huh-1 and HepG2 cells . The endogenous Rac1 was knocked down by two different shRNA vectors in Huh-1 and HepG2 cells with stable p53 knockdown , and the abilities of migration ( A ) and invasion ( B ) of these cells were measured by trans-well assays . The knockdown of Rac1 was shown in Figure 4—figure supplement 3A . Data are presented as mean ± SD ( n=4 ) . **p<0 . 001; Student’s t-test . HCC , hepatocellular carcinoma; shRNA , short hairpin RNA . DOI: http://dx . doi . org/10 . 7554/eLife . 10727 . 017 Our finding that GLS2 interacts with Rac1-GDP to inhibit Rac1 activity suggests that as a direct p53 target , GLS2 could mediate p53’s function in inhibiting Rac1 activity . Notably , while individual knockdown of GLS2 or p53 greatly activated Rac1 , simultaneous knockdown of GLS2 and p53 did not display an additive effect on the Rac1 activity in Huh-1 or HepG2 cells ( Figure 7C ) . Furthermore , GLS2-Flag overexpression largely abolished the promoting effect of p53 knockdown on the Rac1 activity in Huh-1 or HepG2 cells ( Figure 7D ) . These results indicate that GLS2 mediates p53’s function in inhibiting Rac1 activity . Our results have shown that GLS2 inhibited the interaction between Rac1 and its GEFs Tiam1 and VAV1 to down-regulate the Rac1 activity ( Figure 3E–H ) . Here , we investigated whether inhibition of the interaction of Tiam1 and VAV1 with Rac1 is an important mechanism for p53 to down-regulate the Rac1 activity . Knockdown of WT p53 in Huh-1 and HepG2 cells , which greatly decreased GLS2 protein levels ( Figure 7C ) , clearly promoted the interaction of Tiam1 and VAV1 with Rac1 ( Figure 7E ) . Notably , GLS2 knockdown in cells with p53 knockdown did not further promote the interaction of Tiam1 and VAV1 with Rac1 ( Figure 7E ) . Furthermore , GLS2 overexpression largely abolished the promoting effect of p53 knockdown on the interaction of Tiam1 and VAV1 with Rac1 ( Figure 7F ) . These results suggest that blocking the interaction of Tiam1 and VAV1 with Rac1-GDP by GLS2 contributes greatly to p53’s function in inhibiting the Rac1 activity . Collectively , our results strongly suggest that GLS2 mediates p53’s function in suppression of HCC metastasis by inhibiting the interaction of Rac1 GEFs , such as Tiam1 and VAV1 , with Rac1-GDP to down-regulate the Rac1 activity ( Figure 7G ) .
In this study , GLS2 was identified as a novel binding protein and negative regulator for Rac1 . GLS2 bound to Rac1-GDP through its Switch I & II regions , which is also the binding domain for Rac1 GEFs Tiam1 and VAV1 . Thus , GLS2 blocked the binding of Tiam1 and VAV1 to Rac1-GDP , and inhibited the Rac1 activation by Tiam1 and VAV1 . We found that GLS2 inhibited migration and invasion of HCC cells in vitro and lung metastasis of HCC cells in vivo . Blocking the Rac1 signaling by expression of Rac1-T17N or knockdown of Rac1 largely abolished GLS2’s function in inhibiting metastasis . These results demonstrate a novel and important role of GLS2 in suppressing cancer metastasis , and also reveal that GLS2 binding to Rac1-GDP to inhibit Rac1 activity is a critical underlying mechanism ( Figure 7G ) . GLS1 plays a critical role in promoting tumorigenesis through enhancing glutamine metabolism ( Gao et al . , 2009; Thangavelu et al . , 2012; Wang et al . , 2010 ) . It is unclear why GLS1 and GLS2 have contrasting roles in tumorigenesis , although they both function as the glutaminase enzymes . While the glutaminase core domains of GLS1 and GLS2 show high homology , their C-termini show relatively low homology . In this study , we found that GLS2 bound to Rac1 through its C-terminus and inhibited the Rac1 activity to suppress migration and invasion of HCC cells . This effect requires the C-terminus of GLS2 and is independent of its glutaminase activity . In contrast , GLS1 does not bind to Rac1 or inhibit Rac1 activity . Considering the critical role of Rac1 in cancer , our results provide a novel mechanism for the different roles of GLS1 and GLS2 in tumorigenesis , particularly with respect to cancer metastasis . In addition to Rac1 , GLS2 may interact with other proteins to regulate their functions , which in turn contributes to GLS2’s function in tumor suppression . Future studies should shed light on the further mechanisms of GLS2 in tumor suppression . p53 plays a critical role in suppressing cancer metastasis . While extensive work has been done on the mechanisms underlying p53-mediated apoptosis , cell cycle arrest and senescence , the mechanism underlying p53’s function in suppressing cancer metastasis is much less well understood ( Muller et al . , 2011; Vousden and Prives , 2009 ) . p53 has been reported to inhibit Rac1 activity , however , the detailed mechanism is unclear ( Bosco et al . , 2010; Guo and Zheng , 2004; Muller et al . , 2011 ) . Our results show that p53 knockdown down-regulated GLS2 levels and promoted the interaction of Rac1-GDP with its GEFs Tiam1 and VAV1 , and thereby enhanced the Rac1 activity and promoted cancer metastasis . These results demonstrate that GLS2 is a novel and critical mediator for p53 in suppressing metastasis , and also reveal a novel mechanism by which p53 inhibits Rac1 . HCC is one of the most common types of cancer and the third leading cause of cancer death worldwide ( El-Serag and Rudolph , 2007; Jemal et al . , 2011 ) . The high malignancy and poor prognosis of HCC has been related with the high metastatic characteristic of HCC; however , the mechanism underlying HCC metastasis is not well understood ( El-Serag and Rudolph , 2007; Tang , 2001 ) . Considering that GLS2 is frequently down-regulated in HCC ( Hu et al . , 2010; Liu et al . , 2014a; Suzuki et al . , 2010; Xiang et al . , 2015 ) , our findings that GLS2 inhibits HCC metastasis and loss of GLS2 promotes HCC metastasis provide a novel mechanism contributing to high metastatic characteristic of HCC . These results strongly suggest that the GLS2/Rac1 signaling could be a potential target for therapy in cancer , particularly in HCC . In summary , our results demonstrate that GLS2 is a novel negative regulator of Rac1 , and plays a novel and critical role in suppression of metastasis through its negative regulation of the Rac1 activity . Furthermore , our results also reveal that GLS2 is a critical mediator for p53 in suppression of cancer metastasis .
HepG2 ( p53-WT ) and Hep3B ( p53-null ) cells were obtained from American Type Culture Collection ( ATCC , Manassas , VA ) . Huh-1 ( p53-WT ) and Huh-7 ( p53 mutant ) were obtained from the Japanese Culture Collection ( RIKEN BioResource Center , Saitama , Japan ) . All cell lines were authenticated by short tandem repeat profiling . Cells were regularly tested for mycoplasma using Lookout Mycoplasma PCR detection kit ( MP0035 , Sigma , St . Louis , MO ) and only used when negative . The WT p53 were knocked down in HepG2 and Huh-1 cells by 2 different shRNA vectors as previously described ( Zhang et al . , 2013 ) . The pLPCX vectors expressing Flag-tagged WT or deletion mutants of GLS2 were constructed by PCR amplification as we previously described ( Hu et al . , 2010 ) . The pLPCX-Myc-Rac1 and pLPCX-Myc-Rac1-G12V vectors were constructed by using Myc-Rac1 DNA fragment from pcDNA3 . 1-Myc-Rac1 WT and pcDNA3 . 1-Myc-Rac1 G12V vectors , respectively ( He et al . , 2010 ) . The pLPCX-Myc-Rac1-T17N vector was constructed by using a Quikchange II XL Site-Directed Mutagenesis Kit ( Stratagene/Agilent Technologies , San Diego , CA ) . The pLPCX vectors expressing deletion mutants of Myc-Rac1 were constructed by PCR amplification . The pLPCX-GLS1-Flag , pLPCX-Tiam1-HA and pLPCX-VAV1-HA vectors were cloned using PCR amplification . Two lentiviral shRNA vectors against GLS2 ( ID: V3LHS_307701 and V2LHS_71048 ) , two lentiviral shRNA vectors against Rac1 ( ID: V3LHS_317664 and V3LHS_317668 ) and control shRNA vectors were obtained from Open Biosystems ( Huntsville , AL ) . The trans-well system ( 24 wells , 8 μM pore size , BD Biosciences , Franklin Lakes , NJ ) was employed for cell migration and invasion assays as we previously described ( Zheng et al . , 2013; Zhao et al . , 2015 ) . In brief , cells ( 2 × 104 for Huh-1 , and 6 × 104 for HepG2 ) in serum-free medium were seeded into upper chambers for migration assays . Cells on the lower surface were fixed , stained and counted at 24 hr after seeding . For invasion assays , cells ( 4 × 104 for Huh-1 , and 1 × 105 for HepG2 ) were seeded into upper chambers coated with matrigel ( BD Biosciences ) . Cells on the lower surface were fixed , stained and counted at 36 hr after seeding . In vivo lung metastasis assays were performed as previously described ( Li et al . , 2014; Zheng et al . , 2013 ) . In brief , Huh-1 and HepG2 cells ( 2 × 106 in 0 . 1 mL phosphate-buffered saline ) transduced with lentiviral vectors expressing luciferase were injected into 2-month-old male BALB/c nude mice via the tail vein ( n=8 mice/group ) . Lung metastatic colonization was monitored and quantified at different weeks using non-invasion bioluminescence imaging by IVIS Spectrum in vivo imaging system ( PerkinElmer , Waltham , MA ) , and was validated at the endpoint by routine histopathological analysis . All mouse experiments were approved by the University Institutional Animal Care and Use Committee . Standard western blot assays were used to analyze protein expression in cells . The following antibodies were used for assays: anti-Flag-M2 ( F1804 , Sigma; 1:20 , 000 dilution ) , anti-β-Actin ( A5441 , Sigma; 1:10 , 000 dilution ) , anti-Myc ( 9E10 , Roche , Indianapolis , IN; 1:1000 dilution ) , anti-HA ( 3F10 , Roche; 1:1000 dilution ) , anti-Rac1 ( 23A8 , Millipore , Billerica , MA; 1:5000 dilution ) , anti-p-PAK ( Ser199/204 ) ( 09–258 , Millipore; 1:1000 dilution ) , anti-PAK ( 07–1451 , Millipore; 1: 1000 dilution ) , anti-p53 ( FL393 , Santa Cruz , Dallas , TX; 1:2000 dilution ) , anti-Tiam1 ( sc-872 , Santa Cruz; 1:2000 dilution ) , anti-VAV1 ( sc-8039 , Santa Cruz; 1:1000 dilution ) . The anti-GLS2 antibody ( 1: 1000 dilution ) was prepared as previously described ( Hu et al . , 2010 ) . To increase the sensitivity of the GLS2 antibody , endogenous GLS2 in cells was pulled down by IP and detected by western blot assays . The band intensity was quantified by digitalization of the X-ray film and analyzed with the ImageJ software . Co-IP assays were performed as we previously described ( Liu et al . , 2014b ) . For co-IP of GLS2-Flag and Myc-Rac1 proteins , anti-Flag ( M2 , Sigma ) and anti-Myc ( 9E10 , Roche ) agarose beads were used to pull down GLS2-Flag and Myc-Rac1 , respectively . For Co-IP of endogenous GLS2 and Rac1 , the anti-GLS2 antibody and the anti-Rac1 ( 23A8 , Millipore ) antibody were used for IP , respectively . The mouse or rabbit purified IgGs were used as negative controls . To determine potential GLS2 binding proteins , GLS2-Flag protein in Huh-1 cells with stable expression of GLS2-Flag was pulled down by co-IP using anti-Flag ( M2 ) beads and eluted with Flag peptide . Huh-1 cells transduced with control vectors were used as a control for co-IP assays . Eluted materials were separated in a 4–16% sodium dodecyl sulfate polyacrylamide gel electrophoresis and visualized by silver staining using the silver staining kit ( Invitrogen , Carlsbad , CA ) . LC-MS/MS analysis was performed at the Biological Mass Spectrometry facility of Rutgers University as previously described ( Yue et al . , 2015; Zhao et al . , 2015 ) . For Rac1 activity analysis , the GST-p21-binding domain of PAK1 pull-down assays were performed using a Rac1 activation assay kit ( Millipore ) to measure the levels of GTP-bound Rac1 ( Rac1-GTP ) in cells as previously described ( Galic et al . , 2014; Hayashi-Takagi et al . , 2010; Palacios et al . , 2002 ) . The p21-binding domain of the Rac1 effector protein PAK1 binds specifically to the Rac1-GTP ( Hayashi-Takagi et al . , 2010; Palacios et al . , 2002 ) . The levels of precipitated Rac1-GTP were measured by western blot assays using a Rac1 antibody ( 23A8 , Millipore ) and normalized to total Rac1 levels in cells measured by western-blot assays . Total RNA was prepared with the RNeasy Kit ( Qiagen , Hilden , Germany ) . Complementary DNA was prepared using a TaqMan reverse transcription kit , and real-time PCR was performed with TaqMan PCR mixture ( Applied Biosystems , Foster City , CA ) as we previously described ( Hu et al . , 2010; Zhang et al . , 2013 ) . The expression of genes in cells was normalized to the expression of the Actin gene . Glutaminase activity was measured as previously described ( Gao et al . , 2009; Wang et al . , 2010 ) . Briefly , cell lysates were incubated at 37°C for 10 min with the assay mix consisting of 20 mM glutamine , 50 mM Tris-acetate ( pH 8 . 6 ) , 100 mM phosphate , and 0 . 2 mM ethylenediaminetetraacetic acid . The reaction was quenched with the addition of 2 mL of 3 M HCl . Subsequently , the reaction mixture was incubated for 30 min at room temperature with the second assay mix ( 2 . 2 U glutamate dehydrogenase , 80 mM Tris-acetate ( pH 9 . 4 ) , 200 mM hydrazine , 0 . 25 mM ADP , and 2 mM nicotinamide adenine dinucleotide ) . The absorbance was read at 340 nm using a spectrophotometer . The differences in tumor growth among groups were analyzed for statistical significance by analysis of variance , followed by Student’s t-tests using GraphPad Prism software . All other P-values were obtained using two-tailed Student t-tests . **p<0 . 001; *p<0 . 01; #p<0 . 05 . | Healthy cells in the body derive most of their energy from a sugar called glucose . However , cancer cells grow and divide much more rapidly than normal cells and so require larger amounts of energy to sustain themselves . Therefore , many cancer cells can alter their metabolism so that they can obtain more energy from a molecule called glutamine or other alternative sources . Cancer cells obtain glutamine from the blood and use an enzyme called glutaminase to convert it into another type of molecule . Human cells produce two forms of glutaminase called GLS1 and GLS2 . Even though both enzymes share many common features , they have different effects on cancer cells . GLS1 promotes tumor formation , while GLS2 has the opposite effect . However , it is not clear why these enzymes behave so differently . Zhang , Liu et al . now investigate how GLS2 suppresses the progression of tumors . The experiments show that GLS2 , but not GLS1 , can directly bind to a protein called Rac1 that normally promotes the spread of tumor cells around the body . GLS1 inhibits the activity of Rac1 , but this happens independently of the enzyme’s glutaminase activity . Zhang , Liu et al . altered the levels of GLS2 in liver cancer cells and then injected these cells into mice . Cells that had low levels of GLS2 were able to spread and form tumors in distant sites like the lung . In contrast , smaller and fewer lung tumors were observed in mice that had been injected with cells that produced high levels of GLS2 . Zhang , Liu et al . ’s findings reveal a new role for GLS2 that may help to explain why it affects tumor progression differently from GLS1 . Further work is now needed to explore whether targeting Rac1 could be a potential therapy for cancers that have lost GLS2 . | [
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Successful treatment decisions in cancer depend on the accurate assessment of patient risk . To improve our understanding of the molecular alterations that underlie deadly malignancies , we analyzed the genomic profiles of 17 , 879 tumors from patients with known outcomes . We find that mutations in almost all cancer driver genes contain remarkably little information on patient prognosis . However , CNAs in these same driver genes harbor significant prognostic power . Focal CNAs are associated with worse outcomes than broad alterations , and CNAs in many driver genes remain prognostic when controlling for stage , grade , TP53 status , and total aneuploidy . By performing a meta-analysis across independent patient cohorts , we identify robust prognostic biomarkers in specific cancer types , and we demonstrate that a subset of these alterations also confer specific therapeutic vulnerabilities . In total , our analysis establishes a comprehensive resource for cancer biomarker identification and underscores the importance of gene copy number profiling in assessing clinical risk .
To determine the differences between benign and fatal tumors , we first analyzed multiple classes of genomic data from 9442 patients with 16 types of cancer from the TCGA ( outlined in Figure 1—figure supplement 1A; abbreviations are defined in Figure 1—figure supplement 1B ) . For every tumor type and every dataset , we generated Cox univariate proportional hazards models linking the presence or expression of a particular feature with clinical outcome ( described in Supplemental Text 1 ) . We report the Z score for each model , which encodes both the directionality and significance of a particular association . If the presence of a mutation or copy number amplification is significantly associated with patient death , then a Z score >1 . 96 corresponds to a P value < 0 . 05 ( Figure 1—figure supplement 2A–C ) . In contrast , a Z score less than −1 . 96 indicates that the presence of a mutation is associated with survival or that a gene deletion is significantly associated with patient death . We extracted mutation , copy number , gene expression , and clinical information from 16 TCGA cohorts ( summarized in Supplementary file 1 and discussed in additional detail in Supplemental Text 2 ) . To assess the validity of our data analysis pipeline , as well as the accuracy of the reported patient annotations , we first examined the overall survival curves for the 16 tumor types that we profiled . As expected , we observed significant differences in clinical outcome according to a cancer’s tissue-of-origin ( Figure 1—figure supplement 2D ) . Prostate cancer had the least aggressive clinical course , with a median survival time that was not reached in this dataset ( >4600 days ) , while pancreatic cancer conferred the worst prognosis ( median survival time: 444 days ) . Overall , the 5 year survival frequencies of patients in the TCGA were highly similar to the national averages reported by NCI-SEER ( R = 0 . 83 , p < 0 . 0001 ) , suggesting that the patients included in this analysis are broadly representative of the general population ( Figure 1—figure supplement 2E ) . Next , we inferred patient sex on the basis of chromosome-specific gene expression patterns ( Gentles et al . , 2015; van den Berge and Sijen , 2017 ) . Our analysis exhibited >99% concordance with patients’ self-reported sex , further verifying the overall accuracy of the clinical annotations and our data processing pipeline ( Figure 1—figure supplement 2F ) . We first set out to discover whether coding mutations in cancer genomes were associated with patient outcome . We extracted non-silent mutations in each tumor , and then we identified all genes that were mutated in ≥2% of patients in each of the 16 cohorts ( discussed in Supplemental Text 1 ) . We next performed Cox proportional hazards analysis to compare the survival times for patients harboring mutant or wild-type copies of each gene . This analysis uncovered very few mutations that were significantly associated with patient outcome ( Figure 1 and Supplementary file 2A-B ) . We first focused on known oncogenes and tumor suppressors , and found that among the 30 most-frequently mutated cancer driver genes , only two ( EGFR and TP53 ) were associated with prognosis in more than two tumor types ( Figure 1C ) . TP53 mutations were linked to outcome in five of 16 cancer types , though the differences in patient survival were generally small ( Figure 1—figure supplement 3A–B ) . In contrast , many other cancer driver genes were not associated with survival time in any tumor type . While mutations in KRAS , PIK3CA , CDKN2A , BRAF , KMT2D , ATM , SMAD4 , and many other genes were frequently observed , they were never significantly linked with patient outcome ( Figure 1C ) . We next considered the possibility that mutations in specific codons could have prognostic significance not captured when all mutations in a gene are pooled together . To test this , we identified the 30 most-frequently mutated amino acid positions in the TCGA cohorts , and then asked whether patients harboring these alterations had different outcomes than those who did not . IDH1c132 mutations were significantly associated with a favorable prognosis in glioma , but other recurrently-mutated codons ( KRASc12 , PIK3CAc1047 , TP53c273 , etc . ) were largely uninformative ( Figure 1—figure supplement 4A–D ) . Then , we identified ‘hotspot’ residues that were mutated in at least five different patients across all cohorts . Considering only these ‘hotspot’ mutations in each gene also failed to uncover robust survival associations ( Figure 1—figure supplement 4E ) . Finally , we identified cancer type-specific recurrent mutations , but these alterations ( FGFR3c249 in BLCA , CTNNB1c37 in UCEC , etc . ) were similarly uninformative ( Figure 1—figure supplement 4F ) . Next , we sought to test whether the use of targeted therapies had blunted the deleterious effects of certain driver mutations ( e . g . , in BRAF or EGFR ) . However , due to the time-frame of sample collection , very few patients were treated with BRAF or EGFR inhibitors , and removing those patients who had received these therapies failed to significantly affect Z scores ( Figure 1—figure supplement 5A ) . Hyper-mutation within a subset of cancers could increase mutational ‘noise’ and decrease our ability to identify prognostic signatures , but excluding patients with hyper-mutated tumors had minimal effect on the prognostic significance of driver gene mutations ( Figure 1—figure supplement 5B and Supplementary file 2C ) . We then asked whether the presence of mutations in multiple cancer driver genes might cooperate to confer a worse clinical outcome . We found that , in general , patients harboring mutations in two cancer driver genes that were not prognostic alone had the same risk of death as patients with wild-type copies of one or both genes ( Figure 1—figure supplement 5C ) . Lastly , we considered the possibility that the clonality of a mutation might affect its prognostic significance . We calculated the variant allele frequency ( VAF ) for each cancer mutation and tested whether mutations present at clonal levels in single tumors were more likely to be associated with outcome . We found that restricting our analysis to mutations with high VAFs failed to identify more prognostic genes , indicating that patient stratification is unlikely to be improved by assessing only clonal mutations ( Figure 1—figure supplement 6 ) . These analyses suggested that , in general , cancer driver gene mutations lacked significant patient stratification power . This led us to investigate whether mutations in genes other than recurrently-mutated oncogenes and tumor suppressors could affect prognosis . We therefore expanded our analysis to include all genes mutated in ≥2% of patients with a particular tumor type . To account for greatly expanding the number of genes tested , we applied a Benjamini-Hochberg correction with a 5% false-discovery rate to the individual Z scores that we obtained . We uncovered several genes that were linked with prognosis in glioma , but found very few genes significantly associated with death or survival in the other 15 cancer types ( Figure 1D and Supplementary file 2A ) . For instance , in breast cancer and lung adenocarcinoma , 128 and 3996 genes were mutated in ≥2% of patients , respectively , but none of these mutations were significantly correlated with patient outcome at a 5% FDR . In total , these results indicate that most mutations in cancer genomes lack significant prognostic power . In our above analysis , we noted that the five genes with the strongest survival associations were all observed in the GBMLGG ( pan-glioma ) cohort . As glioma appeared to be an exception to our overall finding that mutations are seldom prognostic , we investigated this cohort further . Among the top-scoring genes , we found that PTEN and EGFR mutations conferred dismal prognosis , while mutations in IDH1 , TP53 , and ATRX were associated with favorable prognosis ( Figure 1—figure supplement 7A ) . Mutations in these genes have previously been linked to distinct glioma subtypes ( Ceccarelli et al . , 2016; Kannan et al . , 2012; Suzuki et al . , 2015 ) , and we verified that mutations in IDH1 , TP53 , and ATRX were most frequently observed in low-grade gliomas , while mutations in PTEN and EGFR were most frequently observed in high-grade glioblastomas ( Figure 1—figure supplement 7B ) . However , when we analyzed low-grade gliomas and glioblastomas separately , several of these alterations remained prognostic ( Supplementary file 2D ) . For instance , while IDH1 mutations were more common in low-grade gliomas , they were occasionally observed in high-grade tumors as well , and they were independently associated with prolonged survival in both cohorts ( Figure 1—figure supplement 7C ) . In contrast , when EGFR mutations were observed in low-grade gliomas , they were associated poor outcomes , but EGFR mutations were non-prognostic in high-grade glioblastomas ( Figure 1—figure supplement 7D ) . Thus , in gliomas , mutations contain both subtype-dependent and subtype-independent prognostic information . However , outside of this cancer type and the tumor suppressor TP53 , mutations in most cancer driver genes are non-prognostic . As mutations were largely uninformative , we next set out to determine whether gene copy number conveyed prognostic information . We determined the copy number of each gene at its transcriptional start site and regressed this value against patient outcome in each tumor cohort . We then examined the clinical impact of CNAs affecting the same 30 cancer driver genes that we previously investigated . Surprisingly , we found that the copy number of these oncogenes and tumor suppressors was frequently linked with patient outcome ( Figure 2 and Supplementary file 3A-B ) . Amplification of EGFR , PIK3CA , and BRAF , and deletion of CDKN2A , RB1 and EP300 were strongly associated with shorter patient survival times in four or more cancer types each . Copy number was prognostic even for genes in which mutations were not linked with outcome: for instance , while mutations in PIK3CA were never informative , the copy number of PIK3CA was associated with outcome in breast , colorectal , glioma , lung-squamous , pancreas , and prostate cancers ( Figure 2B and D ) . Overall , among the 30 most frequently-mutated cancer driver genes , we detected 108 significant associations between gene copy number and outcome , compared to 23 associations between mutation and outcome . For 28 out of 30 driver genes , DNA copy number was prognostic in more cancer types than mutational status was . We conclude that determining the copy number of oncogenes and tumor suppressors in a primary tumor can better stratify patient risk than assessing single base-pair mutations . In our analysis thus far , we have treated mutations as a binary variable ( ‘mutant’ vs . ‘not mutant’ ) , while copy number alterations are treated as continuous values . Thus , the greater prognostic significance of tumor CNAs could reflect the fact that individual CNA measurements inherently harbor more information . To test this possibility , we trichotomized CNA values into ‘deletions’ ( <−0 . 3 ) , ‘amplifications’ ( >0 . 3 ) , and ‘copy-neutral’ ( ≥−0 . 3 and≤0 . 3 ) . We then calculated Cox regressions at the same 30 loci using the discretized copy number values . This analysis resulted in 94 significant survival associations , more than four times as many significant features as when mutations were analyzed , and comparable to the number of significant features that resulted using continuous CNA values ( Figure 2—figure supplement 1 ) . This analysis suggests that the greater prognostic significance of CNAs is not simply a consequence of the continuous nature of copy number data . We next investigated whether these oncogene and tumor suppressor CNAs were likely to drive patient mortality , or whether they were passenger genes that changed in copy number along with other , unknown drivers . To assess this question , we combined Z scores from different cancer types using Stouffer’s method ( Stouffer , 1949 ) , and then plotted the pan-cancer meta-Z scores along every chromosome ( Figure 2C ) . This analysis revealed multiple sharp peaks and valleys in the data that overlapped with known driver mutations . The most significant survival-associated copy number changes genome-wide were found on chromosome 9p in a valley that precisely included the tumor suppressor CDKN2A . Z score peaks were found at loci that include oncogenes PIK3CA , EGFR , MYC , CCNE1 , and others . This overlap suggests that , in many instances , the copy number of these oncogenes and tumor suppressors directly influence the risk of cancer patient death . Whole-chromosome aneuploidy has previously been linked to a decreased infiltration of immune cells ( Davoli et al . , 2017; Taylor et al . , 2018 ) . We therefore considered the possibility that CNAs are prognostic via an indirect mechanism; namely that they are found in tumors that lack robust immune infiltration , and this deficient immune response was itself driving patient mortality . However , multiple lines of evidence argue against this interpretation . First , we assessed the association between patient survival and three different measures of tumor sample purity: pathologist-assessed tumor cell fraction , sample purity as judged by ABSOLUTE ( Carter et al . , 2012; Taylor et al . , 2018 ) , and leukocyte infiltration , as judged by methylation analysis ( Taylor et al . , 2018 ) . We found that sample purity was inconsistently-associated with patient outcome ( Figure 2—figure supplement 2 ) . For instance , higher tumor purity determined by either pathological analysis or ABSOLUTE was associated with worse outcome in only one of 16 cohorts , each ( Figure 2—figure supplement 2A–B ) . The lack of a strong correlation between infiltrating cell populations and clinical prognosis suggests that analyte purity is insufficient to explain the relationship between CNAs and patient survival . Secondly , we generated multivariate Cox models that included gene copy number and these three measurements of tumor purity , and we found that driver gene CNAs remain broadly prognostic in these bivariate models ( Figure 2—figure supplement 2C and Supplementary file 3C-E ) . For instance , we discovered that amplification of Cyclin E1 is associated with poor prognosis in ovarian cancer , and this remained true even when our analysis was restricted to high-purity tumor samples and samples that lacked significant leukocyte presence ( Figure 2—figure supplement 2D ) . Thus , while the interrelationship between aneuploidy and immunological tolerance likely plays an important role in tumor development , this analysis suggests that it is not the primary driver of CNA-associated patient mortality . Pathological assessment of tumor stage and grade are important sources of prognostic information , though blinded assessments reveal significant inter-observer discordance ( Allsbrook et al . , 2001; Coons et al . , 1997; Elmore et al . , 2015; Gilks et al . , 2013 ) . We therefore tested whether the CNA biomarkers that we uncovered could affect the stratification conferred by these parameters . We found that Z scores generated from either univariate models or multivariate models that included stage or grade were highly correlated ( R = 0 . 91 and R = 0 . 96 respectively; Figure 2—figure supplement 3A and Supplementary file 4 ) . Overall , 71% of prognostic CNAs in individual cancer types remained prognostic in these multivariate models ( Figure 2—figure supplement 3B ) . Thus , including gene-level copy-number assessment can significantly improve the stratification of patient risk beyond standard clinical parameters ( Figure 2—figure supplement 3C–D ) . Certain mutations were similarly able to yield prognostic information in a stage- and grade-independent manner . However , due to the lower overall significance of most mutations that we identified , the improvements in patient stratification were generally more modest ( Figure 2—figure supplement 3E and Supplementary file 4E ) . Gene-level copy number values also remained prognostic when separating TCGA cohorts by cancer subtype ( Figure 2—figure supplement 4 and Supplementary file 4F-G ) . For instance , CNA Z scores values were highly correlated between the bulk GBMLGG cohort and the individual GBM and LGG subtypes ( R = 0 . 68 and R = 0 . 86 , respectively; Figure 2—figure supplement 4A ) . While analyzing the GBM cohort separately abolished the prognostic significance of EGFR mutations ( Figure 2—figure supplement 4D ) , EGFR amplifications remained associated with outcome in both the LGG and GBM cohorts ( Figure 2—figure supplement 4B–C ) . Amplifications in MYC and PIK3CA were similarly prognostic in multiple tumor subtypes ( Figure 2—figure supplement 4D–E and Supplementary file 4G ) . At other loci , low patient numbers from certain subtypes may obscure the detection of specific biomarkers . For instance , within the KIPAN cohort , 67% of tumors are clear cell carcinomas , 23% of tumors are papillary cell carcinomas , and 10% of tumors are chromophobe carcinomas . CDKN2A deletion is a strong indicator of poor prognosis in the pan-kidney cohort , in clear cell carcinomas , and in papillary cell carcinomas , but did not reach statistical significance when kidney chromophobe carcinomas were analyzed independently ( Figure 2—figure supplement 4F–G ) . In total , these results underscore the ability of driver gene CNAs to improve patient stratification when controlling for tumor identity , though larger cohort numbers may be needed to identify the strongest biomarkers in rare cancer subtypes . Highly-aneuploid tumors tend to harbor mutations in TP53 , and both TP53 mutations and arm-length aneuploidy have previously been associated with poor clinical outcomes ( Davoli et al . , 2017; Petitjean et al . , 2007 ) . Using an ‘aneuploidy score’ for each tumor based on the total number of arm-length alterations ( Taylor et al . , 2018 ) , we verified that TP53-mutant tumors exhibit more aneuploidy than TP53-wild-type tumors ( Figure 3—figure supplement 1A ) , and that total aneuploidy is a poor prognosis factor in several cancer types ( Figure 3—figure supplement 1C ) . To investigate the relationship between gene-level prognostic CNAs , TP53 status , and arm-length aneuploidy , we selected a set of 40 prognostic amplifications and deletions for additional analysis ( Figure 3—figure supplement 2A ) . In multivariate models that included TP53 mutation status , 33 of 40 ( 83% ) gene-level CNAs remained prognostic , demonstrating that these CNAs are not linked with death due to an indirect association with TP53 status ( Figure 3—figure supplement 2A–B ) . Similarly , in multivariate models that included total tumor aneuploidy , 80% of these CNAs were still associated with outcome ( Figure 3—figure supplement 2C–D ) . Finally , as a proxy for the total structural alteration burden , we summed the number of breakpoints ( as indicated by discrete copy number values along a chromosome ) in each tumor ( Figure 3—figure supplement 1B ) . This metric was associated with outcome in multiple tumor types ( Figure 3—figure supplement 1C ) , but 75% of driver gene CNAs remained prognostic in multivariate models that included this score ( Figure 3—figure supplement 2E–F ) . These results indicate that assessing gene-level tumor CNAs can yield more prognostic information than simply screening for TP53 mutations or measuring bulk levels of tumor aneuploidy ( Supplementary file 5 ) . We next set out to determine whether focal copy number alterations and broad copy number alterations could have distinct effects on patient outcome . To investigate this possibility , we compared the prognostic power of focal CNAs ( defined as an alteration ≤3 Mb in length; Krijgsman et al . , 2014 ) and broad CNAs ( defined as all alterations >3 Mb in length ) . Among loci at which both broad and focal alterations were observed , we frequently found that broad CNAs were associated with moderately worse outcomes , while focal CNAs were associated with sharp declines in survival ( Figure 3A–B ) . At some loci , broad CNAs had outcomes that were indistinguishable from copy-neutral tumors , while only focal CNAs were associated with death ( Figure 3C ) . We rarely detected instances in which broad CNAs indicated a worse prognosis than a focal alteration ( Figure 3A ) . We interpret these results as a reflection of aneuploidy-induced fitness penalties ( Sheltzer et al . , 2017; Sheltzer and Amon , 2011 ) : large copy number alterations change the dosage of multiple genes at once and can impair tumor growth , while targeted alterations that specifically affect driver gene copy number maximize malignant potential . Gene copy number alterations typically result in a proportional change in the expression of the affected loci ( Pollack et al . , 2002; Stingele et al . , 2012; Williams et al . , 2008 ) , though instances of dosage compensation have been reported ( Gonçalves et al . , 2017 ) . To test the effects of prognostic CNAs on gene expression , we compared transcript levels and gene copy number changes at 40 prognostic loci and found a significant correlation between the two at 98% of the analyzed genes ( Figure 3—figure supplement 3 ) . Next , we sought to uncover whether these copy number alterations were deadly because they increased or decreased the expression of mutant gene products . That is , we could observe that the amplification of a driver gene is prognostic only in tumors in which that driver gene is also mutated . Interestingly , this is not the case: at 95% of our test loci , gene copy number remained prognostic in multivariate models that also included gene mutation status ( Figure 3D ) . For instance , in colorectal cancer , amplification of EGFR was associated with death even in tumors that lacked EGFR mutations ( Figure 3E ) . In total , these results indicate that even at recurrently-mutated loci , changes in the expression of the wild-type gene can have a profound effect on cancer cell behavior . Together with our observation that focal changes tend to confer a worse prognosis than broad changes , these results support the recently-proposed ‘cancer gene island’ model of tumor genome evolution ( discussed in more detail below; Solimini et al . , 2012 ) . To determine the generality of our findings , we collected independent patient cohorts harboring mutation or copy number data linked to survival outcome ( Supplementary file 1 ) . We then performed univariate Cox proportional hazards analysis on these ‘validation’ cohorts and compared the results to the Z scores obtained from our ‘discovery’ set of TCGA data . First , we identified prognostic mutations within a set of 16 patient cohorts from the International Cancer Genome Consortium ( ICGC ) , comprising 3054 patients analyzed by whole-genome or whole-exome sequencing . Overall , the mutation frequencies and the Z scores of recurrent mutations were highly similar between the ICGC and TCGA cohorts ( R = 0 . 67 , p < 0 . 0001 , and R = 0 . 56 , p < 0 . 0001 , respectively; Figure 4A–B ) . Consistent with our TCGA analysis , mutations in TP53 were associated with outcome in more patient cohorts than any other gene ( Figure 4C and Figure 1—figure supplement 3C–D ) . Other mutations , including in known cancer driver genes , were rarely associated with outcome in individual cancer types and harbored minimal pan-cancer significance ( Figure 4D–F and Supplementary file 6 ) . Mutations in KRAS , PIK3CA , BRAF , APC , PTEN , CDKN2A , and many others were frequently observed but were never correlated with outcome ( Figure 4D ) . We next analyzed 2431 additional patients with CNA data curated by cBioportal , and found numerous amplifications and deletions associated with patient mortality ( Supplementary file 6C ) . In breast cancer , we found prognostic amplifications that were centered around oncogenes , including ERBB2 , MYC , and MDM2 , while prognostic deletions encompassed tumor suppressors CDKN2A , PTEN , and TP53 ( Figure 4G ) . Overall , we observed a highly significant correlation between the meta-Z scores obtained from the TCGA and cBioportal datasets ( R = 0 . 42; Figure 4H ) . Finally , in patient cohorts subjected to both mutation and copy number analysis , we verified that CNAs in driver genes commonly harbored greater prognostic significance than mutations in those same genes ( Figure 4I ) . For instance , in breast cancer , among 25 frequently-mutated genes , mutations in only two genes ( TP53 and GATA3 ) displayed prognostic significance , while CNAs in 12 of those same genes were associated with patient outcome ( Figure 4J ) . In total , these analyses suggest that the survival patterns discovered in the TCGA dataset are conserved across independent cohorts of cancer patients . In particular , while mutations in most cancer driver genes are non-prognostic , copy number alterations in these same genes are tightly linked with patient outcome . In order to discover the biomarkers with the greatest potential clinical relevance , we next identified the individual mutations and CNAs that were consistently associated with outcome across independent patient cohorts . To increase our ability to detect these genetic alterations , we performed survival analysis on an additional set of 2701 primary tumors subjected to targeted sequencing and copy number analysis ( MSKCC_2017; Supplementary file 7 ) ( Zehir et al . , 2017 ) , on 2431 patients from cBioportal cohorts whose tumors had been sequenced ( Supplementary file 6D ) , and on 628 patients from ICGC cohorts subjected to copy number analysis ( Supplementary file 6B ) . Our combined patient dataset therefore included two to six independent cohorts from each of 13 common cancer types , comprising 16 , 580 total patients . These cohorts were collected at different locations , in different patient populations , using different study designs , and the samples were analyzed using different genomic technologies . We reasoned that alterations that were consistently associated with outcome despite these significant differences would represent highly-penetrant biomarkers of patient prognosis . To identify such alterations , we screened for biomarkers that were associated with outcome ( |Z| > 1 . 96 ) in ≥2 independent cohorts , and that were highly significant ( |meta-Z| > 3 . 3 ) across all available cohorts . This approach revealed multiple high-confidence genetic biomarkers of patient outcome that , to our knowledge , were novel , including MDM4 amplifications in prostate cancer , NOTCH2 amplifications in melanoma , and 2q32 deletions in ovarian cancer ( Supplementary file 8 ) . These robust biomarkers allowed a striking stratification of patient risk , and top-scoring CNAs remained prognostic in multivariate models that included commonly-measured prognostic criteria ( Gleason score in prostate cancer , Hepatitis serology in liver cancer , etc . ; Figure 5—figure supplement 1 ) . Consistent with our single-cohort analyses , cross-cohort prognostic CNAs were significantly more common than prognostic mutations , and TP53 was the only gene whose mutation status was associated with outcome in more than one cancer type ( Figure 5—figure supplement 2A ) . We hypothesized that some genetic alterations that were sufficient to affect overall patient survival could impact other facets of cancer behavior as well , including , potentially , drug sensitivity . That is , biomarkers harboring significant prognostic information could potentially contain predictive information as well . We therefore sought to discover whether genetic alterations that drove aggressive disease could also sensitize patient tumors to specific therapeutic regimens . By analyzing a cohort of 1000 patient-derived xenografts ( PDXs ) , we identified several instances in which high-confidence biomarkers were associated with vulnerability to particular anti-cancer agents ( Gao et al . , 2015a ) . For instance , we identified Chr9 deletions that encompassed CDKN2A as a robust biomarker for poor prognosis in breast cancer ( Supplementary file 8 ) . We found that PDXs harboring CDKN2A deletions were profoundly sensitive to combination therapy with a CDK4/6 inhibitor and an mTOR inhibitor ( Figure 5—figure supplement 2B ) , consistent with the fact that a protein encoded by CDKN2A , p16 , functions as a natural inhibitor of CDK4/6 ( Serrano et al . , 1993 ) , p . 4 ) . In contrast , other biomarkers associated with poor prognosis in breast cancer failed to predict sensitivity to this treatment combination , but instead correlated with sensitivity to other agents ( Supplementary file 8 ) . Due to the limited number of drugs tested in PDXs , we expanded our target search to include a recently-described pharmacogenomic profile of cancer cell lines and discovered several additional biomarker vulnerabilities ( Figure 5A–B ) . For instance , we identified mutations in STAG2 as a high-confidence biomarker of poor prognosis in glioma , and we found that STAG2-mutant gliomas were exquisitely sensitive to treatment with the PARP inhibitor olaparib ( Figure 5A ) . In total , we identified highly-significant therapeutic vulnerabilities for 49% of the prognostic biomarkers uncovered by our integrated analysis , providing potential strategies to treat a subset of patients who have the most aggressive cancers .
Modern medicine has vastly prolonged the survival of individuals diagnosed with cancer ( Johnson et al . , 2017 ) . However , increasing evidence suggests that large subsets of patients receive sub-optimal care , and are over-treated or under-treated relative to their level of risk ( Bhatt and Klotz , 2016; Esserman et al . , 2013; Swaminathan and Swaminathan , 2015 ) . To date , many of the genetic alterations that differentiate fatal and benign tumors have remained obscure . Our analysis of prognostic biomarkers from 17 , 879 patients sheds light on these genetic differences , identifies a subset of patients who may benefit the most from aggressive intervention , and suggests therapeutic strategies for tumors harboring certain alterations associated with poor prognosis . A web portal to facilitate access to these results is available at http://survival . cshl . edu/ . As cancers arise due to the accumulation of mutations in growth-promoting oncogenes and growth-inhibitory tumor suppressors , the presence and diversity of these mutations may be expected to dictate a tumor’s clinical course . However , our data suggest that in many cases , they do not . Substantial disagreements exist in the literature on the value of mutation-based prognostic biomarkers , as the same driver oncogenes have been independently reported to be either adverse or non-significant prognostic features ( Guan et al . , 2013; Marabese et al . , 2015; Scoccianti et al . , 2012; Sun et al . , 2013 ) . In this manuscript , we performed an unbiased genome-wide analysis of public datasets with pre-established sample sizes . This approach may therefore bypass certain problems , including post-hoc hypothesis testing , patient-selection bias , and the ‘file-drawer problem’ , that can confound targeted biomarker studies ( Aronson , 2005; Ensor , 2014; Goossens et al . , 2015; Rosenthal , 1979; Scargle , 1999 ) . We consider it possible that , with larger sample sizes or more-specific tumor subtypes , additional prognostic mutations could be identified . Importantly , in most patient cohorts that we collected , tumors were analyzed on multiple genomic platforms , and CNAs were commonly prognostic in the same cohorts in which gene mutations were not . These results underscore our ability to successfully detect biomarkers in cohorts of these sizes , and suggest that , in a head-to-head comparison , copy number alterations provide more useful prognostic information than single-gene mutations . While we identified very few mutations associated with patient outcome , several lines of evidence underscore the potential benefits of continued clinical sequencing efforts . First , our analysis revealed a subset of mutations with tissue-specific prognostic power , including TP53 mutations in breast cancer , RB1 mutations in bladder cancer , and FBXW7 mutations in colorectal cancer . Secondly , most patients in the TCGA cohorts were treated with standard cytotoxic drugs . As targeted and immuno-therapies are increasingly adopted in the clinic , oncogenic mutations that were non-prognostic in the datasets analyzed here may be able to predict sensitivity to specific therapeutic agents ( Gagan and Van Allen , 2015 ) . Thirdly , tumors themselves are composed of sub-clonal populations that harbor distinct sets of mutations , and recent evidence suggests that cancer heterogeneity can influence clinical course ( Jamal-Hanjani et al . , 2017 ) . Thus , interrogating the mutational spectrum at the sub-clonal level may identify prognostic mutations not distinguished in bulk analyses . Though large-scale changes in tumor ploidy have previously been recognized as an indicator of poor outcome ( Friedlander et al . , 1984; Kallioniemi et al . , 1987; Kokal et al . , 1986; Merkel and McGuire , 1990; Zimmerman et al . , 1987 ) , the contributions of copy number alterations in most single genes have remained unexplored . Despite the limited stratification value of mutations in cancer driver genes , we found that copy number alterations of many of these same genes are broadly prognostic . Focal CNAs tended to confer a worse prognosis than broad CNAs , consistent with a model in which large-scale gene dosage imbalances trigger proteotoxic stress and impose a fitness penalty on cancer cells ( Santaguida and Amon , 2015; Sheltzer and Amon , 2011 ) . Moreover , while prognostic CNAs commonly caused proportional changes in target gene expression , most CNAs remained prognostic whether or not they affected the expression of a mutated gene . These results support a ‘cancer gene island’ or ‘cumulative aneuploidy’ model of tumorigenesis , in which cancers accumulate a series of limited copy number changes affecting haplo-sensitive and triplo-sensitive regions ( Davoli et al . , 2013; Solimini et al . , 2012 ) . Identifying the functional consequences of these prognostic CNAs on tumor physiology is a key future goal . Patients whose tumors harbor genetic alterations that drive mortality are in urgent need of improved treatment options . We discovered many instances in which high-confidence biomarkers of aggressive disease also sensitized tumors to specific anti-cancer therapies . By taking advantage of these vulnerabilities , a precision-medicine approach could be applied to both stratify patient risk and identify drug combinations most likely to provide a clinical benefit . Several predicted sensitivities from our work have clinical or mechanistic support , including the use of CDK4/6 inhibitors to treat CDKN2A-deleted tumors , the use of PARP inhibitors to treat STAG2-mutant tumors , and the use of SYK inhibitors to treat RB1-mutant tumors ( Bailey et al . , 2014; Gao et al . , 2015b; Zhang et al . , 2012 ) . Treatment with targeted agents significantly alters the cellular epigenetic and genetic landscape , often culminating in the development of resistance to the applied therapies ( Holohan et al . , 2013 ) . We speculate that secondary alterations that tumors evolve to tolerate these drugs could also alter or blunt the aggressive phenotype caused by the original driver alteration . In this way , targeting a biomarker that confers poor prognosis could both directly lead to improved patient outcomes by triggering a robust clinical response , and indirectly help patients by forcing tumor evolution away from dependence on a driver of aggressive disease .
Patient cohorts analyzed in this study are listed in Supplementary file 1 . For the TCGA analysis , pre-processed files from the Broad Institute TCGA Firehose were used ( https://gdac . broadinstitute . org/ ) . For the TCGA genomic copy number analysis , we used the HG19 segmented SCNAs , corrected for germline SCNAs . Overall survival time was used as a clinical endpoint for all cancer types except PRAD . Overall survival was chosen because it reflects an objective and unambiguous event , it is the gold-standard for oncology clinical trials , and it is widely-available across different studies ( Driscoll and Rixe , 2009 ) . However , as fewer than 2% of the patients in the PRAD cohort died during the follow-up period , ‘days to biochemical recurrence’ was used as a surrogate endpoint . For all cancers , survival or follow-up time from diagnosis were corrected for the days to sample procurement . Primary tumors ( indicated with a ‘01’ in the patient barcode ) were used for every cancer type except SKCM; for this cancer , few primary samples were available , so metastatic samples ( indicated with a ‘06’ in the barcode ) were included for patients in which no primary tumor was available . For additional discussion of the TCGA samples , see Supplemental Text 2 . Pathology-assessed tumor cell fraction was obtained from the TCGA clinical files under ‘Percent_tumor_cells’ . Tumor stage and grade were similarly obtained from the appropriate TCGA clinical files . Mutation , copy number , and clinical data from Release 25 of the International Genome Consortium were downloaded from the ICGC Data Portal ( Zhang et al . , 2011 ) . Overall survival was used as a clinical endpoint for all cohorts except EOPC-DE; due to the few deaths in this cohort , recurrence-free survival was used as an endpoint . Cohorts were chosen based on the availability of WGS or WES data , and were included if they came from a cancer type comparable to the types that were studied in our TCGA analysis . Copy number , mutation , and clinical data from cBioportal were downloaded as pre-processed files from www . cbioportal . org ( Gao et al . , 2013 ) . For the patients described in Zehir et al . ( 2017 ) ( the cBioportal/MSKCC_2017 cohorts ) , only primary tumors were included for all cancer types except melanoma . All processing and analysis was performed using Python . Cox proportional hazard analysis used the R survival package ( https://cran . r-project . org/web/packages/survival/index . html ) to compute Z scores and p values . Justification and further explanation for the use of Cox proportional hazards modeling can be found in Supplemental Text 1 . The rpy2 project was used to control R from python , allowing seamless integration of Z score calculations with data processing and pan-cancer analysis . Pandas DataFrames were used as the primary structure for storing and manipulating data . Additionally , native numpy methods and arrays were for used occasionally for efficiently storing strictly numerical data , for example , as input to Cox proportional hazards models . The statsmodels package ( www . statsmodels . org ) was used for false discovery correction using the Benjamini-Hochberg procedure . Microsoft Excel was occasionally used for final data processing and examination , so a single apostrophe was added before gene names in intermediate data processing steps to protect genes from auto-formatting ( Zeeberg et al . , 2004 ) . Code was structured to allow ease of internal reuse and reproducibility of results . Cox univariate proportional hazards , Cox multivariate proportional hazards , Kaplan-Meier , and Stouffers analysis methods were factored into an analysis library , taking as input the data required to perform the computation as numpy arrays or pandas DataFrames . In addition to the code for statistical analyses , code for processing TCGA clinical files was factored into a common library . This approach allowed the same TCGA clinical file processing code to be executed across a variety of platform analyses , ensuring identical behavior for each platform . The TCGA clinical processing code selected the relevant clinical endpoints and sample procurement data . The processing translated the available clinical data into the required format for Cox proportional hazard models: an endpoint/survival time value and a censor value for each patient . Code to select tumor samples based on cancer type was also included in this library . Raw input data for the mutation analysis needed additional preprocessing before Cox proportional hazard models could be constructed . This preprocessing included removing per-patient headers throughout the data and some data transposition . For all analyses using TCGA mutation data , mutations annotated as silent were excluded . Genes were only included in downstream analyses if they were mutated in 2% or more of the patients in a cancer type cohort . Raw input data for copy number analysis also required substantial preprocessing . Copy number input data consists of per-patient , per-chromosome location maps of copy numbers ( hg19 downloaded from the UCSC Genome Browser; Tyner et al . , 2017 ) . These maps were converted to a single copy number value for each gene . We created an interval tree ( using the intervaltree python package , https://pypi . python . org/pypi/intervaltree ) of the location maps for each chromosome and used the appropriate HGNC to convert chromosome locations to genes for each patient . We used the gene’s transcriptional start site position to look up in the interval tree the copy number value for a gene . This analysis produced an intermediate file of a similar form to the other TGCA platforms , which allowed for straightforward Cox analysis . Note that Cox proportional hazards models are a threshold-independent method of performing survival analysis , and so no minimum or maximum threshold for a copy number alteration was specified . A tumor was defined as having a focal amplification or deletion if its copy number was greater than 0 . 3 or less than −0 . 3 , and the chromosomal interval with a copy number greater than 80% of the copy number at the gene of interest was less than or equal to 3 Mb ( Krijgsman et al . , 2014 ) . To calculate the number of structural alterations per tumor , the number of distinct copy number values per chromosome in the DNA segmentation file was summed for each patient . For each platform and analysis type , we performed a pan-cancer analysis . This analysis created a single Z score for each gene by combining the per gene Z scores from each cancer type using Stouffer’s method . To perform Stouffer’s method , we took the sum of the Z scores for a single gene and divided that sum by the square root of the number of cancer types with Z scores for the gene ( Stouffer , 1949 ) . This meta-Z score was then compared against meta-Z scores obtained similarly from other platform analyses . We performed several additional analyses on mutation data , including double mutation combination Z scores , hotspot codon Z scores , and Z scores corrected for VAFs . For double mutation Z scores , we took the top 30 most common cancer driver genes and performed pairwise combinations . We then calculated Cox proportional hazards for each pair of genes , where a patient was considered to have a pairwise mutation if and only if both genes were non-silently mutated for that patient . Z scores were only calculated for a pair if ( 1 ) neither gene in the pair was statistically significant alone in the univariate analysis and ( 2 ) if both genes were mutated together in at least 10 patients . Per-codon Z scores were calculated for a selected set of hotspot codons . Most cancer types were available in HG37 , so HG37 mutation positions were used to locate codons . Mutations for OV and COADREAD were only available in HG36 , so gene positions were converted to HG37 before codon processing . Per codon Z scores were calculated by first identifying patients with mutations in the relevant gene , then selecting from that set of patients those whose mutations were in the codon of interest . If 2% of patients or more had mutations in the selected codon , a Z score was calculated . VAFs were calculated for 10 of the TCGA cancer types . We analyzed VAF data in two ways . First , we calculated Z scores , only counting a gene as mutated if its VAF was greater than or equal to 0 . 4 . Secondly , we identified the median VAF score per gene , and calculated Z scores only counting a gene as mutated if its VAF was equal to or greater than the median VAF for that gene . CBioPortal was structured similarly to the TCGA analyses , though data processing was not factored into an independent library since each of these datasets was only used in one analysis . Copy number data from one CBioPortal cancer type , blca_mskcc , required initial preprocessing in the manner described above for TCGA copy numbers . Mutations were included if they were annotated as one of these types: In_Frame_Ins , Nonstop_Mutation , Translation_Start_Site , In_Frame_Del , Splice_Region , Frame_Shift_Ins , Frame_Shift_Del , Splice_Site , Nonsense_Mutation , or Missense_Mutation . ICGC analysis was structured similarly to CBioPortal analysis . Mutations were only included in downstream analyses if they were annotated as one of these types: disruptive inframe deletion , disruptive inframe insertion , frameshift variant , inframe deletion , missense variant , splice acceptor variant , splice donor variant , stop gained , or stop lost . Z scores were calculated if a gene was mutated in 2% or more of the patients in a particular cohort . Across independent datasets , cohorts of patients from related cancer types were identified . Mutations or CNAs significantly associated with patient prognosis ( Z > 1 . 96 or Z < −1 . 96 ) in two or more independent cohorts from each cancer type were determined . Then , the subset of these alterations that remained highly-significant ( Z > 3 . 3 or Z < −3 . 3 ) across all cohorts from the same cancer type were classified as high-confidence biomarkers . In some instances , amplifications that spanned continuous chromosomal regions were found to correlate with patient prognosis . These segments were identified manually . For the determinations of therapeutic sensitivity described below , the gene with the minimum meta-Z score ( for deletions ) or maximum meta-Z score ( for amplifications ) within a segment was chosen to represent the segment as a whole . Therapeutic sensitivity data for PDXs was acquired from ( Gao et al . , 2015a ) . To identify mutations that correlated with therapy sensitivity , for each drug or drug combination , a comparison was performed if five or more PDXs had a mutation in a gene of interest , and if five or more PDXs were wild-type for a gene of interest . For genes and therapies fitting these criteria , we next identified instances in which the therapy resulted in a clinical response in the mutant population , defined as an average ‘Best Average Response’<15% tumor growth among PDXs with a mutation in the gene of interest . Finally , for genes and therapies fitting these criteria , we performed a t-test for the ‘Best Average Response’ between PDXs with mutant and wild-type copies of a gene of interest . We reported therapies in which these criteria were met and tumors with mutation were more sensitive to the therapy than tumors with wild-type copies of the gene of interest ( p < 0 . 01 ) . To identify CNAs that correlated with therapy sensitivity in the PDX cohort , amplifications and deletions ( CNA >| . 3| ) were called , and then considered separately . As above , CNAs were included if five or more PDXs exhibited an alteration , and if five or more PDXs did not exhibit that alteration . For genes and therapies fitting these criteria , we next identified instances in which the therapy resulted in a clinical response in the altered population , defined as an average ‘Best Average Response’<15% tumor growth among PDXs with an amplification or deletion in the gene of interest . Finally , for genes and therapies fitting these criteria , we performed a t-test for the ‘Best Average Response’ between PDXs with mutant and wild-type copies of a gene of interest . We reported therapies in which these criteria were met and tumors with a mutation were more sensitive to the therapy than tumors with wild-type copies of the gene of interest ( p < 0 . 01 ) . Therapeutic sensitivity data from cancer cell lines was acquired from ( Iorio et al . , 2016 ) . For this data , two different comparisons were used . First , the calculations described below were performed for cell lines from the specific cancer type that the high-confidence biomarker was identified in . If this analysis yielded no significant vulnerabilities , then the calculations were repeated across all cancer types ( pan-cancer ) . High-confidence mutations were assessed if five or more cell lines in the set of interest had a non-synonymous mutation in that gene , and if five or more cell lines had wild-type copies of that gene . CNAs were assessed if five or more cell lines had an alteration ( deletion or amplification ) of that gene , and if five or more cell lines lacked that alteration . For each comparison , T-tests were performed between the log ( IC50 ) value of every tested compound . For single-cancer type analyses , a threshold of p < 0 . 01 was used to identify significance , while for pan-cancer analyses , a threshold of p < 0 . 0001 was used to identify significance . Code is available on GitHub at https://github . com/joan-smith/genomic-features-survival ( Smith , 2018; copy archived at https://github . com/elifesciences-publications/genomic-features-survival ) . Kaplan-Meier plots were generated using Graphpad Prism . Deletions and amplifications in Kaplan-Meier plots correspond to CNAs > |0 . 3|; deep deletions and high-copy gains correspond to CNAs > |1| . P values reported in KM plots were generated by the log-rank test in Prism . Note that Kaplan-Meier plots are displayed in this manuscript primarily for the ease of visualizing patient outcomes . Z scores were always generated with Cox proportional hazards modeling , which does not require the selection of artificial cut-offs or thresholds for continuous data . The 30 frequently-mutated cancer driver genes were acquired from ( Zehir et al . , 2017 ) . NCI-SEER statistics were downloaded from https://seer . cancer . gov . Total tumor aneuploidy scores , ABSOLUTE-determined purity values , and leukocyte infiltration was obtained from ( Taylor et al . , 2018 ) . Hyper-mutated samples were obtained from ( Bailey et al . , 2018 ) . Lollipop plots were generated using Lollipops software ( Jay and Brouwer , 2016 ) . Density plots were generated with Python scripts using matplotlib ( https://matplotlib . org/ ) . Single base-pair mutations were mapped to codons using PolyPhen-2 ( Adzhubei et al . , 2010 ) . Multiple statistical techniques have been developed to perform survival or ‘time-to-failure’ analysis ( reviewed in Kleinbaum and Klein , 2012 ) . These include Kaplan-Meier analysis , Cox proportional hazards regression , accelerated failure time modeling , and many others . In this paper , we chose to apply Cox proportional hazards regression to analyze cancer survival data . The Cox model is represented by the following function:ht , X= h0 ( t ) e∑i=1nβiXi Where t is the survival time , h ( t , X ) is the hazard function , h0 ( t ) is the baseline hazard , Xi is a potential prognostic variable , and βi indicates the strength of the association between a prognostic variable and survival . In this model , patients have a baseline , time-dependent risk of death [h0 ( t ) ] , modified by time-independent prognostic features that either increase ( βi>0 ) or decrease ( βi<0 ) risk of death . In this paper , we report Z scores , which are calculated by dividing the regression coefficient ( βi ) by its standard error . Cox proportional hazards modeling was chosen for several reasons . First , unlike Kaplan-Meier analysis , Cox models do not require the selection of a threshold or cut-off , so continuous data like gene expression values do not need to be dichotomized . ( Note that in this manuscript , Kaplan-Meier plots are provided for visualization purposes , but the reported Z scores are always from Cox models ) . Secondly , Cox models can accept both continuous and discrete input data , allowing this approach to be used to analyze both binary ( e . g . , mutant vs . non-mutant ) and continuous ( e . g . , gene copy number ) genomic features . Thirdly , Cox models are amenable to both univariate ( i = 1 ) and multivariate ( i > 1 ) analyses . Fourthly , Cox regression allows us to calculate Z scores and a p value for each association , as Z scores represent the number of standard deviations from the mean of a normal distribution . Fifthly , Z scores encode the directionality of an association: poor prognostic factors will exhibit βi values greater than 0 , while favorable prognostic factors will exhibit βi values less than 0 . This allows ‘positive’ and ‘negative’ survival features to be directly compared . Sixthly , Z scores are useful for meta-analyses , as they can be combined using Stouffer’s Method ( Stouffer , 1949 ) :Z= ∑i=1nZik Seventhly , Cox proportional hazards modeling is commonly used in both previous genome-wide survival analyses and in numerous clinical biomarkers studies ( Dhanasekaran et al . , 2001; Fukuoka et al . , 2011; Gentles et al . , 2015; Parker et al . , 2009; Wang et al . , 2005 ) , facilitating comparison with other biomarker discovery efforts . To verify the underlying normality of the Z scores , we generated qq plots for gene copy number values ( Figure 1—figure supplement 2C ) . The resulting distributions for CNAs were generally linear , as expected , with occasional shoulders at low and high Z scores . We similarly calculated Z scores for all genes harboring coding-sequence mutations; however , we discovered that this resulted in plateaus around the origin in multiple cancer types . These aberrations were caused by the occurrence of rare , random mutations in multiple genes that lacked any prognostic power . To eliminate these plateaus , we experimented with different thresholds for mutational analysis . Considering only mutations that occurred in a certain percentage of cancer patients diminished the appearance of the plateaus , but high thresholds also eliminated from consideration mutations in a number of known cancer drivers . We selected a 2% threshold to balance between maintaining the normality of the Z score distribution while also retaining infrequent but significant mutations in driver genes . Note that in many survival analysis papers , a ‘feature selection’ step is included to identify a minimal number of features that can accurately identify at-risk patients . We performed an unbiased , whole-genome analysis without feature selection , to generate a Z score for every gene and for every feature type in the genome . No feature selection step is applied in this work . Patient cohorts that were assembled for the TCGA were collected in order to allow a molecular analysis of the major cancer subtypes found within the United States . Though clinical information was collected for nearly all patients , these cohorts were not specifically chosen in order to conduct survival studies . We posit that our survival analysis is appropriate for several reasons . First , we verified that the overall survival times of patients within the TCGA is highly consistent with national epidemiological data collected by the NCI ( Figure 1—figure supplement 2D–E ) . Secondly , we found that many well-established biomarkers hold prognostic significance in TCGA cohorts , including IDH1 mutations in glioma ( Figure 1—figure supplement 7 ) , TP53 mutations in breast cancer ( Figure 1—figure supplement 3 ) , tumor stage and grade in multiple cancer types ( Figure 2—figure supplement 3 ) , and more . Thirdly , we validated the survival patterns that we describe in the TCGA in several independent patient cohorts , indicating that these are not TCGA-specific phenomena ( Figure 4 ) . Fourthly , in an independent analysis of the quality of clinical annotations in the TCGA ( Liu et al . , 2018 ) , none of the cohort/endpoint combinations chosen for this study were classified as ‘not recommended for use . ’ Fifthly , our efforts build upon a robust body of work that has also performed survival analyses on TCGA cohorts , and , in some cases , similarly validated findings from the TCGA in independent patient populations ( Andor et al . , 2016; Davoli et al . , 2017; Gentles et al . , 2015; Guinney et al . , 2015; Uhlen et al . , 2017 ) . Finally , we note that the TCGA has several benefits over standard investigator-initiated survival studies . Patient samples were collected and analyzed in an unbiased manner , precluding the possibility of the ‘file-drawer problem’ ( failing to publish negative results ) or post-hoc sample size adjustment ( ending patient enrollment when a significant result is found ) . Significantly more molecular data is available from TCGA tumors than in any other comparably-sized dataset , which allows for multivariate and correlational analyses of different facets of tumor genomes . All data from the TCGA and all code from this manuscript are publicly-available , allowing easy replication and extension upon this analysis . | Cancers are not created equal: even when the disease affects the same organ , it can run different courses between individuals . For example , amongst people with early-stage bowel cancer who undergo surgery , 60% will go on to live cancer-free but the remaining patients will see the illness come back within a few years . These differences in outcome are still poorly understood , but they may find their roots in the genetic changes present in tumor cells . Comparing the genomes of healthy and cancerous cells can help to understand which genetic modifications makes a cell go ‘rogue’ and start to multiply uncontrollably . Often , this happens because of a mutation , a change in the letters that compose our genetic code . However , looking at genetic differences between cancerous cells from different patients , or different tumors , can shed light on how certain genetic changes make the disease deadlier or more likely to reoccur . Smith and Sheltzer looked into the genomes of 17 , 879 tumors from patients whose clinical information was also available . The analysis revealed that specific genetic alterations were more common in either deadly or treatable cancers . Most of these changes were not mutations that affected a few DNA letters; instead , they were copy number alterations , whereby large portions of the genetic code are being repeated or deleted . These results suggest that while mutations certainly drive the development of the disease , other changes such as copy number alterations can tell us which cancers will be deadlier . Through this approach , Smith and Sheltzer were also able to identify copy number alterations that were associated with patients responding well to certain drugs . These findings now need to be confirmed on a different set of data . If they hold , new technologies may be developed so that the approach can be used cheaply and easily in the clinic . Ultimately , being able to examine copy number alterations in tumors may help physicians to tailor treatment for a particular cancer , or even a specific tumor . | [
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Bcl-2 family proteins reorganize mitochondrial membranes during apoptosis , to form pores and rearrange cristae . In vitro and in vivo analysis integrated with human genetics reveals a novel homeostatic mitochondrial function for Bcl-2 family protein Bid . Loss of full-length Bid results in apoptosis-independent , irregular cristae with decreased respiration . Bid-/- mice display stress-induced myocardial dysfunction and damage . A gene-based approach applied to a biobank , validated in two independent GWAS studies , reveals that decreased genetically determined BID expression associates with myocardial infarction ( MI ) susceptibility . Patients in the bottom 5% of the expression distribution exhibit >4 fold increased MI risk . Carrier status with nonsynonymous variation in Bid’s membrane binding domain , BidM148T , associates with MI predisposition . Furthermore , Bid but not BidM148T associates with Mcl-1Matrix , previously implicated in cristae stability; decreased MCL-1 expression associates with MI . Our results identify a role for Bid in homeostatic mitochondrial cristae reorganization , that we link to human cardiac disease .
The critical function for Bcl-2 family proteins during apoptosis transpires at the mitochondria and involves remodeling of both the inner and outer mitochondrial membranes to mobilize cytochrome c and release it into the cytosol . In addition to cell death , mitochondrial membranes can reorganize with changes in metabolic conditions ( Hackenbrock , 1966 ) ( Mannella , 2006 ) . Regulation of the inner mitochondrial membrane ( IMM ) into highly organized loops known as cristae is necessary for a multitude of metabolic processes ( Cogliati et al . , 2016 ) ( Rampelt et al . , 2017 ) . Cristae harbor respiratory chain complexes embedded within and peripheral to the membrane and this tight organization is critical for efficient electron transfer ( Lapuente-Brun et al . , 2013 ) and cytochrome c sequestration ( Korsmeyer et al . , 2000 ) . Inefficient oxidative phosphorylation due to disruption of the respiratory chain can lead to mitochondrial disease , which range widely in organ systems and severity ( Moslehi et al . , 2012; Picard et al . , 2016; Wallace , 2013 ) . During apoptosis , the BH3-only protein Bid , is cleaved by caspase-8 ( cBid ) to facilitate both mitochondrial cristae reorganization ( Cogliati et al . , 2013; Frezza et al . , 2006; Scorrano et al . , 2002 ) and outer membrane permeability ( Gross et al . , 1999; Li et al . , 1998; Luo et al . , 1998; Walensky et al . , 2006; Wang et al . , 1996 ) . cBid associates with the multidomain Bcl-2 proteins Bax and Bak through its BH3-domain at the outer mitochondrial membrane ( OMM ) , triggering mitochondrial outer membrane pores ( MOMP ) ( Gross et al . , 1999; Li et al . , 1998; Luo et al . , 1998; Walensky et al . , 2006; Wang et al . , 1996 ) . Bid’s role in regulating cristae structure has been limited to in vitro studies focusing on isolated mitochondria and cBid . cBid’s interaction with the mitochondrial membrane is stabilized in part through an interaction with MTCH2 as well as cBid’s membrane binding domain ( MBD ) , consisting of alpha-helices 4 , 5 , and 6 ( Tae-Hyoung Kim , Yongge Zhao , Wen-Xing Ding , Kim et al . , 2004 ) . Alpha-helix-6 partially embeds within the membrane ( Oh et al . , 2005 ) , and has been shown to be necessary for apoptotic cristae reorganization ( Cogliati et al . , 2013 ) . In addition to its apoptotic function , Bid is also known to be involved in the regulation of other essential cellular processes such as DNA damage and metabolism , acting as rheostat for cell health ( Reviewed in Giménez-Cassina and Danial , 2015; Hardwick et al . , 2012; Zinkel et al . , 2006 ) . Interestingly , full-length Bid can also localize to the mitochondria ( Maryanovich et al . , 2012; Wang et al . , 2014 ) . The role for this association and the consequence for mitochondrial function as well as implication for human disease have not been explored . We reveal a new role for full-length Bid in the regulation of mitochondrial cristae under homeostatic conditions in an approach that integrates cell biology with human genetic studies ( Figure 1 ) . We observe that loss of Bid impairs proper cristae formation in the absence of an apoptotic stimulus both in myeloid cells and left ventricular ( LV ) cardiomyocytes . This function is independent of Bid’s caspase-8 cleavage site ( D59A ) , and BH3-domain . We demonstrate decreased respiration in Bid-/- cells and decreased respiration coupled with decreased ATP production in LV fibers . These deformations become more pronounced in the heart when it is exposed to various cardiac stressors including Epinephrine and Doxorubicin , in both cases leading to decreased LV function in Bid-/- mice . In the case of Epinephrine , these changes correspond to increased cristae damage and fibrosis , phenotypically similar to damage caused by a myocardial infarction ( MI ) in humans . Given the known association between mitochondrial dysfunction , especially respiratory chain deficiencies , and heart disorders ( Schwarz et al . , 2014 ) , we use two human genetics approaches to interrogate an association for BID with human cardiac diseases . We first use PrediXcan , which estimates the genetically determined component of gene expression ( Gamazon et al . , 2015; Gamazon et al . , 2018 ) , applied to a cohort of Vanderbilt University’s de-identified genetic database called BioVU ( Roden et al . , 2008 ) . We reveal a highly significant association between decreased BID expression and MI . We also find that patients with the lowest 5% of BID expression have a > 4 fold increase in MI susceptibility . BID’s role in cardiac diseases is further validated through an investigation of additional independent cohorts including the large-scale CARDIoGRAMplusC4D GWAS datasets ( Schunkert et al . , 2011 ) ( Nikpay et al . , 2015 ) . Secondly , using BioVU exome-chip data , we uncover a gene-level association with MI from low-frequency nonsynonymous variation . Of significance , coding single nucleotide polymorphism ( SNP ) M148T lies within Bid’s membrane binding domain ( MBD ) , that includes alpha-helix-6 . We then demonstrate that the double Bid mutant BidBH3/M148T fails to support proper mitochondrial respiratory function or restore cristae in Bid-/- cells . The Bcl-2 family member , Mcl-1 , has been shown to localize to the mitochondrial matrix ( Mcl-1Matrix ) and facilitate maintenance of respiratory complexes ( Perciavalle et al . , 2012 ) . We also observe a pool of Bid localized to the matrix and find that while WT Bid can interact with Mcl-1Matrix , this matrix association is diminished with BidM148T . Using PrediXcan , we find MCL-1 and MTX-1 , a mitochondrial transporter that associates with the mitochondrial contact site and cristae reorganizing complex ( MICOS ) ( Guarani et al . , 2015 ) are significantly associated with MI , linking susceptibility to MI to two additional genes involved in cristae regulation . Our study provides an integrative approach , summarized in Figure 1 , that spans observations in tissue culture and mice to independent human genetics studies providing direct relevance for our findings in human disease . We shed light on the regulation of mitochondrial cristae and consequently oxidative phosphorylation and reveal an important role for Bid's alpha-helix-6 in regulation of mitochondrial function under homeostatic conditions . Furthermore , this approach provides a model for elucidating previously unrecognized proteins that impact complex genetic diseases .
Consistent with a pro-survival function , Bid-/- myeloid progenitor cells ( MPCs ) display decreased growth rates not due to altered proliferation , but instead as a result of decreased viability ( p<0 . 05 ) ( Figure 2—figure supplement 1a–c ) . Given the critical apoptotic role for Bid at the mitochondria , we evaluated mitochondrial structure in Bid-/- MPCs by transmission electron microscopy ( TEM ) ( Figure 2a and b , Figure 2—figure supplement 2a ) . Compared to Bid +/+ MPCs , mitochondria in Bid-/- MPCs were highly abnormal . Quantitation of the average number of cristae per mitochondrion revealed a significant decrease in the number of cristae in Bid-/- MPCs compared to Bid +/+ MPCs ( p<0 . 0001 ) ( Figure 2c ) . This function is independent of Bid’s apoptotic role , as Bid-/- MPCs stably expressing Flag-HA-tagged full-length Bid mutated in either in its BH3-domain ( FHA-BidBH3 ) or caspase-8 cleavage site D59 ( FHA-BidD59A ) could rescue cristae structure ( p<0 . 0001 ) ( Figure 2c ) . Furthermore , quantitation of the area density of mitochondria per cell revealed a slight decrease in density in the Bid-/- cells compared to Bid+/+ cells ( p<0 . 05 ) , while FHA-BidD59A expressing cells had increased mitochondrial density compared to all other cell lines ( Figure 2d ) . Several groups have reported that cleaved Bid ( cBid ) reorganizes cristae ( Cogliati et al . , 2013; Scorrano et al . , 2002 ) or Bid BH3-peptide narrows cristae junction size ( Yamaguchi et al . , 2008 ) in the presence of isolated mitochondria . Given that myeloid cells have high endogenous protease activity , we anticipated that reintroduction of WT FHA-Bid into Bid-/- MPCs by retroviral transduction may not rescue mitochondrial structure ( Figure 2a–d ) . Indeed , overexpression of full-length WT Bid ( Bid-/- + FHA Bid ) but not FHA-BidBH3 or FHA-BidD59A results in the production of endogenous cBid in the absence of a death stimulus ( Figure 2e ) . Thus , in a myeloid cell line , we observe that Bid’s apoptotic domains must be mutated to fully restore cristae . We next analyzed expression of other BH3-only apoptotic proteins as we anticipated they may be upregulated in the absence of Bid , and considering the known role of Bim in disassembly of mitochondrial Opa-1 oligomers ( Yamaguchi et al . , 2008 ) . We evaluated Bid-/- cellular extracts as well as lysate from left ventricular ( LV ) cardiac tissue which are highly enriched in mitochondria , and find no compensatory upregulation of Bim , Bad , or Puma to account for the observed loss of cristae structure in Bid-/- cells ( Figure 2—figure supplement 2b and c ) . It has previously been shown that full-length Bid can localize to mitochondria in the absence of an apoptotic stimulus ( Maryanovich et al . , 2012; Wang et al . , 2014 ) . To confirm this result , we first evaluated Bid in subcellular fractions of Bid-/- and WT MPCs . We find full-length Bid in a heavy membrane , mitochondrial-enriched fraction absent of cytosolic contamination ( Figure 3a ) . We also observe full-length Bid in mitochondria isolated from liver tissue , both in a crude mitochondrial fraction as well as in a Percoll purified fraction ( Figure 3b ) . To determine the submitochondrial localization of full-length Bid , isolated liver mitochondria were treated with Proteinase K ( PK ) in the presence or absence of SDS . We observe that a pool of Bid remains uncleaved with PK , under conditions in which we observe cleaved Bak ( Figure 3c ) , a protein associated with the OMM . Furthermore , we used an osmotic shock approach to separate and enrich for OMM and mitoplast ( inner membrane and matrix containing fractions ) from isolated liver mitochondria . We find an enrichment of Bid in the mitoplast-containing fraction compared to the OMM ( Figure 3d ) . Taken together , the above results suggest that full-length Bid can localize to the mitochondria during non-apoptotic conditions and is found both at the OMM as well as in the mitoplast . Mitochondria cristae defects in humans can result in severe abnormalities in multiple organ systems , especially the heart ( Brown et al . , 2017; Meyers et al . , 2013 ) . We were interested to know if Bid-/- mice also display cristae abnormalities beyond myeloid cells . TEM of left ventricular tissue isolated from Bid-/- mice revealed striking irregularities both in gross mitochondrial organization between myofibrils as well as loss of normal lamellar cristae structure ( Figure 4a ) . Specifically , without treatment , Bid-/- tissue had overall decreased mitochondrial electron density corresponding to significantly increased cristae width ( p<0 . 0001 ) ( Figure 4b ) . To test how Bid-/- mice respond to an acute stress , we used Epinephrine ( Epi ) to increase the energetic demand on the mitochondria . We assessed both Bid+/+ and Bid-/- mitochondria 18 hr after a dose of 0 . 5 mg/kg Epi and find that while both Bid+/+ and Bid-/- tissues are damaged , the Bid-/- cristae are significantly more deformed ( p<0 . 0001 ) ( Figure 4a and b ) . Interestingly , these damaged cristae are structurally similar to mitochondria observed after induction of an acute myocardial infarction ( MI ) ( Bryant et al . , 1958 ) . Thus , Bid-/- mice have a severe cardiac cristae defect that results in increased susceptibility to acute stress-induced damage . To determine whether the mitochondrial cristae defect in Bid-/- mice translates to decreased cardiac function , we performed echocardiograms on mice . In the absence of a clear mouse model of heart failure ( Breckenridge , 2010 ) , we chose Epi as an acute pharmacological stress due to the fact it causes both a rise in blood pressure with increased left ventricular ( LV ) afterload as well as increased myocardial contractility ( Goldberg et al . , 1960 ) . This results in maximal oxygen demand with potential to reveal a phenotype driven by mitochondrial dysfunction . Bid+/+ and Bid-/- mice were evaluated at baseline ( without treatment ) , 18 hr after acute-intraperitoneal ( IP ) Epi ( 0 . 5 mg/kg ) , and 5 days after Epi treatment to evaluate recovery . Cardiac function does not differ at baseline . However , we find a significant increase in left internal ventricular diameter during diastole ( LVIDd ) ( p<0 . 01 ) as well as during systole ( LVIDs ) ( p<0 . 05 ) 18 hr after Epi ( Figure 4c and d ) . This corresponds to a significant decrease in Ejection Fraction ( EF ) for Bid-/- mice ( p<0 . 01 ) ( Figure 4e ) and a trend for decreased fractional shortening ( FS ) ( p=0 . 1564 ) ( Figure 4—figure supplement 1a ) . Furthermore , we also observe an increase in both end diastolic ( p<0 . 01 ) and end systolic volume ( p<0 . 05 ) ( Figure 4f and g ) with stress . This is consistent with our findings by EM indicating a decreased ability of Bid-/- hearts to maintain proper mitochondrial structure under stress . Decreased LV cardiac function observed in Bid-/- mice is phenotypically similar to observations made by echo in patients during the acute phase of MI ( LV wall dilation and decreased ejection fraction ) ( White et al . , 1987; Di Bella et al . , 2013 ) . Interestingly , at 5 days post-Epi , both the Bid+/+ and Bid-/- mice had restored cardiac function and we find no difference in heart weights at sacrifice ( Figure 4c–g and Figure 4—figure supplement 1b ) . Lastly , we also employed an additional pharmacological myocardial stress in the form of Doxorubicin ( Dox ) ( 3 doses of 7 . 5 mg/kg ) , a chemotherapy drug with heart mitochondrial toxicity ( Hull et al . , 2016 ) . Dox also resulted in a significant decrease in FS and EF ( p<0 . 01 ) ( Figure 4—figure supplement 1c and d ) in Bid-/- mice . Thus , using two different models , Epinephrine , which directly results in increased oxygen demand , as well as the mitochondrial toxic drug Doxorubicin , we find that Bid plays a role in maintaining LV function under stress . Myocardial fibrosis due to cardiomyocyte remodeling after damage is a prominent sequelae of MI , and directly contributes to loss of cardiac function ( Talman and Ruskoaho , 2016 ) . To determine the extent of fibrotic damage , we used Masson’s trichrome staining and quantitatively evaluated whole heart tissue sections ( Figure 5 ) . We find that Bid-/- tissue has significantly increased fibrosis both at the 18 hr and the recovery time point , 5 days post treatment ( p<0 . 05 ) ( Figure 5b and c ) . Interestingly , WT mice display no increase in fibrosis at 18 hr post-Epi; fibrosis developed in WT hearts at 5 days post-Epi . Thus , Bid-/- mice have more fibrosis and increased susceptibility to damage after stress . This result recapitulates the response to cardiomyocyte damage in human MI and suggests that although the Bid-/- mice are able to recover functionally , the long-term damage is more severe . To better understand how loss of Bid alters mitochondrial function , we performed proteomics using Multidimensional Protein Identification Technology ( MudPIT ) on equal concentrations of isolated mitochondrial protein from Bid +/+ and Bid-/- MPCs ( Figure 6—figure supplement 1a ) . We identified a total of 3258 proteins that mapped to unique Entrez gene identifiers . Cross referencing our hits to the Mouse MitoCarta 2 . 0 ( Calvo et al . , 2016 ) , we identified 54 significantly different mitochondrial proteins between the Bid+/+ and Bid-/- samples ( Figure 6—figure supplement 1b and c ) . Our MudPIT results suggested a possible defect in mitochondrial respiratory chain function . To interrogate individual respiratory complexes , we isolated mitochondria from heart tissue of age matched Bid +/+ ( WT ) and Bid-/- mice . We then resolved digitonin-extracted complexes using gradient Native-PAGE , stained with Coomassie blue , and treated with complex-specific substrates to measure enzymatic activity . We observe a decrease in the activity of ATP synthase dimers from Bid-/- heart mitochondria ( p<0 . 05 ) ( Figure 6a and b ) , consistent with the known association between dimerization of ATP synthase in cristae loop formation and stabilization of cristae structure ( Hahn et al . , 2016; Paumard et al . , 2002 ) . Enzymatic activity of additional respiratory complexes and supercomplexes were also evaluated including Complex I ( CI ) and complex IV ( CIV ) . We observed a significant decrease in the activity of complex I within the SCs and a trend for decreased activity of complex IV containing SCs ( Figure 6—figure supplement 1d , e and f ) . Overall , these results are consistent with a role for Bid in maintenance of cristae structure linked to respiratory chain function . We next measured respiration directly from Bid-/- MPCs and LV fibers . Using an Oroboros Oxygraph , we found Bid-/- MPCs displayed significantly decreased oxygen consumption rates ( OCR ) compared to Bid+/+ cells ( p=0 . 008 ) , consistent with a cristae defect . Respiration could be restored in Bid-/- MPCs by re-introduction of FHA-BidBH3 and FHA-BidD59A but not FHA-Bid into Bid-/- MPCs ( Bid-/- v . Bid-/- + FHA-BidBH3 , p<0 . 0001 and Bid-/- v . FHA-BidD59A , p=0 . 0008 ) ( Figure 6c ) . Despite decreased oxygen consumption , one possible explanation for the observed mitochondrial defects in Bid-/- cells could be damage from the generation of reactive oxygen species ( ROS ) . We assessed baseline mitochondrial and cellular superoxide with MitoSOX and DHE , respectively , and found no difference between Bid-/- and Bid +/+ MPCs , however mitochondrial superoxide was increased in Bid-/- cells under conditions of nutrient withdrawal ( p<0 . 01 ) ( Figure 6—figure supplement 2a , b and c ) . Bid’s phosphorylation sites S61 and S78 ( BidAA ) have also been shown to correspond with increased ROS and respiration in hematopoietic stem cells ( Maryanovich et al . , 2012 ) , ( Maryanovich et al . , 2015 ) . Additionally , it was shown that truncated Bid ( tBid ) residues 57–73 had strong binding to MTCH2 ( Katz et al . , 2012 ) , To determine if these phosphorylation sites are involved in full-length Bid’s regulation of cristae function , we made both S61A and S78A point mutations in BH3-mutated Bid followed by stable re-introduction into Bid-/- MPCs ( Bid-/- + FHA-BidBH3AA ) . Interestingly , we find that even in the context of a BH3-mutant , these cells were highly unstable , which we attribute in part to the important role of these phosphorylation sites in the DNA damage response ( Liu et al . , 2011; Zinkel et al . , 2005 ) as well as preventing cleavage of Bid and thus initiation of apoptosis ( Desagher et al . , 2001 ) . We measured TMRE and MitoSOX by flow cytometry , gating on cells positive for human CD25 ( co-expressed with FHA-Bid ) . We find that compared to BidBH3 , FHA-BidBH3AA MPCs do not have altered membrane potential and show only a trend for increased ROS ( p=0 . 1956 ) ( Figure 6—figure supplement 2d and e ) . Thus , our results in MPCs are most consistent with a role for these phosphorylation sites in overall cell viability , by preventing caspase-8 cleavage of Bid ( Desagher et al . , 2001 ) , rather than specifically in the regulation of mitochondrial membrane potential or ROS production . Next , to determine whether the decreased respiration is also observed in mouse cardiac fibers , we evaluated oxygen consumption in Bid-/- and Bid+/+ heart tissue . Respiration of permeabilized left ventricular ( LV ) cardiac fiber bundles ( PmFBs ) was measured in the presence of the complex I ( CI ) substrates malate and glutamate , as well as ADP ( state 3 ) . Bid-/- LV fibers also displayed significantly decreased oxygen consumption compared to Bid+/+ LV fibers ( p=0 . 0103 ) ( Figure 6d ) . To more thoroughly interrogate the mitochondrial defect from Bid-/- hearts , we used a customized instrument platform optimized for permeabilized muscle fibers ( Lark et al . , 2016 ) and simultaneously measured ATP production and O2 consumption . We first analyzed PmFBs in the presence of complex I substrates ( glutamate , malate and ADP ) . Bid-/- fibers had decreased respiratory function as well as decreased ATP production ( p<0 . 05 ) ( Figure 6e and f ) compared to Bid +/+ fibers . Rotenone , ( complex I specific inhibitor ) prevents electron flux through CI and we observe decreased O2 consumption and ATP production as expected . Succinate directly contributes electrons to CII and was added in the presence of rotenone to interrogate CI-independent respiration . Bid-/- PmFBs had decreased respiration in the presence of rotenone and succinate ( p<0 . 001 ) , and decreased ATP production ( p<0 . 05 ) ( Figure 6e and f ) , consistent with our finding that irregular cristae correspond with decreased ATP synthase activity . Oxidative phosphorylation efficiency can be defined as the ratio of ATP to O . Interestingly , despite an overall decrease in respiration and ATP production , Bid-/- PmFBs have similar efficiency to Bid +/+ when using CI substrates . This is consistent with our finding that Bid-/- mitochondria do not have increased ROS or loss of membrane potential ( Figure 6—figure supplement 2a–d ) . However , in the presence of rotenone and succinate , Bid-/- PmFBs have an increased ATP/O ratio ( p<0 . 05 ) ( Figure 6—figure supplement 2f ) . This suggests Bid-/- mitochondria may compensate by bypassing complex I in favor of respiratory complex I I , which is not found in respiratory supercomplexes ( Schägger and Pfeiffer , 2001 ) and therefore would be less impacted by disorganized cristae . Given the observed increased fibrosis in Bid-/- mice , phenotypically similar to post-MI damage in humans , we investigated the clinical relevance of our findings . We applied PrediXcan ( Gamazon et al . , 2015; Gamazon et al . , 2018 ) ( see Materials and methods and Figure 7a ) to test the association of genetically determined BID expression in 29 , 366 patients in BioVU ( Roden et al . , 2008 ) with MI predisposition . Because of the substantial prior support from our studies observed for Bid’s role in heart function and inducing fibrotic damage with acute stress , we evaluated the association with MI risk of BID expression and used Bonferroni adjustment for the number of cardiac traits tested to assess statistical significance . Consistent with our findings in mice , we observed that decreased BID expression is significantly associated with MI ( Figure 7b ) . To quantify the extent of genetic control of BID expression , we performed SNP-based heritability analysis ( Gamazon and Park , 2016 ) . Genotype-Tissue Expression ( GTEx ) project data , despite the breadth of tissues , are still generally underpowered for this analysis ( because of sample size ) , and we therefore utilized a larger transcriptome panel DGN ( n = 922 ) ( Battle et al . , 2014 ) , which is , however , available only in whole blood . The BID heritability estimate was significant ( h2 = 0 . 08 with standard error [SE] of 0 . 026 ) , providing support for genetic regulation ( Figure 7—figure supplement 1a and b ) . We determined the prevalence of MI , coronary atherosclerosis and ischemic heart disease in Vanderbilt University’s Synthetic Derivative ( SD ) , which contains over 2 . 8 million de-identified patient records linked to electronic health records ( Roden et al . , 2008 ) . For comparison , we evaluated the prevalence of MI , coronary atherosclerosis , and ischemic heart disease in individuals with the lowest 5% of BID expression , thus approximating the Bid-/- condition of our mouse model . Within this group , we find a > 4 fold increase in the prevalence of myocardial infarction compared to the rest of the Synthetic Derivative ( Figure 7b ) . Decreased BID expression in heart tissue significantly associated with myocardial infarction ( p=7 . 55×10−3 ) as well as coronary atherosclerosis ( p=8 . 26×10−3 ) , and ischemic heart disease ( p=9 . 7×10−4 ) ( Figure 7b ) . These results are notable , as they not only suggest the impact that loss of Bid would have in humans but also highlight the continuity of phenotypes observed in the Bid-/- mice with human patient data . In order to more precisely characterize the effect of decreased genetically determined BID expression on cardiac phenotypes , we additionally analyzed the recently available GWAS of atrial fibrillation ( N > 1 million individuals ) ( Nielsen et al . , 2018 ) . Notably , we find no significant association between BID genetically determined expression and atrial fibrillation in this dataset ( p=0 . 63 ) , consistent with the lack of significant association in BioVU . Thus , while we identify multiple cardiac traits associated with BID expression , BID’s effect is specific to particular pathophysiologies . To determine whether our findings are unique to BID among other BH3-only and related genes , including BECN1 ( a Bcl-2-interacting protein involved in autophagy ) and MTCH2 ( a Bid-interacting protein ) ( Grinberg et al . , 2005; Katz et al . , 2012; Shamas-Din et al . , 2013 ) , we performed a secondary PrediXcan analysis . The results revealed a unique role for BID among these genes in conferring MI risk ( see Supplementary Information , Materials and methods , and Figure 7—figure supplement 2a–f ) . In a separate BioVU sample set ( see Materials and methods and Figure 7—figure supplement 1c and d ) , we observed a significant correlation ( p=0 . 002 ) between decreased genetically determined BID expression in the aorta and MI . We analyzed the publicly available CARDIoGRAMplusC4D GWAS datasets ( Schunkert et al . , 2011; Nikpay et al . , 2015 ) ( see Materials and methods ) . Consistent with the BioVU discovery and validation results , decreased genetically determined expression of BID in heart was associated ( p=0 . 02 , effect size = −0 . 06 , SE = 0 . 026 ) with MI in CARDIoGRAMplusC4D . Interestingly , several of the SNPs ( in the locus ) nominally associated with MI and CAD clustered within the adjacent BCL2L13 ( Bcl2-rambo ) gene , the most significant being rs2109659 ( p=0 . 004 ) . However , no association with MI in CARDIoGRAMplusC4D was observed with BCL2L13 ( p=0 . 75 ) ( Figure 7—figure supplement 3a and b ) , consistent with the SNPs being regulatory for BID . For completeness , we report the BID associations with cardiac traits using additional tissues ( Sudlow et al . , 2015 ) . Interestingly , all nominally significant associations with other cardiac traits in these tissues in BioVU were consistent with decreased expression of BID ( Figure 7—figure supplement 3c ) . Here we show that site-directed mutagenesis informed by exome association analysis of BID revealed that Bid's alpha-helix-6 directs its role to regulate mitochondrial function . First , we evaluated whether there was an association between coding SNPs within BID and MI risk . Using BioVU , we developed a cohort of 23 , 195 self-reported Caucasian subjects ( median age 63 years [IQR 43 to 57 years] and 52% female ) who had previously undergone genotyping ( Illumina Human Exome BeadChip v1 ) ( see Supplementary Information ) , of whom 1507 were MI cases . In multivariable logistic regression , a significant association was observed between carrier status ( i . e . presence of any missense variant ) and MI ( p=0 . 013; OR 1 . 7 [95% CI 1 . 1–2 . 6] ) . Although this would not meet significance in an unbiased , exome-wide search , we are testing only a single gene for which we have already observed substantial evidence for its role in conferring MI risk . This gene-level association was primarily driven by variants in the membrane binding domain ( MBD ) , including E120D , R123Q and M148T ( Figure 8a ) . Carrier status for MBD variants ( i . e . presence of any missense variant in the MBD ) was strongly associated with MI ( p=0 . 002; OR 8 . 5 [95% CI 2 . 1–33 . 6] ) ( Figure 8b and c ) . Notably , M148T was also associated with MI risk ( p=0 . 029 , OR = 1 . 47 ) in the recent meta-analysis of exome-chip studies involving 42 , 335 patients and 78 , 240 controls of European ancestry , consistent with the BioVU results ( Stitziel et al . , 2016 ) . We next evaluated whether any of these coding variants , particularly those that lie within the MBD , affect Bid’s regulation of mitochondrial function . In particular , the conserved M148 residue lies within Bid’s alpha-helix-6 , which regulates mitochondrial association and cristae remodeling in the context of cBid during apoptosis ( Cogliati et al . , 2013; Oh et al . , 2005; Shamas-Din et al . , 2013 ) . We introduced the M148T mutation in conjunction with full-length BH3-mutated Bid , which can rescue mitochondrial function , into Bid-/- MPCs ( Figure 8d ) . To establish that introduction of the M148T mutant does not disrupt Bid’s overall structure , we evaluated apoptotic function by assessing cell death with TNF-α/Actinomycin D . As expected , Bid-/- MPCs were protected from death compared to Bid +/+ MPCs ( p=0 . 0068 ) . Importantly , Bid-/- + FHA-BidBH3/M148T MPCs displayed similar death kinetics to Bid-/- + FHA-BidBH3 MPCs which has been shown to have some sensitivity to TNF-α/Actinomycin D stimulated death ( Wang et al . , 1996 ) . This indicates that the M148T mutation has no effect on Bid’s apoptotic function in the presence of a mutated BH3 domain ( Figure 8—figure supplement 1a and b ) . We evaluated mitochondrial cristae number in Bid +/+ , Bid-/- + FHA-BidBH3 and Bid-/- + FHA-BidBH3M148T ( double mutant ) as in Figure 2 , and found that the double mutant had significantly less cristae compared to FHA-BidBH3 alone ( p<0 . 01 ) . Interestingly , we found that the double mutant had an increase in overall mitochondrial area density per cell , likely as a compensatory mechanism for decreased cristae function ( p<0 . 01 ) ( Figure 8e–g ) . Respiratory efficiency of MPCs was then assessed using these mutants , directly comparing the BH3-mutant to the double mutant . Expression of the BidBH3/M148T double mutant was insufficient to restore respiratory levels comparable to Bid +/+ or Bid-/- + FHA-BidBH3 MPCs ( Figure 8h ) . Interestingly , the two other SNPs identified in the membrane binding region of Bid also lie within a hydrophobic region of Bid as well as the region predicted to interact with MTCH2 ( Katz et al . , 2012 ) . We made the corresponding mutations , E120D and R124Q in BH3-mutated Bid to determine if these would also result in altered mitochondrial function ( Figure 8—figure supplement 1c ) . Compared to BH3-mutated Bid , BidBH3/E120D MPCs had equivalent respiration . While BidBH3/R124Q MPCs had decreased respiration ( Figure 8—figure supplement 1d ) , it was not significantly different from WT MPCs . Neither BidBH3/E120D nor BidBH3/R124Q MPCs displayed altered sensitivity to TNF-α/Actinomycin D stimulated cell death ( Figure 8—figure supplement 1e and f ) . Our observation that Bid is found within the mitoplast ( Figure 3c and d ) raised the possibility that it is interacting with mitochondrial matrix proteins known to regulate cristae structure . In particular , the anti-apoptotic Bcl-2 family member Mcl-1 has been shown to have a matrix isoform involved in respiratory chain maintenance and mitochondrial metabolism ( Escudero et al . , 2018; Perciavalle et al . , 2012; Thomas et al . , 2013; Wang et al . , 2013 ) . It is known that the BH3-domain of cBid associates with Mcl-1 , to inhibit apoptosis ( Clohessy et al . , 2006 ) . We tested whether full-length Bid associates with WT Mcl-1 , an outer mitochondrial membrane form of Mcl-1OM or the matrix form of Mcl-1 , Mcl-1Matrix . Using FlagHA-tagged Bid expressed in 293 T cells , we were able to immunoprecipitate all three forms of Mcl-1 ( Figure 9a ) . This is in contrast to the other BH3-only protein Bim , which did not associate with Mcl-1Matrix ( Perciavalle et al . , 2012 ) . We then sought to determine the role of helix-6 in this association . We find that FHA-BidM148T displays decreased association with Mcl-1Matrix compared to both WT-Bid and our rescue mutant , FHA-BidD59A . Furthermore , FHA-BidM148T displays increased association with WT Mcl-1 relative to either WT Bid or FHA-BidD59A ( Figure 9b ) . The above results are consistent with a role for helix-6 in Bid’s association with Mcl-1Matrix , in the context of the mitochondrial cristae . Informed by our observation of Bid’s interaction with mitochondrial matrix proteins known to regulate cristae structure organization , we applied PrediXcan to evaluate potential contribution to MI susceptibility for these genes ( see Figure 9—figure supplement 1 ) . Loss of Mcl-1 has previously been shown to result in cardiomyopathy ( Wang et al . , 2013 ) and impaired autophagy leading to heart failure in mice ( Thomas et al . , 2013 ) . We find that decreased genetically determined expression of MCL-1 is significantly associated with MI ( p=0 . 00903 ) ( Figure 9c ) . In addition to MCL-1 , we find that MTX1 ( Metaxin1 ) , a mitochondrial protein transporter that associates with the MICOS complex ( Guarani et al . , 2015 ) , has reduced genetically determined expression significantly associated with MI ( p=1 . 93×10−5 ) . Furthermore we utilized the Synthetic Derivative ( Roden et al . , 2008 ) to gain additional insights into the cardiac traits known to result from loss of Mcl-1 . Using ICD9-codes , we identified 20 , 834 patients diagnosed with an MI ( among the nearly 2 . 8 million patients ) . Using this information , we constructed a contingency table , first looking at the relative risk for control phenotypes ( headache and burn ) as well as known risk factors for MI including hypertension , hypercholesteremia , and diabetes mellitus ( Anand et al . , 2008 ) . Interestingly , we find that patients with a history of cardiomyopathy have a significantly increased relative risk for MI compared to the known risk factors , connecting these two phenotypes genetically ( Figure 9d ) . Thus , we propose a model in which we have evidence from cell lines , mice , and multiple human genetics studies that converge on a role for Bid in the regulation of mitochondrial cristae structure and predisposition to MI ( Figure 9e ) . We further find genetic evidence that decreased expression of two additional genes known to regulate cristae structure , MCL-1 and MTX1 , is also associated with susceptibility to MI . In addition to its apoptotic function , we now add a homeostatic function for Bid at the mitochondria which is dependent on its full-length form in the matrix , and the helix-6 residue M148 , uncovered directly from human exome data . Finally , we find an association between Bid and the matrix form of Mcl-1 mediated by the helix-6 residue M148 , suggesting that Bid may perform its role at the mitochondrial matrix through interaction with Mcl-1 .
Our results add to the body of literature implicating a role for the Bcl-2 family in mitochondrial membrane remodeling in the absence of apoptosis . While full-length Bid is observed at the mitochondria homeostatically ( Maryanovich et al . , 2012; Wang et al . , 2014; Figure 3 ) , the purpose for this localization , especially given that cleaved Bid is potently apoptotic , was unclear . We find that Bid , like Bcl-XL and Mcl-1 ( Chen et al . , 2011; McNally et al . , 2013; Perciavalle et al . , 2012 ) , is critical for the structural and functional maintenance of mitochondrial cristae and this occurs independently of caspase-8 cleavage . The significance of this finding is strengthened by our complementary approach , which integrates cell biology with human genetics data . In both MPCs as well as LV tissue , loss of Bid results in absent or abnormal mitochondrial cristae structure . Acute cardiac stress not only exacerbates this cristae disorganization but leads to cardiac dysfunction in Bid-/- mice , including increased left ventricular diameter and reduced ejection fraction . While mice are able to recover functionally , this ultimately results in increased cardiomyocyte fibrosis , damage similar to that observed after an MI . We propose that the association between Bid and MI can be linked to mitochondrial function . Real-time analysis of permeabilized cardiac fibers revealed that loss of Bid results in decreased respiration and ATP production . Thus Bid-/- cells and tissue function at their maximum efficiency , yet produce less energy , consistent with disrupted respiratory chain formation ( Lapuente-Brun et al . , 2013 ) . Under conditions of stress , Bid-/- mitochondria are unable to meet increased energetic demand , thus decreasing the threshold to cardiac failure , and ultimately myocardial dysfunction . To determine the human disease relevance of our findings , PrediXcan analysis ( Gamazon et al . , 2015 ) was applied to BioVU ( Roden et al . , 2008 ) . The PrediXcan-derived association of BID with MI has important implications . Firstly , the association derives from common genetic variants , and therefore has potential diagnostic implications in the general population . Secondly , use of germline genetic profile to estimate BID expression removes any potential confounding effect the environment or disease itself could have on gene expression . Importantly , we also evaluated the individuals with the lowest BID expression , thus approximating the situation in Bid-/- mice in humans . Strikingly , the lowest 5% of individuals had a > 4 fold increase for incidence of MI . This remarkable result further connects our genetic findings to the cardiac phenotype observed in Bid-/- mice . Lastly , we sought independent validation for BID’s association with MI . We used the publicly available CARDIoGRAMplusC4D GWAS datasets ( Schunkert et al . , 2011 ) ( Nikpay et al . , 2015 ) , and an additional independent cohort of BioVU patients . This result was also unique to BID among other BH3-only proteins . At the coding level , we have also identified SNPs within the membrane binding region of BID associated with MI . In particular , M148T in helix 6 was of interest as two additional downstream residues , K157 and K158 , have been shown to be essential for cristae re-organization in the context of apoptosis ( Cogliati et al . , 2013 ) . This SNP was also found to be significant in a meta-analysis of exome-chip studies of European ancestry ( Stitziel et al . , 2016 ) . To determine the functional consequence of this SNP , we made the corresponding M148T point mutant in Bid and find it fails to fully restore cristae structure , and results in a loss of respiratory function when combined with our rescue BH3-mutant Bid . In contrast , two SNPs in the putative Bid-MTCH2 binding domain ( BidE120D and BidR124Q ) did not alter mitochondrial function compared to WT MPCs . Our results indicating the presence of Bid in the matrix prompted us to determine if the M148T mutant would also impact a possible protein-protein interaction . A strong candidate is the anti-apoptotic Bcl-2 family member Mcl-1 , which was rigorously shown to have a mitochondrial matrix isoform that mediated mitochondrial cristae structure and lipid metabolism independent of Mcl-1’s apoptotic function ( Escudero et al . , 2018; Perciavalle et al . , 2012 ) . We find that this point mutant decreases the association between Bid with Mcl-1Matrix compared to WT and D59A-mutanted Bid ( rescue mutant ) . Interestingly , we also observe that BidM148T associates WT Mcl-1 . We propose that Bid interacts with Mcl-1 in a manner that requires not only a BH3-domain , but also helix- 6 . Based on the NMR structure of Bid ( Chou et al . , 1999; McDonnell et al . , 1999 ) , BidM148T , as well as the previously implicated BidK158 ( Cogliati et al . , 2013 ) , lie in approximately the same plane of helix-6 , in an orientation facing away from helix-3 ( BH3-domain ) in solution . EPR analysis of p15 Bid also places both of these residues in the headgroup region of a lipid bilayer when cBid is inserted into a membrane ( Oh et al . , 2005 ) . It is possible that mutating these residues decreases the affinity of Mcl-1 to full-length Bid in solution by destabilizing the hydrophobic core of Bid adjacent to helix-3 . Alternatively , these mutants might also be predicted to decrease Bid’s association with a membrane . This may be more critical for Bid’s interaction with Mcl-1Matrix in regulating membranes than for its interaction on the surface of mitochondria with WT Mcl-1 and may account for the difference in affinity found by immunoprecipitation . In sum , we have identified a homeostatic role for Bid in the regulation of mitochondrial structure and function extending initial observations in tissue culture to an in vivo model that converges on a unique role for BID in human cardiac disease . We propose that loss of Bid or decreased BID gene expression contributes to cardiac diseases , particularly MI . Furthermore , we provide evidence that this mitochondrial function for Bid is dependent at least in part upon Bid’s alpha-helix-6 , that mediates Bid’s interaction with Mcl-1Matrix , implicating a Bid-Mcl-1 interaction at the matrix in mitochondrial cristae organization . Finally , we find an association between decreased expression of MCL-1 and MTX-1 with susceptibility to MI , linking altered cristae structure with MI . Our integrated approach , combining multiple avenues of investigation , has identified previously unknown proteins involved in complex genetic diseases , and can be used to bridge the gap between basic biological findings and translational science .
All mice were housed , and experiments performed with approval by the IACUC of Vanderbilt University Medical Center in compliance with NIH guidelines . WT ( Bid+/+ ) and Bid-/- mice were back-crossed onto a C57BL/6 background at least nine generations in addition to being re-derived to mice with a pure C56BL/6 background . Age and sex of mice used for experiments are indicated where applicable . Hox11-immortalized MPCs were cultured in IMDM medium with 20% FBS , 100 U/ml penicillin-streptomycin , 2 mM glutamine , 0 . 1 mM β-mercaptoethanol , and 10% WEHI conditioned medium as a source of IL-3 . Cell lines were mycoplasma tested and negative . Cell lines were also authenticated by genotyping . To generate MPCs expressing exogenous wild type or mutant Bid , Bid was cloned into pOZ-FH-C-hCD25 using XhoI and NotI restriction sites ( Nakatani and Ogryzko , 2003 ) . BH3 mutant Bid has amino acids 93–96 of mouse Bid mutated from IGDE to AAAA ( Wang et al . , 1996 ) . The D59A mutant Bid is mutated at the caspase eight cleavage site . M148T , E120D , R123Q , and BH3S61AS78A ( BH3AA ) were designed according to the Quickchange II Site-directed mutagenesis Kit ( Agilent Technologies ) using the pOZ-FH-C-Bid-BH3-mut-hCD25 as a template . Stable cell lines were generated with retroviral transduction using Fugene 6 ( Promega ) or Lipofectamine 2000 ( Thermo Fisher Scientific ) . Please see Table 1 for primer sequences . At the indicated times cells were , washed , incubated with Annexin V-FITC ( Biovision ) in 1X Annexin V staining buffer ( 10 mM HEPES , pH 7 . 4 , 140 mM NaCl , 2 . 5 mM CaCl2 ) . Immediately prior to analysis , propodeum iodide ( Sigma ) was added to a final concentration of 1 μg/ml . TNF-α/Actinomycin D death assays were performed by treating cells with 25 ng/ml TNF-α and 50 ng/ml Actinomycin D in complete IMDM growth medium Samples were analyzed on a Becton-Dickinson flow cytometer and FlowJo analysis software . Cell growth was determined by trypan blue viability . Cells were washed with 0 . 1 M cacodylate buffer and fixed in 2 . 5% glutaraldehyde/0 . 1M cacodylate for 1 hr at room temperature and left at 4°C overnight . The samples were post-fixed in 1% osmium tetroxide and washed 3 times with 0 . 1 M cacodylate buffer . The samples were dehydrated through a graded ethanol series followed by incubation in 100% ethanol and propylene oxide ( PO ) as well as 2 exchanges of pure PO . Samples were embedded in epoxy resin and polymerized at 60°C for 48 hr . For each sample , 70–80 nm ultra-thin sections were cut and mounted on 300-mesh copper grids . Two sections per sample were stained at room temperature with 2% uranyl acetate and lead citrate . Imaging was done on a Philips/FEI Tecnai T-12 high resolution transmission electron microscope with a side mounted 2k × 2 k AMT CCD camera . For initial cell line analysis , a total of 40 images were captured per cell type . Images were quantified at 30 , 000x . LV cardiac tissue was harvested from WT or Bid-/- mice at 18 hours with or without Epinephrine ( 0 . 5 mg/kg ) and immediately fixed and processed as described above . All images were acquired in the Vanderbilt Cell Imaging Shared Resource . Quantitation was done with FIJI ( Fiji Is just ImageJ ) software using a stereology plugin ( Version 0 . 1 ) to create a multipurpose stereological grid ( Gundersen and Jensen , 1987 ) . Horizontal grid lines were overlaid on each image using the same tile density setting for all samples . The end of each line was counted as a point and points on the grid were counted as nucleus , extracellular space , cytoplasm or mitochondria . Total reference points per image were everything except nucleus and extracellular space . Cristae were counted when intersecting the grid line or point , and each crista was counted twice to account for double membranes . Data is represented as either area density ( equivalent to volume density ) , which is the number of mitochondria divided by the number of reference points . Length density ( which is equivalent to surface density ) was calculated as two times the number of cristae intersections divided by the total length of line for all possible intersections . For LV tissue cristae quantitation , 150 individual cristae were measured per treatment condition using the measurement tool in ImageJ software . MPCs were treated as indicated and clarified cell extracts were prepared by lysis in RIPA buffer supplemented with protease ( Complete Mini , Roche ) and phosphatase ( PhosSTOP , Roche ) inhibitor followed by centrifugation at 12 , 000 rcf . Heart tissue extract was also prepared in the same way . Proteins were resolved by SDS-PAGE and transferred to PVDF membrane . Immunoblots were probed with the indicated antibodies and developed using chemiluminescent HRP substrate and autoradiography film . Co-immunoprecipitation was performed on 293T cells transfected by Fugene 6 ( Promega ) with the following Bid constructs: FlagHA-Bid , FHA-BidD59A , FHA-BidM148T and the following Mcl-1 constructs ( a kind gift from Dr . Joseph Opferman ) : pMSVC-puro ( empty vector ) , Mcl-1 ( WT ) , Mcl-1OM , Mcl-1Matrix . Input was removed from equal concentrations of whole cell lysate , followed by immunoprecipitation with Flag-M2 agarose beads ( Sigma ) . Antibodies used: anti-Bid goat ( R and D Systems ) or anti-Bid rabbit polyclonal antibody ( Wang et al . , 1996 ) , anti-Bim H-5 ( Santa Cruz ) , anti-Bad Clone 48 ( BD Biosciences ) , anti-Puma/bbc3 ( Sigma ) , N-terminal ( Sigma ) , anti-HA tag ( Sigma ) , anti-VDAC1 ( Abcam ) , anti-β-Actin ( Sigma ) , anti-GAPDH ( Santa Cruz ) , anti-Bak , NT ( EMD Millipore ) , anti-MnSOD ( Stressgen ) , anti-Mcl-1 ( Rockland Immunochemicals Inc ) anti-Opa1 ( BD Biosciences ) , anti-Calreticulin ( Cell Signaling Technology ) , anti-PDH E2/E3 ( Abcam ) , anti-HRP conjugated anti-rabbit ( GE Healthcare ) , and HRP conjugated anti-mouse ( Novex ) , and HRP conjugated anti-goat ( Santa Cruz ) . Mitochondria were isolated by differential centrifugation from both tissue and cell lines . Unless stated otherwise , all isolations were done at 4°C . Mouse liver mitochondria were isolated using a protocol adapted from Brookes et al . ( Brookes et al . , 2002 ) and heart mitochondria were isolated based on a protocol by JW Palmer et al . ( Palmer , 1977 ) . Liver tissue: harvested livers were placed in ice cold isolation buffer ( IB ) ( 200 mM sucrose , 5 mM HEPES-KOH , pH 7 . 4 , and 1 mM EGTA ) and homogenized in a glass-glass dounce homogenizer . The homogenized tissue was centrifuged at 1 , 000 g and clarified supernatant was centrifuged at 10 , 000 g to pellet mitochondria followed by two wash spins in IB at 10 , 000 g to obtain a final crude mitochondrial pellet . Light membrane was removed based on the protocol by Wieckowski et al . ( Wieckowski et al . , 2009 ) . After a crude pellet was obtained , mitochondria were resuspended in MRB buffer ( 250 MM mannitol . 5 mM HEPES ( pH 7 . 4 ) and 0 . 5 mM EGTA ) and further purified in 30% Percoll gradient ( vol/vol ) , spun for 30 min at 95 , 000 g . Purified mitochondria were isolated with a Pasteur pipette from the bottom of the tube followed by two wash spins at 6 , 3000 g for 10 min . Mitochondria were resuspended in MRB buffer and stored at −80°C . Heart tissue: Hearts were dissected , rinsed with buffer A ( 220 mM mannitol , 70 mM sucrose , 5 mM MOPS , 2 mM EGTA and 0 . 1% BSA , pH 7 . 4 ) and minced into small pieces . Tissue was homogenized in a glass-glass dounce homogenizer . Tissue was then centrifuged at 500 g , supernatant was retained , and the pellet was washed and repeated . Supernatant from both spins were combined at 3 , 000 g to obtain a final mitochondrial pellet . MPCs: MPC mitochondria were prepared based upon the protocol by Wieckowski et al . ( Wieckowski et al . , 2009 ) . At least 2 × 108 cells were harvested , rinsed in cold PBS buffer and re-suspended in isolation buffer ( 225 mM mannitol , 75 mM sucrose , 0 . 1 mM EGTA , and 30 mM Tris-HCl , pH 7 . 4 ) containing 20 µg/ml digitonin to permeabilize the outer membrane . After a 25 min incubation on ice , cells were homogenized with a glass-glass dounce homogenizer until >90% of the cells were damaged ( determined by trypan blue visualization ) . Cell debris was removed with two 5 min spins at 600 g followed by a 7 , 000 g spin for 10 min . The mitochondrial containing pellet was washed in buffer , and spun at 7 , 000 g , washed and followed by a final spin at 10 , 000 g . The mitochondrial pellet was stored in MRB buffer at −80°C . Cytosolic proteins were retained from the supernatant after debris removing spins and spun at 100 , 000 g for 1 hr to separate light membranes . Protein concentration for all isolations was determined by Bradford reagent . Mitochondria were isolated from WT mouse liver , with fragmented mitochondria removed from the pellet after the first fast spin . Isolated mitochondria ( 4 mg/ml ) were then treated with 0 . 5 mg/ml Proteinase K ( Macherey-Nagel GmbH and Co . KG ) in the presence or absence of 1% SDS for 20 min on ice followed by quenching with 5 mM PMSF . Crude liver mitochondria were isolated from two WT mice , and fragmented mitochondria were removed from the pellets after the first fast spin . Pellets were combined , and treated as described in Perciavalle et al . ( 2012 ) with the following modification: 1 . The mitoplast fraction was washed 2x in buffer ( 10 mM KH2PO4 with one-third volume of 10 mM MgCl2 ) to remove contaminating OMM and 2 . isolated OMM was resuspended in buffer followed by a second spin at 100 , 000 g for 1 hr for further purification . Echocardiograms on male Bid +/+ ( WT ) and Bid-/- C57BL/6 mice were performed under 2–3% isoflurane anesthesia using an a VisualSonics Vevo 770 instrument housed and maintained in the Vanderbilt University Institute of Imaging Science core lab . Measurements of the left ventricular internal diameter end diastole ( LVIDd ) and the left ventricular internal diameter end systole ( LVIDs ) were determined from M-mode tracings in triplicate for each mouse . Mice were echoed before ( baseline ) , and 18 hr ( stress condition ) and 5 days after ( recovery ) an IP injection of Epinephrine at 0 . 5 mg/kg per mouse . Female WT ( Bid+/+ ) and Bid-/- C57BL/6 approximately 12–18 weeks of age were treated with 3 doses of Doxorubicin at 7 . 5 mg/kg 5 days apart . Echocardiogram was performed three days after the final dose . Echocardiography was performed using the VEVO2100 digital ultrasound system ( Visual Sonics; Toronto , Ontario ) . Studies were performed using the MS400 18–38 MHz transducer . M-mode images were then processed using the Visual Sonics Software ver2 . 2 . All measurements were made in at least duplicate using the LV trace function . Hearts were excised from mice , weighed , and fixed for a minimum of 12 hr ( overnight ) in 10% formalin and embedded in paraffin . Coronal sections of hearts were cut and stained using H and E and Masson trichrome blue stain by the Translational Pathology Shared Resource ( TPSR ) at Vanderbilt University . Trichrome stained slides were scanned at 40X magnification using the Aperio CS2 Brightfield Scanner or whole slide imaging was performed in the Digital Histology Shared Resource at Vanderbilt University Medical Center ( www . mc . vanderbilt . edu/dhsr ) . Representative 2x and 60x H and E and Trichrome images were acquired on an Olympus BX43 brightfield microscope with a Spot Insight camera . Aperio Imagescope software version 12 . 3 . 28013 was used to define regions of within the left ventricle , excluding edges where stain uptake may have been falsely increased or any visible artifact within the section . Slides were run through a positive pixel algorithm and input parameters were adjusted to detect aniline blue staining and positive pixels are counted and grouped as weak , medium , or strong intensity . Positivity is defined as the total number of positive pixels divided by the total number of pixels in the region of interest . Equal amounts of Bid +/+ and Bid-/- MPC mitochondrial protein were isolated and frozen . Samples were trypsinized and analyzed by MudPIT . MS/MS spectra were identified using SEQUEST software which queried a Uniprot-mouse-reference-canonical_20121112_rev database ( Unknown version , 86222 entries ) . Results were visualized in Scaffold 4 . 5 . 1 software ( Proteome Software Inc . ) and protein identification was limited to two unique peptides per protein and a 5% FDR ( false discovery rate ) for both peptides and proteins . For analysis , samples were ranked based upon Fisher’s exact test done in Scaffold with a significance value of p<0 . 05 . The Mouse MitoCarta 2 . 0 ( Calvo et al . , 2016; Pagliarini et al . , 2008 ) ( Broad Institute ) was used to verify genes encoding mitochondrial proteins . Mitochondria from heart and liver tissue were prepared as described . Complexes were extracted based on the protocol by Wittig , et al . ( Wittig et al . , 2006 ) and run on native gels followed by incubation with complex specific substrates . Specifically , mitochondrial protein was extracted with digitonin at a 6 . 0 g/g detergent/protein ratio for complexes I and IV extraction and 2 . 5 g/g ratio for complex V . After solubilization , samples were spun at 20 , 000 g for 20 min . Supernatant was retained and protein concentration was determined by Bradford reagent ( BioRAD ) . Samples were supplemented with 50% glycerol and 5% Coomassie blue G-250 dye . Equivalent protein concentrations were then loaded onto pre-cast NativePAGE 4–16% Bis-Tris gel ( Invitrogen by ThermoFisher Scientific ) . Samples were run at 4°C for 30 min at 100V and 2 1/2 hr at 300V . Cathode and anode buffers per Wittig , et al . For Coomassie band visualization , gels were stained with NOVEX Colloidal blue staining kit ( Invitrogen ) and de-stained overnight . All in gel activity assays were performed at room temperature . Complex I was developed in 5 mM Tris-HCl buffer ( pH 7 . 4 ) supplemented with 10 mg/ml NADH ( Roche Diagnostics ) and 25 mg of Nitro Blue Tetrazolium ( Sigma ) for 10 min . Complex IV was developed in 50 mM Sodium Phosphate buffer ( pH 7 . 2 ) supplemented with 5 mg of Diaminobenzidine ( DAB ) ( Sigma ) and 100 µl of horse heart cytochrome c ( Sigma ) for at least 30 min . Complex V activity was determined by equilibration of native gels in a 35 mM Tris/270 mM Glycine buffer ( RPI ) ( pH 8 . 3 ) for 1 hr followed by addition of 14 mM MgSO4 ( Fisher ) , Pb ( NO3 ) 2 ( Sigma ) and 8 mM ATP ( Roche ) , adjusted to pH 8 . 6 and incubated until a precipitate appeared . Reactions were quenched with fixation in methanol and gels were scanned for quantitation . Quantitation was done on replicate samples ( n = 3 WT and Bid-/- mice for CI and CIV , n = 4 WT and Bid-/- for CV ) run on the same gel for accuracy , however experiments were done a minimum of three independent times . Analysis was done using the gel tool function of ImageJ software and graphs were generated with GraphPad Prism . MPCs: To determine the basal respiration rate of MPCs , oxygen consumption rates ( OCR ) were measured in an Oroboros O2K oxygraph ( Oroboros Instruments ) . For each genotype , 2 × 106 viable cells , determined by trypan blue exclusion , were added to oxygraph chambers containing 2 ml of culture medium . The average OCR was measured over an interval of stable oxygen flux following addition of cells to the chamber . Cardiac fibers: Initial basal respiration of cardiac myocytes was performed on 2–3 mg of heart fibers extracted from the left ventricle of Bid +/+ and Bid-/- mice ( Veksler et al . , 1987 ) . Fibers were prepared in ice-cold relaxation and preservation solution ( 2 . 77 mM CaK2EGTA , 7 . 23 mM K2EGTA , 6 . 56 mM MgCl2 , 5 . 7 mM Na2ATP , 14 . 3 mM phosphocreatine , 20 mM taurine , 0 . 5 mM dithiothreitol , 50 mM K-MES and 20 mM imidazole , pH 7 . 1 ) . Fibers were permeabilized by incubation at 4°C for 20 min in relaxation and preservation solution containing 50 μg/ml saponin . Fibers were transferred into mitochondrial respiratory solution ( MiRO5: 0 . 5 mM EGTA , 3 mM MgCl2 , 60 mM K-lactobionate , 20 mM taurine , 10 mM KH2PO4 , 20 mM HEPES , 110 mM Sucrose , and 1 g/L BSA , adjusted to pH 7 . 1 with KOH ) Oroboros O2K oxygraph chambers containing MiRO5 buffer were supplemented with 10 mM glutamate , 4 mM malate , and 2 mM ADP . Respiration rate was determined during stabilized oxygen flux . ATP/O of cardiac fibers: Mitochondrial ATP production and O2 consumption were determined as described previously ( Lark et al . , 2016 ) . Briefly , the left ventricle was excised and placed in ice-cold Buffer X containing ( in mM ) : 7 . 23 K2EGTA , 2 . 77 CaK2EGTA , 20 Imidazole , 20 Taurine , 5 . 7 ATP , 14 . 3 Phosphocreatine , 6 . 56 MgCl2-6H2O and 50 MES ( pH 7 . 1 , 295 mOsm ) . Under a dissecting microscope , valves and wall muscle were removed and remaining muscle separated into small bundles and weighed . Less than 3 mg wet weight of tissue was used for each experiment . Fiber bundles were incubated in Buffer X supplemented with 40 μg/ml saponin for 30 min . PmFBs were then washed in ice-cold Buffer Z containing ( in mM ) : 110 K-MES , 35 KCl , 1 EGTA , 5 K2HPO4 , 3 MgCl2-6H2O , and 5 mg/ml Bovine serum albumin ( BSA , pH 7 . 4 , 295 mOsm ) and remained in Buffer Z on a rotator at 4°C until analysis ( <4 hr ) . O2-equilibrated Buffer Z was supplemented with: 5 U/ml HK , 5 U/ml G6PDH , 5 mM D-Glucose , 2 mM NADP+ and 20 mM Creatine Monohydrate . This buffer permitted coupling of glucose-dependent , HK-catalyzed ATP hydrolysis to G6PDH-catalyzed reduction of NADP+ to NADPH in a 1:1 stoichiometry . To measure ATP synthesis , auto-fluorescence of NADPH ( 340/460 ex/em ) was measured continuously at 30°C simultaneously with O2 consumption using a customized system integrating monochromatic fluorescence ( FluoroMax-4 , Horiba Jobin Yvon , Edison , NJ ) via a fiber optic cable ( Fiberguide Industries ) with high-resolution respirometry ( Oroboros Oxygraph-2k , Innsbruck , Austria ) ( Figure 1B ) . Complex I-supported respiration was established with glutamate ( 10 mM ) and malate ( 4 mM ) . ADP ( 75 μM ) was added to determine Complex I-supported ATP synthesis and O2 consumption . Rotenone ( 0 . 5 μM ) was added to inhibit Complex I , followed by the addition of succinate ( 10 mM ) to assess Complex II-supported respiration . Rates of ATP synthesis ( JATP ) were quantified by applying a standard curve generated from ATP titrations in the presence of the enzyme-coupled system and the respiratory substrates . For each step of the experimental protocol , JO2 or JATP were obtained from identical time points and are reported as the mean of >20 s of steady-state data ( >10 individual data points ) . Instrumental background rates ( prior to any substrate additions ) were subtracted from all subsequent values for JO2 and JATP and data were normalized to PmFB weight . ATP:O ratio was calculated by dividing the rate of ATP synthesis by the rate of atomic oxygen consumed using the formula: ATP/O = JATP/ ( JO2*2 ) Intracellular ROS was determined by staining MPCs with either 2 µM MitoSOX or 5 µM DHE for 30 min followed by washing and analysis by flow cytometry . As a positive control , cells were also treated with Antimycin A for 1 . 5 hr prior to staining with MitoSOX . Membrane potential was measured by staining for 30 min with 50 nM TMRE . For Bid-/- + FHA-BidBH3 and Bid-/- + FHA-BidBH3AA MPCs . Cells were also stained for human CD25 and measurements were obtained from CD25 +cells . All samples were analyzed on a Becton-Dickinson flow cytometer and FlowJo analysis software . We performed PrediXcan analysis ( Gamazon et al . , 2015 ) to evaluate potential roles for Bid in myocardial infarction . PrediXcan proposes gene expression as a mechanism underlying disease risk by testing the genetically determined component of expression for association with disease risk . An observed association implies a likely causal direction of effect from the gene expression trait to disease risk since , as can be reasonably assumed , disease risk does not alter germline genetic profile . The genetic component of BID expression was estimated from an imputation model ( Elastic Net ( Gamazon et al . , 2015 ) with mixing parameter α = 0 . 5 ) trained on a reference transcriptome data set ( the Genotype-Tissue Expression ( GTEx ) project ( Gamazon et al . , 2018; Gamazon et al . , 2015 ) . Imputation performance for each analyzed tissue was evaluated using 10-fold cross-validation ( between imputed expression and directly measured expression ) , as previously described ( Gamazon et al . , 2015 ) . The imputation model derived from GTEx left heart ventricle was then applied to genome-wide association study data from BioVU , a Vanderbilt University resource that links human DNA samples and genetic data to de-identified electronic health records ( EHRs ) . The development of BioVU has been previously described ( Roden et al . , 2008 ) . We applied PrediXcan on 29 , 366 individuals ( of whom 5146 are MI cases ) to impute the genetically determined BID expression and to test for correlation with the phenotype of interest . We performed logistic regression with disease status as the response variable and the inferred genetic component of gene expression as predictor . We also evaluated the patients with the lowest BID expression , that is , in the bottom 5% of the expression distribution and closest to a BID ‘knockout’ , to test for enrichment of MI and to directly validate the observed increased fibrotic damage akin to human MI observed in Bid-/- mice . To determine whether the observed association implicated a specific pathophysiology , we applied summary-statistics based PrediXcan ( Barbeira et al . , 2018 ) with atrial fibrillation using a recently released GWAS data in more than 1 million patients ( Nielsen et al . , 2018 ) . In a secondary analysis , we also tested the other members of the BH3-only Bcl-2 family as well as the BID-interacting protein MTCH2 . The connection of MTCH2 with obesity has been explored in the literature ( Bauer et al . , 2009 ) , prompting us to evaluate the PrediXcan association with BMI using the GIANT Consortium dataset ( Locke et al . , 2015 ) . The Synthetic Derivate consists of approximately 2 . 8 million de-identified records that contain basic clinical and demographic information of individuals seen at Vanderbilt University Medical Center . This resource was used to determine the number of patients with the following ICD-9 codes as well as their basic demographic information ( age , sex , and ethnicity ) : Burn ( 949 ) , Headache ( 784 ) , Myocardial Infarction ( 410 ) , Cardiomyopathy ( 425 ) , Hypertension ( 401 . 9 ) , Diabetes mellitus ( 250 ) , Hypercholesteremia ( 272 ) . Caucasian patient numbers were then used to create a 2 × 2 contingency table binned by age group to determine the relative risk ( RR ) of each ICD-9 code with MI . Raw RR risk scores and patient numbers are as indicated in the figure . The CARDIoGRAMplusC4D Consortium consists of multiple large-scale genetic association studies ( e . g . , 14 CAD GWAS studies ) of individuals of European descent totaling 22 , 233 cases and 64 , 762 controls and a later ( larger but more heterogeneous ) meta-analysis of GWAS studies of European , South Asian , and East Asian decent totaling 60 , 801 cases and 123 , 504 controls . These data provide a resource to identify new SNP associations with coronary artery disease or myocardial infarction and facilitate replication of the gene-level ( PrediXcan ) association ( Nikpay et al . , 2015; Schunkert et al . , 2011 ) . The human clinical cohort was derived from BioVU . Genotyping was performed with the Illumina Human Exome BeadChip v1 by the Vanderbilt DNA resources core ( VANTAGE ) using standard quality control procedures . Pre-specified clinical syndromes of cardiac injury were heart failure and MI . Phenotypes were defined by extraction of International Classification of Disease ( ICD9 ) billing codes and application of a code translation table used for phenome-wide association scanning ( Pews ) , a validated method of mapping ICD9 codes to clinical phenotypes within the EMR environment ( Denny et al . , 2013; Denny et al . , 2010 ) , ( Wei et al . , 2017 ) . Analyses of genotype-phenotype associations from the coding SNPs were performed using the R statistical package . Due to the individual rarity of variants , SNPs were collapsed prior to association testing . Pre-specified SNP groupings were: ( 1 ) presence of one or more of any genotyped missense variants in the BID gene , and ( 2 ) presence of one or more genotyped SNPs in the MBD . Association testing between SNPs and clinical phenotypes was performed using multivariable logistic regression with age , gender , systolic blood pressure , cholesterol levels , body mass index ( BMI ) , and hemoglobin A1C included as covariates ( in the case of heart failure , prior MI was also included as a covariate ) . A Bonferroni correction was applied to account for multiple testing , resulting in an adjusted p-value for significance of 0 . 0125 . We also utilized the recent meta-analysis of exome-chip studies of MI , involving 42 , 335 cases and 78 , 240 controls to replicate the coding SNP associations ( Stitziel et al . , 2016 ) . Within each experiment , all pairwise comparisons were made by the indicated statistical test and all relevant and significant comparisons are indicated on the figures or in figure legends . All biological replicates ( denoted as n ) are defined as the same experimental method independently tested on different samples of the same type of cell or mouse model . It should also be noted that one Bid-/- mouse was not included in the statistical analysis of echocardiogram data ( Figure 4 ) at 18 hr as it was a statistical outlier ( Grubbs’ outlier test , p<0 . 05 ) . Graphs and statistical analysis were completed using GraphPad Prism software and the following denote statistical significance: ns = not significant , *p<0 . 05 , **p<0 . 01 , ***p<0 . 005 , ****p<0 . 001 . All error bars indicate SEM ( standard error of the mean ) . Human blood and tissue samples for BioVU were obtained with written informed consent under protocols approved by the Vanderbilt University Medical Center IRB , and PrediXcan analysis for BioVU is encompassed in VUMC IRB# 151187 . As indicated in the IRB , this study does not meet the definition of human subject’s research . The Vanderbilt University Institutional Animal Care and Use Committee approved all experiments ( IACUC #M16000037 , M/14/231 , V/17/001 , M1600220 ) . The authors declare that all relevant data are available within the article and its supplementary information files . Publicly available data on coronary artery disease/myocardial infarction have been contributed by CARDIoGRAMplusC4D investigators and have been downloaded from www . CARDIOGRAMPLUSC4D . ORG . GTEx Consortium ( v6p ) transcriptome/genotype data is available through the GTEx portal ( htt://www . gtexportal . org ) and through dpGap ( Gamazon et al . , 2018 ) . Model definition files are described in Gamazon et al . , 2015 . Code for the following analyses is publicly available: PrediXcan: https://github . com/hakyimlab/PrediXcan S-PrediXcan: https://github . com/hakyimlab/MetaXcan | Cells contain specialized structures called mitochondria , which help to convert fuel into energy . These tiny energy factories have a unique double membrane , with a smooth outer and a folded inner lining . The folds , called cristae , provide a scaffold for the molecular machinery that produces chemical energy that the cell can use . The cristae are dynamic , and can change shape , condensing to increase energy output . Mitochondria also play a role in cell death . In certain situations , cristae can widen and release the proteins held within their folds . This can trigger a program of self-destruction in the cell . A family of proteins called Bcl-2 control such a ‘programmed cell death’ through the release of mitochondrial proteins . Some family members , including a protein called Bid , can reorganize cristae to regulate this cell-death program . When cells die , Bid proteins that had been split move to the mitochondria . But , even when cells are healthy , Bid molecules that are intact are always there , suggesting that this form of the protein may have another purpose . To investigate this further , Salisbury-Ruf , Bertram et al . used mice with Bid , and mice that lacked the protein . Without Bid , cells – including heart cells – struggled to work properly and used less oxygen than their normal counterparts . A closer look using electron microscopy revealed abnormalities in the cristae . However , adding ‘intact’ Bid proteins back in to the deficient cells restored them to normal . Moreover , without Bid , the mice hearts were less able to respond to an increased demand for energy . This decreased their performance and caused the formation of scars in the heart muscle called fibrosis , similar to a pattern observed in human patients following a heart attack . DNA data from an electronic health record database revealed a link between low levels of Bid genes and heart attack in humans , which was confirmed in further studies . In addition , a specific mutation in the Bid gene was found to affect its ability to regulate the formation of proper cristae . Combining evidence from mice with human genetics revealed new information about heart diseases . Mitochondrial health may be affected by a combination of specific variations in genes and changes in the Bid protein , which could affect heart attack risk . Understanding more about this association could help to identify and potentially reduce certain risk factors for heart attack . | [
"Abstract",
"Introduction",
"Results",
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] | [
"cell",
"biology"
] | 2018 | Bid maintains mitochondrial cristae structure and function and protects against cardiac disease in an integrative genomics study |
Mesenchymal ( lamellipodial ) migration is heterogeneous , although whether this reflects progressive variability or discrete , 'switchable' migration modalities , remains unclear . We present an analytical toolbox , based on quantitative single-cell imaging data , to interrogate this heterogeneity . Integrating supervised behavioral classification with multivariate analyses of cell motion , membrane dynamics , cell-matrix adhesion status and F-actin organization , this toolbox here enables the detection and characterization of two quantitatively distinct mesenchymal migration modes , termed 'Continuous' and 'Discontinuous' . Quantitative mode comparisons reveal differences in cell motion , spatiotemporal coordination of membrane protrusion/retraction , and how cells within each mode reorganize with changed cell speed . These modes thus represent distinctive migratory strategies . Additional analyses illuminate the macromolecular- and cellular-scale effects of molecular targeting ( fibronectin , talin , ROCK ) , including 'adaptive switching' between Continuous ( favored at high adhesion/full contraction ) and Discontinuous ( low adhesion/inhibited contraction ) modes . Overall , this analytical toolbox now facilitates the exploration of both spontaneous and adaptive heterogeneity in mesenchymal migration .
Cell migration is a profoundly heterogeneous phenomenon . Indeed , cells can adopt several substantially different migration modalities , including multicellular , amoeboid , and mesenchymal ( also termed lamellipodial or lamellipodial-driven ) migration , which can all be utilized by a broad range of cell types , as well as lobopodial migration , which has been observed specifically in fibroblasts ( Friedl and Wolf , 2010; Sahai , 2005; Petrie and Yamada , 2015; Petrie et al . , 2014; Welch , 2015; Friedl and Alexander , 2011 ) . These migration modes represent 'prespecified' cellular configurations ( i . e . cell states ) that are favored under particular conditions ( Friedl , 2004 ) . Switch-like conversion between these distinct modes is therefore part of the plastic , adaptive/compensatory response of cells to either environmental modulation ( Liu et al . , 2015; Starke et al . , 2014; Ruprecht et al . , 2015 ) or molecular targeting ( Sahai et al . , 2007; Sanz-Moreno et al . , 2008; Somlyo et al . , 2003; Wolf , 2003 ) . At a finer scale , heterogeneity is also evident within these migration modes , arising either stochastically or as an adaptive response to changing cues ( Geiger et al . , 2009; Lämmermann and Sixt , 2009; Lock et al . , 2014; Winograd-Katz et al . , 2009 ) . Yet , partly due to a lack of adequate quantification , it remains unclear to what extent variation within modes occurs either progressively along a continuum or in a switch-like manner between as yet undefined intra-modal subpopulations . Specifically , in the case of amoeboid migration , three discrete sub-modalities have been observed , and these can co-exist under individual conditions ( Welch , 2015; Lämmermann and Sixt , 2009; Yoshida , 2006 ) . By contrast , potentially distinct styles of mesenchymal ( lamellipodial ) migration , including keratocyte-like ( Barnhart et al . , 2015; Keren et al . , 2008 ) and fibroblast-like ( Abercrombie et al . , 1977; Theisen et al . , 2012 ) migration , have been described as arising largely in separate cell types or conditions . Therefore , despite some early suggestions ( Lewis et al . , 1982 ) , it has remained uncertain to what extent divergent sub-modalities of mesenchymal migration spontaneously emerge in parallel within uniform cell populations and conditions , and whether these modes are truly quantitatively distinct , or instead represent extremes in a broad phenotypic continuum . These questions are important because each discrete migration mode , with its unique internal logic and dependencies , may respond differently to altered regulatory cues – thereby defining a multifaceted adaptive response . Such responses are exemplified by the seminal observation that amoeboid motility provides a compensatory escape route for tumor cells when mesenchymal migration is blocked via inhibition of proteolytic mechanisms or by confinement ( Wolf , 2003 ) . Identifying and characterizing such distinct modes within individual cell types and/or conditions may therefore be key to understanding heterogeneity in response to experimental or even clinical interventions . Unlike amoeboid migration , which requires little or no cell-matrix adhesion ( Lämmermann et al . , 2008 ) , mesenchymal migration demands cell adhesion to the extracellular matrix ( ECM ) for effective force application ( Friedl and Wolf , 2010 ) . This adhesion is achieved through the interaction of integrin-mediated cell-matrix adhesion complexes ( CMACs ) with ECM ligands , while applied forces are generated by the F-actin cytoskeleton and actomyosin system ( Geiger et al . , 2009; Humphries et al . , 2015 ) . This arrangement defines the ECM – adhesion – F-actin axis that directly mediates mesenchymal migration ( Parsons et al . , 2010; Schwartz , 2010 ) . Integrins are central to the ECM – adhesion – F-actin axis and are both anchored and activated by binding through their extracellular domains to ECM ligands , such as collagen , laminin , and fibronectin ( Lewis et al . , 1982 ) . The composition , topology , and density of ECM ligands therefore play key roles in mesenchymal migration ( Geiger et al . , 2009 ) . Through interaction with integrin tails , talin acts a critical intracellular regulator of integrin activation ( Calderwood et al . , 1999; Kiss et al . , 2015; Moser et al . , 2009; Tadokoro , 2003 ) , and is also one of several CMAC proteins linking integrins to the F-actin cytoskeleton ( Schwarz and Gardel , 2012 ) . This enables cellular force application from F-actin , through CMACs , to the ECM , thereby driving membrane protrusion/retraction and cell translocation ( Small and Resch , 2005 ) . Furthermore , CMACs function both up- and downstream of the small GTPases Rac1 and RhoA ( Raftopoulou and Hall , 2004 ) , which modulate both F-actin polymerization and actomyosin contractility , the latter through the Rho kinase ( ROCK ) -mediated regulation of non-muscle myosin II ( Ridley , 2003; Riento and Ridley , 2003 ) . In fact , CMACs function as hubs for bi-directional chemical and mechanical information transduction across the plasma membrane , providing command and control of mesenchymal migration ( Hynes , 2002; Lock et al . , 2008 ) . Given the pivotal roles of CMACs and F-actin , their assessment can efficiently provide a broad estimation of how the complex machinery underlying mesenchymal migration is organized ( Lock et al . , 2014; Gardel et al . , 2010; Gupton and Waterman-Storer , 2006; Kim and Wirtz , 2013 ) . Such an approach , generally based on quantitative imaging , is especially effective when multiscale data capturing both cell behavior ( migration ) and organization ( e . g . CMAC and F-actin status ) is derived simultaneously on a per cell basis . This facilitates the leveraging of natural or induced heterogeneity to define: i ) the statistical structure of variation ( e . g . progressive or discrete ) within and between cell populations , as well as; ii ) key trends , dependencies and relationships within the cell migration system ( Lock et al . , 2014; Keren et al . , 2008; Kiss et al . , 2015; Kowalewski et al . , 2015; Ku et al . , 2012; Lee et al . , 2015; Lock and Strömblad , 2010 ) . Here , we extend on this approach by employing a tailored analytical toolbox to detect two quantitatively distinct sub-modalities of mesenchymal migration , thus illuminating the discrete nature of variation within this general migration archetype . We employ this unique suite of analytical tools to further characterize key aspects of the behavior , organization , and regulation of these divergent migration strategies . Collectively , this study now provides conceptual and practical capabilities to the cell migration research community , while also highlighting the importance of distinguishing individual mesenchymal migration modes as a vital precursor to understanding mesenchymal migration as a whole .
To enable the detection of discrete mesenchymal migration sub-modalities , and thereafter , the comprehensive analysis of cellular organization , regulation , and adaptation underlying each mode , we have integrated a unique combination of analytical tools . These are now made freely available as a Matlab Toolbox: 'The Cell Adhesion and Migration Analysis Toolbox' – along with the raw quantitative data underlying this study , sample image data to aid implementation , and explanatory documentation ( see doi:10 . 5061/dryad . 9jh6m ) . The features included in this Toolbox are also specified briefly in 'Materials and methods' . H1299 ( non-small cell lung carcinoma ) cells stably expressing EGFP-paxillin ( marker for CMACs ) and RubyRed-LifeAct ( marker for F-actin ) ( H1299 P/L cells [Lock et al . , 2014] ) were imaged via confocal microscopy on glass coated with 2 . 5 µg/ml fibronectin ( FN ) . H1299 P/L cells moved individually and exclusively via mesenchymal ( lamellipodial ) migration . Nonetheless , we observed and classified two qualitatively different migration modes emerging within the clonally derived H1299 P/L cell population . We termed these migration modes 'Discontinuous' and 'Continuous' , reflecting their contrasting stepwise and smooth motion , respectively ( Figure 1 ) . Specifically , cells migrating in the Discontinuous mode cycle in sequence through at least three recognizable stages of movement , which we term 'lateral protrusion' , 'polarization' , and 'tail retraction' , as described in Figure 1A ( also see Video 1 ) . These stages produce dramatic changes in cell morphology and frequent changes in migratory direction . Such directional changes occur at least in part because lateral protrusions typically develop at approximately 90° to the axis of the preceding tail retraction . Overall , the stepwise nature of Discontinuous migration is highly reminiscent of previous descriptions of fibroblast-like migration ( Theisen et al . , 2012; Abercrombie et al . , 1970; Chen , 1981 ) . By contrast , cells migrating in the Continuous mode move progressively , with less frequent changes in cell morphology and motile direction , in a manner analogous to classical keratocyte-like migration ( Figure 1B and Video 2 ) ( Keren et al . , 2008 ) . Importantly , these migration modes emerge spontaneously in parallel under uniform experimental conditions within a clonally derived cell line ( Figure 1—figure supplement 1 and Video 3 ) . It is noteworthy that cells may traverse the same underlying substrate regions while stably occupying distinct modes ( see Video 3 ) , indicating that local environmental inconsistencies are not the principal cause of differential mode identity . Yet , inter-modal conversion is possible , occurring bi-directionally between modes ( Figure 1—figure supplement 2 and Videos 4 and 5 ) , indicating their profound plasticity . 10 . 7554/eLife . 11384 . 003Figure 1 . Classification of 'Discontinuous' and 'Continuous' modes of mesenchymal ( lamellipodial ) migration . ( A , B ) Confocal image sequences of individual H1299 cells stably expressing EGFP-paxillin ( cell-matrix adhesion complex [CMAC] marker; upper image panels ) and RubyRed-LifeAct ( F-actin marker; lower image panels ) migrating in either Discontinuous ( A ) or Continuous ( B ) modalities on fibronectin ( FN ) -coated ( 2 . 5 µg/ml ) glass . Images are displayed with gray-scale inverted . Numbers denote time ( min ) . Schematics in blue ( Discontinuous , A ) and orange ( Continuous , B ) depict typical cell morphology changes associated with each migration mode . Note stepwise cycles of: lateral protrusion ( in directions of dashed arrows ) ; cell polarization; and tail retraction ( in direction of solid arrows ) that recur during Discontinuous migration , and that lateral protrusion tends to occur at 90° to the preceding tail retraction . During Continuous migration , cell morphology is relatively stable , with many small protrusion ( open arrowheads ) and retraction ( closed arrowheads ) events producing smooth movement . Movies corresponding to cells shown in A and B are available in supporting material ( Videos 1 , 2 ) . ( C ) Quantification of frequencies of Discontinuous and Continuous modes . ( D ) Box plots of cell speed ( µm/sec ) per migration mode . Boxplots show median values and inter-quartile ranges ( IQR , 25% to 75% ) . Notches indicate median +/− 1 . 57 * IQR/√n ( approximates 95% confidence interval of the median , n = number of cell observations , see 'Materials and methods' ) . Statistical discernibility assessed by Wilcoxon rank sum test , p < 0 . 001 . ( E ) Cell trajectories in each mode were assessed via mean squared displacement ( MSD ) analysis and divided into quintiles ( 20% bins ) according to their migration cefficient ( related to speed of movement ) . Median values +/−1 . 57 * IQR/√n ( n = number of cell observations ) of a second MSD measure , persistence time ( related to migration direction stability ) , are plotted per migration coefficient quintile . Number of observations per quintile: Discontinuous = ~ 402; Continuous = ~ 95 . ( F ) A table summarizes results from visual inspection of several cell lines migrating on 2 . 5 µg/ml FN , confirming the emergence of either one or both Discontinuous and Continuous migration modes in these cell types . Example image sequences are presented in Figure 1—figure supplement 3 . ( G ) A table summarizes results from visual inspection of H1299 cells migrating on several different extracellular matrix ligands , confirming the emergence of both migration modes under each condition . Example image sequences are presented in Figure 1—figure supplement 4 . Scale bars = 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 00310 . 7554/eLife . 11384 . 004Figure 1—figure supplement 1 . Discontinuous and Continuous migration modes emerge in parallel under uniform conditions . A confocal image sequence showing the parallel emergence of Discontinuous and Continuous migration modes in H1299 P/L cells labeled with a far red membrane-dye on 5 µg/ml fibronectin-coated glass . ( A ) De-noised ( left ) and segmented ( right ) images show a selected region from within a 4 x 4 image montage . Based on observed cell dynamics ( see Video 3 ) , cells were classified as in either the Discontinuous ( e . g . cell in blue box ) or Continuous mode ( e . g . cell in red box ) . ( B ) Stereotypical Discontinuous mode behaviors are displayed in a series of time points ( numbers denote minutes ) following the cell from the blue box in A . Specifically: Tail Retraction is denoted by solid arrows; Lateral Protrusion and Polarization are denoted by dashed double headed arrows; ( C ) The relatively stable morphology of Continuous mode migration is shown by the cell from the red box in A . It is noteworthy that both highlighted cells sequentially traverse the same region of the coated substrate while maintaining distinct migration modes ( see Video 3 ) , indicating that potential inconsistencies in the local environment could not be responsible for the parallel emergence of distinct modes . Scale bars = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 00410 . 7554/eLife . 11384 . 005Figure 1—figure supplement 2 . Discontinuous and Continuous migration modes can spontaneously inter-convert ( mode switching ) . Confocal image sequences show the spontaneous inter-conversion between Discontinuous and Continuous migration modes by H1299 P/L cells labeled with a far red membrane-dye on 5 µg/ml fibronectin-coated glass . ( A ) De-noised ( upper ) and segmented ( lower ) time sequences ( numbers denote minutes ) of a single cell spontaneously transitioning ( switching ) from the Continuous migration mode to the Discontinuous migration mode ( see tracked cell in Video 4 ) . The approximate timing of the Mode Transition is indicated . ( B ) De-noised ( upper ) and segmented ( lower ) time sequences of a single cell spontaneously transitioning ( switching ) from the Discontinuous migration mode to the Continuous migration mode ( see tracked cell in Video 5 ) . The approximate timing of the Mode Transition is indicated . Scale bars = 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 00510 . 7554/eLife . 11384 . 006Figure 1—figure supplement 3 . Discontinuous and/or Continuous migration modes recur in multiple cell types . Confocal image sequences exemplifying Discontinuous and/or Continuous migration on 2 . 5 µg/ml fibronectin-coated glass by several cell types , including: BT549 ( breast epithelial [ductal carcinoma] ) ; MDA-MB-231 ( breast epithelial [adenocarcinoma] ) ; Hep-3 ( hepatocellular carcinoma ) ; and HS578T ( breast epithelial [carcinoma] ) . Cells were labeled with a membrane dye and imaged at 5-min intervals . Images are displayed with gray-scale inverted . Numbers denote time ( min ) . Movies corresponding to image sequences ( in the order shown ) are available in supporting material ( Videos 6–11 , respectively ) . Scale bars = 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 00610 . 7554/eLife . 11384 . 007Figure 1—figure supplement 4 . Discontinuous and Continuous migration modes recur in H1299 cells adhering to multiple extracellular matrix ligands . Confocal image sequences exemplifying H1299 cell migration in Discontinuous and Continuous modes on glass coated with various purified extracellular matrix ligands , including: fibronectin ( FN ) ; Laminin; Collagen Type 1; and Vitronectin . Cells were labeled with a membrane dye and imaged at 5-min intervals . Images are displayed with gray-scale inverted . Numbers denote time ( min ) . Schematics of sequences on FN detail the characteristic movement and morphology changes associated with each mode , as shown in Figure 1 . See movies corresponding to image sequences ( in the order shown but excluding fibronectin sequences; as this is shown in Videos 1 , 2 ) ( Videos 12–17 , respectively ) . Scale bars = 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 00710 . 7554/eLife . 11384 . 008Video 1 . H1299 P/L cell migration in the Discontinuous mode . High-resolution multiscale imaging of a single H1299 P/L cell expressing EGFP-paxillin ( green , CMAC marker ) and RubyRed-LifeAct ( red , F-actin marker ) during migration in the Discontinuous mode on 2 . 5 µg/ml fibronectin . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 00810 . 7554/eLife . 11384 . 009Video 2 . H1299 P/L cell migration in the Continuous mode . High-resolution multiscale imaging of a single H1299 P/L cell expressing EGFP-paxillin ( green , CMAC marker ) and RubyRed-LifeAct ( red , F-actin marker ) during migration in the Continuous mode on 2 . 5 µg/ml fibronectin . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 00910 . 7554/eLife . 11384 . 010Video 3 . Parallel emergence of Discontinuous and Continuous modes in H1299 P/L cells . Low-resolution imaging of H1299 P/L cells labeled with a membrane dye shows the parallel emergence of both Discontinuous ( e . g . dark blue outline , trajectory shown ) and Continuous ( e . g . light blue outline , trajectory shown ) modes under uniform conditions . Cells have been segmented and tracked to highlight morphologies and trajectories . Note that two cells in the Continuous mode ( yellow and lilac outlines ) pass through the substrate region traversed by the Discontinuous mode cell ( dark blue ) , yet these cells remain in the Continuous mode . This implies that these migration modes are not simply determined by ( possible ) local variations in , for example , ECM substrate ( 5 µg/ml fibronectin ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 01010 . 7554/eLife . 11384 . 011Video 4 . Migration mode transition from Continuous to Discontinuous motility . Low-resolution imaging of H1299 P/L cells labeled with a membrane dye , during random migration on 5 µg/ml fibronectin . Cells have been segmented and tracked to highlight morphologies and trajectories . Note that the cell with light green outline ( trajectory shown ) transitions from Continuous to Discontinuous migration during the course of imaging . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 01110 . 7554/eLife . 11384 . 012Video 5 . Migration mode transition from Discontinuous to Continuous motility . Low-resolution imaging of H1299 P/L cells labeled with a membrane dye , during random migration on 5 µg/ml fibronectin . Cells have been segmented and tracked to highlight morphologies and trajectories . Note that the cell with light green outline ( trajectory shown ) transitions from Discontinuous to Continuous migration during the course of imaging . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 012 Under the conditions described above , ~80% of observations showed cells migrating via the Discontinuous mode , with the remaining 20% moving in the Continuous mode ( Figure 1C , see 'Materials and methods' for sample numbers relating to all experimental data ) . Automated cell tracking ( see 'Materials and methods' , [Lock et al . , 2014] ) revealed that cells in the Discontinuous mode migrate significantly faster than during Continuous migration ( Figure 1D ) . By applying mean squared displacement analysis of cell trajectories to assess measures related to cell speed ( migration coefficient ) and directionality ( persistence time ) , we found that Discontinuous migration is less directionally stable at any given speed ( Figure 1E ) . However , both modes of migration show a positive correspondence between speed and directionality , as recently reported ( Maiuri et al . , 2015 ) . Importantly , an extensive comparison of migratory behaviors in several additional cell lines ( Figure 1F , Figure 1—figure supplement 3 and Videos 6–11 ) and during H1299 cell migration on alternative extracellular matrix ligands ( Figure 1G , Figure 1—figure supplement 4 and Videos 12–17 ) indicated that Discontinuous and Continuous migration modes are consistently recurring phenomena . 10 . 7554/eLife . 11384 . 013Video 6 . BT549 cell migration in the Discontinuous mode . A single BT549 cell migrating in the Discontinuous mode . Cropped from a larger image montage . Cells labeled with a membrane dye during random migration on 2 . 5 µg/ml fibronectin . Image intensity scale inverted and brightness linearly adjusted for visualization ( note: intensity data are not quantified ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 01310 . 7554/eLife . 11384 . 014Video 7 . BT549 cell migration in the Continuous mode . A single BT549 cell migrating in the Continuous mode ( upper centre cell ) . Cropped from a larger image montage ( note , montage stitching can cause observable intensity boundaries in image; however , these have no effect on interpretation ) . Cells labeled with a membrane dye during random migration on 2 . 5 µg/ml fibronectin . Image intensity scale inverted and brightness linearly adjusted for visualization ( note: intensity data are not quantified ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 01410 . 7554/eLife . 11384 . 015Video 8 . MDA-MB-231 cell migration in the Discontinuous mode . A single MDA-MB-231 cell migrating in the Discontinuous mode . Cropped from a larger image montage . Cells labeled with a membrane dye during random migration on 2 . 5 µg/ml fibronectin . Image intensity scale inverted and brightness linearly adjusted for visualization ( note: intensity data are not quantified ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 01510 . 7554/eLife . 11384 . 016Video 9 . MDA-MB-231 cell migration in the Continuous mode . MDA-MB-231 cells migrating in the Continuous mode . Cropped from a larger image montage . Cells labeled with a membrane dye during random migration on 2 . 5 µg/ml fibronectin . Image intensity scale inverted and brightness linearly adjusted for visualization ( note: intensity data are not quantified ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 01610 . 7554/eLife . 11384 . 017Video 10 . Hep3 cell migration in the Discontinuous mode . Hep3 cells migrating in the Discontinuous mode . Cropped from a larger image montage ( note , montage stitching can cause observable intensity boundaries in image; however , these have no effect on interpretation ) . Cells labeled with a membrane dye during random migration on 2 . 5 µg/ml fibronectin . Image intensity scale inverted and brightness linearly adjusted for visualization ( note: intensity data is not quantified ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 01710 . 7554/eLife . 11384 . 018Video 11 . HS578T cell migration in the Continuous mode . A single HS578T cell migrating in the Continuous mode . Cropped from a larger image montage . Cells labeled with a membrane dye during random migration on 2 . 5 µg/ml fibronectin . Image intensity scale inverted and brightness linearly adjusted for visualization ( note: intensity data are not quantified ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 01810 . 7554/eLife . 11384 . 019Video 12 . H1299 P/L cell migration in the Discontinuous mode on laminin . H1299 P/L cells migrating in the Discontinuous mode . Cropped from a larger image montage . Cells labeled with a membrane dye during random migration on 2 µg/ml laminin . Image intensity scale inverted and brightness linearly adjusted for visualization ( note: intensity data are not quantified ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 01910 . 7554/eLife . 11384 . 020Video 13 . H1299 P/L cell migration in the Continuous mode on laminin . A single H1299 P/L cell migrating in the Continuous mode . Cropped from a larger image montage . Cells labeled with a membrane dye during random migration on 50 µg/ml laminin . Image intensity scale inverted and brightness linearly adjusted for visualization ( note: intensity data are not quantified ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 02010 . 7554/eLife . 11384 . 021Video 14 . H1299 P/L cell migration in the Discontinuous mode on collagen type 1 . H1299 P/L cells migrating in the Discontinuous mode . Cropped from a larger image montage . Cells labeled with a membrane dye during random migration on 2 µg/ml collagen type 1 . Image intensity scale inverted and brightness linearly adjusted for visualization ( note: intensity data are not quantified ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 02110 . 7554/eLife . 11384 . 022Video 15 . H1299 P/L cell migration in the Continuous mode on collagen type 1 . H1299 P/L cells migrating in both Discontinuous and Continuous modes . Cropped from a larger image montage . Cells labeled with a membrane dye during random migration on 5 µg/ml collagen type 1 . Image intensity scale inverted and brightness linearly adjusted for visualization ( note: intensity data are not quantified ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 02210 . 7554/eLife . 11384 . 023Video 16 . H1299 P/L cell migration in the Discontinuous mode on vitronectin . A single H1299 P/L cell migrating in the Discontinuous mode . Cropped from a larger image montage . Cells labeled with a membrane dye during random migration on 1 µg/ml vitronectin . Image intensity scale inverted and brightness linearly adjusted for visualization ( note: intensity data are not quantified ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 02310 . 7554/eLife . 11384 . 024Video 17 . H1299 P/L cell migration in the Continuous mode on vitronectin . A single H1299 P/L cell migrating in the Continuous mode . Cropped from a larger image montage . Cells labeled with a membrane dye during random migration on 1 µg/ml vitronectin . Image intensity scale inverted and brightness linearly adjusted for visualization ( note: intensity data are not quantified ) . Time in minutes shown . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 024 Cell migration is the product of membrane dynamics that can be divided into protrusive and retractive processes ( Ridley , 2003 ) . We quantitatively compared the spatial and temporal dynamics of membrane protrusions and retractions between and within each migration mode based on defining membrane dynamics over the minimal imaging interval of 5 min ( Figure 2 ) ( Kowalewski et al . , 2015 ) . This revealed that protrusions share similar size ( area ) distributions in both modes ( Figure 2A ) , while retraction events are more extreme in size during Discontinuous migration , that is , more frequently very small or very large ( Figure 2B ) . When compared within each mode , membrane retractions have a broader size distribution than protrusions in the Continuous mode ( Figure 2C ) , although this is far more striking in the Discontinuous mode ( Figure 2D ) . To investigate membrane dynamics operating over longer time-scales , we calculated the probabilities of protrusions and retractions of a given size based on net cell shape/position changes occurring over intervals of between 1 and 15 image frames ( 5 to 75 min ) . This revealed a relatively unstructured pattern of dynamics in Continuous cells , although retractions tended to be smaller and protrusions larger ( Figure 2E ) . In contrast , Discontinuous migration consistently displayed a wide distribution of retraction sizes ( very small and very large ) , with protrusion sizes uniformly moderate ( Figure 2F ) . Notably , the relatively high probability of very large retraction events in Discontinuous cells corresponds with their observed tendency to undergo dramatic tail retraction events . Collectively , these results explain the spatial characteristics underlying stepwise or smooth cell motion during migration in Discontinuous or Continuous modes , respectively . 10 . 7554/eLife . 11384 . 025Figure 2 . Membrane protrusion and retraction dynamics are temporally decoupled and less coordinated during Discontinuous migration . ( A ) Probability distribution of membrane protrusion sizes ( µm2 ) per 5-min interval during Discontinuous ( blue ) and Continuous ( orange ) migration . Subtraction of the Continuous from Discontinuous protrusion size probability distribution ( inset ) reveals no substantial or structured difference in these distributions ( probability difference , Y axis ) . ( B ) Probability distribution of membrane retraction sizes ( µm2 ) per 5-min interval during Discontinuous ( blue ) or Continuous ( orange ) migration . Subtraction of the Continuous from Discontinuous retraction size probability distribution ( inset ) reveals that retraction sizes tend to be more extreme ( frequently small or large , rarely moderate ) in cells during Discontinuous migration ( probability difference , Y axis ) . ( C ) Subtraction of the protrusion from retraction size probability distribution ( 5 min intervals only ) during Continuous migration reveals little structure in probability differences . ( D ) Subtraction of the protrusion from retraction size probability distribution ( 5 min intervals only ) during Discontinuous migration reveals that retractions in this mode are consistently more extreme in size ( frequently small or large , rarely moderate ) than protrusions . ( E ) Surface plotting of probability differences ( retraction minus protrusion probability , per size ) over various time windows ( 1 to 15 frames; 5 to 75 min ) in cells during Continuous migration . Surface color-coding indicates where protrusions ( blue ) or retractions ( red ) are more common at a particular size . ( F ) Surface plotting of probability differences ( retraction minus protrusion probability , per size ) over various time windows ( 1 to 15 frames; 5 to 75 min ) in cells during Discontinuous migration . ( G ) Signal cross-correlation was calculated between protrusion size and retraction size fluctuations per cell during Continuous migration . Mean cross-correlation values ( Y axis , red = positive , blue = negative ) plotted as a surface , per time lag ( -12 to 12 frames , negative values indicate protrusion leads retraction , positive values indicate retraction leads protrusion ) , per time window ( ranging from 1 to 15 frames; 5 to 75 min ) . ( H ) Signal cross-correlation was calculated between protrusion size and retraction size fluctuations per cell during Discontinuous migration . Mean cross-correlation values ( Y axis , red = positive , blue = negative ) plotted as a surface , per time lag ( -12 to 12 frames , -ve values indicate protrusion leads retraction , +ve values indicate retraction leads protrusion ) , per time window ( ranging from 1 to 15 time points; 5 to 75 min ) . ( I ) Mean ( over all lags , per time window ) absolute cross-correlation values for Continuous ( orange ) and Discontinuous ( blue ) migration , /- 95% confidence intervals , n = number of cells ( see Materials and Methods ) . Statistical discernibility assessed by Friedman testing , p = 0 . 0012 . ( J ) Cells in each migration mode were divided into quintiles ( 20% bins ) based on instantaneous cell speed , and median protrusion areas /- 1 . 57 * interquartile range ( IQR , 25% to 75% ) / √n ( approximates 95% confidence interval of the median , n = number of cell observations , see Materials and Methods ) were calculated per quintile . ( K ) Median retraction areas /- 1 . 57 * IQR/ / √n ( n = number of observations ) were calculated per cell speed quintile . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 025 To understand the temporal dynamics underlying differences in migration mode behavior , we assessed the cross-correlation between protrusion and retraction size signals over time . This revealed strong temporal coordination ( high cross-correlation ) between protrusion and retraction events during Continuous migration , with a single cross-correlation peak around the -1 time-lag . This indicated that protrusion consistently led retraction , but that these dynamics are tightly coupled in time ( Figure 2G ) . This was true regardless of the time frame over which membrane dynamics were defined . In marked contrast , two cross-correlation peaks were apparent in the Discontinuous mode , with one peak stable around the -1 time-lag ( tightly coupled dynamics ) , and a second peak temporally offset with positive lags ( Figure 2H ) . This offset was dependent on the time frame used to define membrane dynamics , but clearly indicates a population of temporally decoupled membrane dynamics , wherein retraction leads protrusion events that are significantly delayed . This confirmed the visual impression of Discontinuous migration as a cyclical , stepwise process with asynchronous retraction ( i . e . tail retraction stage ) preceding protrusion ( i . e . lateral protrusion stage ) . In contrast , the morphological stability observed during Continuous migration is a consequence of the tight coupling of protrusion and retraction , producing motility without large-scale , transient size/shape changes . Visual impressions also suggested the Discontinuous migration mode to be less ordered/coordinated than the Continuous mode . The significantly lower average ( combining all time-lags ) protrusion-retraction cross-correlation values during Discontinuous migration ( Figure 2I ) confirmed reduced coordination of membrane dynamics in this migration mode . To complete our analysis of how membrane dynamics differentially contribute to migration in each mode , we assessed the relationship between cell speed and the size of membrane protrusions ( Figure 2J ) or retractions ( Figure 2K ) . Both modes exhibited similar dependencies . Remarkably , cell speed appears in each case to depend only slightly on the size of membrane protrusions , instead being largely determined by retraction size . The data described above detail significant quantitative differences between both the cell motion and membrane dynamics comprising each migration mode behavior . Yet , how these divergent behaviors emerge from the organization of underlying macromolecular machineries remains unclear . We therefore extended our image analyses ( see Materials and Methods and [Lock et al . , 2014] ) to derive a multivariate dataset of 55 organizational features ( detailed in Figure 3—figure supplement 1 ) defining cellular-scale morphology as well as the state ( e . g . size , number , density , morphology , localization ) and dynamics ( e . g . motion , stability , rates of area/density change ) of critical macromolecular-scale machineries driving cell migration , namely , cell-matrix adhesion complexes ( CMACs; demarcated by EGFP-paxillin ) and F-actin ( demarcated by RubyRed-LifeAct ) . This multiscale organizational data was measured per cell , per time-point , thereby complementing corresponding measures of migration behavior from the same individual cells ( Figure 3—figure supplement 1 ) . Such integrated data allowed exploration of the organizational states that give rise to particular cell migration behaviors , based on information embedded in natural cell heterogeneity ( Lock et al . , 2014; Lock and Strömblad , 2010 ) . To first understand how much overlap exists between the organizational states underpinning Discontinuous and Continuous migration modes , we performed supervised clustering ( canonical vectors analysis , CVA ) of all cell observations based on their organizational features only ( Figure 3A ) . Strikingly , the two migration mode populations showed almost no overlap , indicating that their underlying patterns of organization can be quantitatively distinguished . We next visualized these organizational states via parallel coordinate mapping of 15 key organizational features ( Figure 3B ) . This revealed specific instances of dissimilarity in feature values as well as an overview of the collective organizational signatures associated with each mode . To estimate the contribution of specific features to the divergence between modes , we ranked organizational features by the coefficient values assigned to them within the first canonical vector of the CVA ( Figure 3C ) . We then compared feature value distributions between modes for a subset of these top-ranked features , finding highly significant differences in all cases ( Figure 3D–K ) . These analyses help to define the spectrum of differences in organization that spontaneously arise in parallel with the emergence of Discontinuous and Continuous migration mode behaviors . For instance , cells in the Discontinuous mode are: far smaller; less round ( more protrusive ) ; have fewer , smaller , less dense CMACs ( based on EGFP-paxillin intensity ) with less F-actin association ( based on background-subtracted RubyRed-LifeAct intensity [Li et al . , 2010] ) and shorter lifetimes ( less stable ) , resulting in a drastically decreased total adhesion area . 10 . 7554/eLife . 11384 . 026Figure 3 . Discontinuous and Continuous migration modes reflect quantitatively distinct cell states with unique signatures of underlying organization . ( A ) Canonical vectors analysis ( CVA ) -based multivariate clustering ( based on all 55 organizational features ( see Figure 3—figure supplement 1 ) , canonical vectors ( CVs ) 1 and 2 displayed , percentages indicate proportion of total variance per CV ) of cell observations ( see Materials and Methods ) during Discontinuous ( blue ) or Continuous ( orange ) migration show modes to be quantitatively distinct . Large circles with black outlines indicate population centers of mass . ( B ) Parallel coordinate mapping of key organizational feature values ( per cell observation ) detail the multivariate signatures associated with cells during Discontinuous ( light blue ) or Continuous ( light orange ) migration . Points of difference between these multivariate signatures are emphasized by plotting of median values for Discontinuous ( blue ) and Continuous ( orange ) modes . Wilcoxon rank sum testing ( per feature ) assessed statistically discernable differences ( p < 0 . 001 ) , except where shown in gray italics . ( C ) A list of organizational features ranked by their contribution ( coefficient values in canonical vector 1 , which contains 99% of total variance ) to the separation of migration modes in the canonical vectors space shown in ( A ) . Gray backgrounds highlight features for which value distributions are compared between Discontinuous ( blue ) and Continuous ( orange ) modes in ( D-K ) . ( D-K ) Features compared indicated on boxplot Y-axes . Boxplots show median values and inter-quartile ranges ( IQR , 25% to 75% ) . Notches indicate median /- 1 . 57 * IQR/ / √n ( approximates 95% confidence interval of the median , n = number of cell observations , as in ( A ) . P values reflect Wilcoxon rank sum testing as in ( B ) . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 02610 . 7554/eLife . 11384 . 027Figure 3—figure supplement 1 . Definition of organizational and behavioral features . This table defines the names , relevant measurement units , and a brief description of quantitative features derived through multiscale image analysis . Features are numbered 1-60 in correspondence with the order and numbering presented in Spearman's correlation heatmaps in Figure 4 . Note , of these 60 features , 55 define Organizational features ( light gray , used in multivariate clustering analyses ) , while 5 define the Behavioral features assessed herein ( dark gray ) . Additional terms are defined above relevant columns . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 027 Having defined a spectrum of differences in key organizational features underlying migration modes , we next explored how the correlative relationships between these features might also vary . To this end , we mapped the complete network ( including both organizational and behavioral features ) of pairwise inter-feature Spearman’s correlation coefficients ( rs ) for both Discontinuous ( Figure 4A ) and Continuous ( Figure 4B ) modes . Visual inspection of the heatmaps presented in 4A and 4B suggests that , while some differences in correlations are apparent , general patterns of correspondence are well preserved regardless of migration mode . This is emphasized by calculation of absolute differences in rs values between modes ( Figure 4C ) , wherein the majority of relationships appear virtually unchanged . Indeed , only a very limited number of inter-feature relationships change by more than 0 . 4 rs ( Figure 4D ) . This suggests that changes in inter-feature correlations are unexpectedly selective , given the magnitude of changes in the actual feature values themselves ( Figure 3 ) . To assess this , we compared the observed distribution of absolute differences in rs values to that which would be expected if changes arose randomly from the rs distributions depicted in Figure 4A and 4B ( Figure 4E ) . This confirmed that observed changes are much smaller and less frequent than randomly expected , thus supporting the proposal that reconfiguration of correlative relationships is highly selective . 10 . 7554/eLife . 11384 . 028Figure 4 . Differences in inter-feature relationships between Discontinuous and Continuous migration modes appear highly selective . ( A , B ) Heatmaps of Spearman's rank correlation coefficients ( rs ) for all pairwise combinations between 60 features ( 55 organizational and 5 behavioral ) , derived from cells during ( A ) Discontinuous or ( B ) Continuous migration . Individual correlation coefficient values are color-coded as depicted in the color bar ( blue = negative; red = positive; green = near zero ) . Numbers on X and Y axes ( 1 to 60 ) correspond to the identities of features , as defined in Feature Number column of Figure 3—figure supplement 1 . ( C ) A heatmap summarizes absolute differences in correlation coefficient values , per feature pair , between Discontinuous and Continuous migration modes . Difference values are color-coded as depicted in the color bar ( blue = no difference; red = large difference ) . ( D ) A histogram shows the frequency distribution of absolute differences in correlation coefficient values between Continuous and Discontinuous migration modes . ( E ) A plot of cumulative distribution functions ( CDFs ) comparing the observed distribution of absolute differences in correlation coefficient values ( between Discontinuous and Continuous modes as in [C] and [D] , black line ) and the differences in coefficient values obtained following randomized permutation of Spearman's correlation pairs ( gray line ) . The permuted distribution ( repeated 100 times for all relationships , all values included in CDF ) shows the expected distribution of coefficient differences if inter-feature correlation changes occur randomly . Comparison of observed and permuted CDFs suggests that observed differences in inter-feature Spearman's correlation values are far more selective than randomly expected . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 028 We next analyzed how organizational features correlated to cell speed in each migration mode . As exemplified in Figure 5A , and more extensively in Figure 5B , we found that a proportion of such correlations were essentially equivalent between modes ( e . g . Cell Area vs Cell Speed rs = negative in both modes; Cell Compactness − Cell Speed rs = positive in both modes ) . However , a large number of cell speed correlations were detectable in only one of the two modes ( e . g . Median [CMAC Distance to Border] per Cell vs Cell Speed rs = positive only in Continuous mode; Median [CMAC Area] per Cell vs Cell Speed rs = positive only in Continuous mode ) . Even more salient , a substantial number of correlations to Cell Speed were inverted between modes ( e . g . Median [CMAC Lifetime] per Cell vs Cell Speed rs = negative in Discontinuous mode , positive in Continuous mode; Median [CMAC Average Trailing Edge Speed] per Cell vs Cell Speed rs = positive in Discontinuous mode , negative in Continuous mode ) . Such inversions in organizational feature-to-Cell Speed relationships hint at profound differences in the mechanisms controlling each mode . We also note that , more broadly , most organizational feature-to-Cell Speed correlations were both stronger and more positive during migration in the Continuous mode ( Figure 5C ) . This suggests that Continuous mode migration is more directly dependent on the state of CMAC and F-actin machineries , while alternative machineries may play more dominant roles during Discontinuous migration . 10 . 7554/eLife . 11384 . 029Figure 5 . Cell speed variation is coupled to distinct patterns of remodeling in underlying features and inter-feature relationships within each migration mode . ( A ) A selection of Spearman's correlation coefficient values ( rs ) for organizational feature relationships to cell speed are depicted ( color-coded as in color bar ) . Values reflect medians of populations values . Correlations are considered non-significant ( ns ) if zero is included in the range of the median +/−1 . 57 * inter-quartile range ( IQR , 25% to 75% ) √n ( approximates 95% confidence interval of the median , n = number of cell observations , see 'Materials and methods' ) . This highlights correlations to cell speed that: are equivalent between modes ( e . g . Cell Area , negative correlation in both modes ) ; exist in only one mode ( e . g . Median [cell-matrix adhesion complex ( CMAC ) Area] per Cell , ns in Discontinuous vs positive in Continuous ) ; or are opposite between modes ( e . g . Median [CMAC Lifetime] per Cell , negative in Discontinuous vs positive in Continuous ) . Examples in A are linked to an extensive analysis of cell speed-to-organizational feature correlations in B and C . ( B ) Parallel coordinate mapping of median cell speed-to-organizational feature correlations in Discontinuous ( blue ) and Continuous ( orange ) modes . Relationships are categorized by correlation value similarity or difference between modes , as indicated by the orange; C' ( denotes Continuous ) and blue 'D' ( denotes Discontinuous ) followed by '−'; ( negative rs ) or '+' ( positive rs ) or 'ns' ( non-significant rs ) . ( C ) Bar graphs depict the magnitude of differences in median cell speed-to-organizational feature correlation values from the Discontinuous to the Continuous mode . Correlations tend to be stronger and more positive in cells during Continuous migration . ( D ) Canonical vectors analysis ( CVA ) -based multivariate clustering ( using all 55 organizational features , canonical vectors [CV] 1 , 2 , and 3 displayed , percentages indicate proportion of total variance per CV ) of cell observations during: slow Discontinuous ( 0–33 . 33% of Discontinuous migration speed values , blue ) ; medium Discontinuous ( 33 . 34–66 . 66% , cyan ) ; fast Discontinuous ( 66 . 67–100% , green ) ; slow Continuous ( 0–33 . 33% of Continuous migration speed values , yellow ) ; medium Continuous ( 33 . 34–66 . 66% , orange ) ; or fast Continuous ( 66 . 67–100% , red ) migration . Two orientations of the same three-dimensional clustering are depicted ( upper and lower ) , revealing the separate clustering of each migration mode . Within each mode , progressive differences in cell speed correspond to similarly progressive variations in the position of observations within the multivariate organizational feature ( or cell state ) space . These speed-dependent trends in clustering define trajectories in the cell state space along which cells evolve as cell speed changes . Remarkably , these trajectories are completely distinct when comparing Discontinuous ( dashed lines ) and Continuous ( solid lines ) migration modes . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 029 We have thus far detailed a variety of specific differences between Discontinuous and Continuous migration modes in terms of behavior ( Figure 1 and Figure 2 ) , underlying organizational features ( Figure 3 ) and individual inter-feature correlations ( Figure 4 and Figure 5A–C ) . We next explored how these differences might translate into systemic dissimilarities in the coupling of cell behavior ( i . e . cell migration speed ) and cell organization within each mode . To this end , we first grouped cell observations according to both migration mode and tertiles of cell speed ( 0−33% [slow]; 33−66% [medium]; 66−100% [fast] ) . We then performed supervised CVA clustering of these groups ( Figure 5D ) . This enabled assessment of how cells in each mode cluster in the multivariate cell state space , but also of the trajectories defined through cell state space given variability in cell migration speed ( as described previously , [Lock et al . , 2014] ) within each mode . As also observed in Figure 3A ( based on similar but not equivalent clustering ) , we found that cells in Discontinuous or Continuous modes cluster separately . However , in a striking extension of this result , we also found that variability in migration speed within each mode corresponded to completely independent trajectories in cell state space . This means that different patterns of organizational remodeling are associated with varying cell speed within each mode . Thus , profound differences exist between modes in terms of the underlying strategies that produce and control cell migration speed . We have demonstrated how specific organizational feature values diverge in conjunction with the spontaneous emergence of Discontinuous and Continuous migration modes ( Figure 3 ) . Yet , it remains unclear whether these features are tightly coupled to migration mode identity , or only loosely correlated with these behaviors . We therefore assessed whether directed modulation of these features could drive corresponding and predictable changes in the frequency equilibrium between migration modes . To test for such bottom-up regulation , we applied a selection of targeted molecular perturbations for which we and others have established prior knowledge regarding their effects on the organizational features in question . These perturbations included: ECM ligand ( FN ) concentration modulation ( Gupton and Waterman-Storer , 2006 ) ; talin 1 depletion ( Kiss et al . , 2015 ) ; and ROCK inhibition-mediated reduction of actomyosin contractility ( Lock et al . , 2014; Kim and Wirtz , 2013; Hernández-Varas et al . , 2015 ) ( Figure 6A–C ) . Images exemplifying perturbation effects on cell , CMAC and F-actin morphology are presented in Figure 6—figure supplement 1 . Notably , these molecular perturbations collectively target four core regulatory mechanisms around the ECM – adhesion – F-actin axis , including integrin ligation; integrin activation/clustering; integrin-F-actin linkage; and actomyosin contractility ( Figure 6A ) . Given the pivotal importance of these mechanisms for mesenchymal migration , the effects of the specific molecular perturbations applied herein may be somewhat predictive of a wide array of related regulatory mechanisms . 10 . 7554/eLife . 11384 . 030Figure 6 . Cell-matrix adhesion and actomyosin contractility control the equilibrium between Continuous and Discontinuous migration modes . ( A ) Schematic summary of molecular mechanisms ( green ) associated with the ECM – adhesion – F-actin axis ( gray ) targeted here for perturbation to modulate implicated organizational features and , potentially , corresponding migration mode frequencies . Specifically , altering ECM ligand density ( fibronectin concentration , [FN] ) impacts mechanism 1 ) - integrin ligation and subsequent cell-matrix adhesion complex ( CMAC ) formation and maturation . RNAi-mediated knockdown of talin 1 limits both mechanism 2 ) - integrin activation and mechanism 3 ) - integrin-F-actin linkage , thereby also affecting CMAC formation , maturation and stability . Y-27632-mediated inhibition of ROCK disrupts mechanism 4 ) - actomyosin contractility , affecting F-actin dynamics and CMAC maturation . Images exemplifying the effects of each molecular perturbation are presented in Figure 6—figure supplement 1 . ( B ) Boxplots summarizing the response of the same selection of organizational features to: ECM ligand modulation ( 2 . 5 µg/ml FN vs 10 µg/ml FN , upper row ) ; talin 1 RNAi ( control siRNA vs talin 1 siRNA , 5 µg/ml FN , middle row ) ; or ROCK inhibition ( DMSO vs 6 µM Y-27632 , 5 µg/ml FN , lower row ) . All boxplots in B show median values per condition and inter-quartile ranges ( IQR , 25% to 75% ) . Notches show the median +/−1 . 57 * IQR/ / √n ( approximates 95% confidence interval of the median , n = number of cell observations , see 'Materials and methods' ) . In each case , statistically discernable differences were assessed by Wilcoxon rank sum testing , with resulting p values <0 . 001 . ( C ) Migration mode frequency responses to each perturbation are depicted ( Discontinuous , blue; Continuous , orange ) . Additional conditions were included for ROCK inhibition ( low panel ) , showing a progressive response to 1 µM , 2 µM , and 6 µM Y-27632 as compared to DMSO vehicle control . Note: in addition to depicting specific perturbation-dependent trends , the matrix of results presented in B and C is used , together with spontaneous feature variations depicted in Figure 3D , E , G–I , to logically parse organizational features that are consistently coupled , or just occasionally correlated , with migration mode identity . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 03010 . 7554/eLife . 11384 . 031Figure 6—figure supplement 1 . Comparison of cell , adhesion complex and F-actin morphologies following perturbations targeting the ECM – adhesion – F-actin axis . Confocal images of H1299 cells comparing: ( A ) low concentration ( 2 . 5 µg/ml ) fibronectin ( FN ) -coated glass ( left ) vs high concentration ( 10 µg/ml ) FN-coated glass ( right ) ; ( B ) control siRNA treatment ( left ) vs talin 1 siRNA treatment ( right ) ; and ( C ) control ( DMSO ) treatment ( left ) vs ROCK inhibitor ( Y-27632 , 6 µM ) treatment ( right ) . EGFP-paxillin ( upper panels ) and RubyRed-LifeAct ( lower panels ) signals are shown to demarcate cell-matrix adhesion complexes ( CMACs ) and F-actin , respectively . Images are displayed with gray-scale inverted . ( D ) Talin 1 knockdown was confirmed by immunoblotting . Scale bars = 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 031 Importantly , we found that Continuous mesenchymal migration occurred upon high adhesion ( high FN concentration ) and with unperturbed actomyosin contractility , while Discontinuous migration occurred under conditions of low adhesion ( low FN concentration ) and inhibited contractility ( ROCK-inhibitor ) . Talin knock-down promoted Discontinuous migration ( Figure 6C ) . We then compared: i ) spontaneous differences in feature values between Continuous and Discontinuous migration modes ( displayed in Figure 3D , E , G and I ) ; ii ) perturbation-induced differences in the same features ( Figure 6B ) ; and iii ) perturbation effects on migration mode frequencies ( Figure 6C ) . From this matrix of results , we were able to determine whether a logical coherence was preserved over all these measures . For example , median CMAC Area was spontaneously higher in cells during Continuous migration ( Figure 3I ) , and increasing ECM ligand concentration ( FN ) causes higher CMAC Area values ( Figure 6B , upper left panel ) . It is therefore logically consistent ( if CMAC Area and migration mode behaviors are functionally coupled ) that raising ECM ligand concentration increases the frequency of the Continuous migration mode ( Figure 6C , upper panel ) . In fact , although the applied perturbations produce differing effects on CMAC Area ( increasing or decreasing ) , migration mode frequencies show logically consistent responses in each case . CMAC Area therefore appears likely to be closely coupled to migration mode identity , since there is no evidence that CMAC Area and migration mode identity could be uncoupled . Such coherence was also observed for CMAC Lifetime and Cell Compactness , thus similarly supporting their functional coupling to migration mode determination . In contrast , while the modulation of either ECM ligand concentration or talin 1 expression caused effects on Cell Area and CMAC Number that were consistent with their parallel effects on mode frequencies , ROCK inhibition caused contradictory results: CMAC number and Cell Area were both higher in cells in the Continuous mode ( Figure 3H ) and were increased following ROCK inhibition ( Figure 6B , lower right panel ) , yet ROCK-inhibition caused a decreased frequency of the Continuous mode ( Figure 6C , lower panel ) . These results imply that , although Cell Area and CMAC Number spontaneously correlate with migration mode identities under the original experimental condition , this correspondence can be uncoupled . These organizational features are therefore unlikely to be causal of , or caused by , migration mode identity determination . Taken together , the matrix of results presented delineates organizational features ( CMAC Area , CMAC Lifetime , Cell Compactness ) that may be directly coupled with migration mode determination . More definitively , these results effectively exclude Cell Area and CMAC Number from being ( linearly ) causally linked to this process . Furthermore , these results demonstrate the potent regulatory influence of the specific molecular components fibronectin , talin 1 and ROCK , on migration mode determination . These perturbations also allows us to conclude more broadly that cell-matrix adhesion complexes and actomyosin contractility play key roles in shaping Discontinuous and Continuous mode frequencies . The molecular perturbations described above changed both cellular and macromolecular organization , as well as the frequency equilibrium between Discontinuous and Continuous migration modes . However , it remained unclear to what extent perturbation effects on organizational features may reflect: a ) mode frequency changes ( inter-modal 'adaptive switching' ) , as opposed to b ) organizational remodeling of the modes themselves ( intra-modal 'adaptive stretching' ) . To address the balance between these two adaptive responses , we leveraged data from the comparison of low ( 2 . 5 µg/ml ) and high ( 10 µg/ml ) FN concentrations displayed in Figure 6 . Using only organizational features , we performed principal component analysis ( PCA ) -based unsupervised clustering of cell observations grouped by both FN concentration and migration mode ( Figure 7A ) . Surprisingly , this revealed that the distances ( in PCA-defined cell state space ) between Discontinuous and Continuous migration modes were far greater than the distances induced within each mode by FN concentration variation . Comparing the heterogeneity of individual cells over time ( Figure 7B and C , Figure 7—figure supplement 1 ) and the distances between migration mode centers of mass confirmed the substantial nature of average organizational differences between modes ( equal to or greater than the maximal span of variation observed over 8 hr in the individual cells shown in Figure 7B–C ) . To more intuitively visualize how changing FN concentration shifts the distribution of cell organization within the PCA-defined cell state space ( and between modes ) , we calculated the probability density distributions of cell observations in each mode , for each experimental condition ( Figure 7D–E ) . This revealed that cell observations in each mode populate high probability-density 'valleys' that are at least partially separated by low probability-density 'ridges' . This visualization also emphasized that the major response to FN modulation was a substantial rebalancing of cell organizational states from one valley ( migration mode ) to the other . Collectively , these observations demonstrate that switching ( between modes ) dominates over stretching ( remodeling within modes ) as an adaptive response to FN concentration modulation . The conceptual relation between these two mechanisms of adaptation in cell state space is schematized in Figure 7F . 10 . 7554/eLife . 11384 . 032Figure 7 . Adaptive switching between migration modes is the dominant perturbation response rather than intra-modal remodeling . ( A ) Principal component analysis ( PCA ) -based clustering of cell observations ( based on all 55 organizational features ) , principal components ( PC ) 1 and 2 displayed , percentages indicate proportion of total variance per PC , color-coded by migration mode and experimental condition: Discontinuous mode on 2 . 5 µg/ml FN ( dark blue ) ; Discontinuous mode on 10 µg/ml FN ( light blue ) ; Continuous mode on 2 . 5 µg/ml FN ( red ) ; Continuous mode on 10 µg/ml FN ( orange ) . Large black outlined circles indicate population centers of mass . Note that , despite FN variation exerting large effects on cell organization , differences between migration modes are much larger that differences between conditions within the same mode . ( B ) PCA clustering as in ( A ) , overlaid by the heterogeneous trajectories over time of 4 cells ( I–IV ) within the PCA-based multivariate state space . Cells I–IV belong to the spatially corresponding cell populations defined in ( A ) and are relatively constrained within their respective regions of the cell state space . ( C ) Single time point confocal images of EGFP-paxillin from Cells I–IV from ( B ) . Images are displayed with gray-scale inverted . Corresponding image sequence selections are presented in Figure 7—figure supplement 1 . Scale bars = 20 µm . ( D ) A probability density map based on the coordinates in PCA space of cell observations from the 2 . 5 µg/ml FN condition in ( A ) ( view orientation relative to A is rotated 180 degrees ) . Cells in the Discontinuous mode ( dark blue ) define a deep probability valley that partially overlaps with but is largely distinct from a shallow probability valley defined by cells in the Continuous mode ( red ) . The sum of probabilities equals 1 . ( E ) A probability density map based on the coordinates in PCA space of cell observations from the 10 µg/ml FN condition in ( A ) ( view orientation relative to A is rotated 180 degrees ) . Cells in the Continuous mode ( orange ) define a deep probability valley that overlaps with but is partly distinct from a shallow probability valley defined by cells in the Discontinuous mode ( light blue ) . The sum of probabilities equals 1 . Centers of mass for each population in ( D ) and ( E ) are indicated by capped vertical lines of matching colors . Arrows in ( D ) and ( E ) signify how decreasing or increasing FN concentration , respectively , causes 'switching' from one probability valley ( migration mode ) to the other . ( F ) Schematic summary of the dual adaptive strategies employed by cells , with respect to mesenchymal migration . Modulation of intracellular or extracellular conditions can cause the remodeling of cellular and macromolecular organization locally within a given mode ( 'adaptive stretching' ) . However , the much larger and more frequent response to such perturbations is 'adaptive switching' between discrete migration modes , resulting in substantial rebalancing of Discontinuous and Continuous migration mode frequencies . ( G ) Dendogram ( qualitative ) indicating the major recognized archetypes of cell migration ( top row ) and the new modalities of Continuous and Discontinuous mesenchymal migration described herein ( bottom row ) . Italicized names in brackets correspond to similar/analogous migration modes/terms . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 03210 . 7554/eLife . 11384 . 033Figure 7—figure supplement 1 . Confocal image sequences of cells during Discontinuous and Continuous mode migration on glass coated with low or high fibronectin concentrations . Confocal image sequences of H1299 cells during migration on low concentration ( 2 . 5 µg/ml ) fibronectin ( FN ) -coated glass ( A and B ) or high concentration ( 10 µg/ml ) FN-coated glass ( C and D ) . EGFP-paxillin ( upper panels ) and RubyRed-LifeAct ( lower panels ) signals are shown to demarcate cell-matrix adhesion complexes ( CMACs ) and F-actin , respectively . Images are displayed with gray-scale inverted . Numbers denote time ( min ) . Scale bars = 20 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 11384 . 033
We here present an integrated analytical approach and associated tools , designed to interrogate both spontaneous and induced heterogeneity in single-cell mesenchymal ( lamellipodial ) migration . By deploying these tools , we quantitatively demonstrate that mesenchymal migration is composed of two distinct sub-modalities , each with specific behavioral , organizational , and regulatory characteristics . This marks an important advance that may enhance future investigations of the mesenchymal migration archetype . In particular , interpretations of natural and/or experimentally induced variability will likely become more coherent and precise if data are disaggregated according to Discontinuous or Continuous modes , rather than being unintentionally aggregated across cells with fundamentally different properties and dependencies . Recognition of these modes may therefore facilitate a more accurate understanding of the heterogeneity , adaptability , and regulation of mesenchymal migration as a whole . Our initial classification of cells into Discontinuous and Continuous migration modes was based on observed behavioral differences . Crucially , the same distinctive behaviors emerged in parallel within a variety of cell lines and under a spectrum of conditions , thus supporting the robustness and broad relevance of these behaviors . Yet , given the similarity of Continuous and Discontinuous migrations to previous descriptions of keratocyte-like ( Barnhart et al . , 2015; Keren et al . , 2008 ) or fibroblast-like motility ( Abercrombie et al . , 1977; Theisen et al . , 2012 ) , respectively , the existence of these behaviors per se is not novel . Indeed , early observations even qualitatively suggest the co-existence of similar behaviors ( Lewis et al . , 1982 ) . However , we now present quantitative evidence of the spontaneous , parallel emergence of two distinct yet inter-convertible mesenchymal migration modes , wherein divergent couplings exist between behavioral ( speed ) and organizational characteristics . We thereby provide new insights into the discontinuous structure of heterogeneity within the broad mesenchymal migration archetype . Indeed , by differentiating between progressive ( intra-modal ) and discrete ( inter-modal ) forms of variation , we gained the capacity to compare the roles that these two mechanisms play in adaptive responses to perturbation . Surprisingly , this revealed that adaptive switching between modes is the dominant response to perturbation ( FN concentration modulation ) , with adaptive stretching ( remodelling ) within modes playing only a minor role . The quantitative distinction between Discontinuous and Continuous migration modes is based on systemic differences detected via multivariate clustering of data following visual classification . Importantly , given that cells were classified based on perceived differences in their behavioral dynamics , we excluded behavioral features and focused only on organizational features ( predominantly related to CMAC and F-actin status ) when addressing the question of separation between the modes . As a result , at least four lines of evidence support the discrete nature of Discontinuous and Continuous migration modes . First , supervised multivariate clustering ( canonical vectors analysis , CVA ) of mode-classified cell observations indicated near complete separation between Discontinuous and Continuous modes . The systemic organization underlying these migration modes is therefore , to a very large extent , quantitatively distinct . Second , although separation between modes was not complete when cell observations were clustered in unsupervised PCA space , the topology of probability-density distributions was consistent with the existence of subpopulations in cell state space . Precisely , the high probability-density 'valleys' near the center of mass of each mode contrast with the low probability-density 'ridges' at the boundaries between modes . Importantly , these probability-density distributions are not predetermined outcomes of such PCA clustering , since PCA is unsupervised with respect to mode identity . In fact , these topologies are suggestive of a system comprised of two attractor states ( Huang and Ingber , 2000 ) , whose basins of attraction diverge on either side of a set of potentially unstable or unfavorable configurations . The low probability of these boundary configurations effectively divides mesenchymal migration into two independent sub-modalities . Such attractor states have been described in relation to , for example: gene expression during development and tumorigenesis ( Huang et al . , 2009; Huang , 2009 ) ; cell signaling during motility ( Kim et al . , 2015 ) ; and even in the force-coupled dynamics of CMACs ( Hernández-Varas et al . , 2015 ) . Moreover , the attractor state concept appears analogous to the concept of prespecification originally proposed by Friedl to explain the robust , recurrent and yet switchable characteristics of mesenchymal and amoeboid migration modes ( Friedl , 2004 ) . Interestingly , the limited remodelling within Discontinuous and Continuous modes in response to FN modulation ( as opposed to mode switching ) suggests that these modes may be relatively inflexible , providing additional support for their proposed status as prespecified attractor states . Third , further indicating the discrete nature of Discontinuous and Continuous migration modes , CVA clustered cell observations grouped by both migration mode and cell speed were again largely distinct , supporting the validity of the original CVA clustering result . In addition , this approach also provided a fourth compelling line of evidence by revealing completely independent trajectories through cell state space defined by cells migrating at different speeds within each mode . Remarkably , this indicates that changes in cell speed within each mode were coupled to substantially different patterns of organizational remodelling . These systemic differences were composed in part by specific disparities in Spearman’s correlations between individual organizational features and cell speed within each mode . While a variety of differences were observed , most striking were instances where correlations to cell speed were inverted between modes , such as in the case of adhesion stability ( CMAC lifetime ) – a critical parameter previously linked to causal regulation of cell speed ( Lock et al . , 2014 ) . Interestingly , when comparing modes , the independent patterns of cell speed-coupled reorganization arose despite only subtle differences across the entire network of inter-feature Spearman’s correlations . Indeed , these differences in correlation values were far more limited than would be expected at random , suggesting that a select few features and relationships may control migration mode identity . Taking into account each of these lines of evidence , we conclude that Discontinuous and Continuous migration modes are representative of more than simply cells with different organizational feature values . We propose that these modes in fact represent divergent , yet co-existing strategies of mesenchymal migration . Despite strongly embracing quantitative imaging-based research ( Lock and Strömblad , 2010; Le Dévédec et al . , 2010; Masuzzo and Martens , 2015 ) , the cell migration field has seen remarkably little application of automated approaches to the classification of migrating cells . This is in contrast to the study of mitosis , where automated classification has already proven to be a powerful research tool ( Neumann et al . , 2010; Schmitz et al . , 2010 ) . It is likely that this reflects differences in the degree and structure of heterogeneity in these biological processes . For example , mitosis is a relatively stereotyped process - both morphologically and particularly in the terms of the temporal ordering of events . Both characteristics can increase the accuracy of automated classification approaches ( Held et al . , 2010 ) . While cell migration is also more ( e . g . Discontinuous mode ) or less ( e . g . Continuous mode ) a globally time-ordered process , the underlying events appear far less stereotypical . Given these challenges , we have employed a blinded manual classification approach in this study . However , we note that the CVA clustering results – even excluding behavioral features ( as herein ) – provide evidence that automated classification of migration modes may be feasible in future . This may significantly enhance subsequent quantitative analyses of mode organization , behavior , and regulation . Such automated classification may be in part based on our detailed spatiotemporal analysis of protrusive and retractive membrane dynamics within each mode . This revealed that the major differences between modes were: the size of membrane retraction events; the order and temporal coupling of protrusion and retraction events; and the coordination displayed between protrusive and retractive dynamics . Specifically , cells in the Discontinuous mode produced many very small and occasional very large retraction events , with these retractions tending to lead protrusion , reversing the order detected in Continuous cells . A large time delay also exists between retraction and subsequent protrusion dynamics in Discontinuous cells , and these dynamics are less coordinated ( lower cross-correlation ) . Such substantial differences in membrane dynamics ( especially the inversion of protrusion and retraction orders ) further support the distinct nature of Discontinuous and Continuous migration modes . Furthermore , these analyses both confirmed and explained the visual impressions that originally differentiated Discontinuous from Continuous migration . In addition , given the strong correspondence between retraction ( but not protrusion ) size and cell speed , these analyses now add migration mode and cell speed to motility initiation as aspects of migration where membrane retraction appears to be the deterministic membrane process ( Barnhart et al . , 2015; Kowalewski et al . , 2015; Cramer , 2010 ) . Having quantitatively established the discrete nature and behavioral characteristics of Discontinuous and Continuous migration modes , we sought to understand how their frequency balance is regulated . Importantly , to select candidate molecular regulators , we first considered the differences in organizational feature values that arose spontaneously between the modes . Then , given existing knowledge on how targeting of particular molecular components ( FN concentration [Gupton and Waterman-Storer , 2006]; talin 1 expression [Kiss et al . , 2015]; ROCK signaling [Lock et al . , 2014; Kim and Wirtz , 2013; Hernández-Varas et al . , 2015] ) impact these same features , we designed a series of perturbations intended to drive specific changes in these features , while also monitoring the behavioral balance between modes . This demonstrated that each of these molecular factors plays a key role in shaping the equilibrium between modes . Furthermore , these molecular targets also regulate generic characteristics of the pivotal ECM – adhesion – F-actin axis , including: integrin ligation ( FN concentration ) ; integrin activation and clustering ( talin expression ) ; integrin – F-actin linkage ( talin expression ) ; and actomyosin contractility ( ROCK signaling ) . Hence , the effects of these specific perturbations may also be illustrative of how generalized regulatory mechanisms impact the migration mode equilibrium . Taking this perspective , we observe that while Continuous migration arises under conditions of high adhesion and full contractility , Discontinuous migration is preferred given low adhesion and inhibited contractility . It is noteworthy that both modes emerge under conditions that are distinct from those that favour amoeboid migration ( i . e . low adhesion and high contractility ) . Therefore , the switch between Continuous and Discontinuous ( mesenchymal ) migration modes clearly contrasts to the previously identified conversion between mesenchymal and amoeboid migration . This emphasizes the multifaceted adaptive capacity of migrating cells , which likely expands the contexts within which efficient migration is possible . This plasticity is highlighted by the growing variety of distinct migration modalities now recognized ( Figure 7G ) . Finally , by viewing differences in organizational feature values ( both spontaneous and perturbation-induced ) and mode frequencies as a logical matrix , we could identify organizational features that are coupled to migration mode identity under all observed conditions , as well as features that can be explicitly uncoupled . This provides a first step in parsing correlated features from those with significant causal influence over migration mode identity . Specifically , it is notable that CMAC Area and CMAC Lifetime were previously indicated to causally influence cell migration speed ( Lock et al . , 2014; Kim and Wirtz , 2013 ) , while Cell Compactness was causally downstream of cell speed ( Lock et al . , 2014 ) . Thus , the evidence that these features may be functionally coupled to the determination of migration mode identity is consistent with them being integral to migratory regulation . Conversely , we previously found that Cell Area showed no causal relationship to cell speed ( Lock et al . , 2014 ) , and this is again consistent with it being functionally uncoupled from the process of mode identity determination . Overall , three types of insight were revealed through these perturbation-based analyses , including: the regulatory roles of specific molecules ( fibronectin , talin and ROCK ) ; the putative influence of generic regulatory mechanisms; and the nature of links ( coupled vs uncoupled ) between migration mode identity and commonly measured macromolecular features . Nonetheless , much remains to be learned about the proximal and distal determinants of these migration modes . In conclusion , we have presented and applied a quantitative , imaging-based analytic approach to explore the heterogeneity that naturally emerges within the mesenchymal ( lamellipodial ) archetype of cell migration . We thereby characterized two quantitatively distinct migration modes , here termed Continuous and Discontinuous migrations , that co-exist within the broad mesenchymal migration archetype . We compared these migration modes in terms of: their motion and behavioral dynamics; the specific and systemic organization of key machineries ( CMACs and F-actin ) underlying their motility , and; differences in how behavior ( migration speed ) and organization co-evolve within each mode . Through targeted molecular perturbations , we defined both specific molecular and general mechanisms of bottom-up control over migration mode determination , while also beginning to parse potentially functional ( coupled ) relationships ( between features and modes ) from those that are correlated but non-functional ( uncoupled ) . Finally , our systemic analysis of adaptive responses to perturbation has revealed how discrete mechanisms ( inter-modal adaptive switching ) dominate over progressive remodeling ( intra-modal adaptive stretching ) . This study therefore emphasize the importance of distinguishing and comprehending Discontinuous and Continuous migration modes as a necessary precursor to understanding mesenchymal migration in its totality , while simultaneously providing tools and approaches that enable this endeavor .
We have integrated a comprehensive and unique combination of analytical capabilities and made them freely available as a Matlab toolbox: 'The Cell Adhesion and Migration Analysis Toolbox' – along with the raw quantitative data underlying this study , sample image data to aid implementation , and explanatory documentation ( see doi:10 . 5061/dryad . 9jh6m ) . Briefly , this toolbox includes the following features utilized in this study: Data import and preprocessing based on images and extracted variables ( see Quantitative Image Analysis , below ) ; Smoothing of cell and adhesion trajectories; Membrane protrusion and retraction dynamics extraction; Migration mode classification; Mean square displacement ( MSD ) of cell trajectories; Variable selection for uni- and multivariate analyses; Univariate statitstical analysis; Variable correlation based heat maps; Canonical vector analysis ( CVA ) ; Principal component analysis ( PCA ) ; and two dimensional kernel density estimation . A more detailed description is available within the Matlab Toolbox . All cell lines were acquired directly from the American Type Culture Collection ( ATCC , Manassas , VA ) , and therefore were not further authenticated . The cell lines used herein are not members of the ATCC list of commonly misidentified cell lines . H1299 ( human non-small cell lung carcinoma , ATCC; ATCC# CRL-5803 , mycoplasma negative ) cells stably expressing EGFP-Paxillin and RubyRed-LifeAct , termed H1299 P/L cells , were established and maintained in RPMI 1640 medium ( Gibco – Thermo Fisher Scientific , Waltham , MA ) containing 400 µg/ml geneticin ( G-418 sulfate , Gibco ) with 10% fetal bovine serum ( Gibco ) and 1 mM glutamine , as described previously ( Lock et al . , 2014; Kiss et al . , 2015 ) . BT549 ( ductal breast carcinoma , ATCC; ATCC# HTB-122 , mycoplasma negative ) cells were maintained in RPMI 1640 medium ( Gibco ) containing 10% fetal bovine serum ( Gibco ) and 1 mM glutamine . MDA-MB-231 ( adenocarcinoma , ATCC; ATCC# HTB-26 , mycoplasma negative ) , Hep-3 ( hepatocellular carcinoma , ATCC; ATCC# HB-8064 , mycoplasma negative ) and Hs578T ( breast carcinoma , ATCC; ATCC# HTB-126 , mycoplasma negative ) cells were maintained in DMEM ( Gibco ) containing 10% fetal bovine serum ( Gibco ) . All cells were incubated at 37°C in 5% CO2 . In preparation for imaging , 96-well optical glass-bottomed plates ( Zell-Kontakt , Nörten-Hardenberg , Germany ) were coated with ECM ligands , including either collagen type 1 ( Life Technologies – Thermo Fisher Scientific ) , laminin ( Sigma-Aldrich , St . Louis , MO ) , fibronectin or vitronectin ( both purified from human plasma as described previously [Smilenov et al . , 1992; Yatohgo et al . , 1988] ) . Coating was performed for 2 hr at 37°C followed by blocking with 1% heat-denatured bovine serum albumin ( Sigma-Aldrich ) for 1 hr at 37°C . ECM ligand coating concentrations were 10 µg/ml except where otherwise indicated ( i . e . where fibronectin concentration was varied ) . RNAi-depletion of talin 1 was performed 48 hr prior to imaging or immunoblotting ( with anti-talin [8d4 , 1:500 , Sigma-Aldrich] and anti-tubulin [DM1A , 1:2000 , Fisher Scientific] antibodies as described previously [Kiss et al . , 2015] ) using the following oligonucleotide sequence: ( 5’ -GAA GAU GGU UGG CGG CAUU- 3’ ) ( synthesized by GenePharma , Shanghai , P . R . China ) . A non-targeting oligonucleotide sequence was used as control: ( 5’ -GCG CGC UUU GUA GGA UUCG- 3’ ) . Transfection of 2 x 104 cells was performed in 24-well plates using 20 pmol of siRNA and 2 µl RNAiMAX ( Invitrogen – Thermo Fisher Scientific ) according to the manufacturers instructions . Inhibition of Rho kinase ( ROCK ) was performed starting 1 hr prior to imaging using the Y-27632 inhibitor ( Sigma-Aldrich ) at 1 µM , 2 µM , or 6 µM . Di-methyl sulfoxide ( DMSO , Sigma-Aldrich ) was used as control . Live-cell imaging was initiated 2 hr after replating of 2 x 104 cells per well into ECM-coated 96-well glass-bottomed plates ( see coating details above ) . High-resolution , multiscale imaging ( of CMACs , F-actin and cell migration ) was performed on a Nikon A1R confocal microscope using a PlanApo VC 60X/1 . 4 NA oil-immersion objective ( Nikon , Amsterdam , Netherlands ) . Images were acquired for 8–10 hr at 5 min intervals with a pixel resolution of 0 . 21 µm . Long-term ( 16 hr at 5-min intervals ) , low resolution imaging to monitor for inter-modal transitions was performed on a Nikon A1R confocal microscope using a Plan Apo γ 20X/0 . 75 NA air objective and resonance scanner , producing a pixel resolution of 0 . 82 µm . 3×3 or 4×4 image montages were acquired and stitched . Prior to imaging , cells were labeled at 1:5000 with a far-red membrane dye ( Cell Mask Deep Red , Thermo Fisher Scientific ) for 2 hr and throughout imaging . Normal culture medium minus fetal bovine serum was used during all imaging , and cells were maintained at 37°C in 5% CO2 . Multiscale image sequences ( Nikon ND2 files ) were analyzed using PAD software ( Digital Cell Imaging Laboratories , Keerbergen , Belgium ) , as described previously ( Lock et al . , 2014 ) . Briefly , cell boundaries were detected using the RubyRed-LifeAct signal . Cells touching the image border in any given time frame were excluded . Within each segmented cell , CMACs greater than 0 . 05 µm2 in area were segmented based on EGFP-Paxillin signal . Segmented cells and CMACs were then tracked based on nearest neighbor analysis . Cell trajectories were smoothed using smoothing splines . CMACs were excluded where tracking did not contain at least three consecutive time points . CMACs present in the first and last frames of each image sequence were excluded from the calculation of CMAC Lifetime . All segmentation and tracking was manually inspected and iteratively optimized through parameter tuning . Quantitative features characterizing cell , CMAC , and CMAC-associated F-actin properties were then extracted ( Figure 3—figure supplement 1 ) . CMAC intensity values per channel were corrected by subtraction of mean intensity values in a 1 µm radius around the CMAC , excluding other segmented CMACs . CMAC intensity data were further standardized per experimental repeat relative to the median value of CMAC intensities in the size range between 0 . 15 and 0 . 2 µm2 within the low FN no INH condition . Cell motion was characterized by instantaneous cell speed or via analysis of mean squared displacement ( MSD ) . Mean square displacement ( MSD ) was calculated using a moving window as previously described ( Khorshidi et al . , 2011 ) . The window size was set to 24 frames ( 2 hr ) . The first 12 time lags ( tlag ) , up to 1 hr , of the MSD were fitted to Furth’s formula: MSD=4M ( tlag−tp ( 1−e−tlag/tp ) ) , thus measuring the migration coefficient M and the persistence time tp at each time point . The observations were divided into quintiles of migration coefficient and within each such quintile the mean persistence time for each trajectory was calculated . For visualization of parallel mode emergence ( Figure 1—figure supplement 1 and Video 3 ) and mode transitions ( Figure 1—figure supplement 2 and Videos 4 , 5 ) , long-term low-resolution image data was segmented and tracked ( following advanced denoising , power 15 ) using a custom general analysis pipeline in NIS-Elements ( v4 . 30 , Nikon ) . Collectively , data sets for comparison of fibronectin concentration , talin 1 expression and ROCK inhibition had structures as follows: 2 . 5 µg/ml FN ( 3 experimental repeats , 34 cells , 2525 cell observations , 29651 CMAC observations ) vs 10 µg/ml FN ( 19 experimental repeats , 118 cells , 6528 cell observations , 213406 CMAC observations ) ; talin 1 siRNA ( 4 experimental repeats , 101 cells , 6263 cell observations , 81675 CMAC observations ) vs control siRNA ( 4 experimental repeats , 51 cells , 3154 cell observations , 131679 CMAC observations ) ; High-resolution , multiscale imaging of 6 µM Y27632 ( ROCK inhibitor , 4 experimental repeats , 17 cells , 985 cell observations , 44647 CMAC observations ) vs DMSO control ( 10 experimental repeats , 34 cells , 2666 cell observations , 86794 CMAC observations ) ; long-term , low resolution imaging of 6µM Y27632 ( 4 experimental repeats , 73 cells , 4478 cell observations ) vs 2 µM Y27632 ( 4 experimental repeats , 71 cells , 5198 cell observations ) vs 1 µM Y27632 ( 4 experimental repeats , 69 cells , 4885 cell observations ) vs DMSO control ( 4 experimental repeats , 83 cells , 5968 cell observations ) . Note that the term 'experimental repeats' refers to independent biological repeats , not technical replicates . Data sample sizes were selected based on prior experience from related multiscale analyses in several previous studies ( Lock et al . , 2014 ) . Samples were collected and analyzed without iteration , that is , all relevant available data were used and sample sizes were not altered to increase or decrease statistical power . Cells were classified into Discontinuous or Continuous migration modes according to the behavioral criteria described in Figure 1 based on visual inspection and optimization between two individuals , using a custom platform established in Matlab ( vR2013b , The Mathworks , Natick , MA ) . Single cells were assessed at each time point within each image sequence ( inter-mode transitions , though infrequent , were assessed ) . All data presented herein were assessed concurrently and were both computationally blinded and randomized with respect to experimental conditions . Dead cells and cells in sustained contact were classified as null and excluded from subsequent analyses . Membrane protrusion and retraction dynamics were quantified as described previously ( Kowalewski et al . , 2015 ) . Briefly , consecutive segmented images were compared to define protrusions ( new pixels in cell segment compared to previous frame ) and retractions ( pixels lost from cell segment compared to previous frame ) . To explore dynamics over different time frames , images separated by different intervals were assessed using the same criteria , establishing time window samplings of between 5 min ( 1 frame ) and 75 min ( 15 frames ) . Subtraction of protrusion and retraction size probability distributions was performed as described previously for other feature probability distributions ( Kiss et al . , 2015 ) . Analysis of cross-correlation between protrusion and retraction areas over time was performed on a per cell sequence basis using the Matlab function xcov . Missing values were replaced by the mean value of protrusion/retraction area for that cell trajectory . Time lags of between −60 min ( −12 frames ) and 60 min ( +12 frames ) were assessed , with negative lags indicating that protrusion dynamics lead retraction dynamics , and positive lags indicating that retraction dynamics lead protrusion dynamics . To summarize the mean absolute cross-correlation per migration mode , cross-correlation values at all lags ( −60 to 60 min; −12 to 12 frames ) were averaged for each time window ( 5 min to 75 min; 1 to 15 frames ) , per cell , and then averaged per condition . Nienty-five percent confidence intervals were calculated per time window size . Friedman testing based on distinct cell numbers defined statistically discernable differences in mean protrusion-retraction cross-correlation between modes across the sampled time windows . All membrane dynamics analyses were performed in Matlab ( vR2013b , The Mathworks ) . Supervised multivariate clustering via canonical vectors analysis ( CVA ) was performed based on decompositions of the between-group and within-group covariance matrices following eigenvalue decomposition , as previously described ( Lock et al . , 2014 ) . Feature selection via CVA was performed by ranking absolute feature weighting coefficients from the first canonical vector ( CV1 ) . Unsupervised multivariate clustering using principal component analysis ( PCA ) was performed using singular value decomposition of the normalized data matrix . Probability density surfaces were calculated using a Gaussian kernel density estimation of significantly smaller scale that the overall mode distributions , and hence does not dictate the 'valley' shape of these distributions – rather this is a reflection of the true value distribution . Spearman’s rank correlation coefficients were calculated based on feature values per cell observation . Estimation of the expected distribution of random absolute value changes in Spearman’s correlation between modes was performed by permuting the identity of correlation values in one mode while holding the other constant . This was repeated 100 times and the cumulative distribution function of all differences is displayed . Pairwise testing for differences in feature distributions ( boxplots , parallel coordinates ) was performed using the Wilcoxon rank sum test ( equivalent to Mann-Whitney test ) given all cell observations . Analyses and visualizations described above were performed in Matlab ( The Mathworks ) . Parallel coordinates-based visualization of selected feature values in each migration mode was generated using Knime ( v2 . 12 . 00 , KNIME . com , Zurich , Switzerland ) . Parallel coordinates-based comparison of Spearman’s correlation coefficients between Cell Speed and selected features , as well as visualization of coefficient differences between modes , were generated using R ( v3 . 2 . 0 , R Foundation for Statistical Computing ) and RStudio ( v0 . 98 . 945 , RStudio , Boston , MA ) . | During an animal’s lifetime , many of its cells will move from one location in the body to another . For example , skin cells can migrate to repair wounds . Prior to migration , cells are usually attached to a scaffold called the extracellular-matrix , which helps to hold them in a particular location within a tissue . Individual cells can move in different ways . During a type of movement called mesenchymal migration , the front end of a cell grows outwards and attaches to a different section of the matrix . The rear of the cell is pulled forward and it detaches from the matrix and retracts , which allows the entire cell to move forward . In contrast , during amoeboid migration , the moving cells are only loosely attached to the matrix and move by gliding . There are large variations in how cells move and they can adopt modes that lie between the two extremes of mesenchymal and amoeboid migration . They can also switch between modes depending on their requirements . Shafqat-Abbasi et al . developed a method to analyse how individual human lung cancer cells move . The method uses software to collect data on cell shape , speed of movement and other features from microscopy images of the migrating cells . The experiments reveal that the cells adopt two distinct migration modes , which Shafqat-Abbasi et al . termed 'Discontinuous' and 'Continuous' . The majority of cells migrated in the Discontinuous mode , in which cells moved in many different directions . This was caused by a lack of coordination between the outgrowth of the front end of the cell , and the retraction of the back from the matrix . In contrast , in the cells that migrated using the Continuous mode , an outgrowth consistently led to a retraction , which enabled cells to move in one direction . Further experiments revealed that the mode of migration used by the cells is affected by how tightly they are bound to the extracellular-matrix , and the mechanical forces generated inside the cells to drive the movement . Shafqat-Abbasi et al . ’s method provides an analytical toolbox that other researchers can use to study the mesenchymal migration of animal cells . | [
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RNA-protein ( RNP ) granules have been proposed to assemble by forming solid RNA/protein aggregates or through phase separation into a liquid RNA/protein phase . Which model describes RNP granules in living cells is still unclear . In this study , we analyze P bodies in budding yeast and find that they have liquid-like properties . Surprisingly , yeast stress granules adopt a different material state , which is reminiscent of solid protein aggregates and controlled by protein disaggregases . By using an assay to ectopically nucleate RNP granules , we further establish that RNP granule formation does not depend on amyloid-like aggregation but rather involves many promiscuous interactions . Finally , we show that stress granules have different properties in mammalian cells , where they show liquid-like behavior . Thus , we propose that the material state of RNP granules is flexible and that the solid state of yeast stress granules is an adaptation to extreme environments , made possible by the presence of a powerful disaggregation machine .
The organization of the intracellular space into compartments is fundamental to life; it allows cells to perform complex biochemical reactions in a confined and controlled manner . In addition to membrane-delimited organelles , such as the Golgi apparatus or the endoplasmic reticulum , the cytoplasm contains compartments that are not surrounded by membranes . These non-membrane-bound compartments are frequently linked to gene expression pathways and often contain substantial amounts of RNAs ( Hyman and Brangwynne , 2011; Weber and Brangwynne , 2012; Brangwynne , 2013; Hyman et al . , 2014 ) . Examples in the nucleus include chromatin domains , the nucleolus , and Cajal bodies; examples in the cytoplasm are P bodies , stress granules , and germ granules . Although their functions are not always clear , it is assumed that they facilitate or suppress specific biochemical reactions . Recent studies proposed that P granules—RNA-protein ( RNP ) granules in germ cells of Caenorhabditis elegans—form by liquid–liquid demixing or phase separation from the cytoplasm , so that two phases , a liquid droplet phase and the cytoplasm coexist ( Brangwynne et al . , 2009; Lee et al . , 2013 ) . Such demixed phases of proteins and RNAs may turn out to be a unifying principle of subcellular organization ( Weber and Brangwynne , 2012; Brangwynne , 2013; Hyman et al . , 2014 ) . Indeed , recent findings show that liquid phase separation is not limited to P granules ( Brangwynne et al . , 2011; Aggarwal et al . , 2013; Feric and Brangwynne , 2013; Hubstenberger et al . , 2013; Wippich et al . , 2013; Banjade and Rosen , 2014 ) . Importantly , defined mixtures of RNAs and proteins can also phase separate in a cell-free system , driven by multivalent interactions ( Li et al . , 2012 ) . This suggests that many cellular components may have an intrinsic ability to phase separate and assemble into structures with liquid-like properties . Despite recent evidence for a liquid-like state , others have argued that RNP granules have more solid material characteristics , similar to protein aggregates ( Gilks et al . , 2004; Vessey et al . , 2006; Decker et al . , 2007; Si et al . , 2010; Decker and Parker , 2012; Ramaswami et al . , 2013 ) . This seems particularly likely for P bodies and stress granules , which are stress-inducible RNP granules containing non-translating RNAs and protein factors involved in translation repression or mRNA decay ( Anderson and Kedersha , 2009; Decker and Parker , 2012 ) . P bodies and stress granules form as a response to acute stress conditions , when a cell has to make arrangements to divert valuable resources to cellular survival , and their formation coincides with the formation of protein aggregates , which result from stress-induced protein misfolding . Proteins contained in P bodies or stress granules are characteristically composed of two types of domains: RNA-binding domains ( RBDs ) and domains of low sequence complexity; the latter are also referred to as prion-like ( Gilks et al . , 2004; Decker et al . , 2007; Reijns et al . , 2008; King et al . , 2012; Malinovska et al . , 2013 ) . This term is derived from the fact that they have a characteristic amino acid composition ( mostly polar amino acids such as serine , glycine , asparagine , glutamine and tyrosine ) , which resembles that of yeast prions . Prion domains ( PDs ) and prion-like domains ( PLDs ) have little structure under normal conditions . However , they can undergo spontaneous conversions into an aggregated state , which is characterized by a cross-β structure and referred to as amyloid ( Alberti et al . , 2009 ) . Once formed , the amyloid state can act as a template for the incorporation of further proteins , but it can also be reversed through energy-expending protein disaggregation machines ( Doyle et al . , 2013 ) . Despite some recent progress , RNP granule assembly remains a poorly understood molecular process . In particular , it is not known how RBDs and PLDs cooperate to promote RNP granule formation in living cells . Recent cell-free reconstitution experiments seem to confirm that PLDs in RNP granule components can assemble into amyloid-like fibers and undergo sol–gel or liquid–solid phase transitions ( Han et al . , 2012; Kato et al . , 2012; Kwon et al . , 2013 , 2014 ) . However , PLDs can also cause protein-misfolding diseases , which are typically accompanied by solid RNP aggregates ( Li et al . , 2013; Ramaswami et al . , 2013 ) . These pathological RNP aggregates are similar to those observed in cell-free reconstitution experiments , raising important questions about the relationship of physiological and pathological RNP granules in living cells . More insight into this topic is also desirable in light of the fact that two different models have been proposed to explain RNP granule assembly , which make very different predictions about the material state of RNP granules ( liquid vs solid ) and their mode of assembly ( phase separation vs aggregation ) . In this study , we analyze P bodies and stress granules in budding yeast and mammalian cells . To our surprise , we find a high degree of versatility in RNP granule assembly . We show that physiological RNP granules can have different material properties and behave as liquid-like droplets or solid protein aggregates . We further establish a key role for RNA in RNP granule assembly and demonstrate that PLDs in RNA-binding proteins promote RNP granule formation in a manner that does not involve amyloid-like aggregation . Instead , these domains undergo promiscuous interactions , with other PLDs or with misfolded proteins . We further reveal a central role for ATP-driven disaggregases in maintaining the identity and integrity of RNP granules and propose that the presence of the Hsp104 disaggregase in yeast has enabled the evolution of a unique pathway for RNP granule formation , which resembles a typical protein aggregation reaction .
To analyze the molecular mechanisms underlying the formation of stress-inducible RNP granules , we first focused on the role of prion-like proteins . These proteins have been implicated in RNP granule assembly ( Gilks et al . , 2004; Vessey et al . , 2006; Decker et al . , 2007; Reijns et al . , 2008; Si et al . , 2010; Kato et al . , 2012 ) , but they are also key determinants of protein aggregation ( Alberti et al . , 2009; King et al . , 2012; Malinovska et al . , 2013 ) . Indeed , Decker and colleagues demonstrated in 2007 that the C-terminal domain of the protein Lsm4 is required for P-body formation in yeast . This domain is aggregation-prone and compositionally similar to yeast prion proteins ( Alberti et al . , 2009 ) ( Figure 1—figure supplement 1 ) . Consistent with this , the PD of the yeast prion Rnq1 could functionally replace the PLD of Lsm4 , thus restoring P-body formation in certain genetic backgrounds ( Decker et al . , 2007 ) . However , it remained undetermined whether the PD of Rnq1 adopted an amyloid-like conformation during P-body assembly or whether other properties of this domain were required . To get insight into this question , we analyzed the PD of Rnq1 in yeast cells . First , we studied its subcellular localization in yeast carrying the background prion [PIN+] , a factor that promotes the conversion of the Rnq1 protein and other yeast prion proteins into an amyloid state . In these cells , GFP ( green fluorescent protein ) -tagged Rnq1PD assembled into punctate structures ( Figure 1A ) . To determine whether Rnq1PD could also aggregate in the absence of a co-inducing prion , we expressed it in a [pin−] background . The resulting cells also displayed a punctate fluorescence signal ( Figure 1A ) . However , the pattern of aggregation was different , because not all the signal was concentrated in foci ( Figure 1—figure supplement 2 ) . Next , we analyzed these cells by semi-denaturing detergent-agarose gel electrophoresis ( SDD-AGE ) ; this method can separate SDS ( sodium dodecyl sulfate ) -soluble from SDS-insoluble fractions , SDS insolubility being a hallmark of amyloid polymers ( Alberti et al . , 2010 ) . Using this technique , we found that Rnq1PD formed SDS-resistant polymers in [PIN+] cells ( Figure 1B ) . The Rnq1PD structures in [PIN+] cells could also be stained with the amyloid-specific dye Thioflavin T ( ThT ) ( Figure 1C ) . In contrast , Rnq1PD expressed in [pin−] cells did not form SDS-resistant aggregates ( Figure 1B ) and it could not be stained with ThT ( Figure 1C ) . Thus , we conclude that Rnq1PD can form two types of assemblies in yeast cells: amyloid-like aggregates and non-amyloid assemblies . 10 . 7554/eLife . 06807 . 003Figure 1 . Prion-like domains can access two distinct aggregated states , only one of which is amyloid-like . ( A ) Fluorescence microscopy of yeast cells expressing sfGFP-tagged Rnq1PD in [PIN+] and [pin−] cells . White lines indicate the cell boundaries . Scale bars: 5 µm . Also see related Figure 1—figure supplements 1–3 . ( B ) Semi-denaturing detergent-agarose gel electrophoresis ( SDD-AGE ) of [PIN+] and [pin−] cells containing a plasmid for expression of Rnq1PD-sfGFP . SDS-resistant amyloid polymers show slower migration in comparison to SDS-soluble monomers . Proteins were detected by immunoblotting with a GFP-specific antibody . ( C ) Thioflavin T ( ThT ) staining of [PIN+] and [pin−] cells expressing Rnq1PD-mCherry from a plasmid . Note that only amyloid-like assemblies can be stained with ThT . ( D ) 1 , 6-hexanediol treatment specifically disrupts non-amyloid Rnq1PD assemblies and not amyloids . Fluorescence time-lapse microscopy of [PIN+] ( top panel ) and [pin−] cells ( bottom panel ) expressing Rnq1PD-sfGFP . Time points are before treatment ( Before ) and 38 min after treatment with 10% 1 , 6-hexanediol ( After ) . In the control condition ( Mock ) only media was added . Cells were permeabilized with 10 µg/ ml digitonin . See corresponding Video 1 . ( E ) Cells expressing Rnq1PD-sfGFP were treated with 10% 1 , 6-hexanediol and digitonin for 1 hr to dissolve non-amyloid Rnq1PD assemblies ( Before ) . Hexanediol was washed out and replaced with normal growth media ( After ) , and the cells were observed with fluorescence microscopy . Also see corresponding Video 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 00310 . 7554/eLife . 06807 . 004Figure 1—figure supplement 1 . Lsm4 has a prion-like C-terminal domain ( underlined ) that is enriched for asparagines ( N ) and glutamines ( Q ) and contains hydrophobic residues ( L , V , I , M , F ) . N and Q are highlighted in red and hydrophobic residues are highlighted in green . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 00410 . 7554/eLife . 06807 . 005Figure 1—figure supplement 2 . Rnq1PD shows a different aggregation pattern in [pin−] and [PIN+] cells . The graph shows the foci-to-cytoplasm ratio of yeast cells expressing sfGFP-tagged Rnq1PD ( number of cells analyzed from three independent experiments: [pin−] 95 cells , [PIN+] 91 cells ) . Using the unpaired Student's t-test , the values are significantly different with p = 5 . 5 × 10−08 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 00510 . 7554/eLife . 06807 . 006Figure 1—figure supplement 3 . The amino acid sequence of the prion domain of Rnq1 resembles that of FG repeat-containing low-complexity domains of nucleoporins ( hydrophobic aromatic residues in an asparagine- and glutamine-rich polar sequence background ) . Aromatic hydrophobic residues are highlighted in green and N and Q residues are highlighted in red . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 006 Our next goal was to develop a tool that could differentiate between these two assembled states of Rnq1PD . To do this , we made use of the chemical 1 , 6-hexanediol , an aliphatic alcohol that has previously been shown to perturb nucleoporin-mediated transport across the nuclear pore ( Ribbeck and Gorlich , 2002; Patel et al . , 2007 ) . Many nucleoporins contain domains of low sequence complexity , which are compositionally similar to yeast PDs and form a sieve-like matrix that enables the selective passage of cargo complexes ( Frey et al . , 2006; Frey and Gorlich , 2007; Hulsmann et al . , 2012 ) . Importantly , sieve formation involves weak hydrophobic interactions between phenylalanine–glycine repeats that are embedded in the PLD . When these interactions are perturbed by 1 , 6-hexanediol , nucleocytoplasmic transport ceases ( Ribbeck and Gorlich , 2002; Patel et al . , 2007 ) . The amino acid composition of the PD of Rnq1 is similar to that of nucleoporins ( Figure 1—figure supplement 3 ) , suggesting that Rnq1PD aggregation may likewise be affected by hexanediol . Hexanediol indeed triggered the dissolution of non-amyloid Rnq1PD assemblies , whereas the amyloid form remained unaffected ( Figure 1D , Video 1 ) . Importantly , this process was reversible ( Figure 1E , Video 2 ) . This suggests that hexanediol can differentiate between these two types of assemblies and may thus be a powerful tool to interfere with the formation of structures that depend on weak interactions between sticky PLDs . 10 . 7554/eLife . 06807 . 007Video 1 . Hexanediol treatment specifically disrupts non-amyloid Rnq1PD assemblies and not amyloids . Fluorescence time-lapse microscopy of [PIN+] and [pin−] cells expressing Rnq1PD-sfGFP . All cells were treated with 10 µg/ml digitonin and , where indicated , with 10% 1 , 6-hexanediol . In the control condition only media was added . Related to Figure 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 00710 . 7554/eLife . 06807 . 008Video 2 . Reformation of non-amyloid Rnq1PD assemblies after hexanediol removal . Cells expressing Rnq1PD-sfGFP were treated with 10% 1 , 6-hexanediol and 10 µg/ml digitonin for 1 hr to dissolve non-amyloid Rnq1PD assemblies . Hexanediol was washed out and replaced with normal growth media and the cells were observed by fluorescence time-lapse microscopy . Related to Figure 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 008 The PLD of yeast Lsm4 is required for the assembly of P bodies ( Decker et al . , 2007 ) . However , the PLD of Lsm4 has not only been implicated in RNP granule formation , it also assembles into an amyloid-like state ( Alberti et al . , 2009 ) . Because the induction of an amyloid state is a concentration-dependent nucleation process , the conformational conversion of prion-like proteins into amyloid can be induced in [PIN+] yeast cells by raising their concentration . Thus , by expressing additional Lsm4 from a plasmid in the [PIN+] background , we could genetically drive endogenous Lsm4 into an amyloid state . In this state , endogenous Lsm4 formed one to a few fluorescent puncta in the cytoplasm ( Figure 2A , upper panel ) . Lsm4 was also present in puncta in uninduced cells , but they were smaller ( Figure 2A , left lower panel ) . Previous studies identified these puncta as P bodies ( Decker et al . , 2007 ) . Because P-body formation is strongly enhanced by stress , we next analyzed the localization of Lsm4 after glucose depletion . Now , Lsm4 was present in a few large puncta per cell ( Figure 2A , right lower panel ) , and this stress-induced localization pattern was reminiscent of the pattern formed by Lsm4 in the amyloid conformation . We further observed that other P-body components such as Edc3 and Dcp2 co-localized with amyloid-like Lsm4 structures ( Figure 2B ) . One potential explanation for this is that amyloid formation by Lsm4 occurs during the formation of P bodies . 10 . 7554/eLife . 06807 . 009Figure 2 . The prion-like protein Lsm4 does not adopt an amyloid-like conformation in P bodies . ( A ) Genetically induced and stress-induced Lsm4 assemblies are morphologically similar . Endogenous GFP-tagged Lsm4 in the amyloid-like state and PB state was investigated by fluorescence microscopy . PB formation was induced by glucose starvation for 1 hr ( Stress induction ) . Genetic induction of Lsm4-GFP assembly into an amyloid-like state was through overexpression of unlabeled Lsm4 . White lines indicate the cell boundaries . Scale bars: 5 µm . ( B ) Fluorescence microscopy of Lsm4-GFP cells co-expressing the mCherry-tagged PB proteins Dcp2 or Edc3 . Lsm4 assembly was induced genetically ( amyloid-like state ) or through heat stress at 46˚C for 10 min ( P-body state ) . Note that both types of assemblies show co-localization with Dcp2 and Edc3 . ( C ) Lsm4 in P bodies does not adopt an amyloid-like conformation . Comparative SDD-AGE analysis of Lsm4-GFP in the amyloid-like and PB state . P bodies were induced through heat stress or glucose depletion . ( D ) The Lsm4 RNP complex in P bodies has a different size than the amyloid-like Lsm4 complex . Size exclusion chromatography of Lsm4-GFP in the amyloid-like state and PB state . Cells expressing Rnq1PD from a plasmid were used as a control for amyloid . Molecular size standards were: thyroglobulin ( 660 kDa ) , ferritin ( 440 kDa ) , and catalase ( 240 kDa ) . Cells were stressed in glucose-deficient medium to induce PBs . ( E ) Fluorescence recovery after photobleaching analysis of Lsm4 in the P body state ( induced by glucose depletion ) or amyloid-like state . Dendra2-tagged Lsm4 was expressed from a plasmid . The median half-recovery times were 22 s for the PB state ( n = 6 ) and 131 s for the amyloid-like state ( n = 4 ) . Using the unpaired Student's t-test , the values are significantly different with p = 0 . 0018 . Also see related Figure 2—figure supplement 1 . ( F ) Lsm4-containing P bodies are RNA-dependent , whereas amyloid-like Lsm4 assemblies are not . Cells containing Lsm4-GFP in the PB state ( induced by glucose depletion ) or amyloid-like state were treated with 100 µg/ml cycloheximide and observed by fluorescence time-lapse microscopy after 14 min . See corresponding Video 3 . ( G ) P bodies are not stainable with the amyloid-specific dye ThT . Fluorescence microscopy of ThT-stained cells expressing mCherry-tagged Lsm4 . Lsm4 assembly was induced genetically ( amyloid-like state ) or through glucose depletion ( P-body state ) . Also see related Figure 2—figure supplement 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 00910 . 7554/eLife . 06807 . 010Figure 2—figure supplement 1 . Lsm4 in P bodies shows faster turnover than Lsm4 in amyloid-like assemblies . FRAP analysis was performed using the photoconvertible tag Dendra2 . The recovery of the green fluorescence was followed over time after photoconversion . P-body formation was induced by glucose depletion . The normalized fluorescence intensity is shown . Error bars represent SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 01010 . 7554/eLife . 06807 . 011Figure 2—figure supplement 2 . P-body proteins do not enter into an amyloid-like state upon stress . SDD-AGE analysis of whole cell lysates from yeast cells that expressed GFP-tagged Dcp2 or Edc3 from their endogenous loci . P-body formation was induced through heat stress ( 46°C ) or glucose depletion . The prion-like protein Nrp1-GFP in the amyloid-like state was used as a control ( left ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 011 To investigate this possibility , we analyzed Lsm4 in the P-body state . First , we generated lysates from stressed cells and subjected them to SDD-AGE analysis . As can be seen in Figure 2C , Lsm4 did not become resistant to SDS in response to glucose depletion or robust heat stress . In contrast , SDS-resistant polymers were readily detected , when Lsm4 was genetically driven into an amyloid conformation ( Figure 2C ) . We then prepared lysates from stressed cells in mild detergent ( in the absence of SDS ) and subjected them to size exclusion chromatography ( SEC ) . Lsm4 in the amyloid state formed very large complexes , whereas Lsm4 complexes in stressed cells were much smaller ( Figure 2D ) . As a next step , we investigated the dynamic behavior of Lsm4 in P bodies . To do this , we tagged Lsm4 with the fluorophore Dendra2 . The resulting yeast strain was stressed by removal of glucose , and P body-localized Lsm4-Dendra2 was photo-converted from green to red . The recovery of the green fluorescence in the P bodies was then followed over time . As can be seen in Figure 2E and Figure 2—figure supplement 1 , the turnover of Lsm4 in P bodies was rapid and in the same range as previously reported for mammalian P-body components ( Andrei et al . , 2005; Kedersha et al . , 2005; Aizer et al . , 2008 ) . However , the turnover rate of Lsm4 in the amyloid conformation was much slower ( Figure 2E and Figure 2—figure supplement 1 ) . Next , we tested whether Lsm4 assemblies are dependent on RNA for their formation , as demonstrated previously for other RNP granules ( Andrei et al . , 2005; Teixeira et al . , 2005 ) . Indeed , stress-induced Lsm4-labeled P bodies disassembled in the presence of the translation inhibitor cycloheximide , an inhibitor that traps RNAs on polysomes and thereby depletes P bodies of RNA substrates ( Figure 2F , Video 3 ) . In contrast , Lsm4 in the amyloid state was unaffected by cycloheximide , suggesting that the formation of this structure is independent of RNA . 10 . 7554/eLife . 06807 . 012Video 3 . P-body formation requires RNA . Glucose starved cells containing Lsm4-GFP in the PB state or amyloid-like state ( ‘Prion’ ) were treated with 100 µg/ml cycloheximide and observed by fluorescence time-lapse microscopy . Related to Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 012 Collectively , these findings indicated that the Lsm4 conformation in stressed cells is fundamentally different from the experimentally induced amyloid conformation . To more generally test whether P-body formation depends on amyloid-like aggregation , we treated P body-containing yeast cells with the amyloid-specific dye ThT . As shown in Figure 2G , Lsm4-containing P bodies were ThT negative , whereas amyloid-like assemblies of Lsm4 were readily identified by ThT . Moreover , other P-body proteins with PLDs , such as Edc3 and Dcp2 , did not become SDS-resistant in stressed cells ( Figure 2—figure supplement 2 ) , suggesting that they do not enter into amyloid-like states . Thus , we conclude that amyloid-like conformational conversions are not required for the formation of P bodies in yeast . Our data so far suggest that P-body formation may not be dependent on amyloid-like conformational changes . They further imply that yeast P-bodies are dynamic RNA-dependent structures . Recent findings in C . elegans embryos indicate that P granules—germ line RNP granules related to P bodies—have liquid-like properties and form by demixing from the cytoplasm ( Brangwynne et al . , 2009; Lee et al . , 2013 ) . Thus , we hypothesized that the assembly of yeast P-bodies may be governed by the same physical principle . The morphology of a structure or its ability to fuse can provide important hints about its material state ( Hyman et al . , 2014 ) . Indeed , fluorescence microscopy of yeast P-bodies revealed a smooth spherical surface , in agreement with a liquid-droplet state ( Figure 3A ) . Moreover , P bodies underwent frequent fusion events ( Figure 3A , B ) . Upon fusion , the newly formed body rapidly relaxed into a spherical shape ( Figure 3A ) . Such fast relaxation times indicate that the viscosity of P bodies is relatively low , in agreement with a dynamic liquid-like state . 10 . 7554/eLife . 06807 . 013Figure 3 . Yeast P-bodies are liquid droplets and not aggregates . ( A ) P bodies behave as liquid-like droplets . Fluorescence time-lapse microscopy of stressed cells expressing Lsm4-GFP from the endogenous locus . Time points are indicated in seconds above . Two fusing PBs are indicated by red arrows . White scale bar: 2 µm . ( B ) Quantification of the PB fusion behavior over time using GFP-tagged Lsm4 as a marker . At time point 0 , the medium was changed to synthetic medium lacking glucose ( red curves ) . Control cells received complete synthetic medium ( green curves ) . Values given are PB number/cell and the average PB size [µm2] . At last 150 cells were analyzed . Error bars represent SEM . ( C ) P bodies are sensitive to 1 , 6-hexanediol , suggesting that they are liquid-like . Cells expressing Lsm4-GFP from the endogenous promoter were stressed in medium without glucose for 30 min ( Before ) . 5% 1 , 6-hexanediol or medium ( Mock ) was added and the cells were analyzed after 30 min ( After ) . Scale bars: 5 µm . Also see corresponding Video 4 and related Figure 3—figure supplement 1 . ( D ) The hexanediol effect is reversible . Stressed cells expressing Lsm4-GFP were treated with 5% 1 , 6-hexanediol for 1 hr ( Before ) . Hexanediol was washed out with fresh medium and an image was acquired after 12 min ( After ) . Also see corresponding Video 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 01310 . 7554/eLife . 06807 . 014Figure 3—figure supplement 1 . The aliphatic alcohol 1 , 6-hexanediol causes rapid disassembly of P bodies . Fluorescence microscopy of the P-body marker Edc3-GFP expressed from the endogenous locus . Cells were stressed in medium without glucose for 15 min and 10 µg/ml digitonin with or without 10% 1 , 6-hexanediol was added . Images were taken before hexanediol treatment ( Before ) and 2 min after treatment ( After ) . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 014 A liquid state requires that the molecular interactions are weak and permanently changing ( Hyman et al . , 2014 ) . Solid states instead are based on tight interactions , which are largely invariant over time . We reasoned that hexanediol could be a useful tool to differentiate between liquid-like and solid-like states , because of its ability to interfere with weak hydrophobic interactions . In fact , hexanediol has been used previously to assess the liquid-like nature of germ granules in C . elegans ( Updike et al . , 2011 ) . Indeed , when we added hexanediol to yeast cells , it dissolved P bodies ( Figure 3C , Video 4 ) . Importantly , this effect was rapid ( Figure 3—figure supplement 1 ) and reversible , as P bodies reformed after hexanediol washout ( Figure 3D , Video 5 ) . This suggests that P bodies—similar to germ granules in C . elegans—rely on weak interactions for their formation . Thus , we conclude that yeast P-bodies resemble liquid droplets with physicochemical properties unlike those of solid amyloid-like aggregates . 10 . 7554/eLife . 06807 . 015Video 4 . Hexanediol treatment disrupts P bodies in yeast cells . Cells expressing Lsm4-GFP from the endogenous promoter were stressed in medium without glucose for 30 min . 5% hexanediol or medium ( Control ) was added and the cells were analyzed by time-lapse fluorescence microscopy . Related to Figure 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 01510 . 7554/eLife . 06807 . 016Video 5 . Reformation of P bodies after hexanediol removal . Glucose starved cells expressing Lsm4-GFP were treated with 5% hexanediol for 1 hr . Hexanediol was washed out with fresh medium and the cells were observed by time-lapse fluorescence microscopy . Related to Figure 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 016 Stress granules are related to P bodies; they are induced by stress and function as storage depots for mRNAs ( Anderson and Kedersha , 2008; Decker and Parker , 2012 ) . We therefore wondered whether yeast stress granules show similar liquid-like behavior as P bodies . To investigate this , we tested the effect of hexanediol on stress granules . Remarkably , hexanediol did not affect stress granule integrity , even when applied for extended times or in the presence of digitonin to facilitate hexanediol entry into the cells ( Figure 4A , Figures 4—figure supplement 1 , Video 6 ) . Thus , we conclude that stress granules are distinct from P bodies and may instead have a more solid character . 10 . 7554/eLife . 06807 . 017Figure 4 . Stress granules have properties of solid aggregates , but do not depend on amyloid-like conversions for their formation . ( A ) 1 , 6-hexanediol does not disrupt stress granules . Cells expressing Nrp1-GFP from the endogenous promoter as a marker for stress granules were stressed for 15 min in glucose-deficient medium ( Before ) . An image was taken 6 min after the addition of 10% hexanediol ( After ) . The cells were treated with 10 µg/ml digitonin to make them more permeable to hexanediol . The control ( Mock ) received only medium +10 µg/ml digitonin . Note that P bodies dissolve within less than 2 min under the same conditions ( see Figure 3—figure supplement 1 and Figure 4—figure supplement 1 ) . Also see corresponding Video 6 . ( B ) The amyloid-like and stress granule states of Nrp1 are morphologically similar . Fluorescence microscopy of cells expressing GFP-tagged Nrp1 from the endogenous promoter . Nrp1 assembly was induced genetically ( amyloid-like state ) or through 1 hr glucose starvation ( stress granule state ) . White lines indicate the cell boundaries . Scale bars: 5 µm . ( C ) Genetically induced and stress-induced Nrp1 assemblies are morphologically similar and recruit Pab1 . Fluorescence microscopy analysis of Nrp1-GFP cells expressing mCherry-tagged Pab1 . Stress was induced through heat shock at 46°C 10 min . ( D ) Stress does not induce an amyloid-like state in Nrp1 . SDD-AGE of lysates from cells expressing Nrp1-Cerulean from the endogenous promoter . Nrp1 assembly was induced genetically ( amyloid-like state ) or through stress ( stress granule state , SG ) . Also see related Figure 4—figure supplement 4 . ( E ) Analysis of Nrp1 turnover in the stress granule state ( induced by glucose depletion ) and amyloid-like state . Nrp1-Dendra2 was expressed from a plasmid . The recovery half times of Nrp1 in the SG state ( median = 42 s , n = 4 ) and in the amyloid-like state ( median = 113 s , n = 4 ) are shown . p value = 0 . 0927 ( unpaired Student's t-test ) . Also see related Figure 4—figure supplement 3 . ( F ) Stress granules are not stainable with ThT . Cells expressing mCherry-tagged Nrp1 were treated with ThT . Assembly of Nrp1 was induced genetically ( amyloid-like state ) or through glucose depletion ( stress granule state ) . Also see related Figure 4—figure supplement 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 01710 . 7554/eLife . 06807 . 018Figure 4—figure supplement 1 . P bodies but not stress granules are sensitive to hexanediol . Cells expressing Nrp1-GFP and Edc3-mCherry from the endogenous loci were stressed in medium without glucose for 80 min ( Before ) . 5% 1 , 6-hexanediol or medium ( Mock ) was added and the cells were analyzed by fluorescence microscopy after 2 hr ( After ) . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 01810 . 7554/eLife . 06807 . 019Figure 4—figure supplement 2 . Stress granule-associated and amyloid-like Nrp1 form different complexes in yeast cell lysate . Size-exclusion chromatography of lysates from cells expressing Nrp1-Cerulean from the endogenous promoter . Nrp1 in the amyloid-like state and SG state ( glucose starvation ) is compared . Molecular weight standards were: ferritin ( 440 kDa ) , catalase ( 240 kDa ) , and BSA ( bovine serum albumin ) ( 67 kDa ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 01910 . 7554/eLife . 06807 . 020Figure 4—figure supplement 3 . Analysis of the turnover of Nrp1 in the stress granule and the amyloid-like state . Dendra2-tagged Nrp1 was used for FRAP analysis . The recovery of the green fluorescence was followed over time after photoconversion . The SG state was induced through glucose depletion . The normalized fluorescence intensity in the SG state ( left panel ) and amyloid-like state ( right panel ) are compared . Error bars represent SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 02010 . 7554/eLife . 06807 . 021Figure 4—figure supplement 4 . Prion-like stress granule proteins do not transition into an amyloid-like state . SDD-AGE analysis of endogenous GFP-tagged Pab1 , Pub1 , Ngr1 or Pbp1 in unstressed and stressed yeast cells . Stress granules were induced through heat stress ( 46°C ) or glucose depletion . Nrp1-GFP in the amyloid conformation was used as a control ( left ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 02110 . 7554/eLife . 06807 . 022Video 6 . Hexanediol does not disrupt stress granules . Cells expressing Nrp1-GFP from the endogenous promoter were stressed for 15 min in glucose-deficient medium before the addition of 10% hexanediol . The cells were treated with 10 µg/ml digitonin to make them more permeable to hexanediol . The control received only medium +10 µg/ml digitonin . Note that P bodies dissolve within 2 min under the same conditions ( see Figure 3—figure supplement 1 ) . Related to Figure 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 022 Like the constituent proteins of P bodies , many stress granule proteins contain PLDs . To investigate whether these proteins undergo amyloid-like conformational conversions , we initially focused on one stress granule protein: Nrp1 . Nrp1 is a prototypical stress granule protein in that it contains a RNA-binding domain ( RRM - RNA recognition motif ) and a PLD ( Buchan et al . , 2008 ) . However , Nrp1 can also be converted into an amyloid-like state ( Alberti et al . , 2009 ) . Moreover , it is one of the proteins capable of binding to b-isox ( Kwon et al . , 2013 ) , a chemical that specifically binds to low-complexity domains , which have the ability to undergo amyloid-like conformational conversions ( Kato et al . , 2012 ) . To investigate whether amyloid-like aggregation of Nrp1 is involved in stress granule assembly , we first converted endogenous Nrp1 into an amyloid state , using a genetic approach ( see ‘Materials and methods’ for details ) . In the resulting cells , Nrp1 localized to one or a few bright cytoplasmic foci ( Figure 4B , top panel ) . The same localization pattern was observed in stressed cells ( Figure 4B , lower panel ) . Both structures co-localized with other stress granule proteins such as Pab1 ( Figure 4C ) , which is consistent with the possibility that genetically induced amyloid-like Nrp1 structures are genuine stress granules . Thus , we next investigated whether stress triggers an amyloid-like conversion in Nrp1 using SDD-AGE . However , Nrp1 did not become SDS-resistant in response to stress ( Figure 4D ) . Nrp1 complexes also had a very different molecular size in stressed cells ( Figure 4—figure supplement 2 ) . In addition , we found that the turnover rate of amyloid-like Nrp1 was much slower ( Figure 4E , Figure 4—figure supplement 3 ) . As a next step , we investigated four additional stress granule proteins with PLDs using SDD-AGE . However , none of these proteins formed amyloid-like structures in stressed cells ( Figure 4—figure supplement 4 ) . Furthermore , stress-induced Nrp1 assemblies were ThT negative , whereas genetically induced Nrp1 assemblies were stainable with ThT ( Figure 4F ) . Thus , we conclude that stress granules are more solid-like but like P bodies do not transition into amyloid-like states upon stress . Why do stress granules adopt a different material state than P bodies ? A previous study showed that in heat-stressed cells , the formation of stress granules coincides with the formation of protein aggregates , which result from stress-induced protein misfolding ( Cherkasov et al . , 2013 ) . Thus , we reasoned that stress granules might be functional aggregates that behave in a similar manner as aggregates formed by misfolded proteins . To investigate this , we compared the aggregation behavior of stress granule components and misfolded proteins by time-lapse microscopy . For this purpose , we used yeast strains co-expressing GFP-tagged stress granule proteins ( Pbp1 or Nrp1 ) and mCherry-tagged misfolding-prone proteins ( a mutant variant of luciferase or a thermo-labile variant of Ubc9 , Ubc9ts ) ( Kaganovich et al . , 2008; Gupta et al . , 2011 ) . Indeed , in cells that were exposed to a robust heat shock of 46°C , stress granule proteins co-localized with luciferase or Ubc9ts in punctate structures ( Figure 5A , Figure 5—figure supplement 1 ) . Co-localization was also observed between Nrp1 and several chaperones , such as Hsp42 , Ssa1 , and Hsp104 ( Figure 5—figure supplement 2 ) . This indicates that stress granule proteins and misfolded proteins are co-deposited , as previously suggested ( Cherkasov et al . , 2013 ) . 10 . 7554/eLife . 06807 . 023Figure 5 . Yeast stress granules are functional protein aggregates , which are dissolved by disaggregases . ( A ) The stress granule protein Nrp1 is co-deposited with misfolding-prone proteins during robust heat shock . Fluorescence microscopy of yeast cells expressing Nrp1-GFP from the endogenous locus and mCherry-tagged misfolding-prone proteins ( mutated luciferase and Ubc9ts ) from a plasmid . The cells were exposed to a 10-min heat shock at 46°C . White lines indicate the cell boundaries . Scale bars: 5 µm . Also see related Figure 5—figure supplements 1–3 and Videos 7–9 . ( B ) Nrp1 is less aggregation prone than luciferase . Same as ( A ) , except that the cells were exposed to a mild heat shock at 42°C for 10 min . Note that under these conditions only luciferase forms aggregates . Also see related Figure 5—figure supplement 4 . ( C ) Nrp1 and luciferase coalesce into distinct structures under starvation conditions . Same as ( A ) , except that the cells were stressed by glucose starvation for 1 hr ( -glc ) . See corresponding Video 10 . Also see related Figure 5—figure supplement 5 . ( D ) Dissolution of stress granules is dependent on disaggregases . Fluorescence time-lapse microscopy of yeast cells expressing GFP-tagged Nrp1 from the endogenous locus . Wild-type cells are compared to strains with genetic deficiencies ( Δhsp104 , Δsse1 , or Δsse2 ) . Shown are images after 40 min at 46˚C and during recovery after 80 min at 25˚C . Also see Video 11 . ( E ) Quantification of the number of foci/cell in the strains shown in ( D ) . At least 290 cells were analyzed for each strain from three independent experiments . Error bars are SEM . ( F ) Hsp104 is also required for the disassembly of stress granules induced by glucose starvation . Wild-type or Δhsp104 cells expressing Nrp1-GFP from the endogenous locus were exposed to glucose starvation for 1 hr ( image taken after 40 min at -glc ) and observed 80 min after glucose-containing growth medium was added . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 02310 . 7554/eLife . 06807 . 024Figure 5—figure supplement 1 . The stress granule protein Pbp1 is co-deposited with the misfolding-prone protein Ubc9ts during robust heat shock . Fluorescence microscopy of yeast cells expressing Pbp1-GFP from the endogenous locus and mCherry-tagged Ubc9ts from a plasmid . Cells were exposed to a heat shock at 46°C . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 02410 . 7554/eLife . 06807 . 025Figure 5—figure supplement 2 . The stress granule protein Nrp1 co-localizes with chaperones during robust heat shock . Fluorescence microscopy of yeast cells expressing Nrp1-mCherry from a plasmid and GFP-tagged Hsp42 , Ssa1 , or Hsp104 from the endogenous locus . Cells were exposed to a heat shock at 46°C . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 02510 . 7554/eLife . 06807 . 026Figure 5—figure supplement 3 . Stress granules dissolve faster than protein aggregates . Yeast cells expressing Nrp1-GFP or Pbp1-GFP from the endogenous locus were transformed with plasmids for the expression of mCherry-tagged misfolding-prone proteins ( mutated luciferase or Ubc9ts ) . Cells were exposed to a robust heat shock at 46°C for 2 hr ( time point: 0 hr ) and then transferred back to 25°C and observed after 2 hr ( time point: 2 hr ) . Also see the corresponding Videos 7–9 . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 02610 . 7554/eLife . 06807 . 027Figure 5—figure supplement 4 . A mild heat shock does not lead to co-deposition of stress granule components and misfolding-prone proteins . Fluorescence microscopy of yeast cells expressing Nrp1-GFP from the endogenous locus and mCherry-tagged Ubc9ts from a plasmid . The cells were exposed to a mild heat shock at 42°C . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 02710 . 7554/eLife . 06807 . 028Figure 5—figure supplement 5 . Stress granules and misfolding-prone proteins do not form mixed aggregates under glucose depletion conditions . Yeast cells expressing Pbp1-GFP from the endogenous locus and mutant mCherry-tagged luciferase from a plasmid were exposed to glucose starvation stress ( −glc ) . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 028 Despite the fact that protein aggregates and stress granule components showed strong co-localization , stress granules dissolved faster than misfolded proteins during recovery from stress ( Figure 5—figure supplement 3; Videos 7–9 ) . Importantly , cells that had dissolved their stress granules re-entered into the cell cycle ( Videos 7–9 ) , although they still contained aggregated misfolded proteins . This suggests that the presence of misfolded proteins does not prevent re-entry into the cell cycle and that resumption of growth may be coupled to stress granule dissolution . It also implies that stress granule components are less aggregation-prone than the misfolding-prone model proteins used in our study . To investigate this possibility , we repeated the heat shock experiment at 42°C . Indeed , under mild heat-shock conditions only luciferase and Ubc9ts formed visible aggregates , whereas the stress granule component Nrp1 remained diffusely localized ( Figure 5B and Figure 5—figure supplement 4 ) . These findings suggest that stress granule components behave like misfolding-prone proteins , which reversibly aggregate into stress granules , when cells are exposed to robust environmental stress . 10 . 7554/eLife . 06807 . 029Video 7 . Stress granule and protein aggregate formation and dissolution in stressed cells . Yeast cells expressing Nrp1-GFP from the endogenous locus were transformed with plasmids for the expression of mCherry-tagged mutant luciferase . Cells were exposed to a robust heat shock at 46°C and then transferred back to 25°C . Note that stress granules dissolve faster than protein aggregates . Related to Figure 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 02910 . 7554/eLife . 06807 . 030Video 8 . Stress granule and protein aggregate formation and dissolution in stressed cells . Yeast cells expressing Nrp1-GFP from the endogenous locus were transformed with plasmids for the expression of mCherry-tagged Ubc9ts . Cells were exposed to a robust heat shock at 46°C and then transferred back to 25°C . Note that stress granules dissolve faster than protein aggregates . Related to Figure 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 03010 . 7554/eLife . 06807 . 031Video 9 . Stress granule and protein aggregate formation and dissolution in stressed cells . Yeast cells expressing Pbp1-GFP from the endogenous locus were transformed with a plasmid for the expression of mCherry-tagged Ubc9ts . Cells were exposed to a robust heat shock at 46°C and then transferred back to 25°C . Note that stress granules dissolve faster than protein aggregates . Related to Figure 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 031 Stress granules form under a variety of conditions , and they show different compositions depending on the nature of the inducing stress ( Hoyle et al . , 2007; Grousl et al . , 2009; Buchan et al . , 2011 ) . To determine whether stress granules induced by other stresses behave in a similar way , we exposed yeast to glucose depletion . Glucose removal also caused stress granule formation , and this was accompanied by a low level of luciferase aggregation ( see Video 10 ) . However , in contrast to heat stress conditions , luciferase and stress granule components coalesced into largely distinct structures ( Figure 5C and Figure 5—figure supplement 5 ) . Thus , we conclude that stress granule proteins aggregate under a variety of conditions but that co-aggregation with misfolded proteins is most pronounced during robust heat shock . 10 . 7554/eLife . 06807 . 032Video 10 . The stress granule protein Nrp1 is not co-deposited with misfolding-prone proteins during glucose deprivation . Fluorescence microscopy of yeast cells expressing Nrp1-GFP from the endogenous locus and mCherry-tagged mutants luciferase from a plasmid . Related to Figure 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 032 A previous report proposed a role for disaggregating chaperones in stress granule dissolution ( Cherkasov et al . , 2013 ) . Therefore , we next tested how protein disaggregases affect stress granule formation . Three different proteins promote the disaggregation of protein aggregates in yeast: Hsp104 and two members of the Hsp110 family , called Sse1 and Sse2 ( Glover and Lindquist , 1998; Shorter , 2011; Duennwald et al . , 2012; Rampelt et al . , 2012; Doyle et al . , 2013 ) . We compared wild-type cells and cells in which these proteins had been inactivated genetically . Deletion of either of the disaggregases led to more pronounced stress granule assembly and a delay in stress granule disassembly ( Figure 5D , E , Video 11 ) . However , inactivation of Hsp104 had the strongest effect . Similar findings were obtained for stress granules induced by glucose depletion ( Figure 5F ) . Thus , we conclude that stress granules are functional aggregates and that the components contained in stress granules need to be reactivated by disaggregation before cells can re-enter into the cell cycle . Consistent with this , a recent report showed that cell cycle-associated RNP granules in the multinuclear fungus Ashbya gossypii are functional aggregates that are remodeled by chaperones ( Lee et al . , 2015 ) . 10 . 7554/eLife . 06807 . 033Video 11 . Dissolution of stress granules is dependent on disaggregases . Fluorescence time-lapse microscopy of yeast cells expressing GFP-tagged Nrp1 from the endogenous locus . Wild-type cells are compared to strains with genetic deficiencies ( Δhsp104 , Δsse1 , or Δsse2 ) . Cells were exposed to a robust heat shock at 46°C and then transferred back to 25°C . Related to Figure 5 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 033 Our findings indicate that during severe stress conditions both misfolded proteins and stress granule components co-aggregate . However , despite being deposited in spatial proximity , the two types of proteins do not seem to form mixed aggregates , in particular when exposed to mild stress condition such as glucose depletion . A previous study suggested that misfolded proteins seed the formation of stress granules ( Cherkasov et al . , 2013 ) . However , it remained unclear whether this is a general mode of stress granule formation or only applies to conditions of robust heat stress . Investigation of a seeding function requires experimental control over the aggregated state of a protein in the context of a living cell . To achieve this , we developed a method based on a self-assembling protein fragment , derived from a viral capsid protein ( μNS ) . This fragment assembles into large spherical particles in cells , and these particles could be visualized by adding GFP to the N terminus ( Figure 6A ) . To exclude that these particles are aggregates of misfolded protein , we first tested whether they co-localize with chaperones ( Ssa1 and Hsp104 ) . However , chaperones did not associate with μNS particles ( Figure 6—figure supplement 1 ) , suggesting that the particles are invisible to the protein quality control system . 10 . 7554/eLife . 06807 . 034Figure 6 . Stress granule assembly is redundant and highly adaptable . ( A ) Using a genetically encoded particle to study dynamic interactions in living yeast cells . Left: a fragment of the viral capsid protein μNS comprising the 250 C-terminal amino acids forms self-assembling particles in yeast cells . The particles were visualized using an N-terminal sfGFP tag . Right: interaction assay . Fusion of a protein X to sfGFP-tagged μNS particles allows interaction studies with a mCherry-tagged protein Y . ( B ) μNS particles carrying mutant luciferase on the surface interact with endogenous mCherry-tagged Hsp104 . Cells were observed before and after a 10-min heat shock at 46°C . White lines indicate the cell boundaries . Scale bars: 5 µm . Also see related Figure 6—figure supplements 1 , 2 . ( C ) Same as ( B ) , except that cells were used in which mCherry-tagged Pub1 was expressed from the endogenous locus . Only one representative μNS particle is shown at high magnification . Note that Pub1 only interacts with luciferase in cells exposed to robust heat stress . Also see related Figure 6—figure supplements 3–5 . ( D ) Same as ( C ) , except that mCherry-tagged Nrp1 was mildly overexpressed from a plasmid carrying an ADH1 promoter . Note that Nrp1 interacts with luciferase already in unstressed cells , and that the amount of Nrp1 accumulating on the particle is strongly increased upon heat stress . ( E ) Same as ( D ) , except that the prion-like domain ( PLD ) of Nrp1 ( Nrp1PLD ) or a deletion mutant lacking the PLD ( Nrp1ΔPLD ) was observed at 25˚C . ( F ) The cellular chaperone machinery prevents interactions between misfolded proteins and stress granule components . Cells expressing Nrp1-GFP from the endogenous locus and mCherry-tagged mutated luciferase from a plasmid were exposed to a 10-min heat shock at 42°C or 46°C . The cells in the bottom panel were exposed to 3 mM guanidinium hydrochloride ( GdnHCl ) to inhibit Hsp104 . Also see related Figure 6—figure supplement 6 . ( G ) Same as ( C ) , except that the temperature was increased slowly from 25°C to 46°C ( preconditioning ) . Note that preconditioning prevents co-assembly of stress granules and misfolded proteins . Also see related Figure 6—figure supplement 7 . ( H ) PLDs mediate interactions only when present in high local concentrations . Yeast cells were transformed with plasmids for the expression of GFP-tagged wild-type Nrp1 or deletion mutants lacking the RNA-binding domain ( RBD ) ( Nrp1PLD ) or PLD domain ( Nrp1ΔPLD ) . The resulting cells were exposed to heat shock . ( I ) Upon heat shock , stress granules form on µNS particles that present Nrp1 on the surface . Same conditions as ( C ) and ( D ) . Also see related Figure 6—figure supplements 8–9 . ( J ) Same as ( I ) , except that mutants lacking the RBD ( Nrp1PLD ) or PLD domain ( Nrp1ΔPLD ) were presented on the particle . Note that both mutants are able to recruit full-length Nrp1 at 25˚C . Also see related Figure 6—figure supplement 10–12 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 03410 . 7554/eLife . 06807 . 035Figure 6—figure supplement 1 . µNS particles do not interact with Hsp104 or Ssa1 . Cells expressing tdimer2-tagged Hsp104 and Ssa1 from the endogenous locus were observed before and after heat shock at 46°C . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 03510 . 7554/eLife . 06807 . 036Figure 6—figure supplement 2 . sfGFP-µNS particles carrying mutant luciferase or Ubc9ts on the surface interact with Hsp104 and Ssa1 . Cells expressing tdimer2-tagged Hsp104 and Ssa1 from the endogenous locus were observed before and after heat shock at 46°C . Note that Ubc9ts is already interacting with chaperones in unstressed cells , suggesting that a fraction of Ubc9ts is already misfolded under normal conditions . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 03610 . 7554/eLife . 06807 . 037Figure 6—figure supplement 3 . Misfolded proteins can nucleate stress granule formation under robust heat shock conditions . Same as Figure 6—figure supplement 2 , except that cells were used in which mCherry-tagged Pab1 was expressed from the endogenous locus . Note that Pab1 only interacts with luciferase in cells that are exposed to robust heat stress . For presentation purposes , only one representative μNS particles is shown at high magnification for each experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 03710 . 7554/eLife . 06807 . 038Figure 6—figure supplement 4 . Control experiment showing that µNS particles do not interact with Pub1 or Pab1 . Same as Figure 6—figure supplement 1 , except that strains were used that expressed mCherry-tagged Pub1 or Pab1 from the endogenous locus . For presentation purposes , only one representative μNS particles is shown at high magnification for each experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 03810 . 7554/eLife . 06807 . 039Figure 6—figure supplement 5 . Misfolded proteins do not nucleate stress granule formation in glucose-deprived cells . Same as Figure 6—figure supplement 2 , except that strains were used that expressed mCherry-tagged Pub1 or Pab1 from the endogenous locus , and stress was induced by removing glucose from the medium for 1 hr . For presentation purposes , only one representative μNS particles is shown at high magnification for each experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 03910 . 7554/eLife . 06807 . 040Figure 6—figure supplement 6 . Chemical inhibition of Hsp104 leads to co-aggregation of misfolded proteins and stress granule components even under mild heat shock conditions . Yeast cells expressing Nrp1-GFP from the endogenous locus and mCherry-tagged mutated Ubc9ts from a plasmid were exposed to heat shock at 42°C or 46°C . The cells in the bottom panel were exposed to 3 mM GdnHCl to inhibit Hsp104 . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 04010 . 7554/eLife . 06807 . 041Figure 6—figure supplement 7 . Misfolded proteins and stress granule components do not co-aggregate in preconditioned cells . Same as Figure 6—figure supplement 3 , except that the temperature was increased slowly from 25°C to 46°C ( preconditioning ) . For presentation purposes , only one representative μNS particles is shown at high magnification for each experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 04110 . 7554/eLife . 06807 . 042Figure 6—figure supplement 8 . Control experiment showing that Nrp1-µNS particles do not interact with Hsp104 . A strain expressing tdimer2-tagged Hsp104 from the endogenous promoter was transformed with a construct for the expression of Nrp1-sfGFP-μNS . Note that particle-presented Nrp1 is not recognized by Hsp104 at 25˚C , indicating that it is not misfolded on the particle surface . White lines indicate the cell boundaries . Scale bar: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 04210 . 7554/eLife . 06807 . 043Figure 6—figure supplement 9 . Nrp1 can nucleate stress granules upon glucose starvation stress . Same as Figure 6—figure supplement 8 , except that strains were used that expressed mCherry-tagged Nrp1 and Pub1 and cells were exposed to glucose starvation for 1 hr . For presentation purposes , only one representative μNS particles is shown at high magnification for each experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 04310 . 7554/eLife . 06807 . 044Figure 6—figure supplement 10 . Same as Figure 6—figure supplement 8 except that mutants lacking the RBD ( Nrp1PLD ) or PLD domain ( Nrp1ΔPLD ) were presented on the particle . Note that both deletion mutants are not recognized by Hsp104 at 25˚C , suggesting that they are folded and accessible on the particle surface . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 04410 . 7554/eLife . 06807 . 045Figure 6—figure supplement 11 . The PLD as well as the RBD of Nrp1 can nucleate stress granule formation . Same as Figure 6—figure supplement 10 , except that a strain was used in which mCherry-tagged Pub1 was expressed from the endogenous locus and the cells were exposed to a heat shock at 46°C . For presentation purposes , only one representative μNS particles is shown at high magnification for each experiment . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 04510 . 7554/eLife . 06807 . 046Figure 6—figure supplement 12 . The PLD or RBD of Nrp1 can nucleate stress granule formation through heterotypic interactions . A Pub1-mCherry-expressing strain lacking Nrp1 ( Δnrp1 ) was transformed with a construct for expression of GFP-µNS fused to full-length Nrp1 , Nrp1PLD , or Nrp1RBD . The cells were exposed to a heat shock at 46°C . For presentation purposes , only one representative µNS particle is shown at high magnification . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 046 An earlier study used μNS particles to test for protein–protein interactions in budding yeast ( Schmitz et al . , 2009 ) . In this study , bait proteins were genetically fused to the μNS fragment , which resulted in the presentation of the bait protein on the particle surface . Using this approach , we generated particles that carried either mutated luciferase or Ubc9ts on the surface . We found that both types of particles were recognized by chaperones ( Figure 6B and Figure 6—figure supplement 2 ) , indicating that the misfolded proteins are accessible on the particle surface . Using these particles , we next tested whether misfolded Ubc9ts or luciferase could recruit stress granule components . Under normal growth conditions ( 25˚C ) , the two stress granule proteins Pab1 or Pub1 were not enriched on the particle surface ( Figure 6C and Figure 6—figure supplement 3 , upper panels ) . However , during a robust heat shock Pub1 and Pab1 accumulated on μNS particles ( Figure 6C and Figure 6—figure supplement 3 , lower panels ) . Naked particles on the other hand did not recruit Pab1 or Pub1 ( Figure 6—figure supplement 4 ) , indicating that stress granule assembly was specifically triggered by the misfolded proteins . Furthermore , interaction of Pub1 or Pab1 with luciferase was only observed under robust heat shock and not under glucose depletion conditions ( Figure 6—figure supplement 5 ) . Thus , we conclude that misfolded proteins can nucleate the formation of stress granules under severe heat stress conditions , but not under mild stress conditions or during normal growth . How do stress granule proteins interact with misfolded proteins ? To address this question , we focused on the stress granule protein Nrp1 . Nrp1 has an intrinsically disordered PLD , which can undergo amyloid-like conformational conversions ( Alberti et al . , 2009 ) and shows specific binding to b-isox ( Kwon et al . , 2013 ) . We speculated that the structural flexibility of the PLD could promote the interaction of Nrp1 with misfolded proteins . To test this hypothesis , we mildly overexpressed Nrp1-mCherry in cells carrying luciferase-coated μNS particles . As can be seen in Figure 6D , Nrp1 weakly interacted with the particles , even in the absence of stress . Importantly , when we subsequently applied a robust heat shock , a large amount of Nrp1 accumulated on the surface of the particle ( Figure 6D ) . This suggests that the interaction between Nrp1 and misfolded proteins is sufficient to ectopically induce stress granule formation . Next , we tested a variant of Nrp1 lacking the PLD . Remarkably , this variant was unable to recognize the luciferase-coated particle ( Figure 6E ) . In contrast , the isolated PLD showed robust binding to luciferase particles ( Figure 6E ) , demonstrating that the PLD is sufficient for the interaction with misfolded proteins . Thus , PLDs can promiscuously interact with misfolded proteins , and such promiscuous interactions can nucleate the formation of stress granules , in particular under conditions of robust heat stress . Why do misfolded proteins only nucleate stress granules under robust heat-shock conditions ? We reasoned that under mild stress conditions the activity of the cellular chaperone machinery is sufficient to prevent promiscuous interactions between misfolded proteins and stress granule components . Indeed , when Hsp104 was inhibited , a mild heat shock was sufficient to induce the co-aggregation of endogenous Nrp1 and misfolded proteins ( Figure 6F and Figure 6—figure supplement 6 ) . A mild stress stimulus can protect cells from subsequent severe stress , a phenomenon known as preconditioning ( Parsell and Lindquist , 1993 ) . Thus , we hypothesized that preconditioning could prevent the co-aggregation of stress granule components and misfolded proteins . We preconditioned yeast cells by increasing the temperatures incrementally from 25°C to 46°C . Under these conditions , Pab1 and Pub1 did not co-aggregate with misfolded proteins but formed assemblies in distinct areas of the cell ( Figure 6G and Figure 6—figure supplement 7 ) . This shows that molecular chaperones constantly work to prevent promiscuous interactions with misfolded proteins and that misfolded proteins only act as scaffolds for stress granules when the capacity of the protein quality control machinery is overrun . It also suggests that preconditioned cells are protected from promiscuous interactions with misfolded proteins , presumably because of the up-regulation of chaperones . Our findings suggest that an interaction with misfolded proteins is not required for stress granule formation . To investigate how stress granules form independently of misfolded proteins , we again focused on Nrp1 . We first tested which domains of Nrp1 are required for recruitment to stress granules . As shown in Figure 6H , the RBD alone was sufficient for localization to stress granules , whereas the PLD was not . This is consistent with many other studies , which reported that PLDs are dispensable for RNP granule localization ( Reijns et al . , 2008; Sun et al . , 2011; Cai and Futcher , 2013; Grousl et al . , 2013; Kruger et al . , 2013; Bley et al . , 2014 ) . One potential explanation for the observed dispensability of PLDs is that they mediate interactions that are redundant and weak and thus only manifest when these domains are present at high local concentrations . To test this possibility , we used our particle assay to concentrate stress granule proteins in living cells , thus , creating a molecular environment similar to that in RNP granules . First , we generated particles that carried full-length Nrp1 on the surface . After having confirmed that Nrp1-μNS particles are not recognized by chaperones ( Figure 6—figure supplement 8 ) , we tested whether Nrp1 could ectopically nucleate the formation of stress granules . Indeed , particle-bound Nrp1 was able to recruit additional Nrp1 molecules from the cytosol , and upon heat shock ( Figure 6I , bottom panel ) and glucose depletion ( Figure 6—figure supplement 9 , bottom panel ) stress granules formed on Nrp1 particles . As a next step , we tested which domains of Nrp1 are required for this behavior . We generated two deletion mutants comprising either the RBD or the PLD . We first confirmed that these truncation mutants were not misfolded when presented on the particle surface ( Figure 6—figure supplement 10 ) . Using these particles , we found that both the PLD and the RBD were able to recruit full-length Nrp1 ( Figure 6J ) and promoted the formation of stress granules ( Figure 6—figure supplement 11 and Figure 6—figure supplement 12 ) . Together , these findings indicate that stress granule assembly is highly redundant and that nucleation can proceed in multiple ways , through PLDs or RBDs . Our data further suggest that PLDs work synergistically with RBDs and only function when present at a high local concentration , as during RNP granule assembly . Our findings so far show that P bodies are liquid-like droplets , in contrast to stress granules , which behave as true aggregates . Because of this distinction , we predicted that P bodies do not co-aggregate with misfolded proteins . Indeed , we found that P bodies showed only marginal co-localization with misfolded proteins in cells exposed to robust heat stress ( Figure 7A and Figure 7—figure supplement 1 ) . Co-localization with chaperones , such as Hsp42 , Ssa1 and Hsp104 , was also limited ( Figure 7B and Figure 7—figure supplement 2 ) . We next tested whether P-body formation and dissolution is affected by protein disaggregases . Under normal or mild stress conditions , Hsp104 deficiency did not affect P-body formation . However , upon robust heat stress ( 46°C ) , the P-body protein Edc3 assembled into irregular aggregate-like structures ( Figure 7C ) , which persisted for extended times ( Video 12 ) . These aggregate-like structures co-localized with the stress granule marker Pub1 and misfolded proteins ( Figure 7D ) , suggesting that Edc3 is mistargeted to stress granules in the absence of Hsp104 . Similar findings were made for the P-body protein Lsm4 ( Figure 7—figure supplement 3 ) . Next , we generated strains that expressed Hsp104 at different levels . We found that the amount of Edc3 that co-aggregated with misfolded proteins decreased when the expression level of Hsp104 was increased ( Figure 7E , Video 13 ) . This indicates that maintenance of the liquid-like P-body state requires the continuous action of Hsp104 during acute stress and that P-body components are mistargeted to stress granules , when the disaggregation activity is insufficient . 10 . 7554/eLife . 06807 . 047Figure 7 . Maintenance of yeast P body integrity requires Hsp104 . ( A ) The P-body protein Edc3 shows only minor co-localization with misfolded proteins during robust heat stress . Fluorescence microscopy of endogenous GFP-tagged Edc3 and plasmid-expressed mCherry-tagged mutant luciferase . The cells were stressed at 46°C for 10 min . Cell boundaries are indicated in white . Scale bars: 5 µm . Also see related Figure 7—figure supplement 1 . ( B ) Edc3 shows only limited spatial overlap with Hsp104 . Fluorescence microscopy of plasmid expressed mCherry-tagged Edc3 and Hsp104-GFP expressed from the endogenous locus . The cells were subjected to heat stress . Also see related Figure 7—figure supplement 2 . ( C ) Edc3-positive assemblies show different morphologies and behavior in the absence of disaggregases . Fluorescence time-lapse microscopy of yeast cells expressing GFP-tagged Edc3 from the endogenous locus . Wild-type cells are compared to strains lacking disaggregases ( Δhsp104 , Δsse1 , or Δsse2 ) after 1 hr heat stress at 46˚C and 1 hr 45 min after recovery at 25˚C . Also see Video 12 and related Figure 7—figure supplement 3 . ( D ) P-body components co-assemble with stress granules in heat-stressed cells in the absence of Hsp104 . Yeast cells expressing endogenous Edc3-GFP and endogenous Pub1-mCherry or aggregation-prone mCherry-tagged luciferase from a plasmid in Hsp104-deficient cells were subjected to 1 hr heat stress at 46˚C . ( E ) The amount of stress granule-localized Edc3 ( denoted with S ) decreases with increasing amounts of Hsp104 . P denotes P bodies . Yeast cells were used that expressed Edc3 under the endogenous promotor and mCherry-tagged mutated luciferase from a plasmid . Endogenous hsp104 was deleted and substituted with plasmid-expressed Hsp104 under control of a GPD - glyceraldehyde-3-phosphate dehydrogenase ( high ) , ADH1 - alcohol dehydrogenase 1 ( medium ) , or SUP35 ( low ) promoter . Cells were observed after 1 hr at 46˚C . Also see Video 13 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 04710 . 7554/eLife . 06807 . 048Figure 7—figure supplement 1 . P bodies do not co-aggregate with misfolded proteins under robust heat shock conditions . Edc3-GFP was expressed from the endogenous locus and mCherry-tagged Ubc9ts from a plasmid . Cells were observed before and after heat shock at 46°C . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 04810 . 7554/eLife . 06807 . 049Figure 7—figure supplement 2 . P bodies only show limited co-localization with chaperones under robust heat shock conditions . GFP-tagged Hsp42 or Ssa1 was expressed from the endogenous locus and mCherry-tagged Edc3 from a plasmid . Cells were observed before and after heat shock at 46°C . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 04910 . 7554/eLife . 06807 . 050Figure 7—figure supplement 3 . Lsm4-positive assemblies show different morphologies and behavior in the absence of disaggregases . Fluorescence time-lapse microscopy of yeast cells expressing GFP-tagged Lsm4 from the endogenous locus . Wild-type cells are compared to strains lacking functional genes for disaggregases ( Δhsp104 , Δsse1 , or Δsse2 ) after 1 hr heat stress at 46˚C and 1 hr 45 min after recovery at 25˚C . White lines indicate the cell boundaries . Scale bars: 5 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 05010 . 7554/eLife . 06807 . 051Video 12 . Edc3-positive assemblies show different morphologies and behavior in the absence of disaggregases . Fluorescence time-lapse microscopy of yeast cells expressing GFP-tagged Edc3 from the endogenous locus . Wild-type cells are compared to strains lacking functional genes for certain disaggregases ( Δhsp104 , Δsse1 or Δsse2 ) . Cells were exposed to a robust heat shock at 46°C and then transferred back to 25°C . Related to Figure 7 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 05110 . 7554/eLife . 06807 . 052Video 13 . Edc3 co-aggregates with luciferase in a Hsp104-dependent manner . Endogenous hsp104 was deleted and substituted with plasmid-expressed Hsp104 under control of a GPD ( high ) , ADH1 ( medium ) , or SUP35 ( low ) promoter . Cells expressing mCherry-tagged luciferase from a plasmid and GFP-tagged Edc3 from the endogenous locus were exposed to a robust heat shock at 46°C and then transferred back to 25°C . Related to Figure 7 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 052 Do mammalian P bodies and stress granules behave in a similar way as those of yeast ? To investigate the properties of P bodies and stress granules in mammalian cells , we generated stable HeLa cell lines expressing GFP-tagged G3PB2 or DCP1a as markers for stress granules or P bodies , respectively , using BAC TransgeneOmics ( Poser et al . , 2008 ) . We tested mammalian stress granules and P bodies for three characteristics that define a liquid-like compartment ( Hyman et al . , 2014 ) : first , a liquid compartment should be roughly spherical due to surface tension . Second , the components within the compartment should undergo rapid internal rearrangement and third , two liquid droplets should fuse and relax into one droplet . Indeed , the stress granules and P bodies in our cell lines had a characteristic circular shape ( Figure 8A ) , as expected for a liquid-droplet state . We also noticed that stress granules , in particular in the early stage of stress exposure , merged and formed larger structures over time ( Figure 8B , Video 14 ) , as did P bodies ( Figure 8B , Video 15 ) . In both cases , the structures rapidly relaxed into more spherical structures , in agreement with a liquid-like state . Next , we applied a technique known as ‘half-bleach’ to test for internal mobility within the compartment . In this method , roughly half a structure is bleached , and the distribution of the fluorescence within the photo-manipulated structure is then determined over time ( Brangwynne et al . , 2009 ) . The analysis of such a half-bleach event showed that G3BP2 was redistributed rapidly within stress granules from the unbleached to the bleached area ( because of the small size we cannot perform a similar experiment for P bodies ) ( Figure 8C ) . To further investigate the material properties of these RNP granules , we treated HeLa cells harboring P bodies and stress granules with hexanediol . Hexanediol triggered the disintegration of both types of compartments , whereas a control amyloid structure , Q103-GFP , was unaffected ( Figure 8D ) . Thus , we conclude that P bodies and stress granules have liquid-like properties in mammalian cells: they turn over rapidly , are spherical , and when they fuse they relax into one spherical assembly . This is consistent with previous fluorescence recovery after photobleaching ( FRAP ) studies showing that components within mammalian RNP granules turn over rapidly ( Kedersha et al . , 2000; Andrei et al . , 2005; Kedersha et al . , 2005; Aizer et al . , 2008; Bley et al . , 2014 ) . 10 . 7554/eLife . 06807 . 053Figure 8 . Mammalian RNP granules have liquid-like properties and are distinct from yeast stress granules . ( A ) Mammalian P bodies and stress granules are spherical . Quantification of the circularity of stress granules ( SG ) and P bodies ( PB ) in HeLa cells , which expressed GFP-tagged DCP1a ( PB ) or G3BP2 ( SG ) from BACs ( Poser et al . , 2008 ) . Cells were stressed with 1 mM sodium arsenate for 1 hr . Because of surface tension , liquid-like structures are expected to display a near circular shape ( Hyman et al . , 2014 ) . A perfect circle has a circularity of 1 . See ‘Materials and methods’ for details about how the circularity was determined . The mean and the SD are shown ( n=118 PB , n=165 SG ) . ( B ) P bodies and stress granules show fusion behavior . Time-lapse microscopy of P bodies ( DCP1a-GFP ) and stress granules ( G3BP2-GFP ) in arsenate-stressed HeLa cells . Stress granules undergo frequent fusions at the beginning of a stress stimulus and form large spherical structures in cells after extended exposure to stress . Scale bars: 1 µm . Also see corresponding Videos 14 , 15 . ( C ) The stress granule protein G3BP2 shows fast internal rearrangement within stress granules , consistent with a liquid-like state . Kymograph of a stress granule ( G3BP2-GFP ) induced through arsenate stress and an amyloid ( Q103-GFP ) aggregate in HeLa cells after a half-bleach event ( Brangwynne et al . , 2009 ) . PolyQ aggregates are used as a control for a solid-like structure . Note that only stress granules but not polyQ assemblies show a redistribution of fluorescence from the bleached to the unbleached area ( from left to right ) . ( D ) P bodies and stress granules but not solid-like polyQ aggregates are sensitive to hexanediol . Quantification of the effect of 3 . 5% 1 , 6-hexanediol treatment on stress granules and P bodies induced through arsenate stress , and Q103 aggregates . The normalized mean fluorescence intensities of the structures are plotted over the duration of the treatment . Error bars are SD ( n=5 PolyQ , n=31 SG and n=22 PB ) . ( E ) Mammalian stress granules do not co-aggregate with misfolded proteins . HeLa cells expressing BAC-encoded G3BP2-GFP ( SG ) were transfected with a plasmid coding for mutant luciferase-mCherry . Formation of stress granules was induced by arsenate stress ( 2 hr ) , proteasome inhibition ( 10 µM MG132 , 3 hr ) or heat stress ( 43°C , 2 hr ) . Scale bar: 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 05310 . 7554/eLife . 06807 . 054Video 14 . Stress granules show extensive fusion behavior and form more spherical structures with time . HeLa cells expressing BAC-encoded G3BP2 were observed by time-lapse microscopy . The cells were stressed through arsenate . Related to Figure 8 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 05410 . 7554/eLife . 06807 . 055Video 15 . Two P bodies fuse and rapidly relax into a spherical shape . Time-lapse microscopy of P bodies ( labelled by BAC-encoded DCP1a-GFP ) in arsenate-stressed HeLa cells . Related to Figure 8 . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 055 Our findings demonstrate that mammalian stress granules are more liquid-like than their yeast counterparts . We therefore hypothesized that in mammalian cells , misfolded proteins do not nucleate stress granules . To investigate this , we analyzed the subcellular distribution of stress granules and misfolding-prone luciferase in stressed HeLa cells . As can be seen in Figure 8E , stress granules did not overlap with luciferase aggregates , under conditions of arsenate stress , proteasome inhibition , or heat shock . Thus , we conclude that stress granules are formed in fundamentally different ways in yeast and mammalian cells .
In this study , we investigate two types of stress-inducible RNP granules—P bodies and stress granules . We find that these RNP granules have distinct material properties in yeast cells: whereas P bodies show more liquid-like behavior , stress granules exhibit characteristic properties of solid aggregates . We further show that the formation of yeast stress granules does not involve amyloid-like conformational conversions . Rather , yeast stress granules resemble amorphous protein aggregates . Assembly of these aggregates depends on interactions with RNAs , and PLDs in RNA-binding proteins contribute to granule formation , by promiscuously interacting with other PLDs or with misfolded proteins . Finally , we show that stress granules have very different properties in mammalian cells , where they have liquid-like characteristics and do not behave as aggregates . In summary , these findings show that RNP granule formation is highly flexible , and that under conditions of acute stress , disaggregating machines play a key role in maintaining the identity and integrity of RNP granules ( Figure 9 ) . 10 . 7554/eLife . 06807 . 056Figure 9 . Schematic illustrating the potential molecular mechanisms underlying the formation of stress-inducible RNP granules . ( A ) RNA-protein ( RNP ) granule formation as a two-step assembly process . In the first step , RNAs and RNA-binding proteins associate to form large RNP complexes ( nucleation step ) . In the second step , these RNP complexes coalesce into larger compartments through additional RNA-mediated interactions , but primarily through PLD-mediated weak binding events ( coalescence step ) . PLDs are indicated in red and RBDs indicated in blue . ( B ) Stress-inducible RNP granules have different compositions , which affect their dynamic and material properties . The presumed average interaction strength is indicated in red . Weak interactions increase the vulnerability to hexanediol . An aggregation-prone stress sensor domain ( SD ) is indicated in dark green . DOI: http://dx . doi . org/10 . 7554/eLife . 06807 . 056 Cells must respond rapidly to changing environments . This is particularly important for single-celled organisms such as yeast because they are directly exposed to environmental fluctuations . How can a cell respond rapidly and efficiently to stress , while at the same time solving the task of adjusting the activities of numerous proteins and RNAs ? An increasing number of studies suggest that this can be achieved by building compartments . Such compartments are condensed phases of proteins and RNAs , which exchange components with the surrounding cytoplasm or nucleoplasm ( Brangwynne et al . , 2009 , 2011; Hyman and Brangwynne , 2011; Hyman and Simons , 2012; Li et al . , 2012; Feric and Brangwynne , 2013; Hubstenberger et al . , 2013; Hyman et al . , 2014 ) . Two types of compartments can be distinguished: compartments for localized biochemistry in which specific chemical reactions occur; and compartments for storage , where macromolecules adopt an inactive , yet re-activatable state . The prediction would be that these two types of compartments have different properties . Indeed , a large body of work shows that P bodies are active compartments , involved in processing and degrading mRNAs; stress granules on the other hand do not perform biochemical reactions , but store proteins and RNAs ( Anderson and Kedersha , 2009; Decker and Parker , 2012 ) . P bodies are therefore expected to have different properties than stress granules . A P body should allow for the continuous entry and exit of RNAs and proteins , and the components within a P body should be able to rearrange . A liquid phase meets these demands . Stress granules on the other hand do not need to be liquid-like . Their function is to inactivate proteins and RNAs by removing them from the cytoplasm . Thus , a solid-like state is fully compatible with the function of stress granules . Our findings indeed reveal a remarkable distinction between yeast P-bodies and stress granules . Previous studies have also pointed to differences ( Buchan et al . , 2008; Decker and Parker , 2012; Mitchell et al . , 2013; Shah et al . , 2013 ) . Based on these findings , we propose that yeast P-bodies are liquid-like droplets that form by demixing from the cytoplasm , whereas yeast stress granules form through a liquid–solid phase transition ( Figure 9 ) . We hypothesize that these findings are specific to yeast and do not apply to mammalian cells . Indeed , studies suggest that mammalian cells have developed other ways of controlling stress granule assembly , primarily through posttranslational modifications of stress granule proteins ( Anderson and Kedersha , 2009; Buchan and Parker , 2009; Kedersha et al . , 2013; Wippich et al . , 2013 ) . Future studies will provide further insight into the distinct molecular mechanisms underlying stress granule formation in yeast and mammalian cells . We used the small organic alcohol 1 , 6-hexanediol to differentiate between liquid-like and solid-like cellular structures . Hexanediol disperses liquid germ granules in C . elegans ( Updike et al . , 2011 ) and impairs transport across the nuclear pore ( Ribbeck and Gorlich , 2002; Patel et al . , 2007 ) , two processes that depend on weak interactions between sticky intrinsically disordered domains . We do not yet understand how hexanediol perturbs RNP granules . However , it should be noted that hexanediol is widely used as an additive in protein crystallization studies . Protein crystallization is typically approached in an empirical manner , and its success often depends on the formation of a liquid protein phase by phase separation ( Galkin and Vekilov , 2000; Chen et al . , 2004; Dumetz et al . , 2008 ) . It is believed that additives such as hexanediol inhibit or promote the formation of this liquid phase , or otherwise modify its physicochemical properties , and by doing so favor the formation of a crystal . Thus , we speculate that hexanediol perturbs the weak interactions in liquid-like assemblies , leaving stronger interactions that are characteristic of more solid-like structures intact . This suggests that hexanediol could be a useful tool to probe the material properties of cellular structures . Our observations support the conclusion that yeast stress granules are solid protein aggregates . First , hexanediol disperses P bodies but not stress granules . Second , yeast stress granule components are co-deposited with misfolded proteins . Finally , yeast stress granules are substrates for chaperones and disaggregases . A previous study suggested that stress granule components assemble around misfolded proteins ( Cherkasov et al . , 2013 ) . However , this hypothesis could not be tested directly because it requires control over the nucleation step of protein aggregation . Using an assay to ectopically form protein aggregates , we show that misfolded proteins can indeed nucleate stress granules , in particular when the stress intensity is high and when the overall disaggregation activity is low . However , although yeast stress granules resemble aggregates , they showed no properties of amyloids: they could not be stained with ThT , and we found no evidence that stress granule proteins convert into structures with amyloid-like properties . This suggests that stress granule proteins do not assemble into amyloid-like aggregates under physiological conditions , but rather form amorphous aggregates . We do not yet know which interactions make yeast stress granules more solid-like . However , we speculate that stress granule proteins carry aggregation-prone domains , which act as stress sensors , as previously also proposed by others ( Cherkasov et al . , 2013 ) . Upon stress , these domains could expose interaction sites that promote assembly into amorphous aggregates . We propose that assembly through these sensor domains is largely specific , as this would provide better control over the assembly process and could facilitate dissolution in the recovery phase . In fact , a similar type of assembly has been observed in starved yeast , where several metabolic enzymes form higher order assemblies ( Narayanaswamy et al . , 2009; Noree et al . , 2010; Petrovska et al . , 2014 ) . These enzymes polymerize upon energy depletion , and when yeast cells are replenished with nutrients , they disassemble within minutes . How enzyme assembly can be specific in the crowded environment of a cell is still unclear , but it probably involves interactions via protein surfaces that are sterically and electrostatically compatible . Thus , we propose that yeast stress granules are functional aggregates , which assemble through a controlled process that is driven by a range of specific and promiscuous interactions between proteins and RNAs . Previous studies proposed an important role for prion-like low-complexity domains in RNP granule assembly . This was based on the observation that these domains can undergo conformational conversions into amyloid-like fibers in a cell-free system ( Han et al . , 2012; Kato et al . , 2012 ) . Assembly into these structures was accompanied by the formation of hydrogels , and these hydrogels could reversibly interact with other prion-like proteins in a homotypic or heterotypic manner . Accordingly , a model for RNP granule assembly was proposed that put a strong emphasis on the ability of PLDs to assemble into amyloid-like protein scaffolds ( Han et al . , 2012; Kato et al . , 2012 ) . The problem with this model , however , is that hydrogels only rearrange over long timescales , and thus can hardly provide the dynamic properties that are characteristic of RNP granules in living cells ( Weber and Brangwynne , 2012; Malinovska et al . , 2013; Bley et al . , 2014 ) . To account for this , it was suggested that these amyloid-like fibers are dynamic in vivo , due to regulation by posttranslational modifications ( Han et al . , 2012; Kato et al . , 2012; Kwon et al . , 2013 ) . However , evidence for dynamic fibers in living cells is still lacking . We used self-assembling particles to locally concentrate prion-like proteins in living cells , creating a molecular environment that resembles that in RNP granules . Using this assay , we find that prion-like proteins promote RNP granule formation , but independently of amyloid-like conversions . Thus , above a certain critical concentration , PLDs can promote a phase transition of RNA-binding proteins into RNP granules . Based on these findings , we favor the following two-step model of RNP granule formation: First , RNA-binding proteins bind to RNAs through multivalent interactions via RBDs , forming complexes of RNA and protein; these RNP complexes may already reach a relatively large size . In the second step , these RNP complexes condense into RNP granules driven by promiscuous interactions between PLDs and further associations between RNAs and RBDs . In this model , the PLDs primarily assist the final coalescence step , thus , promoting the condensation of RNPs into large compartments ( Figure 9 ) . In agreement with this , recent findings indicate that PLDs are not essential for RNP granule formation per se but rather promote the fusion of RNP granules and their enlargement into bigger RNP compartments ( Shiina and Nakayama , 2014 ) . However , the function of PLDs may also depend on their specific local environment: In P bodies , the collective properties of PLDs may be important for maintaining a dynamic , liquid-like state , whereas in solid assemblies such as stress granules , PLDs may act as molecular glue to connect RNA-binding proteins with other RNA-binding proteins or misfolded proteins . Thus , PLDs may function as adaptable interaction domains , which do not undergo conversions into structurally well-defined amyloid-like states , but mediate promiscuous interactions with many binding partners in their local environment . Such behavior would be highly favorable , because it allows a high degree of flexibility during compartment formation . We found that in heavily stressed yeast cells , the continuous action of protein disaggregases is required to keep P bodies in a liquid-like state . Interference with protein disaggregation caused the mislocalization of P-body components to stress granules ( Figure 7C–E ) , suggesting that P-body proteins and potentially many other proteins can enter into a stress granule state , but are normally prevented from doing so through molecular chaperones such as Hsp104 . This implies that the molecular composition of stress granules is also a function of the cellular disaggregation activity and that yeast cells try to keep a fraction of the cytoplasm in a dynamic liquid-like state , presumably to maintain their viability . We found that the two Hsp110 disaggregases Sse1 and Sse2 make a significant contribution to the cellular disaggregation activity , but that Hsp104 adopts an essential function , without which yeast cells cannot dissolve stress granules . In contrast to yeast , mammalian cells do not express a Hsp104 homolog in the cytosol . Therefore , we speculate that the ability of yeast to enter into a solid stress granule state has co-evolved with Hsp104 , providing a potential explanation for why yeast and mammalian stress granules have different material properties . In contrast to mammalian organisms , sedentary yeast cells cannot escape from harsh environments . We conjecture that for yeast , formation of solid stress granules is an effective way of preserving the pre-stress state of the cytosol and that a selected set of yeast proteins and RNAs has been modified by evolution to aggregate upon stress . Consistent with this , a recent study showed that the yeast proteome has a higher overall aggregation propensity than the human proteome ( Albu et al . , 2014 ) . When the stress subsides , Hsp104 reactivates the proteins and RNAs preserved in solid stress granules , thus promoting re-entry into the cell cycle . We note that this situation is reminiscent of dormancy , as for example in spores or seeds , where an entire organism enters into a resting state by solidifying its macromolecular components ( Parry et al . , 2014 ) . Future work will show whether controlled entry into a solid phase can provide general protection to a cell in stressful environments .
Cloning procedures were performed as described previously using the Gateway system ( Alberti et al . , 2007 , 2009 ) . For a list of plasmids see Supplementary file 1 . The media used were standard synthetic media or rich media containing 2% D-glucose . The yeast strain backgrounds were W303 ADE+ ( leu2-3 , 112; his3-11 , -15; trp1-1; ura3-1; can1-100; [psi-]; [PIN+] ) or BY4741 ( his3Δ1; leu2Δ0; met15Δ0; ura3Δ0; [psi-]; [PIN+] ) . Yeast gene deletions were performed using a PCR-based approach ( Gueldener et al . , 2002 ) . C-terminal tagging of yeast genes was performed as described previously ( Sheff and Thorn , 2004 ) . The induction of an amyloid state is a concentration-dependent nucleation process . Thus , the prion-like state in Lsm4 was induced by overexpressing untagged Lsm4 in a [PIN+] strain that expressed GFP-tagged Lsm4 from the endogenous promoter . The prion-like state in Nrp1 was induced by using a [PIN+] strain expressing the Nrp1PLD ( Alberti et al . , 2009 ) . For a list of strains see Supplementary file 1 . HeLa cells were cultured in DMEM ( Dulbecco's modified eagle's medium ) supplemented with 10% FBS ( fetal bovine serum ) and penicillin–streptomycin ( all Gibco Life Technologies , United Kingdom ) . Cells were maintained at 37°C in a 5% CO2 incubator . HeLa Kyoto cells containing G3BP2-GFP or DCP1a-GFP BAC constructs were used to visualize stress granules or P bodies , respectively ( Poser et al . , 2008 ) . Transient transfection of HeLa cells with pcDNA3 . 1-Q103 was performed using Jet Prime transfection reagent ( Polyplus , France ) . Transfection with pcDNA3 . 1-Luciferase was performed using Lipofectamine 2000 ( Invitrogen , Carlsbad , California ) . Yeast cells were grown in cultures of 50–100 ml at 25°C to an OD600 not higher than 0 . 5 . The yeast cells were then immobilized on concanavalin A ( Sigma Aldrich , St . Louis , Missouri ) -coated precise glass bottom dishes ( MatTek , Ashland , Massachusetts ) . Microscopy was performed using a DeltaVision microscope system with softWorx 4 . 1 . 2 software ( Applied Precision , United Kingdom ) . The system was based on an Olympus IX71 microscope ( Olympus , Japan ) , which was used with a UPlanSApo 100 × 1 . 4 numerical ( NA ) oil objective . The images were collected with a Cool SnapHQ camera ( Photometrics , Tucson , Arizona ) and a pixel size of 0 . 13 µm . Heat shock experiments were performed using a Warner heating chamber ( Warner instruments , Hamden , Connecticut ) . When indicated , 5–10% of 1 , 6-hexanediol ( Merck , Germany ) solution or 100 µg/ml cycloheximide ( AppliChem , Germany ) was added to the medium to perturb RNP granule integrity . To inhibit Hsp104 , guanidinium hydrochloride ( GdnHCl ) was added to the medium to a final concentration of 3 mM , 3 hr before imaging . All images were deconvolved using standard softWorx deconvolution algorithms ( enhanced ratio , high-noise filtering ) . Shown images are maximum intensity projections of 8–14 individual images . Figures show representative cells . The cell boundaries were introduced by thresholding the bright-field image and overlaying it with the fluorescence image . In all comparative experiments , we used four chamber dishes ( MatTek ) , allowing us to image four different conditions in the same experiment . Analysis of the foci-to-cytoplasm ratio of yeast cells ( Figure 1B ) was performed by manually defining regions of interest and measuring their fluorescence intensity using Fiji ( Schindelin et al . , 2012 ) . The P-body number and size analysis ( Figure 3B ) was performed using Fiji image analysis tools . The image was segmented by thresholding and then analyzed using the analyze particle tool ( size: 0-infinity , circularity: 0–1 ) . The number of total cells was determined by manually counting cells in the bright-field channel using the Fiji cell counter plugin . Cells were grown in 50 ml cultures while shaking at 180 rpm to an OD600 not higher than 0 . 5 . Cultures were then harvested and resuspended in a 10 mM Tris/EDTA ( ethylenediaminetetraacetic acid ) buffer ( pH 7 ) containing 30 µM ThT ( Sigma Aldrich ) and incubated for 20 min . Subsequently , the cells were washed three times in Tris/EDTA buffer without ThT and then resuspended in media . Cells were then immobilized on 1% agar pads . Microscopy was performed on a single photon point scanning confocal Olympus Fluoview 1000 microscope ( Olympus ) . ThT was excited with a 405-nm diode laser . Emission of ThT was recorded in the 480–540 nm range . For co-localization studies , we used cells that expressed mCherry-tagged proteins . mCherry fluorescence was excited with a 561-nm DPSS laser , and emission was recorded in the 570–670 nm range . Cells were scanned in one z-plane using a UPlanSApo 60 × 1 . 35 numerical ( NA ) oil objective and a scan speed of 8 µs/pixel . Images were acquired with a pixel resolution of 0 . 103 µm . Yeast cells were grown in cultures of 50–100 ml at 25°C to an OD600 not higher than 0 . 5 . The cells were immobilized in precise glass bottom dishes using concanavalin A coating . Images were acquired using an Olympus IX81 inverted stand microscope ( Olympus ) with a spinning disk scan head Yokogawa CSU-X1 ( 5000 rpm ) . The system was used with an Olympus UPlanSApo 100 × 1 . 4 oil objective . Acquired images had pixel sizes of 0 . 081 µm . The images were collected with an Andor iXon EM+ DU-897 BV back-illuminated EMCCD ( Andor , Ireland ) . For imaging of Dendra2 , we used a triple-band dichromatic mirror: T-405/488/561 . Images before and after photoconversion were acquired as z-stacks with 0 . 7-µm spacing and a time interval of ∼1 s . The Dendra2 signal was converted in a 1 × 1 pixel area by using the 405-nm diode laser at 6% intensity in 400 repeats for 100 µs each . For image segmentation , we used the 3D tracking tool of the Imaris software ( Bitplane , Switzerland ) . The signal from three areas was obtained for each time point: the photoconverted/bleached foci ( IF ) , a reference focus in a neighboring cell ( IR ) and an area of equal size in the background ( IB ) . The fluorescence signal in the foci was normalized as follows ( Carisey et al . , 2011 ) :IF−pre=∑t=0tbleach−1IF ( t ) −IB ( t ) fprebleach , IR−pre=∑t=0tbleach−1IR ( t ) −IB ( t ) fprebleach , IF_norm ( t ) =IF ( t ) −IB ( t ) IF−preIR ( t ) −IB ( t ) IR−pre . Since yeast cells are small , we had to ensure that photoconversion in one cell did not affect the reference foci in the neighboring cell . Therefore , we estimated the decay kinetics of the reference foci by fitting the following equation to the post-bleach period of IR:y=A∗e ( p∗x ) , where p is the time constant and A is the fluorescence intensity . The average fluorescence level before the photoconversion was deduced by extrapolating the fitted decay curve , yielding IR_estimate . If IR_estimate was smaller than IR_measured , it indicated that fluorescence loss in the reference foci occurred through photoconversion . These cells were excluded from the analysis . To reveal the half-time of recovery , we fitted the values of IF_norm with:y=A ( 1−e−p∗x ) , where p is the time constant and A is the fluorescence intensity . The half time t1/2 was calculated using:t1/2=log ( 0 . 5 ) −p . Data were analyzed , tested for statistical significance , and plotted using R software . Boxes in boxplots extend from the 25th to 75th percentiles , with a line at the median . Live HeLa cells were imaged using the DeltaVision imaging system with softWorx 4 . 1 . 2 software ( described above ) . The system was used with a Plan Apo 60 × 1 . 42 NA oil immersion objective . 15 sections with 200-nm spacing were acquired and the maximum intensity projections were created in Fiji . If indicated , 3 . 5% of hexanediol solution was added to the culture medium to perturb P body and stress granule integrity . Measurement of the circularity of P bodies and stress granules ( Figure 8A ) was performed using a custom-made Matlab routine ( see Supplementary file 2 ) from maximum intensity projections , according to the following equation:Circularity=4πArea ( Perimeter ) 2 , where the value of circularity is 1 for a perfect circle and the value decreases as it deviates from the circular shape . Microscopy to determine the circularity of granules was performed using an Andor spinning disk confocal microscope as described above with a UPlanSApo 100 × 1 . 4 oil objective . The half-bleach ( Brangwynne et al . , 2009 ) in Figure 8C was performed at a pixel resolution of 0 . 08 µm using a spinning disk confocal microscope as described above . Photo-bleaching and imaging was performed with the 488-nm laser . Structures were photo-bleached for 1 . 2 ms within a region of 5 × 5 pixels . SDD-AGE analysis was used to separate amyloid polymers from monomers in whole cell lysates . Yeast cells were grown in cultures of 100 ml at 25°C to an OD600 of 0 . 5 . The cultures were harvested and washed once with water . The pellets were resuspended in 300 µl ice-cold lysis buffer ( 50 mM Tris , pH 7 . 5; 150 mM NaCl; 2 . 5 mM EDTA; 1% ( vol/vol ) TritonX-100; 0 . 33 mM PMSF ( phenylmethylsulfonyl fluoride ) ; 6 . 7 mM NEM; 1 . 25 mM benzamidine; 10 µg/ml pepstatin; 10 µg/ml chymostatin; 10 µg/ml aprotinin; 10 µg/ml leupeptin; 10 µg/ml E-64 ) and added to ice-cold glass beads ( 425–600 µm ) ( Sigma–Aldrich ) . Cells were lysed using mechanical disruption ( Tissue Lyser II , Qiagen , Netherlands ) at 25 Hz for 15 min . Unwanted cell debris and beads were removed by centrifugation and the supernatant was used for SDD-AGE analysis . The supernatants were adjusted for equal protein concentrations and mixed 4:1 with 4 × Sample buffer ( 40 mM Tris acetic acid , 2 mM EDTA , 20% glycerol , 4% SDS , bromophenol blue ) . Samples were incubated for 10 min at room temperature and loaded onto a 1 . 5% agarose gel containing 0 . 1% SDS in 1 × TAE/0 . 1% SDS running buffer . The gel was run at low voltage ( less than 80 V ) to prevent the gel from heating up . Proteins in the gel were detected by immunoblotting with a GFP-specific antibody ( Roche , Switzerland ) . The exposure time was the same for all immunoblots shown in one experiment . SEC was used to separate cellular complexes according to their size . Cells were grown in cultures of 50 ml at 25°C to an OD600 not higher than 0 . 5 . The cultures were harvested and washed once with water . The pellets were resuspended in 500 µl ice-cold lysis buffer ( 50 mM Tris , pH 7 . 5; 150 mM NaCl; 2 . 5 mM EDTA; 1% ( vol/vol ) TritonX-100; 0 . 4 mM PMSF; 8 mM NEM; 1 . 25 mM benzamidine; 10 µg/ml pepstatin; 10 µg/ml chymostatin; 10 µg/ml aprotinin; 10 µg/ml leupeptin; 10 µg/ml E-64 ) and ice-cold glass beads were added ( 425–600 µm ) . Cells were lysed using mechanical disruption ( Tissue Lyser II ) at 25 Hz for 20 min . To pellet cell debris and beads , samples were centrifuged and the supernatant was applied to a SpinX-Centrifuge Filter ( 0 . 22 µm cellulose acetate , Sigma-Aldrich ) . After centrifugation for 5 min at 13 . 000 rpm at 4°C , protein concentration of the supernatant was measured using the Bradford protein assay ( BioRad , Germany ) . Equal amounts were loaded onto the column . To efficiently resolve the protein complexes , we used the TSK-Gel G6000PWXL ( Tosoh Bioscience , Japan ) column with a separation range between 40 kDa and 8 . 000 kDa . The column was equilibrated in running buffer ( 2 × PBS ( phosphate-buffered saline ) ) and calibrated with different standard proteins: Tyroglobulin ( 660 kDa ) , Ferritin ( 440 kDa ) , Catalase ( 240 kDa ) , and BSA ( bovine serum albumin ) ( 67 kDa ) . Subsequently , a dot blot assay was performed using a vacuum slot blot device ( Whatman , United Kingdom ) . The fractions were collected on protein-binding nitrocellulose membrane ( Protran B 85 , Whatman ) and the resulting membrane was used for immunodetection with an anti-GFP antibody . | Genes consist of long stretches of DNA that code for proteins . The DNA is first ‘transcribed’ to produce an RNA molecule , which is then translated into a protein . In most cells , RNA molecules are present within a structure called ribonucleoprotein ( RNP for short ) granules . These contain the protein machinery needed to transport , store , and break down RNAs . P bodies and stress granules are two types of RNP granules found in all cells , from yeast to human . P bodies are present at all times , whereas stress granules assemble when a cell experiences stressful conditions , such as a lack of nutrients or high temperatures . Once the stress has been overcome , the stress granules are disassembled . The precise details of how RNP granules assemble in cells remain poorly understood . One theory suggests that RNP granules form through a physical process called ‘phase separation’ in which RNA molecules and proteins above a certain critical concentration condense to form a liquid droplet . Other research has suggested that RNP granules arise when so-called prion-like proteins spontaneously clump together and start aggregating to form fibers . These granules would behave more like solids than liquids . Kroschwald et al . have now analyzed how P bodies and stress granules form in yeast and human cells using a chemical compound that can distinguish between liquid-like and solid-like structures . The results revealed that P bodies and stress granules behave very differently in yeast cells . While P bodies are indeed liquid droplets , stress granules are more solid in nature and act like protein aggregates . So why is there a difference between the two ? It is known from previous work that when cells are stressed , many proteins misfold and start aggregating . Kroschwald et al . found that the formation of stress granules coincides with the formation of aggregates , suggesting that stress granules themselves are a type of aggregate . Furthermore , stress granule formation does not seem to involve prion-like fibers , but rather prion-like proteins can easily interact with other proteins in a promiscuous way , thus promoting the seeding of stress granules and their growth . Kroschwald et al . next studied human cells and observed that in these cells , both P bodies and stress granules were liquid droplets . These results together suggest that the physical properties and method of assembling P bodies and stress granules can vary from one organism to another . Future work will investigate whether the ability to form solid rather than liquid stress granules provides extra protection to yeast cells when they are stressed . It also remains to be tested whether and how stress granules convert into the pathological RNP aggregates that are often seen in neurodegenerative diseases . | [
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Mutations in Park8 , encoding for the multidomain Leucine-rich repeat kinase 2 ( LRRK2 ) protein , comprise the predominant genetic cause of Parkinson's disease ( PD ) . G2019S , the most common amino acid substitution activates the kinase two- to threefold . This has motivated the development of LRRK2 kinase inhibitors; however , poor consensus on physiological LRRK2 substrates has hampered clinical development of such therapeutics . We employ a combination of phosphoproteomics , genetics , and pharmacology to unambiguously identify a subset of Rab GTPases as key LRRK2 substrates . LRRK2 directly phosphorylates these both in vivo and in vitro on an evolutionary conserved residue in the switch II domain . Pathogenic LRRK2 variants mapping to different functional domains increase phosphorylation of Rabs and this strongly decreases their affinity to regulatory proteins including Rab GDP dissociation inhibitors ( GDIs ) . Our findings uncover a key class of bona-fide LRRK2 substrates and a novel regulatory mechanism of Rabs that connects them to PD .
Parkinson’s disease ( PD ) is the second most common neurodegenerative disease , affecting 1–2% of the elderly population ( Lees et al . , 2009 ) . Environmental and genetic factors contribute to the development of the disease , but its precise etiology still remains elusive ( Burbulla and Krüger , 2011 ) . Genome-wide association studies ( GWAS ) have related 28 genetic risk variants at 24 loci with nonfamilial PD ( Nalls et al . , 2014 ) . Among those , mutations in LRRK2 ( Park8 ) are also found in hereditary forms , pinpointing a shared molecular pathway driving pathogenesis in both familial and non-familial PD and comprising the most common cause of the disease ( Simón-Sánchez et al . , 2009; Satake et al . , 2009 ) . LRRK2 encodes a large protein composed of central kinase and GTPase ( ROC-COR ) domains that are surrounded by multiple protein-protein interaction regions . PD pathogenic LRRK2 mutations map predominantly to the kinase ( G2019S , I2020T ) and the ROC-COR domains ( R1441C/G/H , Y1699C ) , implying that these enzymatic activities are crucial for pathogenesis ( Rudenko and Cookson , 2014 ) . Presently , it is unclear how LRRK2 mutations occurring in different functional domains all predispose to PD . The most common PD-associated LRRK2 mutation is the G2019S amino acid substitution , which activates the kinase two- to threefold ( West et al . , 2005; Khan , 2005; Jaleel et al . , 2007 ) . Since protein kinases are attractive pharmacological targets , this finding has raised hopes that selective LRRK2 inhibition can prevent or delay the onset of PD ( Yao et al . , 2013 ) . Extensive studies of LRRK2 have associated it with diverse cellular processes such as Wnt signaling , mitochondrial disease , cytoskeleton remodeling , vesicular trafficking , autophagy , and protein translation ( Taymans et al . , 2015; Cookson , 2015; Schapansky et al . , 2014; Papkovskaia et al . , 2012 ) . Moreover , several LRRK2 substrates have been reported previously; however , evidence that they are phosphorylated by LRRK2 in a physiological context is generally lacking and proofs are confined to in vitro approaches or to cellular systems using overexpressed kinase ( Jaleel et al . , 2007; Kumar et al . , 2010; Ohta et al . , 2011; Kawakami et al . , 2012; Bailey et al . , 2013; Martin et al . , 2014; Qing et al . , 2009; Chen et al . , 2012; Gloeckner et al . , 2009; Imai et al . , 2008; Gillardon , 2009; Kanao et al . , 2010; Matta et al . , 2012; Xiong et al . , 2012; Yun et al . , 2013; Yun et al . , 2015; Krumova et al . , 2015 ) . Significant off-target effects for LRRK2 compounds that have been used previously further complicate interpretation of the data ( Schapansky et al . , 2015 ) . Overall , there is little consensus on the cellular roles of LRRK2; thus , identification of definitive and verifiable physiological LRRK2 substrates is considered to be one of the greatest challenges in the field ( Schapansky et al . , 2015 ) . Besides mutations in LRRK2 , other genetic risk variants for PD map to the Park16 locus . Among the five genes within this locus is Rab7L1 ( also known as Rab29 ) , which together with LRRK2 increases nonfamilial PD risk . Depletion of Rab7L1 recapitulates the dopaminergic neuron loss observed with LRRK2-G2019S expression and its overexpression rescues mutant LRRK2 phenotypes ( MacLeod et al . , 2013 ) . Rab GTPases comprise ~70 family members in humans , and they are key players in all forms of intracellular vesicular trafficking events ( Stenmark , 2009; Rivero-Ríos et al . , 2015 ) . Apart from Rab7L1 , several other family members have been associated with PD pathogenesis . For example , mutations in Rab39b ( Park21 locus ) predispose to PD in humans ( Wilson et al . , 2014; Mata et al . , 2015 ) . Moreover , overexpression of Rab8a , Rab1 , and Rab3a attenuate α-synclein-induced cytotoxicity in cellular and animal models of PD , suggesting a functional interplay between Rab GTPases and known PD factors ( Cooper , 2006; Gitler et al . , 2008 ) . Recently , another PD-connected protein kinase termed PTEN-Induce Kinase-1 ( PINK1 ) has been reported to indirectly control the phosphorylation of a small group of Rabs including Rab8a at Ser111 ( Lai et al . , 2015 ) . Despite these intriguing links , it is presently unclear whether LRRK2 directly or indirectly modulates Rab GTPases at the molecular level and if so , by which mechanism . High-resolution quantitative mass spectrometry ( MS ) has become the method of choice for confident identification of in vitro and in vivo phosphorylation events ( Roux and Thibault , 2013; Lemeer and Heck , 2009; Olsen et al . , 2006 ) . With current MS instrumentation , proteomics can identify tens of thousands of phosphosites ( Sharma et al . , 2014; Mallick and Kuster , 2010 ) . However , challenges in the phosphoproteomic approaches are to determine functionally relevant residues from these large datasets and to establish direct kinase-substrate relationships . As such , we complement the power of modern phosphoproteomics with parallel genetic , biochemical and pharmacological approaches to establish direct , in vivo LRRK2 substrates . Using fibroblasts derived from two different LRRK2 knock-in mouse lines we identify a subset of Rab GTPases as bona-fide LRRK2 targets . LRRK2 phosphorylates these substrates on an evolutionarily conserved residue situated in their switch II domain both in human and murine cells and in mouse brain . The phosphorylation of Rabs by LRRK2 is direct and strikingly all LRRK2 missense mutations that contribute to PD pathogenesis increase the phosphorylation of at least three Rab GTPases . Further , we establish that different PD pathogenic mutations modulate the interaction with a number of regulatory proteins including guanine dissociation inhibitors ( GDI1/2 ) . In this way , LRRK2 regulates the specific insertion of Rab GTPases into target membranes thereby altering their membrane-cytosol equilibrium .
To search for bona-fide physiological LRRK2 substrates , we performed a dual-phosphoproteomic screening approach using knock-in lines harboring either hyperactive LRRK2 or a LRRK2 variant with wild-type activity but insensitive to a highly selective , newly developed LRRK2 compound . For our first screen ( PS1 ) , we generated a mouse model harboring the LRRK2-G2019S substitution that increases kinase activity two- to threefold ( Figure 1B ) . We derived fibroblasts from these animals and treated them with two structurally different LRRK2 inhibitors , GSK2578215A ( Reith et al . , 2012 ) or HG-10-102-01 ( Choi et al . , 2012 ) ( Figure 1A and Figure 1—figure supplement 1A ) . This screening modality offers three major advantages; first , increased activity of the G2019S-LRRK2 kinase amplifies the chance of finding bona-fide substrates , second , using an isogenic system excludes that measured phosphoproteome changes are due to differences in the genetic background and third , considering only the overlapping population of significantly modulated phosphopeptides of two structurally distinct inhibitors constitutes a very stringent criterion for specifically pinpointing LRRK2 substrates . 10 . 7554/eLife . 12813 . 003Figure 1 . Two unbiased phosphoproteomic screens identify physiological LRRK2 targets . ( A ) Experimental setup of PS1 . LRRK2-G2019SGSK mouse embryonic fibroblasts ( MEFs , n=5 ) were treated with DMSO or each of two structurally distinct LRRK2 inhibitors GSK2578215A or HG-10-102-01 ( 1 µM for 90 min ) . ( B ) LRRK2 immunoprecipitated from either knockout ( -/- ) , wild-type ( wt ) or LRRK2-G2019SGSK ( G2019S ) knock-in MEFs was assessed for phosphorylation of Nictide ( Nichols et al . , 2009 ) peptide substrate in absence or presence of GSK2578215A ( 2 μM ) . Western blot below shows that similar levels of LRRK2 were immunoprecipitated . Error bars are mean ± SD ( n=3 ) . ( C ) Scheme of PS2 . The higher affinity of MLI-2 toward wt-LRRK2 allows specific pinpointing of LRRK2 substrates when comparing the phosphoproteomes of wt and A2016T MEFs . ( D ) Kinase activities of wt ( closed circles ) and A2016T ( open circles ) GST-LRRK2 [1326-2527] purified from HEK293 cells were assayed in the presence of the indicated concentration of MLI-2 ( n=3 ) . ( E ) Decreased levels of pS935-LRRK2 in wt MEFs after treatment with 10 nM MLI-2 . ( F ) Heat map cluster of phosphopeptides in PS1 ( p<0 . 005 ) which are downregulated after treatment with both GSK2578215A and HG-10-102-01 . ( G ) Heat map cluster of downregulated ( FDR=0 . 01 , S0=0 . 2 ) phosphopeptides in PS2 . ( H ) Venn diagram of overlapping downregulated phosphosites in PS1 and PS2 . ( Biorep= biological replicate ) . SD , standard deviation . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 00310 . 7554/eLife . 12813 . 004Figure 1—figure supplement 1 . Two unbiased phosphoproteomic screens identify physiological LRRK2 targets . ( A ) Western blot analysis of wild type ( wt ) and LRRK2-G2019SGSK ( G2019S ) mouse embryonic fibroblasts ( MEFs ) , treated with DMSO ( - ) or 1 µM of GSK2578215A or HG-10-102-01 for 90 min . ( B ) In vitro kinase assay using LRRK2 immunoprecipitated from MEFs ( wt and A2016T ) in the presence of various concentrations of MLi-2 . Phosphorylation of Nictide was quantified by liquid scintillation counting . The western blot below shows that similar levels of LRRK2 were used . Error bars are mean ± SD ( n=3 ) . ( C ) Western blot analysis of pS935-LRRK2 and total LRRK2 levels in wt-LRRK2 MEFs and A2016T-LRRK2 MEFs treated for 60 min with the indicated concentrations of MLI-2 . ( D ) Number of quantified class I phosphorylation sites of PS1 in five biological replicates ( Biorep ) per phenotype analyzed . ( E ) More than 9000 phosphorylation sites are identified in each of the four biological replicates ( Biorep ) of wild type and A2016T MEFs ( PS2 ) . ( F ) Pearson correlations for the phosphoproteomes of PS1 and PS2 ( G ) . SD , standard deviation . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 00410 . 7554/eLife . 12813 . 005Figure 1—figure supplement 2 . Two unbiased phosphoproteomic screens identify physiological LRRK2 targets . ( A ) Venn diagram of significantly regulated ( ANOVA , p<0 . 005 ) sites with GSK2578215A and HG-10-102-01 in PS1 . ( B ) Heat map of regulated phosphosites identified in five biological replicates of MEFs ( LRRK2-G2019SGSK ( DMSO ) , LRRK2-G2019SGSK+ GSK2578215A . and LRRK2-G2019SGSK + HG-10-102-01 ) . ( C ) Clusters identified in ( B ) . ( D ) Volcano plot of all phosphosites of PS2 . Significant sites are in blue and pS935 is indicated . ANOVA , analysis of variance . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 005 The second screen ( PS2 ) added another layer of specificity by combining phosphoproteomics with genetics and chemical biology . For this , we used fibroblasts derived from either wt or A2016T-LRRK2 knock-in mice and treated them with the newly developed , highly potent and selective LRRK2 compound MLI-2 ( Fell et al . , 2015 ) The A2016T substitution does not change basal LRRK2 activity but decreases sensitivity to MLI-2 ~10-fold ( Figure 1C , D and Figure 1—figure supplement 1B ) . At a dose of 10 nM , we observed a substantial decrease in phosphorylation of LRRK2-pS935 , which is associated with LRRK2 kinase activity ( Dzamko et al . , 2010 ) , in wt but not in A2016T cells ( Figure 1E and Figure 1—figure supplement 1C ) . Under these conditions , the phosphoproteome of wt MEFs includes both LRRK2-specific and off-target sites , whereas A2016T ( which is resistant to MLI-2 ) only includes off-targets . Therefore , direct quantitative comparison should reveal true LRRK2 substrates ( Figure 1C ) . Using a state-of-the-art workflow for phosphopeptide enrichment , label-free LC-MS/MS and the MaxQuant environment for stringent statistical data evaluation ( Humphrey et al . , 2015; Cox and Mann , 2008; Cox et al . , 2011 ) , we quantified over 9000 high-confidence phosphosites in each replicate in both screens ( median R=0 . 80 and 0 . 89 in PS1 and PS2 , respectively ) , ( Figure 1—figure supplement 1D–G and Supplementary file 1 ) . Independently acquired proteome measurements verified that the detected phosphorylation changes in PS2 were not due to altered protein abundances ( changes as determined by label-free quantification in MaxQuant ( Cox et al . , 2014 ) were less than twofold [Supplementary file 2] ) . Next , we determined how many of the identified sites were significantly and robustly modulated . As we were interested in capturing the most strongly regulated sites , we required that the fold change had to be at least as strong as pS935-LRRK2 . In PS1 , we thus found 234 significantly regulated sites after treatment with each of the two LRRK2 compounds GSK2578215A and HG-10-102-01 ( ANOVA , p<0 . 005 ) , with 78 sites regulated by both ( Figure 1—figure supplement 2A ) . Hierarchical clustering divided them into several subgroups ( Figure 1—figure supplement 2B–C and Supplementary file 3A ) . Besides revealing potential off-target sites of the two LRRK2 inhibitors , this identified a particularly interesting cluster containing 47 sites that were downregulated after treatment with both compounds ( Figure 1—figure supplement 2C , cluster 5 ) . In PS2 , we identified 204 significantly regulated sites ( two sample t-test , FDR=0 . 01 , S0=0 . 3 ) , when comparing wild-type and inhibitor-resistant LRRK2 fibroblasts , with 128 sites specifically downregulated in the wild type , thus excluding off-target effects ( Figure 1G , Figure 1—figure supplement 2D and Supplementary file 3B ) . Finally , to stringently define LRRK2 substrates , we overlapped the results of the two orthogonal screens . Remarkably , only two phosphosites passed these stringent filtering criteria , our positive control pS935-LRRK2 and the conserved T73 residue of the small GTPase Rab10 ( Figure 1H ) . Rab10 belongs to the Ras family of small GTPases that regulate intracellular vesicular transport , with ~70 members in human . They function as molecular switches in the tethering , docking , fusion , and motion of intracellular membranes ( Stenmark , 2009; Wandinger-Ness and Zerial , 2014 ) . The T73 residue of Rab10 is located in the switch II domain , which is characteristic of Rab GTPases ( Figure 2A ) . This region changes conformation upon nucleotide binding and regulates the interaction with multiple regulatory proteins ( Pfeffer , 2005 ) . Sequence alignment revealed that the equivalent site to T73-Rab10 is highly conserved in more than 40 human Rab-family members , indicating strong functional relevance ( Figure 2B ) . Moreover , superposing the crystal structures of multiple Rab GTPases localizes the equivalent residues to T73-Rab10 in nearly the same position ( Figure 2—figure supplement 1A ) . To investigate whether the phosphorylation of Rab10 by LRRK2 is direct , we performed an in vitro kinase assay using recombinant components . Notably , we found that both wt and LRRK2-G2019S , but neither kinase inactive D1994A mutant nor small molecule-inhibited LRRK2 , efficiently phosphorylated Rab10 , proving a direct kinase-substrate relationship ( Figure 2C ) . Furthermore , incubation of Rab10 with LRRK2 followed by tryptic digestion and MS analysis unambiguously identified T73 as the major phosphorylation site ( Figure 2—figure supplement 1B ) . Given the high conservation of T73-Rab10 , we investigated whether other Rab GTPases were also phosphorylated by LRRK2 in vitro . Therefore , we first measured LRRK2-mediated phosphorylation of Rab8a , Rab1a , and Rab1b , all of which contain a Thr at the site equivalent to T73-Rab10 , by MS or 32P incorporation followed by Edman sequencing . Remarkably , all proteins were rapidly phosphorylated on the predicted LRRK2 phosphorylation site in the switch II domain ( Figure 2D–F and Figure 2—figure supplement 1C–E ) . Next , we compared Rab family members containing Thr sites in the switch II region with those containing a Ser in the equivalent position . Interestingly , while Rabs with threonines ( Rab1b , Rab8a , and Rab10 ) were efficiently phosphorylated , those with the equivalent serine sites ( Rab5b , Rab7a , Rab7L1 , Rab12 , and Rab39b ) were phosphorylated to a drastically lower extent ( Figure 2G ) . This confirms the previously reported in vitro preference for threonines by LRRK2 ( Nichols et al . , 2009 ) . Finally , we performed a side by side comparison of the phosphorylation efficiencies of recombinant Rab8a and Rab7L1 against two previously reported substrates , moesin ( Msn ) and Rps15 ( Jaleel et al . , 2007; Martin et al . , 2014 ) . Msn is a cytoskeletal protein and a well-known in vitro LRRK2 substrate , whereas Rps15 is part of the 40S ribosomal subunit and its phosphorylation on Thr136 has been reported to regulate protein translation in D . melanogaster ( Martin et al . , 2014 ) . In accordance with our previous observations ( Figure 2G ) , phosphorylation levels of RAB7L1 were barely detectable , and even lower than those of Msn and Rps15 . Strikingly , levels of pRab8a were about ten times higher as compared to Rps15 and Msn , two of the best in vitro LRRK2 substrates known to date , demonstrating that Rabs with Thr sites in the switch II domain are primary LRRK2 targets ( Figure 2H , I ) . 10 . 7554/eLife . 12813 . 006Figure 2 . Phosphorylation of Rab GTPases by LRRK2 in vitro . ( A ) Position of threonine 72 in the switch II region of Rab8a ( PDB: 4HLY ) . ( B ) Sequence alignment of Rab10 and other indicated Rab-family members . ( C ) Phosphorylation of Rab10 ( 1 µM ) by wt- , G2019S- or kinase inactive LRRK2-D1994A . Inhibition of LRRK2-G2019S by GSK2578215A or HG-10-102-01 prevents phosphorylation . ( D ) Time course of LRRK2 ( wt ) mediated Rab8a ( 4 µM ) phosphorylation and ( E ) quantification of phosphorylation stoichiometry ( n=3 ) . ( F ) Time course of LRRK2-wt-mediated pT75-Rab1a phosphorylation and MS-based label-free quantification ( n=3 ) . ( G ) In vitro phosphorylation of recombinant Rab proteins ( 4 µM ) by LRRK2-wt . ( H ) Phosphorylation of recombinant Rab7L1 , Rab8a , moesin , and Rps15 by LRRK2 and ( I ) quantification of the signals . For all reactions LRRK2 inhibitors= 2 µM and LRRK2= 100 ng . Error bars indicate mean ± SEM of replicates . MS , mass spectrometry; SEM , standard error of the mean; wt , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 00610 . 7554/eLife . 12813 . 007Figure 2—figure supplement 1 . Phosphorylation of Rab GTPases by LRRK2 in vitro . ( A ) Superposition of the crystal structures of 14 Rab isoforms ( Rab1a , 1b , 2 , 3 , 4 , 6 , 7 , 9 , 12 , 18 , 27 , 30 , 31 , 43 ) . All potential LRRK2 phosphorylation sites ( in grey ) cluster in the same region . ( B ) MS analysis of in vitro phosphorylated Rab10 identified three LRRK2-specific sites ( note that phosphorylation is prevented completely by HG-10-102-01 ) and pT73 as the one with the highest intensity . The collosion-induced dissociation ( CID ) fragmentation spectrum and the Andromeda score ( score ) ( Cox et al . , 2011 ) for the tryptic pT73-Rab10 peptide are shown . ( C ) Phosphorylation of Rab8a and Rab1b by LRRK2-wt . Inhibition of LRRK2 by HG-10-102-01 prevents phosphorylation . ( D ) HPLC trace of tryptic peptides of Rab8a and Rab1b ( E ) after in vitro phosphorylation by LRRK2-wt and sequence analysis of tryptic peptides . Y axis units are relative Cherenkov counts per minute . MS , mass spectrometry; wt , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 007 Because of the high conservation of T73-Rab10 ( Figure 2B ) and the ability of LRRK2 to phosphorylate multiple Rabs in vitro , we inspected our quantitative MS data further to determine whether all sequence and structurally equivalent sites are targets of LRRK2 . This turned out not to be the case as pS72-Rab7a was not regulated in either of our screens . LRRK2 thus phosphorylates only a subset of Rab GTPases in mouse fibroblasts . Surprisingly , we noticed that pS105-Rab12 , which is not phosphorylated by LRRK2 in vitro ( Figure 2G ) , was among the significantly modulated sites in PS1 and also downregulated upon MLI-2 treatment in wt cells as compared to the inhibitor-resistant A2016T mutant in PS2 ( Figure 3A , B ) . However , because of elevated intergroup variability and stringent FDR cut-offs , it was not selected in our first analysis . LRRK2 is found also in lower eukaryotes such as C . elegans and D . melanogaster ( Liu et al . , 2011 ) and T73-Rab10 is conserved in these organisms as well . Also , S105-Rab12 is present throughout the vertebrates ( Figure 3A , B ) . We identified both pT73-Rab10 and pS105-Rab12 multiple times with high identification and phosphosite localization scores ( Supplementary file 1 ) and the MS/MS fragmentation spectra of the corresponding synthetic peptides independently validated the MS results ( Figure 3—figure supplement 1A , B ) . Total protein levels of Rab10 and Rab12 did not change appreciably in the A2016T knock-in model as judged by quantitative MS analysis , ruling out that that the observed phospho-level changes are due to differential protein expression ( Figure 3—figure supplement 2A ) . 10 . 7554/eLife . 12813 . 008Figure 3 . A number of Rab GTPases are physiological LRRK2 substrates . ( A ) MS-quantified pT73-Rab10 peptide intensities in PS1 and PS2 . Sequence alignment of the T73-Rab10 region is shown below . ( B ) Same as ( A ) with pS106-Rab12 . Western blots illustrating phosphorylation of T73-HA-Rab10 ( C ) , S106-HA-Rab12 ( D ) , and T72-Rab8 ( E ) after induction of LRRK2 expression by doxycycline ( 1 µg/ml ) . HG-10-102-01 ( 1 µM ) was added prior to lysis . ( F ) Western blot of homogenized brain lysates from LRRK2-G2019SLilly mice injected with vehicle ( 40% HPβCD ) or with 3 mg/kg MLI-2 ( Biorep= biological replicate ) and ( G ) MS-based quantification of pT72-Rab8 and pS105-Rab12 peptides . ( H ) Cytoscape network analysis of Rab8a interacting proteins determined by affinity-purification mass spectrometry ( AP-MS ) . LRRK2 is in purple and dashed lines in grey show experimentally determined interactions from string database ( http://string-db . org/ ) . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 00810 . 7554/eLife . 12813 . 009Figure 3—figure supplement 1 . HCD MS/MS spectra of synthetic Rab peptides ( A ) Higher energy collision-induced dissociation ( HCD ) MS/MS spectra of the pT73-Rab10 peptide identified in PS2 . The spectrum of the corresponding synthetic peptide is shown below . ( B ) Same as ( A ) but pS105-Rab12 . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 00910 . 7554/eLife . 12813 . 010Figure 3—figure supplement 2 . Quantification of Rab phosphorylation by mass spectrometry ( MS ) . ( A ) MS-based label-free quantification ( MaxLFQ , Cox et al . , 2014 ) of the Rab10 and Rab12 protein intensities in PS2 . ( B ) MS-based quantification of pT73-Rab10 ( left ) and total Rab10 ( right ) derived from HEK293 ( trex flpIn ) cells expressing GFP-LRRK2-G2019S after LRRK2 inhibition ( n=4 ) . ( C ) MS-quantified Rab3-pT86 peptide levels of ectopically expressed Rab3a alone or in combination with LRRK2-G2019S , in presence or absence of HG-10-102-01 ( 3 µM , 3 hr , n=3 ) . A western blot of the same samples is shown below . ( D ) Same as ( C ) but Rab1a was expressed and pT75-Rab1a quantified . ( E ) Same as ( B ) with pT72-Rab8 ( left ) and total Rab8a ( right ) . ( F ) Label-free quantification of pT73-Rab10 and ( G ) pT72-Rab8a from knockout , wt , G2019S , or G2019S treated with HG-10-102-01 ( 3 µM , 3 hr ) MEFs . Total Rab10 and Rab8 protein levels were also quantified ( n=3 ) . ( H ) and ( I ) Western blot analyses of samples used in ( F ) and ( G ) . Open circles indicate imputed values . MEFs , mouse embryonic fibroblasts; wt , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 01010 . 7554/eLife . 12813 . 011Figure 3—figure supplement 3 . Several Rabs stably associate with LRRK2 in cells . ( A ) Western blot of HEK293 cells expressing flag-LRRK2-G2019S , either alone or in combination with HA-Rab8a . ( B ) Volcano plot of MS-quantified Rab8a interactors ( n=4 ) . ( C ) Same as ( A ) with HA-Rab10 or HA-Rab12 . ( D ) and ( E ) Volcano plots of MS-quantified Rab10 and Rab12 interactors . MS , mass spectrometry . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 011 To extend the analysis of LRRK2-mediated phosphorylation of Rab10 , we used human embryonic kidney cells harboring doxycycline-dependent gene expression of LRRK2-G2019S ( HEK293-t-rex-flpIn ) . Expression of the kinase , treatment with either GSK2578215A or HG-10-102-01 and enrichment of Rab10 by immunoprecipitation followed by quantitative MS analysis confirmed a strong , LRRK2-dependent decrease of pT73-Rab10 peptide levels ( Figure 3—figure supplement 2B ) . Polyclonal antibodies recognizing pT73-Rab10 and pS106-Rab12 ( note that the equivalent site is S105 in mouse ) independently verified LRRK2-dependent phosphorylation of both Rab isoforms in HEK293 cells ( Figure 3C , D ) . Next , we evaluated whether more Rab isoforms can be phosphorylated in a LRRK2-dependent manner in human cells , focusing on Rab1a , Rab3a , and Rab8a , all of which contain Thr as predicted LRRK2 phosphorylation site ( Figure 2B ) . Therefore , we first ectopically expressed LRRK2 along with either Rab1a or Rab3a , in presence or absence of HG-10-102-01 and quantified pT75-Rab1a and pT86-Rab3a peptide levels by MS . Whereas T86-Rab3a is clearly a LRRK2 target , Rab1a is not , indicating that overexpression of LRRK2 is not sufficient to phosphorylate all Rabs in cells ( Figure 3—figure supplement 2C , D ) . Next , we inhibited LRRK2 in HEK293-t-rex-flpIn cells expressing LRRK2-G2019S and quantified pT72-Rab8 . Again , we found a strong decrease of pT72 peptide levels upon LRRK2 inhibition with both GSK2578215A and HG-10-102-01 ( Figure 3—figure supplement 2E ) . An antibody raised for specific detection for pT72-Rab8 confirmed these results further ( Figure 2E ) . To analyze LRRK2-dependent phosphorylation of Rabs in an endogenous context , we quantified pT72-Rab8 and pT73-Rab10 peptide levels in MEFs derived from LRRK2 knockout , wt , or G2019SGSK animals . In the knock-out , the decrease was only about twofold compared to wt , implying a very low intrinsic LRRK2 activity in cells . Consistent with the two- to threefold increased in vitro activity of MEFs-extracted LRRK2-G2019SGSK ( Figure 1B ) , our quantitative MS analysis revealed a threefold increase in both pT72-Rab8 and pT73-Rab10 , which was restored to near wt levels by selective LRRK2 kinase inhibition ( Figure 3—figure supplement 2F–I ) . Finally , we globally measured the brain phosphoproteome of LRRK2-G2019SLilly mice injected with vehicle ( 40% HPβCD ) or with MLI-2 ( 3 mg/kg ) . Levels of pT72-Rab8 and pS105-Rab12 were decreased more than twofold upon LRRK2 inhibition , validating our findings in the context of a LRRK2 pathogenic mouse model ( Figure 3F , G ) . Having identified Rabs as physiological substrates of LRRK2 , we next asked if kinase and substrate also stably interact . Indeed , affinity-purification mass-spectrometry ( AP-MS ) showed that transiently expressed epitope-tagged LRRK2 and Rab8a efficiently associated with each other , demonstrating that LRRK2 is able to form stable complexes with Rab GTPases in cells ( Figure 3H and Figure 3—figure supplement 3A , B ) . Similarly , Rab10 as well as Rab12 associate with LRRK2 when transiently overexpressed in HEK293 cells ( Figure 3—figure supplement 3C–E ) . Pathogenic PD LRRK2 mutations predominantly map to the kinase and the ROC-COR ( GTPase ) domains and a PD risk factor coding mutation is also found in the WD-40 domain ( Martin et al . , 2014; Farrer et al . , 2007 ) ( Figure 4A ) . Because it is presently unclear how mutations occurring in distinct LRRK2 functional domains lead to similar disease phenotypes , we decided to investigate if different LRRK2 pathogenic mutations might impact on the phosphorylation status of Rab GTPases . For this , we expressed different disease causing LRRK2 variants along with either Rab8a or Rab10 in HEK293 cells . This revealed that besides PD-associated mutations located in the kinase domain that augment LRRK2 kinase activity , those occurring in the GTPase ( ROC-COR ) or the WD-40 domains also increased pT72-Rab8a and pT73-Rab10 levels in cells ( Figure 4B–E ) . 10 . 7554/eLife . 12813 . 012Figure 4 . LRRK2 pathogenic variants increase phosphorylation of Rab GTPases . ( A ) Scheme of LRRK2 and common PD-associated amino acid substitutions ( in red ) . ( B ) Different LRRK2 versions were co-expressed with Rab8a in HEK293 cells , lysates subjected to immunoblot analysis and ( C ) indicated signals quantified . ( D ) and ( E ) Same as ( B ) but HA-Rab10 was used . ( F ) In vitro phosphorylation of recombinant Rab8a ( 4 µM ) by indicated LRRK2 variants ( 100 ng ) and ( G ) quantification of the signals . HG-10-102-01= 2 µM . Error bars indicate mean ± SEM of replicates ( n=3 ) . PD , Parkinson's disease; SEM , standard error of the mean . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 012 To determine whether this interplay between different functional domains was direct , we next tested whether pathogenic LRRK2 mutations which lie outside the kinase domain also increase Rab phosphorylation in vitro . As expected , compared to wt , the G2019S mutation resulted in a two- to threefold increase in Rab8a phosphorylation . However , the ROC-COR domain R1441C mutation failed to do so , which is consistent with previous data suggesting that these mutations do not directly enhance LRRK2 kinase activity ( Nichols et al . , 2010 ) , indicating that its effect on Rab8a phosphorylation levels is mediated by accessory factors in cells ( Figure 4F , G ) . Rab GTPases consist of a similar core structure comprising highly conserved P-loop , switch I and switch II regions ( Pfeffer , 2005 ) . They cycle between the cytosol , in which they are GDP bound and inactive and specific membrane compartments , where they are activated by GDP/GTP exchange ( Hutagalung and Novick , 2011 ) . In the crystal structure of Rab8a ( Guo et al . , 2013 ) , the LRRK2-mediated phosphorylation site is in the switch II region ( Figure 2A ) , which regulates hydrolysis of GTP and coordinates the binding to various regulatory proteins ( Pfeffer , 2005 ) . We therefore tested whether the phosphomimetic T72E substitution would modulate GDP/GTP binding or interfere with Rab8a protein interactions . Binding affinities of wt and the TE mutant , determined with fluorescently labeled ( N-Methylanthraniloyl , mant ) GDP and non-hydrolysable GTP analogue GMPPNP , did not differ ( Figure 5—figure supplement 1 ) . In contrast , AP-MS revealed that a number of proteins preferentially bind to non-phosphorylatable T72A-Rab8a compared to the T72E phosphomimetic protein ( Figure 5A , B ) . These were Rab GDP dissociation inhibitors α and β ( GDI1 and GDI2 ) , Rab geranyltransferase complex members ( CHM , CHML , and RabGGTA/RabGGTB ) , the Rab8a guanine nucleotide exchange factor ( GEF ) Rabin8 ( Rab3IP ) , a guanine nucleotide activating protein TBC1D15 and the inositol phosphatase INPP5B ( Figure 5C ) . 10 . 7554/eLife . 12813 . 013Figure 5 . LRRK2 controls the interaction of Rabs with regulatory proteins . ( A ) Volcano plots showing interactors of GFP-Rab8a ( T72A ) transiently expressed in HEK293 cells and ( B ) Proteins differentially binding to T72A as compared to T72E . ( C ) Fold changes ( T72A/T72E , n=4 ) of regulated proteins shown in ( B ) . ( D ) Kinetic measurements of the dissociation of mant-GDP from non-phosphorylated and T72 phosphorylated Rab8a by Rabin8 . Observed rate constants ( kobs ) are indicated for each reaction and data points represent mean ( n=3 ) . ( E ) Measurements of mant-GDP dissociation from LRRK2 phosphorylated Rab8a by Rabin8 in absence or presence of λ-phosphatase ( λ-PPase ) or MLI-2 ( 1 µM ) . Error bars are mean ± SD of replicates . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 01310 . 7554/eLife . 12813 . 014Figure 5—figure supplement 1 . Rab8a nucleotide binding experiments . Titration experiment using Rab8a ( wt and T72E ) and fluorescently labeled non-hydrolysable GTP analog ( mant-GMPPNP ) or GDP ( mant-GDP ) . The fluorescence signal is plotted as a function of Rab8a concentration . The dissociation constants ( Kd ) ± SD are indicated . Error bars are mean ± SD ( n=3 ) . SD , standard deviation; wt , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 01410 . 7554/eLife . 12813 . 015Figure 5—figure supplement 2 . Rab8a guanine nucleotide exchange assays . ( A ) Ribbon structure of Rab8a in complex with Rabin8 ( PDB: 4LHY ) . The LRRK2 phosphorylation site ( T72 ) situated in the switch II region and forming close contact with Rabin8 is indicated . ( B ) Kinetics of mant-GDP dissociation from Rab8a ( wt , T72A , and T72E ) by Rabin8 . ( C ) and ( D ) Representation of the observed rate constants ( kobs ) and catalytic efficiencies ( kcat/Km ) for the same reactions . ( E ) ESI-TOF mass determination of Rab8a after in vitro phosphorylation by LRRK2-G2019S ( left ) and after enrichment of phosphorylated Rab8a by ion-exchange chromatography ( right ) . ( F ) Collision-induced dissociation ( CID ) fragmentation spectrum of the tryptic pT72-Rab8a peptide , which was identified after phosphorylation of Rab8a by LRRK2 followed by enrichment of the phosphorylated form by ion exchange chromatography . ( G ) Ni2+-NTA pull-down of Rab8a ( non-phosphorylated or phosphorylated on T72 ) by HIS-tagged Rabin8 using purified components . ( H ) Representation of the observed rate constants ( kobs ) and catalytic efficiencies ( kcat/Km ) for the indicated reactions . Error bars are mean ± SD ( n=3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 015 Rabin8 interacts with membrane-bound Rab8a and activates it by catalyzing the exchange of GDP to GTP ( Westlake et al . , 2011 ) . This in turn triggers retention of Rab effector proteins that mediate downstream vesicular trafficking events . Rabin8 binds to the switch II domain of Rab8a and contacts the conserved , phosphorylatable T72 residue ( Figure 5—figure supplement 2A ) ( Guo et al . , 2013 ) . We found that compared to wt , the T72E phosphomimetic substitution decreased the level of Rabin8-catalyzed mant-GDP displacement from a Rab8-GDP complex ( Figure 5—figure supplement 2B–D ) . To further substantiate this finding , we phosphorylated purified Rab8a using LRRK2 , which resulted in ~60% of T72-phosphorylated protein as determined by total protein MS and LC-MS/MS after tryptic digestion ( Figure 5—figure supplement 2E , F ) . Further enrichment by ion-exchange chromatography yielded a highly enriched ( ~100% ) fraction of pT72-Rab8a ( Figure 5—figure supplement 2E ) . Loading of non-phosphorylated and phosphorylated Rab8a with mant-GDP following incubation with Rabin8 revealed that LRRK2-induced phosphorylation of T72-Rab8a inhibits rates of Rabin8-catalyzed GDP exchange fourfold and decreases Rab8a-Rabin8 interaction ( Figures 5D and Figure 5—figure supplement 2G ) . Both λ-phosphatase treatment of LRRK2-phosphorylated Rab8a and pharmacological inhibition of LRRK2 prevented the decreased GEF activity of Rabin8 toward pT72-Rab8a ( Figure 5E and Figure 5—figure supplement 2H ) . Thus , phosphorylation of Rab8a by LRRK2 can limit its activation by Rabin8 . GDI1 and GDI2 , along with CHM and CHML ( also known as Rab escorting proteins REP1 and REP2 ) form the GDI superfamily and are essential regulators of the Rab cycle . GDIs extract inactive , prenylated Rabs from membranes and bind them with high affinity in the cytosol ( Pylypenko et al . , 2003 ) . The regulatory mechanism by which Rabs are displaced from GDIs to facilitate their insertion into specific target membranes is unknown . The co-crystal structure of GDI1 with the yeast Rab homologue Ypt1 shows that GDIs closely contact the switch II region ( Rak , 2003 ) , which explains why phosphorylation in this domain interferes with the Rab-GDI interaction . Since GDIs are not specific to one Rab isoform ( Seabra and Wasmeier , 2004 ) , we reasoned that phosphorylation of the switch II domain could be a general mechanism of Rab-GDI dissociation . We therefore substituted S106-Rab12 and T73-Rab10 with non-phosphorylatable Ala or phosphomimetic Glu residues and tested their capacity to form complexes with GDIs by immunoprecipitation followed by MS or western blotting . As compared to non-phosphorylatable Rab10 and Rab12 , neither S106E-Rab12 nor T73E-Rab10 was able to bind GDIs , demonstrating the functional importance of these residues ( Figure 6A , B and Figure 6—figure supplement 1A , B ) . 10 . 7554/eLife . 12813 . 016Figure 6 . PD pathogenic LRRK2 mutations interfere with Rab-GDI1/2 association . ( A ) Fold changes ( T73A/T73E , n=3 ) of indicated MS-quantified Rab10 interactors . ( B ) Same as ( A ) but S106A-Rab12 and S106E-Rab12 ( n=4 ) . ( C ) Different LRRK2 versions were co-expressed with Rab8a in HEK293 cells , lysates subjected to immunoblot analysis or immunoprecipitation using α-HA antibodies and indicated signals quantified ( D ) . ( E ) and ( F ) Same as ( C ) with Rab12 expression . ( G ) Scheme for analyzing T72A-Rab8a and T72E-Rab8a subcellular protein distributions in a SILAC experiment . ( H ) SILAC ratios ( Log2 ) of T72E-Rab8a/T72A-Rab8a proteins in the cytosolic and membrane fraction of HEK293 cells . PD , Parkinson's disease . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 01610 . 7554/eLife . 12813 . 017Figure 6—figure supplement 1 . Rab10/12-GDI interactions . ( A ) HA-Rab10 constructs ( wt , T73A , T73E ) were expressed in HEK293 cells and lysates subjected to α-HA immunoprecipitation before western blotting . ( B ) Same as ( A ) using HA-Rab12 ( wt , S106A , S106E ) . wt , wild type . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 017 To further analyze the effect of Rab phosphorylation and GDI dissociation in the context of PD , we expressed LRRK2 variants harboring various pathogenic mutations along with Rab8a in cells and assessed Rab-GDI complex formation by immunoprecipitation . Strikingly , the level of Rab8a-GDI interaction closely correlated with the degree of T72-Rab8a phosphorylation ( Figure 6C , D ) . Similarly , LRRK2-mediated phosphorylation of S106-Rab12 diminished the interaction with GDIs , confirming that the effect is not specific to one Rab isoform ( Figure 6E , F ) . All tested LRRK2 pathogenic mutations that affect kinase activity thus control the interaction of Rabs with GDIs . Finally , to directly test whether disruption of the Rab-GDI interaction results in an altered subcellular distribution of Rabs , we quantitatively determined T72A-Rab8 and T72E-Rab8 protein abundances in SILAC ( Ong , 2002 ) labeled HEK293 cells . This revealed a twofold increase of non-phosphorylatable T72A mutant in the cytosol . Consistently , we detected a significant ( p= 2 . 58*10-3 ) increase of T72E-Rab8 protein levels in the membrane fraction , demonstrating that interference with the Rab-GDI interaction results in an unbalanced membrane-cytosol distribution of Rabs ( Figure 6G , H ) .
Here , we used a state of art MS-based phosphoproteomics workflow in combination with cells of two genetically engineered mouse models as well as a mixture of selective LRRK2 compounds to define LRRK2 targets with high stringency . Starting with almost 30 , 000 identified phosphosites , our screens rapidly narrowed down the candidates to a small number that were consistently and strongly regulated with all tested compounds and genetic models . Only the known phosphorylation site pS935 on LRRK2 itself and a specific residue in the Rab10 GTPase ( T73 ) fulfilled our most specific criteria . LRRK2 kinase is conserved also in flies and worms and this is true of the T73-Rab10 substrate site as well . Further experiments with diverse model systems and techniques all verified the T73-Rab10 site as well as the equivalent sites on many but not all other Rab family members . These include the threonine sites on Rab8a and Rab3a ( T72 and T86 , respectively ) , as well as S106-Rab12 . Rab7a is an important component of the endocytic pathway ( Wandinger-Ness and Zerial , 2014 ) and phosphorylation on S72 has recently been shown to play a functional role in B-cell signaling ( Satpathy et al . , 2015 ) . While our data clearly show that this site is not regulated by LRRK2 in mouse fibroblasts , its regulation by LRRK2 in B cells remains possible , given the high expression levels of LRRK2 in those cell types ( Gardet et al . , 2010 ) . In vitro experiments proved that LRRK2 directly phosphorylates Thr but not the Ser sites in Rab isoforms , in line with its well-established in vitro preference ( Jaleel et al . , 2007; Martin et al . , 2014; Nichols et al . , 2009 ) . We found that Ser sites on Rabs were hardly phosphorylated in vitro but S105-Rab12 ( S106 in human ) was clearly regulated in cells and brain tissue , establishing that accessory factors are required in this case . Consistent with this finding , the major characterized in vivo LRRK2 autophosphorylation site is a Ser residue ( Ser1292 ) ( Sheng et al . , 2012 ) . Our observation of residual Thr72-Rab8 phosphorylation in LRRK2-/- mice implies that one or more other kinase ( s ) are able to act upon this residue . Besides defining Rab GTPases as LRRK2 targets , our screens identified a number of phosphosites as potential LRRK2 targets . However , these were validated by only one of the screens , their regulation was weaker and for many of them regulation may reflect indirect modulation by the LRRK2 kinase . This is likely to account for the difficulty in identifying substrates of this kinase . In a direct comparison of threonine Rab phosphorylation it was much stronger than the known in vitro LRRK2 targets we tested . Overall , the relatively small number of regulated sites in our screens , suggest that LRRK2 is a very specific or low activity kinase . LRRK2 is ubiquitously expressed , but highly abundant in the kidney , lungs , pancreas , and certain cell types of the immune system ( Schapansky et al . , 2015 ) . Therefore , it is possible that different LRRK2 substrates , including Rab isoforms , are phosphorylated in a cell- or tissue-specific manner . Further phosphoproteomic research should shed more light on this open question . We conclude that the threonine sites on Rab family members identified here may not be the only functional ones in the context of LRRK2 , but that they are the most prominent ones . In searching for a functional role for LRRK2-mediated Rab phosphorylation , we noted that it maps onto the switch II region , which is known to mediate GDP/GTP exchange as well as interaction with regulatory proteins . Results from nucleotide affinity measurements make the former mechanism unlikely but AP-MS established phosphorylation-dependent binding of several proteins involved in regulating their cycling between cytosol and membrane compartments . This indicates that direct phosphorylation of Rabs on a conserved residue situated in the switch II domain regulates their movement by controlling the interaction with numerous regulatory proteins . The affinities of GDIs for Rabs are vastly decreased in a manner correlating with the phosphorylation levels induced by different LRRK2 pathogenic variants . Our data thus establish that LRRK2 is an important regulator of Rab homeostasis which is likely contributing to PD development ( Figure 7A ) . Overactive LRRK2 , which results in increased Rab phosphorylation , promotes dissociation from GDIs in the cytosol with concomitant membrane insertion ( Figure 7B ) . In this way , the relative pool of membrane bound and cytosolic Rab is altered , disturbing intracellular trafficking . In particular , PD-associated LRRK2 mutations would shift the membrane-cytosol balance of Rabs toward the membrane compartment , thereby causing accumulation of inactive Rabs in the membranes ( Figure 7B ) . The subtle increase in Rab phosphorylation in cells derived from LRRK2-G2019S knock-in mice is consistent with the long time needed for PD to manifest in humans . 10 . 7554/eLife . 12813 . 018Figure 7 . Model of Rab GTPase phosphorylation by LRRK2 and its outcome . ( A ) Rab GTPases ( Rabs ) cycle between an inactive ( GDP-bound ) and an active state ( GTP-bound ) between cytosol and membranes , respectively . Geranyl-geranyl-modified Rab GTPases in their GDP-bound state are tightly bound by guanine dissociation inhibitors ( GDIs ) in the cytosol . LRRK2 aids the insertion of Rabs in their specific target membrane . After removal of the LRRK2 phosphorylation site , guanine exchange factors ( GEFs ) facilitate exchange of GDP to GTP . This in turn allows binding to effector proteins and membrane trafficking events . Next , a Rab-specific GTPase-activating protein ( GAP ) assists in the hydrolysis of GTP followed by removal of the Rab GTPase from the target membrane by GDIs . ( B ) In pathogenic conditions , in which LRRK2 is hyperactive , RabGTPases have strongly diminished affinities for GDIs . As a result , the equilibrium between membrane-bound and cytosolic Rabs is disturbed , which may contribute to LRRK2 mutant carrier disease phenotypes . Model adapted and modified from ( Hutagalung and Novick , 2011 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 12813 . 018 Intriguingly , our results show that pathogenic LRRK2 mutations outside the kinase domain can also increase Rab phosphorylation . Although our in vitro data clearly shows that this mechanism is indirect , they will still act on the same pathway as kinase domain mutants . Therefore , the same model would also be applicable in this case . Independent evidence that Rabs are likely to be primary LRRK2 substrates comes from LRRK2 knockout animal studies . LRRK2-/- mice and rats have deformed kidneys and lungs , indicative of defects in the autophagosome/lysosome pathway , which depend on properly tuned Rab activity ( Herzig et al . , 2011; Hinkle et al . , 2012; Baptista et al . , 2013 ) . Moreover , it was recently reported that LRRK2 and Rab2a regulate Paneth cell function , which is compromised in Crohn’s disease ( Zhang et al . , 2015 ) . In this context , it is interesting that we found pS70-Rab2a/b to be regulated in our second screen , although it’s very low abundance impedes meaningful statistical interpretation . Taken together these observations make it plausible that LRRK2 regulates Rab2a by direct phosphorylation of S70 in specialized cell types . In conclusion , we prove that LRRK2 induces phosphorylation of Rabs and provide evidence that it deregulates cycling between cytosol and target membrane compartments . It will be interesting to investigate whether the Rab regulatory mechanism uncovered here is of key importance in vesicular trafficking in general . Discovery of a key physiological LRRK2 substrate should inform and accelerate research into PD , including monitoring the efficacy of therapeutic intervention .
MLI-2 ( Fell et al . , 2015 ) was obtained from Merck , GSK2578215A ( Reith et al . , 2012 ) from Tocris or GlaxoSmithKline . HG-10-102-01 was custom synthesized by Natalia Shapiro ( University of Dundee ) as described previously ( Choi et al . , 2012 ) . Doxycycline , ATP , and trypsin were from Sigma . LysC was obtained from Wako . 32P-γATP was from PerkinElmer . GST-LRRK2 ( residues 960-2527 wild type , G2019S , D1994A ) , full-length wild type flag-LRRK2 from Invitrogen and MANT-GDP ( 2'- ( or-3' ) -O- ( N-Methylanthraniloyl ) Guanosine 5'-Diphosphate , Disodium Salt ) and MANT-GMPPNP from Jena Bioscience . GFP beads for affinity purification were from Chromotek . Recombinant Rab10 and Rab1a ( Figure 3A and 3C ) were purchased from mybiosource . Anti-Rab10 and Rab8 were from Cell Signaling Technologies , anti-GFP from Invitrogen , anti-HA high affinity from Roche , anti-GDI1/2 from Sigma , and anti-pS1292-LRRK2 from Abcam . Rabbit monoclonal antibodies for total LRRK2 and pS935-LRRK2 were purified at the University of Dundee ( Dzamko et al . , 2012 ) . Antibodies against Rab8a phospho-Thr72 ( S874D ) , Rab10 phospho-Thr73 ( S873D ) and Rab12 phospho-Ser106 ( S876D ) were generated by injection of the KLH ( keyhole limpet hemocyanin ) -conjugated phospho-peptides AGQERFRpTITTAYYR ( Rab8a ) , AGQERFHpTITTSYYR ( Rab10 ) , AGQERFNpSITSAYYR ( Rab12 ) and IAGQERFpTSMTRLYYR ( where pS/T is phospho-serine/threonine ) into sheep and affinity purified using the phosphopeptides . Antibodies were used at final concentrations of 1 μg/ml in the presence of 10 μg/ml of non-phosphorylated peptide . The following constructs were used: 6His-SUMO-Rab8a wt/T72A/T72E ( DU47363 , DU47433 , DU47436 ) , HA-Rab8 wt/T72A/T72E ( DU35414 , DU47360 ) , 6His-SUMO-Rab5b ( DU26116 ) , 6-His-SUMO-Rab7a ( DU24781 ) , 6-His-SUMO-Rab7L1 ( DU50261 ) . 6-HIS-SUMO-Rab10 ( DU51062 ) , HA-Rab10 wt/T73A/T73E ( DU44250 , DU51006 , DU51007 ) , 6-His-SUMO-Rab12 ( DU52221 ) , HA-Rab12 wt/S106A/S106E ( DU48963 , DU48966 , DU48967 ) , and 6-His-SUMO-Rab39b ( DU43869 ) . Full datasheets are available on https://mrcppureagents . dundee . ac . uk/ . Mice were maintained under specific pathogen-free conditions at the University of Dundee ( UK ) . All animal studies were ethically reviewed and carried out in accordance with Animals ( Scientific Procedures ) Act 1986 , the GSK Policy on the Care , Welfare and Treatment of Animals , regulations set by the University of Dundee and the U . K . Home Office . Animal studies and breeding were approved by the University of Dundee ethical committee and performed under a U . K . Home Office project license . The LRRK2-G2019SGSK knock-in mouse line was generated by a targeting strategy devised to introduce the point mutation G2019S into exon 41 of the LRRK2 gene by homologous recombination in mouse embryonic stem ( ES ) cells . 5’ and 3’ homology arms ( approximately 4 . 8 and 3 . 8 kb , respectively ) flanking exon 41 were generated using Phusion High-Fidelity DNA Polymerase ( New England BioLabs ) on a C57BL/6J genomic DNA template . Similarly a 739 bp fragment carrying exon 41 lying between these two homology arms was isolated and subjected to site-directed mutagenesis with the QuickChangeII site-directed mutagenesis kit ( Stratagene ) to introduce the appropriate point mutation ( GG to TC mutation at bps 107/8 ) . The 5’ and 3’ homology arms and the mutated exon 41 fragments were subcloned into a parental targeting vector to achieve the positioning of the loxP and FRT sites and PGKneo cassette . Gene targeting was performed in de novo generated C57BL/6J-derived ES cells . The targeting construct was linearized and electroporated into ES cells according to standard methods . ES cells correctly targeted at the 3’ end were identified by Southern blot analysis of EcoRV digested genomic DNA using a PCR-derived external probe . Correct gene targeting at the 5’ end and presence of the point mutation was confirmed by sequencing of a ~6 kb PCR product . High-fidelity PCR of ES cell clone-derived genomic DNA using primers spanning the 5’ homology arm generated the latter . Correctly targeted ES cell clones were injected into BALB/c blastocysts and implanted into foster mothers according to standard procedures . Male chimaeras resulting from the G2019S targeted ES cells were bred with C57BL/6J female mice , and germline transmission of the targeted allele was confirmed by PCR . The PGKneo cassette was subsequently removed by breeding germline mice to FLPeR ( Farley et al . , 2000 ) mice expressing Flp recombinase from the Rosa26 locus ( C57BL/6J genetic background ) . Absence of the PGKneo cassette in offspring was confirmed by PCR and subsequent breeding to C57BL/6J mice removed the Flper locus ( confirmed by PCR ) . The line was maintained by breeding with C57BL/6J , and crossing mice heterozygous for the point mutation generated homozygous mice . Standard genotyping which distinguishes wild type from point mutation knock-in alleles was used throughout . Requests for LRRK2 G2019SGSK mice should be directed to: alastair . d . reith@gsk . com . The Michael J . Fox Foundation for Parkinson’s Research generated the A2016T knock-in mice . A targeting vector was designed to introduce an alanine to threonine ( A2016T ) substitution at codon 2016 in exon 41 of the endogenous locus . In addition , an FRT-flanked neomycin resistance ( neo ) cassette was introduced 400 bp downstream of exon 41 . The construct was electroporated into C57BL/6N-derived JM8 ES cells . Correctly targeted ES cells were injected into blastocysts and chimeric mice were bred to B6 . Cg-Tg ( ACTFLPe ) 9205Dym/J ( JAX stock No . 005703 ) to remove the neo cassette and leave a silent FRT site . The resulting animals were crossed to C57BL/6NJ inbred mice ( JAX stock No . 005304 ) for one generation . These mice are available from The Jackson Laboratory and for further information see http://jaxmice . jax . org/strain/021828 . html . The LRRK2-G2019SLilly were generated by Ely Lilly and maintained on a C57BL/6J background . Genotyping of mice was performed by PCR using genomic DNA isolated from ear biopsies . For LRRK2-G2019SGSK knock-in mice , Primer 1 ( 5′-CCGAGCCAAAAACTAAGCTC -3′ ) and Primer 2 ( 5′-CCATCTTGGGTACTTGACC-3′ ) were used to detect the wild-type and knock-in alleles . For LRRK2-G2019SLilly knock-in mice Primer 1 ( 5′-CATTGCGAAGATTGCGGACTACTCAATT-3′ ) and Primer 2 ( 5′-AAACAGTAACTATTTCCGTCGTGATCCG-3′ ) were used to detect the wild-type and knock-in alleles . For LRRK2-A2016T Primer 1 ( 5′-TTGCCTGTGAGTGTCTCTGG-3′ ) and Primer 2 ( 5′-AGGAAATGTGGTTCCGACAC-3′ ) were used to detect the wild-type and knock-in alleles . The PCR program consisted of 5 min at 95°C , then 35 cycles of 30 s at 95°C , 30 s at 60°C and 30 s at 72°C , and 5 min at 72°C . DNA sequencing was used to confirm the knock-in mutation and performed by DNA Sequencing & Services ( MRC–PPU; http://www . dnaseq . co . uk ) using Applied Biosystems Big-Dye version 3 . 1 chemistry on an Applied Biosystems model 3730 automated capillary DNA sequencer . For experiments shown in Figure 3F–G , homozygous LRRK2-G2019SLilly mice ( 3 months of age ) were injected subcutaneously with vehicle ( 40% Hydroxypropyl-β-Cyclodextran ) or MLI-2 ( 3 mg/kg of body mass dissolved in 40% Hydroxypropyl-β-Cyclodextran ) and euthanized by cervical dislocation 1 hr after treatment . Brains were rapidly isolated and snap frozen in liquid nitrogen . No specific randomization method or blinding was applied to experiments . Littermate matched wild type and homozygous LRRK2-A2016T mouse embryonic fibroblasts ( MEFs ) were isolated from mouse embryos at day E12 . 5 resulting from crosses between heterozygous LRRK2-A2016T/WT mice using a previously described protocol ( Wiggin et al . , 2002 ) . Cells were genotyped as described above for mice and wild type and homozygous A2016T knock-in cells generated from the same littermate selected for subsequent experiments . Cells cultured in parallel at passage 4 were used for MS and immunoblotting experiments . Littermate matched wild type and homozygous LRRK2-G2019SGSK MEFs were isolated from mouse embryos at day E12 . 5 resulting from crosses between heterozygous LRRK2-G2019SGSK/WT mice as described previously ( Wiggin et al . , 2002 ) . Wild-type and homozygous LRRK2-G2019SGSK/G2019SGSK MEFs were continuously passaged in parallel for at least 15 passages before being used for MS and immunoblotting experiments . All cells were cultured in DMEM containing 10% FBS , 2 mM L-glutamine , 50 units/ml penicillin , 50 μg/ml streptomycin , and non-essential amino acids ( Life Technologies ) . Littermate matched wild type and homozygous knock-out MEFs were isolated from LRRK2 knock-out mice ( Dzamko et al . , 2012 ) as described previously ( Davies et al . , 2013 ) . All knock-in and knock-out cell lines were verified by allelic sequencing . HEK293 were purchased from ATCC and cultured in Dulbecco’s modified Eagle medium ( Glutamax , Gibco ) supplemented with 10% fetal calf serum , 100 U/ml penicillin , and 100 µg/ml streptomycin . The HEK293-t-rex-flpIn stable cell lines with doxycycline-inducible wild type and mutant forms of LRRK2 have been described previously ( Nichols et al . , 2010 ) . Transient transfections were performed 36-48 hr prior to cell lysis using Lipofectamine 2000 ( Life Technologies ) or FuGene HD ( Promega ) . LRRK2 expression in HEK293-t-rex-flpIn was induced by doxycycline ( 1 μg/ml , 24 hr ) . All cells were tested for mycoplasma contamination and overexpressing lines were verified by Western blot analysis . For HA-Rab immunoprecipitations , HA-agarose ( Sigma ) was washed 3 times with PBS and incubated with lysates at a concentration of 25 μl of resin/mg lysate for 1 hr . Beads were then washed twice with 1 ml PBS and samples eluted in 2 x LDS ( 50 µl per 25 μl of resin ) and centrifuged through a 0 . 22 μm Spinex filter , 2-mercpatoethanol added to 2% ( v/v ) and heated to 70oC for 5 min prior to SDS-PAGE . For GFP pulldowns and immunoprecipitations , cells were lysed in ice-cold NP-40 extraction buffer ( 50 mM Tris-HCl , pH 7 . 5 , 120 mM NaCl , 1 mM EDTA , 6 mM EGTA , 15 mM sodium pyrophosphate and 1% NP-40 supplemented with protease and phosphatase inhibitors ( Roche ) and clarified by centrifugation at 14 , 000 rpm . Supernatants were incubated over night with Rab8 or Rab10 antibodies and bound complexes recovered using agarose protein A/G beads ( Pierce ) . For GFP pull-downs , lysates were incubated with GFP beads for 2 hr ( Chromotek ) . On bead digestion of protein complexes used for MS analysis was performed as described previously ( Hubner et al . , 2010 ) . For subcellular fractionation , SILAC ( Ong , 2002 ) labeled HEK293 cells were counted and mixed in a 1:1 ratio after harvesting in PBS , spun at 1000 rpm at 4oC for 5 min and then resuspended in subcellular fractionation buffer ( 250 mM sucrose , 20 mM HEPES pH 7 . 4 , 10 mM KCl , 1 . 5 mM MgCl2 , 1 mM EDTA , 1 mM EGTA and protease/phosphatase inhibitor cocktail [Roche] ) . Cells were then Dounce homogenized , left on ice for 20 min and spun at 750 g for 5 min . The supernatant spun in an ultracentrifuge ( 100 , 000 g ) for 45 min to obtain cytosolic ( supernatant ) and membrane ( pellet ) fractions . Phos-tag acrylamide and MnCl2 were added to a standard gel solution at a final concentration of 50 μM and 100 μM , respectively . After degassing for 10 min , gels were polymerized by ammonium persulfate and TEMED . Cell lysates used for Phos-tag SDS-PAGE were supplemented with MnCl2 at 10 mM to mask the effect of EDTA in the lysates . After SDS-PAGE , gels were washed 3 times with transfer buffer containing 10 mM EDTA followed by a wash with transfer buffer ( 10 min each ) . Blotting to nitrocellulose membranes was carried out according to a standard protocol . All samples were lysed in SDS lysis buffer ( 4% SDS , 10 mM DTT , 10 mM Tris pH 7 . 5 ) , boiled and sonicated , and precipitated overnight using ice-cold acetone ( v/v= 80% ) . After centrifugation ( 4000 g ) , the pellet was washed at least twice with 80% ice-cold acetone before air drying and resuspension ( sonication ) in either urea ( 6 M urea , 2 M thiorurea , 50 mM Tris pH 8 ) or TFE buffer ( 10% 2-2-2-trifluorethanol , 100 mM ammonium bicarbonate [ABC] ) . Proteins were digested using LysC and trypsin ( 1:100 ) , overnight at 37°C . Peptides for total proteome measurements were desalted on C18 StageTips and phosphopeptides were enriched as described previously ( Humphrey et al . , 2015 ) . Peptides were loaded on a 50 cm reversed phase column ( 75 µm inner diameter , packed in-house with ReproSil-Pur C18-AQ 1 . 9 µm resin [Dr . Maisch GmbH] ) . Column temperature was maintained at 50°C using a homemade column oven . An EASY-nLC 1000 system ( Thermo Fisher Scientific ) was directly coupled online with a mass spectrometer ( Q Exactive , Q Exactive Plus , Q Exactive HF , LTQ Orbitrap , Thermo Fisher Scientific ) via a nano-electrospray source , and peptides were separated with a binary buffer system of buffer A ( 0 . 1% formic acid [FA] ) and buffer B ( 80% acetonitrile plus 0 . 1% FA ) , at a flow rate of 250nl/min . Peptides were eluted with a gradient of 5-30% buffer B over 30 , 95 , 155 , or 240 min followed by 30-95% buffer B over 10 min , resulting in approximately 1 , 2 , 3 , or 4 hr gradients , respectively . The mass spectrometer was programmed to acquire in a data-dependent mode ( Top5–Top15 ) using a fixed ion injection time strategy . Full scans were acquired in the Orbitrap mass analyzer with resolution 60 , 000 at 200 m/z ( 3E6 ions were accumulated with a maximum injection time of 25 ms ) . The top intense ions ( N for TopN ) with charge states ≥2 were sequentially isolated to a target value of 1E5 ( maximum injection time of 120 ms , 20% underfill ) , fragmented by HCD ( NCE 25% , Q Exactive ) or CID ( NCE 35% , LTQ Orbitrap ) and detected in the Orbitrap ( Q Exactive , R= 15 , 000 at m/z 200 ) or the Ion trap detector ( LTQ Orbitrap ) . Raw MS data were processed using MaxQuant version 1 . 5 . 1 . 6 or 1 . 5 . 3 . 15 ( Cox and Mann , 2008; Cox et al . , 2011 ) with an FDR < 0 . 01 at the level of proteins , peptides and modifications . Searches were performed against the Mouse or Human UniProt FASTA database ( September 2014 ) . Enzyme specificity was set to trypsin , and the search included cysteine carbamidomethylation as a fixed modification and N-acetylation of protein , oxidation of methionine , and/or phosphorylation of Ser , Thr , Tyr residue ( PhosphoSTY ) as variable modifications . Up to two missed cleavages were allowed for protease digestion , and peptides had to be fully tryptic . Quantification was performed by MaxQuant , ‘match between runs’ was enabled , with a matching time window of 0 . 5-0 . 7 min . Bioinformatic analyses were performed with Perseus ( www . perseus-framework . org ) and Microsoft Excel and data visualized using Graph Prism ( GraphPad Software ) or R studio ( https://www . rstudio . com/ ) . Hierarchical clustering of phosphosites was performed on logarithmized ( Log2 ) intensities . Significance was assessed using one sample t-test , two-sample student’s t-test , and ANOVA analysis , for which replicates were grouped , and statistical tests performed with permutation-based FDR correction for multiple hypothesis testing . Missing data points were replaced by data imputation after filtering for valid values ( all valid values in at least one experimental group ) . Error bars are mean ± SEM or mean ± SD . Proteomics raw data have been deposited to the ProteomeXchange Consortium ( http://proteomecentral . proteomexchange . org ) via the PRIDE partner repository with the data set identifier PXD003071 . LRRK2 kinase activity was assessed in an in vitro kinase reaction performed as described previously ( Nichols et al . , 2009 ) . For IC50 determination of LRRK2 inhibitor , peptide kinase assays were set up in a total volume of 30 μl with recombinant wild type GST-LRRK2- ( 1326-2527 ) or mutant GST-LRRK2[A2016T]- ( 1326-2527 ) ( 6 nM ) in 50 mM Tris-HCl pH 7 . 5 , 0 . 1 mM EGTA , 10 mM MgCl2 , 0 . 1 mM [γ-32P]ATP ( ~300-600 cpm/pmol ) , and 20 μM Nictide LRRK2 substrate peptide substrate , in the presence of indicated concentration of MLI-2 . After incubation for 20 min at 30°C , reactions were terminated by applying 25 μl of the reaction mixture onto P81 phosphocellulose papers and immersion in 50 mM phosphoric acid . After extensive washing , reaction products were quantified by Cerenkov counting . IC50 values were calculated using non-linear regression analysis using GraphPad Prism ( GraphPad Software ) . The IC50s for GSK2578215A ( Reith et al . , 2012 ) against wild type GST-LRRK2- ( 1326-2527 ) or mutant GST-LRRK2[A2016T]- ( 1326-2527 ) are 10 . 9 nM and 81 . 1 nM , respectively , and the IC50s for HG-10-102-01 ( Choi et al . , 2012 ) vs LRRK2 WT and A2016T are 20 . 3 nM and 153 . 7 nM , respectively . The IC50 of MLI-2 against wild type GST-LRRK2- ( 1326-2527 ) or mutant GST-LRRK2[A2016T]- ( 1326-2527 ) are 0 . 8 nM and 7 . 2 nM ( see Extended Data Figure 1A ) . As MLI-2 displayed greater potency as well as a higher degree of resistance between wild type and A2016T mutation ( 9-fold compared to 7 . 4-fold for GSK2578215A and 7 . 6-fold for HG-10-102-01 ) , we used MLI-2 for MS studies employing LRRK2[A2016T] knock-in MEFs . Purified Rab1b and Rab8a ( 5 μg ) were phosphorylated using recombinant full-length wild type Flag-LRRK2 ( 0 . 2 μg; Invitrogen ) in a buffer containing 50 mM Tris-HCl pH 7 . 5 , 0 . 1 mM EGTA , 10 mM MgCl2 , 0 . 1 mM [γ-32P]ATP ( ~3000 Ci/pmol ) for 1 hr at 30°C . The reactions were stopped by the addition of SDS sample buffer , and reaction products were resolved by electrophoresis on SDS-PAGE gels that were stained with Coomassie blue . The band corresponding to Rab1b/Rab8a was excised and digested overnight with trypsin at 30°C , and the peptides were separated on a reverse-phase HPLC Vydac C18 column ( Separations Group ) equilibrated in 0 . 1% ( v/v ) trifluoroacetic acid , and the column developed with a linear acetonitrile gradient at a flow rate of 0 . 2 ml/min . Fractions ( 0 . 1 ml each ) were collected and analyzed for 32P radioactivity by Cerenkov counting . Phosphopeptides were analyzed by liquid chromatography ( LC ) -MS/MS using a Thermo U3000 RSLC nano liquid chromatography system ( Thermo Fisher Scientific ) coupled to a Thermo LTQ-Orbitrap Velos mass spectrometer ( Thermo Fisher Scientific ) . Data files were searched using Mascot ( www . matrixscience . com ) run on an in-house system against a database containing the appropriate Rab sequences , with a 10 ppm mass accuracy for precursor ions , a 0 . 6 Da tolerance for fragment ions , and allowing for Phospho ( ST ) , Phospho ( Y ) , Oxidation ( M ) , and Dioxidation ( M ) as variable modifications . Individual MS/MS spectra were inspected using Xcalibur 2 . 2 ( Thermo Fisher Scientific ) , and Proteome Discoverer with phosphoRS 3 . 1 ( Thermo Fisher Scientific ) was used to assist with phosphosite assignment . The site of phosphorylation of 32P-labeled peptides was determined by solid-phase Edman degradation on a Shimadzu PPSQ33A Sequencer of the peptide coupled to Sequelon-AA membrane ( Applied Biosystems ) as described previously ( Campbell and Morrice , 2002 ) . Rab8a was phosphorylated using LRRK2-G2019S ( see ‘in vitro kinase assays’ section ) . Non-phosphorylated and phosphorylated Rab8a proteins were separated using ion-exchange chromatography ( Mono S 4 . 6/100; GE Healthcare ) with a linear salt gradient from buffer A ( 20 mM Tris/HCl pH 7 . 5 , 50 mM NaCl , 10% ( v/v ) glycerol ) to buffer B ( as buffer A , but with 1000 mM NaCl ) . The successful enrichment of phosphorylated Rab8a was confirmed by ESI-TOF MS . Rab8a ( 1-183 , wt and T72E ) were subjected to HPLC revealing that the purified proteins were ( >90% ) in the nucleotide-free form . To determine affinities for G-nucleotides , fluorescence measurements were carried out at 20°C in a buffer containing 50 mM Tris pH 7 . 5 , 100 mM NaCl , and 5 mM MgCl2 . Spectra were measured with a PerkinElmer LS50B fluorescence spectrophotometer; 1 µM of methylanthraniloyl ( mant ) labeled GMPPNP and GDP was incubated with increasing concentrations of wild type and T72E Rab8a in 60 µl volumes . Fluorescence of mant-nucleotides was excited at 355 nm and emission spectra monitored from 400 to 500 nm , with emission maxima detected at 448 nm . Intrinsic protein fluorescence and mant-nucleotide background fluorescence was subtracted from the curves . Data collection was performed with the program FL WinLab ( PerkinElmer ) , while further analysis , curve fitting and dissociation constant ( Kd ) determination was done using GraphPad Prism ( GraphPad Software ) . Figure 5E: Purified Rab8a ( 100 μg ) was phosphorylated using LRRK2 G2019S ( 1 . 5 μg ) in a buffer containing 50 mM Tris-HCl pH 7 . 5 , 0 . 1 mM EGTA , 10 mM MgCl2 , 2 mM DTT , 1 mM ATP ( 18 hr , room temperature [RT] ) in a Dispo-Biodialyzer MWCO 1 kDa ( Sigma-Aldrich ) and incubated in 2 l of the same Buffer to allow for ADP exchange . The buffer was subsequently exchanged to a GDP dissociation assay buffer containing 20 mM HEPES-NaOH pH 7 . 5 , 50 mM NaCl , 2 mM DTT , 1 mM MnCl2 , 0 . 01% ( w/v ) Brij-35 using Zeba Spin desalting columns ( Invitrogen ) . Phosphorylated Rab8a ( 50 μg ) was treated with lambda phosphatase ( 5 μg ) for 30 min at 30°C where indicated . To load mant-GDP , Rab8a was incubated with 40 μM mant-GDP in the presence of 5 mM EDTA at 30°C for 30 min . After adding MgCl2 at 10 mM , in order to remove unbound mant-GDP , the buffer was exchanged to a buffer containing 10 mM HEPES-NaOH pH 7 . 5 , 50 mM NaCl , 5 mM DTT , 1 mM MgCl2 using Zeba Spin desalting columns . GDP dissociation reactions were set up in a total volume of 50 μl with 1 μM Rab8a:mant-GDP in 20 mM HEPES-NaOH pH 7 . 5 , 50 mM NaCl , 2 mM DTT , 1 mM MgCl2 , and 0 . 1 mM GDP , and the reaction was started by adding the indicated concentration of Rabin8 ( residues 153-237 ) ( Guo et al . , 2013 ) . Kinetic measurement of the mant fluorescence was carried out in a black half-area 96-well plate with PHERAStar FS ( BMG Labtech ) at RT using a set of filters ( excitation: 350 nm , emission: 460 nm ) . The observed rate constant ( kobs ) and the catalytic efficiency ( kcat/Km ) were calculated as described previously ( Delprato et al . , 2004 ) . Phosphorylation stoichiometry ( 63% ) was calculated by digestion of the protein with trypsin and analyzing the fragments by Orbitrap MS . Figure 5D: Phosphorylated Rab8a was obtained as described in section ‘Phosphorylation of Rab8a’ . GEF assays were performed as described previously ( Eberth and Ahmadian , 2009 ) . Loading of purified nucleotide-free ( both phosphorylated and non-phosphorylated ) Rab8a ( 1-183 ) with 2’ ( 3’ ) -O- ( N-methylanthraniloyl ) -GDP ( mantGDP ) was achieved by incubation with an 1 . 5 molar excess of mantGDP for 2 hr at RT . Unbound mantGDP was removed using a size-exclusion chromatography column . ( Micro Bio-Spin column , Bio-RAD ) . The nucleotide exchange reactions were set up in a total volume of 50 μl in a quartz-glass cuvette ( Hellma Analytics ) with 0 . 5 μM mantGDP-bound Rab8a ( non-phosphorylated or phosphorylated ) using a GEF buffer containing 30 mM Tris pH 7 . 5 , 5 mM MgCl2 , 3 mM DTT and 10 mM KH2PO4 , pH 7 . 4 . Purified Rabin8 ( 144-245 , GEF domain ) was subjected to size exclusion chromatography prior to GEF activity assay to ensure no loss of GEF activity due to storage . Rabin8 was added to a final concentration of 2 μM and incubated for 30 min at 20°C . The reactions were initiated by addition of GTP ( 1 mM cf ) . The dissociation of mant-GDP from Rab8a was monitored every 2 s for a total of 300 s at 20°C using a fluorescence spectrometer ( PerkinElmer , 366 nm excitation and 450 nm emission ) . The observed rate constants ( kobs ) were calculated by fitting the data into a one-phase exponential decay equation without constraints using nonlinear regression in GraphPad Prism ( GraphPad Software Inc ) . Ni2+-NTA beads were pre-equilibrated with buffer containing PBS pH 7 . 4 , 30 mM imidazole and 5 mM MgCl2 . Purified HIS-tagged Rabin8 ( residues 144-460 ) and untagged Rab8a ( 1-183 ) WT or quantitatively phosphorylated pT72 were mixed at equal molar ratios . Individual proteins and a mixture of the proteins were incubated with Ni2+-NTA beads for 1 . 5 hr at 4°C . Beads were washed 3 times with PBS , bound proteins eluted with 500 mM imidazole followed by SDS–PAGE and western blot analysis . | Parkinson’s disease is a degenerative disorder of the nervous system that affects approximately 1% of the elderly population . Mutations in the gene that encodes an enzyme known as LRRK2 are the most common causes of the inherited form of the disease . Such mutations generally increase the activity of LRRK2 and so drug companies have developed drugs that inhibit LRRK2 to prevent or delay the progression of Parkinson’s disease . However , it was not known what role LRRK2 plays in cells , and why its over-activation is harmful . Steger et al . used a 'proteomics' approach to find other proteins that are regulated by LRRK2 . The experiments tested a set of newly developed LRRK2 inhibitors in cells and brain tissue from mice . The mice had mutations in the gene encoding LRRK2 that are often found in human patients with Parkinson’s disease . The experiments show that LRRK2 targets some proteins belonging to the Rab GTPase family , which are involved in transporting molecules and other 'cargoes' around cells . Several Rab GTPases are less active in the mutant mice , which interferes with the ability of these proteins to correctly direct the movement of cargo around the cell . Steger et al . ’s findings will help to advance the development of new therapies for Parkinson’s disease . The next challenges are to identify how altering the activity of Rab GTPases leads to degeneration of the nervous system and how LRRK2 inhibitors may slow down these processes . | [
"Abstract",
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"Results",
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"methods"
] | [
"biochemistry",
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"chemical",
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] | 2016 | Phosphoproteomics reveals that Parkinson's disease kinase LRRK2 regulates a subset of Rab GTPases |
The coordinated control of Ca2+ signaling is essential for development in eukaryotes . Cyclic nucleotide-gated channel ( CNGC ) family members mediate Ca2+ influx from cellular stores in plants ( Charpentier et al . , 2016; Gao et al . , 2016; Frietsch et al . , 2007; Urquhart et al . , 2007 ) . Here , we report the unusual genetic behavior of a quantitative gain-of-function CNGC mutation ( brush ) in Lotus japonicus resulting in a leaky tetrameric channel . brush resides in a cluster of redundant CNGCs encoding subunits which resemble metazoan voltage-gated potassium ( Kv1-Kv4 ) channels in assembly and gating properties . The recessive mongenic brush mutation impaired root development and infection by nitrogen-fixing rhizobia . The brush allele exhibited quantitative behavior since overexpression of the cluster subunits was required to suppress the brush phenotype . The results reveal a mechanism by which quantitative competition between channel subunits for tetramer assembly can impact the phenotype of the mutation carrier .
Lotus japonicus Gifu ( wild-type , accession B-129 ) ( Handberg and Stougaard , 1992 ) , Miyakojima ( accession MG-20 ) ( Kawaguchi et al . , 2001 ) and brush ( EMS mutant SL0979-2 , Gifu ) ( Perry et al . , 2003 ) plants were used . The BRUSH TILLING line SL1484-1 was obtained from the L . japonicus TILLING facility ( John Innes Centre , Norwich , UK ) . The seed bag numbers of critical lines are listed in Supplementary file 3 . Seeds were scarified with sandpaper , sterilized for 10 min in 4% sodium hypochlorite , and imbibed overnight in sterile water at 4°C . Hairy roots were generated using the Agrobacterium rhizogenes strain AR1193 ( Stougaard et al . , 1987 ) . Nodulation experiments were carried out by inoculating plants grown in pots or weck jars containing a sand-vermiculite mixture and Fåhraeus ( Fahraeus , 1957 ) media with Mesorhizobium loti MAFF303099 expressing DsRed ( Markmann et al . , 2008 ) . Transgenic roots were visualized with either a stereomicroscope ( Leica M165FC ) or confocal laser scanning microscope ( Leica SP5 ) . Hairy roots were stained for GUS and sectioned as described previously ( Chiasson et al . , 2014 ) . Plants were cultivated in growth cabinets at 22°C ( 16 hr light/8 hr dark ) . All complementation and GUS experiments were carried out a minimum of three times and displayed similar results . Crossings were performed as described previously ( Jiang and Gresshoff , 1997 ) . Primers and plasmids used for all experiments are listed in Supplementary file 1 and Supplementary file 2 , respectively . F2 plants from a cross between brush and MG-20 were used for fine mapping using SSR markers as described ( Groth et al . , 2013 ) . Primer sequences were obtained from the Kazusa DNA Research Institute website ( http://www . kazusa . or . jp/lotus/markerdb_index . html ) . The region was further refined using identified SNPs . The brush target interval between TM2432 and SNP3 ( approximately 103 kb ) was sequenced by Sanger sequencing . The brush genome was also reassembled after next-generation sequencing to identify mutant-specific polymorphisms . Nuclear DNA ( see below ) of brush seedlings was subjected to next-generation sequencing at Eurofins MWG , Germany , using an Illumina HiSeq 2000 ( Illumina , USA ) with a read length of 2 × 100 bp . Genes in the brush target region were annotated after sequencing using Genscan ( Burge and Karlin , 1997 ) and Artemis ( Rutherford et al . , 2000 ) . CLC Genomics Workbench ( CLC bio , Denmark ) was used to analyze the sequencing data . Four-week-old brush seedlings were transferred to the dark for 2 days before leaf material was harvested . Approximately 2 g of ground powder was resuspended in 20 ml ice-cold HB buffer ( 10 mM Tris , 80 mM KCl , 10 mM EDTA , 1 mM spermine , 1 mM spermidine , 0 . 5 M sucrose , 0 . 5% triton X-100 , 0 . 15% β-mercaptoethanol , pH 9 . 4 with NaOH ) by gentle shaking on ice . The solution was filtered through two layers of Miracloth ( Calbiochem , Merck , Germany ) . The flow-through was transferred to a 15-ml Falcon tube and the nuclei were pelleted at 4°C by centrifugation ( 1800 x g ) and washed two times by resuspension in HB buffer . The final pellet was resuspended in 500 μl CTAB buffer ( 55 mM cetyltrimethylammonium bromide , 1 . 4 M NaCl , 20 mM EDTA , 100 mM Tris , pH 8 ) , and incubated at 60°C for 30 min . 500 μl chloroform:isoamylalcohol ( 24:1 ) was added and mixed by inverting the tube several times . After a centrifugation step at 8000 x g ( 4°C ) for 10 min , the upper phase was transferred to a new tube . 5 μl of RNase ( 10 mg/ml stock concentration ) was added and incubated at 37°C for 30 min . 0 . 6 volumes ice-cold isopropanol was added and mixed by inverting the tube several times . The nuclear DNA was then precipitated at −20°C overnight and centrifuged for 10 min at 16 , 000 x g and 4°C . The supernatant was discarded and the pellet was washed with 70% ethanol and resuspended in 55 μl TE buffer . Yeast two-hybrid interaction assays were conducted with the haploid yeast strain AH109 ( Clontech ) . Split ubiquitin interaction assays were carried out in the haploid strain THY . AP4 ( Obrdlik et al . , 2004 ) . THY . AP4 and plasmids for split-ubiquitin were obtained from the Arabidopsis Biological Resource Center ( http://abrc . osu . edu/ ) . Plasmids used for both interaction assays are shown in Supplementary file 2 . Bait and prey plasmids were introduced via double transformation using the lithium acetate method ( Gietz and Schiestl , 2007 ) and selected on media lacking leucine and tryptophan ( -LW ) . The interacting protein pair of CCaMK and CYCLOPS was used as a control for yeast two-hybrid ( Yano et al . , 2008 ) . Positive transformants were restreaked on -LW , then used to inoculate overnight cultures in liquid -LW media . Overnight cultures were diluted to OD600 of 0 . 5 in sterile water and diluted 10-fold . 5 μl was spotted on –LW or solid media lacking leucine , tryptophan , adenine , and histidine ( -LWAH ) . Yeast plates were incubated at 28°C for 3–5 days . All interaction assays were independently conducted a minimum of three times . BRUSH and brush coding sequences were cloned for Xenopus expression with a custom Golden Gate cloning strategy using a modified backbone obtained from the Standard European Vector Architecture 2 . 0 database ( Martinez-Garcia et al . , 2015 ) . The backbone ( with flanking bacterial transcriptional terminators ) was derived from pSEVA191 ( http://wwwuser . cnb . csic . es/~seva/ ) and was chosen to alleviate toxicity issues uncovered while cloning CNGC . IVA sequences into pUC-based Golden Gate backbones and pGEMHE ( Liman et al . , 1992 ) . A ccdB cassette compatible with Golden Gate cloning ( Binder et al . , 2014 ) was amplified and inserted into the AvrII/SacI sites of pSEVA191 to create the LII backbone pSEVA191 1–2 . The coding sequences of BRUSH and brush were then combined in a BsaI cut-ligation with modules containing the T7 promoter as well as the 5’UTR and 3’UTR sequences of β-globin mRNA ( amplified from pEMHE ) . The same backbone was used to express the constructs for BiFC analysis , where LI Golden Gate B-C or D-E parts encoding for the N-terminal ( VN ) or C-terminal ( VC ) portions of mVenus ( Offenborn et al . , 2015 ) were inserted . Plasmids were assembled in a 15 µl reaction containing 100 ng of each LI plasmid and backbone , 1 . 5 µl CutSmart buffer ( NEB , Germany ) , 1 . 5 µl 10 mM ATP , 0 . 75 µl BsaI ( NEB ) , 0 . 75 µl T4 ligase ( NEB ) . The reaction was then cycled 6 times ( 10 min at 37°C , 10 min 16°C ) in a PCR machine , followed by incubation at 37°C ( 10 min ) and 65°C ( 20 min ) . Capped RNA ( cRNA ) synthesis , oocyte injection , and voltage-clamp recordings were performed as described ( Becker et al . , 2004; Müller-Röber et al . , 1995 ) . cRNA was synthesized with a mMESSAGE mMACHINE T7 Transcription Kit ( ThermoFisher , Germany ) and oocytes were injected ( General Valve Picospritzer III , Parker Hannifin Corp . ) with approximately 25 ng cRNA or with RNase-free water as a control . Injected oocytes were stored at 18°C in ND96 solution ( 96 mM NaCl , 2 mM KCl , 1 mM CaCl2 , 1 mM MgCl2 , 5 mM HEPES , 10 mM sorbitol , pH 7 . 4 with NaOH ) adjusted to 220 mOsm/L with sorbitol and supplemented with 25 µg/ml gentamycin until use . Measurements were recorded 2 to 3 days after injection using the two-electrode voltage-clamp technique with a Turbo Tec-10Cx amplifier ( NPI electronic GmbH ) . During two-electrode voltage clamp measurements , oocytes were constantly perfused with bath solution composed of 30 mM CaCl2 , 10 mM MES-Tris pH 7 . 4 , adjusted to 220 mOsm/L with mannitol and supplemented with either 100 µM 8-Bromo-cAMP ( Sigma ) or 100 µM 8-CPT-cAMP ( BioLog ) . For analysis of channel permeabilities , CaCl2 was exchanged as indicated in the figure legends with 5 mM CaCl2 , 15 mM CaCl2 , or 60 mM KCl . Starting from a holding potential of −40 mV , voltage steps from +60 to −160 mV in 20 mV increments were applied ( PatchMaster , HEKA Electronics Inc . ) . For localization , YFP was fused to the C-terminus of BRUSH or brush . Oocytes were imaged by confocal microscopy 2 to 3 days after injection with BRUSH-YFP and brush-YFP cRNA ( Leica TCS SP5 , excitation: 488 nm , detection: 525–575 nm ) to confirm expression . The same protocol was used for BiFC experiments , except that cRNAs were mixed 1:1 prior to injection . For analysis of gene expression after rhizobial inoculation , Lotus japonicus Gifu seeds were germinated and grown on half-strength B5 agar plates for 14 days . Six plants were planted per weck jar containing sand/vermiculite with Fåhraeus media . After 7 days , root tissue from a single jar was collected and pooled ( represents a biological replicate ) for the Day 0 time point . Mesorhizobium loti MAFF303099 expressing DsRed was added to the remaining jars and tissue was collected in the same manner after 12 days . To analyze gene expression after RNAi , positive hairy roots were isolated from individual plants 6 weeks after inoculation with Mesorhizobium loti MAFF303099 DsRed . For both experiments , root tissue was ground in liquid nitrogen and RNA was extracted with a Spectrum Plant Total RNA Kit ( Sigma ) . Genomic DNA was removed using a Turbo DNA-free Kit ( Ambion ) and total RNA ( 1 μg for the time course and 200 ng for RNAi ) was used for cDNA synthesis with Superscript III ( ThermoFisher ) . cDNA was then checked for genomic DNA contamination by PCR . Expression of CNGC . IVA cluster genes after rhizobia inoculation was analyzed by qPCR using SYBR Select Master Mix ( Applied Biosystems ) with a CFX96 real-time PCR machine . brush expression after RNAi was analyzed by qPCR using mi-real-time EvaGreen Master Mix ( Metabion ) with a QuantStudio 5 Real-Time PCR System ( ThermoFisher ) . In both cases , the plotted data point for each biological replicate represents the mean of three technical replicates . The relative expression was calculated with the 2-ΔΔCT method ( Schmittgen and Livak , 2008 ) using eEF-1Aα ( GenBank: BP045727 ) as the reference . Arabidopsis thaliana protein sequences were obtained from The Arabidopsis Information Resource ( TAIR ) . A multiple sequence alignment was generated using MUSCLE in CLC Main Workbench ( CLC bio , Denmark ) . A Maximum Likelihood phylogenetic tree was calculated using UPGMA ( 100 bootstrap iterations were performed ) . One-way ANOVA statistical analysis of data followed by a post-hoc Tukey’s multiple comparisons test and t-tests were calculated using GraphPad Prism . | Plants constantly monitor and respond to changes in their environment . Central to this surveillance system is the movement of calcium ions into and out of cells . Calcium ions are normally kept at very low levels inside of cells and subtle changes in these levels relay information about the external environment . In the case of plant roots , changes in the concentration of calcium ions herald essential information about soil conditions and the presence of microorganisms , and in turn trigger appropriate responses . Calcium ion signals are essential for peas , beans and other members of the legume family to form close relationships ( known as symbioses ) with soil bacteria called rhizobia . As such , many studies of calcium signalling have focused on root symbioses , particularly in a model legume called Lotus japonicus . Previous studies have identified one mutant version of this plant , called brush , which develops abnormal roots with brush-like arrays of root hairs near the tip . The brush mutant was also unable to form a symbiosis with rhizobia , and structures that allow the bacteria to enter the plant stopped developing before they were complete . However , the gene responsible had not been identified . Chiasson , Haage et al . set out to identify the responsible mutation . At first the brush mutation escaped identification because a key experiment gave an unexpected result . The introduction of a normal , or wild type , copy of the proposed gene – referred to as BRUSH – into the brush mutant did not correct the problems with its roots . Further analysis revealed that it was actually the ratio between BRUSH and brush expression levels that was critical for determining how the plant’s roots developed . The mutation in brush causes a small change in a protein belonging to the CNGC family . These proteins act as channels and allow ions to move across cell membranes . Further experiments found that the channel formed by the mutated CNGC protein is leaky and allows calcium ions to enter the cell in the absence of any cue from the environment . The leaky entry of calcium ions likely confuses the plant’s surveillance system , which disturbs the normal development of the root . It is also likely that the brush mutation’s effects on calcium signaling also interfere with the entry of rhizobia into the roots . These findings provide important insights into the function of CNGCs and reveal how a small change in a channel protein can have far reaching effects on an organism . | [
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] | 2017 | A quantitative hypermorphic CNGC allele confers ectopic calcium flux and impairs cellular development |
Accurate lineage reconstruction of mammalian pre-implantation development is essential for inferring the earliest cell fate decisions . Lineage tracing using global fluorescence labeling techniques is complicated by increasing cell density and rapid embryo rotation , which hampers automatic alignment and accurate cell tracking of obtained four-dimensional imaging data sets . Here , we exploit the advantageous properties of primed convertible fluorescent proteins ( pr-pcFPs ) to simultaneously visualize the global green and the photoconverted red population in order to minimize tracking uncertainties over prolonged time windows . Confined primed conversion of H2B-pr-mEosFP-labeled nuclei combined with light-sheet imaging greatly facilitates segmentation , classification , and tracking of individual nuclei from the 4-cell stage up to the blastocyst . Using green and red labels as fiducial markers , we computationally correct for rotational and translational drift , reduce overall data size , and accomplish high-fidelity lineage tracing even for increased imaging time intervals – addressing major concerns in the field of volumetric embryo imaging .
Accurate lineage tracing and precise tracking of single cells in pre-implantation embryos are essential for a mechanistic understanding of the first cell fate decisions during mammalian development ( Welling et al . , 2016; Pantazis and Bollenbach , 2012 ) . Selective plane illumination microscopy ( SPIM ) has the potential to play a major role in achieving comprehensive , non-invasive imaging of mammalian pre-implantation development . During these early steps of development , a major fraction of embryos ( n = 9/19 , 45% in this study ) exhibit confounding rotational and spatial drift ( Videos 1 , 2 and 3 ) , which often leads researchers to exclude these embryos from analysis , drastically decreasing efficiency , losing valuable data , and potentially biasing downstream results ( Strnad et al . , 2016; Motosugi et al . , 2005 ) . While high-imaging rates have helped to overcome these challenges for samples like zebrafish embryos , they demand increased data storage capacities . Moreover , higher frame rates increase photodamage from laser overexposure and are hence less applicable for highly sensitive mouse embryos ( Strnad et al . , 2016; Takenaka et al . , 2007 ) . Labeling strategies using green-to-red photoconvertible fluorescent proteins ( pcFPs ) allow for visualization of both the entire population of cells in green and a selected population in red . This combination of global and sparse labeling yields great potential for facilitating lineage tracing and trophectoderm ( TE ) and inner-cell-mass ( ICM ) fate assignments after photoconversion ( Kurotaki et al . , 2007 ) . However , to our knowledge these sparse labels have not been combined with SPIM - presumably because photoconversion has been limited by the need for axially unconfined , potentially photodamaging , intense violet light ( Post et al . , 2005 ) . Our recent report of a novel photochemical mechanism called “primed conversion” overcomes this long-standing problem by using dual-wavelength illumination with blue 488nm and far-red 730nm laser light instead ( see Mohr and Pantazis , 2018 ) for a review ) . Importantly , primed conversion allows for confined photoconversion of small volumes in three dimensions ( 3D ) by selectively intersecting the two laser beams in a common focal spot , yielding axial confinement unachievable by 405 nm photoconversion ( Dempsey et al . , 2015; Mohr et al . , 2016 ) . The discovery of the mechanism responsible for primed conversion enabled the rational engineering of primed convertible ( “pr-” ) variants of most pcFPs ( Mohr et al . , 2017; Turkowyd et al . , 2017 ) with improved brightness and photostability , essential properties for long-term imaging in a SPIM ( Mohr et al . , 2017 ) . Here , we show that primed conversion of single pr-pcFP-labeled cells in early stages of mouse development allows for computational correction of spatial and rotational drift , which minimizes uncertainties in tracking and lineage tracing . Accurate tracking is achievable even for larger imaging intervals further reducing laser exposure to the sensitive specimen .
Previously , we and others found that pr-pcFP variants based on the Eos-family of Anthozoa-derived pcFPs efficiently undergo primed conversion and exhibit high levels of photostability and brightness ( Mohr et al . , 2017; Turkowyd et al . , 2017 ) . In order to assess which protein of the Eos-family is most suitable for long-term cell tracking and lineage tracing experiments in mouse embryos , we directly compared pr-mEos2 and pr-mEosFP . We injected mouse zygotes with mRNAs encoding for the histone fusions H2B-pr-mEos2 or H2B-pr-mEosFP and imaged them at different stages to observe their developmental progression . Embryos injected with mRNA encoding for H2B-pr-mEosFP showed no visible signs of developmental impairment , similar to un-injected control embryos ( Figure 1—figure supplement 1a and 1b ) . In contrast , H2B-pr-mEos2-injected embryos showed partly divided , seemingly connected nuclei and prematurely arrested in development ( n=30/30 ) ( Figure 1—figure supplement 1b ) . This apparent inability to separate the nuclei during cell division is likely due to a residual tendency of mEos2 to oligomerize , as proposed previously ( Zhang et al . , 2012 ) . As a consequence , we identified primed convertible mEosFP ( pr-mEosFP ) as the optimal fluorescent protein variant for in vivo primed conversion in the mouse embryo followed by long-term imaging . Next , we investigated whether a single round of green-to-red photoconversion at the four-cell stage would create a sufficiently large pool of red-converted protein that could be followed throughout development until the blastocyst stage . For this purpose , we performed confined primed conversion in a confocal system as previously described ( Mohr et al . , 2016 ) to photoconvert a single nucleus of an H2B-pr-mEosFP expressing embryo at the four-cell stage . Primed converted embryos were then transferred and monitored for 60 hours during early embryo development in a custom built SPIM suitable for long term imaging of mouse embryos ( Figure 1a ) . To compensate for signal dilution of the H2B-pr-mEosFP signal over time primarily due to cell division , the laser power was gradually increased throughout the imaging sessions . Embryos subjected to photoconversion of a single cell developed normally and the red daughter cells of the initially primed converted cell were clearly distinguishable from non-converted green cells up to the blastocyst stage ( Figure 1b; Figure 1—figure supplement 2a ) . In addition , primed conversion itself did not impede the development of photoconverted embryos compared to non-converted control embryos ( Figure 1—figure supplement 2b ) . As cells converted at the four-cell stage can be visualized up to the blastocyst stage , we wondered whether such sparsely labeled subsets of cells could aid computational reorientation and automated lineage tracing in embryos that exhibit dramatic spatial and rotational drift ( Videos 1 , 2 and 3 ) . Of note , while we initially imaged our embryos with time intervals of 7 . 5 or 15 minutes , we found that increased sampling frequency did not recover successful lineage tracing for rotating embryos: the percentage of embryos showing spatial and rotational drift prohibitive of automated lineage tracing in our experiments with 5-minute imaging time intervals ( =50% , n=8 ) was similar to those imaged with larger time intervals ( =45% embryos imaged every 7 . 5 or 15 minutes , n=11 ) . To accomplish accurate tracking , we developed a computational pipeline , referred to as “primed Track” , for automated segmentation , cell tracking , and lineage tracing . Primed Track uniquely takes advantage of the sparse red cell population to correct for spatial and rotational drift as well as to simplify lineage reconstruction ( Figure 2a ) . In the 5-dimensional ( 5D , that is 3 spatial dimensions , time , color ) imaging data , cells were first segmented based on size , shape , and fluorescence taking into account both color channels . The use of increasing laser power to compensate for red signal dilution mainly due cell division resulted however in increasing background fluorescence . To discern red signal from increasing background signal , we took advantage of the dual nuclear labeling that allowed us to identify weaker fluorescent red nuclei at advanced time points by their overlap with the green signal in which lower autofluorescence was detected ( Figure 2a , left column ) . Background signal that was falsely segmented in the green channel due to increased illumination could be excluded by ignoring spots detected outside of a defined radius of the embryo . The ability to select parameters that match the brightness , size , and shape of the embryos combined with fluorescence information of two channels makes the segmentation both robust and flexible for use in different experiments . In addition , the dual color information allowed for cell distinction in instances otherwise rendered ambiguous through high cell density and proximity of nuclei . For instance , we were able to distinguish nuclei that would have been identified as a single nucleus even after manual validation ( Figure 2—figure supplement 1a-c ) . In a second step , the embryo was positioned at its fluorescence center of mass , cropped and rotated , such that the red center of mass was oriented to the same side of the embryo in every time frame to compensate for rotational and spatial drift ( Figure 2a , middle column; Videos 4 , 5 and 6 ) . The resulting high-quality 5D cropped and registered datasets were reduced to only 34±11% of the original size ( Figure 2—figure supplement 2 ) . The automatic tracking of a realigned embryo resulted in greatly improved lineage tracing fidelity compared to a naïve state-of-the-art lineage-tracing algorithm that was not able to reconstruct a lineage tree from rotating and spatially drifting embryos imaged with a time interval of 15 , 7 . 5 or 5 minutes ( Bitplane Imaris cell lineage package ) ( Figure 2b; Figure 2—figure supplement 2 ) . Of note , none of the existing state-of-the-art lineage tracing tools such as Ilastik , TrackMate and the TGMM software ( Amat et al . , 2014 ) were designed to compensate for heavy rotational and spatial drift and are therefore incapable of calculating lineage trees from these embryos ( Figure 2—source data 1 ) . Separating the green and red channels to generate two less complex datasets during lineage reconstruction further increased the fidelity of lineage tracing versus a dataset consisting of the green channel alone ( Figure 2a , right column; Figure 2—figure supplement 3 ) . We assessed the power of primed Track by comparing the lineage trees obtained i ) without corrections , ii ) after embryo realignment with all algorithmic corrections , and iii ) after final manual review by calculating the total distance between these lineage trees ( see Materials and methods for more details ) ( Zhang and Shasha , 1989 ) . We were able to recover all rotating embryos that we acquired using this image analysis pipeline and the resulting lineage trees required a minimal amount of time for manual corrections ( 0 . 5-1 . 5 hours per lineage tree ) . The observation that the registration of embryos based on the dual labeling with primed Track allows for reliable lineage tracing despite heavy embryo rotation suggests that decreasing the imaging frequency will have limited effect on lineage tracing ability . To test the robustness of primed Track , we removed time points from datasets from both rotating as well as non-rotating embryos to examine lineage tracing capacity at a decreased sampling rate . Naturally , larger imaging time intervals increase embryo displacement in consecutive time points and exacerbate the accurateness of lineage tracing ( Figure 3 ) . Using primed Track to correct for spatial and rotational drift results in reliable reconstruction of the lineage trees even at imaging time intervals of 30 minutes for a rotating embryo and 40 minutes for a non-rotating embryo ( Figure 3; Figure 3—figure supplement 1 ) . While the registration of non-drifting embryos does not increase lineage tracing accuracy for high 5-minute sampling rates , the gain of fidelity in lineage tree reconstruction of these embryos greatly benefits from our presented approach when the imaging frequency is reduced ( Figure 3 ) . However , in general , one should keep in mind that sampling rates above 40 minutes will decrease the possibility to precisely infer cell divisions and assign daughter cells to their correct mother . Still , the opportunity to reduce laser exposure while maintaining accurate tracking and lineage tracing potential offers a great advantage for long-term imaging experiments of sensitive specimen . In summary , primed Track enables fast , automated , high fidelity lineage tracing of mammalian pre-implantation development combined with reduced illumination time and data volume , key considerations for handling and analyzing data by the biological community ( Pantazis and Supatto , 2014 ) . A recently published study presents a compelling image analysis framework that enables the long-term tracking of cells during gastrulation and early organogenesis in the post-implantation embryo ( McDole et al . , 2018 ) . Primed Track complements such efforts by enabling accurate fate mapping of mouse pre-implantation embryos . The ability to correct for both spatial and rotational drift overcomes the previous requirement to exclude spinning embryos from the analysis using primed Track . Furthermore , primed conversion of photoconvertible proteins in combination with primed Track enables the experimenter to still achieve reasonable lineage tracing quality with datasets acquired at lower sampling rate . The timescales and intensities at which the fluorescent signal of photoconvertible proteins can be observed depend on the expression system ( i . e . stable vs . transient expression , promotor choice ) as well as the stability of the fusion protein . While we present tracking and lineage tracing of embryos labeled with a relatively highly-expressed and stable H2B-pr-pcFP fusion protein , it is important to take into consideration that low abundant protein fusions may require higher illumination power for visualization , potentially impacting sample integrity . Such cases will in particular benefit from our primed Track pipeline , as it facilitates imaging with longer time intervals while preserving high fidelity in cell tracking and lineage tracing . In the future , implementing primed conversion to take place inside a SPIM used for volumetric imaging will allow for repeated manual or automatic primed conversion of nuclei once the red fluorescence signal intensity drops below a user-defined threshold . Such pulse-chase experiments can then be extended even longer , ultimately being only limited by the rate of new green pr-pcFP synthesis . The combination of confined primed conversion of pr-pcFPs with primed Track will allow researchers to get more accurate insight into the dynamic processes responsible for cell fate decisions in the early mammalian embryo .
The coding sequences for pr-mEosFP and pCS2+-H2B-pr-EosFP were obtained by PCR amplification from pQE32-pr-mEosFP ( Addgene No . 99213 ) and pRSET-pr-mEos2 ( gift from Dominique Bourgeois ) and cloned into pCS2+-H2B-Dendra2 using AgeI and SnaBI , hence replacing the Dendra2 coding sequence to obtain pCS2+-H2B-pr-EosFP and pCS2+-H2B-pr-Eos2 . mRNA was synthesized using the mMESSAGE mMACHINE kit ( ThermoFisher Scientific ) , followed by poly-A-tailing ( ThermoFisher Scientific ) , and purified using a Qiagen RNAeasy kit according to manufacturer guidelines . C57Bl/6 wild-type females ( Janvier Labs , France ) were superovulated by hormone priming , mated to C57Bl/6 males ( RRID:IMSR_JAX:000664 ) , and mated females were euthanized by CO2 asphyxiation . Embryos were recovered by flushing oviducts as described previously ( Mohr et al . , 2016; Plachta et al . , 2011 ) . Embryos were cultured at 37°C and 5% CO2 in KSOM + AA medium covered with mineral oil . mRNA constructs were microinjected into the pro-nucleus at 50 ng/µl or in both cells in two-cell stage embryos , following standard protocols . All these experiments were approved by the veterinary authority of the canton Basel Stadt , Switzerland . Confined primed conversion of single nuclei was performed on mouse embryos at the four-cell stage as previously described in great detail ( Mohr et al . , 2016 ) . Right after confined primed conversion was performed , the four-cell stage embryos were transferred to a pre-equilibrated , custom built inverted SPIM setup suitable for long term imaging of mouse embryos and continuously cultured/imaged until they reached blastocyst stage . For each embryo , a z-stack consisting of 80 planes , 3 μm apart , was acquired every 5 , 7 . 5 or 15 min . To establish a reference , mouse embryos were lineage traced using the state-of-the-art Imaris lineage tracing package ( Bitplane , CH ) . The automated high-fidelity mouse embryo drift correction and lineage-tracing algorithm described here is explained in detail below . 5D movies of photoconverted mouse embryos were processed with the following pipeline using a custom MATLAB code implemented in Imaris ( Bitplane , CH ) . All codes of primed Track can be downloaded from this code repository: https://git . bsse . ethz . ch/scu_public/primed_track ( copy archived at https://github . com/elifesciences-publications/primed_track ) ( Ponti , 2018 ) . To assess the power of our newly created algorithm , we sought to compare the lineage trees obtained with i ) no corrections , ii ) after embryo realignment with all algorithmic corrections , and iii ) after final manual review . We quantified the effects of the corrections and validations on the quality of the lineage trees by calculating the total distance between the lineage trees using the implementation of the tree Zhang-Shasha edit distance algorithm ( Zhang and Shasha , 1989 ) by Tim Henderson and Steve Johnson ( Henderson and Johnson , 2013 ) . The zss algorithm assigns a ( user-defined ) cost for each node insertion , removal , and update necessary to transform an ordered tree into another , and gives therefore a quantitative measure of dissimilarity of the two trees . Small tracking differences between corrected and uncorrected trees , however , can result in quite large tree distances if the zss algorithm is applied to the complete trees . A correction that relinks one cell to its mother cell in just one time point causes the whole branch to be flagged as incorrect , and the longer the branch , the higher the distance between the trees . In other words , the earlier the tracking error occurs , the larger the distance; yet , only the first time point in the track is incorrect , and its penalty should be the same whether it happens at the beginning of the time series or the end . To circumvent these issues , we applied the algorithm to a condensed version of the lineage trees . The condensed tree retains only the branch points of the original lineage tree ( i . e . the cell divisions ) . Also , each branch point stores information about the original number of child nodes in its branches ( i . e . the number of time points the daughter cells were tracked until their next cell division ) . The distance between condensed trees will flag positions where cell divisions were tracked incorrectly and tracks that have different lengths , without causing an explosion in the reported distance . Since our acquisitions started at the four-cell stage , we aimed to build a tree for each of the original four cells ( one containing the progeny of the primed converted cell ) . The final , manually curated lineage was used as ground truth to quantify the effects of the various algorithmic correction steps . The sets of trees across correction schemes were assigned to each other by minimizing the spatial and temporal distance of their origins . After condensation , their pairwise distances were calculated . All distances were summed to give the total lineage tree difference . In addition , spurious trees that resulted from bad segmentation and tracking were not used for the distance calculation , since they already indirectly affected the difference of the tree from which they were erroneously detached . | A mouse embryo starts with one cell , which divides to create identical daughters that quickly start to multiply . Within three to four days , certain cells begin to specialize and take on specific roles . Scientists want to track these early events to understand how they give rise to an individual formed of huge numbers of cells organized in specialized tissues . To do so , researchers genetically manipulate embryos so that each cell produces fluorescent molecules that ‘glow’ under light . These embryos are grown inside a special microscope for several days . Images are taken regularly and then processed by specialized software that automatically tracks the fluorescent cells and their daughters over time . This helps reconstruct the history of each cell , and which structures they give rise to . However , many embryos move and turn around between images , and so software packages often lose track of which cell was which . Taking images more frequently is not possible because each imaging event exposes the embryo to light , which can damage its fragile cells . To address this problem , Welling , Mohr et al . made embryonic cells produce a special fluorescent marker , which is normally green but can be converted to red . Then , a technique known as primed conversion was used so that only one cell in a four-cell embryo would glow red . Welling , Mohr et al . designed a piece of software , baptized ‘primed Track’ , that can use this red cell ( and its daughters ) to reorient the embryo during image analysis and reliably identify and match any mother cell to its daughters . The new approach means the experiments require fewer imaging events , but also fewer embryos because even the ones that move a lot can be studied . This should help scientists look into how early life processes give rise to specialized cells , and even explore the fate of cells in other tissues . | [
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] | 2019 | Primed Track, high-fidelity lineage tracing in mouse pre-implantation embryos using primed conversion of photoconvertible proteins |
The RAS family of proteins is amongst the most highly mutated in human cancers and has so far eluded drug therapy . Currently , much effort is being made to discover mutant RAS inhibitors and in vitro screening for RAS-binding drugs must be followed by cell-based assays . Here , we have developed a robust set of bioluminescence resonance energy transfer ( BRET ) -based RAS biosensors that enable monitoring of RAS-effector interaction inhibition in living cells . These include KRAS , HRAS and NRAS and a variety of different mutations that mirror those found in human cancers with the major RAS effectors such as CRAF , PI3K and RALGDS . We highlighted the utility of these RAS biosensors by showing a RAS-binding compound is a potent pan-RAS-effector interactions inhibitor in cells . The RAS biosensors represent a useful tool to investigate and characterize the potency of anti-RAS inhibitors in cells and more generally any RAS protein-protein interaction ( PPI ) in cells .
RAS is the most prominent oncogene identified in cancer . Mutation in RAS proteins can be found in approximately 30% of all human tumors ( Downward , 2003; Prior et al . , 2012 ) ( http://cancer . sanger . ac . uk/cosmic ) prompting interest in the discovery of anti-RAS therapeutics . However , there are still no RAS-targeted drugs currently available in the clinic even though such molecules could prove widely efficacious in many human cancers as front-line drugs for therapy . Some forms of cancer , like pancreatic cancer , present late and are difficult therefore to treat ( Kleeff et al . , 2016 ) but these contain a high proportion of KRAS mutations and are thus potentially susceptible to RAS-binding drugs . RAS has been regarded as undruggable partly because so far attempts to interfere with the protein have not been efficacious ( Cox et al . , 2014 ) . RAS is a membrane-bound small GTPase switching between an inactive GDP-bound state and an active GTP-bound state . RAS signaling to the cell nucleus occurs after interaction of RAS-GTP with its effectors to trigger the activation of downstream signaling pathways . This activation thereby promotes cell survival and cell proliferation ( Wennerberg et al . , 2005 ) via gene modulation so that the blockade of mutant RAS signaling in tumors cells is an attractive therapeutic option . There are several ways in which this could be achieved ( Athuluri-Divakar et al . , 2016; Burns et al . , 2014; Spiegel et al . , 2014; Zimmermann et al . , 2013 ) but methods such as implementing farnesylation inhibitors have limited success due to side effects ( Berndt et al . , 2011; James et al . , 1995; Whyte et al . , 1997 ) . One avenue that has largely been avoided in inhibiting RAS is the interaction with its effectors , such as RAF , RALGDS and PI3K . However , the effectiveness of the orthosteric RAS-effector PPI inhibition was shown using intracellular antibodies ( Tanaka and Rabbitts , 2003; Tanaka et al . , 2007 ) ( herein called macrodrugs ( Tanaka and Rabbitts , 2008 ) to distinguish them from conventional small molecule drugs ) and a single domain intracellular antibody that blocks effector interaction sites of RAS-GTP . This PPI inhibition can prevent tumor growth in xenograft models and tumor initiation in a transgenic mouse model ( Tanaka and Rabbitts , 2010; Tanaka et al . , 2007 ) . Other macrodrugs , such as DARPins ( Guillard et al . , 2017 ) , have also been shown to be effective in interfering with RAS PPIs . Moreover , for many years , RAS was regarded as a protein without any pockets suitable for small molecule interactions ( McCormick , 2016 ) but recent studies have described compounds that are able to bind RAS-associated pockets ( Gentile et al . , 2017; Lito et al . , 2016; Maurer et al . , 2012; Ostrem et al . , 2013; Patricelli et al . , 2016; Shima et al . , 2013; Sun et al . , 2012; Waldmann et al . , 2004; Welsch et al . , 2017 ) . Most of the current RAS inhibitors have been selected and identified through in vitro techniques ( Ostrem et al . , 2013; Trinh et al . , 2016; Upadhyaya et al . , 2015; Welsch et al . , 2017 ) but cell-based assay technologies are needed to assess initial hits for efficacy before hit to lead development is undertaken . Indeed , a robust cell-based assay is a mandatory step in any drug discovery programme , as it provides insights into the behavior of compounds in physiological conditions , including cell permeability , stability and potency in the cellular complexity of a whole cell . We now describe a toolbox of mutant and wild-type RAS BRET-based biosensors that can be used to assess PPI between activated , GTP-bound RAS ( KRAS , HRAS or NRAS ) and effectors such as CRAF , RALGDS or PI3K in living cells . We validate the toolbox using a published anti-RAS intracellular domain antibody ( hereafter named iDAb RAS ) ( Tanaka et al . , 2007 ) , which is an inhibitor of RAS PPI to establish the RAS biosensor resource . We have further used this methodology to test a RAS-binding compound ( herein referred to as 3344 ) that we have derived from an in vitro medicinal chemistry programme starting with an intracellular antibody fragment . By monitoring the change in BRET2-specific signal in transfected HEK293T cells expressing different RAS-effector donor-acceptor combinations , we have been able to characterize the pan-RAS-effector PPI inhibitor properties of 3344 . This inhibitory mechanism shown using the BRET biosensor toolbox was supported by the crystal structure of KRAS with bound 3344 , showing binding to a pocket close to the RAS switch . Therefore , the BRET2 toolbox we describe here is a critical resource and is available for all investigators in the international effort to produce anti-RAS drugs , that can be employed in the treatment of cancers with RAS mutations .
RAS biosensors were developed for use in the BRET2 method ( Bacart et al . , 2008 ) as a real-time system allowing the monitoring of protein-protein interactions and their inhibition in live cells . The scheme used is outlined in Figure 1A . The intracellular localization of BRET donor RAS proteins was recapitulated by expressing the full-length proteins including the CAAX box , which is the farnesylation site for trafficking to the plasma membrane . The CAAX sequences were fused to the carboxy terminal end of the Renilla Luciferase variant 8 ( RLuc8 ) to act as the donor molecule in BRET2 ( De et al . , 2007 ) ( for simplicity of the nomenclature , CAAX has been omitted from the RAS construct names ) . We used available structural data for RAS/effector and RAS/iDAb complexes to optimize the proximity of donor and acceptor moieties . Hence , RLuc8 was fused to the amino termini of full-length RAS family proteins and the GFP2 ( Ramsay et al . , 2002 ) fused to the C-termini of the effectors ( RALGDS , CRAF , PI3K ) or of the iDAbs . Other parameters can influence the BRET2 signal such as the linker length between RLuc8/RAS and effector-iDAb/GFP2 . For our study , we observed a higher BRET signal with a ( GGGS ) 3 linker between RLuc8-KRASG12D construct , a ( GGGS ) 3 linker between the CRAF RBD-GFP2 molecule and a ( GGGS ) 2 linker between iDAb RAS-GFP2 construct ( Figure 1—figure supplement 1A ) . Therefore , we implemented these observations to all our BRET biosensors ( Supplementary file 1 ) . When donor and acceptor plasmids are transfected into HEK293T cells ( although any cell line of choice would be suitable ) , the resultant cells are fluorescent and bioluminescent if treated with the luciferase substrate ( coelenterazine 400a ) . If an interaction occurs between RAS and a partner-GFP2 fusion , bringing the RLuc8 and GFP2 within 100 Å , an energy transfer occurs from the RLuc8-RAS donor to the GFP2 acceptor and a BRET2 signal is achieved ( Figure 1A , middle panel ) . Inhibitors of the donor-acceptor molecule interaction will decrease the BRET signal whilst maintaining the RLuc8 bioluminescence and GFP2 fluorescence signals ( Figure 1A , right hand panel ) . The BRET signal ( or BRET ratio ) is calculated as the light emitted by the GFP2 acceptor constructs ( at 515 nm ) upon addition of coelenterazine 400a , divided by the light emitted by the RLuc8 donor constructs ( at 410 nm ) ( Pfleger et al . , 2006 ) . A background BRET signal is only observed with the donor-only construct where the RLuc8 plasmid is transfected alone into the cells ( Figure 1—figure supplement 1B ) and this signal is therefore subtracted from that BRET ratio . As shown in Figure 1—figure supplement 1B , un-transfected cells and those transfected with GFP2-only construct have a negligible auto-luminescence and emission at 515 nm upon addition of the BRET substrate and are not considered in the calculation of the BRET ratio . BRET donor saturation assessments were first carried out with the RAS effector RAS binding domains ( RBDs ) to evaluate the optimal levels of expression plasmid transfection for the competition experiments ( Figure 1B ) . All of the effector domains were found to interact specifically with KRASG12D since the BRET signal reached a donor saturation level ( Figure 1B ) . Further , all the transfected plasmids expressed the proteins at equivalent levels as indicated by western blot analysis ( Figure 1C ) and their expression does not modify KRASG12D expression ( Figure 1—figure supplement 2A shows the increase of acceptor protein level has little effect of donor protein levels ) . To further characterize this BRET2 system , we used the dominant negative mutant KRASS17N , which does not interact with the effectors ( Cool et al . , 1999; Nassar et al . , 2010; van den Berghe et al . , 1997 ) , as a donor . We found that the BRET signal increased linearly with the concentration of acceptor for all the RAS binding domains . This result is typical of non-specific interactions ( Mercier et al . , 2002 ) , confirming the S17N mutant does not interact with the effectors and supports the sensitivity of this system ( Figure 1—figure supplement 2B ) . We initially characterized the biosensor pairs with the iDAb RAS that is known to interact with mutant KRAS on the switch regions ( Tanaka et al . , 2007 ) , compared with a non-relevant anti-LMO2 iDAb ( Sewell et al . , 2014; Tanaka et al . , 2011 ) that was designated as iDAb control in this study ( herein called iDAb Ctl ) . Introduction of mutations in the three CDRs of the iDAb RAS to generate a dematured iDAb RAS ( iDAbdm RAS ) , was shown to reduce its affinity towards RAS-GTP from 6 . 2 nM to ~1 μM affinity ( Assi et al . , 2010 ) . While this did not alter the protein expression ( Figure 1—figure supplement 2C , D ) , there was an expected BRET signal reduction ( Figure 1—figure supplement 2C ) . Indeed , it significantly increased the BRET50 ( an approximation of the relative affinity of the acceptor fusion for the donor fusion proteins , corresponding to the acceptor/donor ratio necessary to reach 50% of the BRETmax ) and significantly reduced the BRETmax ( an approximation for the total number of complex RAS/iDAb and the distance between the donor and the acceptor within the dimer ) , which together are consistent with a decreased affinity of this mutant iDAb toward RAS . Therefore , the results obtained with the iDAb RAS confirmed the sensitivity and accuracy of the RAS biosensors . Finally , we tested the inhibition of interaction between RAS and its effector partners using BRET in a competition assay . HEK293T cells were transiently transfected with KRASG12D , each of the RAS-effector domain and a competitor ( non-GFP2 ) version of the iDAb RAS or iDAb control . This competition showed that iDAb RAS , but not the control , drastically decreased the BRET ratio of all the interactions tested ( Figure 1D ) . These results confirmed that the BRET2 biosensors enable monitoring of PPI inhibition of KRASG12D with each of the four effectors tested by the anti-RAS single domain antibody . Our major purpose in the development of the RAS BRET2 biosensors was to create a validation tool for compounds that bind to RAS and interfere with its PPI in living cells . We have identified compounds that bind to KRAS using in vitro screening and one compound 3344 ( chemical structure and 1-D NMR characterization shown in Figure 1—figure supplement 3A–C ) binds to KRASG12V with an affinity of 126 nM using 1H Carr-Purcell-Meiboom-Gill ( CPMG ) NMR ( Baldwin and Kay , 2009 ) ( data are shown in Figure 1—figure supplement 3D ) . In vitro competition studies of 3344 binding to KRASG12V in waterLOGSY NMR show the anti-RAS scFv inhibits 3344 binding to KRAS ( Figure 1—figure supplement 3E ) . In view of the in vitro inhibition by the anti-RAS scFv of 3344 binding to RAS and because the iDAb RAS interferes with BRET signal in cells ( Figure 1D ) , 3344 was used for validation of the BRET2 toolbox for RAS-effector PPI inhibitors . In the subsequent experiments reported here , we compare 3344 with an initial compound ( Abd-2 ) obtained through a SPR in vitro screening , which binds HRAS/KRAS with low affinity . It is the precursor of the 3344 compound and both share the same benzodioxane group ( the structures of 3344 and Abd-2 are shown in Figure 1—figure supplement 3A , F ) . These compounds have been selected from a medicinal chemistry programme in order to validate the BRET-based RAS biosensors . HEK293T cells were transiently transfected with BRET pairs and , after 24 hr to allow protein expression , the cells were seeded in 96-well plates . The compounds were added at different concentrations ( 5 , 10 and 20 μM ) and incubated on cells for a further 20 hr before the BRET reading . For each assay , the donor protein was RLuc8-KRASG12D and the acceptor proteins were PI3Kα RBD-GFP2 , PI3Kγ RBD-GFP2 , CRAF RBD-GFP2 or RALGDS RA-GFP2 . We observed a dose response reduction in BRET signal for the assays with compound 3344 but not with the Abd-2 indicating that only 3344 interferes with the RAS-effector PPI ( Figure 1E ) . To rule out the possibility of false positive compounds ( for instance , that might interfere directly with the BRET signal ) , we included control BRET-based biosensors . We tested the RAS compounds with the iDAbs RAS biosensors , either with RLuc8-LMO2 donor and iDAbdm LMO2 ( a dematured anti-LMO2 iDAb ( Sewell et al . , 2014 ) ) acceptor ( Figure 1—figure supplement 3G ) , RLuc8-KRASG12D donor with the iDAb RAS acceptor ( Figure 1—figure supplement 3H ) , or RLuc8-KRASG12D donor with the iDAbdm RAS acceptor ( Figure 1—figure supplement 3I ) . Abd-2 has no effect on any of these assays while 3344 only interferes , in a dose response , with KRASG12D/iDAbdm RAS-induced BRET without affecting the expression of the biosensors ( Figure 1—figure supplement 3J ) . Hence , the inhibitory effects of 3344 on KRASG12D-effectors interactions are not simply due to interference with the BRET assay . The RAS binding domain of the effector molecules lack some regulatory domains , which impedes a direct study of RAS inhibitors on pathways downstream of RAS . To reduce this limitation , we developed an optimized RAS biosensor of the full-length CRAFS257L mutant ( herein named CRAFFL ) since the S257L mutation increases ERK phosphorylation ( Razzaque et al . , 2007 ) and because we found that CRAFFL interacts with KRASG12D but not with KRASS17N ( Figure 2—figure supplement 1A ) . We performed a competition assay with the iDAb RAS confirming that it impedes the BRET2 signal due to the binding of CRAFFL with KRASG12D , in a dose response mode , whereas the iDAb control had no effect ( Figure 2A ) . There was no alteration in CRAFFL and KRASG12D protein expression due to the transfection of the iDAbs , shown by western analysis ( Figure 2—figure supplement 1B ) . In addition , iDAb RAS inhibition significantly decreased the phosphorylation of MEK1/2 and ERK1/2 kinases ( Figure 2B shows western blot data , quantitated in Figure 2C ) , confirming results affecting endogenous ERK phosphorylation by iDAb RAS interaction with RAS ( Tanaka and Rabbitts , 2010 ) . We further tested the ability of the small molecule 3344 to inhibit the KRASG12D/CRAFFL biosensor and the downstream biomarker pathways with either a long incubation ( 20 hr , Figure 2D–F ) or a short incubation ( 3 hr , Figure 2—figure supplement 1D–F ) to further validate the specificity of inhibition . Indeed , long-term incubation with the compound may indirectly inhibit RAS downstream pathways by affecting autocrine mechanisms involved in secondary activation of RAS pathways ( Arthur and Ley , 2013; Zhang et al . , 2011 ) . We compared the effect of Abd-2 and 3344 on the BRET pair and found a significant decrease in BRET signal with 3344 that occurred in a dose-dependent manner ( Figure 2D and Figure 2—figure supplement 1D ) without modifying RAS or CRAF expression ( as shown by western analysis , Figure 2—figure supplement 1C , G ) . Western blots using anti-pMEK and anti-pERK showed that 3344 also significantly inhibited MEK1/2 and ERK1/2 phosphorylation whilst Abd-2 did not ( Figure 2E , quantified in Figure 2F and Figure 2—figure supplement 1E–F ) . Therefore , these observations show a specific and functional effect of the inhibition of interaction between RAS and CRAFFL by the 3344 with a long and short incubation . Some compounds have been previously characterized that bind selectively on the cysteine of KRASG12C mutant ( Lito et al . , 2016; Ostrem et al . , 2013; Patricelli et al . , 2016 ) . We assessed whether our compound 3344 was able to interfere with binding of a range of mutant KRAS Gly12 proteins , including G12C , with CRAF in BRET assays . Analysis of the BRET2 signals from interaction of KRASG12A , KRASG12C , KRASG12V and KRASG12R with CRAFFL showed a dose response effect of compound 3344 but not Abd-2 ( Figure 2G ) . The corresponding BRET biosensor acceptor and donor proteins are equally expressed after transfection as judged by western blot analysis ( Figure 2—figure supplement 1H ) . Therefore , using this new set of validated RAS biosensors , we show that the compound disrupts mutant KRAS/CRAFFL interaction in cells . In turn , this leads to inhibition of the RAF/MEK/ERK downstream signaling pathway ( that emanates from the transfected protein expression ) . We extended the repertoire of biosensors by analyzing wild-type KRAS ( KRASWT ) donor molecule and also assessed if epidermal growth factor ( EGF ) -stimulated MEK/ERK phosphorylation ( Burgering et al . , 1993; Lange-Carter and Johnson , 1994 ) could be altered through the interaction of a KRASWT/CRAFFL BRET2 biosensor protein pair . Although the iDAb RAS binds weakly to RASWT in transfected mammalian two-hybrid reporter cells ( Tanaka et al . , 2007 ) , we first established if the BRET2 signal from RLuc8-KRASWT and GFP2-CRAFFL PPI could be inhibited by the iDAb RAS in the BRET transfection assay . HEK293T cells were transfected with the BRET pair and serum was removed for 24 hr , stimulated for 5 min with EGF and the BRET ratio directly determined after the stimulation . EGF treatment brings KRASWT and CRAFFL fusion proteins in a closer proximity and enhances the number of KRASWT/CRAFFL dimers because the BRETmax value increases from 4 . 02 to 10 . 01 ( Figure 3—figure supplement 1A ) . A dose response inhibition of the BRET2 signal was observed with iDAb RAS , but not iDAb control ( Figure 3A ) , which correlated with the reduction of pMEK1/2 and pERK1/2 detected by western blots ( Figure 3B and quantified in Figure 3C ) . This shows that the RAS BRET2 biosensors can be used to couple PPI effects and signaling effects . We conducted parallel BRET2 dose response experiments with the 3344 , compound compared to Abd-2 , implementing EGF stimulation and using the KRASWT/CRAFFL biosensor with short and long incubation times ( 3 hr and 20 hr , respectively ) . Compound 3344 inhibits this interaction in a dose-response manner ( Figure 3D and Figure 3—figure supplement 1D ) and prevents the phosphorylation of MEK1/2 and ERK1/2 kinases ( Figure 3E , quantified in Figure 3F and Figure 3—figure supplement 1E–F ) . Protein levels per se were not affected by the BRET2 transfectants by either the iDAb expression ( Figure 3—figure supplement 1B ) or Abd-2 or 3344 treatments ( Figure 3—figure supplement 1C , G ) . In conclusion , use of the 3344 with the BRET2 RAS biosensors confirms this compound is a pan-KRAS-effector PPI inhibitor . We have also explored the second best-characterized RAS effector family , the RAS-PI3Kα-AKT pathway ( Castellano and Downward , 2011 ) by establishing a KRASG12D/full-length PI3Kα ( herein PI3KαFL ) biosensor . In this case , we required a tripartite system as we observed that co-expression of the p85α regulatory subunit with PI3KαFL-GFP2 was required to obtain detectable , specific and optimized BRET signal from interaction of KRASG12D and PI3KαFL ( Figure 4—figure supplement 1A ) . KRASS17N mutant showed no specific interaction with PI3KαFL further confirming the accuracy of this biosensor ( Figure 4—figure supplement 1A ) . We validated the BRET biosensor by showing that the iDAb RAS impaired that interaction in a dose-dependent manner , whereas the iDAb control did not ( Figure 4A ) . Western blot analysis showed some reduction in PI3K and RAS proteins , specifically concordant with expression of the iDAb RAS ( Figure 4—figure supplement 1B ) and there was also a dose response reduction of phosphorylation of the downstream biomarker AKT at Ser473 ( Figure 4B and quantified in Figure 4C ) . Implementing the same biosensor assay treated with the compound 3344 for 3 or 20 hr , we confirmed this compound interferes with the KRASG12D/PI3KαFL interaction ( Figure 4D–F and Figure 4—figure supplement 1D–F ) without loss of protein ( Figure 4—figure supplement 1C , G ) . Abd-2 has no effect on the phosphorylation of AKT that results from KRASG12D/PI3KαFL interaction . Conversely , 3344 does affect RAS-PI3K interaction and AKT phosphorylation . When increasing doses of either Abd-2 or 3344 were used in the BRET-transfected cells , we observed dose response reduction of BRET signal with 3344 but not Abd-2 ( Figure 4D and Figure 4—figure supplement 1D ) . Associated with this inhibition , was a reduction in the downstream biomarker AKT Ser473 phosphorylation ( Figure 4E , quantified in Figure 4F and Figure 4—figure supplement 1E–F ) . 3344 inhibits RAS-PI3Kα PPI and thus signaling through AKT . The KRAS , NRAS and HRAS family members are conserved proteins that have an almost identical amino-acid domain ( G domain ) from residues 1–166 but a C-terminal hypervariable domain ( Wennerberg et al . , 2005 ) . We have extended the RAS biosensor toolbox to include NRAS and HRAS . We used full-length NRASQ61H and HRASG12V mutants to build these new RAS biosensors for use with the various effector RBDs . These mutants were used at the positions Q61 and G12 , for NRAS and HRAS respectively , as these are the positions most frequently mutated in human cancer involving NRAS and HRAS mutants ( Cox et al . , 2014 ) . Titration of the RAS donor and CRAFFL acceptor proteins show that the RLuc8-NRASQ61H and RLuc8-HRASG12V proteins interact and reach plateau BRET signals with GFP2-CRAFFL ( Figure 5—figure supplement 1A ) . Furthermore , the BRET2 signal is diminished by increasing levels of the iDAb RAS but not the iDAb control ( Figure 5—figure supplement 1B–D ) as expected from the analysis of the effects of the anti-RAS intracellular antibody ( Tanaka and Rabbitts , 2010; Tanaka et al . , 2007 ) . We further evaluated the efficacy of the RAS-binding compounds Abd-2 and 3344 in binding to NRAS and HRAS using a BRET assay in which the RAS protein donors were co-expressed with either PI3K , CRAF or RALGDS acceptors ( Figure 5A–D ) . While the low-affinity Abd-2 compound does not interfere with the BRET signal in any of the NRAS and HRAS BRET assays using either effector RBDs ( Figure 5A , B ) or full-length CRAF ( Figure 5C , D ) , the compound 3344 disturbs the BRET2 signal in a dose-response manner in all these RAS interactions ( Figure 5 and Figure 5—figure supplement 1E , F ) . Therefore , the BRET-based RAS biosensors characterization of 3344 shows this compound as a pan-RAS-effector interactions inhibitor that binds KRAS , NRAS and HRAS . The implementation of our RAS BRET2 toolbox showed that the compound 3344 is able to bind the transfected RAS protein products at the plasma membrane and interfere with their effector interaction . In addition , the downstream signaling was impeded . The mechanism of the interaction inhibition was corroborated by X-ray crystallography of KRASQ61H soaked with compound 3344 . Figure 6A shows that 3344 binds to KRAS in a previously identified pocket ( Maurer et al . , 2012; Sun et al . , 2012 ) close to the switch regions where the effectors interact with RAS ( Table 1 has the refinement statistics for the X-ray data ) . The superimposition of the structures of three RAS-effector protein complexes with the structure of KRAS-3344 complex shows that parts of 3344 would overlap with the bound effector structures , suggesting that the competition effect of 3344 can be explained by straightforward steric hindrance ( Figure 6B ) . We further confirmed that 3344 could interfere with the endogenous RAS-effector PPI in two human cancer cell lines ( viz . colorectal adenocarcinoma DLD-1 cells expressing KRASG13D and non-small cell lung carcinoma H358 cells expressing KRASG12C ) . The cells were serum starved 24 hr and stimulated 10 min with EGF in the presence of increasing amounts of 3344 , followed by western blot protein analysis to detect phosphorylated AKT Ser473 or phosphorylated ERK ( Figure 6C , D ) . 3344 decreases EGF-induced pAKT and pERK1/2 abundance in both cell types with an observed IC50 of ~5–10 μM without any change in the total levels of AKT or ERK1/2 . Therefore , 3344 can interfere with endogenous RAS signaling in human cancer cell lines . As our BRET2 results show direct interference of RAS-effector PPI by 3344 , we conclude that this is the mechanism of inhibition of the biomarkers in the tumor cell assay .
BRET-based biosensors have been successfully used to discover and characterize small molecules inhibitors ( Beautrait et al . , 2017; Corbel et al . , 2011; Lavoie et al . , 2013; Mazars and Fåhraeus , 2010; Robinson et al . , 2014 ) . The development of such biosensors involves the optimization of multiple parameters such as the fusion position of the RLuc8 and GFP2 moieties on their respective protein N- or C-terminus and the determination of the appropriate quantity of donor and acceptor plasmids for intracellular expression . Notably , the latest parameter has to be optimized in order to avoid the titration of active compounds if transient protein expression is used ( Couturier and Deprez , 2012 ) . In this study , we have engineered and optimized a complete set of RAS biosensors that includes several different mutant forms of KRAS and other family members ( viz . mutant NRAS and HRAS ) . This toolbox allows the monitoring of RAS-effector interactions and the assessment of RAS PPI inhibition by a macrodrug ( iDAb RAS ) and 3344 , a new anti-RAS small molecule derived from an intracellular antibody fragment , in living cells . Furthermore , when the full-length biosensors were used , we could couple the RAS PPI inhibition to the signaling effects , thereby providing additional insights into the behavior of RAS inhibitors . The inhibition of RAS PPI by 3344 in cells was demonstrated by the RAS biosensors toolbox and validated by X-ray crystallography . 3344 binds to a hydrophobic pocket near to the effector-binding switch regions of RAS ( Figure 6 ) . Whereas 3344 does not make direct contact with the switch regions , the BRET data show that the binding geometry and potency of 3344 is sufficient to interfere with the interaction of RAS-effector molecules that bind close to the 3344 site . While the RAS biosensors rely on transfection and expression of RAS with one of its partner proteins rather than observations of endogenous proteins , it nevertheless offers several advantages for the study of RAS-effector interactions inhibition . It provides a direct and quantitative measurement of the PPI interference with inhibitors ( i . e . small molecules or macrodrugs ) , which could allow the comparison of different compounds ( e . g . for structure-activity-relationship studies ) or macrodrugs and therefore the selection of more potent inhibitors . It is also sensitive and consequently requires a small quantity of cells to study the inhibition of the interaction . Nonetheless , 3344 prevents endogenous RAS-dependent signaling in two different human tumor cell lines at a lower concentration ( IC50 around 5 μM ) ( Figure 6C , D ) than in the BRET assay with observed IC50 around 20 μM . This difference probably reflects the expression levels of the target proteins in the two assays , where the BRET2 assay relies on transient transfection . Indeed , the overexpression in HEK293T cells probably produces higher amount of mutant RAS/effector proteins than the endogenous counterparts in cancer cells . Therefore , it might be more difficult to quantitatively inhibit the exogenous RAS/effector interaction than the endogenous one with 3344 compound . Generating stable BRET2 cell lines could minimize this difference . Another advantage of this toolbox has been shown by using the iDAb RAS as an acceptor within the RAS biosensors allowing a recapitulation of the published features of this intracellular single domain antibody . Therefore , the biosensors are also important tools to study RAS protein interactions in living cells and their effect on the RAS downstream pathways before being tested in cancer cell lines . RAS biosensors use should not be limited to the discovery and characterization of RAS inhibitors . Indeed , studies suggested that isoform and residue- or codon-specific RAS mutants show differences in their ability to engage effectors and signaling properties ( Hunter et al . , 2015; Nakhaeizadeh et al . , 2016; Yan et al . , 1998 ) . Accordingly , RAS biosensors could also be a methodology to decipher RAS isoform/mutant properties in cells . Our toolbox is an available resource for RAS-drug development programmes , and more generally for the RAS community , since our results demonstrate the possibility of using these RAS biosensors as a generic method to characterize cell-potent RAS-binding compounds or RAS-binding macrodrugs . The BRET2 biosensor system could also be used for direct screens of PPI inhibitors with libraries of compounds . However , because initial compounds from a library are not expected to have high affinity for their target , relatively weak interactions between donor and acceptors should be involved in the generation of BRET2 signal . This provides a further use of intracellular domain antibodies where reduction of affinity ( dematuration ) from a tool initially used for target validation , can be achieved to make a screening tool . Thus , the method is an approach that is transferable to other PPI situations required for drug development programmes in cancer or any other clinical indication .
HEK293T human embryonic kidney cells , DLD-1 cells and H358 cells were grown in DMEM medium ( Life Technologies ) supplemented with 10% FBS ( Sigma ) and 1% Penicillin/Streptomycin ( Life Technologies ) . Cells were grown at 37°C with 5% CO2 and were tested using a MycoAlert Mycoplasma Detection Kit ( Lonza ) and found to be mycoplasma-free before use . RNA was extracted from 5 × 106 DLD-1 or H358 cells using the RNeasy Plus Mini Kit ( Qiagen ) according to the manufacturer’s instructions . cDNA was synthesized from 1 . 5 to 2 μg RNA using SuperScript II Reverse Transcriptase ( Invitrogen ) . Primers were designed to amplify KRAS DNA and incorporate HindIII and BamHI restriction sites for subcloning: 5’- TAAGCAAAGCTTATGACTGAATATAAACTTGTGGTAG-3’ and 3’-GAAAATTAAAAAATGCATTATAATGTAAGGATCCTAAGCA-5’ DNA was amplified using Phusion High-Fidelity DNA Polymerase ( New England Biolabs ) and , following digestion with HindIII and BamHI , the DNA was cloned into pBlueScript II SK ( + ) ( Stratagene ) . Plasmid DNA was prepared from indivudial DH5α transformants using a QIAprep Spin Miniprep Kit ( QIAGEN ) . KRAS mutations were verified by Sanger sequencing ( Source Bioscience ) of at least six clones from each cell line . The KRAS mutations in the two human cancer cell lines were confirmed as KRASG13D in DLD-1 and KRASG12C in H358 . For dose response experiments ( BRET and western blot ) , drugs were prepared in 100% DMSO at 10 mM . Cells were treated with Abd-2 or 3344 compounds at concentration of 5 , 10 or 20 μM for 3 hr ( short-term incubation ) or 20 hr ( long-term incubation ) . The compounds were diluted in the BRET medium: OptiMEM no phenol red ( Life Technologies ) supplemented with 4% FBS and with a final concentration of 0 . 2% DMSO . For serum starvation studies with the BRET assay , cells were grown 24 hr in the presence of OptiMEM no phenol red supplemented with 1% FBS and stimulated with 50 ng/mL EGF ( Life Technologies ) for 5 min at 37°C . For serum-starvation studies of cancer cell lines , cells were grown 24 hr in the presence of DMEM without FBS and stimulated 10 min with 50 ng/mL EGF . The compound was incubated for 3 hr before the EGF stimulation at 2 , 5 , 10 and 20 μM . The generation of the mutant and wild-type KRAS was PCR site-directed mutagenesis using pPGK-KRASG12D-CAAX-P2A-Puro as a template ( a gift from Jennifer Chambers ) . The following full-length KRAS mutants have been produced: KRASG12A , KRASG12C , KRASG12D , KRASG12V , KRASG12R , KRASS17N and KRASWT , all with carboxy terminal CAAX . All RAS cDNAs ( KRAS mutants , KRASWT , NRASQ61H and HRASG12V-CAAX ) were cloned between NotI/XbaI of the pEF-RLuc8-MCS plasmid . LMO2 was amplified by PCR and cloned between NcoI/XhoI sites of the pEF-MCS-RLuc8 plasmid . CRAF RBD ( 1-149 ) , PI3Kα RBD ( 161-315 ) , full-length PI3Kα ( a gift from Roger Williams and Olga Perisic ) , PI3Kγ RBD ( 190-315 ) , RALGDS RA ( 788-884 ) , iDAb RAS , iDAbdm RAS and iDAb LMO2 ( iDAb control ) were amplified by PCR and cloned between NcoI/XhoI sites of the pEF-MCS-GFP2 plasmid . The full-length CRAFS257L was cloned between NotI/XbaI sites of pEF-GFP2-MCS as well as the iDAbdm LMO2 . All RAS and effectors are human sequences except RALGDS RA ( mouse ) . All the RAS BRET constructs DNA and protein sequences have been listed in the supplementary file 1 . The BRET experiment protocols have been adapted from previous studies ( Lavoie et al . , 2013; Pfleger et al . , 2006 ) . For all BRET experiments ( titration curves and competition assays ) 650 , 000 HEK293T were seeded in each well of a six well plates . After 24 hr at 37°C , cells were transfected with a total of 1 . 6 μg of DNA mix , containing the donor + acceptor ± competitor plasmids , using Lipofectamine 2000 transfection reagent ( Thermo-Fisher ) . Cells were detached 24 hr later , washed with PBS and seeded in a white 96 well plate ( clear bottom , PerkinElmer ) in OptiMEM no phenol red medium complemented with 4% FBS . Cells were incubated for an additional 20–24 hr at 37°C before the BRET assay reading . BRET2 signal was determined immediately after addition of coelenterazine 400a substrate ( 10 μM final ) to cells ( Cayman Chemicals ) , using an Envision instrument ( 2103 Multilabel Reader , PerkinElmer ) with the BRET2 Dual Emission optical module ( 515 nm – 30 nm and 410 nm – 80 nm; PerkinElmer ) . Total GFP2 fluorescence was detected with excitation and emission peaks set at 405 nm and 515 nm , respectively . Total RLuc8 luminescence was measured with the Luminescence 400–700 nm-wavelength filter . The BRET signal or BRET ratio corresponds to the light emitted by the GFP2 acceptor constructs ( 515 nm – 30 nm ) upon addition of coelenterazine 400a divided by the light emitted by the RLuc8 donor constructs ( 410 nm – 80 nm ) . The background signal is subtracted from that BRET ratio using the donor-only negative control where only the RLuc8 plasmid is transfected into the cells . The normalized BRET ratio is the BRET ratio normalized to a negative control ( DMSO , no competitor or iDAb control ) during a competition assay . Total GFP2 and RLuc8 signals were used to control the protein expression from each plasmid . Cells were washed once with PBS and lysed in SDS-Tris buffer ( 1% SDS , 10 mM Tris-HCl pH 7 . 4 ) supplemented with protease inhibitors ( Sigma ) and phosphatase inhibitors ( Thermo-Fisher ) . Cell lysates were sonicated with a Branson Sonifier and the protein concentrations determined by using the Pierce BCA protein assay kit ( Thermo-Fisher ) . Equal amounts of protein ( 10 μg ) were resolved on 10 or 15% SDS-PAGE and subsequently transferred onto a PVDF membrane ( GE ) . The membrane was blocked either with 10% non-fat milk ( Sigma ) or 10% BSA ( Sigma ) in TBS-0 . 1% Tween20 and incubated overnight with primary antibody at 4°C . After washing the membrane was incubated with HRP conjugated secondary antibody for 1 hr at room temperature ( RT , 25°C ) . The membrane was washed with TBS-0 . 1% Tween and developed using Pierce ECL Western Blotting Substrate ( Thermo-Fisher ) and CL-XPosure films ( Thermo-Fisher ) . Primary antibodies include anti-phospho-p44/22 MAPK ( ERK1/2 ) ( CST ) , anti-p44/42 MAPK ( total ERK1/2 ) ( CST ) , anti-phospho-MEK1/2 ( CST ) , anti-MEK1/2 ( CST ) , anti-phospho-AKT S473 ( CST ) , anti-AKT ( CST ) , anti-pan-RAS ( Millipore ) , anti-GFP ( Santa Cruz Biotechnologies ) , anti-β-actin ( Sigma ) . Secondary antibodies include anti-CMYC HRP-linked ( Novus Biologicals ) , anti-mouse IgG HRP-linked ( CST ) and anti-rabbit IgG HRP-linked ( CST ) . The waterLOGSY NMR method ( Dalvit et al . , 2001 ) was used to measure RAS ligand interaction ( Huang et al . , 2017 ) . WaterLOGSY experiments were conducted at a 1H frequency of 600 MHz using a Bruker Avance spectrometer equipped with a BBI probe . All experiments were conducted at RT , 25°C . 3 mm diameter NMR tubes with a sample volume of 200 μL in all experiments . Solutions were buffered using an H2O PBS buffer corrected to pH 7 . 4 . The sample preparation is exemplified as follows; the compound ( 10 μL of a 10 mM solution in DMSO-d6 ) was added to an Eppendorf tube before sequential addition of the H2O PBS buffer ( 163 . 6 μL ) , D2O ( 20 μL ) , and protein ( 6 . 4 μL , 311 . 8 μM ) . The resulting solution was vortexed to mix and transferred to a 3 mm NMR tube prior to the NMR analysis . For competition experiments using anti-RAS scFv , protein preparation for NMR was carried out in a similar manner; the compound ( 10 μL of a 10 nM solution in DMSO-d6 ) was added to an Eppendorf tube before sequential addition of the H2O PBS buffer ( 146 . 4 μL ) , D2O ( 20 μL ) , protein ( 6 . 4 μL , 311 . 8 μM ) and anti-RAS scFv ( 17 . 2 μL , 116 . 6 μM ) . The resulting solution was vortexed to mix and transferred to a 3 mm NMR tube prior to the NMR analysis . Negative controls ( compound alone ) were prepared in a similar manner , in order to obtain an end volume of 200 μL . All reactions involving moisture-sensitive reagents were carried out under a nitrogen atmosphere using standard vacuum line techniques and glassware that was flame-dried before use . Anhydrous solvents were prepared following the procedure outlined ( Pangborn et al . , 1996 ) . Water was purified by an Elix UV-10 system . All other solvents and reagents were used as supplied ( analytical or HPLC grade ) without prior purification . Brine refers to a sat . aq . solution of NaCl . In vacuo refers to the removal of solvent by the use of a rotary evaporator attached to a diaphragm pump . Thin layer chromatography was performed on normal phase Merck silical gel 60 F254 aluminum-supported thin layer chromatography sheets . Visualization of spots was either by absorption of ultra violet light ( λmax 254 nm ) , or by thermal development after staining with 1% aq . KMnO4 . Flash column chromatography was performed on Kieselgel 60 silica in a glass column , under a positive pressure . NMR spectra were recorded on Bruker Avance spectrometer ( AVIII 600 ) in the deuterated solvent stated . The field was locked by external referencing to the relevant deuteron resonance . Chemical shifts ( δ ) are reported in parts per million ( ppm ) . The multiplicity of each signal is indicated by: app . ( apparent ) , s ( singlet ) , br s ( broad singlet ) , d ( doublet ) , t ( triplet ) , q ( quartet ) , dd ( doublet of doublets ) or m ( multiplet ) . Coupling constants ( J ) are quoted in Hz and are reported to the nearest 0 . 1 Hz . Low-resolution mass spectra were recorded on an Agilent 6120 spectrometer operating in positive or negative mode , from solutions of MeOH . Accurate mass measurements were run on either a Bruker MicroTOF internally calibrated with polyalanine , or a Micromass GCT instrument fitted with a Scientific Glass Instruments BPX5 column ( 15 m x 0 . 25 mm ) using amyl acetate as a lock mass , by the mass spectrometry department of the Chemistry Research Laboratory , University of Oxford , UK . m/z values are reported in Daltons . A solution of 2-bromo-6-methoxyphenol 1 ( 2 . 50 g , 12 . 3 mmol ) in CH2Cl2 ( 80 mL ) was cooled to −78°C before dropwise addition of BBr3 ( 1 M in heptane , 14 . 8 mL , 14 . 8 mmol ) . The resulting mixture was warmed to room temperature and stirred for 2 hr before being poured onto an ice/water ( 200 mL ) and stirred for 30 min . The organic phase was separated , washed with water ( 100 mL ) and brine ( 100 mL ) , dried ( Na2SO4 ) , filtered and concentrated in vacuo to give the desired 3-bromobenzene-1 , 2-diol two as a brown oil ( 2 . 24 g , 11 . 9 mmol , 97% ) , which was used in the next step without further purification . A solution of diol 2 ( 1 . 00 g , 5 . 35 mmol ) in DMF ( 20 mL ) was treated sequentially with K2CO3 ( 1 . 77 g , 12 . 8 mmol ) , and 1 , 2-dibromoethane ( 507 µL , 5 . 88 mmol ) before being heated to 60°C for 18 hr . The reaction was then cooled down before addition of water and brine ( 1:1 , 50 mL ) and EtOAc ( 100 mL ) . The organic phase was washed further with water and brine ( 1:1 , 4 × 50 mL ) , dried ( Na2SO4 ) , filtered and concentrated in vacuo to give the crude material as a brown oil . Purification on silica gel ( pentane/EtOAc , 4:1 ) afforded the desired 5-bromo-2 , 3-dihydrobenzo[b][1 , 4]dioxine three as a clear oil ( 1 . 11 g , 5 . 19 mmol , 97% ) . Bromide 3 ( 600 mg , 2 . 79 mmol ) was added to a vial before addition of 1 , 4-dioxane/water ( 5:1 , 8 mL ) ; the solution was degassed before sequential addition of K2CO3 ( 1 . 16 g , 8 . 37 mmol ) , 4-chloro-3-methoxyphenyl boronic acid ( 572 mg , 3 . 07 mmol ) , and Pd ( dppf ) Cl2 ( 100 mg , 0 . 140 mmol ) . The vial was sealed and the reaction heated to 100°C for 18 hr , cooled down and concentrated in vacuo . The residue was purified on silica gel ( pentane/EtOAc , 9:1 ) to afford the desired 5- ( 4-chloro-3-methoxyphenyl ) −2 , 3-dihydrobenzo[b][1 , 4]dioxine four as a clear oil ( 745 mg , 2 . 70 mmol , 97% ) . 1H NMR ( 600 MHz , CDCl3 ) δ 7 . 39 ( 1H , d , J 8 . 1 Hz ) , 7 . 11 ( 1H , s ) , 7 . 08 ( 1H , dd , J 8 . 2 , 1 . 7 Hz ) , 6 . 91–6 . 89 ( 3H , m ) , 4 . 31–4 . 28 ( 4 hr , m ) , 3 . 94 ( 3H , s ) ; 13C NMR ( 150 MHz , CDCl3 ) δ 154 . 5 , 143 . 9 , 140 . 6 , 137 . 5 , 130 . 0 , 129 . 6 , 122 . 6 , 122 . 4 , 121 . 4 , 121 . 1 , 117 . 0 , 113 . 5 , 64 . 4 , 64 . 1 , 56 . 2; m/z ( ESI+ ) 277 ( [M + H]+ ) ; HRMS ( ESI+ ) [C15H14ClO3] requires 277 . 0631 , found 277 . 0591 . Chloride 4 ( 75 mg , 0 . 272 mmol ) , Cs2CO3 ( 266 mg , 0 . 866 mmol ) , 3- ( ( dimethylamino ) methyl ) aniline ( 61 mg , 0 . 408 mmol ) , XPhos ( 13 mg , 0 . 027 mmol ) and Pd ( OAc ) 2 ( 3 mg , 0 . 014 mmol ) were added sequentially to a vial and degassed with N2 for 5 min . Degassed 1 , 4-dioxane ( 2 mL ) was then added , the vial sealed and heated to 100°C for 18 hr . The mixture was cooled down , diluted with EtOAc ( 30 mL ) , and washed with a 50/50 solution of water and brine ( 2 × 30 mL ) . The organic phase was dried ( Na2SO4 ) and concentrated in vacuo . Purification by column chromatography on silica gel ( CH2Cl2/MeOH , 9:1 ) afforded the desired 4- ( 2 , 3-dihydrobenzo[b][1 , 4]dioxin-5-yl ) -N- ( 3- ( ( dimethylamino ) methyl ) phenyl ) −2-methoxyaniline 3344 as a yellow oil ( 102 mg , 96% ) . 1H NMR ( 400 MHz , MeOD ) δ 7 . 26 ( 1H , d , J 8 . 3 Hz ) , 7 . 20 ( 1H , dd , J 7 . 6 , 0 . 2 Hz ) 7 . 12 ( 1H , d , J 2 . 0 Hz ) 7 . 08–7 . 04 ( 2H , m ) , 7 . 00 ( 1H , dd , J 8 . 3 , 2 . 0 Hz ) , 6 . 88 ( 1H , dd , J 7 . 6 , 2 Hz ) , 6 . 83 ( 2H , J 7 . 8 , 0 . 2 Hz ) , 6 . 78 ( 1H , dd , J 7 . 8 , 2 . 0 Hz ) , 4 . 25–4 . 20 ( 4H , m ) , 3 . 87 ( 3H , s ) , 3 . 45 ( 2H , s ) , 2 . 27 ( 6H , s ) , NH was not observed; 13C NMR ( 125 MHz , CDCl3 ) δ 150 . 2 , 145 . 5 , 145 . 3 , 142 . 2 , 139 . 4 , 133 . 2 , 132 . 4 , 131 . 7 , 130 . 3 , 123 . 5 , 123 . 0 , 122 . 9 , 122 . 0 , 120 . 1 , 118 . 3 , 117 . 1 , 116 . 7 , 113 . 6 , 65 . 8 , 65 . 5 , 65 . 1 , 56 . 4 , 45 . 3; m/z ( ESI‒ ) 38 ( [M‒H]‒ ) ; HRMS ( ESI‒ ) [C24H25N2O3] requires 389 . 1865 , found 389 . 1841 . Typical experimental parameters for Carr-Purcell-Meiboom-Gill ( CPMG ) NMR spectroscopy were the following: total echo time , 40 ms; relaxation delay , 2 s; and number of transients , 264 ( Abboud et al . , 2016 ) . The PROJECT-CPMG sequence ( 90°x-[T−180°y-T- 90°y-T−180°y-T]n-acq ) was applied . Water suppression was achieved by presaturation . Prior to Fourier transformation , the data were multiplied with an exponential function with 3 Hz line broadening . The CPMG experiments were conducted at a 1H frequency of 700 MHz using a Bruker Avance with 5 mm inverse TCI 1 hr/13C/15N cryoprobe . All experiments were conducted at RT and lapsed 128 scans . 3 mm diameter NMR tubes with a sample volume of 200 μL were used in all experiments . Solutions were buffered using a D2O PBS buffer corrected to pH 7 . 4 . The sample preparation is exemplified as follows: for a 5 μM GST-KRASG12V sample: 55 μM of the 3344 compound ( 1 . 1 μL of a 10 mM solution in DMSO-d6 ) was added to an Eppendorf before sequential addition of the D2O PBS buffer ( 194 . 0 μL ) and GST-KRASG12V ( 4 . 9 μL of a 205 μM solution , the protein is in an H2O buffer for stability reason ) . The resulting solution was vortexed to be fully mixed and transferred to a 3 mm NMR tube before the run . Negative controls ( compound alone , without the KRAS protein ) were prepared in a similar manner , in order to obtain an end volume of 200 μL . CPMG experiments were carried out at a fixed 3344 concentration ( 55 μM , optimal concentration for these CPMG NMR experiments ) and a variable GST-KRASG12V concentration . The amount of GST-KRASG12V was increased from 0 μM until the signals of the compound completely disappear in the proton NMR at 20 μM . Seven measurements were done in total with 0 μM , 2 . 5 μM , 5 μM , 7 . 5 μM , 10 μM , 15 μM and 20 μM of GST-KRASG12V . The integrations of the protons acquired were all compared to the compound alone ( with no KRAS ) in order to obtain a percentage decrease for each concentration of KRAS . Three different proton signals were used and a mean was calculated for each run . KRAS concentration experiments were run in triplicate and a mean was also calculated for each concentration . Concentration and percentage of decrease were plotted and Kd fitting was run on the generated curve using Origin 2017 software with the following function: A* ( 1/ ( 2*C ) ) * ( ( B + x + C ) -sqrt ( ( ( B + x + C ) ^2 ) - ( 4*x*C ) ) ) where A is the maximum % of inhibition ( i . e . 100 ) , B is the Kd , C is the concentration of compound and x the concentration of KRAS protein necessary to reach 100% of signal reduction of the compound . KRASG12V cDNA was cloned into the pGEX vector in-frame with an N-terminal Glutathione-S transferase ( GST ) tag . pGEX-GST-KRASG12V was transformed into E . coli BL21 ( DE3 ) cells . Bacterial cells were cultured at 37°C to an OD600 of 0 . 5 and induced with IPTG ( isopropyl 1-thio-beta-D-galactopyranoside , final concentration 0 . 1 mM ) at 16°C overnight . The bacteria cultures were harvested by centrifugation and the cell pellets re-suspended in 50 mM Tris-HCl pH8 . 0 , 140 mM NaCl , 1 mM mercaptoethanol supplemented with complete protease inhibitor ( Roche ) . The GST-fusion proteins were purified by glutathione-sepharose column chromatography ( GE Healthcare ) and eluted with 50 mM Tris-HCl pH8 . 0 , 10 mM reduced glutathione , 1 mM mercaptoethanol , 5 mM MgCl2 . KRASQ61H cDNA was cloned into the pRK-172 vector in-frame with an N-terminal 6xHis-tag and TEV protease recognition site . The plasmid containing KRASQ61H sequence was transformed into E . coli B834 ( DE3 ) pLysS cells , which were grown in 25 mL LB medium with 50 μg/mL Carbenicillin and 34 μg/mL Chloramphenicol for 16 hr , prior to inoculation of 1L LB medium . Protein expression was induced at OD600 = 0 . 6 by addition of IPTG to a final concentration of 0 . 5 mM and cells grown overnight at 16°C . Bacteria were harvested by centrifugation and sonicated in 50 mM Tris-HCl , pH 7 . 5 , 500 mM NaCl , 5 mM MgCl2 and 10 mM imidazole and EDTA-free protease inhibitor cocktail ( Roche Diagnostics ) . Proteins were purified using nickel agarose beads ( Invitrogen ) and bound proteins were eluted batch-wise in 50 mM Tris-HCl , pH 7 . 5 , 500 mM NaCl , 5 mM MgCl2 and 300 mM imidazole . RAS protein samples were concentrated using Vivapore 10/20 mL concentrator ( 7 . 5 kDa molecular weight cut-off; Sartorius Vivapore ) to a final volume of approximately 1 mL . Nucleotide exchange for crystallographic samples was carried out following published procedures ( Herrmann et al . , 1996 ) . RAS proteins were further purified by gel filtration on a HiLoad Superdex 75 10/300 GL column ( GE Healthcare ) in a buffer containing 20 mM HEPES pH 8 . 0 , 150 mM NaCl , 5 mM MgCl2 and 1 mM DTT at a flow rate of 0 . 5 mL/min . Fractions corresponding to the protein were pooled and concentrated to 45–75 mg/mL for crystallization trials . Protein concentration was determined by extinction coefficient ( ε280 = 12045 L/mol/cm ) . Protein purity was analyzed by SDS-PAGE stained with Coomassie Brilliant Blue . scFv recombinant protein was expressed and purified as described elsewhere ( Tanaka et al . , 2007 ) . For X-ray diffraction experiments , KRASQ61H-GppNHp crystals were grown by vapour diffusion at 4°C by mixing 1 . 5 + 1 . 5 volumes of KRAS solution at a concentration of 75 mg/mL KRASQ61H , with 8–15% w/v Polyethylene Glycol 3350 and 0 . 2 M lithium citrate pH 5 . 5 . The resulting crystals are termed crystal form I hereafter . Prior to X-ray data collection , crystals were cryo-protected by addition of 20% glycerol to the crystallization buffer and flash-cooled in liquid nitrogen . 3344 was initially dissolved at 200 mM in 100% DMSO and sequentially mixed in a ratio of 1:1 with crystallization buffer ( 8–15% w/v Polyethylene Glycol 3350 , 0 . 2 M lithium citrate 7 . 0 and 20 mM Tris-HCl pH 7 . 0 ) to give a final concentration of compound of 50 mM and 25% DMSO in a 5 μL drop . Soaked crystals were flash-cooled in liquid nitrogen prior to data collection using the final DMSO concentration on the soaking drop as cryo-protectant . X-ray diffraction data were collected at beamline ID30A-1 ( Bowler et al . , 2015; Bowler et al . , 2016; Nurizzo et al . , 2016; Svensson et al . , 2015 ) at The European Synchrotron Radiation Facility ( ESRF , Grenoble , France ) . The structure of KRASQ61H GppNHp-3344 was solved by molecular replacement using a KRAS169Q61H GPPNHP-Abd-2 , ( PDB ID 5OCO ) as a search model within the program Phaser ( McCoy , 2007; McCoy et al . , 2007 ) . Structures were manually adjusted using COOT ( Emsley et al . , 2010 ) and refined using REFMAC ( Murshudov et al . , 1997 ) . Crystal Form I ( KRASQ61H ) has six KRAS molecules in the asymmetric unit , assembled as a hexamer . Electron density maps averaged with six-fold non-crystallographic symmetry ( NCS ) were used to improve the definition of the bound compounds . Refinements were also performed with the six fold NCS applied . The refined models were validated using PROCHECK ( Laskowski et al . , 1993a ) , MolProbity ( Chen et al . , 2010 ) and Phenix software packages ( Adams et al . , 2010; Laskowski et al . , 1993b ) . Figures were created using PyMOL ( Schrodinger ) . Data collection and refinement statistics are summarized in Table 1 . All quantifications were performed using ImageJ or Prism 7 . 0 c ( GraphPad Software ) , BRET titration curves and statistical analysis were performed using Prism 7 . 0 c ( GraphPad Software ) . Data are typically presented as mean ± SD or SEM as specified in the figure legends . Statistical analyses were performed with a one-way ANOVA followed by Dunnett’s post-hoc tests or Sidak’s post-hoc tests unless otherwise indicated in the figure legends . *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 , ****p<0 . 0001 . Structure files and coordinates have been deposited to PDB under this accession number: 6F76 . | A group of proteins known as the RAS family plays a critical role in controlling animal cell growth and division . RAS proteins are normally active only some of the time , but genetic mutations can create permanently active forms of the proteins . These constantly interact with other proteins called effectors . In response , cells multiply uncontrollably and give rise to cancers . In an attempt to find new cancer treatments , researchers across the globe are trying to develop inhibitor drugs that prevent RAS and effector proteins from interacting . New drugs are often tested in laboratory experiments that directly apply the drugs to the proteins that they are designed to work on . But in some cases a drug may work wellin the laboratory but fail to work when used in cells . Unfortunately , there are few ways to judge how well inhibitor drugs work inside living cells . Bery et al . have now developed RAS biosensors – a collection of proteins that bind to RAS and produce light more brightly when RAS interacts with effector proteins in living cells . Tests on cells treated with an antibody that works inside cells and is known to prevent interactions between RAS and effector proteins confirmed that the RAS biosensors work well . Bery et al . then used the RAS biosensors to show that a new RAS inhibitor works in human cancer cells . The RAS biosensors are available upon request to researchers across the globe . They should form an important tool for testing potential treatments for cancers that contain mutated RAS proteins . | [
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] | 2018 | BRET-based RAS biosensors that show a novel small molecule is an inhibitor of RAS-effector protein-protein interactions |
Schistosomiasis is a debilitating parasitic disease infecting hundreds of millions of people . Schistosomes use aquatic snails as intermediate hosts . A promising avenue for disease control involves leveraging innate host mechanisms to reduce snail vectorial capacity . In a genome-wide association study of Biomphalaria glabrata snails , we identify genomic region PTC2 which exhibits the largest known correlation with susceptibility to parasite infection ( >15 fold effect ) . Using new genome assemblies with substantially higher contiguity than the Biomphalaria reference genome , we show that PTC2 haplotypes are exceptionally divergent in structure and sequence . This variation includes multi-kilobase indels containing entire genes , and orthologs for which most amino acid residues are polymorphic . RNA-Seq annotation reveals that most of these genes encode single-pass transmembrane proteins , as seen in another resistance region in the same species . Such groups of hyperdiverse snail proteins may mediate host-parasite interaction at the cell surface , offering promising targets for blocking the transmission of schistosomiasis .
Schistosomiasis is a chronic and debilitating disease suffered by over 200 million people worldwide ( Evan Secor , 2014; Lo et al . , 2018 ) . It is caused by infection with schistosome trematode parasites that are transmitted by aquatic snails ( Lo et al . , 2018 ) . Infection can be treated with regular doses of a single drug , praziquantel ( Doenhoff et al . , 2009 ) . However , it has become increasingly clear that mass drug administration alone will not adequately control schistosomiasis , and that successful elimination of transmission requires intervention at the snail stage ( Lo et al . , 2018; Sokolow et al . , 2018 ) . Immunogenetic interactions between snails and schistosomes represent a crucial stage in the parasite life cycle that can be targeted to block transmission . Parasite resistance is highly heritable in snails , and there is substantial strain-by-strain interaction between hosts and parasites ( Richards and Shade , 1987; Richards et al . , 1992; Knight et al . , 1999; Webster et al . , 2004; Webster and Woolhouse , 1998; Theron et al . , 2014 ) . Genetically diverse parasite cultures can infect nearly all snails , while bottlenecked laboratory strains of parasite can only infect a subset ( Theron et al . , 2008 ) , suggesting a ‘trench warfare’ model in which numerous alleles are maintained in both host and parasite populations because each matches a different phenotype in the other species ( Seger , 1988; Stahl et al . , 1999 ) . There are likely to be snail genes with large effects on resistance to particular parasite genotypes ( Lewis et al . , 2001 ) . Finding these genes will open two potential avenues for disease mitigation . First , they may uncover mechanisms of infection by the parasite that could be therapeutically targeted . Second , they may facilitate genetic manipulation of wild snail populations so as to reduce parasite transmission by eliminating alleles that permit infection by certain schistosome genotypes ( Theron et al . , 2014; Reardon , 2016; Famakinde , 2018; Maier et al . , 2019 ) . For invertebrates , highly strain-specific responses to parasites remain unexplained ( Schmid-Hempel , 2005; Schulenburg et al . , 2007 ) , as these taxa lack the combinatorial immune system found in vertebrates with its vast and adjustable repertoire of recognition molecules . Invertebrate resistance to macroparasites or parasitoids often involves large-effect loci ( Carton et al . , 2005 ) , but most of these are poor candidates for mediating strain specificity , as they reflect generic enhanced defenses conveying a constitutive fitness cost ( Kraaijeveld and Godfray , 1997; Webster and Woolhouse , 1999; Koella and Boëte , 2002 ) , or encode signaling molecules ( Hita et al . , 2006 ) or effectors ( Goodall et al . , 2006 ) rather than recognition molecules . Strain specificity may be conveyed by suites of highly diverse host genes that act synergistically , especially if these interact with similarly varying and coevolving sets of parasite genes to mediate host-parasite recognition ( Schmid-Hempel , 2005; Schulenburg et al . , 2007; Cerenius and Söderhäll , 2013 ) . Such loci will not necessarily produce a large phenotypic signal unless several are clustered in the same genomic region . The neotropical snail Biomphalaria glabrata has been the focus of recent efforts to develop genomic resources for schistosomiasis vector biology , although the 916 Mb , repetitive genome remains poorly assembled ( reference genome BglaB1 N50 = 48 kb; Adema et al . , 2017; Tennessen et al . , 2017 ) . B . glabrata snails can be readily challenged with Schistosoma mansoni miracidia under controlled laboratory conditions , with successful infections diagnosed by subsequent shedding of cercariae ( Bonner et al . , 2012 ) . To date , four genomic regions have been identified in which allelic variation influences resistance to infection by S . mansoni ( Knight et al . , 1999; Goodall et al . , 2006; Tennessen et al . , 2015a; Tennessen et al . , 2015b ) . An F2 mapping cross between resistant B . glabrata strain BS-90 and a susceptible strain yielded two RAPD markers linked to resistance ( Knight et al . , 1999 ) . One of those was subsequently aligned to a contig on Linkage Group ( LG ) XII ( Tennessen et al . , 2017 ) , although the identities of any candidate genes to which it may be linked remain unclear ( the other could not be uniquely mapped ) . Two other genomic regions ( sod1 and RADres; Goodall et al . , 2006; Tennessen et al . , 2015a ) influence S . mansoni infection in B . glabrata population 13–16-R1 which is admixed from Caribbean and Brazilian populations and has been maintained free from parasites for several decades as a large laboratory population with substantial segregating variation . However , together those two regions explain only 7% of the variance in resistance in 13-16-R1 , suggesting that other resistance loci remain undiscovered in this population . In B . glabrata from Guadeloupe , the Guadeloupe Resistance Complex ( GRC ) shows an 8-fold effect on the odds of S . mansoni infection ( Tennessen et al . , 2015b; Allan et al . , 2017 ) via a hemocyte-mediated mechanism ( Allan et al . , 2018a ) that also affects the proteome ( Allan et al . , 2019 ) and microbiome ( Allan et al . , 2018b ) . Seven clustered GRC genes encode hyperdiverse single-pass transmembrane ( TM1 ) proteins that appear to recognize parasite-associated molecules ( Tennessen et al . , 2015b ) , likely including saccharides ( Allan and Blouin , 2018 ) . Here , we use a genome-wide association study to pinpoint a new resistance region in snails , with a very large ( over 15-fold ) effect on odds of infection by schistosomes . It comprises a cluster of highly polymorphic transmembrane genes , and as GRC ( =PTC1 ) was the first such cluster described in Biomphalaria , we designate this second region as Polymorphic Transmembrane Cluster 2 ( PTC2 ) . Using PacBio , we have vastly improved the assembly of the B . glabrata genome , allowing us to fully characterize the chromosomal vicinity of PTC2 . Transcriptomic data show that , like GRC , PTC2 harbors exceptionally divergent suites of TM1 genes , suggestive of coevolutionary dynamics . These results support a general immunogenetic scenario in which clusters of highly polymorphic TM1 genes mediate host-parasite interaction .
In pooled whole-genome sequencing of 600 infected and 600 uninfected 13–16-R1 snails ( 298x and 333x coverage , respectively ) , a single genomic region showed by far the greatest difference in allele frequencies between pools ( Figure 1A; Supplementary file 1A ) . The highest outliers occurred in a 450 kb section of LG XII , here called PTC2 . Genetic divergence between pools ( FST ) at numerous PTC2 variants exceeds 0 . 1 , a value unobserved among one billion simulated neutral variants , which is therefore significant even if corrected for the nearly 7 million empirical variants examined ( p<0 . 01 ) . Many variants even show FST over 0 . 2 , more than twice the FST at sod1 and RADres . By subsequently genotyping indel polymorphisms at PTC2 in individual snails , we observed three alleles at intermediate frequency ( R: 44% , S1: 24% , and S2: 32% ) . Infection was rare for RR homozygotes ( 12 . 9% ) , and much more common for S1S1 ( 75 . 3% ) and S2S2 ( 29 . 6% ) homozygotes , a difference in infection odds of over 15-fold ( i . e . infection odds of 0 . 15 vs . 3 . 0; Figure 1B ) . Heterozygotes showed intermediate phenotypes . There was weak partial dominance of S1 over R ( observed Clopper-Pearson 95% confidence interval of infection probability for S1R = 51 . 0–61 . 8%; expected intermediate phenotype = 44 . 1% ) , such that relative to RR , carrying an S1 allele increases the odds of infection 5 . 9-fold ( p=6 × 10−42 ) while a second S1 allele further increases the odds of infection 2 . 7-fold for a 15 . 9-fold difference ( p=1 × 10−4 ) . The S2 allele acts additively , such that each S2 allele increases the odds of infection 1 . 5-fold ( p=6 × 10−5 ) . We confirmed the PTC2 signal using an independent set of 392 snails from 13-16-R1 that had previously been phenotyped ( Tennessen et al . , 2015a ) ( p=7 × 10−12 for R vs . S1; p=4 × 10−5 for R vs . S2; Figure 1—figure supplement 1 ) . These snails had also been genotyped at sod1 and RADres , revealing that all three loci had significant independent associations when included together in the same model ( p≤10−4 for each ) , with no evidence for epistasis ( p>0 . 05 for interaction terms ) . Segregating variation at PTC2 has a stronger association with odds of infection than that of any other known B . glabrata locus ( Tennessen et al . , 2015a; Tennessen et al . , 2015b ) . The BS-90 RAPD marker ( Knight et al . , 1999 ) is only 5 Mb and 23 cM from PTC2 ( Figure 1—figure supplement 2; Tennessen et al . , 2017 ) . This marker is predicted to be 17 cM ( range 6–33 cM ) from a causal locus , which could therefore plausibly be PTC2 ( Supplementary file 1B ) . Using PacBio whole-genome assemblies from snails homozygous for each of the three PTC2 alleles ( Supplementary file 1C ) , we find striking sequence and structural divergence among the haplotypes ( Figure 2 ) . Alignable regions show 3 . 3% nucleotide divergence on average ( SD = 2 . 1% ) . A majority of PTC2 sequence shows no similarity among alleles; the percentage of sequence that could even be aligned ranged from 12 . 9% ( R onto S2 ) to 40 . 0% ( S1 onto R ) . All three PTC2 haplotypes harbor unique insertions tens of thousands of bp in size , some of which contain complete coding genes , such that each genotype carries a distinct combination of genes ( Figure 2 ) . Shared orthologous genes at PTC2 show many nonsynonymous differences and in some cases homology can only be identified at the protein , not DNA , level ( Figure 2—figure supplement 1; Figure 3 ) . This degree of polymorphism is unusually high for conspecific haplotypes in most genomic regions in any taxon ( Leffler et al . , 2012 ) . In contrast to PTC2 , 89 . 5% of sequence on other contigs can be aligned between assemblies , with a mean of 0 . 4% nucleotide divergence . It is not obvious how a chromosomal rearrangement ( e . g . inversion ) could maintain more than two distinct haplotypes , and in any case we see no evidence for one in our assemblies . Using RNA-Seq data from homozygotes of each genotype to identify expressed genes , we fully annotated PTC2 ( Supplementary file 1D , E ) . Of the eleven PTC2 genes , eight are predicted to be TM1 genes , including all five genes that are shared among the three haplotypes ( genes 1 , 2 , 4 , 5 , and 9; Figure 2; Figure 3 ) . Of the three non-TM1 genes , gene 3 and gene 11 show homology to TM1 genes 2 and 8 , respectively , but without the TM1 domains . Gene 6 contains a conserved protein domain of unknown function ( DUF2732 ) . Only 11% of B . glabrata genes are TM1 genes , so they would be unlikely to constitute eight of eleven genes by chance alone ( p<10−5 ) . PTC2 TM1 genes are all between 166 and 530 codons , have TM1 domains that are displaced from the N-terminus ( Figure 3—figure supplement 1 ) , and like the rest of PTC2 they are highly polymorphic ( Figure 3 ) , with amino acid level divergence exceeding 50% in several cases . Sequences similar to both R and S haplotypes are present in other B . glabrata populations without admixed histories ( genomic and transcriptomic sequence from Brazilian strain BB02 , Adema et al . , 2017; transcriptomic sequence from Guadeloupe , Tennessen et al . , 2015b; Figure 3—figure supplement 2 ) , suggesting that they co-occur throughout the species range and the polymorphism is old . Synonymous divergence among alleles is higher than nonsynonymous , and a phylogeny of concatenated genes shows 24% synonymous divergence from the midpoint root . Thus , haplotypes are more consistent with an ancient origin ( 24 million years assuming a neutral mutation rate of 10−8 per year ) rather than recent divergence via selection for protein diversity . Other than the transmembrane segment , these genes contain no known protein domains or homology to sequences outside of gastropods , nor are they homologous to GRC genes . Some show homology to each other and/or to other genes near PTC2 or elsewhere in the genome , but amino acid level sequence similarity among paralogs is low ( <50% ) . The phenotypic effects of individual genes and polymorphisms will be an exciting subject for future work involving knockdowns or knockouts ( Allan et al . , 2017; Abe and Kuroda , 2019 ) and/or additional RNA-Seq from multiple individuals allowing quantification of expression differences . Both GRC and PTC2 suggest a model of snail-schistosome interaction via molecular recognition ( either of the parasite by the host , or of the host by the parasite ) that is mediated by TM1 gene polymorphism . Across metazoans , TM1 genes often play a role in immunological recognition , and include B- and T-cell receptors , Toll-like receptors , major histocompatibility complex genes , and similar host defense genes ( Pahl et al . , 2013 ) . Other polymorphic clusters of host transmembrane genes are used by parasites as receptors for host recognition and invasion ( e . g . human glycophorins and Plasmodium; Malaria Genomic Epidemiology Network et al . , 2017 ) , and at least one of the GRC TM1 genes controls shedding of S . mansoni cercariae ( Allan et al . , 2017 ) . One PTC2 TM1 gene is present only on the R haplotype and is an obvious candidate if it functions to recognize the parasite . However , allelic divergence among shared genes could also be important , and an R-specific gene alone would not explain the difference between S1 and S2 . In contrast to GRC , in which a completely dominant allele confers resistance , all three alleles in PTC2 differ in their susceptibility , and allelic associations are additive or show partial dominant susceptibility ( Figure 1B ) . This pattern suggests that multiple loci along the haplotypes may jointly contribute to phenotype by interacting with different combinations of parasite molecules such as SmPoMucs ( Roger et al . , 2008 ) or other glycoproteins ( Allan and Blouin , 2018 ) to determine the outcome of infection . As with GRC , we suspect non-neutral host-parasite coevolutionary processes have shaped sequence polymorphism at PTC2 . The inferred ancient origin ( >20 million years ) of PTC2 is inconsistent with a neutral coalescent process . Because 13–16-R1 is admixed from geographically isolated populations , we can’t infer natural allele frequencies or compare the site frequency spectrum to a neutral expectation for a randomly-mating population , as was possible for GRC ( Tennessen et al . , 2015b ) , though these allelic lineages do segregate in natural populations ( Figure 3—figure supplement 2 ) . Therefore , while the remarkable structural and nonsynonymous polymorphism appears adaptive , it is difficult to distinguish among plausible scenarios including overdominance ( Woolhouse et al . , 2002 ) , negative-frequency dependent selection ( Woolhouse et al . , 2002; Koskella and Lively , 2009; Bento et al . , 2017 ) , adaptive introgression from distantly related species ( Hedrick , 2013 ) , or selection for an epistatically-interacting supergene ( Thompson and Jiggins , 2014 ) . Introgression appears unlikely , as it would have had to occur twice independently to generate three distinct haplotypes , and all of the closest relatives of B . glabrata occur allopatrically in Africa ( DeJong et al . , 2001 ) . Therefore , the most plausible explanations involve some form of long-term balancing selection . Although S . mansoni is not native to the neotropics ( Desprès et al . , 1993 ) , selection may have been driven by other trematodes , a clade that has ubiquitously infected snails for millions of years ( Blair et al . , 2001 ) and which can be a strong selective force favoring rare alleles ( Koskella and Lively , 2009 ) . Schistosomes castrate snails ( Faro et al . , 2013 ) but wild snails show no sign of evolving universal resistance , suggesting that the R haplotype is either specific to parasite genotype or else costly to fitness in some undetected manner . The R allele has persisted within the 13–16-R1 population for decades in the absence of challenge by parasites , so any fitness cost must be relatively weak or context-dependent . We have thus far only observed an effect on one parasite strain , PR-1 , precluding inferences about gene-for-gene interaction . Nevertheless , a system of polymorphic matching alleles could explain the substantial schistosome-strain by snail-strain interaction in compatibility that is often observed ( Richards and Shade , 1987; Richards et al . , 1992; Knight et al . , 1999; Webster et al . , 2004; Webster and Woolhouse , 1998; Theron et al . , 2014 ) , including schistosome-infection dose-response curves that fit a simple phenotype-matching model ( Theron et al . , 2008; Theron et al . , 2014 ) . If more than one PTC2 gene contributes to resistance , then synergistic interactions among these genes and other unlinked loci could begin to explain the pronounced variation in host-parasite compatibility ( Schulenburg et al . , 2007 ) . In other invertebrates , highly polymorphic haplotypes can be major-effect loci for infection and show striking coevolutionary signatures including variable presence/absence of genes . For example , the PR-locus mediating bacterial resistance in Daphnia via matching-allele interactions also features haplotypes of vastly different sizes ( differences > 60 kb at both this locus and PTC2 ) with large non-homologous sections , and these contain glycosyltransferase genes that could mediate host-pathogen compatibility ( Bento et al . , 2017 ) . Similarly , the APL1 immune factor impacting Plasmodium development in Anopheles consists of adjacent paralogs that differ in copy number among species and show extreme diversity within species ( Rottschaefer et al . , 2011; Mitri et al . , 2020 ) . Thus , while invertebrates lack the acquired immunity of vertebrates and its associated adaptive genetic variation ( Spurgin and Richardson , 2010 ) , their defenses can show similar nuance conveyed by molecular diversity ( Loker et al . , 2004; Cerenius and Söderhäll , 2013 ) . However , the evolutionary consequences may not match those for vertebrate acquired immunity loci like the major histocompatibility complex or immunoglobulins , where sequence diversity per se tends to enhance immune effectiveness though perhaps at the cost of autoimmunity . For invertebrates , increased numbers of distinct immunogenetic sequences may not necessarily lead to increased resistance if parasites also use these sequences to recognize hosts or mount evasion strategies ( Schmid-Hempel , 2005 ) . If discarding immune genes is often as advantageous as gaining them , the result could be a patchwork of genes as observed at PTC2 . A more appropriate vertebrate analog might be blood groups used by parasites to invade host cells , for which haplotypic differences often include loss of functional genes , and diversity has been maintained for millions of years by balancing selection ( e . g . Dantu group , Malaria Genomic Epidemiology Network et al . , 2017; ABO group , Ségurel et al . , 2013 ) . The generation and maintenance of such divergent haplotypes remain to be fully explained and could reflect long-term fluctuating selection among alleles with different combinations of specificity and cost ( Seger , 1988; Ashby and Boots , 2017 ) . As with mosquitoes ( Marshall et al . , 2019 ) , a promising strategy for disease control involves recruiting the natural immunogenetic variation of vectors ( Reardon , 2016 ) . The successful implementation of CRISPR/Cas9 in gastropods ( Abe and Kuroda , 2019 ) will facilitate the creation of genetically modified snails having enhanced immunity to block disease transmission ( Maier et al . , 2019 ) . As large-effect loci , the TM1 clusters are excellent candidates to target in such efforts . However , more work is needed to characterize the functional effects of these genes , as well as the molecular and evolutionary dynamics between hosts and parasites . For example , if host polymorphism is adaptive , it may not be readily replaced in natural populations . Furthermore , gene-by-gene interactions between snail and schistosome genotypes could permit the rapid evolution of parasite counterstrategies . In the context of ancient trench warfare coevolution , it is unlikely that a universally resistant snail could be generated by a single genetic change , although successive changes could enhance parasite resistance enough to impact patterns of transmission ( Theron et al . , 2014 ) . As an alternative to genetic modification , future work could leverage the hypothesis that the snail TM1 proteins bind to key schistosome molecules that mediate invasion of the host . One could use TM1 proteins to find such molecules , as with snail fibrinogen related proteins and schistosome SmPoMucs ( Roger et al . , 2008 ) , or GRC and galactose ( Allan and Blouin , 2018 ) . More broadly , clusters of immune recognition loci with elevated functional diversity have long been used to track and predict patterns of adaptive variation across populations and species ( Sommer , 2005; Spurgin and Richardson , 2010 ) . Thus , we anticipate that this class of genes will play a central role in disease control as the molecular aspects of vector biology are fully brought to bear on schistosomiasis .
We used the Oregon State University population of 13–16-R1 that has been maintained as a large population ( hundreds ) since the mid-1970s ( Bonner et al . , 2012 ) . 13–16-R1 is descended from snails collected in Brazil and Puerto Rico ( Richards and Merritt , 1972; Sullivan and Richards , 1981 ) but its exact history is not entirely clear . Our population has been maintained in the absence of parasite exposure , and therefore under relaxed selective pressure in regard to parasite resistance . We used mice to maintain the schistosome parasites and to produce miracidia for challenge experiments . Infection is through contact with inoculated water and involves minimal discomfort . Infected rodents are euthanized with CO2 prior to showing clinical signs of disease and are dissected to recover parasitic eggs . Animal numbers were held to the minimum required for the research . Institutional approval: Oregon State University Animal Care and Use Protocols 4749 and 5115 . We challenged snails of the 13–16-R1 population with PR-1 miracidia , following previous methods ( Bonner et al . , 2012 ) . In brief , we arbitrarily chose 1700 outbred juvenile snails ( 4–6 mm diameter ) , challenged them each with five miracidia , and classified them as infected or uninfected . About 40% of snails became infected . From these , we randomly selected 600 infected and 600 uninfected snails for sequencing . These sample sizes were chosen based on a simulation of variants with minor allele frequencies ≥ 0 . 2 , with copies randomly assigned to 600 infected and 600 uninfected individuals at the expected sequencing coverage depth ( script SimulatePools . pl at https://github . com/jacobtennessen/GOPOPS/ ) , which revealed that FST between simulated sequencing pools was unlikely to exceed 0 . 05 ( p<10−5 ) and very unlikely to exceed 0 . 1 ( p<10−9 ) and therefore we had substantial power to detect larger FST differences . We divided the empirical pools into two technical replicates , and four pools ( each combination of infected/uninfected and technical replicate ) were sequenced across six lanes of the Illumina HiSeq 3000 ( paired-end reads of 151 bp ) at the Center for Genome Research and Biocomputing ( CGRB ) at Oregon State University ( Illumina data at NCBI SRA , BioProject Accession PRJNA638474 ) . Infected snails contain DNA from S . mansoni , which could potentially generate false sequence variants correlated with resistance . To prevent this , we converted reads to FASTA format , used BLASTN ( version 2 . 6 . 0 ) to identify reads that matched the S . mansoni reference genome ( v . 5 . 2 , Berriman et al . , 2009 ) with an E-value cutoff of 1e-040 , and then filtered all such reads , as well as their mate pairs , from all downstream analysis . Filtered FASTQ files , having had adapters removed with Cutadapt ( version 1 . 15 , Marcel , 2011 ) and trimmed with Trimmomatic ( v . 0 . 30 , Bolger et al . , 2014; options: LEADING:20 TRAILING:20 SLIDINGWINDOW:5:20 MINLEN:50 ) , were aligned using BWA version 0 . 7 . 12 ( command: bwa mem -P -M -t 4; Li and Durbin , 2009 ) initially to reference genome BglaB1 and ultimately to our PacBio assemblies ( Figure 2—figure supplement 2 ) . All reads marked as secondary alignments were filtered out of the sam files . We used SamTools version 1 . 3 ( Li et al . , 2009 ) to convert these to sorted bam files ( commands: samtools view -bT; samtools sort ) and generate pileup files ( command: samtools mpileup -t DP -A ) . From these files , we estimated allele frequencies at each variant within each pool , and calculated FST in overlapping 10 kb windows across the genome , using the scripts MakeFreqTableFromPooledPileup . pl ( options: -a 0 . 1 and -d 15 ) and FstFromJoinedFreqTablesWindow . pl ( default options ) , available at https://github . com/jacobtennessen/GOPOPS/ . We only considered windows with at least 20 single-nucleotide polymorphisms , in order to exclude associations that are supported by few variants and which are therefore likely to be spurious . To more precisely estimate genotype-phenotype associations at the LG XII candidate region , we genotyped candidate loci from the region in individual snails . We designed primers for genotyping using Primer-BLAST ( https://www . ncbi . nlm . nih . gov/tools/primer-blast/ ) on the consensus of our assemblies and used them for PCR amplification ( Supplementary file 1F ) . These surrounded indels , such that after initial confirmatory sequencing in test samples , samples could be genotyped with PCR and gel electrophoresis alone . We genotyped the candidate locus in 1570 of the original 1700 phenotyped snails , including 1165 of the 1200 samples used in the genome-wide association study . Furthermore , in order to independently validate the candidate region , we also genotyped it in 392 snails ( also 13–16-R1 ) from a set of 439 that had been phenotyped several years previously ( Tennessen et al . , 2015a ) . We tested for effects between genotype and phenotype using logistic regression , following our standard approach ( Tennessen et al . , 2015a; R Development Core Team , 2020; Source code 1 ) . Specifically , we first coded infection as binary ( 1 or 0 ) and each allele as either additive ( ‘add’: 0 , 1 , or 2 copies of the allele ) , dominant ( ‘dom’: 1 or 0 for presence/absence of allele ) , or recessive ( ‘rec’: 1 if homozygous , 0 otherwise ) . We first confirmed an independent effect of both S alleles relative to the R allele with model glm ( infection~S1add+S2add , family = binomial ) , and then we found the best-fitting parameter combination ( minimum Akaike information criterion ) which was model glm ( infection~S1dom+S1rec+S2add , family = binomial ) . The positive effect of both S1dom and S1rec on infection odds was interpreted as partial dominance ( i . e . increased susceptibility if the allele is present plus additional increased susceptibility for homozygotes ) . We tested for epistasis by first adding terms for RADres and sod1 ( known to act additively; Tennessen et al . , 2015a ) to the model and then testing if interaction terms among loci were significant . We generated three PacBio assemblies from inbred snail lines homozygous for the three PTC2 alleles . ( Supplementary file 1C; assemblies NCBI Genome , BioProject Accession PRJNA639204 ) . The first assembly ( homR ) used snail line R68 , which is derived from 13-16-R1 and is highly resistant to S . mansoni strain PR-1 , as described previously ( Tennessen et al . , 2015a ) . We pooled and sequenced these snails in 15 SMRT cells ( 78x coverage ) on the Pacific Biosciences Sequel I at the CGRB . We assembled the resulting raw sequences using the HGAP4/FALCON assembler ( Chin et al . , 2016; options: Genome Length 1 Gb , Seed coverage 30 , Min Map Concordance 70 ) . Similarly , the other two assemblies ( homS1 and homS2 ) were generated using the same methodology from snail lines i90 ( 6 SMRT cells , 58x coverage ) and i171 ( 5 SMRT cells , 46x coverage ) , respectively . By default , we treat homR as the reference genome unless stated otherwise . To assign PacBio contigs to the existing linkage map ( Tennessen et al . , 2017 ) , we aligned 46 , 023 fragments of 100 bp each from BglaB1 ( the published genome ) that had previously been screened for uniqueness and used for targeted capture ( Tennessen et al . , 2017 ) using BLASTN ( version 2 . 6 . 0 ) with default parameters . PacBio contigs were then assigned to linkage groups if at least one unique fragment from a mapped BglaB1 contig aligned to it , and if at least 75% of these matching unique fragments pertained to the same linkage group . We thus assigned 1489 homR contigs to linkage groups , representing 635 Mb; these assignments were supported by a median of seven mapped fragments per homR contig , with an average of 96% of fragments per homR contig mapping to the same linkage group . In the vicinity of PTC2 , we assessed sequence similarity with dot plots . Each assembly was broken into overlapping 600 bp segments ( script ChopFastaStaggered . pl at https://github . com/jacobtennessen/MiSCVARS/ ) , which were tested for sequence similarity in pairwise comparison using BLAT ( Kent , 2002; options: stepSize = 1 -minScore = 300 ) followed by script AssessBlatChopped . pl ( at https://github . com/jacobtennessen/MiSCVARS/ ) . To estimate average genomic sequence similarity outside of PTC2 , we used BLASTN ( version 2 . 6 . 0 ) to identify pairs of orthologous contigs between homR and homS1 ( our two best assemblies , which should represent random samples of 13–16-R1 in regions unlinked to PTC2 ) , and performed a similar BLAT comparison for all such pairs in which both contigs were over 2 Mb . For assemblies homR and homS2 , PTC2 is split between two contigs each ( Supplementary file 1C ) . We manually combined these contigs into continuous haplotypes . For homR , the ends of contigs R-35 and R-304 both align to each other with 99 . 9% similarity for 22 kb , indicating that they are in fact directly adjacent and the assembly algorithm was overly conservative in failing to join them ( Figure 2—figure supplement 3 ) . For the homS2 contigs , raw reads aligning to contig S2-78 overlapped with raw reads aligning to contig S2-773 , indicating a gap of only 12 . 6 kb which was confirmed by alignment to BglaB1 ( Figure 2—figure supplement 4 ) . Although BglaB1 is annotated , many genes were likely missed , especially those spanning multiple contigs . Furthermore , some PTC2 haplotypes may contain genes missing from the reference genome . Therefore , we performed RNA-Seq on snail lines homozygous for each of the three PTC2 haplotypes in order to identify all expressed proteins on each haplotype . Samples were prepared as described previously ( Tennessen et al . , 2015b ) . A single sample from each homozygous genotype was included in the same lane of the Illumina HiSeq 3000 at the CGRB ( single-end reads of 151 bp; Illumina data at NCBI SRA , Bioproject Accession PRJNA639026 ) . This single-sample approach precludes quantifying expression in a rigorous way ( Supplementary file 1E ) , but not our goal of assembling transcriptomes for the purpose of annotation . We performed a de-novo annotation of each PTC2 haplotype . Each haplotype-specific RNA-Seq dataset was adapter and quality trimmed using Cutadapt ( version 1 . 15 , Marcel , 2011; options: -q 15 , 10 ) and de-novo assembled into a transcriptome assembly using Trinity ( Grabherr et al . , 2011; default assembly parameters ) . Transcriptome assemblies were reduced to longest open reading frames using TransDecoder ( Haas et al . , 2013 ) by first identifying the longest open reading frames ( TransDecoder . LongOrfs ) , then using BLAST ( Altschul et al . , 1990 ) to map the longest open reading frames to the UNIPROT ( UniProt Consortium , 2019 ) gastropod protein database ( options: -max_target_seqs 1 -outfmt 6 -evalue 1e-5 ) , and finally by predicting protein sequences from the assembled transcripts ( TransDecoder . Predict ) . AUGUSTUS ( Stanke et al . , 2004 ) gene prediction training model was built from the UNIPROT Biomphalaria dataset . BUSCO ( Seppey et al . , 2019 ) was run on the homS1 genome assembly for use across all assemblies . Single copy orthologs found by BUSCO were used to make the SNAP ( Korf , 2004 ) gene prediction training set . A snail-specific repeat library was constructed using data from BglaB1 ( Giraldo-Calderón et al . , 2015; Adema et al . , 2017; https://www . vectorbase . org ) , and mollusca-specific repeats from Repbase ( Bao et al . , 2015 ) , and these repeats were then masked using RepeatMasker ( Smit et al . , 2013 ) . De-novo gene prediction was run with MAKER ( Cantarel et al . , 2008 ) on the repeat-masked genome assembly using the TransDecoder reduced transcriptome assembly as EST evidence , the UNIPROT Biomphalaria proteins as protein evidence , and de-novo gene prediction was conducted using SNAP and AUGUSTUS using the constructed prediction models . We used these automated annotations , along with predictions from genomic sequence from GENSCAN ( Burge and Karlin , 1997 ) , and putative orthologous transcripts in the reference genome project ( Giraldo-Calderón et al . , 2015; Adema et al . , 2017 ) identified with BLASTN ( version 2 . 6 . 0 ) , to guide manual alignment of RNA-Seq reads . Putative coding genes were rejected and subsequently ignored if they showed homology to transposable elements ( e . g . RNA transcriptase or transposase ) which are very abundant in the snail genome , if the open reading frame was less than 100 codons , or if the sequence could not be confirmed via manual alignment of RNA-Seq reads ( sequences in NCBI GenBank , Accessions MT787302-MT787323 ) . Secondary structure was predicted using TMHMM v . 2 . 0 ( Sonnhammer et al . , 1998 ) . To investigate the phylogenetic history of alleles , we first focused on the coding sequence of the two most conserved TM1 genes ( 4 and 5 ) as these could be aligned the most unambiguously . We searched for similar sequences in the genomic and transcriptomic data of reference genome BglaB1 generated from BB02 ( Giraldo-Calderón et al . , 2015; Adema et al . , 2017; https://www . vectorbase . org ) and RNA-Seq data from Guadeloupe population GUA ( Tennessen et al . , 2015b; Bioproject Accession PRJNA264063 ) . We conducted phylogenetic analysis using RAxML ( options: -N 100 m GTRCAT; Stamatakis , 2006 ) and displayed trees with FigTree version 1 . 4 . 4 ( http://tree . bio . ed . ac . uk/software/figtree/ ) . We also aligned and concatenated coding sequence from the 13–16-R1 alleles of the five genes present on all three haplotypes and used SNAP version 2 . 1 . 1 ( https://www . hiv . lanl . gov/content/sequence/SNAP/SNAP . html; Korber , 2000 ) to calculate nonsynonymous and synonymous divergence among alleles , and to infer synonymous site divergence from the midpoint root of a three-taxon neighbor-joining tree . | Schistosomiasis is a widespread parasitic disease , affecting over 200 million people in tropical countries . It is caused by schistosome worms , which are carried by freshwater snails . These snails release worm larvae into the water , where they can infect humans – for example , after bathing or swimming . Treatment options for schistosomiasis are limited . Eliminating the freshwater snails is one way to control the disease , but this is not always effective in the long term and the chemicals used can also harm other animals in the water . Another way to manage schistosomiasis could be to stop the worms from infecting their snail host by breaking the parasites’ life cycle without killing the snails . It is already known that some snails are naturally resistant to infection by some strains of schistosomes . Since this immunity is also inherited by the offspring of resistant snails , there is likely a genetic mechanism behind it . However , very little else is known about any genes that might be involved . Tennessen et al . therefore set out to identify what genes were responsible for schistosome resistance and how they worked . The experiments used a large laboratory colony of snails , whose susceptibility to schistosome infection varied among individual animals . To determine the genes behind this variation , Tennessen et al . first searched for areas of DNA that also differed between the immune and infected snails . Comparing genetic sequences across over 1 , 000 snails revealed a distinct region of DNA that had a large effect on how likely they were to be infected . This section of DNA turned out to be highly diverse , with different snails carrying varying numbers and different forms of the genes within this region . Many of these genes appear to encode proteins found on the surface of snail cells , which could affect whether snails and worms can recognize each other when they come into contact . This in turn could determine whether or not the worms can infect their hosts . These results shed new light on how the snails that carry schistosomes may be able to resist infections . In the future , this knowledge could be key to controlling schistosomiasis , either by releasing genetically engineered , immune snails into the wild ( thus making it harder for the parasites to reproduce ) or by using the snails’ mechanism of resistance to design better drug therapies . | [
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Native PKD2-L1 channel subunits are present in primary cilia and other restricted cellular spaces . Here we investigate the mechanism for the channel's unusual regulation by external calcium , and rationalize this behavior to its specialized function . We report that the human PKD2-L1 selectivity filter is partially selective to calcium ions ( Ca2+ ) moving into the cell , but blocked by high internal Ca2+concentrations , a unique feature of this transient receptor potential ( TRP ) channel family member . Surprisingly , we find that the C-terminal EF-hands and coiled-coil domains do not contribute to PKD2-L1 Ca2+-induced potentiation and inactivation . We propose a model in which prolonged channel activity results in calcium accumulation , triggering outward-moving Ca2+ ions to block PKD2-L1 in a high-affinity interaction with the innermost acidic residue ( D523 ) of the selectivity filter and subsequent long-term channel inactivation . This response rectifies Ca2+ flow , enabling Ca2+ to enter but not leave small compartments such as the cilium .
Polycystic kidney disease proteins ( PKDs ) , or polycystins ( PC ) , are divided into two distinct gene families . The four PKD1 members ( PKD1 , PKD1-L1 , PKD1-L2 , PKD1-L3 ) are large proteins ( ~1700–4300 amino acids ) with 11 putative transmembrane segments ( TM ) and a large autocleaved N-terminal extracellular domain . In contrast , the three PKD2 members are often included in the TRP ion channel family because they have 6 TM domains and a putative selectivity filter loop between TM5 and TM6 ( Ramsey et al . , 2006 ) . These include PKD2 , ( PC2 , TRPP1; formerly TRPP2 ) , PKD2-L1 ( PC2-L1 , TRPP2; formerly TRPP3 ) and PKD2-L2 ( PC2-L2 , TRPP3 , formerly TRPP5 ) ( Wu et al . , 2010 ) . Members of the PKD1 and PKD2-subfamilies are often reported to associate in the plasma membrane , although the nature of these complexes is not understood . PKD1 + PKD2 were purported to form mechanosensitive ion channels in the primary cilia of kidney collecting duct epithelia ( Nauli and Zhou , 2004; Nauli et al . , 2003 ) , a hypothesis that has recently been challenged ( DeCaen et al . , 2013; Delling et al . , 2016 ) . In humans , autosomal dominant polycystic kidney disease ( ADPKD ) is associated with loss-of-function mutations in genes that encode for either PKD1 or PKD2 ( Wu and Somlo , 2000 ) . Complete loss of either Pkd1 or Pkd2 in mice results in embryonic lethality with defects in formation of the kidney , pancreas and heart ( Boulter et al . , 2001; Lu et al . , 1997; Kim et al . , 2000; Wu et al . , 2000 ) . As in human ADPKD , adult Pkd2WS25/– mice have kidney cysts , renal failure , and die early ( Wu et al . , 2000 ) . PKD1-L1 + PKD2-L1 heteromers form a calcium channel complex in the primary cilia from embryonic fibroblasts and retinal pigmented epithelial cells ( DeCaen et al . , 2013 ) . Mice lacking Pkd2-L1 exhibit a form of heterotaxy ( intestinal malrotation ) in ~50% of offspring , suggesting that the channel modulates the ciliary Sonic Hedgehog ( SHh ) pathway during early development ( Delling et al . , 2013 ) . A putative PKD1-L3 + PKD2-L1 channel was reported in mouse taste buds and proposed to be the acid receptor required for sour taste ( Kawaguchi et al . , 2010 ) , but Pkd2-L1 knockout mice do not appear to have a deficit in sour taste perception ( Nelson et al . , 2010 ) . Of all the members of the PKD family , we found that only PKD2-L1 forms a functional homotetrameric channel when heterologously expressed on the plasma membrane ( DeCaen et al . , 2013 ) . Information about the physiological function of the endogenous homomeric PKD2-L1 is sparse . Emerging evidence suggests that single channel openings of homomeric PKD2-L1 channels are sufficiently large ( >100 pS ) to generate action potentials in the medullo-spinal cerebrospinal fluid contacting neurons ( CSF-cNs ) located in the ependymal layer of the brainstem ( Orts-Del'Immagine et al . , 2014; Orts-Del'immagine et al . , 2012 ) . Since channels containing PKD2-L1 subunits are constitutively-active and calcium-permeant , dysregulation of their activity could lead to aberrant cytoplasmic calcium regulation , especially in smaller compartments such as the primary cilia of epithelial cells ( DeCaen et al . , 2013 ) , the dendritic bulb of cerebrospinal fluid-contacting neurons ( Orts-Del'Immagine et al . , 2014 ) and the apical processes of the type III taste receptors ( Huang et al . , 2006; Ishimaru et al . , 2010 ) . Based on results using GCaMP3-targeted calcium sensors and current clamp recordings , primary cilia membranes were depolarized and had higher resting calcium concentrations ( ~ −20 mV , [Ca2+]in ~700 nM ) compared to the cell body ( -50 mV , [Ca2+]in < 100 nM ) ( Delling et al . , 2013 ) . Interestingly , the endogenous PKD1-L1 + PKD2-L1 channel complex inactivates in response to high cytoplasmic [Ca2+] when measured directly from the primary cilium ( IC50 =540 nM ) ( DeCaen et al . , 2013 ) . When measured through heterologous co-expression , the PKD1-L3 + PKD2-L1 channel was initially activated , then inactivated by extracellular Ca2+ ( 5–10 mM ) ( Chen et al . , 2015 ) , which was also observed when homomeric PKD2-L1 channels were expressed in in Xenopus oocytes ( Chen et al . , 1999 ) . It is unclear if calcium regulates the channels composed of PKD2-L1 directly or indirectly through an internal or external binding site . Here we have taken the reductionist approach of measuring the PKD2-L1 homomeric channel in a heterologous system in order to understand its mechanism of calcium-dependent inactivation . We first confirm previous studies that PKD2-L1 is modestly voltage-dependent ( Shimizu et al . , 2009 ) and show that this voltage dependence is independent of any divalent ion blocking mechanism . We then demonstrate that PKD2-L1 inactivation is initiated by the accumulation of internal Ca2+ . Surprisingly , we find that removal of the cytoplasmic EF-hands and coiled-coil motifs do not alter PKD2-L1 inactivation . We observe that Ca2+ ions conduct inwardly through the selectivity filter of PKD2-L1 , whereas outward Ca2+ conductance blocks the channel , followed by long-term inactivation . This type of Ca2+-dependence appears to be unique to PKD2-L1 among TRP channels tested to date and is triggered by Ca2+ coordination with D523 in the selectivity filter , which is also the site responsible for divalent metal and trivalent lanthanide metal block .
The voltage dependence of murine PKD2-L1 ( previously named TRPP2 , now called TRPP1 ) was previously characterized under conditions of high ( 11 mM ) internal magnesium ( Shimizu et al . , 2009 ) . To separate rectification due to divalent ion block ( Voets et al . , 2003; Nadler et al . , 2001; Lucas et al . , 2003; Topala et al . , 2007 ) from intrinsic voltage dependence , we first examined human PKD2-L1 in symmetrical 144 mM [Na+] in the absence of divalent ions ( free Ca2+ and Mg2+ <1 nM , Figure 1A ) . From a holding potential of 0 mV , we applied a +100 mV prepulse and then measured tail currents after immediate hyperpolarization to varying potentials ( Figure 1B , C ) . Plots of the instantaneous maximum tail currents ( Max . Itail ) were nearly linear ( ohmic ) at all potentials . However , the steady-state current ( Iss ) measured after the tail current decay outwardly rectified at potentials above +50 mV . At negative potentials PKD2-L1 appears to simply deactivate as estimated by the rate constants of tail current decay ( τ = 20–60 ms , Figure 1 B ) . In the on-cell configuration , the decay of ensemble-averaged single channel events closely matched macroscopic tail current decay ( Figure 1—figure supplement 1 ) . 10 . 7554/eLife . 13413 . 003Figure 1 . PKD2-L1 channel deactivation is voltage-dependent . ( A ) Diagram depicting symmetrical divalent free ( DVF ) conditions . ( B ) Top , voltage protocol used to generate tail currents . Representative currents from untransfected HEK cells ( gray traces ) and those expressing PKD2-L1 channels ( black traces ) . Tail currents were fit to a single exponential ( magenta trace ) , averaged and plotted relative to voltage ( Inset , Error ± SEM , N = 4–7 cells ) . ( C ) Sodium current density-voltage relationships measured at the maximum of the tail current and at steady state . Results from repeated trials of the voltage protocol are indicated ( Error ± SEM , N = 4–7 cells ) . ( D ) Right , Representative TRPM7 DVF Na+ currents recorded under ( black traces ) and after the addition of 1 . 8 mM CaCl2 ( blue traces ) . Left , Resulting TRPM7 current density-voltage relationships ( Error ± SEM , N = 8 cells ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 00310 . 7554/eLife . 13413 . 004Figure 1—figure supplement 1 . Decay of hyperpolarization-induced tail currents from PKD2-L1 channels . ( A ) Single channel current events triggered by five hyperpolarization steps ( top ) in control conditions and after treatment with dibucaine . Dashed lines are 12 . 1 pA increments that separate multiple open channel events ( inward , 121 ± 3 pS ) . Level ‘C’ indicates the level at which all PKD2-L1 channels are closed . Open channel levels ‘O’ are subscripted with the number of simultaneous open events ( up to 11 ) . ( B ) Ensemble average of single channel records from 20 hyperpolarizing pulses reveals the time course of tail current decay ( black trace ) . ( C ) Tail current decay measured in the whole-cell configuration . Note that all current traces are graphed on the same time scale and are activated by the same voltage protocol shown at the top of panel A . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 004 We considered the possibility of voltage or time-dependent monovalent ion block of the pore in the absence of divalent ions . However , single channel amplitudes appear to remain constant over the time course of the pulse , indicating a lack of fast ( flicker ) block ( Figure 1—figure supplement 1 ) . Also , replacing Na+ by K+ did not change the current amplitude or rate of decay ( Figure 1C ) . Although we cannot rule out a site that binds and blocks all inward monovalent cations , we argue that the most likely interpretation is that the channel simply deactivates after hyperpolarization ( see Discussion ) . It is important to note that the onset of the PKD2-L1 tail current is very fast ( <120 μs , data not shown ) , without an obvious ‘hook’ to indicate overlapping activation and inactivation time courses as observed in delayed rectifying potassium channels ( hERG and Kv3 . 1b ) ( Labro et al . , 2015; Vandenberg et al . , 2012 ) . The tail currents of PKD2-L1 are unusual compared to other cation-nonselective TRP channels . For example , currents conducted by TRPM7 are ohmic and do not decay upon membrane hyperpolarization ( Figure 1D ) . However , TRPM7 becomes outwardly rectifying when calcium is added to the external sodium solution ( and , as is well-known for TRPM6/7 , blocked by internal Mg2+ ) ( Topala et al . , 2007; Li et al . , 2007 ) , indicating that TRPM7 rectifies in the presence of a physiological level of extracellular calcium , and possibly other divalent ions . Taken together , these data demonstrate that human PKD2-L1 channels are voltage-dependent , as previously described for the murine orthologue by Nilius and colleagues ( Shimizu et al . , 2009 ) . Thus , PKD2-L1 voltage dependence which is independent of cation block is likely shared between murine and human orthologs and may be a unique among the TRP family of ion channels . The following experiments focus on another unusual feature of PKD2-L1 we call ‘long-term inactivation’ which is dependent on the accumulation of internal calcium ions . High external Ca2+ ( 10 mM ) potentiates human ( Chen et al . , 1999 ) but not murine PKD2-L1 ( Shimizu et al . , 2009 ) . To examine calcium’s modulation of the current under physiological Ca2+ conditions , we expressed human PKD2-L1 and measured PKD2-L1 currents under voltage clamp in 2 mM [Ca2+]ex . As with high external calcium , PKD2-L1 current was initially potentiated but , interestingly , was then completely and apparently irreversibly inactivated over 8 min ( Figure 2—figure supplement 1A , B ) . During the voltage ramp , we observed a negative shift of Erev from 8 to 0 mV , reflecting a loss in the available calcium-permeable PKD2-L1 channels and/or a shift in the ion composition of the patched cells ( Figure 2—figure supplement 1C ) . Similar shifts in equilibrium potentials due to accumulation of internal cations have been reported for cells overexpressing P2X2 channels ( Li et al . , 2015 ) . Since potentiation ( 4 min ) and inactivation ( 8 min ) were slow , we hypothesized that internal accumulation of Ca2+ could account for the delayed onset of both . We integrated the tail currents triggered by −60 mV repolarizations to estimate the number of calcium ions accumulating within the cell ( Figure 2—figure supplement 1A , C ) . We found that Ca2+ moving through PKD2-L1 saturates the internal calcium buffers ( 5 mM BAPTA and intrinsic ) and accumulates to concentrations >10 μM ( Figure 2—figure supplement 1D ) . The time course of BAPTA saturation by PKD2-L1-mediated increases in Ca2+ occurs after 4 min , suggesting that internal calcium accumulation is responsible for channel inactivation . Consistent with these observations , we varied internal [Ca2+] and observed that cells patched with high initial [Ca2+] ( ≥ 450 nM ) inactivates 1–4 min sooner than cells patched with lower internal free-Ca2+ ( ≤ 100 nM; Figure 2A , B ) . Finally , when the buffering strength is increased ( 15 mM BAPTA , 5 mM EGTA ) , PKD2-L1 currents potentiate , but do not inactivate , over a 10 min time course ( black trace , Figure 2B ) . 10 . 7554/eLife . 13413 . 005Figure 2 . High intracellular Ca2+ irreversibly inactivates PKD2-L1 . ( A ) Representative PKD2-L1 currents captured at 1 , 4 and 8 min time points recorded with the indicated buffered [free- Ca2+]; 2 mM Ca2+ in the external solution . ( B ) Time course of outward peak current density recorded under the indicated conditions ( Error ± SEM , N = 7–10 cells ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 00510 . 7554/eLife . 13413 . 006Figure 2—figure supplement 1 . The time course of PKD2-L1 inactivation correlates with internal Ca2+ accumulation . ( A ) Top , Voltage ramp applied at 0 . 5 Hz to activate PKD2-L1 currents expressed in HEK 293T cells . Bottom , Representative PKD2-L1 currents captured at 1 , 4 , and 8 min time points in physiological [Ca2+] ( 2 mM [Ca2+] external , initial 100 nM [free Ca2+] internal ) . Inset right , corresponding integrated tail currents elicited by repolarization to -60 mV . B ) Plot of the average Ipeak ( measured at 100 mV ) and Itail density ( measured at -60 mV ) over an 8 min time course . ( C ) Relationships between the cumulative integrated Itail currents and the change in measured reversal potential ( Erev ) over time . ( D ) Time course of the estimated total internal [Ca2+] accumulation and [free Ca2+] using 5 mM BAPTA-buffered intracellular solution ( see Materials and methods , N= 9 cells , Error ± SEM ) . The blue area under the free Ca2+ curve is the ‘good buffering’ range ( 0 . 68–683 μM ) for BAPTA ( kD = 216 μM ) ( Ben-Johny et al . , 2015 ) ; the calculated free [Ca2+] above this range is thus less accurate . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 006 To determine whether internal Ca2+ could alter PKD2-L1 channel activity when the endogenous buffering conditions were undisturbed , we patch-clamped cells expressing PKD2-L1 in the on-cell configuration and measured the outward single channel events in response to uncaged internal calcium ( Figure 3 ) . Immediately upon uncaging Ca2+-bound NP-EGTA by a 1 s , 405 nm laser pulse , PKD2-L1 open probability ( Po ) was reduced by 27-fold . The channel was identified as PKD2-L1 since its conductance was consistent with PKD2-L1 homomers ( outward 198 pS , inward 121 pS ( DeCaen et al . , 2013 ) , the conductance was not observed in untransfected HEK-293T cells , and the single channel conductance and macroscopic whole cell currents were blocked by dibucaine ( Figure 1—figure supplement 1 ) . These results demonstrate that PKD2-L1 is long-term-inactivated at high internal [Ca2+] and that this process can occur independent of detectable channel potentiation . 10 . 7554/eLife . 13413 . 007Figure 3 . Uncaging internal Ca2+ blocks outward single channel openings . ( A ) Outward PKD2-L1 single channel events measured in the on-cell configuration; holding potential = 80 mV . Ca2+ dependent fluorescence was measured using Fluo-3; internal Ca2+ was caged with NP-EGTA . Blue arrows indicate the time points at which Ca2+ was uncaged using a 1 s 405 nm UV pulse . Images before and after uncaging are shown above the single channel record . ( B ) Expanded time scales of the record in A , illustrating the rapidity of current block . Right , Normalized open probability histograms measured in control ( gray ) and after uncaging cytosolic Ca2+ ( red ) . The open probability ( Po ) of PKD2-L1 was significantly reduced after the UV pulse ( N= 4 cells , Error ± SEM; asterisk indicates p<0 . 005; Prior to UV pulse , Po = 0 . 0057 ± 0 . 001; after UV pulse Po = 0 . 0002 ± 0 . 0002 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 007 What is the mechanism by which internal Ca2+ inactivates the PKD2-L1 channel ? The C-terminus of PKD2-L1 contains an oligomerization domain ( OD , 588–608 ) ( Chen et al . , 2015 ) , putative EF-hands ( 633–665 ) ( Li et al . , 2002 ) and a coiled-coil domain ( CC , 690–737 ) ( Yu et al . , 2012 ) . We generated truncations above and below each of the C-terminal motifs to determine their effect on channel function ( Figure 4A ) . We found that the most drastic truncation , located before the first coiled-coil domain ( Stop-588 ) , abolished PKD2-L1 channel function ( Figure 4C ) . Surface expression of the 588-Stop C-terminal cleavage mutant was unaltered in comparison to Wt PKD2-L1 , as assayed by membrane biotinylation ( Figure 4—figure supplement 1 ) . The intact OD might be critical for channel function or be a subunit oligomerization site , as previously proposed ( Zheng et al . , 2015 ) . However , truncating the PKD2-L1 channel just after the OD domain ( Stop-608 ) , or before and after the EF-hands and coiled-coil domain , did not alter normal current density or voltage dependence ( Figure 4B–E ) . With the exception of the 508-stop truncation , all truncations were potentiated , then inactivated in normal intracellular [Ca2+] ( Figure 4B ) . Thus the EF-hands and coiled-coil domain do not appear to be required for PKD2-L1’s calcium-dependent potentiation or inactivation ( nor for potentiation by calmidazolium , see below ) . 10 . 7554/eLife . 13413 . 008Figure 4 . PKD2-L1 channel C-terminal truncations do not alter Ca2+-dependent inactivation . ( A ) Cartoon depicting the locations of the C-terminal truncation mutants relative to the putative intracellular motifs . ( B ) Representative PKD2-L1 currents and the time courses of the potentiation and inactivation for the truncation mutants relative to the Wt channel . Currents were recorded in physiological [Ca2+] ( same conditions as described in Figure 2 ) . ( C ) Representative currents activated by +10 mV voltage steps from -100 to +100 mV from the Wt and truncated PKD2-L1 channel . ( D ) Corresponding voltage dependence of the kinetics of channel opening ( τpeak ) and channel closure ( τtail ) . ( E ) The average PKD2-L1 channel current density measured at +100 mV . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 00810 . 7554/eLife . 13413 . 009Figure 4—figure supplement 1 . Cell surface expression of the non-functional PKD2-L1 truncation mutants . Transfected HEK 293T cells expressing N-terminally HA-tagged PKD2-L1 ( HA-2L-1 ) and PKD2-L1 C-terminal truncation ( HA-2-L1 Stop-588 ) were labeled with biotin . Total lysates and streptavidin-precipitated biotinylated proteins were run on a single gel and analyzed by immunoblot with anti-HA ( upper panel ) . Biotinylated protein immunoprecipitated by streptavidin shows that the PKD2-L1 ( Stop-588 ) truncation mutant can be detected on the plasma membrane ( fourth lane ) . Na+/K+-ATPase serves as a positive control for successful membrane biotinylation and streptavidin IP ( middle panel ) . Whole cell lysates ( WCL ) detected with anti-HA ( bottom panel ) serve as the loading control . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 009 The results above exclude the possibility that PKD2-L1’s cytoplasmic EF-hands and the coiled-coil domains are involved in inactivating its current or in trafficking the channel to the membrane . When measuring the voltage dependence of the PKD2-L1 channel under physiological Ca2+ conditions , we observed that PKD2-L1 channels exhibit a biphasic current-voltage relationship , in which the outward current decreases at potentials beyond the reversal potential for calcium ( ECa ) ( Figure 5A ) . By altering extracellular calcium ( [Ca2+]ex ) , we noted that net nonselective outward current always declined positive to ECa ( Figure 5A ) . Repetition of the protocol demonstrated that PKD2-L1 channels had completely inactivated; that is , PKD2-L1 currents could no longer be elicited during the recording period ( Figure 5—figure supplement 1 ) . This data suggests that outward PKD2-L1 currents were stably blocked and/or inactivated by outward moving Ca2+ ions . We tested TRPM7 , TRPV1 , and TRPV3 for similar behavior and found that none of their outward currents exhibited this biphasic dependence ( Figure 5—figure supplement 1 , TRPV1 data not shown ) . Comparing the PKD2-L1 outward current to the inward tail current elicited by repolarization to −60 mV ( Figure 5—figure supplement 1 ) , we observed that the outward current was blocked , whereas the inward tail current remained relatively constant . These observations suggest that Ca2+ induced block and subsequent inactivation of all outward current , while inward flux was unimpeded . To examine this phenomenon , we used a voltage protocol designed to capture the rates of Ca2+- dependent block and inactivation ( Figure 5B , C ) . A series of voltage clamp steps 10–30 mV negative to ECa were applied so that inward tail currents and outward currents were stable over time . Thus , inward Ca2+ could permeate PKD2-L1 without blocking or inactivating the channel . Then , the depolarization step was increased to 28–42 mV more positive than ECa , where the outward currents were immediately blocked by outwardly moving calcium ions . In contrast , tail currents triggered by repolarization to -60 mV were not immediately blocked , but progressively decreased after each depolarization positive to ECa , indicating that the inward PKD2-L1 currents were not long-term- inactivated until after the outward current was blocked by Ca2+ . By plotting the sum of the durations of the depolarization steps positive to ECa against the tail current magnitude ( Figure 5B , C ) , we estimated the rate of inactivation , τinact ≈ 2 s ( 1 . 8 ± 0 . 2 s; 2 . 1 ± 0 . 2 s; 1 . 7 ± 0 . 2 s for the [Ca2+]ex conditions 10 μM , 1 mM and 20 mM respectively ) . 10 . 7554/eLife . 13413 . 010Figure 5 . Block of PKD2L-1 by outward Ca2+ triggers channel inactivation . ( A ) Left , Exemplar currents measured with the indicated [Ca2+]ex ( inset ) . Right , resulting outward current densities at the indicated ECa ( N = 6–7 , Error ± SEM ) . ( B ) PKD2-L1 currents recorded from separate cells activated in the presence of 10 μM , 1mM and 20 mM [Ca2+]ex by a series of depolarizations ( 0 . 2 Hz ) hyperpolarized and depolarized relative to ECa . ( C ) Top , resulting normalized peak and tail currents ( Error ± SEM , N =4–5 cells ) . Bottom , PKD2-L1 inactivation rate in each [Ca2+]ex condition ( 10 μM , 1 mM and 20 mM ) was determined by single exponential fitting of the decay of the tail current amplitude ( τinact . = 1 . 8 ± 0 . 2 s; 2 . 1 ± 0 . 2 s; 1 . 7 ± 0 . 2 s , respectively ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 01010 . 7554/eLife . 13413 . 011Figure 5—figure supplement 1 . Outward PKD2-L1 current is blocked at membrane potentials positive to ECa . ( A ) Left , Scheme depicting the ionic conditions and voltage protocol used to measure the voltage dependence of several TRP channels . Right , Whole-cell currents measured from HEK-293T cells expressing TRPM7 , TRPV3 and PKD2-L1 . In black are the currents activated by depolarizations negative to ECa . In red are currents activated by depolarizations positive to ECa . All scale bars as for TRPM7 . ( B ) Peak current density-voltage relationship . Results from repeated trials of the voltage protocol are indicated ( Error ± SEM , N =4–9 cells ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 011 The above results suggest that outward-going calcium ions through PKD2-L1 block the pore and induce a long period of channel inactivation . Thus , we sought to reduce the affinity for the Ca2+ coordination sites within the selectivity filter to relieve the outward current block by Ca2+ and thus reduce inactivation . We previously reported that the PKD2-L1 conductance could be abolished by double serine or alanine mutations , to Asp523 and Asp525 ( D523S:D525S or D523A:D525A ) , whereas mutation of Asp530 ( D530N ) did not alter the conductance ( DeCaen et al . , 2013 ) . We also tested individual alanine substitutions to either position ( data not shown ) , which also abolished the PKD2-L1 conductance . These data suggest that residues D523 and D525 are critical positions within the PKD2-L1 selectivity filter . Since D523 is the most conserved residue among the PKD2 family members , we propose that it is essential for block , inactivation , and its modest Ca2+ selectivity ( Figure 6A ) . Since asparagine ( Asn , N ) has a similar volume ( ≈ 11 nM ( Nauli and Zhou , 2004 ) ) but its carboxamide side chain is not as electronegative as the carboxylate found in the native aspartate ( Asp , D ) , we generated individual substitutions of Asn for D523 and D525 . We found that at similar current densities , D523N mutant-associated currents did not inactivate over an 8 min time course , whereas D525N currents first potentiated and then inactivated similar to the Wt channel ( Figure 6B ) . To determine if the D523N mutations altered outward Ca2+-dependent inactivation , we used the same depolarization protocol as used in Figure 5B and observed that the outward peak and inward tail currents were stable over time and potential , indicating that Ca2+ does not block or inactivate the D523N current ( Figure 6C , D ) . Furthermore , the voltage dependence of the D523N outward current is not biphasic at potentials more positive to ECa ( Figure 6—figure supplement 1 ) . 10 . 7554/eLife . 13413 . 012Figure 6 . Filter mutant D523N is not blocked by outward Ca2+ and is not inactivated . ( A ) Amino acid alignment of the pore regions of PKD2 family members . The black bar indicates the selectivity filter . ( B ) Representative currents and time course of the average tail and peak current densities for the selectivity filter mutants ( Error ± SEM , N =7 cells ) . ( C ) Left , representative D523N currents activated by a series of depolarizations ( 0 . 2 Hz ) negative ( +120 mV ) and positive ( +180 mV ) to ECa ( 138 mV ) . Right , resulting normalized peak and tail current amplitudes ( Error ± SEM , N =5 cells ) . ( D ) The relative permeability ( Px/PCs ) of Na+ , K+ and Ca2+ for Wt and mutant channels . These values were calculated using the measured reversal potentials from the steady state voltage-current relationships in Figure 6—figure supplement 1 , and tabulated in Figure 6—source data 1 ) . ( E ) Top , representative tail currents activated by repolarizing -60 mV from the indicated potentials . Bottom , Corresponding voltage dependence of the channel closing ( deactivation ) kinetics ( τtail;Error ± SEM , N=6–8 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 01210 . 7554/eLife . 13413 . 013Figure 6—source data 1 . A table listing the relative permeabilities of cations through the PKD2-L1 channels as estimated by the measured reversal potentials ( Erev ) . Relative permeabilities ( Px/PCs ) of Na+ , K+ and Ca2+ compared to Cs+ were calculated ( see Methods ) based on the measured reversal potential ( N= 4–9 , Error ± SEM ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 01310 . 7554/eLife . 13413 . 014Figure 6—figure supplement 1 . The current-voltage relationships of PKD2-L1 channels in the presence of different extracellular cations . Left , Representative current traces activated by 300 ms , +5 mV depolarizations from -100 to +100 mV; holding potential = −60 mV; recorded from the indicated PKD2-L1 channel/mutant . The external concentration of each ion was 140 mM ( no other cations except those indicated ) . Right , Corresponding outward Ipeak current densities ( N= 4–9 , Error ± SEM ) . Intracellular Mg2+ was omitted . Free [Ca2+] was calculated to be 15 nM ( 15 mM BAPTA and 5 mM EGTA ) to prevent Ca2+ accumulation and inactivation of the PKD2-L1 current . The external free-Ca2+ in the Na+ , Cs+ and K+ conditions ≈ 10 μM; ECa = 88 mV . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 014 These data demonstrate that Ca2+ does not block the D523N current in the outward direction and thus the ensuing inactivation does not occur . The selectivity for Ca2+ of the filter mutant channels D523N and D525N are reduced 19- and 8- fold ( respectively ) compared to the wild type channel ( PCa/PCs = 7 ) , as estimated by the change in Erev when measured in Na+ , K+ , Ca2+ and Cs+ extracellular conditions ( Figure 6D , Figure 6—figure supplement 1 , Figure 6—source data 1 ) . The speed of tail current decay of the D523N channel was enhanced ≈ 20-fold ( measured at −100 mV , Wt τTail = 43 ms ± 3; D523N τTail = 2 . 3 ms ± 0 . 2 ) , whereas those from the D525N mutation were much less affected ( D525N τTail = 37 ms ± 2; Figure 6E ) . Despite dramatically altering PKD2-L1 kinetics and selectivity , the D523 mutation retains sensitivity to modulation by the membrane-permeant calmodulin antagonist , calmidazolium ( Wt EC50 = 2 . 6 μM ± 6; D523N EC50 = 9 μM ± 7; also see DeCaen et al . [DeCaen et al . , 2013] ) and block by the amide local anesthetic , dibucaine ( Wt IC50 = 31 μM ± 5; D523N IC50 = 23 μM ± 3; Figure 7—figure supplement 1 , Figure 7—source data 1 ) . These results suggest that modulation by calmidazolium and block by dibucaine are preserved in the D523N mutant , suggesting that these compounds alter PKD2-L1 function through sites allosterically coupled to the filter . Together , these results demonstrate that while both D523 and D525 participate in Ca2+-selectivity of the pore , position D523 is the most critical to Ca2+- inactivation . Since Asp523 apparently forms the high affinity coordination site for Ca2+ that is responsible for Ca2+-selectivity and initiates outward Ca2+-dependent inactivation , we hypothesized that it might also be the binding site for divalent metals and trivalent metal antagonists . The transition metals , cadmium and zinc , blocked the PKD2-L1 Wt current ( IC50 = 25 μM ± 5; IC50 = 156 μM ± 15 ) and the D525N filter mutant ( IC50 = 40 μM ± 8; IC50 = 209 μM ± 28 ) , but were ineffective antagonists of the D523N channel at concentrations below 1 mM ( Figure 7A , B; Figure 7—source data 1 ) . Trivalent cations , such as lanthanum and gadolinium , are commonly used non-specific blockers of Ca2+-permeable channels . We found that the PKD2-L1 channel is 20-times more sensitive to block by Gd3+ ( IC50 = 9 μM ± 3 ) and La3+ ( IC50 = 3 μM ± 4 ) than reported for members of the TRPV and TRPA families ( IC50 ≥ 200 μM , Figure 7C , D ) ( Leffler et al . , 2007; Banke , 2011; Xu et al . , 2002 ) . This potency was preserved in the D525N filter mutant channel ( IC50 = 11 μM ± 3 and 1 μM ± 4 , respectively ) . However , the D523N channel displayed anomalous mole fraction behavior in the presence of La3+ , conducting through the channel at low to mid-µM concentrations ( 1–100 μM ) , but blocking the basal current in the millimolar range ( Figure 7E ) . These findings suggest that D523 forms a coordination site for divalent and trivalent metal cations . 10 . 7554/eLife . 13413 . 015Figure 7 . Loss of transition metal block in selectivity filter mutant , D523N . ( A , B ) Effects of Zn2+ and Cd2+ on PKD2-L1 currents . Top , Exemplar PKD2-L1 currents activated by voltage ramps at the indicated [Zn2+] . Bottom , Corresponding time courses of the Zn2+ block of the Wt and D525N PKD2-L1 channels . The internal buffer was 15 mM BAPTA and 5 mM EGTA , which prevented Ca2+ accumulation and inactivation of the PKD2-L1 currents . ( B ) Divalent metal concentration-dependent block of the Wt , D523N and D525N channels ( Error ± SEM , N = 4–6 cells ) . ( C–E ) Effects of trivalent metal block . Left , Exemplar PKD2-L1 currents activated by voltage ramps in the presence of La3+ . Right , Corresponding time courses of the La3+ block of the indicated PKD2-L1 channels . ( D ) Concentration-dependent block of PKD2-L1 and D525N channels by trivalent ions , and ( E ) potentiation of the D523N filter mutant channel ( Error ± SEM , N = 4–5 cells ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 01510 . 7554/eLife . 13413 . 016Figure 7—source data 1 . A table listing the potencies ( IC50 ) of PKD2-L1 current antagonism by dibucaine and transition metals . Concentration of half current inhibition ( IC50 ) was estimated by fitting the concentration-percent current block relationship using the Hill equation ( see Materials and methods ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 01610 . 7554/eLife . 13413 . 017Figure 7—figure supplement 1 . Calmidazolium activation and dibucaine block is preserved in the D523N filter mutant channel . ( A ) Left , Exemplar Wt and D523N currents activated by voltage ramps in the presence of two concentrations of the local anesthetic , dibucaine ( Dbc ) . Right , Corresponding time course of current block . ( B ) Left , Exemplar Wt and D523N currents activated by voltage ramps in the presence of two concentrations of the cell permeable calmodulin antagonist , Calmidazolium ( Cmz ) . Right , Corresponding time course of current potentiation . ( C ) Dibucaine concentration-dependent block of the Wt and D523N channels ( Error ± SEM , N=4–5 cells ) . ( D ) Calmidazolium concentration-dependent potentiation of the Wt and D523N filter mutant ( Error ± SEM , N = 4–5 cells ) . Intracellular [Ca2+] was buffered with 15 mM BAPTA and 5 mM EGTA to prevent Ca2+ accumulation and inactivation of the PKD2-L1 currents . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 017
Regulation of ion channels by cytoplasmic calcium is a common theme in biology ( Clapham , 2007; Yu and Catterall , 2004 ) and can occur through direct binding to the channel protein or through adaptor or modulatory proteins . Calcium binding proteins like calmodulin often bind to Ca2+-permeant ion channels and alter their function ( Ben-Johny et al . , 2015 ) . Calcium coordinating motifs within channel proteins , such as cytoplasmic EF-hands found in voltage gated CaV channels , alter channel inactivation based on localized calcium accumulation . Here , we have shown a direct mechanism of calcium regulation of the PKD2-L1 channel , in which outward Ca2+ binds in the selectivity filter and initiates inactivation of the channel . Calcium and other ions pass inwardly through the PKD2-L1 channel as long as the electrochemical gradient permits; when channel densities are high , this is sufficient to significantly shift internal ion concentrations as seen by the slowly shifting Erev . However , depolarizations above ECa lead to channel block and inactivation ( as proposed in Figure 8A ) . To our knowledge , this is the only reported ion channel with Ca2+-dependent rectification due to pore block-inactivation , and so far is unique to PKD2-L1 . 10 . 7554/eLife . 13413 . 018Figure 8 . Proposed kinetic scheme of PKD2-L1 channel states and a hypothetical model of Ca2+ coordination sites in the selectivity filter . ( A ) Calcium clamp of restricted spaces , such as primary cilia , by PKD2-L1 . Resting [Ca2+] ≅ 500–700 nM in primary cilia . PKD2-L1 channels in the cilia membrane are potentiated before inactivation by [Ca2+] < ~500 nM , but simply inactivated at [Ca2+] > ~700 nM . ( B ) Model of Wt and D523N filters with proposed Ca2+ movement in response to membrane potential . The PKD2-L1 low affinity Ca2+ ( D525 ) and high affinity ( D523 ) binding sites are colored yellow and red , respectively , corresponding to residues N921 and D918 in the TRPA1 structure ( PDB: 3J9P ) as a substitute for the undetermined PKD2-L1 structure . ( C ) Proposed four channel state scheme C = closed; O = open; Oblock = open channels blocked by outwardly moving Ca2+ and I = inactivated ( long-term ) . Corresponding rate constants ( K ) are indicated between the three channel states ( K1 = opening , K-1 = closure , K2 = inactivation ) . The colored arrows indicate the direction of channel state stimulus by either membrane depolarization ( +Vm ) or outwardly moving calcium ions ( Ca2+ ) . The recovery from calcium-dependent inactivation is shown as a shortened arrow since inactivation is irreversible on the time scale of the experiments ( <20 min ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13413 . 018 Many members of the TRPA , C , M and V families are potentiated and inactivated by Ca2+ dependent mechanisms ( Gordon-Shaag et al . , 2008; Zhu , 2005 ) . Thus far , the proposed mechanism ( s ) by which Ca2+ inhibits TRP channels are indirect , such as with PI ( 4 , 5 ) P2-depletion , Ca2+-regulated kinases , phosphatases , and phospholipases , via both calmodulin-dependent and independent processes ( Gordon-Shaag et al . , 2008; Zhu , 2005 ) . We showed that Ca2+ occupancy in the filter blocks the outward PKD2-L1 current and triggers its long-term inactivation . Although mutating the selectivity filter of TRPA1 ( D918 ) can abolish extracellular Ca2+-dependent potentiation and inactivation , the effect of extracellular Ca2+ on these processes follows elevation of intracellular calcium and is proposed to operate through an indirect , unknown mechanism ( Wang et al . , 2008 ) . For PKD2-L1 , outward Ca2+ current inactivates the channel after binding within the selectivity pore . Although we cannot rule out allosteric modulation by calmodulin or kinases during inactivation , involvement of the C-terminal EF-hand and coiled-coil are unlikely since no effect was observed on inactivation when these motifs were removed . Likewise , for the TRPA1 channel , removing the EF-hands did little to alter the onset of potentiation and inactivation ( Wang et al . , 2008 ) . Our observations from our PKD2-L1 EF-hands truncation are in agreement with results from the PKD2-L1 splice variants expressed in the liver , where a naturally occurring C-terminal EF-hand truncation is found to have no effect on Ca2+-dependent inactivation ( Li et al . , 2002 ) . Thus if C-terminal modulation is involved , the location for binding of a modulator would have to be located above the oligomerization domain , which appears to be critical for PKD2-L1 function , as the trafficking of the Stop-588 mutant was the same as the full length channel . We have shown that even potent ( BAPTA , 5 mM ) cytoplasmic Ca2+ buffers can be saturated when PKD2-L1 channels are overexpressed . Once occupied by outwardly moving calcium ions , the PKD2-L1 channels irreversibly inactivates in seconds ( τ = 2 . 1 s ) . Thus our results explain the inactivation phenomenon observed in Chen et al . ( Chen et al . , 1999 ) and describe a direct level of Ca2+ feedback inhibition of the PKD2-L1 channel . Given that cytoplasmic [Ca2+] ranges from 50 nM to as high as 10 µM ( near mouths of channels ) , what is the physiological relevance of outward calcium current regulation in PKD2-L1 channels ? Because of the cell’s large volume ( typically 1–3 pL ) , it is unlikely that the concentration of calcium in the cytosol would ever reach sufficient concentrations to move outwardly through PKD2-L1 channel and trigger inactivation . However , because PKD2-L1 channels have large inward conductances ( PKD2-L1: 120–150 pS and PKD1-L1 +PKD2-L1 = 86 pS ) ( DeCaen et al . , 2013; Chen et al . , 1999; Shimizu et al . , 2009 ) with modest selectivity for Ca2+ ( PCa/PCs PKD2-L1 and PKD1-L1 +PKD2-L1 = 6–15 ) ( DeCaen et al . , 2013; Shimizu et al . , 2009 ) , the expression of just a few channels ( 1–10 ) in small cellular compartments could result in large changes in local Ca2+ concentrations . For example , the homomeric PKD2-L1 is enriched in the membrane of the dendritic nob of CSF contacting neurons , which have small volumes ( 113 fL ) ( Orts-Del'Immagine et al . , 2014; Orts-Del'Immagine et al . , 2016 ) . Here , PKD2-L1’s alkaline-pH-stimulated channel activity ( Po = 0 . 02 ) would increase the dendritic nob cytoplasmic calcium by ≈ 22 μM per channel opening . In smaller compartments , like the primary cilium ( 0 . 5 fL ) , where roughly 30 heteromeric PKD1-L1 +PKD2-L1 channels are expressed , ATP stimulation ( Po = 0 . 01 ) would increase the cilioplasmic calcium by ≈ 31 μM per channel opening . Thus , prolonged PKD2-L1 ( or PKD1-L1 +PKD2-L1 ) channel openings could result in increases in cellular compartment [Ca2+] up to the external [Ca2+] . Most important , we hypothesize that PKD2-L1 maintains Ca2+ concentration in the 500 -700 nM range observed in primary cilia ( Nelson et al . , 2010; Wu et al . , 2000 ) . As shown in Figure 2 , entering Ca2+ transiently potentiates PKD2-L1 up to ~750 nM [Ca2+]In - above this level , inhibition dominates . This behavior would serve as [Ca2+] clamp , a feedback mechanism that counteracts Ca2+ loss via diffusion into the cytoplasm ( Figure 8B ) . This mechanism would ensure that the primary cilium is a specialized calcium compartment , even though no calcium diffusion barrier exists between cilium and cytoplasm ( Nelson et al . , 2010 ) . However , the Ca2+ exchangers and intrinsic buffers present in each compartment are not known , limiting a more detailed interpretation of this mechanism . We have established that D523 within the filter is responsible for Ca2+-selectivity as ions pass inward , and for block by Ca2+ as it moves out of the cell ( Figure 6 , Figure 6-figure supplement 1 ) . Independent substitutions of the aspartate filter residues 523 and 525 with asparagine resulted in a significant reduction of Ca2+-selectivity , while independent alanine substitutions for either residue resulted in a non-conducting channel . Based on these observations we propose that this unique calcium conductance is achieved with two ion coordinating sites involving filter residues D523 and D525 . We speculate that Ca2+ unidirectional conductance is achieved by a ‘knock-on’ ionic interaction , where the outer residue ( D525 ) coordinates Ca2+ with weaker affinity than the inner residue ( D523 ) . In this model , the interaction of Ca2+ with D523 is broken by repulsion by a second Ca2+ occupying the outer D525 . When the membrane potential is more depolarized than ECa , Ca2+ occupies the high-affinity binding site ( D523 ) . Since there is no inner Ca2+ coordinating site to electrostatically ‘knock-off’ Ca2+ from D523 , it is not displaced from this site and outward current is blocked . Since Ca2+ does not block the D523N channel , we propose that Ca2+ is able to move in both directions in the mutant channel’s selectivity filter ( Figure 8B ) . This effect can be explained by the loss of the high-affinity binding site within the innermost portion of the selectivity filter . Since D523 is conserved within the PKD2 family , it is possible that other members of PKD2 family may share this same unidirectional calcium conductance , although the selectivities of homomeric PKD2 and PKD2-L2 channels have yet to be defined . We established that D523 is the metal binding site for divalent and trivalent ions within the selectivity filter of PKD2-L1 . The potency ( IC50 ) of PKD2-L1 block by Gd3+ was shifted from 9 μM to greater than 1 mM in the D523N channel , which reflects a loss of affinity >6 . 7 kcal/mol . Conversely , the D523N mutant conducts the smaller trivalent La3+ at µM concentrations , whereas the Wt and D525N channels are blocked within this concentration range . These seeming incompatible observations are reminiscent of La3+ anomalous mole fraction effects reported in Wt members of the TRPC family ( C3 , C4 , C5 and C6 ) ( Jung et al . , 2003; Strübing et al . , 2001; Hofmann et al . , 1999 ) . Thus , whether trivalent and divalent metals permeate or block TRP channels is likely determined by the strength of their electrostatic interactions with acidic residues that line the selectivity filter . A simple preliminary four state model of PKD2-L1 is shown in Figure 8C . We have defined the resting state found at negative potentials prior to depolarization as the closed state ( C ) , although PKD2-L1 has some of constitutive activity even at −100 mV ( PO = 0 . 007 ) . After depolarization , our analysis of macroscopic and single channel currents demonstrates that channel openings are transiently increased upon hyperpolarization ( Figure 1—figure supplement 1 ) . We proposed that these large , slowly decaying tail currents represent the population of channels transitioning from the open ( O ) to closed ( C ) state . The closed to open transition ( K1 ) can be measured by the kinetics of the peak currents during membrane depolarization ( <10 mV ) . While it is possible that PKD2-L1 gating may have voltage dependent inactivation properties akin to some delayed rectifying potassium channels ( IKr ) ( Warmke and Ganetzky , 1994; Trudeau et al . , 1995 ) , our experiments did not capture this kinetic property . Further biophysical characterization will be necessary to test this possibility . The PKD2-L1 channel opening ( K1 ) and closure ( K-1 ) are reversible and modestly voltage dependent , increasing 1 . 5x and 2 . 2x with 100 mV membrane potential shifts , respectively . Outwardly moving Ca2+ rapidly block the open channel state ( Oblock ) and trigger a slower long-term inactivation process ( K2 ) . The rate of inactivation ( K2 ) is dependent on outward Ca2+ current and is irreversible on the time scale of the experiments . Interestingly , the enhanced onset of channel opening and closing found in the D523N mutant may be related to the lack of Ca2+ selectivity and Ca2+-dependent inactivation . These observations demonstrate that the rates of PKD2-L1 gating can be altered by mutating positions within the selectivity filter , suggesting that permeant ion coordinating sites are partly involved . This feature has precedence in BK channels , where permeant ions alter the opening and closing transitions but do not alter the calcium- or voltage-dependent activation pathways ( Thompson and Begenisich , 2012; Piskorowski and Aldrich , 2006 ) . It is possible that PKD2-L1 pore blockade by outwardly moving Ca2+ ion ( s ) are displaced by inwardly Na+ current and a slow Ca2+ unbinding process prolongs the time course of channel closure . That is , hyperpolarization helps clear Ca2+ ions from the PKD2-L1 selectivity filter . This effect may be analogous to displacement of quaternary ammonium ion ( s ) by inward K+ current conducted by KV channels found in the squid giant axon ( Armstrong , 1971 ) .
HEK 293T cells were transiently transfected with the mammalian cell expression plasmid pTracer CMV2 containing the human PKD2-L1 gene ( isoform 1 ) . Cells were seeded onto glass coverslips and placed in a perfusion chamber , enabling changes in extracellular conditions . Data generated from cells patched in the whole cell configuration with <1 GΩ of resistance and >90 pA of leak current ( Vmem =-60 mV ) were not analyzed due to insufficient voltage control . Cell-attached single channel data with seal resistance <8 GΩ were not analyzed . Unless otherwise indicated , the pipette electrode solution contained ( in mM ) : CsMES ( 80 ) , NaCl ( 20 ) , HEPES ( 10 ) , MgCl2 ( 2 ) , Cs4-BAPTA ( 5 ) ; CaCl2 was added to achieve 90 nM free Ca2+ and pH was adjusted to 7 . 4 with CsOH ( MaxChelator ( Bers et al . , 2010 ) ) . The standard bath solution contained NaCl ( 150 ) , HEPES ( 10 ) , CaCl2 ( 2 ) and pH was adjusted with NaOH . When testing the relative permeability of monovalent cations , the bath solution contained ( in mM ) : X-Cl ( 150 ) , HEPES ( 10 ) and the pH was adjusted with X-OH; X is the indicated monovalent cation . When testing the relative permeability of Ca2+ , the bath solution contained: NMDG ( 100 ) , CaCl2 ( 20 ) , HEPES ( 10 ) , and pH was adjusted with CaOH2 . All saline solutions were adjusted to 300 mOsm ( ±5 ) with mannitol , if needed . Data was analyzed by Igor Pro 7 . 00 ( Wavemetrics , Lake Oswego , OR ) . The reversal potential , Erev was used to determine the relative permeability of monovalent cation X to Cs+ ( PX/PCs ) according to the following equation:PxPCs= αCseαxe[exp ( ΔErevRT/F ) ] where Erev , α , R , T and F are the reversal potential , effective activity coefficients for cation x ( i , internal and e , external ) , the universal gas constant , absolute temperature , and the Faraday constant , respectively . Based on out measurements of the Erev under our semi-bi-ionic conditions , the PKD2-L1 permeability of Cs+ and NMDG+ ions are equivalent , but are much less permeant to other cations tested ( Ca+ , K+ and Na+ ) . The effective activity coefficients ( αx ) were calculated using the following equation:αx=γx[X] where γx is the activity coefficient and [X] is the concentration of the ion . For calculations of membrane permeability , activity coefficients ( γ ) were calculated using the Debye-Hückel equation: 0 . 74 , 0 . 72 , 0 . 69 , and 0 . 29 correspond to Na+ , K+ , Cs+ and Ca2+ , respectively . To determine the relative permeability of divalent cations to Cs+ , the following equation was used:PxPCs= {αCsi[exp ( ErevFRT ) ][exp ( ErevFRT ) +1]}4αxe Erev for each cation condition was corrected to the measured liquid junction potentials ( -4 . 4 to 3 . 4 mV ) . Single channel events ( Figure 1—figure supplement 1 and Figure 3 ) were measured with standard extracellular saline in the pipette and high potassium saline in the bath ( in mM ) : KCl ( 125 ) , NaCl ( 20 ) , HEPES ( 10 ) and CaCl2 ( 2 ) to neutralize the resting membrane potential . To determine the time course of channel opening ( τpeak ) and closure ( τtail ) the peak current during the depolarization ( △ mV ) and the tail currents during repolarization ( −60 mV ) were fit using the following exponential equation:f ( x ) =B+A exp[ ( 1τ ) x] where τ is the time constant . The internal accumulation of calcium during experiments in Figure 2—figure supplement 1 was estimated using the following equation:=Q×r×charge1 . 6x10−19Coulombs × Ca2+2 charges × ( 6 . 02x1023mol ) −1 × ( 2 . 14x10−12 L ) −1 where Q is the cumulative integrated Itail current and r ( 0 . 199 ) is the product of the extracellular calcium ( 2 mM ) to sodium ( 150 mM ) ratio multiplied by the relative permeability of calcium for sodium ions ( PCa/PNa = 14 . 9 ) . With a starting condition of 100 nM free Ca2+ , the cumulative [free Ca2+] was estimated using MaxChelator ( Bers et al . , 2010 ) , where 5 mM BAPTA was used as an internal Ca2+ buffer . We assume that the 5 mM BAPTA perfusing the cell was the dominant buffer over intrinsic mobile and immobile buffers which ‘typically’ bind Ca2+ in the range of 40 bound/1 free ( Zhou and Neher , 1993 ) . HEK293 cells have an average radius of 8 μM and a volume of ~2100 μM ( Nauli and Zhou , 2004 ) , ( 2 . 1×10–12 L ) . Single channel current magnitude and open time was estimated at the resting membrane potentials for PKD2-L1 channel in CSF-contacting neurons ( −55 mV ) ( Orts-Del'Immagine et al . , 2016 ) , PKD2-L1 ( DeCaen et al . , 2013 ) and PKD1-L1 +PKD2-L1 channels from primary cilia ( −17 mV ) ( Delling et al . , 2013 ) . PKD2-L1 transfected HEK cells were incubated with extracellular o-nitrophenyl EGTA ( NP-EGTA; 4 mM ) and Fluo-3-AM ( 4 mM ) for 30 min at room temperature . Cells were visualized with an Olympus FV1000 confocal microscope equipped with an SIM scanner . After establishing a high resistance seal using electrodes with 3–5 mΩ pipette resistance in the on-cell configuration , the cell membrane potential was held at +80 mV and cytoplasmic calcium was uncaged by a 500 ms , 405 nm laser pulse . All images were analyzed using ImageJ ( NIH ) and IgorPro 7 ( Wavemetrics ) . Biotinylation of cell-surface proteins was performed using EZ-Link Sulfo-NHS-SS-Biotin ( Thermo Fisher Scientific , Waltham , MA ) according to the manufacturer’s instructions . In brief , 48 hr after transfection , HEK 293T cells were washed with PBS , and EZ-Link Sulfo-NHS-SS-Biotin applied to living cultured cells expressing HA , HA-2L1 , HA-2L1 ( Stop-588 ) for 30 min at 4°C . Cells were lysed , and the biotinylated proteins were precipitated using streptavidin agarose beads . The eluted proteins were analyzed by immunoblotting . | Most of our cells have a single tiny-hair like structure called a primary cilium that projects outwards from the cell surface . Many cilia contain an ion channel protein called PKD2-L1 that allows calcium ions to pass through the membrane that surrounds each cell . There are many different calcium channels and they are found in a variety of locations in cells to control the levels of calcium ions within various cell compartments . Channels on the cell surface allow calcium ions from the external environment to enter a compartment called the cytoplasm . Under normal conditions , calcium ions always flow into cells because they are much more abundant outside the cell than inside . Despite the absence of a membrane barrier between cilia and cytoplasm , calcium ions are maintained at a higher level in the cilium than in the cytoplasm . How is this difference maintained ? DeCaen , Liu et al . now show that PKD2-L1 is first stimulated and then inactivated by calcium ions inside the cilia . Under controlled conditions , calcium ions exiting PKD2-L1 block the channel and trigger its long-term inactivation . This inactivation can be overcome by preventing calcium ions from accumulating inside the cell . Further experiments show that cytoplasmic regions of the PKD2-L1 protein that are able to bind to calcium ions are not responsible for this unusual channel behavior . DeCaen , Liu et al . propose that the strange effect of calcium ions on the channel acts to maintain calcium ions in cilia at higher levels than those found in the cytoplasm . Future challenges include understanding how PKD2-L1 is stimulated by calcium ions and to find out the consequences of this activity . | [
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] | 2016 | Atypical calcium regulation of the PKD2-L1 polycystin ion channel |
Active whisking is an important model sensorimotor behavior , but the function of the cerebellum in the rodent whisker system is unknown . We have made patch clamp recordings from Purkinje cells in vivo to identify whether cerebellar output encodes kinematic features of whisking including the phase and set point . We show that Purkinje cell spiking activity changes strongly during whisking bouts . On average , the changes in simple spike rate coincide with or slightly precede movement , indicating that the synaptic drive responsible for these changes is predominantly of efferent ( motor ) rather than re-afferent ( sensory ) origin . Remarkably , on-going changes in simple spike rate provide an accurate linear read-out of whisker set point . Thus , despite receiving several hundred thousand discrete synaptic inputs across a non-linear dendritic tree , Purkinje cells integrate parallel fiber input to generate precise information about whisking kinematics through linear changes in firing rate .
Tactile sensation is an active process whereby sensory information is acquired through self-initiated movement . Effective sensory processing therefore involves the interplay between motor and sensory systems , incorporating multiple feedback loops ( Diamond et al . , 2008; Matyas et al . , 2010; Bosman et al . , 2011 ) . Rodents use coordinated whisker movements for tactile exploration and discrimination , rhythmically sweeping their whiskers back and forth to scan their surroundings . The rodent whisker system thus provides an attractive model to tackle questions related to active sensory processing ( O'Connor et al . , 2002; Crochet et al . , 2011 ) and sensorimotor integration ( Kleinfeld et al . , 2006 ) . Amongst multiple processing regions in the brain , the cerebellar cortex is a major site of sensorimotor integration , but little is known about its role in active whisking . Of the many brain regions involved in whisking behavior , the trigeminal and facial nuclei of the brainstem , thalamus , and neocortex have received the most attention ( Carvell and Simons , 1988; Lichtenstein et al . , 1990; Carvell et al . , 1996; Fee et al . , 1997; Kleinfeld et al . , 1999; Brecht et al . , 2004; Yu et al . , 2006; Leiser and Moxon , 2007; Herfst and Brecht , 2008; Diamond et al . , 2008; Curtis and Kleinfeld , 2009; Hill et al . , 2011; Crochet et al . , 2011; Petreanu et al . , 2012 ) . Recently , physiological and anatomical studies have unveiled a whisking central pattern generator located in the reticular formation of the ventral medulla that produces rhythmic signals to muscles that generate whisking ( Moore et al . , 2013 ) . The lateral hemispheres of the cerebellum , in particular lobule Crus I , are strongly implicated in these vibrissae sensorimotor loops ( Shambes et al . , 1978; Bosman et al . , 2011; Proville et al . , 2014 ) . Growing evidence suggests synchronization of activity between the cerebellum and other whisker-related brain regions both under anesthesia and during active whisking in the awake state ( O’Connor et al . , 2002; Ros et al . , 2009; Popa et al . , 2013 ) . Sensory-evoked responses are observed in Crus I following whisker stimulation ( Shambes et al . , 1978; Bower et al . , 1981; Chadderton et al . , 2004; Bosman et al . , 2010 ) , and whisker movements can be evoked by optogenetic activation of this lobule ( Proville et al . , 2014 ) , but the principles by which cerebellar neurons encode features of whisking remain to be determined . In this study , we set out to identify two key aspects of whisking behavior representation in Purkinje cells ( PCs ) , the final stage of information processing and sole output of the cerebellar cortex . Firstly , which kinematic features are represented by PCs ? Whisking is a rhythmic process characterized both by fast oscillatory forward and backward movements , as well as slower positional changes ( Hill et al . , 2011 ) . Distinct brain regions make different functional contributions to the encoding of this behavior ( Kleinfeld et al . , 2006 ) . For example , within the neocortex , the phase of whisking is strongly represented in primary somatosensory cortex ( vS1 , Curtis et al . , 2009 ) , whereas slower changes ( e . g . the set point and amplitude of whisker movement ) are more closely correlated with activity in primary motor cortex ( vM1 , Carvell et al . , 1996; Hill et al . , 2011 ) . To understand how the cerebellum fits within the various nested loops of the rodent whisker system , it is necessary to establish the kinematic parameters that are most relevant in the modulation of cerebellar activity . Further , it is important to establish how salient features of whisking are encoded in the activity of single neurons . This information is essential if we are to understand the underlying computational principles of the cerebellar circuit . Changes in both the rate and timing of action potential firing may play a role in sensorimotor encoding ( De Zeeuw et al . , 2011 ) , and it has been proposed that the cerebellum may serve a general function as a linear coding device ( Fujita , 1982 ) . However , these concepts have not been directly confirmed in the intact brain , and thus the cerebellar coding scheme ( s ) employed in the representation of whisker movement is unknown . To address these issues , we have made patch clamp recordings from cerebellar PCs in awake , behaving mice to determine the influence of convergent sensory and motor input on the output patterns of the cerebellar cortex during natural whisking . PCs integrate hundreds of thousands of discrete synaptic inputs ( Palkovits et al . , 1977 ) across a complex non-linear dendritic tree ( Llinás and Sugimori , 1980a , 1980b; Finch and Augustine , 1998; Roth and Häusser , 2001 ) . Despite this complexity , our experiments demonstrate that single PCs accurately encode ongoing whisker movements via bidirectional modulation of simple spike firing rate in a linear manner . Our results establish the presence of a whisking coordinate system in the cerebellum and reveal the computational algorithm employed during sensorimotor processing .
We performed patch clamp recordings in Crus I of cerebellar cortex while using high-speed videography to track the whisker movements of awake head-fixed mice ( Figure 1A–C ) . Mice spent variable amounts of time engaged in free whisking , spontaneously switching between periods of whisking and non-whisking behavior ( mean whisking bout: 1 . 68 ± 0 . 04 s , range: 0 . 50 – 29 . 71 s; mean quiet period: 7 . 82 ± 0 . 25 s , range: 0 . 5 – 153 . 1 s ) . PCs ( n = 70 from 47 mice ) could be identified via classification of their two distinct spike waveforms - simple spikes ( SS ) and complex spikes ( CS ) - evident in both cell-attached and whole cell recordings ( Figure 1C ) . 10 . 7554/eLife . 10509 . 003Figure 1 . Alteration of Purkinje cell activity during free whisking . ( A ) Videography of a head-restrained mouse with four traced whiskers ( from row C , labeled in green ) . ( B ) Simplified diagram of the cerebellar circuit ( cf: climbing fiber; gc: granule cell; PC: Purkinje cell; pf: parallel fiber; mf: mossy fiber; MI: molecular layer interneuron ) . ( C ) PC electrical activity in awake behaving mice , acquired via cell-attached and whole cell patch clamp recordings . Asterisks highlight the incidence of complex spiking . ( D ) Observed behavior of PC that increased simple spike ( SS ) frequency during spontaneous whisker movements ( gray shading ) , including ( top ) traced whisker position ( green; upward deflections indicate protraction ) , ( middle ) corresponding SS and CS trains , and ( bottom ) SS instantaneous firing rate histogram ( bin size: 100 ms ) . ( E ) Observed behavior of PC that decreased SS frequency during spontaneous whisking . ( F ) Scatter plot showing relative SS firing rate changes during whisking with respect to non-whisking baseline firing rates for all significantly modulated units ( p<0 . 05 , n = 47 , Mann-Whitney-Wilcoxon test ) . Red and blue symbols indicate increasing ( n = 40 ) and decreasing ( n = 7 ) PCs , respectively . ( G ) Relative SS firing rate changes with respect to baseline firing rate between quiet wakefulness and free whisking for all modulated cells ( red: increasing PCs , blue: decreasing PCs ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 00310 . 7554/eLife . 10509 . 004Figure 1—figure supplement 1 . Influence of locomotion on simple spike rate alteration during free whisking . ( A ) The fraction of time from all recordings ( n = 47 ) that mice were engaged in locomotion during whisking was very small . ( B ) Distribution of whisker positions during a single recording session within epochs of whisking alone ( pink ) , and whisking and locomotion ( yellow ) . Locomotion was associated with a protraction of whisker position . ( C ) Mean whisker position during whisking alone , and during whisking plus locomotion for all recordings in which mice ran ( n = 10 , see Materials and methods ) . On average , locomotion was associated with a protraction of 5 . 5 ± 1 . 7 degrees . ( D ) SS firing rates during whisking alone and with locomotion . Running was associated with a significant elevation ( p = 0 . 03 ) in SS rate across the population . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 00410 . 7554/eLife . 10509 . 005Figure 1—figure supplement 2 . Complex spike rate alteration during free whisking . ( A ) Relative CS firing rate changes during whisking with respect to non-whisking CS rates for all significantly modulated units ( n = 33 , Mann-Whitney-Wilcoxon test , p<0 . 05 ) . Red and blue symbols depict PCs with increasing ( n = 22 ) and decreasing ( n = 11 ) firing rates respectively . ( B ) Scatter plot of relative changes in CS- versus SS rate with respect to baseline firing rate for PCs that displayed significant modulation in both CS and SS during whisking ( n = 22 ) . No correlation was observed between directions of CS and SS modulation for individual PCs . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 005 Although tactile whisker stimulation can change the rate of both SS and CS ( Bower and Woolston , 1983; Loewenstein et al . , 2005; Bosman et al . , 2010 ) , it is not known whether free whisking also affects PC activity . We therefore compared PC firing rates during epochs of whisking and non-whisking behavior . In the absence of movement , PCs fired SSs at high frequencies ( 61 . 9 ± 3 . 7 Hz , range: 20 . 1 – 187 . 9 Hz , n = 70 ) , while the basal rate of CSs was low ( 1 . 6 ± 0 . 1 Hz , range: 0 . 5 – 3 . 3 Hz , n = 70 ) . These values are consistent with other recordings made from lateral cerebellar PCs in awake animals ( Fu et al . , 1997a , 1997b; Lang et al . , 1999; Bosman et al . , 2010 ) . During bouts of free whisking , a large proportion of PCs ( 47 out of 70 cells , ~67% ) exhibited significant changes in SS rate ( p<0 . 05 , Mann-Whitney-Wilcoxon test ) . While the majority of PCs ( 40/47 ) increased their firing rates ( Figure 1D , Video 1 ) , in some PCs ( 7/47 ) SS rates decreased during whisking ( Figure 1E , Video 2 ) . Whisker movement was associated with an overall enhancement of the activity of PCs ( Figure 1F , G ) , although the direction and amplitude of SS rate change during whisking was not related to baseline SS firing rates ( r = 0 . 06 , p=0 . 69 , n = 47 ) . SS rate changes were therefore non-uniform in both magnitude and sign across the population , with individual PCs exhibiting changes ranging from +128% to -34% during whisking periods ( Figure 1G ) . We also compared variability in the timing of simple spiking during periods of whisking and non-whisking behavior by measuring the coefficient of variation ( CV ) of SS firing . Overall , the CV was close to 1 both when mice were at rest ( 0 . 9 ± 0 . 1 , range: 0 . 3 – 6 . 2 , n = 70 ) , and when they actively moved their whiskers ( 0 . 8 ± 0 . 1 , range: 0 . 3 – 2 . 5 , n = 70 ) . Significant changes in SS variability occurred during free whisking in the majority of PCs that displayed rate modulation ( 44/47 PCs; see ‘Materials and methods’ ) . Moreover , nearly one third ( n = 20/70 ) of PCs demonstrated changes in SS firing regularity alone , suggesting that the temporal patterning of SS might independently encode whisker-related signals . In summary , SS activity – either rate , CV , or both - was altered in nearly all PCs ( 67/70 ) . 10 . 7554/eLife . 10509 . 006Video 1 . Increased simple spike activity during whisking . SS activity of a single PC during 15 s of voluntary whisking behavior . Left: Movements of the ipsilateral whisker pad were recorded via high-speed infrared videography . Top: changes in whisker angle for three adjacent row-C whiskers . Bottom and Audio: Simultaneously recorded SS activity from Crus I PC ( raster and audio 2x down-sampled for audiovisual clarity ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 00610 . 7554/eLife . 10509 . 007Video 2 . Reduced simple spike activity during whisking . SS activity of a single PC during 10 s of voluntary whisking behavior . Left: Movements of the ipsilateral whisker pad were recorded via high-speed infrared videography . Top: Fluctuations in the angle of the C3 whisker . Bottom and Audio: Simultaneously recorded SS activity from Crus I PC ( video slowed down 2x for audiovisual clarity ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 007 Locomotion can alter whisking behavior: whisker protraction is correlated with running speed ( Sofroniew et al . , 2014 ) . During the course of our PC recordings , mice rarely ran on the treadmill ( fraction of all whisking bouts spent running was 3 . 5%; Figure 1—figure supplement 1A ) . However , in recordings that included periods of locomotion ( n = 10 ) , whisker movements were more protracted ( Figure 1—figure supplement 1B , C ) and SS firing rates were elevated , independent of the sign of whisking-related modulation ( Figure 1—figure supplement 1D; p=0 . 03 , paired t test ) . These results indicate that running may influence both whisking behavior and whisker-driven SS activity in the cerebellum . Significant differences in CS firing rates were also observed between whisking and non-whisking conditions in a large proportion of PCs ( Figure 1—figure supplement 2B; 33 out of 70 cells , p<0 . 05 , Mann-Whitney-Wilcoxon test ) . A common phenomenon in many regions of the cerebellar cortex is an inverse relationship between SS and CS rate changes during behavior ( Graf et al . , 1988; De Zeeuw et al . , 1995; Barmack and Yakhnitsa , 2003; Badura et al . , 2013 ) . However , there was no consistent relationship between whisker-related changes in CS and SS frequency ( Figure 1—figure supplement 2B; r = 0 . 13 , p=0 . 57 ) . Overall , whisker movements are associated with pronounced alterations in PC activity , indicating that external drive to Crus I changes activity within cerebellar circuits during bouts of whisking . To define the relationship between whisker movement and PC simple spiking , we correlated whisker position with the incidence of SSs . Whisking bouts were aligned at their onset to compare the amplitude and duration of whisker movements with firing rate changes both in increasing ( Figure 2A , B ) and decreasing ( Figure 2C ) PCs . Remarkably , the changes in PC SS firing rate during whisking bouts closely mimicked whisker movement ( compare upper and lower panels in Figure 2A ) , irrespective of the direction of the rate change ( Figure 2B , C ) . Beyond an overall change in CS rate , there was no correspondence between the amplitude/duration of whisker movement and the incidence of CSs ( Figure 2—figure supplement 1 ) . Therefore , increases and decreases in SS , but not CS , rate are intimately related to changes in whisker position . 10 . 7554/eLife . 10509 . 008Figure 2 . Purkinje cell simple spike discharges reliably track whisker movements . ( A ) Whisker movements and corresponding simple spike raster from a single PC across 20 epochs of free whisking . Neuron demonstrates increased SS frequency during movement . ( B ) Peri-event time histogram ( PETH ) for the same PC , obtained by averaging SS rate across trials illustrated in ( A ) , overlaid with averaged whisker position ( in green ) . Note the close relationship between SS firing rate change and mean whisker position . ( C ) PETH for a PC demonstrating reduced SS frequency during movement . The close relationship between SS firing rate change and mean whisker position is preserved . ( D ) Normalized cross-correlations between whisker position and SS discharge for exemplar PCs . The peak ( red for PC1; shown in A , B ) or trough ( blue for PC2; shown in C ) indicates the temporal relationship between whisker position and spiking . PC1 leads whisker movement by 8 ms ( difference between red and yellow dashed lines ) , while PC2 lags movement by 27 ms ( difference between blue and yellow dashed lines ) . Gray shade demonstrates 95% confidence interval . ( E ) Temporal relationship between whisker movement and SS discharge for all modulated PCs ( bin size: 20 ms ) . More units show lead ( negative latency to movement ) than lag ( positive latency to movement ) with respect to behavior . Inset: zoomed-in histogram between -100 ms and 100 ms . Black line is best fit of two summed Gaussians . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 00810 . 7554/eLife . 10509 . 009Figure 2—figure supplement 1 . Complex spike relationship to whisker movement . CS peri-event time histograms ( PETHs ) overlaid with average whisker positions for the same cells shown in Figure 2 . Note the absence of the close relationships between CS firing rate change and whisker movement , in contrast to SS rate . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 00910 . 7554/eLife . 10509 . 010Figure 2—figure supplement 2 . Temporal relationship between whisker movement and SS firing rate for strongly and weakly modulated PCs . ( A ) Cross-correlation between whisker position and SS discharge for individual whisking bouts ( gray lines ) of a strongly modulated PC . Gray circles: time of peak correlation for individual bouts . Black line: average cross-correlation for all bouts . Red line: time of average peak correlation ( -12 ± 5 ms ) . ( B ) Cross-correlation between whisker position and SS discharge for individual whisking bouts ( gray lines ) of a weakly modulated PC . Gray circles: time of peak correlation for individual bouts . Black line: average cross-correlation for all bouts . Red line: time of average peak correlation ( 26 ± 7 ms ) . ( C ) Relationship between correlation strength and latency for all PCs . ( D ) Relationship between SS modulation strength and latency for all PCs . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 01010 . 7554/eLife . 10509 . 011Figure 2—figure supplement 3 . Relationship between SS firing rate and whisking offset . Normalized cross-correlations measured at whisking onset ( solid lines ) and offset ( dashed lines ) for example PCs shown in Figure 2 . The temporal relationship between whisking and SS modulation is preserved at movement offset . Gray shade demonstrates 95% confidence interval . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 011 Because Crus I receives both motor and sensory whisker-related inputs ( Shambes , et al . , 1978; Bower et al . , 1981; Proville et al . , 2014; ) , SS firing rate changes could reflect either efferent motor command , or re-afferent sensory input . To establish whether changes in PC activity reflect transitions to free whisking in a feed-forward ( efferent ) or feedback ( re-afferent ) manner , we determined the temporal relationship between neural activity and behavior . For this purpose , we calculated the cross-correlation between SS firing rate and whisker position , with the analysis centered on the time of whisking onset ( Figure 2D ) . Robust correlations between whisker movement and SS discharge were observed with both large and small whisking-related changes in firing rate ( Figure 2—figure supplement 2A , B ) , and in a manner that was largely independent of the latency between movement and change in firing rate ( Figure 2—figure supplement 2C ) . Two-thirds of PCs ( 31/47 ) exhibited SS firing rate changes that preceded whisking onset by a mean of -34 . 6 ± 7 . 9 ms ( Figure 2E ) , and no clear relationship was observed between the latency and magnitude of whisking related alterations in SS rate ( Figure 2—figure supplement 2D ) . Temporal relationships were preserved when the cross-correlation was calculated based on whisking offset ( Figure 2—figure supplement 3 ) . On average , PC SS discharge led whisker movement by ~18 ms ( n = 47 ) at the population level , implying that SS alterations in Crus I predominantly reflect efferent rather than re-afferent signals . However , the distribution of temporal correlations was broad ( range: -178 ms to 324 ms ) , and non-unimodal ( Figure 2E , Hartigan's dip test; p<0 . 001 , n = 38 ) , indicating that whisking behavior is represented on multiple timescales amongst neighboring PCs within Crus I . Delayed PC responses relative to whisking may result from additional processing of sensory-driven information and/or recurrent motor-related signals during free whisking . In addition , the transmission of whisker signals via parallel fibers ( Wilms and Häusser , 2015 ) of distant granule cells may account for long latency PC responses within the cerebellar cortex . To establish whether the activity of PCs in Crus I represents salient kinematic parameters of whisking , we directly quantified the relationship between PC SS firing rate and whisker position . Plotting whisker position as a function of SS frequency revealed strong linear correlations: linear regression analysis indicated significant correlations in over 60% of the PCs ( R2 = 0 . 958 ± 0 . 005 , p<0 . 05 , ANOVA , n = 44 ) . Two classes of encoding schemes were observed . Unidirectional PCs ( Figure 3A ) displayed linear changes in SS frequency within a range of positions corresponding to only forward or backward movements relative to the whisker resting point ( defined as whisker angle during non-movement; see ‘Materials and methods’ ) . These were the most common type of response ( 37/44 ) and were associated with either increases or decreases in SS rate . In bidirectional PCs , SS frequency both increased and decreased across the full range of movement ( Figure 3B ) , with SS frequency encoding both protracted and retracted whisker positions ( 7/44 PCs ) . Across the PC population , we observed uni- and bidirectional PCs that encoded whisker position with both positive slopes ( corresponding to increased SS rate during forward movements ) and negative slopes ( decreased SS rate during forward movements ) . To compare representation of whisker position across the population of PCs , SS firing rate was normalized with respect to the spontaneous firing rate and whisker position was normalized with respect to the whisker resting point . Notably , both classes of PC displayed almost perfect linear relationships ( unidirectional: R2 = 0 . 950 ± 0 . 004; bidirectional: R2 = 0 . 959 ± 0 . 005 ) between relative SS firing rate change and whisker position ( Figure 3C ) . In unidirectional cells , SS frequency at resting point often differed between bouts of movement and non-movement . Bidirectional PCs never exhibited such large baseline shifts in firing frequency and were capable of continuously representing whisker position through bouts of whisking and non-whisking via alteration of SS frequency . 10 . 7554/eLife . 10509 . 012Figure 3 . Purkinje cell simple spike frequency linearly encodes whisker position . ( A ) Relationship between SS rate and whisker position for PC with strong linear tuning in the forward direction only ( unidirectional PC ) . Linear regression was performed for whisker positions anterior of the resting point ( horizontal dashed line ) . Vertical dashed line shows the cell’s spontaneous firing rate ( FR ) . This cell showed linear reductions in SS frequency during forward movement . Blue and red shaded areas represent decreases and increases in SS FR , respectively . ( B ) Relationship between SS rate and whisker position for PC with strong linear tuning in both forward and backward directions ( bidirectional PC ) . Linear fit encompassed the entire range of SS FR modulation . This cell showed increases in SS frequency during forward movement , and decreases in SS frequency during backward movement . ( C ) Summary of all unidirectional PCs ( n = 37 , gray lines ) and bidirectional PCs ( n= 7 , black lines ) with significant linear correlations between SS FR and whisker position ( ANOVA , p<0 . 05 , R2 > 0 . 86 ) . FR change and whisker position were normalized with respect to spontaneous firing rate and resting point , respectively . Inset: distribution of gain , defined as the slope of individual linear fit , for both unidirectional ( gray ) and bidirectional ( black ) units . Note bidirectional PCs have higher gain values than unidirectional PCs on average , implying they are more sensitive to changes in whisker position during movement . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 01210 . 7554/eLife . 10509 . 013Figure 3—figure supplement 1 . Linear encoding range of individual PCs . ( A ) Relationship between whisker encoding range and PC gain . A strong inverse relationship was observed between linear encoding range and gain of SS modulation , indicating that high gain neurons encode a relatively smaller range of whisker position than low gain neurons . ( B ) Fraction of time spent within linear encoding range with respect to individual PC gain . The fraction of time that a whisker spent within the linear encoding range of an individual PC was independent of gain . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 01310 . 7554/eLife . 10509 . 014Figure 3—figure supplement 2 . Inactivation of contralateral motor cortex does not degrade cerebellar representation of whisker position . ( A ) PC recordings in Crus I were performed during transient inactivation of contralateral motor cortex ( M1; via local muscimol injection , see ‘Materials and methods’ ) . ( B ) Relative SS firing rate changes during whisking with respect to non-whisking baseline firing rates for significantly modulated units ( n = 7/15 , Mann-Whitney-Wilcoxon test , p<0 . 05 ) . Pink and teal symbols depict increasing ( n = 6 ) and decreasing ( n = 1 ) PCs , respectively . ( C ) Relationship between SS rate and whisker position for PC with strong linear tuning in the forward direction . Vertical dashed line shows the cell’s spontaneous firing rate ( FR ) . This cell showed linear reductions in SS frequency during forward movement . ( D ) Summary of all PCs ( n = 6 , gray lines ) with significant linear correlations between SS FR and whisker position . FR change and whisker position were normalized with respect to spontaneous firing rate and resting point , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 014 While the gain of SS firing rate changes with respect to whisker position ( slope of curves in Figure 3A–C ) varied across cells ( mean = 15 . 8 ± 2 . 1 Hz/degree ) , bidirectional PCs demonstrated significantly higher gain values in comparison to unidirectional PCs ( bidirectional: 33 . 5 ± 4 . 4 Hz/degree , unidirectional: 12 . 5 ± 2 . 0 Hz/degree , p<0 . 001 , Mann-Whitney-Wilcoxon test ) , indicating that bidirectional PCs are more sensitive to small-amplitude whisker movements ( Figure 3C , inset ) . A strong inverse relationship was observed between the linear encoding range and the gain of individual PCs ( Figure 3—figure supplement 1A ) indicating that high-gain PCs linearly encode a relatively smaller portion of all possible whisker angles than low-gain PCs . However , the fraction of time that a whisker spent within a PC’s linear encoding regime was approximately constant across the population , with no relationship to PC gain ( Figure 3—figure supplement 1B ) . This suggests that both high- and low-gain PCs make similar contributions to the linear encoding of whisker position within the cerebellar cortex . Taken together , these results demonstrate that individual PCs in Crus I represent whisker position linearly and within distinct ranges of movement . The high resting firing rate of individual PCs enables bidirectional representation through both increases and decreases in SS firing rate . In the majority of PCs , whisker-related changes in SS lead , or are coincident with , movement ( Figure 2D ) , indicating the dominance of efferent rather than re-afferent drive to the cerebellar cortex . We explored whether the contralateral primary motor cortex , a source of excitatory input to Crus I ( Proville et al . , 2014 ) , could provide an efferent drive during voluntary whisking . We performed patch clamp recordings from PCs in Crus I while locally inactivating contralateral vM1 via muscimol injection ( Figure 3—figure supplement 2A; see ‘Materials and methods’ ) . Seven out of 15 PCs displayed significant SS firing rate modulation ( Figure 3—figure supplement 2B; p<0 . 05 , Mann-Whitney-Wilcoxon test ) with a mean latency to whisker movement of -24 . 6 ± 11 . 9 ms ( n = 7 ) . Linear representation of whisker position by SS firing rate was preserved irrespective of vM1 inactivation ( Figure 3—figure supplement 2C , D ) . Thus , inactivation of vM1 does not degrade the cerebellar representation of whisking , and whisker-related input to the cerebellum is derived from other cortical or subcortical processing stations . Whisking is a rhythmic process and neuronal firing locked to specific phases of the whisking cycle has been observed at multiple processing stations in the brain ( Yu et al . , 2006; Leiser and Moxon , 2007; Curtis and Kleinfeld , 2009; Crochet et al . , 2011 ) . To examine whether PCs also encode this parameter , rapidly varying phase information was extracted from whisking bouts by applying the Hilbert transform during epochs of rhythmic whisking ( Figure 4A ) . The relationship between simple spiking and phase was then assessed within rhythmic whisking epochs to determine whether PCs were more likely to fire at a particular phase . Only PC recordings that coincided with longer periods of exploratory rhythmic whisking were included in this analysis ( n = 31 , see ‘Materials and methods’ ) . In almost every cell ( 30/31; p>0 . 05 , Kuiper test ) , no significant phase tuning was observed ( Figure 4B , C ) . However , one PC did exhibit strong phase preference ( modulation depth of 4 . 8 ) . In this cell , a reduction in SS rate was observed during whisking and the incidence of spiking preferentially occurred during whisker retraction ( Figure 4C ) . Overall , the vast majority of PCs did not encode rhythmic variations in whisker position within individual whisking cycles , suggesting that phase information is represented by a small fraction of PCs . 10 . 7554/eLife . 10509 . 015Figure 4 . Most Purkinje cells do not encode the phase of whisking cycle . ( A ) Example of rhythmic whisker movement ( green trace ) , and corresponding phase ( orange ) derived from the Hilbert transform of the raw position trace . ( B ) Modulation depth of phase tuning for population of PCs . Phase tuning is absent in SS patterns of all but one PC ( NS; not significant , n = 30/31 ) . ( C ) Polar plot depicts the phase tuning of five representative cells that did not demonstrate phase tuning ( brown ) and one strongly modulated PC ( orange ) , which showed SS firing locked to mid-point of whisker retraction . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 015 To quantify how well individual PCs represent whisker position , we attempted to reconstruct the trajectory of whisker movement from our recordings of SS activity . A transfer function between SS activity and whisker position was computed from a portion of each recording to capture the underlying linear characteristics of the system ( n = 18 PC recordings longer than 300 s , see ‘Materials and methods’ ) . Calculated transfer functions were applied to trains of SS activity from the remainder of the recording to test whether the transfer functions could predict the dynamics of whisker movement . Using spike train information from single PCs , it was possible to accurately recover the dynamics of whisker movements over many seconds ( Figure 5A ) . In most PCs , the movement reconstruction derived from the transfer function was highly reminiscent of another kinematic parameter – the set point , which denotes the slowly varying midpoint between the protracted and retracted angles of a single whisking cycle . We therefore measured the correlation coefficient between reconstructed whisker position and set point derived from measurements of actual behavior to evaluate the quality of our decoding . Reconstructions based on PC SS activity were excellent predictors of actual whisker set points ( Figure 5B; range of correlation coefficient: 0 . 23 – 0 . 89 , mean = 0 . 57 ± 0 . 05 , p<0 . 001 , n = 18 ) . Therefore , we conclude that single PCs reliably encode whisker set point , and that bidirectional changes in the frequency of simple spiking afford a linear representation of whisker trajectory during voluntary movement . 10 . 7554/eLife . 10509 . 016Figure 5 . Reconstruction of set point trajectories from simple spike activity of single Purkinje cells . ( A ) Reconstruction of whisker movement from single PC SS train based on the calculated transfer function . Whisker set point information ( purple ) is accurately reconstructed ( black trace , bottom ) using SS activity from a single PC ( down-sampled x3 for visual clarity ) , highlighting the strong linear relationship between simple spiking and slow whisker kinematics . Correlation coefficient value between reconstruction and set point is 0 . 78 . ( B ) Correlation coefficients between whisker set point and linear reconstruction from individual PCs ( gray open circles ) . Black filled circle: mean ± SEM across all cells ( n = 18 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10509 . 016
The computational algorithm by which the cerebellar cortex encodes sensorimotor input has been widely debated . Changes in rate ( Walter and Khodakhah , 2009 ) and pauses in firing ( De Schutter and Steuber , 2009 ) have been proposed as mechanisms of sensorimotor encoding . While indirect evidence has been found for both schemes , our recordings in behaving animals provide direct support for the proposal that the cerebellar cortex is optimized to perform as a linear coding device ( Fujita , 1982; Walter and Khodakhah , 2006 , 2009 ) . The strong linearity of PC SS output is surprising given the range of non-linear ionic conductances present across the PC dendritic tree ( Llinás and Sugimori , 1980a , 1980b ) and suggests that non-linear dendritic conductances might compensate for non-linear synaptic integration within passive PC dendrites ( Roth and Häusser , 2001 ) , or alternatively are disengaged during free whisking . For cerebellar circuits , linear computation may provide the optimal means of performing pattern separation ( Albus , 1971; Marr , 1969; Walter and Khodakhah , 2009 ) , affording graded sensorimotor representations ( Heiney et al . , 2014 ) that are less prone to saturation , and increasing the dynamic range of the system . Both increases and decreases in firing rate are linear with respect to whisker position , suggesting that molecular layer inhibition plays a crucial role in maximizing the range of cerebellar operation ( Park et al . , 2012 ) . The majority of PCs in Crus I represent changes in a simple parameter of whisking behavior , the set point , via SS rate changes . Because of the close and robust correspondence between SS rates and movements , we were able to reconstruct whisker trajectories with high accuracy using spike trains from individual cells ( Figure 5 ) . Several PCs converge onto single target neurons ( Palkovits et al . , 1977 ) in the deep cerebellar nucleus ( DCN ) , ensuring that set point information is propagated downstream , where integration of PC signals ( Person and Raman , 2012a ) with different rate functions ( Figure 3 ) could further extend the dynamic range of DCN neurons to movement . In this arrangement , convergence of positively and negatively modulated PC postsynaptic potentials could degrade set point representation , and therefore it seems likely that these two classes of PC have distinct cellular targets in the DCN ( Person and Raman , 2012b ) . In contrast to set point , phase information is only sparsely encoded in the cerebellar cortex ( Figure 4 ) . However , phase information may be reconstituted in the DCN via spatiotemporal convergence of weakly tuned PCs ( De Zeeuw et al . , 2011; Person and Raman , 2012b ) . Overall , our data indicate that whisker set point is represented by SS frequency in the majority of Crus I PCs and further suggest that additional phase tuning may restrict SS firing to precise times within the whisking cycle . It is unclear whether the signals we recorded correspond to single or multiple whisker movements . Tactile receptive fields of Crus I PCs can encompass multiple whiskers ( Bosman et al . , 2010 ) , and a similar mapping of motor responses will be helpful to establish how these signals are integrated across the entire cerebellar cortex . Neurons in the cerebellar cortex potentially have access to discrete ‘motor’ and ‘sensory’ representations of whisking behavior: specifically , efferent copy of planned or current movement from cortical and sub-cortical centers , and re-afferent signals providing continuous sensory feedback from the trigeminal nuclei and sensory neocortex about the consequences of voluntary movement . Necessarily , sensory re-afferent signals are delayed with respect to movement ( Bower et al . , 1981 ) as they are required to propagate from the periphery , and whisker movements are themselves delayed with respect to muscle activity ( by a few tens of milliseconds ) owing to inertia ( Berg and Kleinfeld , 2003 ) . In our recordings , the majority of PCs exhibited changes in SS activity that preceded or were coincident with movement , consistent with a feed-forward as opposed to feedback representation of whisking in the cerebellum . The primary motor cortex does not provide the source of this efferent drive , as the cerebellar representation remains intact during transient vM1 inactivation . Processing stations in the midbrain and the brainstem are therefore likely candidates to provide information to the cerebellum about whisker position during voluntary movement .
The nervous system retains an internal representation of whisking in the cerebellum via a simple linear encoding regime . This grants a remarkable degree of flexibility to fine-tune and coordinate whisker movement by providing fast online feedback , and to disambiguate representations of self- and externally generated sensory signals ( Wolpert et al . , 1998 ) . The resolution with which movement trajectories can be recovered from single neurons suggests that the cerebellar cortex may be an interesting alternative target for brain-machine interface devices that seek to restore movement via online decoding of neural signals ( Kohler et al . , 2009 ) . By providing rapid information about current or future movement , the cerebellar machinery may circumvent long delays in cortical feedback loops , serving effective sensory processing and motor control during active whisking ( Rahmati et al . , 2014 ) . Crucially , our findings confirm that by rendering an internal representation of the whisker system , PC spike train dynamics are highly informative about movement trajectories , facilitating active sensation and tactile exploration .
The care and experimental manipulation of animals was performed in accordance with institutional and United Kingdom Home Office guidelines . 47 C57BL/6 mice ( 4–8 weeks old ) of both genders were used in this study . Animals were housed in a 12-hr reverse light-dark cycle and all experiments were carried out during the dark phase . Prior to recording , mice were anesthetized with 1–2% isoflurane under aseptic conditions , and a lightweight head-post was attached to the skull using glue ( Histoacryl , Braun Corporation , USA ) and acrylic dental cement ( Kemdent , UK ) . A circular chamber was built with cement over the lateral hemisphere of the cerebellum to allow subsequent access for electrophysiological recording . A non-steroidal anti-inflammatory drug ( Carprofen; 5 mg/kg ) was provided via intra-peritoneal administration during surgery to support recovery . Implanted mice were given 2–5 days for recovery , during which time Buprenorphine ( 0 . 8 mg/kg ) jelly was used for postoperative analgesia . On the day of the recording , mice were first anesthetized with isoflurane ( 1–2% ) , and a small craniotomy ( 1–1 . 5 mm ) was drilled over lobule Crus I . The dura was removed with fine forceps and the craniotomy was covered with 1 . 5% low-melting point agar and a silicone-based sealant ( Kwik-Cast; World Precision Instruments , USA ) . Ipsilateral whiskers were partially trimmed with one whisker row left untouched ( row C or D ) . At least two hours following these procedures , habituation and recording sessions were started . Mice were carefully placed on a cylindrical treadmill and the head-post was gently loaded into a fixation clamp to painlessly immobilize the head . At least one hour of habituation was allowed for the mice to be acclimated to the recording environment . Habituated mice showed normal grooming , whisking , and locomotion behaviors on the treadmill . After removal of sealant and agar , recordings were performed in the dark in a single session lasting up to 3 hr . Whole cell and cell-attached patch clamp recordings were made from cerebellar PCs in awake mice using a Multiclamp 700B amplifier ( Molecular Devices , USA ) . Recordings were made in Crus I ( -7 ± 0 . 2 mm posterior , and 3 . 4 ± 0 . 3 mm lateral of bregma ) at depths of 350–1500 μm from the pial surface using borosilicate glass pipettes ( 6–8 MΩ ) filled with internal solution containing ( in mM ) : 135 K-gluconate , 7 KCl , 10 HEPES , 10 phosphocreatine , 2 Mg-ATP , 2 Na2-ATP , and 0 . 5 Na2-GTP ( pH 7 . 2 , 280–290 mOsm ) . Purkinje cells were readily identified by their high spontaneous firing rates and the presence of complex spikes . Data were filtered at 10 kHz and digitized at 25 kHz using an ITC-18 interface ( Instrutech Corporation , USA ) and acquired on a computer using Axograph X software ( www . axograph . com ) . In whole cell recordings , resting membrane potentials were recorded immediately after formation of whole cell configuration and series resistances ranged between 20 and 40 MΩ . No current was injected and membrane potentials were not corrected for liquid junction potentials . To transiently inactivate vM1 , a small hole was made in the skull above contralateral vM1 ( 1 mm anterior and 1 mm lateral of bregma ) and a guided cannula was inserted 500–600 μm from the pia and fixed with dental cement during head-post implant . The gamma-aminobutyric ( GABA ) agonist muscimol ( ThermoFisher , USA ) was administered ( 0 . 6 μl of 1μg/μl solution dissolved in 0 . 9% saline , delivered at a rate of 0 . 1 μl/min ) via a Hamilton syringe into the guide cannula . In pilot experiments , extracellular population recordings ( 4 shank silicon probe; 4 x 2 tetrode , NeuroNexus , USA ) were used to confirm that M1 multi-unit activity was completely abolished within 10 min of injection . For cerebellar recording , injections were made at the end of the habituation session . Electrophysiological recordings began 10 min after infusion and lasted up to 2 hr . Due to the long-lasting effect of muscimol ( up to 3 hr ) , it was not possible to compare single PC activity before and after cortical inactivation . Under infrared light illumination , whisker movements were filmed with a high-speed camera ( Genie HM640; Teledyne Dalsa Inc , USA ) operating at 250 frames per second . Video acquisitions were controlled by Streampix 6 software ( Norpix , Canada ) and externally triggered by TTL pulses generated via the ITC-18 in order to synchronize video and electrophysiological acquisition . Whisker positions were tracked offline using open source software ( Clack et al . , 2012 ) - http://whiskertracking . janelia . org - and transferred into a graphical user interface in MATLAB ( Mathworks ) for analysis . Whisker azimuth angles were measured along the medial-lateral axis ( medial-lateral line: 0 degree , forward movement: increasing angle , backward movement: decreasing angle ) ; protraction corresponded to increasing whisker angles . Because whiskers , especially those from the same row , move in synchrony , we used one of the traced whiskers for all the analysis , as changing whisker did not affect the results . We excluded whisker epochs that were shorter than 500 ms and whisker twitches with single back-and-forth deflection smaller than 5 degrees . Whisking epochs were further separated into periods of rest and locomotion . Locomotion episodes were identified as treadmill movement lasting at least 100 consecutive frames ( 400 ms ) . The traced whisker position was first low-pass filtered at 30 Hz using a 4-pole Butterworth filter run in forward and reverse directions , and subsequently up-sampled to 1 kHz . Whisking set point was derived by low-pass filtering whisker angle at cutoff frequency 6 Hz . Rhythmic whisking epochs were isolated to determine phase information and cells with >20 s of rhythmic whisker movement were included to evaluate phase tuning . Whisker phase was defined as the angle of the Hilbert transform on band-pass filtered ( 6–30 Hz ) whisker angle . A phase of zero corresponds to maximal protraction and a phase of ± π denotes maximal retraction in a whisk cycle . Action potentials were detected offline automatically in Axograph X . SS and CS were sorted according to their distinct waveforms in MATLAB with a manual verification step . The clean separation of SS and CS was confirmed using Peri-CS SS-histograms ( Zhou et al . , 2014 ) . In all PC recordings , histograms showed ~10 ms pauses in SS activity following a CS . PCs were tested to determine if they exhibited significant changes in SS and CS firing rate between epochs of non-whisking and free whisking in air . On average , 40 episodes of whisking for each cell were used to quantitatively assess how whisking behavior modulated PC firing rate . Spike rates were calculated for individual whisking and non-whisking epochs as the total number of spikes divided by the duration of an epoch . Comparisons of the spike rates were made between quiet epochs and whisking epochs using a Mann-Whitney-Wilcoxon test where p<0 . 05 was recognized as a significant difference . Overall firing rates during whisking and non-whisking were calculated by averaging the spike rates of all epochs comprising the two respective conditions . To generate peri-event time-histograms , spike trains were aligned by the onsets of whisking bouts and averaged across trials . Corresponding whisking epochs were aligned at the onset and averaged to reveal the mean whisker movement within bouts . Coefficient of variation ( CV ) of inter-simple spike-interval ( ISI ) was defined as the standard deviation of ISI divided by its mean , where a CV > 1 implies high variance and low regularity . To resolve whether ISI distributions during non-whisking and whisking epochs were significantly different , a 2-sample Kolmogorov-Smirnov test were performed where p<0 . 05 was deemed to be significant . To determine SS instantaneous firing rates , we used a 100 ms wide rectangular window function and calculated a moving average with 1 ms steps . For cells showing significant SS firing rate modulations during whisking , we truncated spiking and whisker position data into 3-s segments centered on individual whisking onsets/offsets ( 1 s preceding- and 2 s post-onset/offset ) . To examine the temporal relationship between SS discharge changes and behavioral transitions , normalized cross-correlations between PC instantaneous firing rate and whisker position were computed for individual data segments and averaged across segments . The time at the nearest maxima/minima ( peak/trough ) above the upper/lower 95% confidence bounds in the normalized cross-correlation provided the temporal delay between the two signals . To test if the latency distribution was non-unimodal , we computed Hartigan’s Dip test on the empirical distribution from -100 ms to 100 ms ( n = 38 cells ) with 5 or 10 ms bin size . To determine the directionality of PCs in encoding whisker position , a firing directionality ratio was calculated based on the normalized linear fitting curve for each cell by dividing the minimal value of firing rate change on one of the two directions ( increase or decrease ) by the other , giving an index value between zero and one . PCs with a ratio value of zero ( n = 37 cells ) were classified as unidirectional cells , whereas PCs with positive values were classified as bidirectional ( n = 7; ratio range: 0 . 4–0 . 9 ) . To identify whether PCs SS rates were significantly modulated by the circular whisking variable phase , a 2-sample Kuiper test ( p<0 . 05 ) was used to compare the distribution of phase information at all times with its distribution at spike times . We divided the phase information into 20 bins and calculated a histogram of the spike events . This histogram was then normalized by the amount of time spent in individual bin to generate values concerning firing rate . The modulation depth of phase was computed as the maximal firing rate minus the minimal firing rate divided by the mean firing rate . All data are presented as mean ± SEM unless otherwise stated . | Many animals actively move their whiskers back and forth to explore their surroundings and search for objects of interest . This behavior is important for navigation and the animals’ sense of touch . It relies on specialized circuits of cells in the brain to carry information about whisker movement patterns and process the touch signals . A region of the brain called the cerebellum is highly connected to these circuits , but its role in the voluntary movement of whiskers is not clear . Chen et al . aimed to address this question by using a technique called patch clamping to measure the electrical activity of individual neurons in the mouse cerebellum . The experiments revealed that individual cells in the cerebellum called Purkinje cells track whisker movements in real time , and with virtually no delay , through both increases and decreases in their activity . Also , Chen et al . found that the patterns of electrical activity in these cells closely mimicked the positions of the whiskers as they moved . These results tell us that cells in the cerebellum use a simple code to represent whisker position during voluntary movement . Chen et al . ’s findings present the first experimental evidence that the cerebellum applies a type of code known as a linear code to represent the voluntary movements of whiskers . The next challenge is to find out how contact with whiskers alters movement-related signals in the cerebellum . | [
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"neuroscience"
] | 2016 | The cerebellum linearly encodes whisker position during voluntary movement |
We have previously proposed that complexin cross-links multiple pre-fusion SNARE complexes via a trans interaction to function as a clamp on SNARE-mediated neurotransmitter release . A recent NMR study was unable to detect the trans clamping interaction of complexin and therefore questioned the previous interpretation of the fluorescence resonance energy transfer and isothermal titration calorimetry data on which the trans clamping model was originally based . Here we present new biochemical data that underscore the validity of our previous interpretation and the continued relevancy of the trans insertion model for complexin clamping .
The tightly regulated release of neurotransmitters is key to all information processing in the neural circuitry . The fusion of a synaptic vesicle to release the neurotransmitters is mediated by the SNARE ( Soluble N-ethylmaleimide-sensitive factor Attachment protein REceptor ) complex , which forms between vesicle and target membranes as v-SNAREs emanating from transport vesicles assemble with t-SNAREs emanating from target membranes ( Sollner et al . , 1993; Weber et al . , 1998; Jahn and Scheller , 2006 ) . Key proteins regulating SNARE-mediated fusion at the synapse are the calcium sensor synaptotagmin and complexin ( CPX ) ( Brose et al . , 1992; McMahon et al . , 1995; Fernandez-Chacon et al . , 2001; Giraudo et al . , 2006; Sudhof and Rothman , 2009 ) . Genetic and physiological studies in a number of model systems show that CPX inhibits the spontaneous release of neurotransmitters and is also essential for synchronous exocytosis ( Huntwork and Littleton , 2007; Maximov et al . , 2009; Yang et al . , 2010; Martin et al . , 2011; Cho et al . , 2014 ) . CPX ‘clamps’ the SNARE assembly process to prevent the continuous release of neurotransmitters ( Giraudo et al . , 2006 ) . It does so by stabilizing the SNAREs in an otherwise unavailable ‘intermediate’ energetic state in which the four helix bundle is about 50% zippered ( Li et al . , 2011 ) . Based on the X-ray crystal structure of CPX bound to a mimetic of this half-zippered intermediate in which only the N-terminal portion ( residues 26–60 ) of v-SNARE , VAMP2 , is present ( SNAREΔ60 ) , we proposed a molecular model for the clamping of the SNARE assembly by CPX ( Kümmel et al . , 2011 ) . We found that the CPX central helix ( CPXcen , the SNARE-binding domain ) binds one SNAREpin while the accessory helix ( CPXacc , the clamping domain ) extends away and bridges to a second SNAREpin . The CPXacc interacts with the t-SNARE in the second SNAREpin , occupying the v-SNARE binding site , thus inhibiting the full assembly of the SNARE complex . Further , the intermolecular trans clamping interaction of CPX organizes the SNAREpins into a ‘zig-zag’ topology that is incompatible with opening a fusion pore ( Krishnakumar et al . , 2011; Kümmel et al . , 2011 ) . We used isothermal titration calorimetry ( ITC ) to characterize the interaction of the CPXacc with the t-SNARE , fluorescence resonance energy transfer ( FRET ) analysis to establish the angled conformation of CPXacc which allows the trans clamping interaction , and the cell–cell fusion assay ( Hu et al . , 2003 ) to functionally test the zig-zag model for CPX clamping ( Krishnakumar et al . , 2011; Kümmel et al . , 2011 ) . Recently , Rizo , Rosenmund , and colleagues ( Trimbuch et al . , 2014 ) have re-examined the clamping interaction of CPX and have raised concerns regarding the interpretation of the ITC and FRET data and the use of the cell–cell fusion assay as an in vitro system to study CPX clamping ( Krishnakumar et al . , 2011; Kümmel et al . , 2011 ) . Here we address these concerns and argue that the trans clamping model we had previously proposed remains relevant .
In our earlier paper we used ITC experiments to confirm that the CPXacc interacts with the t-SNARE in the truncated pre-fusion SNARE complex ( SNAREΔ60 ) as predicted by the X-ray crystal structure ( Kümmel et al . , 2011 ) . To measure this interaction , we blocked the central helix binding site by pre-binding the SNAREΔ60 complex with a truncated form of CPX ( CPX-48; residues 48–134 ) before titration . In the recent report by Trimbuch et al . ( 2014 ) the authors suggest that the 1 . 5 molar excess of the CPX-48 that was used to block the CPXcen binding does not saturate the central helix binding site and the heat observed upon addition of CPX to blocked SNAREΔ60 arises from the completion of CPXcen binding rather than from interactions involving the CPXacc . This was primarily based on their ITC data which showed that CPX-47 ( CPX47–134 ) binds to truncated complex SNARE∆60 with an affinity constant ( Kd ) = 2 . 39 ± 0 . 19 µM and to non-truncated SNARE complex with Kd = 339 ± 9 nM ( Trimbuch et al . , 2014 ) . The binding constant for full-length CPX and a non-truncated SNARE complex is reported to be ∼20 nM ( Pabst et al . , 2002 ) and , given that CPX-48 has an intact central helix including all SNARE-interacting residues ( residues 48 , 52 , 69 , and 70 ) ( Chen et al . , 2002 ) , the expectation would be that CPX-48 and full-length CPX bind to the non-truncated SNARE complex with similar affinities . This discrepancy prompted us to repeat their ITC experiments and , under our experimental conditions , the Kd for CPX-48 binding to the post-fusion SNARE complex was 43 ± 7 nM ( Figure 1A , Table 1 ) , much closer to the value reported for the full-length CPX ( Pabst et al . , 2002 ) . We found further that CPX-48 bound the pre-fusion SNAREΔ60 with a Kd = 457 ± 47 nM ( Figure 1B , Table 1 ) , or about five times more tightly than reported by Trimbuch et al . ( 2014 ) . These binding constants ensure the near saturation ( ∼96% ) of the central helix binding site under the conditions ( 1 . 5 molar excess of CPX-48 ) reported in Kümmel et al . ( 2011 ) . Thus , with the blocked SNAREΔ60 , only the interaction between CPXacc and t-SNARE would be measured . This is supported by the fact that the interaction affinity could be modulated by the mutations in CPXacc . Hydrophobic mutations ( D27L , E34F , R37A ) increased the binding affinity ( approximately eightfold stronger than the wild-type , Table 1 ) , while the introduction of charged residues ( A30E , A31E , L41E , A44E ) abolished the interaction ( Kümmel et al . , 2011 ) . 10 . 7554/eLife . 04463 . 003Figure 1 . Characterization of interaction of complexin ( CPX ) -48 with pre- and post-fusion SNARE complex by isothermal titration calorimetry . Representative thermograms of CPX-48 titrated into post-fusion SNARE ( A ) or pre-fusion SNARE∆60 complex ( B ) . The solid lines represent the best fit to the corresponding data points using non-linear least squares fit with the one-set-of-sites model . The results of the fit from 2–3 independent trials are shown in Table 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 04463 . 00310 . 7554/eLife . 04463 . 004Table 1 . Affinity constants ( Kd ) for complexin ( CPX ) binding to SNARE complexes measured by isothermal titration calorimetryDOI: http://dx . doi . org/10 . 7554/eLife . 04463 . 004ComplexinBinding partnerBinding affinity ( Kd ) ReferenceCPX1–134Ternary SNARE19 nMPabst et al . ( 2002 ) CPX48–134Ternary SNARE43 ± 7 nMThis studyCPX48–134SNAREΔ60457 ± 47 nMThis studyCPX1–134Blocked SNAREΔ60 ( 1 . 5-fold excess of CPX48–134 ) 16 µMKümmel et al . ( 2011 ) Super-clamp CPX1–134 ( D27L E34F R37A ) Blocked SNAREΔ60 ( 1 . 5-fold excess of CPX48–134 ) 2 µMKümmel et al . ( 2011 ) CPX1–134Blocked SNAREΔ60 ( 3-fold excess of CPX48–134 ) 15 . 2 ± 1 . 4 µMCurrent studyCPX26–83Blocked SNAREΔ60 ( 3-fold excess of CPX48–134 ) 23 . 9 ± 0 . 1 µMCurrent study To restore confidence in our ITC data reported in Kümmel et al . ( 2011 ) , we also repeated the ITC binding experiments using 2 . 5–3-fold molar excess of CPX-48 to completely block the CPXcen binding ( ≥99% ) . We found that CPX binds to the blocked SNAREΔ60 with a binding affinity of 15 . 2 ± 1 . 4 µM ( Figure 2A , Table 1 ) , matching well the Kd ∼16 µM reported in Kümmel et al . ( 2011 ) . We note that in these experiments as well as those reported in Kümmel et al . we titrated full-length CPX ( residues 1–134 ) into the blocked SNAREΔ60 , and not the minimal functional domain ( residues 26–83 ) as we had implied ( “we used a complexin construct comprising both the central and accessory helices [residues 26–83]” [Kümmel et al . , 2011] ) , and we apologize for this reporting error . We have now additionally carried out the ITC experiments with the minimal functional domain ( CPX26–83 ) and find that this truncated version also binds to the blocked SNAREΔ60 , albeit with slightly weaker affinity ( Kd = 23 . 9 ± 0 . 1 µM ) compared with full-length CPX ( Figure 2B , Table 1 ) . Taken together , the data strongly support our earlier conclusion that the ITC binding studies carried out with CPX titrated into blocked SNAREΔ60 correctly reflect the binding of CPXacc to t-SNARE . Consistent with this , we have recently also been able to characterize the binding of mammalian CPXacc to Drosophila t-SNAREs using blocked Drosophila pre-fusion SNARE complex ( Cho et al . , 2014 ) . Mutations in the CPXacc predicted to enhance or decrease the binding of CPXacc to t-SNARE exhibit corresponding binding profiles in ITC experiments ( Cho et al . , 2014 ) in support of the trans insertion model ( Kümmel et al . , 2011 ) . 10 . 7554/eLife . 04463 . 005Figure 2 . Interaction of complexin accessory helix ( CPXacc ) with the t-SNARE groove for full-length and truncated ( residue 26–83 ) CPX characterized by isothermal titration calorimetry . Full-length ( A ) or CPX26–83 ( B ) were titrated into pre-fusion SNAREΔ60 complex with the CPX central helix ( CPXcen ) binding site blocked with CPX-48 to exclusively measure the CPXacc–t-SNARE clamping interaction . The solid lines represent the best fit to the corresponding data points using non-linear least squares fit with one-set-of-sites-model and results of the fit are shown in Table 1 . All experiments were conducted in triplicate and a representative thermogram is shown . DOI: http://dx . doi . org/10 . 7554/eLife . 04463 . 005 Further , to directly monitor the multiple binding modes of CPX to the pre-fusion SNAREpin , we carried out new ITC experiments where we titrated full-length CPX into unblocked SNAREΔ60 . As our initial ITC data had suggested that the accessory helix of super clamp CPX ( residues 1–134 with D27L , E34F , R37A; scCPX ) binds to SNAREΔ60 with ∼10× higher affinity ( Table 1 ) than wild-type ( Kümmel et al . , 2011 ) , we used scCPX for this analysis . As shown in Figure 3A , titration of scCPX into the unblocked SNAREΔ60 results in a thermal graph characteristic of a reaction involving multiple binding sites , demonstrating that CPX has more than one binding site per SNAREΔ60 complex . The data can be best approximated using the independent thermodynamic parameters for the CPXcen and CPXacc interaction , with the assumption that both the truncated SNAREΔ60 and scCPX are bivalent ( Figure 3B ) . We observed a qualitatively similar titration curve for wild-type CPX ( Figure 3C ) but , since the CPXacc interaction with SNAREΔ60 is much weaker , the fitting with multiple binding sites was not resolved in detail . 10 . 7554/eLife . 04463 . 006Figure 3 . Isothermal titration calorimetry indicates multivalent interactions between SNAREΔ60 and CPX . ( A ) Calorimetric titration of super-clamp complexin ( scCPX; residues 1–134 , with D27L , E34F , R37A mutations ) into pre-fusion SNAREΔ60 complex describes a multi-site interaction of CPX . The solid lines represent the predicted binding thermogram assuming that both scCPX and truncated SNAREΔ60 are bivalent with well-defined independent thermodynamic parameters describing CPX central helix and CPX accessory helix binding ( B ) . ( C ) Representative thermogram of full-length wild-type CPX ( residues 1–134 ) titrated into unblocked SNAREΔ60 . DOI: http://dx . doi . org/10 . 7554/eLife . 04463 . 006 We suspect that experimental factors such as the buffer conditions , purification methodology ( i . e . , presence or absence of affinity tags ) , and the method used to determine the protein concentration ( Bradford/BCA or A280 ) might contribute to the variability in the ITC data between the two papers . The latter is of particular significance since the quantitation of small proteins/peptide ( <7 kDa ) is highly dependent on the method used . However , we are unable to pinpoint the differences in protocol between the two studies since several relevant experimental details ( e . g . , whether affinity tags were present or what method was used for protein quantification ) are not described in Trimbuch et al . Another discrepancy with regard to the ITC measure may derive from the way the blocked SNAREΔ60 complex was assembled . In our experience , assembly of SNARE complex with VAMP60 or similarly truncated VAMP using only the concentration–dilution cycles as carried out in the Trimbuch et al . report results in heterogeneous samples , with non-productive aggregates and un-assembled components not very effectively removed by the concentration cycle . In our experimental regimen we always purified the truncated SNARE complex on a Superdex 75 gel filtration column to ensure good quality of the assembled complexes . Column-purified SNAREΔ60 complexes were subsequently incubated with gel-filtration purified CPX-48 to form the blocked SNAREΔ60 complex used in the ITC experiments . We also note that Pabst et al . ( 2002 ) used a more stringent purification protocol ( Mono-Q purification ) even though they used a different SNAP25 construct for the preparation of the SNARE complexes for their ITC studies and report Kd values similar to our findings ( Pabst et al . , 2002 ) . The samples used by Trimbuch et al . for the NMR analysis appear to be homogeneous , however . We used FRET analysis to establish that the angled conformation of CPXacc also occurs in solution and is not dictated by crystal packing ( Kümmel et al . , 2011 ) . We placed the donor probe on SNAP25 , acceptor probe on CPXacc , and used donor quenching to track the positioning of the CPXacc in the pre- and post-fusion SNARE complex ( Krishnakumar et al . , 2011; Kümmel et al . , 2011 ) . The FRET data were consistent with the CPXacc locating parallel to the SNARE complex in the fully-assembled SNARE complex , but moving away from the SNAREs in the pre-fusion half-zippered complex ( Krishnakumar et al . , 2011; Kümmel et al . , 2011 ) . In Trimbuch et al . ( 2014 ) , based on NMR analysis , it was stated that the CPXacc helix is poorly structured even when bound to the SNARE complex and exhibits higher flexibility with c-terminal truncation of VAMP . The authors therefore suggested that the low FRET state we observed in the pre-fusion SNAREpin and assign to the ‘angled’ conformation can be explained by the enhanced flexibility of the CPXacc in this complex . Even though this is not in contradiction to the trans clamping model we have proposed , there are several lines of evidence ( Krishnakumar et al . , 2011; Kümmel et al . , 2011 ) arguing against this interpretation . ( 1 ) We observe a well-defined ‘high’ and ‘low’ FRET state for CPX bound to the post- and pre-fusion SNARE using two different FRET pairs with different R0 values ( Stilbene/Bimane , R0 ∼27Å and Bimane/Oregon Green , R0 ∼38Å ) , and the FRET distances in these CPX–SNARE complexes match very well with the predicted distance in the pre- and post-fusion crystal structures ( Kümmel et al . , 2011 ) . Further , a study by Choi et al . suggests that a completely unstructured CPX would result in a higher FRET signal than a more structured form ( Choi et al . , 2011 ) , so the decrease in FRET efficiency for CPX bound to SNAREΔ60 is not consistent with increased disorder . ( 2 ) For both the FRET pairs tested , acceptor placed at residue 31 on CPXacc shows weaker FRET compared with residue 38 , consistent with the idea that the CPXacc extends away from the pre-fusion SNARE complex , with dye on residue 31 locating further away from the donor ( on SNAP25 ) than the dye on residue 38 ( Kümmel et al . , 2011 , Figure 4 ) . ( 3 ) In the accompanying paper ( Krishnakumar et al . , 2011 ) , we tested the effect of VAMP truncation on the orientation of CPXacc and observed the same well-defined low FRET state ( ∼15% FRET ) for CPX bound to SNARE∆69 , SNARE∆65 , or SNARE∆60 complexes ( Krishnakumar et al . , 2011 , Figure 1B ) . But NMR analysis for same or similarly truncated SNARE complexes showed equally dramatic enhancement of local flexibility in CPXacc with increasing truncation on the VAMP c-terminus ( SNARE∆68 versus SNARE∆62 versus SNARE∆60 in Trimbuch et al . ( 2014 ) , Figure 2C , F ) . ( 4 ) Finally , the FRET signal we observed in the ‘low’ FRET state is higher than the FRET signal obtained when we used a CPX construct with enhanced flexibility ( CPX–GPGP ) in which a helix-breaking GPGP linker was inserted between the central and accessory helices of CPX for either SNARE∆60 or the post-fusion SNARE complex ( Kümmel et al . , 2011 , Figure 4D ) . Trimbuch et al . argued that , since we observe only a small change in FRET signal for CPX–GPGP compared with CPX–SNARE∆60 , the results obtained with the GPGP mutant are not conclusive . To address this concern we tested CPX–GPGP bound to SNARE∆60 and SNARE complex using the medium range Bimane/Oregon green FRET pair ( R0 ∼38Å ) . As shown in Figure 4 , the GPGP insertion results in a dramatic decrease in the FRET signal , with the CPXacc locating further away from both SNARE∆60 and the SNARE complex . These data suggest that the increased flexibility does not explain the decreased FRET signal in the pre-fusion truncated SNARE complex . 10 . 7554/eLife . 04463 . 007Figure 4 . Bimane–Oregon green fluorescence resonance energy transfer ( FRET ) experiments probing the effect of flexibility on complexin ( CPX ) orientation in pre- and post-fusion CPX–SNARE complexes . The FRET labeling positions were residue 193 on SNAP25 ( Bimane ) and residue 38 on CPX ( Oregon green ) . Fluorescence emission spectra of Bimane only ( black ) , Oregon green only ( grey ) , and Bimane–Oregon green labeled CPX–SNARE complexes containing VAMP2 ( residues 25–96 , orange ) , VAMP60 ( residues 25–60 , blue ) confirms the angled ( low FRET ) configuration of CPX accessory helix ( CPXacc ) in the pre-fusion SNARE complex ( VAMP60 ) . The near complete loss of FRET for the flexible CPX mutant ( helix breaking GPGP insertion between CPXacc and CPC central helix ( CPXcen ) ; CPX–GPGP , dashed lines ) compared with the wild-type ( WT , solid lines ) in both pre- and post-fusion SNARE complexes shows that the difference in FRET signal observed with WT CPX is not due to increased flexibility of the CPXacc in the pre-fusion complex . FRET distances in these CPX–SNARE complexes determined from the quenching of the donor ( Bimane ) fluorescence is shown ( table ) and standard deviations are reported from 2–3 independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 04463 . 007 Thus , taken together , our data are consistent with our earlier conclusion that the low FRET state corresponds to a defined angled configuration on the CPXacc in the pre-fusion complex . This is corroborated by functional data showing that the inhibitory function of CPX requires a fully-folded and rigid CPX helix ( Radoff et al . , 2014 ) , and introducing flexibility within the minimal functional domain of CPX ( residue 26–83 ) results in a reduction or loss of clamping ability corresponding to the degree of instability introduced ( Kümmel et al . , 2011; Cho et al . , 2014; Radoff et al . , 2014 ) . Using NMR , Trimbuch et al . were unable to detect any interaction between a peptide corresponding to CPXacc only ( CPX26–48 ) and SNARE∆60 ( Trimbuch et al . , 2014 ) . This experiment is in agreement with our ITC measurements , which did not detect an interaction between SNARE∆60 and CPX26–48 alone ( unpublished ) . We suspect that the conformations sampled by CPX26–48 are dependent on its protein context , and this might account for the different binding affinities observed for the shorter ( CPX26–48 ) compared with the longer ( CPX26–83 and CPX1–134 ) constructs . For example , our circular dichroism ( CD ) measurements showed that CPXacc ( residues 26–48 ) by itself is unfolded , but CPX26–83 forms a stable α-helical structure ( Figure 5A ) . 10 . 7554/eLife . 04463 . 008Figure 5 . Circular dichroism ( CD ) and dynamic light scattering ( DLS ) experiments . ( A ) CD spectra of the complexin ( CPX ) accessory domain ( CPX26–48 ) and the CPX minimal functional domain ( CPX26–83 ) . The continuous CPX construct ( 26–83 , blue ) with uninterrupted accessory and central domain shows the characteristics of an α-helical protein . In contrast , the isolated CPX accessory domain ( 26–48 ) shows little secondary structure and appears mostly unfolded . This may explain why no interaction between the CPX accessory region and SNAREΔ60 was observed by NMR . ( B ) DLS analysis showing the formation of the high-order oligomers of CPX–SNAREΔ60 in the concentration range used in the NMR experiments in Trimbuch et al . ( 2014 ) . Experimental average particle radius of pre-formed CPX26–83/SNAREΔ60 ( black dots ) , ncCPX26–83/SNAREΔ60 ( blue dots ) , and CPX26–83/SNARE ( green dots ) at varying concentration is shown . The solid lines ( same color scheme ) represent the average gyration radius of the oligomers calculated from the semi-quantitative model described in ‘Materials and methods’ . DOI: http://dx . doi . org/10 . 7554/eLife . 04463 . 008 However , Trimbuch et al . ( 2014 ) also did not observe any dramatic shifts and/or broadening of the CPXacc cross-peaks , as would be expected for insertion into the t-SNARE groove ( Kümmel et al . , 2011 ) , when they used CPX26–83 . We note that the prediction from our model is that the trans CPXacc–t-SNARE interactions would lead to the formation of CPX/SNAREΔ60 polymers , which would not be visible in NMR studies due to line broadening . Thus , if there is a trans interaction , then the NMR technique may not be well suited for its interrogation . This cross-linked high-order oligomeric state is evident qualitatively in the ITC experiments where we titrated full-length CPX into unblocked SNAREΔ60 ( Figure 3 ) . Dynamic light scattering ( DLS ) analysis ( Figure 5B , black circles ) similarly suggests that CPX26–83/SNAREΔ60 complexes begin to oligomerize at the concentration regime used in Trimbuch et al . ( 25–50 μM ) . The DLS data are consistent with a model predicting the size of the oligomers ( see ‘Materials and methods’ for details on the model , Figure 5B , black line ) which incorporates the Kd of ∼25 µM obtained from our ITC analysis of the CPX26–83 and blocked SNAREΔ60 interaction . In contrast , we observe only a small change in the apparent size if we disrupt the binding of CPXacc to t-SNARE either by introducing non-clamping mutations ( ncCPX26–83; A30E , A31E , L41E , A44E ) in CPXacc ( Figure 5B , blue circles ) or by blocking the t-SNARE groove using a fully assembled ternary SNARE complex ( Figure 5B , green circles ) . Based on our oligomerization model , this small change in the particle size could only be fitted by assuming a very low affinity interaction ( Kd ≥ 250 µM ) such as might arise from non-specific aggregation ( Figure 5B , blue and green lines ) . We suspect that the concentrations used in the NMR studies ( ∼25–50 μM ) ( Trimbuch et al . , 2014 ) , which correspond to the lower end of the range of concentration used for NMR studies , were adjusted to avoid oligomerization and line broadening effects . Because small proteins are difficult to quantitate and because our Kd values , which depend on concentration measurements for their accuracy , are only approximate , it is plausible that , under the NMR conditions of Trimbuch et al . , most of the CPX–SNAREΔ60 complexes were monomeric , with only a small fraction in an oligomeric form . Thus , under the experimental conditions used by Trimbuch et al . , CPXacc might not have an appreciable interaction with the t-SNARE groove , and the minor bound fraction would not be visible in NMR spectra owing to the large size of the cross-linked oligomers , assuming that there is no exchange between the monomeric and oligomeric forms of CPX–SNAREΔ60 . Therefore , we believe that the negative NMR data in Trimbuch et al . do not preclude a trans interaction between CPX and the SNAREΔ60 complex . In Trimbuch et al . , the authors tested the CPXacc/t-SNARE insertion model using the autaptic neuronal culture derived from CPX triple KO mice ( CPX TKO ) . They found that the super clamp and non-clamp CPX mutations , which are predicted to enhance or decrease CPXacc–t-SNARE binding , respectively , did not have the expected effect on evoked or spontaneous release . Based on this , they concluded that functional data do not support the CPXacc insertion/zig-zag model ( Trimbuch et al . , 2014 ) and raised concerns regarding the use of the cell–cell fusion assay ( Giraudo et al . , 2006 , 2009; Krishnakumar et al . , 2011; Kümmel et al . , 2011 ) as an in vitro system to study CPX clamping . These results and conclusions must be viewed with caution since the studies in knockout neurons , particularly in autaptic neuronal cultures , reveal a more restricted role of CPX in the neurotransmitter release ( Reim et al . , 2001; Xue et al . , 2007 , 2008; Yang et al . , 2013 ) . Specifically , the inhibitory function of CPX has not been observed in the autaptic system: that is , both evoked and spontaneous release are reduced in CPX TKO mice ( Reim et al . , 2001; Xue et al . , 2008; Trimbuch et al . , 2014 ) . In contrast , other preparations like rodent mass cultured neurons with reduced CPX expression and invertebrates lacking CPX exhibit reduced evoked release and enhanced spontaneous release ( Huntwork and Littleton , 2007; Yang et al . , 2010; Cho et al . , 2014 ) . Therefore , these systems may be more relevant to examine the mechanism of CPX function in regulating vesicle fusion , particularly in its ability to inhibit and regulate spontaneous release . In rescue experiments with rodent mass cultured neurons in which CPX was knocked down , CPX mutants that either enhanced or disrupted the CPXacc–SNARE interactions ( super-clamp and poor clamp , respectively ) resulted in corresponding reductions and enhancements in spontaneous release compared with wild-type CPX controls ( Yang et al . , 2010 ) . Recent experiments at the Drosophila neuromuscular junction , which in the absence of CPX exhibits large increases in spontaneous release , directly examined the role of a CPX zig-zag array in regulating spontaneous release . Using genetic rescue approaches , CPX mutants predicted to prevent the formation of the zig-zag array ( CPX–GPGP ) disrupt the ability of CPX to clamp spontaneous release , while mutations predicted to enhance the CPXacc–t-SNARE interaction ( super-clamp mutation ) exhibit a strong clamping ability ( Cho et al . , 2014 ) . These results taken together are consistent with a CPX cross-linking model mediating the CPX clamping function to regulate spontaneous release , even though recent reports suggest that the inhibitory and activating functions of CPX may be separable ( Yang et al . , 2010; Cho et al . , 2014; Lai et al . , 2014 ) . In addition , these in vivo studies ( Yang et al . , 2010; Cho et al . , 2014 ) support many of the observations made first with the cell–cell fusion assay ( Giraudo et al . , 2006 , 2008 , 2009; Krishnakumar et al . , 2011; Kümmel et al . , 2011; Li et al . , 2011 ) , underscoring the validity and the relevancy of the in vitro cell–cell fusion assay to study CPX clamping . As an alternative to the insertion/zig-zag model , Trimbuch et al . advanced an ‘electrostatic hindrance model’ for CPX clamping ( Trimbuch et al . , 2014 ) . This was based on the finding that increasing or decreasing the net negative charge on CPXacc inhibits or stimulates neurotransmitter release , respectively ( Trimbuch et al . , 2014 ) . We note that a number of both in vitro and in vivo studies on CPXacc modification argue strongly against the electrostatic hindrance model ( Yang et al . , 2010; Kümmel et al . , 2011; Cho et al . , 2014; Radoff et al . , 2014 ) . Introducing two ( L41E , A44E ) or four ( A30E , A31E , L41E , A44E ) negatively charged mutations in the mammalian CPXacc has been shown to entirely abolish or severely diminish the interaction of CPXacc with the mammalian or Drosophila t-SNARE , respectively ( Kümmel et al . , 2011; Cho et al . , 2014 ) . Consistent with this finding , the similar mutations have been shown to abrogate the clamping function of CPX in both in vitro cell–cell fusion assay ( Kümmel et al . , 2011 ) and in vivo rescue experiments with knockdown neurons ( Yang et al . , 2010 ) . Further , in contrast to the electrostatic model , increasing the hydrophobicity of CPXacc has been shown to enhance the clamping function of CPX ( Yang et al . , 2010; Kümmel et al . , 2011; Cho et al . , 2014 ) .
We believe that the new in vitro data presented above and physiological data published recently ( Cho et al . , 2014 ) should dispel the concerns raised by Trimbuch et al . regarding the plausibility of the insertion/zig-zag model . Specifically , ITC binding experiments with blocked SNARE complexes , where a 2 . 5–3-fold excess of blocking peptide was present , have corroborated the interaction of CPXacc with the t-SNARE groove in a pre-fusion ( truncated ) SNARE complex as observed in the crystal structure ( Figure 2 and Kümmel et al . , 2011 ) . Additionally , titration of scCPX into unblocked truncated SNARE complex revealed multiple binding sites consistent with independent binding sites for CPXcen and scCPXacc , thus describing the essential feature of the trans clamping interaction of CPX ( Figure 3 ) . FRET analysis ( using two FRET pairs with different R0 values ) with ‘flexible’ CPX construct ( CPX–GPGP ) has clearly demonstrated that the low FRET state corresponding to the angled conformation , which describes the trans clamping interaction , is due to discrete conformational change and not because of increased CPX flexibility ( Figure 4 and Kümmel et al . , 2011 ) . Lastly , the physiological relevance of the insertion model was established by independent genetic rescue experiments in the Drosophila neuromuscular junction , wherein mutations in CPX that are predicted to prevent the formation of the SNAREpin array were found to disrupt the ability of CPX to clamp spontaneous release ( Cho et al . , 2014 ) . All of our data are consistent with a model in which CPXacc from one SNAREpin interacts in trans with the t-SNARE groove of a second SNAREpin . As we emphasized in Kümmel et al . , we do not propose that the interactions are necessarily exactly as observed in a single crystal structure for which scCPX was used and where hydrophobic accessory helix residues ( which are hydrophilic in wild-type CPX ) interact with hydrophobic residues in a partially assembled SNARE complex . Details of the interaction between wild-type CPXacc and t-SNARE must be different ( they are also weaker , explaining why the wild-type CPX clamps less well ) , and we can well imagine a scenario in which wild-type CPXacc interacts with a slightly different surface of the assembling SNARE complex . In fact , we found that a single mutation in the CPXacc ( F34M ) results in two distinct binding interfaces for scCPX , but both giving rise to the same zig-zag topology . Both interactions involved the same face of CPXacc and t-SNARE , although the binding site on t-SNARE was extended by two helical turns for the mutant ( Kümmel et al . , 2011 ) . The consideration that interactions of wild-type and scCPX and the SNARE complex are not identical does not , however , exclude the possibility that a trans interaction of some sort is responsible for clamping . We also suspect that yet unknown additional interactions in the pre-fusion complex , which are not represented in our crystal structure , may further stabilize the trans clamping interaction ( Cho et al . , 2014; Radoff et al . , 2014 ) . We do not consider the trans clamping model proven , but it remains the most well-defined model for which a good amount of evidence is available .
The constructs used in this study are GST-PreScission-VAMP2Δ60 ( human VAMP2 residues 29–60 ) , pET15b-oligohistidine-thrombin-VAMP2 ( human VAMP2 residues 29–96 ) , pET28-oligohistidine-synataxin1A ( containing rat syntaxin 1a residues 191–265 ) , oligohistidine-SUMO-SNAP25N ( containing human SNAP25A residues 7–82 ) , oligohistidine-SUMO-SNAP25C ( containing human SNAP25A residues 141-203 ) and pET15b-oligohistidine-thrombin-CPX ( containing human complexin1 residues 1-134 ) ; residues 1–134 with flexible GPGP insert between residues 49–50 ( CPX–GPGP ) , residues 26–83 ( CPX26–83 ) ; residues 26–83 with non-clamping mutations A30E , A31E , L41E , A44E ( ncCPX26–83 ) . Truncated CPX constructs , including residues 48-134 ( CPX-48 ) , residues 26-48 ( CPX26-48 ) was purified as oligohistidine-SUMO constructs . All constructs were expressed and purified as described previously ( Krishnakumar et al . , 2011; Kümmel et al . , 2011 ) . To ensure high quality , all proteins were purified on a High-load Superdex 75 ( 16/60 , GE Healthcare; Piscataway , NJ ) gel filtration column . ITC experiments were carried out as described previously ( Krishnakumar et al . , 2011; Kümmel et al . , 2011; Cho et al . , 2014 ) . To assemble the post-fusion and pre-fusion SNAREΔ60 complex , Syntaxin 1a , SNAP25N , SNAP25C , and VAMP2 or VAMPΔ60 were mixed ( molar ratio of 1:1 . 2:1 . 2:1 . 2 for the post-fusion complex and 1:1 . 2:1 . 2:1 . 6 for pre-fusion SNAREΔ60 complex ) and incubated overnight at 4°C . The assembled complexes were purified from non-productive aggregates and unassembled components by gel filtration ( High-load Superdex 75 16/60 , GE Healthcare ) . To form the blocked SNAREΔ60 complex , purified SNAREΔ60 complex was mixed with 2 . 5 molar excess of CPX-48 and incubated overnight at 4°C to ensure complete binding . Then , we titrated CPX-48 back into this initial mixture of SNAREΔ60 and CPX48-134 . If there was heat signal , we added more CPX-48 to the mixture . This process was repeated until negligible residual heat signal was observed ( control experiment ) . Then we titrated full length CPX1-134 into the exact same mixture of blocked SNAREΔ60 as used in the control experiment . By comparing the full length CPX titration with the control titration , we determined the binding interaction between CPX N-terminus and C-terminal unzippered t-SNARE . To ensure buffer uniformity to measure the weak interactions , CPX variants and the different SNARE complexes were extensively dialyzed ( 4 L for 4 hr followed by another 4 L overnight ) into the same phosphate buffered saline ( PBS ) buffer ( pH 7 . 4 , 137 mM NaCl , 3 mM KCl , 10 mM sodium phosphate dibasic , 2 mM potassium phosphate monobasic , 0 . 25 mM TCEP ) before the ITC analysis . The concentrations of dialyzed proteins were determined by bicinchoninic acid ( BCA ) protein assay kit ( Thermo Scientific; Waltham , Ma ) and/or Bradford assay ( Bio Rad; Hercules , CA ) with bovine serum albumin ( BSA ) as the standard . ITC experiments were performed with a Microcal ITC200 ( Malvern Instruments , UK ) . Typically , ∼200 µl of SNARE solution was loaded into the sample cell and ∼60 µl of CPX solution was loaded into the syringe . The protein concentrations used for the titration were as follows: 110 µM CPX-48 titrated into 5 . 8 µM SNARE ( Figure 1A ) ; 210 µM CPX-48 titrated into 14 µM SNAREΔ60; ∼360 µM CPX1–134 or CPX26–83 titrated into ∼20 µM blocked SNAREΔ60 ( Figure 2 ) ; 150 µM wild-type or scCPX1–134 titrated into 7 . 5 µM SNAREΔ60 ( Figure 3 ) . The heat change from each injection was integrated and then normalized by the moles of CPX in the injection . The thermographs were analyzed by non-linear least squares fit with the one-set-of-sites-model in Microcal Origin ITC200 software package to obtain the stoichiometric number ( N ) , the molar binding enthalpy ( ΔH ) , and the association constant ( Ka ) . The equilibrium dissociation constant ( Kd ) , the binding free energy ( ΔG ) , and the binding entropy ( ΔS ) were calculated using the thermodynamic equations:Kd=1Ka . ΔG=ΔH−TΔS=−RTln ( Ka ) . FRET measurements were carried out as described previously ( Krishnakumar et al . , 2011; Kümmel et al . , 2011 ) . SNAP25 D193C was labeled with the donor probe , Bimane ( Monochlorobimane , Invitrogen ) and the scCPX Q38C was labeled with acceptor Oregon green ( Oregon green 488 maleimide , Invitrogen ) . The proteins were labeled using 10× molar excess of dye in 50 mM Tris buffer , pH 7 . 4 , containing 150 mM NaCl , 10% glycerol , and 1 mM TCEP . Following overnight incubation at 4°C , the excess dye was separated from the labeled proteins using a NAP desalting column ( GE Healthcare ) . The labeling efficiency was calculated using ε396 = 5300 L M−1 cm−1 for Bimane and ε496 = 76 , 000 L M−1 cm−1 for Oregon green , and the protein concentration was measured using the Bradford assay with BSA as standard . Typically , the labeling efficiency was >95% for both Bimane-SNAP25 and Oregon green-CPX . The double-labeled CPX–SNARE complexes were assembled overnight at 4°C and purified by gel filtration on a Superdex 75 ( 10/30 , GE Healthcare ) gel filtration column . All fluorescence data were obtained on a Perkin-Elmer ( Waltham , MA ) LS55 luminescence spectrometer operating at 25°C . Excitation and emission slits of 5 nm were used in all measurements . Fluorescence emission spectra were measured over the range of 410–600 nm with the excitation wavelength set at 396 nm . The donor probe concentration was adjusted to 2 µM in all samples . CD spectra of peptides corresponding to the CPX accessory domain alone ( residues 26–48 ) or the minimal functional domain ( residues 26–83 ) were recorded in PBS using Chirascan CD spectrometer ( Applied Photophysics , UK ) at 25°C from the range of 260 nm–198 nm at 1 nm bandwidth . CPX–SNAREΔ60 complexes containing wild-type CPX1–134 were assembled and purified as previously described ( Krishnakumar et al . , 2011; Kümmel et al . , 2011 ) . DLS experiments were carried out on a DynaPro NanoStar instrument ( Wyatt Technology; Santa Barbara , CA ) at a wavelength of 663 . 76 nm operating at 4°C . Protein samples were centrifuged ( 10 min at 13 , 000×g ) and data were collected using DynaPro disposable cuvettes . Autocorrelations for 20 s were collected over 15 acquisitions . Points were eliminated if the intensity fluctuated by more than 15% from the average . Data were analyzed with DYNAMICS 7 . 1 . 7 . 16 software ( Wyatt Technology ) . In DLS there is oligomerization of CPX–SNAREΔ60 . We can try to model this process as an equilibrium between bound and unbound CPX–SNAREΔ60 , wherein the bound state is generated by the interaction of CPXacc with the t-SNARE groove . Thus , this equilibrium can be written as: ( 1 ) CPX−SNAREΔ60 + CPX−SNAREΔ60 ⇄KdCPX−SNAREΔ60−CPX−SNAREΔ60 . which , in terms of concentration , gives: ( 2 ) [un−bound CPX−SNAREΔ60]2 = Kd * [bound CPX−SNAREΔ60] . Keeping in mind that ( 3 ) [un−bound CPX−SNAREΔ60] + [bound CPX−SNAREΔ60] = [CPX−SNAREΔ60]initial , where [CPX–SNAREΔ60]initial is the initial concentration , the equilibrium concentrations can be calculated from ( 2 ) and ( 3 ) as: ( 4 ) un−bound CPX−SNAREΔ60] = Kd ( 1+4[CPX−SNAREΔ60]initialKd−1 ) 2 . ( 5 ) [bound CPX−SNAREΔ60] = [CPX−SNAREΔ60]initial−Kd ( 1+4[CPX−SNAREΔ60]initialKd−1 ) 2 . Then , the probability that an accessory helix is bound is given by: ( 6 ) p=[bound CPX−SNAREΔ60][CPX−SNAREΔ60]initial=1−Kd ( 1+4[CPX−SNAREΔ60]initialKd−1 ) 2[CPX−SNAREΔ60]initial , and the average number of monomers is equal to: ( 7 ) N=p1−p+1=2[CPX−SNAREΔ60]initKd ( 1+4[CPX−SNAREΔ60]initKd−1 ) . We will model the gyration radius of an N-mer as: ( 8 ) r = N0 . 5 r0 , where r0 is the distance between two particles in the array and the reflection of monomer size . The r value is quantitative for long ideal polymers but is only semi-quantitative for non-ideal oligomers under consideration here . The experimental results can be well approximated by Equation 8 using r0 = 3 . 5 nm and the CPXacc–t-SNARE interaction described by Kd = 25 µM for CPX26–83 titrated into blocked SNAREΔ60 . This r0 value is reasonable considering the dimensions of the CPX–SNAREΔ60 unit within the zig-zag array . Hence , the oligomerization we observe by DLS is consistent with the Kd values we measured by ITC ( Figure 2 ) . In support of the idea that the CPXacc–t-SNARE clamping interaction results in the oligomeric state , CPX–SNARE complexes containing either ncCPX26–83 ( A30E , A31E , L41E , A44E ) or the full ternary SNARE complex show only a small change in the average particle radius . This behavior can be modeled using Equation 8 with r0 = 3 . 8 nm and only if we assume a very low affinity interaction , namely Kd = 250 µM for ncCPX into SNAREΔ60 and Kd = 300 µM for CPX into the full ternary SNARE complex . This suggests that this change in particle size might be a result of non-specific aggregation . Note that r0 values for specific and non-specific oligomerization are not necessarily expected to be identical . | Molecules called neurotransmitters are used to carry signals between neurons . The neurotransmitters in the first neuron are stored in small bubble-like structures called synaptic vesicles . When this neuron is ready to send a signal to a second neuron , the membrane that encloses the synaptic vesicle fuses with the cell membrane that surrounds the neuron . This involves SNARE proteins in the vesicle membrane interacting with similar proteins in the cell membrane to form a SNARE complex , which then proceeds to ‘zip’ the two membranes together . Other proteins are involved in the fusion process and the release of the neurotransmitters . For example , complexins bind to SNARE proteins during the formation of the SNARE complex in order to temporarily halt the fusion process . This ‘clamping’ interaction ensures that the neurotransmitters are released at the appropriate time . Researchers have proposed two different models of the clamping interaction . In the trans clamping model a region in the complexins called the accessory helix extends forward and clamps SNARE proteins that are present on the two membranes . An alternative model explains clamping in terms of electrostatic interactions between the accessory helix and the two membranes . These interactions are repulsive because the accessory helix and the membranes are all negatively charged . Now Krishnakumar , Li et al . —including some of the researchers who first proposed the trans clamping model—have used a variety of biochemical techniques to re-examine the clamping interaction . These experiments support the idea that the accessory helix binds to and clamps a SNARE protein , as suggested by the trans clamping model . The results of recent in vivo experiments on fruit flies have also provided support for the trans clamping model , although further work is need to compare the models in both in vitro and in vivo systems . | [
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] | 2015 | Re-visiting the trans insertion model for complexin clamping |
We demonstrate that prostate cancer can be identified by flow cytometric profiling of blood immune cell subsets . Herein , we profiled natural killer ( NK ) cell subsets in the blood of 72 asymptomatic men with Prostate-Specific Antigen ( PSA ) levels < 20 ng ml-1 , of whom 31 had benign disease ( no cancer ) and 41 had prostate cancer . Statistical and computational methods identified a panel of eight phenotypic features ( CD56dimCD16high , CD56+DNAM-1- , CD56+LAIR-1+ , CD56+LAIR-1- , CD56brightCD8+ , CD56+NKp30+ , CD56+NKp30- , CD56+NKp46+ ) that , when incorporated into an Ensemble machine learning prediction model , distinguished between the presence of benign prostate disease and prostate cancer . The machine learning model was then adapted to predict the D’Amico Risk Classification using data from 54 patients with prostate cancer and was shown to accurately differentiate between the presence of low-/intermediate-risk disease and high-risk disease without the need for additional clinical data . This simple blood test has the potential to transform prostate cancer diagnostics .
Early diagnosis and treatment increase curative rates for many cancers . The WHO considers that the burden of cancer on health services can be reduced by early detection and that this is achievable via three integrated steps: 1 ) awareness and accessing care , 2 ) clinical evaluation , diagnosis , and staging , 3 ) access to treatment ( http://www . who . int/mediacentre/factsheets/fs297/en/ ) . Although the clinical introduction of the Prostate-Specific Antigen ( PSA ) test in 1986 increased the early diagnosis of localized prostate cancer ( Catalona et al . , 1991; Hankey et al . , 1999 ) , elevated PSA levels are not necessarily indicative of prostate cancer because PSA levels can be raised by prostatitis , other localised infections , benign hyperplasia and/or factors such as physical stress . Contrastingly , 15% of men with ‘normal’ PSA levels typically have prostate cancer , with a further 15% of these cancers being high-grade ( https://prostatecanceruk . org/prostate-information/prostate-tests/psa-test ) . The reliable diagnosis of prostate cancer based on PSA levels alone is therefore not possible and confirmation using invasive biopsies is currently required . In 2011/12 approximately 32 , 000 diagnostic biopsies ( 28 , 000 TRUS and 4 , 000 TPTPB ) were performed by the NHS in England ( NICE , 2014 ) . Although the transrectal ultrasound guided prostate ( TRUS ) biopsy is the most commonly used technique , it is limited to taking 10 to 12 biopsies primarily from the peripheral zone of the prostate and has a positive detection rate between 26% and 33% ( Aganovic et al . , 2011; Nafie et al . , 2014a; Naughton et al . , 2000; Yuasa et al . , 2008 ) . The Transperineal Template Prostate biopsy ( TPTPB ) is a 36 core technique that samples all regions of the prostate and delivers a better positive detection rate between 55% and 68% ( Dimmen et al . , 2012; Nafie et al . , 2014b; Pal et al . , 2012 ) . However , invasive biopsies are painful and associated with a significant risk of potentially serious side-effects such as urosepsis and erectile dysfunction ( Chang et al . , 2013 ) . Given the potential challenges of invasive tests and the risk of significant side-effects , considerable interest in the potential of non-invasive blood or urine-based tests/approaches ( ‘liquid biopsies’ ) for diagnosing disease has developed ( Quandt et al . , 2017 ) . Liquid biopsies can provide information about both the tumour ( e . g . circulating cells , cell-free and exosomal DNA and RNA ) and the immune response ( e . g . immune cell composition and their gene , protein , and exosome expression profiles ) . Liquid biopsies are minimally invasive and enable serial assessments and ‘live’ monitoring speedily and cost-effectively ( Quandt et al . , 2017 ) . Based on the reciprocal interaction between cancer and the immune system , we have proposed that immunological signatures within the peripheral blood ( the peripheral blood ‘immunome’ ) can discriminate between men with benign prostate disease and those with prostate cancer and thereby reduce the dependency of diagnosis on invasive biopsies . To this end , we have previously shown that the incorporation of a peripheral blood immune phenotyping-based feature set comprising five phenotypic features CD8+CD45RA-CD27-CD28- ( CD8+ Effector Memory cells ) , CD4+CD45RA-CD27-CD28- ( CD4+ Effector Memory cells ) , CD4+CD45RA+CD27-CD28- ( CD4+ Terminally Differentiated Effector Memory Cells re-expressing CD45RA ) , CD3-CD19+ ( B cells ) , CD3+CD56+CD8+CD4+ ( NKT cells ) into a computation-based prediction tool enables the better detection of prostate cancer and strengthens the accuracy of the PSA test in asymptomatic men having PSA levels < 20 ng/ml ( Cosma et al . , 2017 ) . Herein , we have extended this new approach to determine if phenotypic profiling of peripheral blood natural killer ( NK ) cell subsets can also discriminate between the presence of benign prostate disease and prostate cancer in the same cohort of asymptomatic men . We also investigate the potential of the peripheral blood dataset to discriminate between low- or intermediate-risk prostate cancer and high-risk prostate cancer in those men having prostate cancer .
Herein , we consider a ‘feature’ to be a single phenotypic variable ( as determined using flow cytometry ) or a pre-grouped set of phenotypic variables , as shown in Table 1 . It was not possible to discriminate between men with benign prostate disease and men with prostate cancer based on differences between phenotypic features/profiles due to their similarity ( Table 1 , Figure 1 , Figure 2 ) . These findings highlight the difficulty in identifying combinations of features that can best identify the presence of cancer . These difficulties are compounded by the challenge of identifying the best combination of predictors which comprise n number of features , and that features within a combination , ideally , should not correlate . It is important to evaluate correlations between features , because if two features are highly correlated , then only one of these could serve as a candidate predictor . However , there may be occasions where both features are needed and besides the impact of this on the dimensionality of the dataset , there is no other negative impact . Furthermore , when two features are highly correlated and are important , it may be difficult to decide which feature to remove . Figure 3 shows the correlations between features , where +1 . 0 indicates a strong positive correlation between two features , and −1 . 0 indicates a strong negative correlation between two features . The Kolmogorov-Smirnov and Shapiro-Wilk tests of normality were carried out to determine whether the dataset is normally distributed , as this would determine the choice of statistical tests , that is whether to use parametric ( for normally distributed datasets ) , or non-parametric ( for not normally distributed datasets ) tests . The results of the normality tests are shown in Table 2 . The results revealed that only 7–8 features ( depending on the normality test ) were normally distributed ( with p>0 . 05 ) , and for the remaining features the p value was less than 0 . 05 ( p<0 . 05 ) which indicates that there is a statistically significant difference between the distribution of the data of those features and the normal distribution . Based on the results of the test , we can conclude that the dataset is not normally distributed . Given that most features in the dataset are not normally distributed , the Kruskal-Wallis ( also called the ‘one-way ANOVA on ranks’ , a rank-based non-parametric test ) tests were used to check for statistically significant differences between the mean ranks of the NK cell phenotypic features in men with benign prostate disease and patients with prostate cancer rather than its parametric equivalent ( one-way analysis of variance , ANOVA ) . Although the Kruskal-Wallis test did not return any significant differences in the mean PSA values between individuals with benign disease and those with prostate cancer ( χ2=0; p=0 . 949 , Figure 4 ) , statistically significant differences at the alpha level of α=0 . 05 in the mean ranks of the CD56brightCD8+ ( ID14 , p=0 . 007 ) , CD56+NKp30+ ( ID15 , p=0 . 008 ) , CD56+NKp30- ( ID16 , p=0 . 031 ) , CD56+NKp46+ ( ID17 , p=0 . 023 ) populations in men with benign prostate disease and those with prostate cancer ( Table 3 ) were observed . This initial analysis provided insight into which phenotypic features might be good candidates for distinguishing between the presence of benign disease and prostate cancer . The next step was to examine whether using these as inputs into a machine learning algorithm can achieve this . An Ensemble Subspace kNN classifier was developed for the task at hand . The section which follows explains the approaches that were used to compare the diagnostic accuracy of the classifier when using the subset of features derived from the statistical analysis , and those features which were selected as a combination using the Genetic Algorithm ( GA ) for feature selection . The GA was used to identify a subset of features that , as a combination , provide an NK cell-based immunophenotypic ‘fingerprint’ which can determine if an asymptomatic individual with PSA levels below 20 ng ml-1 has benign prostate disease or prostate cancer . This fingerprint , or feature set , would then be used to construct a diagnostic/prediction model . Given that GAs stochastically select multiple individuals ( i . e . features ) from the current population ( based on their ‘fitness’ ) , each run can return different results . A common approach to identifying the best solution ( s ) is , therefore , to run the algorithm several times to obtain the frequency of the solution ( s ) . Since the aim herein is to identify the most commonly occurring subset of NK cell phenotypic predictors , the GA was applied to the dataset and the most frequent subset of features returned was considered as being the best and most promising . Let fc denote the number of times ( frequency ) a combination was returned during the n number of runs , then the relative frequency of a combination ( Rfc ) can be calculated using formula ( Equation 1 ) , ( 1 ) Rfc=fcn Table 4 shows the most frequent feature combinations returned at the end of each of the 30 runs when setting λ to different values . In Table 4 , λ is the number of features in a combination . No . different comb is the number of unique combinations returned during the n number of runs ( i . e . n = 30 ) for a given λ; Comb . with highest freq is the combination which was returned most frequently during the n number of runs; Freq of Comb . is the frequency of the most common combination found in the previous column; Relative Freq . ( % ) is computed using formula ( Equation 1 ) converted to a percentage . As the optimum number of features is not known , the GA was run by setting λ=2 , 3 , … , n where n is the total number of features in the dataset . Table 4 shows the results for the first 10 combinations . The results indicate that the combination comprising four features is the most promising in terms of its ability to discriminate between benign prostate disease and prostate cancer on NK cell phenotypic data alone . Features 2 , 20 , 27 , 28 , were returned in all 30 runs when searching for the best combination comprising of four features . Furthermore , features 20 , 27 , 28 were returned together in all combinations comprising more than three features ( see feature ID’s in combinations λ=4 to λ=10 in Table 4 ) . These results strongly suggest that these are good predictors when grouped . The fact that the same combination was returned in 30 iterations is a strong indicator that these four features are the most reliable for distinguishing between the presence of benign prostate disease and prostate cancer . Although the statistical analysis presented in Table 3 determined that features: ID14: CD56brightCD8+ , ID15: CD56+NKp30+ , ID16: CD56+NKp30- , and ID17: CD56+NKp46+ were the only ones with values which were significantly different in the two groups at α=0 . 05 , and for which p values were therefore less than 0 . 05 , none of the features selected by the statistical analysis were returned by the GA when searching for the best combination of features for discriminating between the presence of benign prostate disease and prostate cancer . The features selected by the GA were: ID2: CD56dimCD16high , ID20: CD56+DNAM-1- , ID27: CD56+LAIR-1+ , and ID28: CD56+LAIR-1- . Referring back to Figure 3 and the correlation values between the selected features 2 , 20 , 27 , 28 , 14 , 15 , 16 , 17 , it is shown that these features do not have a strong positive correlation . There is a strong negative correlation between features 27 and 28 , but we decided to keep both features since these were selected by the feature selection method . The next step in the analysis involves evaluating the predictive performance of the feature subsets returned by the statistical test and by the GA . The features identified from the statistical and GA approaches were input into the proposed Ensemble Subspace kNN classifier to determine whether it can learn these features and discriminate between the presence of benign prostate disease and prostate cancer . For transparency of the machine learning model , it was important to keep the predictor selection and machine learning processes separate . The feature selection algorithm identified a set of novel NK cell phenotypic features for diagnosing the presence of prostate cancer which will be used to construct a transparent prediction tool . This section describes the outcome of experiments that were performed to determine the predictive performance of various feature subsets using the Ensemble Subspace kNN model , which was designed for the task . Machine learning classifiers that are constructed using small training sets have a large variance which means that the estimate of the target function will change if different training data are used ( Skurichina and Duin , 2002 ) . It is therefore expected , and normal , that classifiers will exhibit some variance . This means that small changes in input variable values can result in very different classification rules . To ensure that the proposed approach does not suffer from low variance , we evaluated the performance of the classifier using the 10-fold cross-validation approach which was repeated 30 times , for which the average and standard deviation of each run were recorded . Multiple runs of 10-fold cross-validation are performed using different partitions ( i . e . folds ) , and the validation results are averaged over the runs to estimate a final predictive model . Each run of the cross-validation involves randomly partitioning a sample of data into complementary subsets , for which one subset is used as the training set , and the other is used as the validation subset . Cross validation randomly partitions the dataset into training and validation sets to limit overfitting problems , and to provide an insight into how the model will generalise to an independent dataset which was not previously seen by the model . A random seed generator was used to generate a different sequence of values each time the k-fold was run , and this was reseeded using a seed that was created using the current time . It is normal that a classifier returns a different validation accuracy in each fold and run , since it is training and validating on different samples . The aim is to create a low variance classifier , meaning that the results of each validation test are close together . The closer the results of each validation test , the more robust the classifier . To evaluate the predictive performance of various feature subsets derived from the computational and statistical feature selection approaches , each of these feature subsets was input into an Ensemble Subspace kNN classifier . Applying 10-fold validation resulted in 10 different partitions of the dataset of approximately 64 randomly selected samples for training and 7 randomly selected samples for validation in each partition ( 1 dataset comprising 63 training cases and 8 validation cases; and 9 datasets comprising 64 validation cases and 7 validation cases ) . All samples went through validation at some point during the evaluations . We consider 10-fold cross validation to be suitable given the small size of the dataset and the fact that sufficient samples are needed during the training process . Table 5 shows the results of the comparison when running the 10-fold validation 30 times using six sets of features: 1 ) the four features selected by the GA; 2 ) the four features which were returned by the Kruskal-Wallis statistical test ( STAT ) ; 3 ) combined features selected by the GA and the statistical test ( GA+STAT ) ; 4 ) PSA values combined with features selected by the GA and the statistical test ( PSA+GA+STAT ) ; 5 ) PSA values alone as a predictor ( PSA ) ; and 6 ) using all 32 features ( All features ) . The averages of the Area Under the Curve ( AUC ) , Optimal ROC Point ( ORP ) False Positive Rate ( FPR ) of the AUC , ORP True Positive Rate ( TPR ) of the AUC , and Accuracy ( ACC ) of each fold are provided . The last column of Table 5 shows the Rank of each model , where 1 is the best model and 6 is the worst . The results of each k-fold were averaged , and these average values are plotted in the box plot shown in Figure 5 . As shown in Table 5 , combining the features selected by the GA ID2: CD56dimCD16high , ID20: CD56+DNAM-1- , ID27: CD56+LAIR-1+ , ID28: CD56+LAIR-1-; with the four features which were returned by the Kruskal-Wallis statistical test as features with values which were statistically significant between individuals with benign prostate disease and patients with prostate cancer , ID14: CD56brightCD8+ , ID15: CD56+NKp30+ , ID16: CD56+NKp30- , ID17: CD56+NKp46+ yielded the highest classification accuracy , with AUC = 0 . 818 , ORP FPR = 0 . 201 , ORP TPR = 0 . 836 and Accuracy = 0 . 821 . PSA values input into the classifier resulted in weak classification performance , AUC = 0 . 698 , ORP FPR = 0 . 217 , ORP TPR = 0 . 609 , and Accuracy = 0 . 692 . Although PSA is used as a screening test in clinical practice for identifying prostate cancer in men , it is the weakest of all the predictors . Importantly , predictive accuracy improved when PSA is combined with GA+STAT flow cytometry features ( PSA+GA+STAT ) : AUC = 0 . 812 , ORP FPR = 0 . 208 , ORP TPR = 0 . 832 , and ACC = 0 . 815 . Combining PSA with the NK cell phenotypic fingerprint increased accuracy by +0 . 123 points when compared to using PSA alone . The closer the standard deviation value is to 0 the less spread out are the results across the 30 runs , and hence the classifier variability is low ( see Table 5 ) . This results in a low variance classifier . A low standard deviation indicates that the data points tend to be close to the mean ( also called the expected value ) of the set , whereas a high standard deviation indicates that the data points are spread out over a wider range of values . Observing the data shown in Table 5 and Figure 5 for each evaluation measure ( i . e . AUC , ORP TPR , ORP FPR , Accuracy ( ACC ) ) , the aim is to have a high AUC and low Std . ; low ORP FPR and low Std . ; high ORP TPR and low Std . ; and high Accuracy and low Std . The results show that the classifier achieved the best performance when using the GA+STAT input and the results using k-fold across the 30 runs returned the lowest mean standard deviation and hence the least variability in the results . The results reveal that using the GA+STAT predictors delivers a more reliable classification model with regards to training and validation on new data which will be generated in the future using the prediction model . The GA+STAT prediction model achieved the best performance , in that the ORP FPR was the lowest , and the AUC , ORP TPR , and Accuracy ( ACC ) were the highest compared to the other prediction models . The experimental results are promising and the proposed prediction model is expected to achieve even higher classification accuracy in identifying the presence of prostate cancer in asymptomatic individuals with PSA levels < 20 ng ml-1 based on peripheral blood NK cell phenotypic profiles as more data become available in the future . Table 5 shows the performance of the classifier when using various feature subsets . When using the GA+STAT features , the AUC is higher , and FPR is lower ( this is an important distinction ) than when using all features or the other alternative feature subsets . The most important aspect is that better performance was achieved using a much smaller set of biomarkers ( features ) , which indicates that we have identified a fingerprint for detecting the presence of prostate cancer in asymptomatic men with PSA levels < 20 ng ml-1 which is indeed significant from a clinical perspective . Feature selection is important , as the fundamental aim of this project is to develop a subset of phenotypic biomarkers that is smaller than the original set of biomarkers ( i . e . 32 biomarkers in total ) which can confidently identify the presence of prostate cancer . Ultimately , the approach will be embedded into a software application to be used by clinicians , and the aim is to create an interface that requires the clinician to input a few values ( features ) , that is 8 instead of 32 . Importantly , identifying a small subset of 8 features which is needed for detecting the presence of prostate cancer , results in the construction of an explainable disease detection and categorization model . Working with a small set of the most promising biomarkers provides a better understanding of the disease and allows cancer immunobiologists and clinicians to focus on performing further laboratory evaluations using the specific subset of biomarkers , in a more cost effective and less time-consuming manner . The next step in the analysis is to determine whether statistically significant differences exist between the average AUC performance values of the classifier when using the various feature subsets , for which Friedman’s two-way Analysis of Variance ( ANOVA ) test was used . It was also important to observe whether including the PSA test values significantly strengthens the diagnostic accuracy and capacity . The average k-fold values across the 30 runs for each feature set were computed . A matrix C was derived which holds the results of the classifier when using one of five feature subsets . Friedman’s chi-square statistic compares the mean values of the columns of matrix C . The test returned a statistically significant difference in the AUC predictive performance depending on which type of feature subset was input into the classifier , χ2 ( 4 ) =106 . 55 , p=3 . 968E-22 . This suggests that the mean AUC ranks of at least one feature subset are significantly different than the others . The mean ranks were as follows: GA = 12 . 050 , STAT = 10 . 733 , GA+STAT = 20 . 283 , PSA = 3 . 067 , PSA+GA+STAT = 18 . 867 . A post hoc test was run alongside the Friedman test to pinpoint which feature subsets differ from each other . Post hoc analysis using a Bonferroni correction was used to reduce the likelihood of erroneously declaring a statistically significant due to multiple comparisons ( a Type I error ) . Table 7 shows the results of multiple comparisons and adjusted p values . There were statistically significant differences between group 8 ( GA+STAT vs . GA ) and 10 ( PSA vs . PSA+GA+STAT ) ( p=0 . 001 ) . We can conclude that GA+STAT returned a significantly higher AUC than PSA , and the difference between their mean ranks is diff = 17 . 217 . PSA returned a significantly lower AUC than PSA+GA+STAT ( p=0 . 002 ) , and the difference between their mean ranks is diff=-15 . 800 . The continuing , significant clinical challenge resides in distinguishing men with low- or intermediate-risk prostate cancer which is unlikely to progress ( for both of which ‘active surveillance’ is the most appropriate approach ) , from men with intermediate disease which is likely to progress and men with high-risk prostate cancer ( both of which require treatment ) . The diagnosis of men with low-risk or small volume intermediate-risk prostate cancer as having prostate cancer is unhelpful as these men will very rarely require treatment . The inappropriate assignment of men to potentially life-threatening invasive procedures and life-long surveillance for prostate cancer has significant psychological , quality of life , financial , and societal consequences . Furthermore , the definitive diagnosis of prostate cancer currently requires painful invasive biopsies with which is associated a risk of potentially life-threatening urosepsis in 5% of individuals . We , therefore , undertook experiments to train the proposed Ensemble Subspace kNN model to predict the D’Amico Risk Classification for those patients with prostate cancer ( see subsection ‘The cancer patients dataset used for building the risk prediction modelin Methods ) , in terms of Low/Intermediate ( L/I ) risk and High ( H ) risk disease using NK cell phenotypic data alone . The Ensemble model was modified to take as input all 32 features ( described in Table 1 ) , and was trained to classify the disease in patients with prostate cancer as being L/I or H risk disease ( see Figure 9 in Materials and methods ) . Hence , given a new patient record , which comprises of 32 inputs , the model predicts whether the patient is D’Amico L/I risk ( not clinically significant ) or H ( clinically significant ) risk . The flow charts in Figure 6 illustrate the process to detect the presence and risk of prostate cancer and patient outcomes . Of those 54 patient records , a total of 10 randomly selected records ( 5 from the L/I group and 5 from the H group ) were extracted from the dataset such that they can be used at the testing ( mini clinical trial ) stage . To ensure thorough experiments , a rigorous methodology was adopted . More specifically , a 10-fold cross validation method was adopted , and the experiments were run in 30 iterations , for which each iteration provided an average validation result across 10 folds . Each iteration consists of 10 different ‘train and validation’ data arrangements ( hence 300 tests were carried out using a different mix of train and validation records ) . The 10 test records were input into each trained model ( i . e . iteration ) to predict their accuracy , and to evaluate the model when it is trained and validated using different variations of patient data . The model can highly accurately differentiate between L/I risk group and H risk group patients . The k-fold validation results across 30 iterations were AUC: 0 . 98 ( ±0 . 03 ) ; FPR: 0 . 03 ( ±0 . 05 ) , TPR: 0 . 99 ( ±0 . 01 ) , Accuracy: 0 . 99 ( ±0 . 02 ) ; and results using the test set were AUC: 0 . 98 ( ±0 . 03 ) ; FPR: 0 . 03 ( ±0 . 05 ) , TPR: 0 . 99 ( ±0 . 01 ) , Accuracy: 0 . 97 ( ±0 . 02 ) . Accuracy has been near perfect in all iterations ( i . e . using different train and validation data cases in each iteration ) . Figure 7 illustrates the performance of the model obtained across the 30 runs during the k-fold cross validation and independent testing using the 10 patient samples . The results demonstrate that the proposed model predicts with near-perfect accuracy , the result of the D’Amico Risk Classification ( L/I vs High ) using NK cell phenotypic data alone , and without requiring the PSA , Gleason , and tumor stage data . The dataset that was utilized to identify the biomarker ( that comprised eight features ) for detecting the presence of prostate cancer ( i . e . benign prostate disease vs prostate cancer ) in 71 men , and thus it was large enough to perform the combinatorial feature selection task for finding the best subset of features . The GA that was used for the combinatorial feature selection task is described in Section Computational Methods . Given that detecting the presence of prostate cancer and its risk if present are two different tasks , it is expected that the biomarkers for those tasks will be different since a different target is given to the GA ( i . e . the target for the prostate cancer detection model comprises 0 ( benign prostate disease ) and 1 ( prostate cancer ) values; the target for the prostate cancer risk prediction model comprises 0 ( L/I risk ) and 1 ( High risk ) values ) . For the L/I vs H risk task , the dataset was small ( n = 54 men ( L/I = 36 , H = 16 ) ) , and we could not perform the combinatorial feature selection task with confidence . Hence , it was decided to use the entire feature set for the risk prediction task . The results obtained from the risk prediction model were very promising as shown experimentally , and this provided the confidence to report these preliminary results . The combinatorial feature selection task to identify the best subset of features for the risk prediction task will be performed once a larger dataset is available . Herein , we demonstrate that all 32 phenotypic features are required to distinguish between low/intermediate risk cancer ( L/I ) and high risk ( H ) cancer . However , we expect to be able to identify smaller subset ( s ) of these features as the datasets increase and the prediction model is retrained on the larger dataset . As indicated above , the generation and delivery of additional datasets is beyond the scope of this paper .
We have previously shown that incorporating peripheral blood immune phenotyping-based features into a computation-based prediction tool enables the better detection of prostate cancer and , furthermore , strengthens the accuracy of the PSA test in asymptomatic individuals having PSA levels < 20 ng/ml ( Cosma et al . , 2017 ) . The phenotypic feature set which was shown to be discriminatory between benign disease and prostate cancer comprised CD8+CD45RA-CD27-CD28- ( CD8+ Effector Memory cells ) , CD4+CD45RA-CD27-CD28- ( CD4+ Effector Memory Cells ) , CD4+CD45RA+CD27-CD28- ( CD4+ Terminally Differentiated Effector Memory Cells re-expressing CD45RA ) , CD3-CD19+ ( B cells ) , CD3+CD56+CD8+CD4+ ( NKT cells ) . Using samples from the same cohort of asymptomatic individuals , herein we have further investigated the phenotype and function of NK cell subsets . Using a combination of statistical and computational feature selection approaches , we have identified a subset of eight phenotypic features CD56dimCD16high , CD56+DNAM-1- , CD56+LAIR-1+ , CD56+LAIR-1- , CD56brightCD8+ , CD56+NKp30+ , CD56+NKp30- , CD56+NKp46+ which distinguish between the presence of benign prostate disease and prostate cancer . These features were used to implement a prediction model . The kNN machine learning approach developed in our previous study ( Cosma et al . , 2017 ) has been extended to an Ensemble of kNN learners to improve performance in identifying patterns in even more complex data . As was observed in our previous study , flow cytometry predictors significantly outperform the PSA test . The findings presented herein significantly reinforce our previous finding ( Cosma et al . , 2017 ) that complementing the PSA prediction model with a subset of flow cytometry-based phenotypic predictors can significantly increase the accuracy of the initial prostate cancer test and reduce misclassification . The performance of the prediction model which was built using the phenotypic ‘signature’ presented in our previous study -CD8+CD45RA-CD27-CD28- , CD4+CD45RA-CD27-CD28- , CD4+CD45RA+CD27-CD28- , CD3-CD19+ , CD3+CD56+CD8+CD4+ ( Cosma et al . , 2017 ) , is similar to the model which was built using the NK cell-based phenotypic signature presented herein , CD56dimCD16high , CD56+DNAM-1- , CD56+LAIR-1+ , CD56+LAIR-1- , CD56brightCD8+ , CD56+NKp30+ , CD56+NKp30- , CD56+NKp46+ . Specifically , the prediction model using the five flow cytometry features identified in Cosma et al . , 2017 achieved Accuracy: 83 . 33% , AUC: 83 . 40% , ORP TPR: 82 . 93% , FPR: 16 . 13% , whereas the prediction model presented herein achieved AUC: 85 . 3% , ORP FPR: 15 . 7% , ORP TPR: 86 . 2% , Accuracy: 85 . 5% . Across the 30 runs the average performance of the prediction model presented herein is AUC: 81 . 8% , ORP TPR: 83 . 6% , FPR: 20 . 1% , Accuracy: 82 . 1% . The difference in the performance of the model presented in the first study ( Cosma et al . , 2017 ) and the study described herein is a consequence of different data and prediction models being used in each study . Given that the phenotypic features that were used to create the prediction models were different , the studies resulted in different prediction models . In particular , the model presented previously ( Cosma et al . , 2017 ) was based on a kNN classifier , and herein the kNN classifier was extended to construct an Ensemble Subspace kNN method which comprised several kNN classifiers ( see Figure 9 ) . The dataset used herein was more complex , and it was therefore necessary to create a more complex classifier . At this point in the studies , it is not possible to determine which set of phenotypic features is better at identifying prostate cancer . However , it is evident that both approaches have significant promise . Since the publication of our previous study ( Cosma et al . , 2017 ) , the model developed for that study was used to predict the outcomes of a further 20 new patients which were previously unseen by the prediction model . The model correctly identified the presence of prostate cancer in 19 of the 20 patients ( data not shown ) . Encouragingly , the prediction models generated in the study reported upon herein selected phenotypic features that are associated with the expression of activating receptors NKp30 , NKp46 , and DNAM-1 by NK cells . Pasero et al . , 2015 demonstrated that these activating receptors , in addition to NKG2D , are involved in the recognition of prostate cancer cell lines . Furthermore , they identified that the intensity of NKp30 and NKp46 expression on the surface of NK cells isolated from the peripheral blood of patients with metastatic prostate cancer was predictive of time to hormone ( castration ) resistance and overall survival . This suggests that our computational analysis is selecting phenotypic features that are of biological/clinical relevance . Thus far , our identification of disease predictive phenotypic immune features has been limited to effector immune populations ( T , B , and NK cells ) . The responsiveness of these cells is known to be influenced by the presence of innate immune cell populations that can be polarized by the tumor toward an immunosuppressive state ( Vitale et al . , 2014; Anderson et al . , 2017 ) . Therefore , future studies will investigate the identification and inclusion of phenotypic features from innate immune subpopulations such as monocytes and neutrophils into prediction models to assess whether their inclusion enhances predictive capability and enables a better assessment of patient prognosis in line with the D’Amico Risk Classification . The proposed machine learning model was adapted to predict the D’Amico Risk Classification of patients with prostate cancer using NK cell phenotypic data alone . Experiments with data from 54 patients revealed the significant potential of using the proposed machine learning model for determining if men with prostate cancer are in the low-/intermediate- or high-risk groups , without the need for additional clinical data ( i . e . PSA , Gleason , clinical stage data ) . One limitation of the current study is that the small patient numbers required for low- and intermediate-risk patients to be grouped . Future work , for which additional sample collections are required , will train the model to separately predict low- , intermediate- and high-risk cancer . Future work involves collecting more patient samples to conduct further testing of the proposed machine learning models . In terms of future work from a computational perspective , once we have a larger patient dataset we plan to design deep learning models and compare their performance to the conventional machine learning model which was proposed in this paper . Currently available screening methods and tests for prostate cancer lack accuracy and reliability , the consequence of which is that many men unnecessarily undergo invasive tests such as biopsy and/or are misdiagnosed as having the disease . Furthermore , a biopsy involves removing samples of tissue from the prostate and it is an extremely uncomfortable procedure which also puts men at risk of developing life-threatening infections . As biopsy results are not definitive , there is a significant potential for misdiagnosis and over- and under- treatment . It is therefore essential that new non-invasive approaches such as blood tests that are more accurate than the Prostate Specific Antigen ( PSA ) test are developed to reduce misdiagnosis and unnecessary procedures . Misdiagnosis unnecessarily subjects many men to lifelong monitoring for prostate cancer which can have undesirable psychological and quality of life side-effects , as well as place a significant financial burden on the NHS and other healthcare systems . This paper proposes a computerised model , which detects the presence of prostate cancer in men by analyzing immune system cells in the blood . The model uses the data from the blood tests and artificial intelligence-based computing ( machine learning ) to more accurately detect the presence of prostate cancer . A preliminary model has also been presented to detect the clinical risk that any prostate cancer which is present poses . The tool has two elements , the first detects whether a man has prostate cancer . If prostate cancer is detected , the second element will detect the clinical risk of the disease ( low , intermediate , high ) and thereby enable the clinician to decide whether the patient requires no further investigation/treatment ( ‘watch and wait’ ) or whether further investigation and treatment are required . To our knowledge , these are the first studies to employ computational modeling of peripheral blood NK cell phenotyping data for the early detection of cancer and its clinical significance . They also illustrate the potential for this approach to decipher clinically relevant immune features that can distinguish between benign prostate disease and prostate cancer in asymptomatic individuals for whom the management and treatment strategy is unclear . Of translational importance is that our prediction models are interpretable , can be explained to patients and clinicians and can be continually refined and improved as data are collected . The novelty of this approach is that it interrogates the immunological response to the tumour , not the tumour itself and that it requires a simple blood test ( liquid biopsy ) . Based on current practice , we expect that this approach could avoid up to 70% of prostate biopsies , thereby sparing men with benign prostate disease or low-risk prostate cancer from unnecessary invasive procedures with which are associated significant side-effects . Furthermore , more accurate diagnosis would reduce the demands of healthcare provision and resources associated with treatment and continual surveillance , thereby reducing costs and improving healthcare . We envisage that , in the future , men with a mildly elevated PSA will also undergo an immune status test and those with a suspicion for significant prostate cancer will then undergo an MRI . Although the current study focuses on prostate cancer , its fundamental principles and approaches are highly likely to be applicable across many , if not all , cancer entities .
Peripheral blood samples were obtained from individuals suspected of having prostate cancer that attended the Urology Clinic at Leicester General Hospital ( Leicester , UK ) between 24th October 2012 and 15th August 2014 . Only patients who had provided informed consent and met the criteria of being biopsy naive , a benign feeling Digital Rectal Examination ( DRE ) with a PSA level of < 20 ng ml-1 and agreeing to undergo a simultaneous 12 core TRUS biopsy and a 36 core transperineal template prostate biopsy ( TPTPB ) were included in the study . Further details regarding the TPTPB technique are provided in Nafie et al . , 2014b . A total of 71 males ( 30 patients diagnosed with benign disease and 41 patients diagnosed with cancer , as confirmed by pathological examination of TPTPB biopsies ) met the criteria . Of the 30 patients diagnosed with benign disease; 9 patients were diagnosed with High Grade Prostatic Intraepithelial Neoplasia ( PIN ) , 10 patients were diagnosed with Atypia and 2 patients were diagnosed with Atypical Small Acinar Proliferation . The remainder were diagnosed with benign disease . Of the men diagnosed with prostate cancer , 16 had Gleason 6 disease , 23 had Gleason 7 disease and 2 had Gleason 9 disease on biopsy-based evidence . The clinical features of individuals with benign disease and patients with prostate cancer are provided in Table 9 . Data derived from the 41 individuals with prostate cancer were extracted from the dataset shown in Table 9 . All 41 patients had PSA < 20 ng ml-1 . However , three of the 41 patients who had a High D’Amico risk were removed because their clinical profiles were very different from those of other high risk patients . They were patients with either a Gleason score 3+3 or had a benign biopsy . In the future , we aim to collect more data from such infrequent patient groups to train the algorithms on patients with such clinical profiles . The remaining 38 patients had PSA levels < 20 ng ml-1 and belonged to the D’Amico L/I risk group . Data were collected from an additional 16 patients with prostate cancer who were diagnosed as having a D’Amico High risk profile ( see Table 10 ) . Thus , the new cancer patient dataset comprised 54 patients with prostate cancer , of which 38 patients belonged to the D’Amico L/I risk group and all had PSA<20 ng ml-1 , and 16 patients belong to the D’Amico H risk group and have PSA 4 . 3 ng ml-1≤ PSA ≤ 2617 ng ml-1 . The 16 patients were diagnosed with Gleason scores of: 4+4 = 8 ( n = 2 ) , 5+4 = 9 ( n = 2 ) , and 4+5 = 9 ( n = 11 ) , and 1 patient was diagnosed with small cell cancer . The combined dataset ( i . e . 38+16 = 54 ) comprised 15 patients with Gleason 6 ( 3+3 ) , 18 patients with Gleason 7 ( 3+4 ) , 5 patients with Gleason 7 ( 4+3 ) , 2 patients with Gleason 8 ( 4+4 ) , 11 patients with Gleason 9 ( 4+5 ) , 2 patients with Gleason 9 ( 5+4 ) , and 1 patient with small cell cancer . Since 11 of those 16 patients had a PSA > 20 ng ml-1 , their data could only be utilised for building the prostate cancer risk prediction model , as the detection model focuses on detecting prostate cancer in asymptomatic men with PSA< 20 ng ml-1 . Peripheral blood ( 60 ml ) was collected from all patients using standard clinical procedures . Aliquots ( 30 ml ) were transferred into two sterile 50 ml polypropylene ( Falcon ) tubes containing 300 μl of sterilized Sigma Aldrich Lithium Heparin ( 1000 U/ml , Merck Millipore ) . Anti-coagulated samples were transferred to the John van Geest Cancer Research Centre at Nottingham Trent University ( Nottingham , UK ) and processed immediately upon receipt ( always within 3 hr of collection ) . Peripheral blood ( 60 ml ) was mixed with Phosphate Buffered Saline ( PBS , 30 ml , Lonza ) and layered over Ficoll-Paque ( GE Healthcare Life Sciences ) in Leucosep tubes ( 20 ml blood per tube ) and then centrifuged at 800 g for 20 min . The peripheral blood mononuclear cell ( PBMC ) fraction was harvested and washed twice with PBS before being re-suspended in Hyclone fetal bovine serum ( FBS , GE Healthcare Life Sciences ) . Viable cells were counted using trypan blue ( 0 . 1 % v/v trypan blue , Santa Cruz ) and a haemocytometer . Cells were frozen in 90% v/v FBS , 10% v/v DMSO ( Santa Cruz ) in aliquots of 10 × 106 PBMC/vial and stored in liquid nitrogen until phenotypic analysis . At the time of analysis , one vial from each patient was thawed by mixing with 10 ml ‘thaw’ solution ( 90% v/v RPMI ( Lonza ) ) , 10% v/v CTL wash solution ( Cellular Technology Limited ) and 10 μl of Novagen Benzonase ( Merck Millipore ) at room temperature . PBMCs were centrifuged at 400 g for 5 min followed by resuspension in 1 ml of RPMI ( supplemented with 10% v/v FBS , 1% v/v L-glutamine ( Lonza ) ) . Cells were rested for 1 hr at 37 , after which viable cells were counted using trypan blue dye ( Santa Cruz ) exclusion . For each monoclonal antibody ( mAb ) panel shown in Table 11 , 1 × 106 cells were washed and incubated in 100 μl of Wash Buffer ( PBS +2% w/v Calbiochem bovine serum albumin ( BSA , Merck Millipore ) +0 . 02% w/v sodium azide ( Sigma ) ) containing the relevant mAb cocktail for 15 min , after which cells were washed with 1 ml PBS and then incubated in 1 ml LIVE/DEAD Fixable Violet dead stain ( Thermo Fisher Scientific ) for 30 min . All incubations were performed at 4 protected from light . The cells were washed with PBS and then re-suspended in Beckman Coulter Isoton isotonic buffered saline solution . Data ( on viable cells ) were acquired within 1 hr using a 10-color/3-laser Beckman Coulter Gallios flow cytometer and analyzed using Beckman Coulter Kaluza v1 . 3 data acquisition and analysis software . Controls used a Fluorescence Minus One ( FMO ) approach . A typical gating strategy for the analyses is presented in Figure 8 . Initially , the GA by Ludwig and Nunes , 2010 was adopted to identify the best subset of features ( i . e . predictors ) , and thereafter a prediction model was constructed using the Ensemble classifier . This section also explains the metrics adopted for evaluating the performance of the prediction model . The GA is a metaheuristic , commonly used to generate solutions to optimization and search problems . Given the large number of combinations , the process of selecting the best subset of flow cytometry features for creating the prediction algorithm is performed using a GA . The GA adopted in the experiments was developed by Ludwig and Nunes , 2010 . The particular GA performs combinatorial optimization to identify a subset of features that comprises the optimum feature set , in which the order of features has no relation with their importance . The algorithm works by maximising the mutual information between the target y ( where y can have a value 1 for cancer or 0 for benign ) and the input features ( i . e . these are the 32 features listed in Table 1 ) . Mutual information is the measure of the mutual dependence between the two variables , i . e . an input feature and the target . Adopting a GA eliminates the computational effort which is necessary to evaluate all the possible combinations of features . The fitness function of the GA ( Ludwig and Nunes , 2010 ) is based on the principle of max-relevance and min-redundancy ( mRMR ) , for which the objective is that the outputs of the selected features present discriminant power , thereby avoiding redundancy . The principle of max-relevance and min-redundancy corresponds to searching the set of feature indexes that are mutually exclusive and correlated to the target output . Let m×n be a feature-by-patient matrix , X=[xij] with m features and n patients . Thus , the matrix element xij is the flow cytometry value i of patient j . Let y be a vector of size 1×n which holds the diagnosis of each patient ( 1 for cancer and 0 for benign ) . Hence , each patient x is mapped to a diagnosis y . The GA takes three inputs: 1 ) the feature-by-patient matrix X; 2 ) the vector y which holds the corresponding labels for each patient record; and 3 ) the desired number of features , λ . The GA returns the IDs of the best subset of features , where the subset has size λ . GAs stochastically select multiple features from the current population and thus each run of the GA can return different results . Consequently , we proposed an approach to identify the best subset of features by running the algorithm several times and then obtaining the frequency of the subsets . This section discusses the machine learning classifier which was developed for the task of identifying the presence of benign prostate disease or prostate cancer using the identified subset of phenotypic features . The challenging task is that a suitable and reliable classifier must be developed using only 72 patient records . A limitation is that classifiers that have been trained on small sample size data are likely to be unstable because small changes in the training set cause large changes in the classifier . It was for this reason that the Ensemble machine learning classifier was preferred as an approach for developing a more stable and reliable classifier . Ensemble classifiers achieve stability and reliability by constructing many ‘weak’ classifiers instead of a single classifier and then combine the weak classifiers ( i . e . weak learners ) to create a more powerful decision rule than that constructed when using a single classifier . In clinical applications , it is important to construct prediction models which have a low bias , meaning that the classifier suggests fewer assumptions about the form of the target function . Because Ensemble learning makes fewer assumptions about the form of the target function , it was considered to be a suitable classifier for the task . Several techniques for combining the classifiers of an Ensemble model exist and these include Boosting , Bagging , and Random Subspace Dimension . In the proposed method , the Random Subspace Dimension approach was utilised as a strategy for combining the kNN classifiers , to create the Ensemble of kNN classifiers . In machine learning , the Random Subspace Method ( Ho , 1998 ) , also called attribute bagging ( Bryll et al . , 2003 ) or feature bagging , is an Ensemble learning method which attempts to reduce the correlation between estimators in an Ensemble by training them on random samples of features instead of the entire feature set . In the Random Subspace method , classifiers are constructed in random subspaces of the data feature space . These classifiers were combined by simple majority voting in the final decision rule , and we used the k Nearest Neighbor method ( see Figure 9 ) . In particular , we used the Random Subspace ensemble-aggregation method coupled with k Nearest Neighbours weak learners to produce an Ensemble of classifiers , and this resulted to a better classification rule . Thus , the Random Space modifies the training data set , builds classifiers on these modified training sets , and then combines them into a final decision rule by simple or weighted majority voting . Figure 9 provides an overview of the architecture of the proposed kNN Ensemble learning , and the description that follows explains the architecture in more detail . Let m be the number of dimensions ( variables ) to sample in each learner minus 1 . Let d be the number of dimensions in the data , which is the number of predictors in the data matrix X . Let n be the number of learners in the ensemble . The basic random subspace algorithm performs the following steps using the above-mentioned parameters: A variety of relevant evaluation metrics were adopted for the task of evaluating the performance of the machine learning prostate cancer presence and risk prediction models . Prostate cancer presence prediction models: Let |TP| be the total number of patients with cancer who were correctly classified as having cancer; |TN| be total the number of individuals with benign disease who were correctly classified as having benign disease; |FP| be the total number of individuals with benign disease who were incorrectly classified as having cancer; |FN| be the total number of patients with cancer who were incorrectly classified as having benign disease; |P| be the total number of patients with cancer that exist in the dataset , where |P|=|TP|+|FN|; and |N| be the total number of individuals with benign disease that exist in the dataset , where |N|=|FP|+|TN| . The following commonly used evaluation measures can be defined . ( 2 ) Accuracy=|TP|+|TN||TP|+|FP|+|FN|+|TN| , ∈[0 , 1] . ( 3 ) TPR=|TP||TP|+|FN| , ∈[0 , 1] . ( 4 ) TNR=|TN||TN|+|FP| , ∈[0 , 1] . ( 5 ) FNR=|FN||TP|+|FN|=1-Sensitivity , ∈[0 , 1] . ( 6 ) FPR=|FP||FP|+|TN|=1-Specificity , ∈[0 , 1] . The closer the values of Accuracy , True Positive Rate ( i . e . TPR , Sensitivity ) and True Negative Rate ( i . e . TNR , Specificity ) are to 1 . 0 , then the better the classification performance of a system . The Receiver Operating Characteristic ( ROC ) is an effective measure for evaluating the quality of a prediction model’s performance . The ROC curve has an optimal ROC point which comprises two values: the False Positive Rate ( FPR ) and the True Positive Rate ( TPR ) values . The optimal ROC point is computed by function ( Equation 7 ) for finding the slope , S . ( 7 ) S=Cost ( P|N ) -Cost ( N|N ) Cost ( N|P ) -Cost ( P|P ) ×NP , where Cost ( N|P ) is the cost of misclassifying a positive class ( i . e . cancer ) as a negative class ( i . e . benign ) ; Cost ( P|N ) is the cost of misclassifying a negative class , as a positive class; P , and N , are the total instance counts in the cancer and benign class , respectively . The optimal ROC point is identified by moving the straight line with slope S from the upper left corner of the ROC plot ( FPR=0 , TPR=1 ) down and to the right , until it intersects the ROC curve . The Area Under the ROC Curve ( AUC ) is another important performance evaluation metric which reflects the capacity of a model capacity to discriminate between the data obtained from individuals with benign disease and patients with cancer . The larger the AUC , the better the overall capacity of the classification system to correctly identify benign disease and cancer . Prostate cancer risk prediction models: When applying the above-mentioned measures to evaluate the performance of the risk prediction models , the Positive class , P , was changed to be the High-risk group and the Negative class , N , was changed to be the L/I group . | With an estimated 1 . 8 million new cases in 2018 alone , prostate cancer is the fourth most common cancer in the world . Catching the disease early increases the chances of survival , but this cancer remains difficult to detect . The best diagnostic test currently available measures the blood level of a protein called the prostate-specific antigen ( PSA for short ) . Heightened amounts of PSA may mean that the patient has cancer , but 15% of individuals with prostate cancer have normal levels of the protein , and many healthy people can have high amounts of PSA . This blood test is therefore not widely accepted as a reliable diagnostic tool . Other methods exist to detect prostate cancer , yet their results are limited . A small piece of the prostate can be taken for analysis , but results from this invasive procedure are often incorrect . Scans can help to spot a tumor , but they are not accurate enough to be conclusive on their own . New tests are therefore urgently needed . Prostate cancer is often associated with changes in the immune system that can be detected through a blood test . In particular , the appearance of a type of white blood ( immune ) cells called natural killer cells may be altered . Yet , it was unclear whether measurements based on these cells could help to detect prostate cancer and assess the severity of the disease . Here , Hood , Cosma et al . collected and examined the natural killer cells of 72 participants with slightly elevated PSA levels and no other symptoms . Amongst these , 31 individuals had prostate cancer and 41 were healthy . These biological data were then used to produce computer models that could detect the presence of the disease , as well as assess its severity . The algorithms were developed using machine learning , where previous patient information is used to make prediction on new data . This work resulted in a new detection tool which was 12 . 5% more accurate than the PSA test in detecting prostate cancer; and in a detection tool that was 99% accurate in predicting the risk of the disease ( in terms of clinical significance ) in individuals with prostate cancer . Although these new approaches first need to be validated in the clinic before being deployed , they could ultimately improve the detection and diagnosis of prostate cancer , saving lives and reducing the need for further tests . | [
"Abstract",
"Introduction",
"Results",
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] | [
"computational",
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] | 2020 | Identifying prostate cancer and its clinical risk in asymptomatic men using machine learning of high dimensional peripheral blood flow cytometric natural killer cell subset phenotyping data |
Blood-sucking insects experience thermal stress at each feeding event on endothermic vertebrates . We used thermography to examine how kissing-bugs Rhodnius prolixus actively protect themselves from overheating . During feeding , these bugs sequester and dissipate the excess heat in their heads while maintaining an abdominal temperature close to ambient . We employed a functional-morphological approach , combining histology , µCT and X-ray-synchrotron imaging to shed light on the way these insects manage the flow of heat across their bodies . The close alignment of the circulatory and ingestion systems , as well as other morphological characteristics , support the existence of a countercurrent heat exchanger in the head of R . prolixus , which decreases the temperature of the ingested blood before it reaches the abdomen . This kind of system has never been described before in the head of an insect . For the first time , we show that countercurrent heat exchange is associated to thermoregulation during blood-feeding .
The entire life of insects highly depends on the environmental temperature ( Ta ) that impacts their body temperature ( Tb ) , thus influencing their behaviour and physiology . Each species possesses a temperature range within which individuals can remain active; but within that range , their performance ( e . g . food seeking or mating ) is still temperature-sensitive ( Huey and Stevenson , 1979 ) . At temperatures below or above the activity range , insects can potentially experience thermal stress causing drastic physiological effects and even death . Nevertheless , different traits have evolved to avoid or minimise the effect of thermal stress due to extreme temperatures . In poikilotherms ( i . e . Tb = Ta ) , these strategies include behavioural ( e . g . postural control of exposition to solar radiation ) , biochemical [e . g . heat-shock proteins ( HSPs ) synthesis] , and physiological ( e . g . diapause ) adjustments . However , some of them exhibit morpho-physiological adaptations that allow them regulating their own Tb independently of Ta ( Heinrich , 1993; May , 1979 ) . If many insect species can avoid thermal stress simply by changing their posture or hide from sun exposure , some others , such as haematophagous ( blood-feeding ) insects , must expose themselves to high temperatures and their deleterious effects during blood-feeding ( Lahondère and Lazzari , 2012 ) . Indeed , most of them feed on endothermic vertebrates ( e . g . birds ) whose blood temperature can be as high as 40°C . On the other hand , in order to escape the defensive reactions of hosts against biting , they must ingest as much blood as possible , as fast as they can . Thus , they must cope with the rapid entry of a large volume of warm fluid , which temperature ( Tblood ) can greatly exceeds their own body temperature . This potentially generates thermal stress , and some species respond by synthesising HSPs , a cellular protective mechanism ( Benoit et al . , 2011; Paim et al . , 2016 ) . Other blood-feeding insects use thermoregulatory processes to protect themselves from overheating . For instance , Anopheles mosquitoes decrease their abdominal temperature by using evaporative cooling . They emit through the anus and then keep at the tip of their abdomen a droplet of fluid composed of urine and fresh blood , which evaporates cooling the abdomen , thus reducing the thermal shock and the associated stress caused by the blood-meal ( Lahondère and Lazzari , 2012 ) . Haematophagous Diptera such as mosquitoes and tsetse flies can obtain a full blood-meal in less than 2 min ( Lehane , 2005; Lahondère and Lazzari , 2015 ) . The duration of their exposure to excessive heat is thus much shorter than kissing bugs , for example , that need between 15 and 20 min to feed to repletion . Moreover , kissing bugs can take blood meals that can attain up to 10 times their own unfed weight ( Lehane , 2005 ) . So , the heat gain at each feeding event is much higher in bugs than in mosquitoes or flies . Interestingly , it has been demonstrated that heating is deleterious for the physiology of kissing bugs . When they are exposed to a sub-lethal temperature of 30°C , bugs show delayed moulting , sterility and changes in their respiratory metabolism ( Okasha , 1968a , Okasha , 1968b , Okasha , 1968c , Okasha , 1968d 1970; Okasha et al . , 1970 ) . Feeding on hot blood induces the synthesis of HSPs ( Paim et al . , 2016 ) , but the increase in their expression level is relatively low compared to other insects ( Benoit et al . , 2011 ) In the present work , we studied how the major Chagas disease vector Rhodnius prolixus copes with the excess of heat associated to blood-feeding . We performed real-time thermography and measured HSP expression , to analyse the extent to which these bugs are exposed to heat stress during feeding . Based on these results , we then performed a morpho-functional analysis of the R . prolixus head to gain insights on its structural organisation and how it could be implicated in heat management . Finally , we used synchrotron-based high-resolution X-ray imaging to see how the ingestion pumps work and to assess their role in blood displacement inside the insect . Based on the results obtained during this study , we propose a 3-D ( three-dimensional ) model of the R . prolixus head , as well as the existence of a countercurrent heat exchange mechanism through which this species can protect itself against heat stress during blood-feeding .
To understand how R . prolixus manages the heat flow associated with the ingestion of a blood-meal , we first performed a real-time thermographic analysis of the dynamics of body warming during the entire feeding process ( Figure 1A; SI Video 1 ) . Before the blood intake , Tb = Ta for all body parts of the insect . But once the insect started to feed , the different parts of its body did not exhibit the same temperature ( i . e . heterothermy ) ( Figure 2A–B ) . Indeed , while the temperature of the proboscis ( Tp ) was high and close to the temperature of the blood , that of the abdomen ( Tabd ) was close to the ambient temperature , whereas the temperature of the head ( Th ) and that of the thorax ( Tth ) remained intermediate . Thus , a marked thermal gradient was established along the insect body: Tp >Th > Tth>Tabd . Interestingly , Tp and Th oscillated during feeding , certainly in concordance with the variations in the activity of the ingestion pumps , while Tth and Tab did not , remaining stable during the entire blood intake . These results show that R . prolixus is able to minimise the amount of heat reaching the abdomen during feeding ( Figure 2A–B ) . We then characterised regional heterothermy in R . prolixus by establishing different combinations of both ambient and blood temperature , to see how the temperature of the different parts of the body would be affected during blood-feeding ( Figure 1B ) . At Ta = 16°C , we observed a clear thermal gradient along the insect bodies either when fed on blood at 32°C or 37°C ( two-way ANOVA , p-values<0 . 01 for all comparisons , see Table 1 , Figure 2C , SI Figure 2—figure supplement 1 , SI Table 1 ) . At Tblood = 37°C , the proboscis was 14°C warmer than the abdomen . A temperature gradient between the proboscis and the abdomen was also found in insects fed with blood at 32°C , 37°C and 42°C for all the different other Ta conditions . Moreover , as Ta increased , Tb increased , but we noted that the temperature of the proboscis was more influenced by Tblood , while the abdomen tended to be more stable and did not vary as much , even if the temperature of ingested blood was higher ( Figure 2C , Figure 2—figure supplement 1 , Figure 2—source data 1 ) . Our results showed that the different body regions warmed up differentially as a function of both the environment and blood temperatures ( two-way ANOVA , p-values<0 . 01 for all comparisons , Table 1 ) and that the temperature of the proboscis was close to Tblood , whereas the abdomen ( Tabd ) remained close to the temperature of the environment ( Ta ) . In between the temperature gradient was not linear , but it exhibited a clear decrease at the posterior region of the head ( Figure 2B ) , revealing that this region is particularly involved in heat transfer . In order to examine the role of haemolymph circulation in heterothermy , we analysed the body temperature during blood-feeding of insects with severed dorsal vessels . As a result of the interruption of haemolymph circulation , heterothermy was greatly reduced . The thermal gradient observed was largely reduced in this group of insects ( Tp ( 37 . 5 ± 0 . 25°C ) , Th ( 36 . 5 ± 0 . 33°C ) , Tth ( 35 . 92 ± 0 . 37°C ) and Tabd [35 . 38 ± 0 . 32°C] ) in comparison to intact insects ( Figure 2D ) . The mean temperature of the proboscis did not statistically differ from control bugs ( Student t-test , n = 5 , n . s ) , but the comparison of other regions , that is head tip , between eyes , thorax and centre of the abdomen , revealed significant differences ( p-values<0 . 001 in all cases ) . To evaluate to what extent haemolymph circulation could be involved in reducing thermal stress during feeding , we compared the expression of HSP70 and HSP90 in insects with either intact or severed dorsal vessel . Whereas HSP90 did not evince a significant differential expression , HSP70 expression was significantly higher in insects with a severed dorsal vessel , a condition that impeded countercurrent ( Figure 2E , Figure 2—source data 2 , Student t-test p<0 . 01 two-tailed ) . Because heat seemed to keep confined in the head during blood-feeding , we then performed an analysis of functional morphology , in order to disentangle the underlying mechanism allowing R . prolixus to avoid abdominal warming during feeding . The head of R . prolixus , like other triatomine bugs , has a tubular shape , which shrinks a little anteriorly at the basis of the antennae and on its posterior part , just before the thorax . The brain is confined to the posterior region of the head , whereas the anterior region lodges the large cibarial pump musculature . Besides , there are large areas of haemocoel , both dorsally and ventrally to the nervous system , where haemolymph circulates ( Figure 3 ) . To better understand the flow of the ingested blood inside the alimentary channel , we analysed the muscular activity of the cibarial and pharyngeal pumps using synchrotron-based X-ray imaging and computed tomography ( SI Video 2 and Video 3 ) . An in vivo analysis during feeding unravelled several relevant information . First , the cibarial pump works as a piston , producing a pulsed wave of blood towards the gut . Second , the small pharyngeal pump , barely considered in the literature about kissing bugs , also shows a pulsed activity , at the same frequency , but in an opposite phase to the pharyngeal pump ( Figures 5 and 6 ) .
Rhodnius prolixus , as other haematophagous insects ( e . g . mosquitoes , bed bugs ) , experiences heat stress during each feeding event , due to the engorgement of relatively big quantities of warm blood . The synthesis of heat shock proteins after a blood meal confirms heat stress during feeding , even though the increase in the HSP expression is lower than other insects ( Benoit et al . , 2011; Paim et al . , 2016 ) . Here , we have shown that R . prolixus also possesses anatomical specificities and adaptations that allow this species to minimize the heat transfer to its abdomen , thus reducing the heat shock associated with feeding . While the head is the most exposed to heat , the amount of heat reaching the thorax and the abdomen is dramatically reduced . Based on our integrative study combining real-time infrared thermography with functional morphology methods , such as classical histology , X-ray in vivo-imaging and µCT , and molecular biology , we have been able to get a functional picture of the way kissing bugs quickly get rid of the excess of heat entering their body when they feed on an endothermic host . For a better perception , we built a 3-D representation of the head , thus revealing the spatial arrangement of the different structures and their relationships ( Figure 7 ) . Anatomically , the structures conform to a countercurrent heat exchanger between the alimentary canal and the circulatory system . The countercurrent is characterised by two currents of fluids circulating in opposite directions inside closely associated and parallel structures . The warmest fluid loses heat by conduction transferring it to the coolest one . In the area where the aorta is in close contact with the oesophagus , cool haemolymph coming from the end of the abdomen withdraws heat from the just ingested blood that is flowing into the alimentary canal in the opposite direction . Then , the haemolymph flows in the semi-open space ( i . e . haemolymphatic sinus ) and bathes the warm muscles of the ingestion pump . Finally , in the anterior part of the head , the haemolymph is conducted to project through the haemocoel , thus circulating in close contact with the body wall , releasing heat towards the environment ( Figure 7 ) . The continuous flow of both fluids is required for an efficient heat exchange . This appears to be assured by the coordinate action of the two ingestion pumps , that iscibarial and pharyngeal , which contraction on opposite phase results in a continuous flow of warm blood to the gut , resembling the Windkessel effect of the vertebrate aorta ( See SI Video 1 ) . On the other hand , the peristaltic contracting waves of the dorsal vessel do the same with the cool haemolymph , but in the opposite direction . The close contact between the aorta and the oesophagus facilitates the exchange of heat , which returns to the head . Consequently , only a relatively small amount of heat leaves the head ( Figure 2A ) . In this way , by sequestering the heat in the head , this countercurrent heat exchanger allows the insect to minimise heat transfer to the thorax and the abdomen , thus maintaining a regional heterothermy ( Figure 2 ) . Our proposed model for the countercurrent exchanger here is based on both anatomical and experimental evidence . Countercurrent heat exchangers are present in some insect species , where they modulate the flow between the thorax and the abdomen , of heat excess produced by the activity of flight muscles . In Cuculiinae winter moths , two heat exchangers are localised in the thorax and at the junction between the thorax and the abdomen . These exchangers enable these insects to fly at very low Ta by sequestering heat in their thorax . Air sacs also help to restrain heat propagation to the abdomen ( Heinrich , 1987 ) . In bumblebees , the exchanger is located in the narrow passage of the petiole and helps them to exchange heat via the haemolymph between the thorax and the abdomen ( Heinrich , 1976 ) . As in previous examples , Xylocopa carpenter bees produce endogenous heat during flight . The countercurrent exchanger occurs in the petiole area where the aorta presents many loops thus facilitating heat dissipation via the abdomen ( Heinrich and Buchmann , 1986 ) . Robber flies ( Diptera: Asilidae ) also use their abdomen as a heat dissipater ( Morgan and Shelly , 1988 ) . All these anatomical particularities help these insect species to maintain a regional heterothermy , and has been speculated that this could protect critical organs ( e . g . gonads ) from an eventual thermal stress due to extreme temperature , keeping others ( e . g . muscles ) at optimal higher temperatures . The heat-exchange system of R . prolixus is unique , in the sense that it is located in the head and that it is involved in the management of the heat flow derived from the food intake . Countercurrent heat exchangers are not restricted to invertebrates , they also occur in vertebrates such as birds ( Arad et al . , 1989 ) , fishes ( Carey et al . , 1971; Stevens et al . , 1974 ) , and mammals ( Scholander and Schevill , 1955 ) . In these animals , heat transfer occurs between arteries reaching zones of potential heat loss ( e . g . appendices ) and veins carrying cold blood , in order to recuperate heat back to the body core ( e . g . artic birds and mammals ) or to active muscles ( e . g . fishes ) . The heat exchanger found in the head of R . prolixus is associated to other features that facilitate heat dissipation during feeding , as the elongated form of the head , which increases the surface/volume ratio and the relatively glabrous cuticle , typical of hemipterans . For instance , bumblebees show a regional difference in depth of insulation: the regions where heat needs to be stored are densely piled ( thorax , dorsal part of the abdomen ) whereas the ventral part of the abdomen , which serves as a thermal radiator , is completely free on hair ( Heinrich , 1976 ) . In some syrphid flies that produce heat endothermically with thoracic muscles , the removal of hair from the thorax leads to an increase of the cooling rate of about 30% compared to hairy flies of the same species ( Heinrich , 1987 ) . Winter moths that need to keep heat to be able to fly in the cold maintain a high thoracic temperature and minimise heat loss by means of a thick insulated pile ( Heinrich , 1987 ) . Moreover , the heart of R . prolixus is unusual in that all eight ostia are grouped in a short section at the very end of the abdomen ( segment VII ) ( Chiang et al . , 1990 ) , where the temperature of the haemolymph is the coolest ( Figure 2A and B ) inside the insect body . In other Hemiptera species , ostia are grouped at the posterior region of the abdomen ( Hinks , 1966 ) . In the case of the haematophagous bugs R . prolixus and Triatoma infestans , they are located further in the terminal part abdomen , grouped in the VII and VIII abdominal segments ( Hinks , 1966; Barth , 1980; Chiang et al . , 1990 ) . This is not the case for all blood-sucking insects . For instance , Stomoxys calcitrans has three pairs of ostia which spanned five abdominal segments ( Cook and Meola , 1988 ) and in Anopheles gambiae hemolymph enters the heart through six pairs of incurrent abdominal ostia and one pair of ostia located at the thoracic-abdominal junction ( Glenn et al . , 2010 ) . It is worth mentioning also that , as mosquitoes , bugs eliminate drops of urine during feeding ( SI Video 1 ) . Although the size and the time that these drops remain attached to the tip of the abdomen are both reduced , and evaporative cooling is hardly probable to occur as in Anopheles ( Lahondère and Lazzari , 2012 ) , these drops may reduce the temperature of the terminal part of the abdomen , precisely in the area where the haemolymph enters the heart to be pulsed forwards . The aorta is open on its dorsal part in the cephalic portion and goes through the cibarial pump muscles . The warmed haemolymph is thus poured around these muscles that are also warmed by both contractions and direct contact with the alimentary canal which contains the circulating warm blood . The wide sinus , in which the insect haemolymph circulates in contact with the insect integument , probably facilitates heat loss by convection and radiation . Finally , two tracheas extend along the head , next to both the pharyngeal pump ( Figure 3C–D ) and the alimentary canal ( Figure 4C–D , SI Video 3 and SI Video 4 ) ( Ramírez-Pérez , 1969 ) . These tracheas may also help decrease the temperature of the head by circulating air and by internal evaporative cooling . Internal evaporative cooling is a common strategy in species , such as plant juice feeders ( Prange , 1996 ) , which can obtain water easily . In the tsetse fly Glossina morsitans , a decrease of 2°C of the Tb occurs when the Ta exceeds the thermal upper limit of tolerance thanks to evaporation via the spiracles ( Edney and Barrass , 1962 ) . The process of evaporative cooling via the ventilatory system is also documented in grasshoppers and beetles with a decrease of the Tb up to 8°C ( Prange , 1990 ) . In R . prolixus during feeding , there is an increase in the rate of exchange of CO2 , O2 , and water vapour: this is thought to be linked with spiracle opening ( Leis et al . , 2016 ) . If the hypothesis of heat loss via the trachea is true in R . prolixus , it could resemble to respiratory countercurrent mechanisms found in the nose of mammals , birds , and reptiles . In these animals , the heat exchanger cools the blood flowing to the brain to reduce overheating ( Tattersall et al . , 2006 ) . This hypothesis is of course quite speculative , but deserves to be further explored . The present work has shed some light on different mechanisms , adaptations and strategies helping R . prolixus to avoid the thermal stress associated with blood-feeding on endothermic animals . The 3-D reconstruction of the head based on histological sections and imaging techniques support the hypothesis for a countercurrent occurring in the head of this species helping the insect to regulate its own temperature during blood-feeding . Until now , the only thermoregulatory mechanism associated with blood-feeding known in insects , was evaporative cooling performed by Anopheles mosquitoes ( Lahondère and Lazzari , 2012 ) . Our work in R . prolixus not only unravelled a new species possessing thermoregulatory abilities , suggesting that it may be also present in other haematophagous insects , but also revealed a new original mechanism , based on morphological and physiological specific adaptations . It is possible that countercurrent heat exchangers could be more frequent and diversified than commonly thought .
Experimental bugs came from laboratory colonies of Rhodnius prolixus Stål ( 1859 ) ( Heteroptera: Reduviidae: Triatominae ) , reared at the Insect Biology Research Institute ( IRBI , Tours , France ) , the Universidade Federal de Minas Gerais ( Brazil ) and at the Department of Physiology , University of Saskatchewan ( Canada ) . Insects were maintained under a 12:12 hr light/dark regime at 25 ± 1°C and 60–70% relative humidity . Insects are fed weekly on heparinised sheep blood via an artificial feeder ( Núñez and Lazzari , 1990 ) . Fifth-instar larvae were isolated after ecdysis and kept starved for 8–12 days until being used for thermographic experiments , X-ray imaging and computed tomography . Unfed adults were used for histological preparations because of their bigger size . Infrared thermography is a non-invasive technique that accurately measures the temperature at the surface of the insect body during the entire process of blood-feeding . Light microscopy was performed on insect heads following the procedure described by Reisenman and collaborators ( Reisenman et al . , 2002 ) . Briefly , freshly decapitated heads were fixed for 3 hr in a mixture of 2 . 5% glutaraldehyde and 2 . 0% paraformaldehyde in phosphate buffer ( pH 7 . 3 ) with glucose and CaCl2 added . After gradual dehydrated in 100% ethanol , they were embedded via propylene oxide in Durcupan ACM ( Electron Microscopy Sciences no . 14040 ) . We used glass knives mounted on a motorised microtome to serially section blocks at 2–5 µm . Sections were stained on a hot plate with Toluidine Blue-Basic Fuchsin and mounted on a slide with DPX ( Electron Microscopy Sciences no . 13510 ) . Photomicrographs were adjusted for brightness and contrast by using Adobe Photoshop CS2 . A total of 10 individuals were prepared , sectioned , stained and analysed . The 3-D reconstruction of the head structure and morphology was performed by compiling both transversal and sagittal serial histological sections and using 3ds Max software ( Autodesk ) . Insects that had their dorsal vessel severed or sham operated ( body wall opened and closed again ) were fed on blood using an artificial feeder ( blood temperature 39°C , environment 28°C ) . Even though sham bugs fed a bit faster than bugs with their dorsal vessel severed , all bugs reached about five times their initial weight . After 2 hr , insects were dissected to collect their midguts for individual RNA extraction using the Nucleospin RNA II Kit ( Macherey-Nagel ) . RNA was treated with DNase according to the manufacturer’s instructions and eluted in 20 µL of ultra-pure RNase-free water . Then , purified RNA was quantified by measuring 260 nm wavelength absorbance and 0 . 5 µg was used for cDNA synthesis with 0 . 5 µg of random hexamers ( Promega ) using the M-MLV reverse transcriptase system ( Promega ) in a final volume of 25 µL . The cDNA was used in qPCR assays , using the StepOne Plus real time quantitative PCR system ( Applied Biosystems ) to evaluate HSP70 and HSP90 expression . Each reaction was run in duplicate and contained 20 ng of cDNA , specific primers for HSP70 ( forward: 5’-gaaatcgtactggttggtgga-3’ , reverse: 5’-cgccataggctacagcttca-3’ ) or HSP90 ( forward: 5’-ggacccatcaagactggaga−3’ , reverse: 5’- agcaatggttcccagattgt - 3’ ) at a final concentration of 300 nM each and 5 µL of 2x Power SYBR Green PCR Master Mix ( Applied Biosystems ) in a final volume of 10 µL . Amplification conditions were 95°C for 10 min , 40 cycles of 95°C for 15 s and 60°C for 1 min . A reverse transcription negative control ( without reverse transcriptase ) and a non-template negative control were included to confirm the absence of genomic DNA and to check for primer-dimer or contamination in the reactions , respectively . To ensure that only a single product was amplified , the melting curve was analysed . The relative amount of gene product in each sample was determined with 2-ΔΔCt method ( Livak and Schmittgen , 2001 ) using α-tubulin ( forward: 5’-tttcctcgatcactgcttcc-3’ , reverse: 5’-cggaaataactggggcataa- 3’ ) as a reference gene ( Paim et al . , 2012 ) . After verification of normality and homoscedasticity by means of the Shapiro-Wilk and F-test ( GraphPrism ) , HSP expression levels ( n = 5 individuals per group ) were compared by means of a Student t-test for independent samples and the significance of the difference evaluated as two-tailed p-value . | Many insect species have adopted the blood of birds and mammals as their main or even only food . Yet , blood is not freely available in nature , but it circulates inside vessels hidden under the skin of animals much bigger than the insect and capable of defending themselves from getting bitten . To succeed in getting a meal , blood-sucking insects must be able to feed quickly and take in as much blood as possible . Each time that they do this , a huge amount of warm fluid enters their body in just a few minutes . The blood temperature can be up to 20° or 25°C warmer than the insect itself . Moreover , an insect called a kissing bug may ingest up to 10 times its own weight in only fifteen minutes . The consequence is overheating and potentially harmful thermal stress . Kissing bugs do not seem to suffer any harmful consequence of taking massive meals from warm-blooded animals . But why ? The answer was unexpected: they simply do not warm up when they take a blood meal . However , it was not known how they manage to cool down the ingested blood . By combining classical methods of studying anatomy with state of the art technologies , Lahondère et al . discovered that kissing bugs possess a sophisticated heat exchanger inside their heads . It works by transferring the heat associated with the ingested blood to the haemolymph ( insect blood ) ; these fluids circulate in opposite directions inside ducts that are close to each other in the head . The discovery of a new system used by insects to cope with thermal stress expands our knowledge of insect physiology and opens new lines of research . The kissing bug heat exchanger could also serve as inspiration for equivalent technological systems . Last but not least , kissing bugs spread the parasites that cause Chagas disease in the Americas . Finding ways to disrupt the heat exchanger could prevent kissing bugs from feeding on blood , and so help to control the spread of disease . | [
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"ecology"
] | 2017 | Countercurrent heat exchange and thermoregulation during blood-feeding in kissing bugs |
How insects navigate complex odor plumes , where the location and timing of odor packets are uncertain , remains unclear . Here we imaged complex odor plumes simultaneously with freely-walking flies , quantifying how behavior is shaped by encounters with individual odor packets . We found that navigation was stochastic and did not rely on the continuous modulation of speed or orientation . Instead , flies turned stochastically with stereotyped saccades , whose direction was biased upwind by the timing of prior odor encounters , while the magnitude and rate of saccades remained constant . Further , flies used the timing of odor encounters to modulate the transition rates between walks and stops . In more regular environments , flies continuously modulate speed and orientation , even though encounters can still occur randomly due to animal motion . We find that in less predictable environments , where encounters are random in both space and time , walking flies navigate with random walks biased by encounter timing .
Olfactory search strategies depend on both an animal’s locomotive repertoire and the odor landscape it navigates . Navigational strategies have been investigated in a variety of odor plumes , each exhibiting a particular structure in space and time . The statistics of these plumes govern what information is available to the animal as it navigates , which in turn dictates the sequence of behaviors it can use to find its target . In some environments , such as the diffusion-dominated odor landscapes of Drosophila larvae , concentrations vary relatively smoothly from point to point . Accordingly , larvae can progress toward odor sources by sampling odor gradients spatially and temporally ( Gomez-Marin et al . , 2011; Gepner et al . , 2015; Hernandez-Nunez et al . , 2015; Schulze et al . , 2015 ) . Similarly , adult flies in gradients can walk up the gradient by monitoring the odor intensity difference across their antennae pairs ( Borst and Heisenberg , 1982; Gaudry et al . , 2013 ) . In the absence of stable gradients , odor landscapes may still be relatively simple when the airflow is laminar . This is true for modest wind speeds and in the near-surface laminar sublayer of turbulent flows , provided the source and average wind directions are not shifting and the surface is smooth ( Crimaldi and Koseff , 2001 ) . Variations in odor concentration are generally slow – odor encounters can last longer than seconds ( Crimaldi and Koseff , 2001; Álvarez-Salvado et al . , 2018 ) . Furthermore , the largely unidirectional and steady wind provides a reliable cue about odor source location . Experiments in walking flies suggest that , in this case , high-frequency fluctuations in odor concentration might be ignored and upwind progress may result from temporal integration of the odor concentration ( Álvarez-Salvado et al . , 2018 ) . Flies turn upwind at the onset of spatially uniform blocks of natural ( Álvarez-Salvado et al . , 2018; Steck et al . , 2012 ) or optogenetic fictive odors ( Bell and Wilson , 2016 ) , and turn downwind or initiate a local search when the odor is lost . Steady odor ribbons have provided an informative experimental paradigm used extensively in insect navigation studies ( Budick and Dickinson , 2006; van Breugel and Dickinson , 2014 ) . Like near-surface flows and spatially uniform odor blocks , ribbons are spatiotemporally simple . Walking and flying moths accelerate and turn upwind upon entering the straight ribbons , and cast perpendicular to the wind or counterturn when losing it ( Mafra-Neto and Cardé , 1994; Cardé and Willis , 2008; Kuenen and Carde , 1994; Kanzaki and Sugi , 1992; Vickers and Baker , 1994; Kennedy and Marsh , 1974; Baker and Haynes , 1989; Haynes and Baker , 1989; Baker and Vickers , 1997 ) . Flying flies navigate them similarly , combining upwind surges with counterturns back into the ribbon after passing through . In these plumes , odor encounters are very brief ( ~100 ms ) and also exhibit some degree of temporal irregularity due to the animal’s self-motion as it randomly crosses the ribbon ( van Breugel and Dickinson , 2014 ) . Still , the locations of encounters are highly predictable , and this spatial regularity is naturally exploited by reflex-dominated strategies such as surging and counterturning ( Pang et al . , 2018 ) . By contrast , in the bulk of turbulent flows ( Cardé and Willis , 2008; Murlis et al . , 1992; Murlis et al . , 2000; Riffell et al . , 2008; Yee et al . , 1993 ) or on rough surfaces where shifting winds and obstacles such as grass , shrubs , and branches can perturb the laminar boundary layer ( Schlichting , 1960; Hunt et al . , 1978 ) , odor landscapes are irregular in both space and time . Measurements of odor concentrations in forests ( Murlis et al . , 2000; Webster and Weissburg , 2001; Moore et al . , 1994 ) show that not only are local concentration gradients less indicative of odor source location , but importantly , odor encounters are intermittent , occurring as a random sequence of brief bursts . Theory suggests that in complex intermittent plumes , the timing of odor encounters may provide important information to the navigator ( Balkovsky and Shraiman , 2002; Vergassola et al . , 2007 ) . Indeed , moths follow tight trajectories upwind while navigating within a turbulent plume , much narrower than those in steady ribbons ( Mafra-Neto and Cardé , 1994; Kanzaki and Sugi , 1992; Baker and Haynes , 1989 ) . These narrow tracks were recapitulated for moths navigating pulsed ribbons , provided the pulse frequency was high enough , again implicating encounter timing in upwind progress ( Mafra-Neto and Cardé , 1994 ) . There is an important distinction between experiments informed by steady odor ribbons versus those informed by spatiotemporally complex plumes . In steady ribbons , navigational behaviors can be tied to individual odor encounters because the location of the ribbon can be measured and the time-dependent odor signal perceived by the animal can be inferred from its trajectory . In spatiotemporally complex plumes , behaviors can at most be correlated with plume statistics , as the time when each individual filament hits the animal is unknown . In this context , analyzing how animals use the timing of individual encounters to navigate would require simultaneous measurement of behavior with odor . Here we identified the navigational principles of walking fruit flies in spatiotemporally complex odor plumes resembling those in naturalistic settings . In particular , we investigated how these navigational principles are shaped by the temporal features of the individual odor encounters made with the rapidly fluctuating plume . We exploit a technical advance that relaxes the tradeoff between restricting odor dynamics and animal motion: an attractive odor that can be imaged in real time with unrestrained walking flies . By passing this odor in a laminar airflow and perturbing it with random lateral air jets , we generate a spatiotemporally complex plume whose statistics approximate those of turbulent plumes near boundaries ( Celani et al . , 2014; detailed comparison with theory in Results ) . This odor allows us to study walking fly olfactory navigation by directly connecting navigational behaviors to individual odor encounters . Consistent with prior studies ( Budick et al . , 2007; David et al . , 1982 ) , we find that flies on average walk upwind within the odor plume cone . However , upwind bias does not result from an accumulation of orientation changes following every odor encounter . Instead , flies execute stochastic , stereotyped 30-degree saccades at a rate independent of the duration or frequency of odor encounters . Upwind bias results not from modulating turn magnitude or frequency but rather turn direction: the randomly-occurring saccades are more likely to be oriented upwind when the frequency of odor encounters – but not their duration or concentration – is high , suggesting an important role for precise odor timing detection ( Gorur-Shandilya et al . , 2017; Szyszka et al . , 2014; Shusterman et al . , 2011; Park et al . , 2016 ) . Prior studies have shown that flies increase the walking speed at the onset of uniform odor blocks ( Álvarez-Salvado et al . , 2018; Jung et al . , 2015; Gao et al . , 2013 ) . In our spatiotemporal plume , flies spend only a fraction of time ( ~15% ) experiencing detectable odor concentrations , and we expectedly do not find an appreciable increase in walking speed . However , flies do markedly modulate their rate of walking and stopping . In contrast to turn decisions , the rates of these walk-stop transitions are strongly tied to the frequency of encounters . We model stops and walks as a double , inhomogeneous Poisson process and find using maximum likelihood estimation and cross-validation that stop rates reset at every encounter before decaying back to a baseline rate . This suggests that individual encounters prolong the flies’ tendency to continue walking but only for a brief time . Meanwhile , walks are triggered by accumulating evidence from multiple encounters while stopped . Using agent-based simulations , we show that this modulation of stops and walks shaped by the timing of odor encounters greatly enhances navigation performance . Together , our results suggest that navigation within spatiotemporally complex odor plumes is shaped by the sequence of encounters with individual odor packets . Both electrophysiological and behavioral measurements indicate that Drosophila – along with other insects , mammals , and crustaceans , among others – can precisely encode odor timing within their signal transduction cascade ( Gorur-Shandilya et al . , 2017; Park et al . , 2016; Smear et al . , 2011; Schaefer and Margrie , 2007 ) . Our findings suggest that Drosophila leverage this capability to navigate their olfactory world .
To investigate how freely-walking insects navigate odor plumes that are complex in both space and time , we developed a wind-tunnel walking assay for Drosophila melanogaster ( Figure 1A ) . The large size of our 2D arena ( 300 × 180 × 1 cm3 ) allowed us to simultaneously image several flies in the dark with minimal mutual interactions . The main flow was set to 150 mm/s , chosen as sufficiently strong for flies to tax upwind , but not so strong that they remained stationary ( Yorozu et al . , 2009 ) . Plumes that fluctuated in space and time were generated by injecting odors at the center of an air comb and perturbing the laminar flow with lateral jets stochastically alternating at a Poisson rate of 10/s . To visualize the flow , we injected smoke , which is turbid , into the center of the air comb and imaged it in the infrared at 90 Hz . Serendipitously , we noticed that when we placed starved flies in the assay with the fluctuating smoke , flies walked upwind toward the source ( Figure 1B , Video 1 ) in a manner reminiscent of their behavior when we injected an attractive odor such as ethyl acetate . We reasoned that if this attraction to smoke were olfactory , the imaged smoke intensity could then provide a proxy for odor concentration , allowing us to visualize dynamic odor plumes simultaneously with fly behavior ( Figure 1C–E and Video 2 ) . Smoke is a complex stimulus ( Figure 1—figure supplement 1A ) , containing not only CO2 and volatile chemicals , but also heat , humidity , and airborne particles . We , therefore , set out to verify that the attraction to smoke is olfactory . For this purpose , we used a simplified environment consisting of a standing odor ribbon , which we generated in our assay by maintaining the laminar flow and odor injection , but turning off the lateral jets ( Figure 1—figure supplement 1B ) . First , we compared behavioral statistics in smoke to those in the attractive odors ethyl acetate ( EA ) and apple cider vinegar ( ACV ) . The likelihood that flies were in the narrow band near the smoke ribbon increased with smoke concentration ( Figure 1—figure supplement 1C ) , before saturating at a sufficient dose ( Figure 1—figure supplement 1D ) , a result reproduced in both EA and ACV ( Figure 1—figure supplement 1E–H ) . We then tested contributions from carbon dioxide sensing and vision , using Gr63a-/- ( Jones et al . , 2007 ) and norpA-/- ( Bloomquist et al . , 1988 ) mutants , respectively . Both mutants retained the ability to localize the odor source at a level comparable to wild-type flies ( Figure 1—figure supplement 1I–J ) . To test whether humidity played a major role , we saturated the airflow with 80% humidity and found that source localization was reduced but still significantly above random ( Figure 1—figure supplement 1I–J ) . Finally , we tested the olfaction directly using Orco-/- mutants ( Larsson et al . , 2004 ) , as well as anosmic flies ( Gr63a-/- , Orco-/- , Ir8a-/- , and Ir25-/-; Ramdya et al . , 2015 ) . In both sets of mutants , the ability to find the odor source was completely abolished . Orco-/- mutants ( but not the anosmic flies ) exhibited a slight repulsion to the smoke ribbon , which we attributed to an aversive response to carbon dioxide ( Figure 1—figure supplement 1I-J; Larsson et al . , 2004; Suh et al . , 2004 ) . Thus , flies’ attraction to smoke is driven mainly by olfaction . To quantify the time-dependent stimulus experienced by each fly during navigation , we averaged the signal intensity in a small area ( 1 . 10 mm2 ) near its antennae ( Figure 1B inset , Figure 1E , and Video 2 ) . The onset and offset of odor encounters were defined as the times when the signal crossed a sensory threshold , which we set to 2 . 5 SD ( σ ) above the background noise . We refer to the periods when the odor is above threshold as ‘odor encounters , ’ or ‘encounters’ for short , and periods when the odor is below the threshold as ‘blanks . ’ We verified that the results and conclusions presented below remained unchanged for thresholds between 2 . 0σ and 3 . 5σ . Using the 2 . 5σ threshold , the error in the timing of odor encounters was estimated to be less than 25 ms ( Figure 1—figure supplement 2 and Materials and methods ) . Using this setup , we then examined how walking flies navigate odor plumes that fluctuate in both space and time . We first quantified the statistics of the odor environment the flies must navigate . Our odor plume was highly intermittent , composed of spatiotemporally localized filaments breaking continuously in time ( Figure 1B and Video 1 , lateral jets on ) . Odor mean intermittency – the fraction of time the odor was above the sensory threshold – ranged several orders of magnitude in the conical extent of the plume ( Figure 1F ) . The average signal intermittency was low across the arena , increasing from about 10−6 at the border of the plume to about 0 . 12–0 . 39 at the center line , depending on the distance to the source ( see also Figure 1—figure supplement 3A ) . Still , navigating flies perceived intermittencies ranging over a decade and a half , with an average intermittency around 0 . 11 ( Figure 1—figure supplement 3B ) , resembling values measured ( Murlis et al . , 2000 ) in natural settings . At fixed locations from the source , odor encounter ( Figure 1G ) and blank ( Figure 1H ) durations spanned a wide range of time scales ( exponents ranging from −3 . 3 close to the source to −2 . 1 far from the source ) , gradually approaching the power law ∼t-3/2 theoretically predicted for turbulent odor plumes in the atmospheric boundary layer ( Celani et al . , 2014 ) . The distribution of encounter and blank durations experienced by navigating flies spanned an even greater range and were closer to a power law ( Figure 1I ) . On average , flies experienced brief odor encounters ( mean duration ~200 ms ) at a mean frequency of 4 Hz . Even beyond their variability and brevity , encounters were also highly unpredictable in location . To quantify this , we calculated the likelihood to receive an odor encounter in 1 s , assuming one walks straight at 10 mm/s radially outward from a fixed point . Predictability in the location of future odor encounters would then manifest as a directional dependence of this likelihood . Within the conical extent of the plume , the likelihood was nearly isotropic with respect to walking direction , whereas near the plume edges , likelihoods were skewed toward the centerline of the plume cone ( Figure 1J ) . Within the conical extent of the odor plume , therefore , the location of future odor encounters was uncertain . Despite this uncertainty , flies remained largely in the plume cone and were able to successfully locate the odor source ( Figure 1K ) . However , during fluctuating winds without odor , they could not locate the source ( Figure 1L ) . How are fly orientation and speed shaped on average by an odor signal exhibiting this degree of spatiotemporal complexity ? To compare these behaviors to those in an odorless environment , we presented the complex plume in 15 s blocks by closing and opening the odor valve every 15 s but maintaining the alternating lateral jets throughout the trial . This produced an environment in which a 15 s block of complex odor plume alternated with a 15 s block of fluctuating wind only ( Video 3 ) . When the odor was on , odor encounters were frequent , but randomly experienced in time ( Figure 2A–B ) . As expected , flies were more likely to be oriented upwind when the odor was on ( Figure 2C ) , as previously reported ( Álvarez-Salvado et al . , 2018; Steck et al . , 2012; Bell and Wilson , 2016; Budick and Dickinson , 2006; Kennedy and Marsh , 1974; Murlis et al . , 1992; Flügge , 1934 ) . However , unlike for flies walking into a spatially homogeneous odor block ( Álvarez-Salvado et al . , 2018; Jung et al . , 2015; Gao et al . , 2013 ) , changes in average angular speed were minor , with a less than 10% change between blocks ( Figure 2D ) . Walking speeds were similarly unmodulated , again in contrast to walking flies in homogenous odor blocks ( Figure 2E; Álvarez-Salvado et al . , 2018 ) . This is not inconsistent , however , since encounters were so brief ( ~200 ms ) , the integration timescales for speed modulation measured previously ( Álvarez-Salvado et al . , 2018 ) would only produce <10% increase in either ground or angular speed . Though changes in ground speed were minor , we noticed a high incidence of stopping in our spatiotemporally complex plume ( Figure 1D ) . The prevalence of immobility has been noted before in walking flies navigating homogenous odor blocks ( Álvarez-Salvado et al . , 2018 ) , though its role in navigation was not investigated . We suspected that stopping might form a critical component of intermittent plume navigation for walking flies . Indeed , walk-to-stop and stop-to-walk transition rates were strongly modulated during the transitions between odorized and non-odorized blocks ( Figure 2F ) . Natural odors ACV and EA elicited similar navigational trends in angular and ground speeds , orientation , and stopping rate when presented in these 15 s blocks ( Figure 2—figure supplement 1 ) . Together , this suggested that turning and stopping comprised the bulk of the navigational repertoire for walking flies in spatiotemporally complex plumes . This prompted us to next examine how the sequence of individual odor encounters experienced by navigating flies precisely shapes their decisions to turn , walk , and stop . Flies reorient upwind soon after flying into an odor ribbon ( van Breugel and Dickinson , 2014 ) or walking into a homogeneous odor block ( Álvarez-Salvado et al . , 2018 ) . We therefore calculated the change in fly orientation following an individual encounter , finding that within 2 s of an encounter onset , flies of any orientation biased their orientation upwind ( Figure 3A and Figure 3—figure supplement 1 ) . Since encounter frequency was on the order of a few Hz , flies receiving one encounter were likely to receive more within the 2 s window . Upwind bias may therefore reflect an accumulated effect from repeated odor encounters . Partitioning the data into encounters followed by 0 , 1-3 , or 4+ further encounters within 2 s , we found that odor encounters followed closely by many others elicited much stronger upwind bias than did isolated ones ( Figure 3B ) . To quantify this more precisely , we calculated a running average of encounter frequency Wfreq ( t ) by convolving the binary vector of encounter onset times with an exponential filter ( timescale τ = 2 s ) , and plotted upwind orientation as a function of encounter frequency ( Figure 3C ) . All orientations were reflected over the x-axis , whereby 0° is upwind and 180° is downwind . The trend was strongly monotonic , with an intercept of 88 . 6° at 0 Hz – flies experiencing no encounters were oriented nearly equally upwind and downwind – and a slope of 21 . 6°/Hz ( p < 10−4 ) – flies experiencing a frequency of 3 Hz would be oriented just 25o off the upwind direction . If no further encounters were received , this monotonic trend dropped steadily to a slope of 4 . 5°/Hz ( not significantly different from 0 , p > 0 . 05 ) after 5 s ( Figure 3C ) . This suggests that repeated interactions with the plume biased the fly upwind , and after some time without encounters , flies were again uniformly oriented . The amount of time a fly is exposed to odor increases with each subsequent encounter . Does upwind bias result from the number of individual odor interactions , the cumulative duration of these encounters , or both ? If , for example , all encounters were 200 ms long , then tripling encounter frequency would also triple perceived odor duration – frequency and duration would be perfectly correlated . But if orientation depended on odor duration alone , the dependency on frequency noted above would arise simply as a consequence of this correlation . Prior results suggest that walking flies bias orientation and speed by filtering odor in time ( Álvarez-Salvado et al . , 2018 ) , so we suspected that odor duration might contribute to some or all of the upwind bias . To investigate this possibility , we defined a running average of odor duration Wdurt analogously to Wfreq ( t ) by exponentially filtering the binary vector of odor intermittency ( 1 during encounters , 0 during blanks ) . We disassociated Wfreqt and Wdurt by holding one constant to a small range , and plotting upwind orientation against the other . Surprisingly , with this analysis , only the correlation of orientation with encounter frequency remained ( Figure 3D-E ) . We also investigated the possibility that odor concentration contributed to upwind turning by defining Wconct analogously using the raw signal . While we have not quantified the exact relationship between odor concentration and image intensity , our dose-response results ( Figure 1—figure supplement 1C-D ) suggest that they are monotonically related , so a correlation would exist to first order . Linearly regressing upwind orientation simultaneously against Wfreqt , Wdurt , and Wconct , revealed Wfreqt as the sole explanatory variable ( p < 1e-6 , p > 0 . 05 , p > 0 . 05 , respectively; Figure 3F ) . Together , these results indicate that in the intermittent , spatiotemporally complex plumes in this experiment , upwind orientation was driven by the frequency , but not by the duration or concentration , of odor encounters . The lack of a clear upwind bias following an isolated encounter ( Figure 3A and Figure 3—figure supplement 1B ) suggested that reorientations may not be simply an encounter-elicited reflex . To characterize reorientations , we first thresholded angular speed to identify turn events ( Figure 4—figure supplement 1 , and Materials and methods ) . We found that individual turns occurred not in a continuum of angles but rather in discrete saccades of 30°±10° either left or right ( Figure 4A ) , consistent with previous studies in non-odorized environments ( Geurten et al . , 2014 ) . Moreover , the contribution to upwind bias from the inter-saccade sections of the trajectories was not significant ( Figure 4B ) . This indicates that the discrete saccadic turns were responsible for upwind progress during navigation . The waiting time between saccades obeyed an exponential distribution with timescale τ = 0 . 75 s±0 . 17 , or a Poisson rate of about 1 . 3 turns per second ( Figure 4C ) . Surprisingly , this turn rate was insensitive to either encounter frequency or duration ( Figure 4C ) . This presented a puzzle: if flies turned left and right at discrete angles and a constant rate , they were effectively executing a random walk on the circle . Since angular random walks randomize orientations in time , how would flies orient upwind ? Partitioning the turn angle distribution into bouts of low ( < 1 Hz ) and high ( > 4 Hz ) encounter frequency resolved this puzzle . For high frequencies , the distribution of turn angles exhibited the same ±~30° peaks , but now with an upwind lobe much larger than the downwind one ( Figure 4D ) . Thus , odor encounter frequency biased the direction of turns , while leaving the magnitude and rate of turns unchanged . These findings could be recapitulated with a simple stochastic model of turning , in which walking flies execute Poisson turns at a constant rate . The magnitude of each turn is chosen randomly from the measured distribution , and the likelihood pT that the turn is directed upwind is a sigmoidal function of the encounter frequency . Specifically , pT= 1+exp-αWfreq-1 ( Figure 4E ) . This model produces unbiased turns ( p=0 . 5 ) in the absence of odor encounters and a high likelihood of upwind turns ( p∼1 ) when encounters are very frequent . We estimated the parameters from a maximum likelihood fit to the data , obtaining a distribution of parameters by performing the estimation on 500 distinct subsets of the measured data ( Materials and methods ) . The distribution of estimated gains α clustered tightly around a mean of α=0 . 242 1/Hz ( Figure 4F ) , indicating that the parameter estimates were robust . Simulating this model with the mean of the estimated parameters closely reproduced the dependence of upwind orientation on encounter frequency ( Figure 4G ) . Together , these findings indicated that in the spatiotemporally complex plume , odor encounters did not initiate reflexive upwind turning . Rather , odor encounters increased the likelihood that stochastically-occurring , saccadic left/right turns were directed upwind . Walking flies navigating spatiotemporally complex plumes stopped frequently ( Figure 1D ) , and the rate of both stopping and starting depended strongly on the presence of odor ( Figure 2F ) . To connect walk-stop transitions to individual encounters , we first calculated the likelihood to be walking or stopped during the 2 s after an encounter ( Figure 5—figure supplement 1A ) . Walking flies were more likely to remain walking after an encounter ( versus random times ) , while stopped flies were more likely to initiate a walk . Notably , even a single encounter was sufficient to initiate walks , and higher encounter frequencies biased this further ( Figure 5—figure supplement 1B ) . This implicated both individual encounters and encounter history in decisions to walk or stop . In contrast , we found no change in walking speed following encounters ( Figure 5—figure supplement 1C ) , even when encounter frequencies were appreciable ( Figure 5—figure supplement 1D ) . How does the sequence of encounters shape a fly’s decision to walk or stop ? After an odor encounter , flies walked for longer periods before stopping , compared to random ( Figure 5A ) . Thus , encounters reduce stopping likelihood , and flies experiencing higher encounter frequencies walked for longer ( Figure 5B-C ) . In addition , the time to stop following an encounter was the same , whether the encounter was isolated or part of a clump containing 3+ encounters in 1 s ( Figure 5A ) . The times to stop were approximately exponentially distributed . We therefore modeled stop decisions as a Poisson process with a time-dependent stopping rate λw→s ( t ) =λw→s ( w ( t ) ) , where w ( t ) is the binary vector of encounter onset times . We considered various models for the dependency of the stopping rate on the encounter sequence w ( t ) . In the last encounter model , λw→s ( t ) drops to the same given value at each encounter , before decaying back to baseline with some characteristic time τs ( Figure 5D ) . In the accumulated evidence model , λw→s ( t ) decreases further at every odor encounter , and therefore remains at a lower value when encounters are more closely spaced ( Figure 5H ) . In the encounter duration model , λw→s ( t ) switches between a low value during encounters and a higher value during blanks ( Figure 5I ) . These models contain various parameters dictating the baseline rates and timescales , which we fit to the data using maximum likelihood estimation . As in the turn model , we obtained a distribution of parameters by carrying out the estimation on 500 distinct subsets of the data – quantifying the robustness of each parameter ( Figure 5—figure supplement 2A ) . We found that the time-to-stop statistics were explained well by the last encounter model ( Figure 5E–G ) but not by the other models ( Figure 5H–I ) . Our parameter fits indicate that at each encounter , the stopping rate drops to 0 . 17 s−1 , before rising with timescale 0 . 25 s to a background rate of 0 . 78 s−1 . In the accumulated evidence model , the distribution of parameter estimates were broad , and often the parameters were estimated close to the imposed bounds ( which ranged 2 orders of magnitude ) , suggesting that this model was not robust to the data ( Figure 5—figure supplement 2B ) . The parameters of the encounter duration model were narrowly peaked , but the predictions were poor , so the model was incomplete ( Figure 5—figure supplement 2C ) . In both these latter models , the distribution of walk durations following encounters was not higher than those following random times ( compare black and dotted black lines in Figure 5A E to those in Figure 5H–I ) . Thus , our data indicate that flies continuously adjust their likelihood to stop while navigating and that the rate of stops decreases by a factor of nearly five at the onset of each encounter . This decrease in stop rate at each encounter is brief , less than 1 s , suggesting that the ongoing perception of frequent encounters retains the flies in an active , walking state , but when encounters are interrupted , stops are frequent . Next , we quantified the rate of stop-to-walk transitions . In contrast to stops , the time to walk was significantly shorter following a clump of encounters than an isolated encounter ( Figure 6A ) , implicating the history of encounters in walk initiation . In addition , the cumulative number of encounters received during a stop bout was rather independent of stop length , ~ 0 . 75-1 . 25 encounters for stops between 2 and 6 s long ( Figure 6B ) . This observation would not rule out models in which the walking likelihood accumulated with every encounter , nor those in which the rate jumped to a large , fixed value at each encounter . Therefore , we modeled walk decisions with three models analogous to those used for the stop decisions . In the accumulated evidence model , the rate increases by the same amount at each encounter ( Figure 6D ) , while in the last encounter model , the walk rate λs→w ( t ) increases to a set value at each encounter , before decaying to baseline ( Figure 6H ) . In the accumulated evidence model , stopped flies receiving a clump would initiate walks sooner than those receiving a single encounter . In the encounter duration model , the rate switches between a higher value in encounters and a lower value in blanks ( Figure 6I ) . The time-to-walk statistics were fit well by the accumulated evidence model ( Figure 6E-G ) but not the other two models ( Figure 6H-I , Figure 6—figure supplement 1 ) . The estimated baseline walking rate is λ0=0 . 29 s−1 , so stopped flies will on average remain stopped for ~3 s if they receive no signal . This rate increases at each encounter by Δλ=0 . 41s-1 , before decaying to baseline λ0 with a timescale of 0 . 52s . Though our model predicts that a higher frequency of encounters will elicit an earlier walk , Δλ is comparable to the base rate – more than doubling the transition rate – so even a single encounter is sufficient to elicit a walk , as observed in Figure 6B and Figure 5—figure supplement 1B . Together , this suggests that stopping forms a key component of the navigational strategy , and that stop and walk decisions are stochastic events whose rates of occurrence depend on the precise timing of recent encounters . To test how these behavioral algorithms individually affected navigational performance , we incorporated our findings into an agent-based simulation ( Figure 7A ) . We simulated 10 , 000 individual virtual flies navigating using the turn , stop-to-walk , and walk-to-stop models that we found in our data . In these simulations , we calculated both the likelihood that agents reach the source as well as the time taken to do so . Virtual flies implementing all three encounter-modulated behaviors navigated largely in the plume cone ( Figure 7B ) and converged to the source ( Figure 7C ) , similarly to real flies ( 10 . 5% of real flies and 7 . 8% of virtual agents reached within 15 mm of the source ) . Visually , the simulated tracks resembled the measured tracks , containing non-linear , circuitous routes toward the source , as well as wide loops ( Figure 7B ) . To meaningfully test the contribution of the walk , stop , and turn decisions in effective navigation , we systematically replaced each time-dependent rate with its average , so that overall biases were retained but the dependency on encounters was not . Without encounter-modulated turning , adding stopping and walking decisions alone improved performance marginally ( Figure 7D ) . With encounter-modulated turning present , however , the addition of either walk or stop decisions obeying our models both markedly increased the chance of finding the source and markedly reduced the search time . Together , this indicates a key benefit of encounter-driven stop-walk modulation when navigating spatiotemporally complex plumes .
Odor plumes can vary widely in spatiotemporal structure depending on the geometry of the surroundings and the nature of the airflow . In turbulent flows , the duration of odor encounters and blanks are power-law distributed , spanning a wide range of values from milliseconds to a few seconds long ( Celani et al . , 2014 ) . While these flows become laminar near very smooth boundaries , the presence in natural terrains of obstacles , wind shifts , source motions , surface roughness , and boundary layer instabilities can cause smooth odor streams to break up into complex filaments ( Cardé and Willis , 2008; Murlis et al . , 1992; Murlis et al . , 2000; Riffell et al . , 2008 ) . In our wind tunnel , we generate such perturbations by perturbing the laminar flow with stochastically alternating air jets near the upwind end . The key feature of this environment is that the statistics of the resulting odor patches are broadly distributed and approximate those in the atmospheric boundary layer ( Figure 1G–H , Figure 1—figure supplement 3 ) , while allowing us to image behavior and signal simultaneously . The intermittent nature of turbulent odor plumes has inspired a number of theoretical navigational algorithms that treat odor signals as a train of event times ( e . g . as in w ( t ) ) , ignoring encounter information about concentration and duration ( Balkovsky and Shraiman , 2002; Vergassola et al . , 2007 ) . Indeed , information-theoretic analysis has indicated that precise measurements of odor concentration may confer less benefit than coarse measurements across space or time ( Boie et al . , 2018 ) . In ‘infotactic’ searches ( Vergassola et al . , 2007 ) , agents successfully navigate turbulent plumes by updating an internal spatial model of the plume structure , using only the arrival time of individual encounters . Analysis of insect flying trajectories ( Pang et al . , 2018 ) and Caenorhabditis elegans larvae crawling patterns ( Calhoun et al . , 2014 ) indicate that encounter-timing-driven infotaxis may form part of the navigation repertoire when concentration gradients are absent or difficult to measure . Beyond theory , various experiments have shown that in intermittent plumes the frequency of encounters strongly shapes navigational behavior . The starkest indication of this in insect olfaction is the response of flying moths , Cadra cautella and Heliothos Virescens ( Mafra-Neto and Cardé , 1994; Vickers and Baker , 1994; Baker and Vickers , 1997; Carde and MafraNeto , 1997 ) , and walking moths , Bombyx mori ( Kanzaki and Sugi , 1992 ) , to pheromone plumes . In turbulent plumes and plumes pulsed at sufficient frequency , moths follow tight , narrow trajectories toward the source , whereas when the pulsing frequency is too low or the ribbon is static , they execute more zigzagging motion . To explain this , a model has been proposed in which an internal counterturning tendency is suppressed or reset by plume hits ( Kennedy and Marsh , 1974; Baker and Vickers , 1997 ) . A loose analogy could be made with our findings . Moths move crosswind and execute counterturns to find the plume , but once within the plume cone , high-frequency odor encounters cause them to suppress counterturns and surge upwind . Analogously , walking Drosophila move crosswind and execute a local search to get inside the plume but once inside a complex plume cone they execute random left/right saccades , with frequent odor encounters biasing these saccades upwind . In both cases , the timing and frequency of odor encounters suppress exploration and drive progress toward the source . Like moths , flying flies navigating static odor ribbons counterturn back into them after passing through , effecting a similar upwind zigzag motion , though with smaller angles ( Budick and Dickinson , 2006; van Breugel and Dickinson , 2014 ) . An alternative explanation to the internal counterturning model in moth is that flies simply counterturn after losing the plume ( van Breugel and Dickinson , 2014 ) . The duration of encounters as flying flies pass through a static ribbon are brief – 10–250 ms – not unlike the encounters we measure in the plume used here . Further , due to the erratic zigzags of flies as they cross the ribbon , encounters are perceived somewhat randomly in time . Thus , it was suggested that since the statistics of perceived odor signals end up resembling those in turbulence , this plume loss-initiated counterturning might be a generic navigational strategy , occurring in spatiotemporally complex plumes as well ( van Breugel and Dickinson , 2014 ) . At least for walking flies , we find that this is not the case . Turns occur stochastically , with rates independent of how long flies spend in the odor and the frequency of encounters ( Figure 4C ) . But there is an important distinction between intermittency in flies crossing standing ribbons and those navigating dynamic plumes . In ribbons , intermittency is generated by animals’ self-motion , creating a strong correlation between the likelihood of an odor encounter and spatial location . The location of expected plume encounters is in this sense highly predictable , which makes counterturning an effective strategy . Within the cone subtended by our dynamic plumes , the frequency and duration of encounters are less correlated with location and direction , and can occur even when the fly is stopped . This makes the location of future hits less predictable ( Figure 1J ) , so within the plume cone , reactive strategies such as counterturning might be ineffective . An important finding here is that the duration of odor encounters plays no role in navigation ( Figure 3D–F ) . This was unexpected , since a recent systematic quantification of navigation algorithms in walking Drosophila found that flies bias their orientation upwind by integrating odor concentration ( Álvarez-Salvado et al . , 2018 ) . In that model , the concentration is normalized , so reorientations are accounted for primarily by the duration of the odor . However , the odor signals were relatively slow – pulsed from 0 . 1 to 1 Hz – giving encounter durations an order of magnitude larger than in the plume used here . This suggests that for rapid , intermittent signals , encounter frequency drives navigation , while for slower signals such as those expected in the boundary layer of a smooth surface , the duration of odor exposure matters . Effective navigation may therefore combine two important features of the temporal odor signal: its rectified derivative ( giving encounter onset times ) and it is integral ( giving odor exposure time ) . Future studies interpolating between these extremes could elucidate if and how animals weight these two distinctly informative contributions . A second important finding is that stopping forms a key component of the search strategy for walking flies ( Figures 5–7 ) . Stopping and waiting for encounters allows flies to receive odor encounters from dynamic plumes without wandering off-track or expending energy . We find that in deciding to walk , flies accumulate evidence from individual encounters , so walks are more likely following a clump of encounters than a single one . Theoretical work has shown that evidence accumulation from odor encounters can inform internal representations of plume structure to drive successful navigation in gradient-less plumes ( Vergassola et al . , 2007; Calhoun et al . , 2014 ) , an interesting possibility still to be examined . Filtering and integrating odor concentration drives navigation in odor plumes with longer encounters and less regularity ( Álvarez-Salvado et al . , 2018 ) , suggesting that evidence accumulation – be it from odor duration or frequency – is a generic feature of olfactory navigation in a variety of environments . More work is required to understand the neural circuits and computations responsible for enacting stop and walk decisions . There is evidence that the transcription factor FoxP plays a role in value-based decision making , implicating these mutants as possible targets for future studies ( DasGupta et al . , 2014 ) . Finally , encounter-elicited stopping might be unique to walking Drosophila and larvae ( Tastekin et al . , 2018 ) , since remaining stationary is more difficult in flight . Still , the reflexive counterturns that flying Drosophila execute after losing a plume ( van Breugel and Dickinson , 2014 ) do bear a loose resemblance to the increased stop rates following a drop in encounter frequency , so these decisions may have a common origin , but a different behavioral response . Our visualizable signal is conventional smoke , a complex odor consisting of various aromatic compounds ( Figure 1—figure supplement 1A ) . Gross fly behaviors in smoke are largely reminiscent of those in other known attractive odors , both in straight ribbons and complex intermittent plumes ( Figure 1—figure supplement 1; Figure 2—figure supplement 1 ) . Still , we expect differences in wind conditions , odor identity , and odor valence to modulate finer motor control in navigation ( Jung et al . , 2015 ) . For example , the responses to the onset of the 15 s blocks of spatiotemporally complex signals were less pronounced in ACV than in smoke or ethyl acetate , with the latter being more similar to each other than to ACV ( Figure 2 , and Figure 2—figure supplement 1 ) . Moreover , Orco-/- flies that lack major olfactory input , but are intact in CO2 sensing , showed mild aversion around the center of the straight smoke plume , illustrating how different components contribute to the perception of the odor mixture . It is surprising that despite the rich locomotive repertoire of walking Drosophila , a large part of their olfactory navigational strategy can be reduced to four actions – left turn , right turn , walk , and stop . A recent , systematic study of the locomotive structure of walking Drosophila in various windless odor environments has similarly found that behaviors fall into a limited number of states comprising a hierarchical hidden Markov model ( Tao et al . , 2019 ) . While the identity of the odor and fly individuality affect the transition rates between these states , new states do not emerge in different conditions . These findings are consistent with ours . A natural extension would be to study how fly individuality and odor identity affect transition rates in our model , and which conditions would indeed require an extended behavioral space . Finally , an important aspect not explored in our work is learning . The navigational algorithms we have found in the plume used here are shaped by odor information from the recent past , over timescales no longer than a few seconds . Animals can learn odor landscapes over longer periods , by associating odor cues with spatial location . Desert ants , Cataglyphis fortis , have been shown to use learned olfactory scenes for homeward navigation in the absence of other directional cues ( Buehlmann et al . , 2015 ) . Similarly , in mice , efficient foraging strategies can overtake an otherwise local gradient ascent strategy , if prior information about the odor scene is available ( Gire et al . , 2016 ) . It is possible that the stochastic random walk strategies we observe here could be replaced with more stereotyped maneuvers if flies were sufficiently preconditioned to the environment . How the navigational strategies we have observed here are affected by conditioning , either with repeated trials or with reward feedback , provides a fruitful direction for future studies .
All analysis was performed using custom-written Matlab and Python scripts . The fly lines used in this study are available upon request . The data are available at https://doi . org/10 . 5061/dryad . 4j0zpc87z and the scripts used to perform the experiments , track flies , and extract relevant behavioral data are available on https://github . com/emonetlab/fly-walk ( copy archived at swh:1:rev:6a9266effbdc305c2e6177a7b6786e295cb48a2c ) and scripts used to run simulations are available on https://github . com/emonetlab/fly-walk-sims ( copy archived at swh:1:rev:be9bb7a93eb4963ca0515144940694412304f633 ) . We thank the following people for making their Matlab scripts , utilized for generating plots in this work , freely available: Ben Mitch , Panel; Holger Hoffman , Violin; Kelly Kearney , legendflex; Yair Altman , export_fig; David Legland , geom2D; Rob Campbell , shadedErrorBar . | When walking along a city street , you might encounter a range of scents and odors , from the smells of coffee and food to those of exhaust fumes and garbage . The odors are swept to your nose by air currents that move scents in two different ways . They carry them downwind in a process called advection , but they also mix them chaotically with clean air in a process called turbulence . What results is an odor plume: a complex ever-changing structure resembling the smoke rising from a chimney . Within a plume , areas of highly concentrated odor particles break up into smaller parcels as they travel further from the odor source . This means that the concentration of the odor does not vary along a smooth gradient . Instead , the odor arrives in brief and unpredictable bursts . Despite this complexity , insects are able to use odor plumes with remarkable ease to navigate towards food sources . But how do they do this ? Answering this question has proved challenging because odor plumes are usually invisible . Over the years , scientists have come up with a number of creative solutions to this problem , including releasing soap bubbles together with odors , or using wind tunnels to generate simpler , straight plumes in known locations . These approaches have shown that when insects encounter an odor , they surge upwind towards its source . When they lose track of the odor , they cast themselves crosswind in an effort to regain contact . But this does not explain how insects are able to navigate irregular odor plumes , in which both the timing and location of the odor bursts are unpredictable . Demir , Kadakia et al . have now bridged this gap by showing how fruit flies are attracted to smoke , an odorant that is also visible . By injecting irregular smoke plumes into a custom-built wind tunnel , and then imaging flies as they walked through it , Demir , Kadakia et al . showed that flies make random halts when navigating the plume . Each time they stop , they use the timing of the odor bursts reaching them to decide when to start moving again . Rather than turning every time they detect an odor , flies initiate turns at random times . When several odor bursts arrive in a short time , the flies tend to orient these turns upwind rather than downwind . Flies therefore rely on a different strategy to navigate irregular odor plumes than the ‘surge and cast’ method they use for regular odor streams . Successful navigation through complex irregular plumes involves a degree of random behavior . This helps the flies gather information about an unpredictable environment as they search for the source of the odor . These findings may help to understand how other insects use odor to navigate in the real world , for example , how mosquitoes track down human hosts . | [
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] | 2020 | Walking Drosophila navigate complex plumes using stochastic decisions biased by the timing of odor encounters |
Dpp , a member of the BMP family , is a morphogen that specifies positional information in Drosophila wing precursors . In this tissue , Dpp expressed along the anterior-posterior boundary forms a concentration gradient that controls the expression domains of target genes , which in turn specify the position of wing veins . Dpp also promotes growth in this tissue . The relationship between the spatio-temporal profile of Dpp signalling and growth has been the subject of debate , which has intensified recently with the suggestion that the stripe of Dpp is dispensable for growth . With two independent conditional alleles of dpp , we find that the stripe of Dpp is essential for wing growth . We then show that this requirement , but not patterning , can be fulfilled by uniform , low level , Dpp expression . Thus , the stripe of Dpp ensures that signalling remains above a pro-growth threshold , while at the same time generating a gradient that patterns cell fates .
During development , tissue growth must be precisely coupled with patterning to ensure that the right number of cells can contribute to the various substructures within each organ ( Restrepo et al . , 2014 ) ( Baena-Lopez et al . , 2012; Bryant and Gardiner , 2016; Hariharan , 2015; Irvine and Harvey , 2015; Johnston and Gallant , 2002; Wartlick et al . , 2011a ) . Not surprisingly , many signalling molecules that specify positional information also control growth ( Baena-Lopez et al . , 2012; Restrepo et al . , 2014 ) . This has been particularly well demonstrated in Drosophila wing imaginal discs , epithelial pockets that grow during larval stages and eventually give rise to the wing proper , the wing hinge and a part of the thorax called the notum ( Figure 1A ) . Segregation of wing imaginal discs into the territories that give rise to these three structures is controlled by a series of signalling events involving EGFR , JAK/STAT , Notch , and Hedgehog signalling , culminating in sustained expression of Wingless and Dpp in orthogonal stripes until the end of the third instar ( Blackman et al . , 1991; Neumann and Cohen , 1996; Zecca et al . , 1995 ) . Both Wingless and Dpp are essential for growth ( Baena-Lopez et al . , 2009; Burke and Basler , 1996; Restrepo et al . , 2014; Spencer et al . , 1982; Wartlick et al . , 2011b ) . Here , we focus on the role of Dpp , which is expressed along the anterior-posterior ( A/P ) compartment boundary in a pattern that cuts across the prospective notum , hinge and wing proper ( Figure 1A ) . We look specifically at the prospective wing , which forms from a central region of the disc called the pouch . A wide range of evidence suggests that , in this region , Dpp acts as a morphogen . Graded distribution of the endogenous protein has not been directly visualized for lack of a suitable antibody against the mature secreted protein . However , the nested pattern of expression of target genes and the patterning activity of ectopic Dpp are strongly indicative of graded signalling activity ( Lecuit et al . , 1996; Nellen et al . , 1996; Schwank and Basler , 2010; Zecca et al . , 1995 ) which is high around the A/P boundary , low further away , and undetectable at the lateral edges of the disc . High signalling activity , within and around the stripe of Dpp expression , is marked by immunoreactivity against phosphorylated Mad ( P-Mad ) and the expression of spalt-major ( salm ) while low signalling activity suffices to activate optomotor blind ( omb ) expression over a wider area of the prospective wing ( Burke and Basler , 1996; Lecuit et al . , 1996; Nellen et al . , 1996; Tanimoto et al . , 2000 ) . In wing imaginal discs , Dpp signalling controls gene expression indirectly , through repression of a transcriptional repressor encoded by the brinker gene ( Martín et al . , 2004 ) . Thus , the inverse gradient of Brinker expression provides yet another means of detecting Dpp signaling activity ( Schwank et al . , 2008 ) . 10 . 7554/eLife . 22546 . 003Figure 1 . Growth of the prospective wing requires Dpp expression within the pouch . ( A ) Diagram highlighting the three domains of wing imaginal discs and the stripe of Dpp expression . ( B ) Diagram of the two conditional alleles we created , showing the region deleted from the genome and the inserted fragment . ( C–F’ ) . Inactivation of dppFRT-CA in the pouch ( with rotund-gal4 UAS-Flp ) leads to derepression of brinker and reduced growth ( shown here in discs fixed at 96 hr and 120 hr AEL ) . The edge of the pouch is marked by the weak inner ring of Hth expression . However , since the outer ring is more readily visible , this is the marker we used to measure pouch size ( thus overestimating ) . ( G , H ) Quantification of the area enclosed by the Hth outer ring at the two stages ( each dot/square represents one imaginal disc ) . ( I , J ) Wings from control ( I ) and experimental ( J ) adults . The scale bar , which represents 50 μm , applies to panels ( C-F’ ) . In panels ( G and H ) statistical significance of the difference between experimental and control samples was assessed with Student’s t-test , assuming equal variance and a Gaussian distribution ( p<0 . 0001 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 00310 . 7554/eLife . 22546 . 004Figure 1—source data 1 . Pouch area . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 00410 . 7554/eLife . 22546 . 005Figure 1—figure supplement 1 . Inactivation of Dpp specifically in the pouch . ( A–B ) Expression of HA-Dpp and Brinker in the unexcised dppFRT-PSB allele ( normal Dpp activity ) . Note the repression of brinker expression on either side of the central stripe of Dpp . ( C–D ) Pouch-specific inactivation of Dpp expression from dppFRT-PSB by nubbin-gal4 UAS-Flp . Most HA immunoreactivity has disappeared at 96 hr AEL , although some is still detectable at 72 hr AEL ( not shown ) . As Dpp disappears , brinker becomes derepressed . ( E–F ) . Pouch-specific inactivation of Dpp expression from dppFRT-CA by rotund-gal4 UAS-Flp . As with nubbin-Gal4 , Dpp is no longer detectable in the pouch from 96 hr AEL . On all panels , the edge of the pouch ( marked with a white dotted line ) was estimated from tissue folds that could be observed in the DAPI channel ( not shown ) . Scale bar = 50 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 005 As a morphogen , Dpp is a pattern organiser . For example , graded Dpp signalling determines the position of wing veins , particularly veins 2 and 5 , through regulation of salm and omb ( Campbell and Tomlinson , 1999; Jaźwińska et al . , 1999; Minami et al . , 1999 ) . Dpp also clearly contributes to growth . Indeed , in the absence of Dpp signalling , wings ( and other appendages ) fail to grow ( Bangi and Wharton , 2006; Restrepo et al . , 2014; Spencer et al . , 1982 ) . The pro-growth role of Dpp is in part mediated through regulation of Myc ( Doumpas et al . , 2013 ) , although a comprehensive understanding of growth regulation by Dpp signalling remains lacking . In wild-type imaginal discs , proliferation is approximately uniform while Dpp signalling is graded . Therefore , there is no apparent correlation between the level of Dpp signalling and the growth rate . How does a graded signal trigger a uniform response ? Experiments involving the creation of abrupt differences in signalling suggested that local differences in Dpp signalling activity , that is , the spatial gradient of signalling , could be the trigger of growth ( Rogulja and Irvine , 2005 ) . This would provide an elegant mechanism for growth termination as the gradient would be expected to become shallower during growth ( Day and Lawrence , 2000 ) . However , there is no evidence that smooth differences in signalling activity associated with the endogenous gradient control growth . An alternative model is that the temporal gradient ( the local relative increase in signalling activity ) could be the trigger of proliferation ( Wartlick et al . , 2011b ) , a model that has also been questioned ( Harmansa et al . , 2015; Schwank et al . , 2012 ) . In agreement with the notion that Dpp controls growth through repression of brinker , imaginal discs lacking both Dpp and Brinker proliferate extensively ( Martín et al . , 2004; Schwank et al . , 2008 ) . Importantly , only the lateral region of the pouch ( as well as the prospective hinge ) overproliferates , while the medial area proliferate normally . Thus , depending on the distance from the stripe of Dpp , the cells of the pouch have a different propensity to proliferate . The main role of the Dpp/Brinker system would be to equalize this difference ( Schwank et al . , 2008 ) . Thus , the inherent tendency of lateral cells to proliferate is slowed down by Brinker , while in medial cells Dpp emanating from its central stripe prevents Brinker-mediated suppression of growth . Despite strong evidence in support of the above model , Akiyama and Gibson recently suggested that the central stripe of Dpp expression is dispensable for wing growth , and that the prospective pouch requires a source of Dpp in the anterior compartment to achieve growth ( Akiyama and Gibson , 2015 ) . To control Dpp activity , these authors created a conditional dpp allele ( here referred to as dppFRT-TA ) by deleting an essential exon and replacing it with a rescuing fragment flanked by Flp Recombination Targets ( FRTs ) . They found that inactivation of this allele at the A/P compartmental boundary in the center of the medial region , had no adverse effect on growth . Inactivation was deemed effective within the pouch because no immunoreactivity against pro-Dpp was detectable there . This led the authors to conclude that the central stripe of Dpp , from where the Dpp gradient originates , is not required for growth . To account for the continued growth observed in the absence of the Dpp stripe , they suggest that perhaps low level Dpp originating from the anterior compartment could suffice to promote growth in the pouch . Here we show , with two new validated conditional alleles , that deletion of the central stripe of Dpp is deleterious to growth . We then investigate and compare the requirements of Dpp within the pouch for growth versus patterning .
To generate means of reliably controlling Dpp activity , we devised two conditional dpp alleles , dppFRT-CA and dppFRT-PSB , that can be inactivated by Flp ( Figure 1B ) . In both cases , hemaglutinin ( HA ) tags were included to enable detection of endogenously produced mature Dpp . Flp was then expressed in various patterns to trigger excision of the essential exon . First , Dpp production was inactivated throughout the prospective wing either with rotund-gal4 and UAS-Flp in homozygous dppFRT-CA or with nubbin-Gal4 and UAS-Flp in homozygous dppFRT-PSB . No HA immunoreactivity ( HA-Dpp ) could be detected in the pouch from 96 hr after egg laying ( AEL ) onward ( Figure 1—figure supplement 1 ) , indicating efficient gene inactivation . HA ( i . e . Dpp ) was still detectable in the prospective hinge and notum , as expected since Gal4 activity was mostly confined to the pouch . Immunostaining with anti-Brinker showed that brinker expression was derepressed throughout the pouch ( Figure 1C–F and Figure 1—figure supplement 1 ) , confirming that Dpp signalling was eliminated there . Note that the down-regulation of Brinker around residual Dpp expression in the hinge did not extend into the pouch ( arrowhead in Figure 1—figure supplement 1D ) , suggesting that Dpp produced in the hinge has little effect on gene expression in the pouch . In both experiments , growth was markedly impaired , an effect that was quantified for dppFRT-CA by marking the edge of the pouch with anti-Homothorax ( anti-Hth ) ( Azpiazu and Morata , 2000; Casares and Mann , 2000 ) and measuring the enclosed area at 96 and 120 hr AEL ( Figure 1G–H ) . The pouch of experimental discs ( dppFRT-CA; rotund-Gal4 , UAS-Flp ) was significantly smaller than that of their wild-type siblings at equivalent stages . It was , however , not completely eradicated , perhaps because of delayed dpp inactivation or residual BMP signalling by glass bottom boat ( gbb ) ( Ray and Wharton , 2001 ) . Since the dppFRT-CA; rotund-Gal4 , UAS-Flp genotype is viable , the growth deficiency was also readily apparent in the adults that emerged ( Figure 1I , J ) . These results confirm that production of mature Dpp within the pouch is required for this tissue to grow and that Dpp originating from outside the pouch does not compensate . To assess whether Dpp is continuously required for wing growth , we first inactivated dppFRT-PSB at different times by Flp expressed from a hsp70-Flp transgene . Larvae were heat shocked at 48 , 72 and 96 hr AEL and wing imaginal discs were fixed at 120 hr AEL . Staining with anti-HA confirmed the efficiency of gene inactivation although occasional spots of residual HA-Dpp expressing cells could be detected ( Figure 2 ) . Inactivation of dpp at 48 and 72 hr AEL resulted in widespread derepression of brinker , confirming the impairment in Dpp signalling . Heat shocking at 48 and 72 hr AEL resulted in markedly reduced growth , while later excision ( 96 hr AEL ) had a milder effect . The relatively weak impact of heat shocks at 96 hr could be due to perdurance of Dpp or downstream events . Alternatively , any effect on growth might be hard to detect beyond this time because the growth rate of imaginal discs decreases with age ( Johnston and Sanders , 2003 ) . We conclude that the results of timed inactivation experiments show that Dpp must be continuously produced at least up to 96 hr , perhaps beyond , for the prospective wing to grow . 10 . 7554/eLife . 22546 . 006Figure 2 . Temporal requirement of Dpp for growth . ( A–D ) Imaginal discs at 120 hr AEL following inactivation of dppFRT-PSB by induction of hsp70-Flp at the indicated times . Inactivation of Dpp leads to ubiquitous derepression of brinker , with the exception of residual HA-Dpp expressing clones ( Representative examples are shown ) . ( E ) The total surface area of discs heat shocked at 24 , 48 , 72 , and 96 hr AEL was measured and normalised to the average surface area of control discs ( n = 4 for 24 hr AEL and n = 20 for the other time points ) . Area measurement for each time point was compared to the control area ( no heat shock ) with a one-way ANOVA . The p value was highly significant ( <0 . 0001 ) for every side by side comparison except for 96 hr AEL vs 120 hr AEL . Scale bar = 50 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 00610 . 7554/eLife . 22546 . 007Figure 2—source data 1 . Total disc area . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 007 Our findings so far indicate that Dpp must be produced in the pouch and during the 48–96 hr AEL period in order for the wing to grow . In this region , the major expression domain of Dpp is in a stripe along the A/P boundary ( Masucci et al . , 1990 ) . It is therefore expected that , as shown in Figure 3 , inactivation of Dpp specifically in this stripe would eradicate Dpp expression in the pouch and lead to growth impairment . Surprisingly , inactivation of dppFRT-TA with Flp expressed under the control of dpp-Gal4 ( dppFRT-TA dppBLK-Gal4 UAS-Flp ) was reported to have no adverse effect on growth ( Akiyama and Gibson , 2015 ) . In this genetic background , expression of salm and omb was disrupted , indicating that Dpp production was indeed impaired . It was therefore suggested that the stripe of Dpp expression may not be needed for growth because of the existence of another source of Dpp outside the stripe ( Akiyama and Gibson , 2015 ) . Indeed , long-term lineage tracing by G-TRACE suggests that progenitors of cells located anterior to the stripe could express Dpp ( Evans et al . , 2009 ) , at least at some point during development . To gain further information on the pattern of dpp expression in the wing pouch , we created a reporter line ( dppFRT-REP ) expressing the readily detectable marker CD8-GFP from the endogenous dpp locus . An excisable cassette expressing Dpp was included upstream of the CD8-GFP coding sequences ( Figure 3—figure supplement 1A ) to allow expression of functional Dpp during embryogenesis , which requires two functional alleles . Thus , during embryogenesis , CD8-GFP is not expressed and the two alleles produce wild-type Dpp . Only after expression of Flp does this allele act as a reporter , in the domain of Flp expression . Cassette excision was induced after embryogenesis with rotund-Gal4 and UAS-Flp , making CD8-GFP a reporter of dpp transcription in the pouch . At 72 , 96 and 120 hr AEL , GFP was only detectable along the A/P boundary ( Figure 3—figure supplement 1B–D ) . Thus , anterior to the stripe , the activity of the dpp promoter must either be very low or take place before 72 hr AEL . Therefore , it is unlikely to promote growth , at least after this time period . This conclusion spurred us to re-assess the role of the Dpp stripe in growth . 10 . 7554/eLife . 22546 . 008Figure 3 . Growth of the prospective wing requires the endogenous stripe of Dpp expression . ( A–F ) Inactivation of dppFRT-CA in the normal domain of Dpp expression ( with dppBLK-Gal4 UAS-Flp ) leads to depression of brinker and reduced growth ( shown here in discs fixed at 96 and 120 hr AEL ) . A zone of brinker repression can be seen in the prospective hinge around weak residual Dpp expression ( arrowhead in B , D ) . ( G ) Quantification of the pouch area ( area enclosed by the outer ring of Hth ) in control and experimental discs ( each dot/square represents a disc ) . Asterisks in panels G denote the statistical significance of the difference between experimental and control samples , using Student’s t-test , assuming equal variance and a Gaussian distribution . ( H–J ) Inactivation of dppFRT-TA in the normal domain of Dpp expression ( with dppBLK-Gal4 UAS-Flp ) only leads to Brinker derepression after growth has taken place . At earlier stages ( 90 and 96 hr AEL ) , Brinker is repressed , indicating residual Dpp signaling activity . Scale bar = 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 00810 . 7554/eLife . 22546 . 009Figure 3—source data 1 . Pouch area . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 00910 . 7554/eLife . 22546 . 010Figure 3—figure supplement 1 . A reporter inserted at the locus shows that dpp expression is confined to the stripe along the A/P boundary . ( A ) Diagram describing the dppFRT-REP allele , designed to act as a reporter following excision of the FRT-flanked HA-Dpp-containing cassette . B-D . Expression of CD8-GFP from dppFRT-REP within the pouch at 72 , 96 , and 120 hr AEL . Expression is only seen in the stripe . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 01010 . 7554/eLife . 22546 . 011Figure 3—figure supplement 2 . Comparison of various dpp-Gal4 strains . Three dppBLK-Gal4 lines ( kept separately in three laboratories ) were characterised molecularly , as described in Materials and methods . DNA sequencing of the genomic region flanking the transgene shows that the insertion site is the same for all three strains . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 01110 . 7554/eLife . 22546 . 012Figure 3—figure supplement 3 . Inactivation of dppFRT-PSB in the domain of dpp expression abolishes growth . ( A–D ) Inactivation of dppFRT-PSB in the normal Dpp expression domain with dppBLK-Gal4 UAS-Flp leads to derepression of brinker and growth defects . Note the repression of brinker at the posterior end of the disc ( arrowheads in C and D ) . ( E ) Quantification of the pouch area in control and experimental discs . Each dot/square represents a disc . The statistical difference ( p<0 . 0001 ) was calculated with a t-test , assuming equal variance and a Gaussian distribution . Scale bar = 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 012 We tested the role of the endogenous stripe of Dpp in wing growth by inactivating our conditional alleles with UAS-Flp and dpp-Gal4 . To enable comparison with the results of Akiyama and Gibson ( Akiyama and Gibson , 2015 ) , we chose the same dppBLK-Gal4 transgene ( Staehling-Hampton et al . , 1995 ) . This strain was generated many years ago and kept separately in our respective laboratories . We therefore characterised the different dppBLK-Gal4 lines by splinkerette PCR ( Potter and Luo , 2010 ) . Although the three stocks displayed sequence polymorphisms , they all carried the dppBLK-Gal4 transgene at the same location , confirming that they all originated from the same initial stock and could be used interchangeably ( Figure 3—figure supplement 2 ) . The dppBLK-Gal4 UAS-Flp combination was introduced in dppFRT-CA and dppFRT-PSB homozygotes to inactivate dpp within the stripe . In both cases , efficiency of excision was assessed by staining imaginal discs with anti-HA , which marks functional , mature Dpp in the unexcised alleles . At 96 hr AEL , HA immunoreactivity was eliminated from the whole disc , except in a previously characterised zone located outside of the pouch , in the posterior prospective hinge ( Foronda et al . , 2009 ) ( arrowhead in Figure 3B , D and Figure 3—figure supplement 3C , D ) . Such residual expression is reproducible and likely represents an area where dppBLK-Gal4 does not recapitulate the endogenous Dpp expression domain , as noted previously ( Akiyama and Gibson , 2015 ) . However , in the rest of the disc , including the whole pouch , the dppBLK-Gal4 UAS-Flp combination appeared to trigger efficient recombination and hence inactivation of dpp . Importantly , this was associated with derepression of brinker ( Figure 3B , D ) and a marked reduction ( 84% ) of pouch size at the end of the growth period ( Figure 3G and Figure 3—figure supplement 3E ) . The lack of growth noted above is in contrast with the report that dppFRT-TA dppBLK-Gal4 UAS-Flp imaginal discs attain a normal size and express Brinker throughout the pouch at 120 hr AEL ( Akiyama and Gibson , 2015 ) . This is in stark contradiction with the model that Dpp stimulates growth through repression of Brinker and that Brinker expression in the pouch is incompatible with growth ( Schwank et al . , 2008 ) . To investigate this apparent inconsistency , we re-examined dppFRT-TA dppBLK-Gal4 UAS-Flp imaginal discs , not only at 120 hr AEL but also at earlier stages . We confirmed that the discs attain a normal size and express Brinker at 120 hr AEL ( Figure 3J ) . However , at 90 and 96 hr AEL , during the growth phase , Brinker was repressed within the pouch ( Figure 3H , I ) , a clear indication that Dpp signalling is still active at these stages . We suggest that , in this genotype , Dpp signalling is eradicated but only after most growth has taken place . These results suggest that the TA allele may not be as readily inactivated by dppBLK-Gal4 UAS-Flp as the PSB and CA alleles . The efficacy of gene inactivation was assessed for all three alleles by expressing Flp from a hs-Flp transgene under identical heat-shock conditions and measuring brinker expression by qRT-PCR . The results show that brinker expression was derepressed in all cases but less so with dppFRT-TA than with dppFRT-PSB and dppFRT-CA ( Figure 4A ) . These results indicate that dppFRT-TA is less readily excised than the other two alleles . Allele ‘excisability’ was also assessed functionally by measuring imaginal disc size following heat-shock-induced expression of Flp at different times ( Figure 4B–K ) . Growth was impaired in a more pronounced manner with dppFRT-PSB and dppFRT-CA than with dppFRT-TA , especially with a heat shock at 72 hr AEL , a time when inactivation of Dpp signalling has a strong effect on growth ( see quantification in Figure 4K ) . Therefore , molecular and functional assays suggest that the dppFRT-TA allele may not be as readily inactivated as our alleles , perhaps because of differences of sequence context around the FRT sites . We note that one of the FRTs of dppFRT-TA is flanked by a LoxP site , which could conceivably impair recombination . In any case , our results show that precluding striped expression of Dpp along the A/P boundary does interfere with wing growth . 10 . 7554/eLife . 22546 . 013Figure 4 . Inactivation efficiency for three conditional alleles of dpp . ( A ) Efficiency of inactivation for dppFRT-PSB , dppFRT-CA and dppFRT-TA by Flp expressed from hsp70-Flp induced at 102 hr AEL . Level of brinker mRNA , normalized to that in non-heat-shocked controls , was assessed by qRT-PCR at 120 hr AEL . Each bar shows average mRNA level +/- SEM . A two-way ANOVA test showed statistically different brinker expression between dppFRT-PSB and dppFRT-TA ( p=0 . 0041 ) as well as between dppFRT-CA and dppFRT-TA ( p<0 . 0001 ) . ( B–J ) Imaginal discs of the same genotypes were fixed and stained with anti-Brinker at 120 hr AEL , following a heat shock at 72 or 96 hr AEL or in the absence of heat shock ( control ) . As can be seen , the 72 hr heat shock did not impair growth as much in dppFRT-TA as it did in dppFRT-PSB and dppFRT-CA . ( K ) Quantification of disc surface area ( normalized to average surface area of control discs ) at 120 hr AEL for the nine conditions shown in panels ( B–J ) . Each bar represents data for 10 discs . Asterisk denotes statistical significance , as assessed by a two-way ANOVA test ( p=0 . 029 ) . Scale bar = 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 01310 . 7554/eLife . 22546 . 014Figure 4—source data 1 . Primers for qPCR . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 01410 . 7554/eLife . 22546 . 015Figure 4—source data 2 . Normalised Brk mRNA levels . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 01510 . 7554/eLife . 22546 . 016Figure 4—source data 3 . Total disc area . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 016 Our results so far show that Dpp expression from the endogenous stripe is required for the growth of wing precursors . They do not address , however , whether a spatial or temporal gradient is necessary . To investigate this question , we took advantage of our conditional alleles to eliminate endogenous dpp expression while at the same time inducing uniform constant expression from a transgene . The rotund-Gal4 and UAS-Flp combination was used to simultaneously excise the FRT cassettes of dppFRT-CA and Tubα1-FRT-f+-FRT-dpp , a transgene previously shown to trigger intermediate signalling activity , sufficient to activate omb but not salm expression ( Zecca et al . , 1995 ) . As expected , in the resulting ‘rescued’ discs , Omb was expressed uniformly , although at a reduced level and Brinker was repressed . ( Figure 5A–D ) . However , pMad immunoreactivity was at the low level normally seen in the lateral region ( Figure 5E , F ) , suggesting that the level of signalling achieved by Tubα1-dpp is similar to that present far from the normal stripe of Dpp . About half the discs of this genotype reached an approximately normal size at the end of the third instar while the other half overgrew slightly ( as is the case for the disc shown in Figure 5B ) . Sustained growth was confirmed by assessing proliferation rates with anti-pH3 staining of discs dissected from late larvae crawling in the food . As shown in Figure 5I–L , ‘rescued’ and wild-type discs proliferated at approximately the same rate while discs lacking dpp proliferated at a lower rate in the pouch area . This result suggests that uniform and constant Dpp signalling is sufficient to promote growth in the pouch . It also suggests that the level of signalling needed to promote growth is much lower than that needed to produce peak p-Mad immunoreactivity . 10 . 7554/eLife . 22546 . 017Figure 5 . Low level uniform Dpp expression suffices for growth but not patterning . ( A–H ) Comparison of wild-type discs ( A , C , E , G ) to discs lacking endogenous Dpp in the pouch and expressing weak uniform Dpp instead ( B , D , F , H ) . Uniform Dpp allows discs to reach a relatively normal size , although with a variably deformed shape ( representative examples are shown ) . Omb is expressed in experimental discs , an indication of active Dpp signaling , but at a relatively lower level than in control discs ( samples shown in A and B were stained and imaged under identical conditions ) . Note also the repression of Brinker and the loss of pMad expression in experimental discs . In contrast to their relatively normal size , experimental discs show abnormal vein patterning , with only two vein territories recognizable instead of the normal five ( marked by the absence of DSRF immunoreactivity ) ( G , H ) . ( I–K ) pH3 immunoreactivity shows that , in control and rescued discs , proliferation is sustained seemingly normally ( I , J ) while proliferation in the pouch of non-rescued discs is depressed ( K ) Quantification show in L is based on 14 rescued discs , 9 controls and 11 unrescued discs . Statistical significance was assessed with a Student’s t-test , assuming equal variance and a Gaussian distribution . Mitotic density ( pH3 spots/area ) was determined for each individual disc using a code written in Fiji ( see Figure 5—source data 1 ) . ( M–O ) Wings from the above genotypes . A majority of examined experimental wings ( 15/20 ) had excess vein tissue ( O ) while the remainder ( 5/20 ) had one central vein around the position of the A/P boundary and another ( not visible ) along the margin ( I ) . Each micrograph is representative of 7–10 discs . Scale bar = 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 01710 . 7554/eLife . 22546 . 018Figure 5—source data 1 . PH3 density . DOI: http://dx . doi . org/10 . 7554/eLife . 22546 . 018 Since veins form at stereotypical positions in Drosophila wings , they provide a convenient marker of patterning . The five longitudinal veins are distinctly specified by various signalling pathways ( reviewed in [Blair , 2007] ) . Most relevant for this paper , the positioning of veins 2 and 5 is dependent on Dpp signalling . Prospective veins can be recognised in late imaginal discs as zones of DSRF ( Drosophila serum response factor ) repression ( Montagne et al . , 1996; Nussbaumer et al . , 2000 ) . Staining with anti-DSRF showed that the prospective vein pattern was markedly disrupted in ‘rescued’ discs ( Figure 5G , H ) , with only two zones of repressed DSRF remaining , one around the D/V boundary , where vein 1 normally forms under the control of Wingless ( Couso et al . , 1994; Rulifson and Blair , 1995 ) , and one around pro-veins 3 and 4 , which are specified by Hedgehog in the wild type ( Blair , 2007 ) . The areas of DSRF repression corresponding to veins 2 and 5 were conspicuously missing . Because some of the ‘rescued’ larvae survived to adulthood , we were able to further assess , in adult wings , the extent of growth and patterning that uniform Dpp promotes . A majority of these wings appeared to be made entirely of crumpled vein material ( Figure 5O ) , which made it difficult to assess size . This phenotype can be explained by the vein-specifying role of Dpp in pupal wings ( Sotillos and de Celis , 2006 ) . Nevertheless , a minority of ‘rescued wings’ were remarkably well formed ( Figure 5N ) , perhaps because they experienced lower Dpp signalling at the pupal stage , below the threshold for vein specification . In these wings , vein patterning was disrupted , but reproducibly so , with a broad swath of vein tissue forming near the A/P boundary . Crucially , these wings reached a remarkably large size ( compare Figure 5M and N ) . This result suggests that uniform , low level Dpp signalling promotes near-normal growth although this is not adequate for patterning .
Dpp behaves as a classic morphogen in wing imaginal discs of Drosophila . It is produced from a stripe of cells along the A/P boundary and spreads from there to activate the nested expression of target genes , which in turn position longitudinal veins . In addition to providing patterning information in the prospective wing , Dpp also promotes growth via repression of brinker . How graded Dpp signalling leads to homogenous proliferation has been the subject of discussion but until recently , there has been general agreement that the stripe of Dpp is required for growth . This basic tenet was recently challenged with a conditional dpp allele that can be inactivated in time and space by Flp ( here referred to as dppFRT-TA ) . Inactivation in the normal domain of Dpp expression , with Flp driven by a disc-specific dpp regulatory element , was reported to have minimal impact on growth ( Akiyama and Gibson , 2015 ) . The authors suggested that Dpp expressed from a source in the anterior half of the pouch could suffice to sustain growth . Consistent with this suggestion , inactivation of dpp throughout the pouch with nubbin-Gal4 UAS-flp led to strong growth reduction ( Akiyama and Gibson , 2015 ) , an observation that we confirmed with our conditional alleles ( dppFRT-CA and dppFRT-PSB ) and two pouch-specific sources of Flp . However , inactivation of our alleles with dppBLK-Gal4 UAS-Flp ( the same source of Flp used by Akiyama and Gibson , 2015 ) led to a severe impairment in growth ( Figure 3 and Figure 3—figure supplement 3 ) , in contrast to the finding with dppFRT-TA . Our analysis of brinker expression during the growth period in the various mutant backgrounds allows us to reconcile the apparent discrepancy between our data and those of Akiyama and Gibson ( 2015 ) . We suggest that our alleles ( dppFRT-CA and dppFRT-PSB ) are more readily inactivated than the one generated by Akiyama and Gibson ( 2015 ) ( dppFRT-TA ) . Thus , in the dppFRT-TA; dpp-Gal4 UAS-Flp genotype , cells expressing Dpp within the stripe would linger long enough to provide sufficient signalling activity for brinker repression ( Figure 3H , I ) and hence growth . As time goes on , these lingering cells would progressively undergo excision so that at the end of third instar , no signalling would remain , explaining the widespread derepression of brinker seen at the late 120 hr AEL stage ( Akiyama and Gibson , 2015 ) . Since , with our conditional allele , inactivation of Dpp in the endogenous stripe leads to growth impairment , we conclude that , during normal development , this source of Dpp is needed for growth , although as discussed below , this can be overcome with low-level exogenously expressed Dpp . How does the Dpp gradient emanating from the Dpp stripe promote growth ? Our finding that uniformly expressed Dpp is sufficient for growth suggests that a spatial gradient of signalling is not required . Moreover , the tubulin promoter , which was used to drive uniform expression , is expected to be constant over time . Therefore , our result could be taken as evidence against the model that growth depends on continuously rising signalling activity ( Wartlick et al . , 2011b ) , although it could be argued that even under a condition of uniform expression , signalling could rise if Dpp became more stable over time . Nevertheless , we prefer the simple model whereby , in the prospective wing , Dpp signalling over a threshold would be permissive for growth . The level of this threshold is still to be precisely measured . In the experiment illustrated in Figure 5 , growth rescue by uniform Dpp in the pouch correlates with repression of brinker , consistent with the growth equalization model ( Schwank et al . , 2008 ) . Although Akiyama and Gibson showed that dppFRT-TA dppBLK-Gal4 UAS-Flp discs express brinker uniformly at 120 hr AEL ( Akiyama and Gibson , 2015 ) , as we have shown ( Figure 3H , I ) , brinker only becomes derepressed in this genotype after growth has occurred . The observations that Dpp expression from the Tubα1-dpp transgene ( Figure 4 ) or residual Dpp from a few cells within the stripe ( as we propose is occurring in the dppFRT-TA dppBLK-Gal4 UAS-Flp background ) , stimulate growth suggest that relatively low level signalling suffices for growth throughout the pouch ( i . e . the prospective wing ) . As we have shown , this level of signalling is below that needed to produce substantial pMad immunoreactivity but higher than that needed to repress brinker . Better tools to tune the level of Dpp signalling will be needed to assess the relationship between signalling activity and growth at all stages . Our results have significantly clarified the spatial requirement of Dpp . As we have shown , Dpp must originate from the pouch for this tissue to grow: in several experimental conditions ( Figure 3B , D , Figure 1—figure supplement 1C–F , Figure 3—figure supplement 3C–D ) , Dpp produced outside the pouch could not overcome the absence of Dpp within the pouch . We cannot discriminate at this point whether the boundary between these tissues acts as a barrier to the spread of Dpp or whether these sources of Dpp are too weak to have an impact in the pouch . In any case , these observations confirm our assertion that growth is normally sustained by Dpp produced at the A/P boundary . Dpp signalling above a relatively low threshold is permissive for growth within the pouch throughout wing development . For this activity , the signalling gradient is irrelevant . By contrast , the signalling gradient is essential for patterning as it specifies the domains of salm and omb expression and thus the positions of veins . Thus , the dual role of Dpp in growth and patterning requires that it is expressed in a stripe . Late inactivation of Dpp impairs patterning , suggesting that the gradient information could be read at the end of the growth period . It remains to be determined how the two processes - growth and patterning - are coordinated to ensure the reproducible formation of the adult wing .
Two conditional dpp alleles , illustrated in Figure 1B , were created for this study . In one allele , dppFRT-CA , the exon encoding mature Dpp was deleted and replaced with the same sequence flanked by FRT and modified so that it would encode two HA tags downstream of the three furin cleavage sites . For the other allele , dppFRT-PSB , a portion of the first coding exon including the signal sequence was replaced by a FRT-flanked fragment encoding full-length HA-tagged Dpp ( 3xHA tag ) . See Source data 1 for the full sequence . Both alleles are homozygous viable with no apparent morphological phenotype . Both are fully inactivated by Flp-mediated excision of the FRT cassette . We also generated a reporter allele , dppFRT-REP , by inserting the DNA fragment shown in Figure 3—figure supplement 1 in the attP site of the deletion allele used to generate dppFRT-CA ( see Figure 1B ) . In this construct , CD8-GFP coding sequences are located downstream of an HA-Dpp excisable cassette . See Source data 1 for the full sequence . The dppFO allele ( Akiyama and Gibson , 2015 ) , referred to here as dppFRT-TA was obtained from Matt Gibson ( Stower’s Institute ) . Tubα1-FRT-f+-FRT-Dpp was described previously ( Zecca et al . , 1995 ) . The other strains used for this study were obtained from the Bloomington stock centre . They include rotund-Gal4 ( rn-Gal4 ) , nubbin-Gal4 ( nub-Gal4 ) , tubulin-Gal80ts ( II ) ( tub- Gal80ts ) , UAS-Flp ( X ) , hs-Flp ( X ) and hs-Flp ( III ) . For Splinkerette PCR , DNA from single flies was isolated and digested with BglII . Afterwards , it was amplified following the Splinkerette PCR protocol for Drosophila melanogaster ( Potter and Luo , 2010 ) . Three dppBLK-Gal4 lines ( which were kept in three labs for extended time ) were analysed: dppBLK-TA-Gal4 ( Akiyama and Gibson , 2015 ) , dppBLK-CA-Gal4 ( kept in London ) and dppBLK-PSB-Gal4 ( kept in Zürich ) . The following primers were used: SPLNK#1 + 5’SPLNK#1-GAWB for the first PCR round and SPLNK#2 + 5’SPLNK#2-GAWB for the second PCR round ( see Figure 4—source data 1 for primer sequences ) . The PCR products were isolated on a 2% agarose gel and sequenced with the primer 5’SPLNK-GAWB-SEQ . The size of the fragment differed for the three strains , probably because of polymorphism that accumulated during maintenance of the stocks . However , sequencing of the fragment showed that in all three cases , the insertion sites were identical , in the 5’UTR of CG6896 ( MYPT-75D ) . Third instar larvae were heat shocked for 30 min at 102 hr AEL and wing discs were dissected in PBS at 120 hr AEL , before being transferred to PBS-Tween 20 . Samples were spun down , and the pellets were snap-frozen in liquid nitrogen , stored at −80°C or processed immediately . RNA from the dissected discs was extracted with the Macherey-Nagel NucleoSpin RNA isolation kit , and cDNA was obtained with the Roche Transcriptor high fidelity cDNA synthesis kit . Quantitative PCR was performed in triplicates using the MESA Green qPCR Mastermix Plus for SYBR assay . All measurements were normalized to actin-5C , alpha-tubulin and TATA box binding protein mRNA levels . See Figure 4—source data 1 for primer squences . Imaginal discs were fixed in 4% paraformaldehyde for approximately 30 min before immunofluorescence staining . The following antibodies were used: α-Brinker ( Aurelio Telemann , EMBL; 1/500 ) , α-Brinker ( Hillary Ashe , University of Manchester; 1/500 ) ; α-HA ( Cell Signalling; 1/3000 or 1/500 ) , α-Hth ( Richard Mann , Columbia University; 1/500 ) , α- Phospho-Histone H3 ( Abcam; [HTA28] phospho S28; 1/500 ) , α− Phospho-Smad1/5 ( Cell Signalling; 41D10 #9516; 1/100 ) α-DSRF ( Active Motif; Cat 39093 Lot 03504001; 1/500 ) , α-Omb ( Gert Pflugfelder , University of Mainz; 1/500 ) , and Alexa-conjugated secondary antibodies ( Thermo Scientific Waltham , MA; 1/500 ) . Images were acquired either with a Zeiss LSM710 or a Leica SP5 confocal microscope . Every experiment was repeated at least once . All data were analysed using Fiji ( ImageJ ) and GraphPad Prism . Error bars denote standard deviation ( SD ) unless stated otherwise , and the statistical tests used to evaluate significance are described in the figure legends . Statistical significance is denoted as follows: ns: p>0 . 05 , *p≤0 . 05 , **p≤0 . 01 , ***p≤0 . 001 , ****p≤0 . 0001 . | From the wings of a butterfly to the fingers of a human hand , living tissues often have complex and intricate patterns . Developmental biologists have long been fascinated by the signals – called morphogens – that guide how these kinds of pattern develop . Morphogens are substances that are produced by groups of cells and spread to the rest of the tissue to form a gradient . Depending on where they sit along this gradient , cells in the tissue activate different sets of genes , and the resulting pattern of gene activity ultimately defines the position of the different parts of the tissue . Decades worth of studies into how limbs develop in animals from mice to fruit flies have revealed common principles of morphogen gradients that regulate the development of tissue patterns . Morphogens have been shown to help regulate the growth of tissues in a number of different animals as well . However , how the morphogens regulate tissue size and what role their gradients play in this process remain topics of intense debate in the field of developmental biology . In the developing wing of a fruit fly , a morphogen called Dpp is expressed in a thin stripe located in the centre and spreads to the rest of the tissue to form a gradient . Bosch , Ziukaite , Alexandre et al . have now characterised where and when the Dpp morphogen must be produced to regulate both the final size of the fly’s wing and the number of cells the wing eventually contains . The experiments involved preventing the production of Dpp in the developing wing in specific cells and at specific stages of development . This approach confirmed that Dpp must be produced in the central stripe for the wing to grow . Matsuda and Affolter and , independently , Barrio and Milán report the same findings in two related studies . Moreover , Bosch et al . and Barrio and Milán also conclude that the gradient of Dpp throughout the wing is not required for growth . Further work will be needed to explain how the Dpp signal regulates the growth of the wing . The answer to this question will contribute to a better understanding of the role of morphogens in regulating the size of human organs and how a failure to do so might cause developmental disorders . | [
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] | 2017 | Dpp controls growth and patterning in Drosophila wing precursors through distinct modes of action |
Defense against infection by Mycobacterium tuberculosis ( Mtb ) is mediated by CD4 T cells . CCR2+ inflammatory monocytes ( IMs ) have been implicated in Mtb-specific CD4 T cell responses but their in vivo contribution remains unresolved . Herein , we show that transient ablation of IMs during infection prevents Mtb delivery to pulmonary lymph nodes , reducing CD4 T cell responses . Transfer of MHC class II-expressing IMs to MHC class II-deficient , monocyte-depleted recipients , while restoring Mtb transport to mLNs , does not enable Mtb-specific CD4 T cell priming . On the other hand , transfer of MHC class II-deficient IMs corrects CD4 T cell priming in monocyte-depleted , MHC class II-expressing mice . Specific depletion of classical DCs does not reduce Mtb delivery to pulmonary lymph nodes but markedly reduces CD4 T cell priming . Thus , although IMs acquire characteristics of DCs while delivering Mtb to lymph nodes , cDCs but not moDCs induce proliferation of Mtb-specific CD4 T cells .
Inflammatory monocytes ( IMs ) express Ly6c , CD11b and CCR2 , a chemokine receptor that facilitates emigration of IMs from the bone marrow ( Serbina and Pamer , 2006 ) . Although IMs make important contributions to innate immune defense during infection , recent studies also implicate IMs in priming of CD4 T cell responses during fungal , viral and parasitic infections ( Traynor et al . , 2000; Leon et al . , 2007; Edismo et al . , 2009; Hohl et al . , 2009; Rivera et al . , 2011 ) . Experiments using CCR2-deficient mice also suggested that monocytes contribute to T-cell-mediated defense against Mtb . IMs , however , can also serve as permissive host cells for Mtb in later stages of infection , suggesting that IMs can both restrict and enhance Mtb infection ( Antonelli et al . , 2010 ) . Because recruitment of IMs in CCR2-deficient mice is defective throughout early , intermediate and late stages of infection , it has not been possible to specifically define the role of monocytes at different times during infection ( Peters et al . , 2001; Scott and Flynn , 2002; Peters et al . , 2004 ) . Therefore , we used CCR2-DTR mice to transiently deplete IMs and other CCR2-expressing cells during discrete stages of Mtb infection .
Administration of DT to these mice depletes inflammatory monocytes from the lung and mLN ( Figure 1—figure supplement 1 ) . CCR2-DTR mice that received three doses of DT surrounding the time of aerosol infection did not differ from control mice in terms of mycobacterial growth in the lungs ( Figure 1A ) , indicating that CCR2-expressing cells are not required for the establishment of pulmonary infection following Mtb inhalation . Previous studies have demonstrated that CD4 T-cell priming occurs approximately 7–10 days following inhalational challenge ( Wolf et al . , 2007; Gallegos et al . , 2008 ) . Depletion of CCR2-expressing cells 7 , 9 and 11 days following infection resulted in a threefold increase in the number of live Mtb in the lungs on day 15 ( Figure 1B ) . To further investigate this finding , we depleted monocytes 7–11 days following infection and quantitatively cultured Mtb from lungs and lymph nodes 12 days following infection . Although monocyte-depleted and control mice had similar Mtb CFUs in lungs at this time ( Figure 1C ) , Mtb CFUs were markedly reduced in mLNs of mice treated with DT ( 1D ) . 10 . 7554/eLife . 01086 . 003Figure 1 . Depletion of inflammatory monocytes during the second week of M . tuberculosis infection abrogates transport of live bacteria to mLNs and increases pulmonary bacterial burden . ( A ) CFU plots from the lungs of CCR2-DTR mice receiving DT or PBS on days −1 , 0 and 1 and harvested on day 7 . ( B ) CFU plots of the lungs of CCR2-DTR mice receiving DT or PBS on days 7 , 9 and 11 and harvested on day 15 post infection . CFU counts from the lungs ( C ) and mLN ( D ) of CCR2-DTR mice given DT treatment on days 7 , 9 and 11 and harvested on day 12 . Each dot represents an individual mouse . Error bars denote SEM . Data are representative of two independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 01086 . 00310 . 7554/eLife . 01086 . 004Figure 1—figure supplement 1 . DT administration depletes IMs from the lungs and mLNs of infected mice . CCR2-DTR mice received PBS or DT on days 7 , 9 and 11 post infection and were harvested on day 12 when flow cytometry was performed to quantify the IM population in the lungs ( A ) and mLN ( B ) . Data are representative of three independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 01086 . 004 We reasoned that reducing live M . tuberculosis transport from the lung to the draining lymph node following monocyte depletion on days 7–11 post infection might delay priming of Mtb-specific CD4 T cells . To address this , we adoptively transferred naïve , Thy1 . 1-marked , ESAT-6-specific CD4 T cells ( C7 T cells ) into CCR2-DTR mice prior to aerosol Mtb infection ( Gallegos et al . , 2008 ) . Monocytes were depleted 7–11 days following infection , and mediastinal lymph nodes and lungs were harvested 12 and 21 days following infection . In comparison to mLNs obtained from PBS treated mice , mLNs obtained from monocyte-depleted mice were smaller , with significantly reduced total and CD4 T cell numbers ( Figure 2A ) . C7 T cells had proliferated in mLNs of control mice 12 days after infection but had not expanded in monocyte-depleted mice ( Figure 2B , D ) . Reduced C7 T cell proliferation in mLNs resulted in reduced C7 T cell frequencies in lungs of monocyte-depleted mice 21 days following infection ( Figure 2C , D ) . Transfer of naïve C7 T cells to control mice resulted in a four to fivefold reduction of Mtb CFUs in lungs , suggesting that supplementing the endogenous T cell repertoire with additional naive , ESAT-6-specific CD4 T cells enhances immune defense against Mtb infection ( Figure 2E ) . Depletion of monocytes abrogated enhanced protection resulting from addition of naive C7 T cells ( Figure 2E ) . 10 . 7554/eLife . 01086 . 005Figure 2 . Priming of ESAT-6-specific CD4 T cells is reduced in CCR2-DTR mice depleted during the second week of infection . ( A ) Total cell counts and CD4 T cell counts in mLNs of mice on day 12 post infection after treatment with DT or PBS on days 7 , 9 and 11 . CCR2-DTR mice received naive ESAT-6-specific C7 T cells the day before infection and were treated with DT or PBS on days 7 , 9 and 11 , mLNs ( B ) were harvested on day 12 , lungs ( C ) were harvested on day 21 and ESAT-6-specific C7 T cells were visualized . ( D ) Cumulative data from experiments shown in ( B ) and ( C ) showing the total number of ESAT-6-specific C7 T cells in mLNs and lungs . ( E ) CFU plots of lungs of mice that received naive EAST-6-specific C7 T cells and were treated with DT or PBS , as indicated , and were harvested on day 21 post infection . Each dot represents an individual mouse . Five mice per group are included in the bar graphs shown in ( A ) . Error bars denote SEM . Data are representative of three independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 01086 . 005 Although IMs represent the most prevalent CCR2-expressing cell population , subsets of NK cells , dendritic cells and CD4 T cells can also express CCR2 . To exclude the possibility that depletion of a non-monocyte cell population resulted in loss of Mtb transport from lungs to mLNs , we purified IMs from CCR2-GFP mice by sorting GFP+ cells that did not express NK1 . 1 , CD4 , FLT3 and C-kit . This strategy did not require antibody staining for trafficking molecules ( CCR2 and CD11b ) and eliminated NK cells , CD4 T cells , dendritic cell progenitors and hematopoietic stem cells , yielding a highly purified ( >99% pure ) population of IMs ( Figure 3—figure supplement 1 ) . We adoptively transferred 2 × 106 IMs on day 8 and 10 of infection while depleting CCR2+ cells on days 7 , 9 and 11 . The transferred IMs were detectable in lungs and mLNs and down-regulated expression of CCR2 and CD11b and up-regulated expression of MHC class II , CD11c and CD103 during trafficking from the lung to mLNs ( Figure 3A , B ) . Administration of IMs to monocyte-depleted CCR2-DTR mice enhanced live Mtb transport to mLNs , as detected 12 days following infection ( Figure 3C ) . Infusion of IMs to WT mice did not enhance delivery of Mtb to mLNs , suggesting that IMs are not limiting during the first 2 weeks of Mtb infection . Adoptive transfer of IMs to monocyte-depleted CCR2-DTR recipient mice rescued priming of C7 T cells ( Figure 3D , E ) . 10 . 7554/eLife . 01086 . 006Figure 3 . Adoptive transfer of highly purified inflammatory monocytes can rescue antigen transport and CD4 T cell priming in DT treated CCR2-DTR mice . CCR2-DTR mice were infected and treated with DT or PBS on days 7 , 9 and 11 and received highly purified CD45 . 1+ IMs on days 8 and 10 . ( A ) Mice were euthanized on day 12 post infection and flow cytometry was performed on lungs and mLNs to track the engraftment of adoptively transferred IMs . ( B ) The expression of cell surface markers CD11b , MHC II , CDIIc and CD103 by adoptively transferred IMs in the lung and mLN was determined . ( C ) CFU plots from day 12 mLNs of CCR2-DTR mice rescued with IMs . Dots marked in red represent mice that received double sorted IMs of greater than 99% purity . ( D ) CCR2-DTR mice received a dose of naive ESAT-6-specific C7 T cells the day before infection and were treated with DT on days 7 , 9 and 11 and received purified IMs or PBS on days 8 and 10 . Lungs were harvested on day 15 and ESAT-6-specific C7 T cells were visualized . ( E ) Cumulative data of experiment shown in ( D ) showing the total number of ESAT-6-specific C7 T cells in day 15 lungs . ( F ) CCR2-DTR mice were depleted on days 7 , 9 and 11 and received CCR2 WT or CCR2 KO IMs on days 8 and 10 . Day 12 mLNs were harvested for CFU counts . Each dot represents an individual mouse . Error bars denote SEM . Data are representative of two independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 01086 . 00610 . 7554/eLife . 01086 . 007Figure 3—figure supplement 1 . Sorting of IMs from CCR2-GFP mice . FLT3- , cKit- , GFP+ cells were sorted from the bone marrow of CCR2-GFP mice . FACS plots show pre- and post sorting purity of the cells . Data are representative of two independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 01086 . 007 While the CCR2 chemokine receptor is required for egress of IMs from bone marrow ( Serbina and Pamer , 2006 ) , the role of CCR2 in trafficking of IMs into infected lungs or from the sites of lung infection to draining lymph nodes remains incompletely resolved . To determine whether CCR2 is required for trafficking to lungs and mLNs during Mtb infection , we purified IMs from CCR2-deficient , CCR2-GFP mice for adoptive transfer into Mtb-infected , CCR2-DTR recipient mice that were monocyte depleted between 7 and 11 days following infection . Quantitative culture 12 days following Mtb infection demonstrated that wild-type and CCR2-deficient IMs were equivalent at delivering Mtb to mLNs , indicating that CCR2-signaling does not contribute to trafficking of IMs between these sites ( Figure 3F ) . Although IMs acquired characteristics of dendritic cells and are essential for Mtb transport from infected lungs to mLNs , it remained unclear whether IMs directly primed Mtb-specific CD4 T cells . Therefore , we transferred C7 T cells into MHC class II-deficient CCR2-DTR mice , depleted monocytes 7–11 days following infection and adoptively transferred MHC class II-expressing purified monocytes on days 8 and 10 following infection . In contrast to our previous experiments with MHC class II-expressing recipients , C7 T cells were not primed in MHC class II-deficient recipients of MHC class II-expressing monocytes ( Figure 4A ) . Adoptively transferred IMs trafficked to mLNs ( Figure 4—figure supplement 1 ) and , as demonstrated in the previous experiments , corrected Mtb delivery to draining lymph nodes to levels seen in mice that had not been monocyte-depleted ( Figure 4B ) . Thus , defective antigen transport to the draining mLN could not explain the lack of CD4 T cell proliferation . In a reciprocal experiment , we transferred MHC class II-deficient IMs into Mtb-infected , MHC class II-expressing CCR2-DTR mice on the same schedule described above and quantified the C7 T cell response . Figure 4C demonstrates that the magnitude of the C7 T cell response was similar in monocyte-depleted mice rescued with either MHC class II-expressing or deficient monocytes . Taken together , these experiments indicate that IMs primarily serve as carriers of Mtb , delivering live bacteria to mLNs and enabling but not directly priming CD4 T cells . 10 . 7554/eLife . 01086 . 008Figure 4 . IMs do not prime CD4 T cells directly . CCR2-DTR mice that were either MHC class II KO or WT received naive ESAT-6-specific C7 T cells the day before infection and were treated with DT or PBS on days 7 , 9 and 11 and received purified IMs on days 8 and 10 . ( A ) ESAT-6-specific C7 T cells in lungs harvested on day 15 . ( B ) Mtb CFUs from mLNs of mice harvested on day 12 . ( C ) CCR2-DTR mice received naive ESAT-6-specific C7 T cells the day before infection and were treated with DT or PBS on days 7 , 9 and 11 and received either MHCII WT or MHCII KO IMs on days 8 and 10 . Flow cytometry was performed on lungs harvested on day 15 . ( D ) zDC-DTR mice received naive ESAT-6-specific C7 T cells the day before infection and cDCs were depleted on day 7 , 9 and 11 , and the number of ESAT-6-specific C7 T cells was determined in mLNs harvested on day 12 . ( E ) The number of CFU in mLNs of PBS and DT treated zDC-DTR mice . ( F ) FLT3LKO mice received naive ESAT-6-specific C7 T cells the day before infection and lungs were harvested on day 15 to quantify the ESAT-6-specific C7 T cells . Each dot represents an individual mouse . Error bars denote SEM . Data are representative of two independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 01086 . 00810 . 7554/eLife . 01086 . 009Figure 4—figure supplement 1 . Adoptively transferred IMs traffick to the mLNs of MHC Class II KO mice . MHC class II KO/CCR2-DTR mice infected with Mtb and depleted on days 7 , 9 , and 11 received a dose of purified IMs on days 8 and 10 and were harvested on day 12 when the total number of transferred IMs per mLN was quantified . Each group contains five mice . Data are representative of two independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 01086 . 00910 . 7554/eLife . 01086 . 010Figure 4—figure supplement 2 . Priming of ESAT-6-specific C7 T cells is reduced in zDC-DTR mice . zDC-DTR mice received naive ESAT-6-specific C7 T cells the day before infection and cDCs were depleted on days 7 , 9 and 11 . On day 12 mLNs were harvested to quantify the number of ESAT-6-specific C7 T cells . Data are representative of two independent experiments containing four mice per group . DOI: http://dx . doi . org/10 . 7554/eLife . 01086 . 01010 . 7554/eLife . 01086 . 011Figure 4—figure supplement 3 . Mtb traffick to the mLN of FLT3LKO mice is unimpaired . FLT3LKO mice were infected with Mtb and mLNs were harvested on day 12 for CFU analysis . Each dot represents an individual mouse . Data are representative of two independent experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 01086 . 011 Classical DCs in lymph nodes and their progenitors express the cDC-specific transcription factor zbtb46 , whereas IMs do not . To determine whether classical DCs are required for Mtb-specific CD4 T cell priming , we used zDC-DTR mice , in which DT administration leads to a loss of classical DC populations ( Meredith et al . , 2012a; Meredith et al . , 2012b ) . We transferred C7 T cells into zDC-DTR mice , infected the mice with Mtb and administered DT on days 7–11 following infection . Depletion of classical DCs resulted in markedly reduced C7 T cell expansion ( Figure 4D , Figure 4—figure supplement 2 ) but did not reduce the number of live Mtb in mLNs ( Figure 4E ) . FLT3L-deficient mice , which lack classical DCs but not IMs , also have markedly reduced CD4 T cell responses following Mtb infection ( Figure 4F ) despite the presence of Mtb in the mLN ( Figure 4—figure supplement 3 ) . These results indicate that classical DCs , while dispensable for trafficking of live Mtb from infected lungs to mLNs , are essential for CD4 T cell priming . These experiments have refined our understanding of the role of IMs during the initiation of adaptive immune defense against M . tuberculosis infection . During the second week of murine infection , IMs play the critical role of delivering live Mtb to draining mLNs , an indispensable step for Mtb-specific CD4 T cell priming . Our results extend previous reports that IMs influence CD4 T cell priming ( Leon et al . , 2007; Hohl et al . , 2009; Nakano et al . , 2009; Rivera et al . , 2011 ) by demonstrating that IMs serve as transporters of live bacteria from the site of infection to the site of T cell priming . In contrast to reports from other disease models demonstrating that IMs differentiate into DCs and then directly prime CD4 T cells ( Cheong et al . , 2010; Zigmond et al . , 2012 ) our adoptive transfer studies using MHC class II KO mice reveal that IMs , despite acquiring characteristics of DCs , do not prime Mtb-specific CD4 T cells in vivo . Why IMs fail to prime DCs in this setting remains unclear . Evidence that moDCs generated in vitro can stimulate T cell responses suggests that IMs can process and present antigen ( Schreurs et al . , 1999 ) . Recovery of several hundred adoptively transferred IMs from the mLNs of infected mice also suggests that sufficient numbers of IM were present to prime T cells . One possible explanation is that IMs , by virtue of being infected with Mtb , are ineffective at stimulating Mtb-specific lymphocytes , in contrast to resident dendritic cells that have not been infected ( Wolf et al . , 2007 ) . Antigen transport and T cell priming are often considered the two major functions of DCs . A number of studies , however , have demonstrated that multiple DC subsets can be involved in priming of naive T cells ( Itano et al . , 2003 ) . Other studies characterizing CD8 T cell responses to cutaneous viral infection have demonstrated a role for CD103+ DCs in transport and priming with potential contributions by inflammatory monocytes ( Bedoui et al . , 2009; Edismo et al . , 2009 ) . One previous study has suggested that IMs carry fungal antigens from a site of cutaneous inoculation to draining LNs and transfer antigens to LN resident DCs ( Ersland et al . , 2010 ) . Our study extends this finding to live pulmonary infection with Mtb . IMs transport bacteria to lymph nodes and transfer antigen to classical dendritic cells prior to CD4 T cell priming . The mechanism of antigen transfer in mLNs remains undefined . It is possible that cross-dressing ( Wakim et al . , 2011 ) , in which infected cells pass MHC molecules to uninfected cells , plays a role during Mtb infection . We did not detect transfer of MHC class II molecules from host cells to adoptively transferred , MHC class II-deficient monocytes . The number of MHC/peptide complexes required per APC to stimulate T cell responses , however , may be very small and below our level of detection . That said , given our finding that MHC class II-deficient IMs effectively complemented monocyte-depleted , MHC class II sufficient mice while MHC class II-expressing monocytes could not correct deficient T cell priming in MHC class II-deficient , monocyte-depleted mice , we believe that transfer of MHC class II molecules in either direction plays a minimal role in T cell priming during Mtb infection . Future studies will determine whether live Mtb are transferred to classical DCs , or whether transfer principally involves the movement of processed or unprocessed proteins from infected monocytes to uninfected DCs .
C57BL/6 and MHC class II-deficient mice were purchased from the Jackson Laboratory . The generation of ESAT-6-specific C7 TCR transgenic , CCR2-DTR , CCR2-GFP and zDC-DTR mice were previously described ( Wolf et al . , 2007; Gallegos et al . , 2008; Hohl et al . , 2009 ) . For depletion experiments , mice were injected i . p . with 20 ng/g body weight DT . All mice were bred and maintained under specific pathogen-free conditions at the Memorial Sloan Kettering Research Animal Resource Center . Sex-and age-matched controls were used in all experiments according to institutional guidelines for animal care . All animal procedures were approved by the Institutional Animal Care and Use Committee of the Memorial Sloan-Kettering Cancer Center . M . tuberculosis Erdman was grown in 7H9 media , and log phase cultures were diluted to 8 × 106 bacilli per millimeter and sonicated before infection with an aerosol exposure system ( Glass-Col ) . The volume of suspension , and exposure time were calibrated to deliver ∼100 CFU per animal . To determine infection dose , three mice were killed 1 day after infection and lungs were homogenized in PBS/0 . 05% Tween-80 , and half the lung homogenate was plated . At various intervals after infection the left lung was harvested from individual mice and homogenized in PBS/0 . 05% Tween-80 . Serial dilutions were made in PBS/0 . 05% Tween-80 and plated onto Middlebrook 7H10 agar ( BD Biosciences ) . After 3 weeks of incubation at 37°C in a 5% CO2 atmosphere , colonies were counted . Inflammatory monocytes were harvested from the bone marrow of CCR2-GFP mice . CD4 T cells and NK+ cells were removed using antibody depletion kits from Miltenyi Biotec . The remaining cells were then stained with anti FLT3 PE and anti C-kit PE antibodies and EGFP+ , PE- cells were sorted by the Memorial Sloan-Kettering Cancer Center flow cytometry core facility . The cells were re-suspended in PBS and 2 × 10 ( Kipnis et al . , 2003 ) cells per mouse were injected via the tail vein . All data are presented as the arithmetic mean ± SEM . Statistical validation was done with the Student’s t test . p<0 . 05 were considered significant , p>0 . 05 were considered insignificant . | Tuberculosis is a disease that kills more than one million people every year . It is caused by mycobacteria , notably Mycobacterium tuberculosis , and the World Health Organization estimates that about one third of the world’s population has latent tuberculosis , although only one person in 10 goes on to develop an active infection . Understanding why some individuals develop active infections , whereas most do not , could help with the development of a vaccine to prevent tuberculosis and/or new treatments for the disease . Disappointing results from vaccine trials and the emergence of drug-resistant strains of tuberculosis have increased the need for more research into the interactions between mycobacteria and the human immune system . Tuberculosis is spread when an infected person coughs or sneezes and someone else inhales the mycobacteria spread by the first person . When M . tuberculosis first enters the human respiratory tract , the innate immune system tries to identify and destroy cells that have been infected . However , if this initial response is not effective , the M . tuberculosis can persist in the lungs and trigger the adaptive immune response . This involves CD4 T cells working to eliminate the infection , but our understanding of the adaptive immune response is not complete . Samstein et al . probed the role that immune cells known as inflammatory monocytes play in the adaptive immune response . Previous research has suggested that inflammatory monocytes may develop into dendritic cells that directly prime the CD4 T cells to respond when the lung has been infected . However , Samstein et al . demonstrate that the inflammatory monocytes carry M . tuberculosis from the lungs of infected mice to the draining lymph nodes during the second week of infection . These monocytes develop many of the characteristics of dendritic cells , but they do not activate the CD4 T cells . Samstein et al . show that dendritic cells , contrary to previous evidence , are not necessary for the transport of the M . tuberculosis from the lungs to the draining lymph nodes . Without the dendritic cells , however , fewer CD4 T cell are primed in the lymph nodes . Samstein et al . suggest that the inflammatory monocytes play a crucial role by transporting the live bacteria to the lymph nodes . And once in the lymph nodes , the monocytes transfer invading antigens to dendritic cells to initiate the production of the CD4 T cells to lead the fight against the infection . | [
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] | 2013 | Essential yet limited role for CCR2+ inflammatory monocytes during Mycobacterium tuberculosis-specific T cell priming |
Standard treatment for metastatic prostate cancer ( CaP ) prevents ligand-activation of androgen receptor ( AR ) . Despite initial remission , CaP progresses while relying on AR . AR transcriptional output controls CaP behavior and is an alternative therapeutic target , but its molecular regulation is poorly understood . Here , we show that action of activated AR partitions into fractions that are controlled preferentially by different coregulators . In a 452-AR-target gene panel , each of 18 clinically relevant coregulators mediates androgen-responsiveness of 0–57% genes and acts as a coactivator or corepressor in a gene-specific manner . Selectivity in coregulator-dependent AR action is reflected in differential AR binding site composition and involvement with CaP biology and progression . Isolation of a novel transcriptional mechanism in which WDR77 unites the actions of AR and p53 , the major genomic drivers of lethal CaP , to control cell cycle progression provides proof-of-principle for treatment via selective interference with AR action by exploiting AR dependence on coregulators .
The androgen-activated androgen receptor ( AR ) is both the major driver of prostate cancer ( CaP ) progression and the main target for treatment of metastatic CaP . An initial remission after AR-targeting androgen deprivation therapy ( ADT ) almost inevitably results in recurrence because CaP cells acquire resistance to ADT and continue to rely on AR activity ( Karantanos et al . , 2015; Dai et al . , 2017 ) . With few exceptions , failure of ADT is responsible for the ~27 , 000 CaP deaths in the United States annually ( Siegel et al . , 2017 ) . Novel , alternative approaches to block the AR action that drives CaP to the lethal stage are highly sought . Current ADT prevents interaction between AR and its androgenic ligands , thus targeting the AR ligand-recognition function . AR’s effector function as a transcription factor controls expression of Androgen Response Element ( ARE ) -driven genes ( Heinlein and Chang , 2004; Heemers and Tindall , 2007 ) and ultimately dictates androgen-regulated CaP cell behavior . Targeting AR’s transactivation function may prevent or overcome resistance associated with current ADT and lead to more CaP-specific inhibition of AR activity ( Heemers , 2014 ) . Interfering with AR action at the post-receptor level ( Heemers , 2014 ) requires an understanding of the molecular mechanisms by which AR controls expression of target genes that drive CaP progression . Increasingly sophisticated systems biology and bioinformatics techniques have provided insights to the AR-dependent transcriptome , the AR cistrome , and the composition of genomic AR binding sites ( ARBSs ) in CaP cells ( Horie-Inoue and Inoue , 2013; Mills , 2014 ) . Combined results from these endeavors suggest that gene specificity may exist in AR control over androgen-dependent gene expression . This possibility is in line with previous reports that coregulators , master regulators of transcription that are recruited to ARE-bound AR , preferentially control androgen regulation of subsets of AR target genes ( Marshall et al . , 2003; Ianculescu et al . , 2012; Xu et al . , 2009; Heemers et al . , 2009 ) . Coregulators have long been of interest as therapeutic targets for CaP ( Heemers and Tindall , 2005; Chmelar et al . , 2007 ) . Expression of 50 of the ~200 AR-associated coregulators is deregulated in clinical CaP specimens . Such aberrant expression often correlates with aggressive disease and poor outcome ( Heemers and Tindall , 2010 ) and is one of the mechanisms that lead to resistance to conventional ADT . Many coregulators possess enzymatic functions for which inhibitor ( s ) are already available ( e . g . ( Wang et al . , 2011 ) ) . Combinations of chemical library-screening and Chem-Seq approaches have identified novel coregulator-targeting drugs ( Jin et al . , 2014 ) while advances in peptidomimetics and multivalent peptoid conjugates are allowing for disruption of selective coregulator-AR interactions ( Ravindranathan et al . , 2013; Wang et al . , 2016 ) . The true potential of coregulators as alternative targets to block AR action in CaP and their contribution to AR-dependent transcription that drives CaP progression , however , remains unknown . The few studies so far ( Marshall et al . , 2003; Ianculescu et al . , 2012; Xu et al . , 2009; Heemers et al . , 2009 ) have taken into account only the impact of coregulators on androgen regulation of a handful of well-characterized exogenous or endogenous ARE-driven genes , or genome-wide gene expression profiles . In most cases investigations have been limited to the study of a single coregulator , without considering its relevance to clinical CaP progression , or redundancy or cooperativity between coregulators interacting with ARE-bound AR . Here , we use an integrated approach to systematically define the contribution of 18 clinically relevant coregulators to androgen responsiveness of 452 bona fide AR target genes . Our results demonstrate a previously unrecognized level of gene specificity and context-dependence in reliance of AR target gene expression on coregulators , and the corresponding AR target gene sets contribute differentially to CaP initiation and progression . Analysis of the molecular basis and associated cell biology of coregulator-dependent AR target gene expression indicates transcriptional codes exist in which AR cooperates with select coregulator ( s ) and transcription factors to control transcription of a subset of its target genes . Our identification of a WDR77-dependent functional interaction between AR and p53 provides a rationale for a coregulator-dependent alternative to target for therapy the major drivers of lethal CaP progression .
A systematic analysis of the role for coregulators in regulation of AR function requires a sizable set of ARE-driven AR target genes that can be interrogated coordinately . System biology approaches per se do not provide an unambiguous signature of AR target genes . Expression profiles of androgen-regulated genes do not distinguish between direct AR target genes , which are androgen-responsive because of direct AR-ARE interaction , and indirect AR target genes , which are androgen-regulated secondary to the action of a direct AR target gene . ChIP-chip , ChIP-Seq and ChIP-exo studies document androgen-dependent recruitment of AR throughout the genome but are prone to artifacts , only a small fraction of isolated ARBSs undergo independent ChIP validation , and the association between an ARBS ( s ) and androgen regulation of an adjacent gene often remains elusive . We reasoned that integrating information on the genome-wide location of ARBSs , transcriptional start site ( TSS ) position , and androgen-responsive gene expression would result in identification of bona fide direct AR target genes . ARBSs present within 300 Kb of TSSs of RefSeq genes after androgen treatment ( Wang et al . , 2009 ) of AR-positive LNCaP cells were retrieved , and the overlap between the corresponding RefSeq gene list and androgen-dependent CaP gene expression profiles ( Wang et al . , 2009; DePrimo et al . , 2002; Nelson et al . , 2002; Segawa et al . , 2002; Febbo et al . , 2005; Velasco et al . , 2004; Ngan et al . , 2009; Waghray et al . , 2001; Xu et al . , 2001 ) was defined . This approach narrowed down a set of 12 , 629 ARBSs to 900 putative direct AR target genes ( Figure 1—figure supplement 1 ) . A custom ( Agilent 8 × 15 k ) gene expression oligoarray was developed to assess simultaneously expression of these genes ( Supplementary file 1 , panel A ) . Oligoarray performance was assessed using RNA from LNCaP cells treated with the synthetic androgen R1881 or vehicle . Prior to oligoarray assay , real-time RT-PCR analysis of AR target genes PSA , FN1 , and SCAP ( Cleutjens et al . , 1996; Cleutjens et al . , 1997; Heemers et al . , 2004; Bolton et al . , 2007 ) verified androgen-responsiveness of cells and RNA quality . Stimulation of LNCaP cell growth under these treatment conditions was verified via Ki67 immunocytochemistry and trypan blue exclusion experiments ( Figure 1—figure supplement 2 ) . Oligoarray data revealed 452 genes with at least 2-fold change in expression in response to androgens ( Supplementary file 1 , panel B ) . These genes included well-characterized AR target genes such as PSA ( Cleutjens et al . , 1996; Cleutjens et al . , 1997 ) , TMPRSS2 ( Wang et al . , 2007 ) , FN1 ( Bolton et al . , 2007 ) and SERPINB5 ( Zhang et al . , 1997 ) , as well as genes that are less readily recognized as AR target genes , e . g . RALB , MPRIP , GNL1 , GNB4 , GUCY1A3 , ARHGAP11A , WASF3 and RAB27A ( Figure 1—figure supplement 1 ) . Androgen treatment increased expression of 241 ( 55% ) of these genes , while reducing expression of the remaining 211 . Androgen dependence and directionality of androgen regulation of >90% of the genes were also present in an independent AR-positive cell line , VCaP ( Korenchuk et al . , 2001 ) ( Supplementary file 2 , panel A , data not shown ) . The kinetics of androgen response ( Supplementary file 2 , panel B ) was consistent with behavior of direct AR target genes , that is , androgen-induction of genes was notable at earlier time points ( 4 hr ) than androgen-suppression ( 8 hr ) . ChIP verified androgen-dependent recruitment of AR to predicted AREs within ARBSs ( Figure 1—figure supplement 1 ) . No preference for particular chromosomes was noted , but consistent with other reports ( Horie-Inoue and Inoue , 2013 ) , ARBSs were predominantly located in enhancer regions and intergenic regions ( Figure 1—figure supplement 1 ) . The vast majority ( 84 . 6% ) of ARBSs in the 452-AR target genes overlapped with androgen-induced H3K4me2 ChIP-Seq peaks , an epigenetic marker for active AR-dependent transcription ( He et al . , 2010 ) . Cistrome motif analyses ( Liu et al . , 2011 ) of the DNA sequences that correspond to ARBSs demonstrated significant enrichment for the consensus AR binding motif . The top 10 enriched motifs also included the highly similar and sometimes interchangeable binding motifs for related glucocorticoid and progesterone nuclear receptors , as well as AR-interacting pioneering factor FoxA1 and the related FoxA2 , general transcription factor GTF2A1 and transcription factors ( TFs ) such as STAT1 that are known to interact with ARE-bound AR ( Figure 1—figure supplement 1 ) . Ingenuity Pathway Analysis ( Krämer et al . , 2014 ) ( IPA ) indicated significant enrichment for cell functions associated with cancer; cell growth ( specifically of CaP cells ) , death and movement , lipid metabolism ( Supplementary file 3 , panel A ) . These processes have been independently reported to be under androgen control in CaP cells ( Dehm and Tindall , 2006; Heemers et al . , 2006 ) . These diverse and complementary analyses of the 452 gene signature indicate that the isolated 452 gene set is suitable for the proposed studies . AR-associated coregulators that are most relevant to CaP aggressiveness will provide the most clinically useful insights . At the onset of this study , 181 coregulators that interact physically and functionally with AR had been identified ( Supplementary file 1 ) . For 51 AR-associated coregulators protein expression was deregulated between CaP and benign prostate . Differential expression of 22 of these 51 correlated with more aggressive CaP features and shorter disease-free survival after prostatectomy ( Heemers and Tindall , 2010 ) . These 22 coregulators , which likely represent critical contributors to AR activity in CaP progression , were analyzed further . LNCaP cells were transfected with specific siRNAs to individually silence each coregulator . Silencing efficacy and specificity were verified using real-time RT-PCR . No adverse effects on cell death and cell appearance were seen after knock-out of any of the 22 coregulators . Silencing of GAK , HIP1 , RAD9A or SMAD3 , however , did decrease markedly AR protein expression ( Supplementary file 4 ) . These 4 coregulators were excluded from subsequent experiments ( Figure 1A ) to avoid confounding interpretation of subsequent experiments . The relevance of the remaining 18 AR-associated coregulators ( Figure 1B ) to androgen responsiveness of AR target gene expression was determined next . Transfection of LNCaP cells with siRNAs that individually targeted each coregulator was combined with R1881 treatment ( Heemers et al . , 2009 ) . For ≥95% of AR target genes ( n > 35 tested ) , real-time RT-PCR analysis verified the oligoarray pattern of gene expression ( Supplementary file 2 ) . Figure 1B summarizes changes in androgen-responsiveness that occurred with silencing of each coregulator individually . The fraction of genes affected varied widely among different coregulators , ranging from 0% for RCHY1 siRNA transfection to 57% ( 258/452 genes affected ) for p300 knock-down . These results demonstrate considerable gene-specific preference in the contribution of individual coregulators to the androgen regulation of AR target gene expression . Further analyses took into account the effect of loss of coregulator expression on the magnitude of androgen regulation of AR target gene expression . Genes for which androgen regulation was decreased by specific coregulator knock-down as compared to control transfection were scored as negative; those for which androgen regulation increased upon coregulator loss were scored as positive ( Supplementary file 5 ) . Strikingly , each coregulator studied could simultaneously increase the androgen-responsiveness of a subset of AR target genes under its control ( thus acting as a coactivator ) and decrease androgen-responsiveness of other genes that rely on it for androgen regulation ( acting as corepressor ) ( Figure 1B ) . These results reveal previously unrecognized context-dependency in the manner by which coregulators govern androgen regulation of gene expression . Next , the effect of loss of coregulator expression on the direction of androgen regulation of gene expression was analyzed . AR target genes for which loss of coregulator expression altered the absolute level but not the direction of androgen regulation ( e . g . , upregulated by androgens in both control and knock-down conditions ) were considered as having consistent directionality whereas those in which silencing coregulator expression changed both the magnitude and the direction of androgen regulation ( e . g . , from up- to down-regulated ) were considered as exhibiting ‘opposite’ directionality ( Supplementary file 5 ) . The vast majority of genes fell into the consistent category ( Figure 1B ) . Androgen-responsiveness of the same AR target gene could be affected by multiple coregulators . 2 to 4 coregulators affected the androgen-responsiveness of the vast majority of genes . For a few genes only , that number of coregulators ranged from 0 to 14 . For instance , androgen-responsiveness of the genes encoding GNB4 and RAB27A was modified by 4 or 12 , respectively , individual coregulators ( Supplementary file 5 ) . ChIP studies using antibodies directed against 6 representative coregulators verified the correlation between androgen-dependent recruitment of NCOA3 , SMARCA4 or WDR77 to AREs and the pattern of androgen-responsiveness of GNB4 and RAB27A in LNCaP cells . Conversely , coregulators which loss did not affect the androgen regulation of these genes , such as PKN1 or NCOA2 , were not found at AREs of these genes . Yet other coregulators ( e . g . EP300 ) were present at relatively high basal level at these AREs , but modification of the androgen regulation of the corresponding gene was not noted unless there were marked changes in recruitment of the coregulator to those AREs ( for instance RAB27A ) ( Supplementary file 5 ) . Time course studies were performed to determine the kinetics of representative coregulator recruitment to AREs in these genes . At 1 hr , 4 hr , 16 hr , and 48 hr after treatment , cells were harvested for ChIP analysis of WDR77 , NCOA3 , and AR . Robust androgen-induced recruitment of AR was seen after 1 hr , 4 hr , or 16 hr at AREs in both genes , which became less pronounced ( RAB27A ) or not detectable ( GNB4 ) at 48 hr . Androgen-stimulated binding of WDR77 and NCOA3 occurred at all time points at AREs of both genes ( Supplementary file 5 ) . The kinetics by which androgen regulation of GNB4 and RAB27A is increased after siRNA-mediated silencing of WDR77 and NCOA3 was defined in real-time RT-PCR studies in LNCaP cells . Consistent with a lag between recruitment of AR and changes in androgen responsiveness of its target genes ( Massie et al . , 2011 ) , androgen stimulation of GNB4 and RAB27A was first seen at 4 hr . At 16 hr androgen treatment , silencing of both WDR77 and NCOA3 increased the level of androgen regulation of GNB4 as well as RAB27A . For both coregulators and both genes studied , this effect was more pronounced at 48 hr . The siRNA-mediated decrease in WDR77 and NCOA3 expression respectively , however , was similar after 1 hr , 4 hr , 16 hr , or 48 hr of treatment ( Supplementary file 5 ) . These results indicated no marked differences in the kinetics of coregulator recruitment to AREs in these target genes . The possibility that multiple coregulators work in concert to control androgen regulation of individual target genes was examined further . Pairwise comparison between all coregulator-dependent AR target gene sets was done to determine the overlap in number of genes . With few exceptions ( e . g . EP300-BAG1 ) the overlap in genes between different coregulator-dependent signatures was less than 30% . Among the 136 pair-wise comparisons , only 42 ( 30 . 8% ) and 31 ( 22 . 7% ) have significant overlap at the level of p<0 . 05 and p<0 . 01 , respectively ( Figure 1—source data 2 ) . For instance , between STAT3- and WDR77-dependent gene sets , 30 genes overlapped , which corresponds to 31% and 28% of gene signatures , respectively ( p=0 . 99 for significance of this overlap , Figure 1C ) . Of note , the directionality in androgen regulation was preserved for only 15 genes ( or 16% and 14% ) ( Figure 1C , insert ) . Because of these findings , we analyzed whether AR target genes could be grouped based on the degree to which their androgen-responsiveness depends on specific coregulators . For this analysis , effect of coregulator loss on magnitude of androgen regulation , but not its direction , was considered . RCHY1 knock-down results were not included as loss of this coregulator did not affect androgen-regulation of any of the 452 AR target genes . Relatively small groups of AR target genes for which androgen-dependency was either decreased or increased clustered together ( Figure 1D ) . Mutual exclusivity in coregulator dependency of androgen regulation was noted for some AR target gene groups ( blue line , EP300 ) , and androgen-responsiveness of other gene sets was affected by 2 or more coregulators ( e . g . orange line , EP300 and SMARCA4 ) . This low level of coregulator cooperativity was supported further by Pearson correlation analyses ( Figure 2—figure supplement 1 ) . Findings of striking AR target gene preference and context-dependence among coregulators indicated that specific coregulator dependency can differentiate between molecular modes of androgen action in CaP . Since kinetics studies ( Supplementary file 5 ) did not reveal marked differences in the timing of coregulator recruitment to AREs , the possibility that the composition of ARBSs associated with individual coregulator-dependent AR target gene signatures differs was examined . DNA sequences corresponding to ARBSs were retrieved , expanded by 1 kb at the 5’ and 3’ ends and analyzed using Cistrome Project tools ( Liu et al . , 2011 ) . A total of 283 significantly overrepresented TF binding motifs were identified ( Figure 2—source data 3 ) . The number of overrepresented motifs in ARBSs of an individual gene signatures ranged from 4 to 127 ( Figure 2A ) and did not correlate with the number of genes per coregulator-dependent gene signature or corresponding number of ARBSs ( some genes harbor >1 ARBS ) . The predominant motif overrepresented in ARBSs from the 452 gene list and the 17 subgroups was one that matched the binding site for AR . ARBSs that did not contain an overrepresented ARE harbored a related , interchangeable motif such as that recognized by glucocorticoid receptor ( GR ) ( e . g . , BAG1- and HTATIP2-dependent genes ) . Motifs known to be enriched in close proximity to an ARE , such as Forkhead family members , were overrepresented in subgroups except for those in NCOA3-dependent AR target genes . Consensus binding sites for other TFs were shared by ARBSs that are present in multiple AR target gene sets . Strikingly , however , each individual coregulator-dependent AR target gene set harbored in its ARBSs at least one overrepresented TF binding motif that was not found to be overrepresented in ARBSs in other AR target gene lists or in the overarching 452 gene set ( Figure 2—source data 3 ) . For instance , multiple TF binding sites that are enriched selectively in STAT3-dependent gene ARBSs but not in other subsignatures correspond to TFs that function in development , organogenesis and stemness ( e . g MEIS2 , NANOG , SOX2 ) ( Figure 2—source data 3 ) . This recalls the emerging role for STAT3 and AR in cancer stemness ( Li et al . , 2015; Kregel et al . , 2013; Kregel et al . , 2014 ) and the overlap in NANOG binding sites with a subset of ARBSs ( Jeter et al . , 2016 ) . Unsupervised clustering confirmed gene set-specific TF binding site clustering in ARBSs ( Figure 2B ) , and suggested that selective motif enrichments serve as the molecular basis for coregulator-dependent clustering of AR target genes . Despite the overall low correlation between coregulator-dependence of AR target genes ( Figure 2—figure supplement 1 ) , we obtained 4 combined gene sets by combining the 4 pairs of gene sets with highest Pearson correlation ( ≥0 . 25 ) and identified significantly overrepresented TF motifs in each of them . The results of analyses for these individual or combined gene sets ( Figure 2—figure supplement 1 ) confirm the observation of selective enrichment of TFBSs . The possibility that individual coregulator-dependent AR target gene sets control different aspects of androgen-dependent CaP cell biology was explored . Using Ingenuity Pathway Analysis ( IPA ) , we determined the association of each of these individual gene signatures with biological functions . The entire 452 AR target gene set was associated with 36 categories , whereas individual AR target gene signatures were associated with 0 ( e . g . CAV1 ) to 34 ( for SMARCA4 ) categories ( Figure 2C , Supplementary file 3 ) . Also , contrary to the wide range of biological processes associated with the entire 452-gene set , coregulator-dependent AR target gene sets tended to involve specific biological processes ( Supplementary file 3 ) . For instance , SMARCC1-dependent AR target genes associated significantly with functions involved in cell death and survival . Similarly-sized gene sets such as those associated with SMARCA4 ( n = 124 ) and KAT5 ( n = 142 ) were associated with a widely different number of categories ( 34 and 3 , respectively ) . The top 10 IPA canonical pathways most significantly associated with each gene set also markedly differed in composition ( Supplementary file 3 ) . These results indicate that coregulators may contribute selectively to specific androgen-dependent biological processes that make up the androgen response of CaP cells . IPA analysis on combined or individual gene sets with highest correlation confirmed conclusions of selective enrichment ( Figure 2—figure supplement 1 ) . The CaP-specificity and clinical relevance of different coregulator-dependent AR target gene signatures was evaluated using prostate and CaP gene expression profiles that are available in the public domain ( Figure 2D ) . First , the possibility that androgen regulation of the coregulator-dependent gene sets differ between benign prostate and CaP was studied . mRNA expression profiles derived from microdissected benign prostate epithelial cells from patients who were either treated with dutasteride , a dual SRD5A inhibitor that prevents conversion of testosterone to the most bioactive androgen dihydrotestosterone , or vehicle prior to radical prostatectomy were compared using gene set enrichment analysis ( GSEA ) ( Subramanian et al . , 2005 ) . Significant enrichment was determined by FDR q-value . The expression of the overarching 452 gene signature was negatively enriched in benign cells from patients who received dutasteride , indicating androgen regulation of this gene set in normal benign prostate epithelial cells . Similar negative enrichment was found for the majority of evaluable coregulator-dependent subsignatures . For 4 gene subsignatures ( PARK7- , FHL2- , WDR77- , and EP300-dependent ) , however , no changes were seen between dutasteride-treated versus vehicle-treated patients , indicating no androgen regulation of these genes in normal benign epithelial prostate cells . One signature , CTNNB1 , was slightly enriched in dutasteride-treated patients . Second , mRNA expression profiles derived from localized CaP and benign prostate were compared . Consistent with previous observations ( Tomlins et al . , 2007; Heemers et al . , 2011 ) , expression of the 452 gene signature was significantly and negatively enriched in CaP compared with benign prostate . Analyses for the 17 coregulator-dependent AR target gene sets indicated significant negative enrichment for 4 signatures . Third , GSEA was done on CaP gene expression profiles from patients who experienced biochemical failure versus those who did not and on tissues from patients with localized CaP versus metastatic CaP . In each study , the global 452 AR target gene set had a significant NES in the most aggressive state ( biochemically recurring or metastatic CaP ) but one or more of the 17 coregulator-dependent subsignatures showed no enrichment between recurring and non-recurring CaPs or between localized CaP or CaP that had spread . As AR controls a transcriptional program in normal prostate , which could cloud the assessment of cancer specificity of the gene sets studied , GSEA was done also using gene expression profiles from CaP and normal bone marrow , the most common site of metastatic CaP seeding . This comparison confirmed significant positive enrichment in cancer for all but 2 AR target gene sets ( SMARCA4- and NCOA2-dependent ) . Finally , GSEA using profiles from luminal and basal prostate epithelial cells verified luminal origin of the signatures studied here . Importantly , the size of the gene lists did not correlate with the NES significance . These findings suggest differential involvement of select coregulator-dependent AR target gene sets in initiation and progression of CaP and validate CaP-specificity and luminal association of the signatures studied . The results above pointed to the existence of discrete coregulator-dependent mechanisms of AR action that may control select aspects of CaP cell biology , differ in clinical relevance , and be governed by specific coregulator-AR-TF interactions ( Figure 3A ) . An integrated review of results for the 17 individual AR target gene sets was done in search for evidence for such transcriptional codes . First , data from Cistrome studies were mined to isolate TF binding sites that were enriched selectively in ARBSs of no more than 2 coregulator-dependent AR target gene signatures ( Figure 2—source data 3 ) . Next , TFs predicted to bind to these motifs were prioritized based on their significance to clinical CaP progression and functional relevance to CaP cell biology . p53 , a well-known tumor suppressor with TF function that regulates apoptosis , cell senescence , cell cycle progression , and DNA repair , and undergoes gain-of-function mutations during CaP progression ( Robinson et al . , 2015; Hong et al . , 2015 ) , best fit those selection criteria ( Figure 2—source data 3 ) . Consensus binding motifs for p53 were enriched selectively in ARBSs of WDR77-dependent AR target genes . Five ( cell death and DNA replication , recombination and repair ) of 7 IPA-identified biological processes that associated significantly with the WDR77-dependent AR target genes were very consistent with p53 function ( Supplementary file 3 ) . WDR77-dependent AR target genes included for instance MYC , a well-known p53 target gene with pivotal roles in CaP aggressiveness and progression . In addition , IPA identified p53 as an upstream regulator function of WDR77- and AR-regulated genes ( p=1 . 37 . e−7 ) . That p53 and WDR77 may cooperate to regulate CaP cell response to androgens was examined first in Co-IP studies . Despite previous reports of functional interaction between AR and p53 ( Dean and Knudsen , 2013; Guseva et al . , 2012; Cronauer et al . , 2004; Shenk et al . , 2001; Gurova et al . , 2002 ) , these proteins have never been found to interact directly . However , interaction between WDR77 and AR has been described ( Li et al . , 2013 ) . Therefore , the possibility that p53 and WDR77 interact physically was explored . Co-immunoprecipitation assays were done in both directions ( i . e . immunoprecipitation for p53 and immunoblotting for WDR77 , and immunoprecipitation for WDR77 followed by western blotting for p53 ) in LNCaP cells , which express wild-type p53 . These studies revealed that WDR77 and p53 are part of the same immune complex ( Figure 3B and C ) . These same experiments were performed in VCaP cells , which confirmed the presence of p53 and WDR77 in the same protein complex regardless of the order in which the immunoprecipitation or -blotting was done ( Figure 3D and E ) . Next , the overlap in p53- and WDR77-dependence of androgen-responsive gene expression was defined . LNCaP cells were transfected using siRNA targeting p53 or WDR77 , or non-targeting control siRNA . Cells were then treated with R1881 or vehicle as above . Illumina genome-wide HTv4 BeadChip analyses were done , and genes that relied on p53 or WDR77 for androgen-responsiveness were identified as above . As shown in Figure 3F , androgen regulation of a set of 272 genes was affected similarly by knockdown of p53 or WDR77 ( p<2 . 2E-16 ) . Remarkable consistency was noted in the impact on directionality of androgen-regulation of these genes after silencing of p53 or WDR77 . 76 . 9% of genes for which androgen-responsiveness was increased after p53 knock-down overlapped with genes for which level of androgen-regulation was enhanced upon silencing of WDR77 . Conversely , 88 . 2% of genes for which androgen-responsiveness is enhanced after loss of WDR77 showed an increased level of androgen regulation also in p53 siRNA-transfected cells . Similarly , 68 . 3% of genes for which androgen-responsiveness decreased after loss of p53 overlapped with genes for which level of androgen-regulation was diminished upon silencing of WDR77; and 67% of genes for which level of androgen-responsiveness was lessened after loss of WDR77 showed decreased androgen regulation also in p53 siRNA transfection condition . For none of the androgen-regulated genes isolated , inconsistency in the directionality of androgen regulation was observed between p53 and WDR77 silencing . These results strongly supported co-operativity between WDR77 and p53 in androgen-regulation of select AR-dependent genes . The ability of cistrome data to predict TF-coregulator interactions was validated by co-immuoprecipitation of STAT3 and IRF1 , for which binding sites are enriched selectively in the ARBSs of STAT3- dependent genes ( Supplementary file 6 ) . Gene expression studies following siRNA-mediated silencing of IRF1 and STAT3 confirmed considerable ( n = 413 ) overlap in androgen-responsive genes ( p<2 . 2E-16 ) ( Supplementary file 6 ) . The directionality of androgen regulation of affected genes was preserved in IRF1- or STAT3-knockdown condition . Comparison of IPA results ( Supplementary file 3 ) from STAT3-IRF1 and p53-WDR77 interactions indicated both shared and unique molecular functions ( Supplementary file 6 , p<2 . 2E-16 ) . As AR and p53 have been proposed as drivers of lethal CaP ( Robinson et al . , 2015; Hong et al . , 2015 ) , the relevance of WDR77-p53 interaction for late stage disease was studied further . Recent NextGen sequencing studies have identified p53 mutants that are enriched in castration-recurrent ( CR- ) CaP , but the contribution of these p53 mutants to AR-dependent transcription is poorly understood ( Cronauer et al . , 2004; Shenk et al . , 2001; Gurova et al . , 2002 ) . WDR77 and p53 were part of the same immunocomplex in VCaP cells ( Figure 3D–E ) that endogenously express gain-of-function p53 R248Q . We therefore examined the effect of CaP-specific p53 mutants on androgen regulation of representative WDR77-dependent AR target genes in LNCaP cells via co-expression of 9 clinically relevant CaP p53 mutants ( Figure 4A ) . These 9 p53 mutants have been detected recently in tissue and blood from CR-CaP patients ( Hong et al . , 2015 ) . Higher nuclear expression level of p53 mutants than wild-type p53 was observed , which is reminiscent of observations in patient specimens ( Haffner et al . , 2013 ) . Increased p53 nuclear expression did not impact on nuclear content of AR or WDR77 ( Figure 4B ) . Despite some heterogeneity in the contribution of different p53 mutants , overall androgen regulation of GNB4 and RAB27A was maintained in the presence of added mutant p53 ( modeling clinically relevant heterotetramerization between wild-type and mutant p53 ( e . g . [Muller and Vousden , 2014] ) , compared to empty vector or wild-type p53 ( Figure 4C ) . These findings suggest that activity of the AR-WDR77-p53 transcriptional code is maintained in CR-CaP that expresses mutant p53 . This conclusion is consistent also with GSEA analyses using the WDR7-dependent gene expression signature and gene expression profiles from clinical CR-CaP cases that express mutant p53 versus those that express wild-type p53 ( Grasso et al . , 2012 ) . No significant normalized enrichment score was obtained between 2 groups of cases . Co-IP studies in which 2 representative p53 mutants , C135Y and N239T , were expressed in a p53-null LNCaP subline ( Guseva et al . , 2012 ) confirmed interaction of mutant p53 with WDR77 ( Figure 4D ) . To gain more insight into the action of the novel AR-WDR77-p53 transcriptional code , IP-mass-spectrometry analysis was performed on nuclear fractions from androgen- versus vehicle-treated LNCaP cells . IP experiments in which either p53 or WDR77 antibodies were used independently identified 3 proteins ( 14-3-3 sigma , hnRNPU and PGAM5 ) as part of the AR-WDR77-p53 immunocomplex . PGAM5 , which has previously been reported to play a role in regulation of cell death ( Vaseva et al . , 2012; Wang et al . , 2012 ) but was not known to be relevant to AR signaling or CaP biology , was prioritized for validation . First , immunohistochemistry for PGAM5 was performed on tissue microarrays that contain 29 benign prostate and 151 CaP tissues . Expression of PGAM5 was significantly higher in CaP than in benign prostate ( score of 2 . 24 vs 1 . 58 , p=0 . 00059 , t-test ) and was higher also in CaPs of Gleason scores 7–10 than in CaP of Gleason score 6 ( p<0 . 05 , t-test ) ( Figure 5A , B ) . These findings were in line with queries of the Oncomine and cBioPortal databases which indicated also overexpression of PGAM5 in CaP versus benign prostate , and increased PGAM5 expression with increasing Gleason grade and CaP progression ( Figure 5C ) . These results indicated that PGAM5 , as other AR-associated coregulators studied here , is overexpressed in CaP where it correlates with more aggressive CaP behavior . The implications of PGAM5 function for WDR77-dependent AR action were determined next . PGAM5 exists in 2 isoforms ( long and short ) . Co-IPs using antibodies directed against AR or p53 for IP confirmed the presence of both PGAM5 forms in the AR-WDR77-p53 complex in LNCaP cells ( Figure 5D ) . Androgen treatment induced recruitment of PGAM5 as well as p53 to ARE-containing regions within the genes encoding GNB4 and RAB27A , to which WDR77 and AR also bind ( Figure 5E , F ) . Knock-down of PGAM5 mirrored the effect of WDR77 loss on androgen regulation of target genes such as RAB27A and GNB4 . Genome-wide oligoarray expression profiling in LNCaP cells showed that loss of PGAM5 altered the androgen responsiveness of 218/272 of the p53 and WDR77-dependent genes ( p<2 . 2E-16 ) ( Figure 5G ) , and for each of those genes the impact on directionality of androgen regulation was the same for PGAM5 loss as that observed after silencing of WDR77 and p53 . At the molecular level , loss of WDR77 altered androgen-dependent pattern of coimmunoprecipitation of p53 by AR ( Figure 5H ) , and silencing of either WDR77 or PGAM5 prevented androgen-dependent recruitment of p53 to WDR77-dependent target genes ( Figure 5I ) . Moreover , ChIP-re-ChIP expertiments using an antibody targeting p53 for ChIP and an antibody against AR for Re-ChIP confirmed androgen-stimulated co-recruitment of AR and p53 to these genes , which was decreased following siRNA-mediated silencing of WDR77 . Similar results were obtained in parallel experiments in which ChIP targeted AR and Re-ChIP was directed at p53 ( Figure 5I ) . In combination , these data demonstrate a novel mechanism in which AR-associated coregulators WDR77 and PGAM5 control androgen-dependent recruitment of p53 to ARBSs in a subset of AR target genes . We set out to verify the IPA results of the WDR77- , p53- and PGAM5-dependent androgen-responsive gene signature , which indicated preferential roles in regulation of cell survival , cell death and cell proliferation ( Supplementary file 3 ) . Since the regulatory proteins WDR77 and PGAM5 may be differentially involved in other transcriptional complexes , which could confound interpretation of results , 4 representative WDR77-dependent AR target genes , GNB4 and RAB27A as well as HES6 and AGR2 ( previously reported as ARE-driven genes and identified in our 452 gene-signature also ) ( Sharma et al . , 2013; Ramos-Montoya et al . , 2014 ) were included in these analyses . siRNA-mediated silencing of all regulators and target genes markedly reduced cell viability of LNCaP cells , both under normal culture conditions and under androgen-stimulated conditions ( Figure 6A , B ) . Similar effects were observed when experiments were repeated in p53-null LNCaP sublines that are stably transfected with expression constructs encoding p53 mutants C135Y and N239T or in VCaP cells that express p53 R248Q ( Figure 6C , D ) . Except for GNB4 , for which effects were modest , propidium iodide FACS studies in LNCaP cells indicated that this reduced cell viability may be due to slow G1 phase progression and G1/S transition ( Figure 6E ) . Western blot analysis on parallel samples showed marked increases in expression of gamma-pH2AX , supporting potential effects on cell cycle stage , early apoptosis and/or DNA damage ( Figure 6F ) .
This first systematic analysis of the individual contribution of multiple coregulators to androgen regulation of several hundred bona fide AR target genes has revealed remarkable diversity in the molecular modulation of AR-dependent transcription . By integrating results from diverse analyses , important novel insights in AR action in CaP , including 2 novel AR-dependent transcriptional codes ( AR-WDR77-p53 and AR-STAT3-IRF1 ) and a novel AR-associated coregulator ( PGAM5 ) , have been derived . These findings strengthen the concept and feasibility of selective ADT . Previous efforts to target for therapy the heterogeneity in AR action have focused on enhancing AR action in a tissue-specific manner via development of selective AR modulators ( Pihlajamaa et al . , 2015 ) . The alternative , namely blocking AR action in specific tissues while not affecting it in organs where its sustained activity is required for maintenance of normal function , has not yet been attempted . We reasoned that a better understanding of the contribution of coregulators , the master regulators for nuclear receptor ( NR ) -mediated transcription , to ARE-driven gene expression in CaP would facilitate such a CaP-selective approach to treatment . The studies described above demonstrate that AR action in CaP can be broken down in coregulator-dependent fractions . Of note , results were derived from the same cell type , in which AR is activated by the same non-metabolizable ligand that is administered using a standardized timing and dosing scheme , and relies on the same endogenously expressed cell-type specific array of coregulators to execute AR-dependent transcription . Our work has thus isolated a previously unrecognized intracellular level of heterogeneity in AR action that differentially uses critical regulators of transcriptional machinery to induce AR target gene subsets . The finding that androgen action depends on different coregulation of transcription is consistent with previous studies that mostly involved silencing of one coregulator and a handful ARE-driven gene fragments or global androgen-dependent gene expression patterns ( Marshall et al . , 2003; Ianculescu et al . , 2012; Xu et al . , 2009; Heemers et al . , 2009 ) . The scope and nature of our approach revealed , for the first time , patterns of ( co ) regulation of AR action in CaP cells and its molecular underpinning and biological relevance . Androgen regulation of most AR target genes could be affected by more than 1 coregulator , but the overlap in different contributing coregulators between target genes was limited . When AR target genes were grouped based on androgen-responsiveness to coregulators , striking differences in ARBS TF binding site composition were found between the AR target gene subsignatures . These findings are reminiscent of a previously proposed model in which DNA not only serves as a binding site for TFs , but also as an allosteric ligand for DNA-bound NRs ( Meijsing et al . , 2009 ) . In this model , the DNA sequence at the NR binding site controls the composition of transcriptional complexes formed at that site . This fits also with the concept of the Androgen Response Unit ( ARU ) in which variability in ARE-driven transcription was attributed to sequence composition of the regions close to AREs to which other proteins bind and cooperate with AR ( Robins et al . , 1994 ) . Our methodical documentation of the extent of this variability , and the identification of select candidate contributing TF binding sites , provide the first glimpses of differential composition of AR transcriptional complexes at individual target genes . The proposed transcriptional codes and the model of cooperative or mutually exclusive coregulator-coregulator interactions support the diversity and modularity in the coregulator component of transcriptional complexes ( Malovannaya et al . , 2011 ) . Context-dependency in the contribution of individual coregulators as activator or repressor of AR activity , described for LSD1/KDM1A before ( Cai et al . , 2011 ) , may be related to the presence of multiple functionally diverse isoforms ( Djebali et al . , 2012 ) of coregulator genes in a target cell , or to specific post-translational modifications on the intracellular pool of a coregulator . That heterogeneity in coregulator-dependent AR target gene subsignatures is associated with different cell biology processes and clinical CaP progression is novel . This result has important implications for therapeutic intervention as it lends further credence to the concept of selective , CaP-specific forms of ADT . Theoretically , blocking a CaP-specific segment of AR transcriptional output that controls aggressive CaP cell behavior and clinical progression will lead to CaP remission while bypassing resistance and avoiding side effects of conventional ADT ( Heemers , 2014; Nguyen et al . , 2015 ) . The AR-WDR77-p53-dependent transcriptional mechanism highlighted here exemplifies such clinical relevance and therapeutic potential: it mediates cell cycle progression , is androgen-regulated specifically in CaP but not benign prostate , and -unlike other AR target gene signatures- is maintained in CR-CaP . The resulting protein-protein and protein-DNA interactions contribute to AR’s control over CaP cell survival , apoptosis and proliferation , which has long been recognized but is poorly understood at the molecular level ( Ta and Gioeli , 2014; Wen et al . , 2014 ) . The identification of PGAM5 , as novel contributing AR coregulator fits with its role in regulation of cell death ( Vaseva et al . , 2012; Wang et al . , 2012 ) . Functional interaction between AR and p53 , which have recently been isolated as 2 major genomic drivers of lethal CaP progression ( Robinson et al . , 2015; Hong et al . , 2015 ) , has been reported , but remains poorly understood ( Dean and Knudsen , 2013; Guseva et al . , 2012; Cronauer et al . , 2004; Shenk et al . , 2001 ) . Findings that p53 can both down- and upregulate AR-dependent transcription , however , relied on reporter constructs or one AR target gene ( typically PSA ) , and , most often , exogenously overexpressed AR and/or p53 . Context-dependence such as that observed above for coregulator contribution to AR-mediated transcription may explain these discrepancies . None of these studies detected direct interaction between AR and p53 . Interaction between AR and WDR77 has been shown ( Li et al . , 2013 ) and our studies show that WDR77 mediates recruitment of p53 to AR . Under ADT , p53 undergoes gain-of-function mutations that affect its TF function and protein interactome , increasing metastatic potential and facilitating CaP cell growth ( Hong et al . , 2015; Muller and Vousden , 2014; Muller and Vousden , 2013; Nesslinger et al . , 2003; Vinall et al . , 2006 ) . p53 mutations are present in >50% of CR-CaP cases . WDR77 is expressed also in clinical CR-CaP ( Peng et al . , 2008 ) , and our data indicate that interaction between WDR77 and mutant p53 is maintained . Deciphering the interactions among WDR77 , and wild-type and mutant p53 , other components of this AR-dependent transcriptional code , at AR and p53 recruiting genomic sites may , therefore , lead to novel , much needed treatments for CR-CaP . Interfering with a select segment of AR action may be feasible through modulation of coregulator-dependent interaction between AR and secondary TFs that jointly control the expression of ARE-driven gene signatures . Recent developments in peptidomimetics and multivalent peptoid conjugates have allowed for disruption of interaction between AR and some of its coregulators in CaP cell lines and xenografts as well as in ex vivo CaP explants ( Ravindranathan et al . , 2013; Wang et al . , 2016 ) . The ability of polyamides to prevent binding of AR-interacting TF Oct1 to its genomic binding sites supports the possibility of inhibiting TF-DNA interactions ( Obinata et al . , 2016 ) . The AR-WDR77-p53 transcription code is one example of an entirely novel molecular mechanism in which coregulator action brings together 2 major clinically relevant drivers of lethal CaP progression to control expression of a subset of AR target genes . Our identification of a STAT3- and IRF1-dependent transcriptional code that differs in biological function from AR-WDR77-p53 collaboration underscores the likelihood for other similar or related ( e . g . , coregulator-coregulator cooperativity ) mechanisms . Yet other models of androgen action may not have been captured because of the selection criteria of our assays . The cut-off of 300 Kb between ARBS and TSS , although sizable , may have prevented identification of genes which form AR transcriptional complexes over longer distances via chromatin looping ( Shang et al . , 2002; Hsieh et al . , 2014 ) . The requirement of 2-fold androgen regulation may have failed to isolate AR target genes for which androgen regulation is not as pronounced . Our design may have overlooked the contribution of lncRNAs ( Yang et al . , 2013 ) to AR-dependent transcription . The 18 AR-associated coregulators studied here are only a small fraction of the >270 identified to date ( DePriest et al . , 2016 ) . Selection of coregulators for inclusion relied on knowledge of their differential protein expression in CaP . When these studies were conceived , results of NextGen sequencing efforts using clinical CaP specimens had not been reported , and genomic alterations that could affect coregulator function irrespective of changes in expression level could not be taken into account . A retrospective analysis of 18 coregulators included here was done using data from the 8 NextGen CaP studies on ~1500 clinical CaP specimens that are publicly available through the cBio dataportal ( Cerami et al . , 2012 ) . In combination , somatic mutation and copy number alterations that affect these 18 genes were present in less than 5% of clinical cases ( Supplementary file 7 ) . The exception was NCOA2 , for which copy number increases fit with overexpression at the protein level as determined by immunohistochemistry . In conclusion , our alternative approach to define systematically the contribution of coregulators to AR-dependent gene expression in CaP revealed that AR action in CaP partitions according to defined fractions . These segments may be amenable to future development into alternative forms of ADT that inhibit only the most clinically relevant portion of AR action .
LNCaP ( RRID:CVCL_1379 ) and VCaP ( RRID:CVCL_2235 ) cells were obtained from the ATCC and cultured as before ( Heemers et al . , 2011; Schmidt et al . , 2012 ) . LNCaP cells in which p53 expression is silencing were obtained from the Guseva laboratory ( Guseva et al . , 2012 ) . All cell lines are used for no more than 10 passages . Cells were authenticated by STR profiles and validated further by the consistency of their AR- and androgen-responsiveness . Cells were Mycoplasma-tested every 6 months; all tests were negative . R1881 was purchased from DuPont ( Boston , MA ) . Antibodies that were used are listed below . siGenome On Target Plus SmartPools were purchased from Thermo-Scientific ( Lafayette , CO ) . Western blotting was done as described ( Schmidt et al . , 2012 ) . Cells were seeded , transfected and treated as before ( Schmidt et al . , 2012 ) . RNA isolation , cDNA synthesis and real-time RT-PCR were done as before ( Schmidt et al . , 2012 ) . Primers were synthesized by Integrated DNA Technologies ( IDT , Coralville , Iowa ) . Primer sequences used to quantitate expression of PSA , FN1 , SCAP , SERPINB5 and GAPDH have been described ( Heemers et al . , 2009; Heemers et al . , 2007a; Heemers et al . , 2007b ) . Other primer sequences are listed below . Cells were harvested in PBS ( Life Technologies , Waltham , MA ) and spun down . After resuspension of cells in 200 µl PBS , 2 ml 70% ethanol was added dropwise and cells were kept on ice before adding 50 µl RNAse A ( 10 mg/ml ) and 50 µl propidium iodide ( 50 µg/ml , Sigma-Aldrich , St . Louis , MO ) . After incubation at 37C for 30 min , cells were sorted using a Becton Dickinson LSR II flow cytometer . Data was analyzed using ModFit software . Cell viability was assessed as before ( Schmidt et al . , 2012 ) . Cells were washed with ice-cold PBS and lysed in cell lysis buffer [20 mM Tris , pH 8 . 0; 150 mM NaCl; 5 mM MgCl2; 0 . 5% NP40; 1X EDTA-free protease inhibitor cocktail ( Roche ) ] for 1 hr at 4°C . The protein content of the cell lysates was determined using a Bradford assay . The cell lysate was precleared with 50 µL of lysis-buffer-equilibrated Dynabeads protein G ( Life Technologies ) or protein G agarose ( for Figure 3B ) for 1 hr at 4°C . 2 mg precleared lysate was incubated with 6 µg of antibody ( for p53 IP , use p53 ( D01 ) #sc126 , p53 ( FL393 ) #sc6243 , p53 ( pAB421 ) #OP03 in equal ratios ) at 4°C for overnight . The next day , the antibody–protein complexes were precipitated using Dynabeads ( protein G ) at 4°C for 3 hr . Immunoprecipitated complexes were washed 4 times with wash buffer [20 mM Tris , pH 8 . 0; 150 mM NaCl; 5 mM MgCl2; 1X EDTA-free protease inhibitor cocktail] , eluted with 20 µL 2x SDS-PAGE Novex sample buffer , heated at 70°C for 10 min and the supernatant was subjected to western blotting . RNA was isolated from cells using Trizol ( Life Technologies ) , purified on RNeasy columns ( Qiagen , Germantown , MD ) and checked for integrity using an Agilent 2100 Bioanalyzer . RNA was obtained and subjected to quality control as above . HumanHT-12 v4 Beadchip analysis was done by Roswell Park Cancer Institute Genomics Shared Resource as per the manufacturer’s recommendations . The raw intensity of Illumina HumanHT-12 v4 gene expression array was scanned and extracted using BeadScan , with the data corrected by background subtraction in GenomeStudio module . The lumi module in the R-based Bioconducter Package was used to transform the expression intensity into log2 scale ( Du et al . , 2008 ) . The log2 transformed intensity data were normalized using Quantile normalization function . We used the Limma program in the R-based Bioconductor package to calculate the level of gene differential expression for each comparison . Briefly , a linear model was fit to the data ( with cell means corresponding to the different condition and a random effect for array ) , and selected contrast for each comparison was performed . For each comparison , we obtained the list of differentially expressed genes ( ≥2 fold change ) constrained by FDR <0 . 05 . Illumina BeadChIP data have been deposited in GEO under accession numbers GSE66977 and GSE81780 in MIAME-compliant format . Datasets containing Gene Symbol identifiers and corresponding expression values were uploaded into the application . Each gene symbol identifier was mapped to its corresponding human/mouse/rat orthologue cluster in the Ingenuity Knowledge Base ( Krämer et al . , 2014 ) . An absolute fold change cutoff of 1 . 4 was set to identify molecules whose expression was significantly differentially regulated ( differentially expressed genes , DEGs ) . The IPA Downstream Effects Analysis ( DEA ) was used to identify the biological functions and/or diseases that were most significant to the dataset . A right-tailed Fisher’s Exact test was used to calculate a p-value determining the probability that each biological function and/or disease assigned to these data sets is due to chance alone . Furthermore , DEA was used to predict increases or decreases of these biological functions and/or diseases occurring after androgen activation by integrating the direction change of the DEGs into a z-score algorithm calculation . Functions and/or diseases with z-scores ≤ −2 or ≥2 were considered significant . Canonical pathways analysis identified the canonical pathways from the IPA library that were most significant to the data set . The significance of the association between the data set and the canonical pathway was measured in 2 ways: ( 1 ) the ratio of the number of molecules from the data set that map to the pathway divided by the total number of molecules that map to the canonical pathway , and ( 2 ) calculation of a p value using Fisher’s Exact test to determine the probability that the association between the genes in the dataset and the canonical pathway is explained by chance alone . Upstream Analysis was used to identify the cascade of upstream transcriptional regulators ( transcription factors , enzyme , cytokine , growth factor , miRNA , compound or drug ) that could explain the observed gene expression changes in these datasets , by measuring an overlap in p-value with Fisher’s Exact test and by measuring the activation z-score as well to infer the activation states of the predicted transcriptional regulators . Cistrome project tools were used to analyze the ARBS genomic regions of different coregulator-dependent AR target gene expression signatures for the presence of consensus TF binding sites . A heatmap was generated using the R gplots package to visualize the association of the gene signatures with the presence of TF binding sites in ARBS genomic regions from Cistrome . After 16 hr of treatment with either R1881 ( 5 nM ) or vehicle , nuclear extracts were prepared from LNCaP cells using the nuclear extract kit ( Active Motif ) and samples were immunoprecipitated as described above . The eluted samples were run on 10% Bis-Tris Novex NuPAGE gels ( Life Technologies ) and were silver-stained according to the manufacturer’s protocol ( Silver stain kit , Amersham ) . The bands were cut into smaller pieces to minimize excess polyacrylamide and were first reduced with 100 mM DTT and further alkylated with 150 mM iodoacetamide . All bands were digested in-gel by adding 50 ng trypsin in 50 mM ammonium bicarbonate to each gel band for 16 hr at 37°C . Peptides were extracted in 50% acetonitrile with 5% formic acid and dried using speed vac . The dried pellet was resuspended in buffer A ( 1% acetic acid ) for LC-MS analysis . The mass spectrometric analysis was performed using a Dionex Ultimate 3000 LC system coupled to Finnigan LTQ-Obitrap Elite hybrid mass spectrometer ( Thermo Fisher Scientific ) equipped with nanoelectrospray ion source . Five μL volumes of the peptide mixture was resolved on a 15 cm Dionex HPLC column ( 75 μm ) filled with 2 µm C18-resin . The peptides were loaded with buffer A and eluted with a 2% to 70% acetonitrile gradient of Buffer B ( acetonitrile/0 . 1% formic acid ) at a flow rate of 300 nl/min for 110 min . The digest was analyzed using the data dependent multitask capability of the instrument acquiring full scan CID mass spectra to determine peptide molecular weights and product ion spectra to determine amino acid sequence in successive instrument scans . The data was processed and analyzed by searching the human reference sequence database ( ftp://ftp . ncbi . nlm . nih . gov/refseq/H_sapiens/ ) with the programs Mascot and Sequest , The search results were further analyzed using the search program X ! Tandem which is bundled into the program Scaffold . The Scaffold data was filtered based on a 1 . 0% FDR at the protein level and 2 positively identified peptides with a peptide threshold at 0 . 1% FDR . The relative abundance of protein in the IP and control samples was determined by comparing the total spectral counts identified for each protein . ChIP and ChIP-re-ChIP analyses were done using EZ ChIP kit ( EMD Millipore , Billerica , MA ) per the manufacturer’s instructions with minor modifications . After crosslinking , cell pellets were lysed and shearing using a Diagenode Bioruptor Plus Sonicator using 3 × 10 cycles of 1 min on and 1 min off at the medium setting at 4C . Protein G agarose was replaced by Protein G DynaBeads ( Thermo-Scientific ) ( 60 µl/reaction , no pre-clearing ) . For p53 ChIP , 3 p53 antibodies ( DO1 ( Santa Cruz ) , FL393 ( Santa Cruz ) , and pAB421 ( Calbiochem ) were used in combination at equal ratios and at a total amount of 2 µg antibody per reaction . Real-time RT-PCR on ChIP’ed DNA and data analysis were done as described ( Schmidt et al . , 2012 ) . Primer sequences used to amplify ARE-containing regions are included below . ChIP-re-ChIP experiments started from 3 times the amount of sheared DNA that was used for a regular ChIP experiment and 6 µg antibody ( targeting p53 or AR ) per reaction . Elution was done via incubation for 30 min at 37C in TE buffer that was supplemented with 10 mM DTT ( Wang et al . , 2007 ) . For Re-ChIP , eluates were diluted 1/50 in dilution buffer and IP was done using 6 µg IgG and p53-targeting antibodies , or IgG and AR-targeting antibody following the EZ ChIP protocol . LNCaP cells were cultured at a density of 3 × 105 per well in 6 well plates in medium supplemented with charcoal-stripped FBS . Two days later , cells were treated with 5 nM R1881 or ethanol vehicle for 48 hr . After 48 hr , cells were harvested in PBS and equal ratio cells to 0 . 4% trypan blue dye was mixed and cells were counted using a Countess II FL cell counter ( Life Technologies ) . LNCaP cells were seeded on coverslips at a density of 1 . 5 × 105 in medium supplemented with charcoal-stripped FBS and treated in the same way as for the trypan blue dye exclusion studies . For immunofluorescence , cells were washed twice with PBS and fixed with cold methanol for 4 min at −20C . Fixed cells were incubated in blocking solution ( 1% BSA in PBS ) for 1 hr . The cells were then incubated for 1 hr at room temperature with 1:500 dilution of Ki67 antibody ( Abcam ) in 1% BSA in PBS in a humidified chamber . After washing with PBS , the cells were incubated with the fluorescently labeled Alexa fluor 488 ( Cell signaling ) secondary antibody for 45 min at room temperature in a humidified chamber . After incubation , cover slips were washed 5 times with PBS , dipped once in distilled water , DAPI stained and mounted in Vectashield medium ( Vector laboratories ) . Images were acquired using an inverted EVOS FL imager ( Life Technologies ) at 10x magnification . For presentation purposes , images were merged and contrast enhanced using Image J 1 . 43 . Unstained sections of tissue microarrays ( TMAs ) PR954 , PR753 , PR483 were obtained from US Biomax . Sections were baked overnight at 58°C in a dehydration oven . Deparaffinization , sections were hydrated in three separate solutions of Xylene ( 20 min in the first and 20 s each in the last two solutions ) , followed by three separate solutions of 100% ethanol ( 4 min in the first and 20 s in each of the last two solutions ) , followed by 95% ethanol for 1 min and then , rinsed in distilled water for 5 min . Sections were incubated in 3% hydrogen peroxide for 5 min to prevent endogenous peroxidase activity and washed in distilled water for 15 min . Vectastain , Universal Elite ABC kit was used for immunostaining ( Cat# PK-6200 ) . Epitopes were retrieved by boiling slides for 30 min in a citrate-based buffer provided as antigen unmasking solution ( Vector Laboratories ) . Non-specific binding was blocked with normal horse serum for 30 min . Affinity-purified , polyclonal rabbit antibody against PGAM ( Abcam , cat # ab126534 , 1:500 ) was used as the primary antibody . The incubation time for primary antibody was overnight at 37°C for 1 hr . A diaminobenzidine ( DAB ) substrate kit ( Vector Laboratories; cat # SK-4100 ) was used for visualization according to manufacturer’s instruction . The slides were counterstained with hematoxylin QS solution ( Cat# H-3404 , Vector Laboratories ) and rehydrated by immersion into 80% , 95% and 100% ethanol followed by dipping in Xylene twice each time for two minutes . The intensity and distribution of positive staining was evaluated . A standard 4-point scale was employed for intensity , with cores being scored as negative ( no staining ) , 1 + ( weak staining ) , 2+ ( moderate staining ) and 3+ ( strong staining ) . The distribution of positive staining was evaluated as percentage of positive cells ( 0–100 ) . The vast majority of cores stained homogeneously ( 100% ) . For pair-wise point comparison of categorical variables , Fisher’s exact test was used . For pair-wise comparisons involving continuous variables , Student’s t-test was used . For populations with unequal variances , Welch's t test was used . If normality is not satisfied ( Kolmogorov-Smirnov test ) even after log-transformation , wilcoxon rank sum test was used . For multi-group comparisons , ANOVA analysis was performed . To derive the statistical significance of the overlaps between lists of genes , hypergeometric tests were used . All tests were two-tailed , with p=0 . 05 significance cutoff . Each microarray experiment was performed in triplicate ( i . e . , three times with independently isolated samples ) . To confirm the data obtained by microarray analysis , we verified a subset of the target gene changes by independent real time RT-PCR analysis . To eliminate potential batch effects , the samples were randomly assigned to different plates using the OSAT program to ensure that the distribution of sample groups was even across plates . Antibodies used for these studies include AR ( N-20 , SantaCruz Biotechnologies , for ChIP ) , AR ( 441 , SantaCruz Biotechnologies , for immunoblotting ) , AR ( PG21 , EMD Millipore , for ChIP-re-ChIP ) , AIB1 ( NCOA3 , 39797 , Active Motif ) , BRG1 ( SMARCA4 , G-7 , SantaCruz Biotechnologies ) , β-actin ( 4967L , Cell Signaling ) , IRF1 ( 8478S , Cell Signaling ) , MEP50 ( WDR77 , 2823S , Cell Signaling ) , p300 ( C-20 , SantaCruz Biotechnologies ) , p53 ( DO-1 , SantaCruz Biotechnologies ) , p53 ( FL-393 , SantaCruz Biotechnologies ) , p53 ( pAB 421 , Calbiochem ) , PGAM5 ( ab126534 , abcam ) , pHistone H2a . z ( S139 , Cell Signaling ) , PKN1 ( 610686 , BD Biosciences ) , STAT3 ( 12640S , Cell Signaling ) , TIF2 ( NCOA2 , 610984 , BD Biosciences ) and Ki67 ( abcam , ab15580 ) . Multiple primers were designed and used for these studies . For real-time RT-PCR , primers include AGR2 ( F: TGTTTGTTGACCCATCTCTGACA and R: TCTTCAGCAACTTGAGAGCTTTC ) , AOF2 ( F: CCACAACAGACCCAGAAGGT and R: CTGGGTGGACAAGCACAGTA ) , ARHGAP11A ( F: GGTTCCCTTGGATGATCTGA and R: TGGTCTCCTAAGGACCCTGTT ) , ATP11A ( F: AGGGAGAACCACATCGAAAG and R: CGAAGAATCTGCTCCTTTGC ) , BAG1 ( F: GCAGCAGTGAACCAGTTGTC and R: CAACGGTGTTTCCATTTCCT ) , CAMKK2 ( F: GTCTCACCACGTCTCCATCA and R: GCCAACTTGACGACACCATA ) , CAV1 ( F: CCACCTTCACTGTGACGAAA and R: CCCAGATGTGCAGGAAAGAG ) , CTNNB1 ( F: GCTTGGTTCACCAGTGGATT and R: GTTGAGCAAGGCAACCATTT ) , FHL2 ( F: GGTACCCGCAAGATGGAGTA and R: CTCATAGCAGGGCACACAGA ) , GAK ( F: CAGCAGAAGGTGTGGAGTCA and R: CTCGGGGACAGGTTGTAGAC ) , GNB4 ( F: GGGAAGGGTAAACGTGTTAGATT and R: GCCACTGTACAAATAGAGGAATGA ) , GNL1 ( F: CACCCCACAGGACCCTAGTA and R: GCTGCTCAAGTCCACTTTCC ) , GUCY1A3 ( F: GATTCTTCCCGGCATCATAA and R: GATTCACAAACTCGCTGCAA ) , HES6 ( F: CTGCCGGCTACATCCAGT and R: ATGGACTCGAGCAGATGGTT ) , HIP1 ( F: CCAGCGGAAGACTCAAGAAC and R: CTGACTGGGCAGAAGTTTCC ) , HTATIP2 ( F: GAAACAGAAGCCCTGTCGAA and R: CAATGAGCGTGACTTTGGAA ) , IRF1 ( F: GGATTCCAGCCCTGATACCT and R: CACCTCCAAGTCCTGCATGT ) , KAT5 ( F: CAGATCACACTCCGCTTCAA and R: CACTGGAGTTGCTGGTGAAA ) , MEF2A ( F: AGCACATTGTGGGAGAGAGACTGA and R: TGGCTTGGCCATTTTTCCTGAGCA ) , MPRIP ( F: GGTTTGCAGCAATGGAAGAA and R: CTTCGATGGCTGAGATGGTG ) , NCOA1 ( F: CTCTGGATTCAGGGCTTCTG and R: GTTCGGCAGTTGTTGTCAAA ) , NCOA2 ( F: GGCAAGAAGAGTTCCCATGA and R: CTGCTCTCATGGTGCTGGTA ) , NCOA3 ( F: CACATGGGAGTCCTGGTCTT and R: GGTTCCCAGTATTGCCAGAA ) , NET1 ( F: CTGTGGTCAGAGATGCTGGA and R: GGGTCATGGTAGGCCTTTCT ) , PARK7 ( F: TGGCTAAAGGAGCAGAGGAA and R: ATGACCACATCACGGCTACA ) , PGAM5 ( F: GCAAAGTCAGCACAGATCTG and R: CATCTGCGCGGTGGATGTAG ) , PKN1 ( F: GCCATCAAGGCTCTGAAGAA and R: GTCTGGAAACAGCCGAAGAG ) , RAB27A ( F: CTGCCAATGGGACAAACATA and R: CCGTAGAGGCATGACCATTT ) , RAD9A ( F: GTGCGGAAGACTCACAACCT and R: CAGGAGAGAAGGGCAGAACA ) , RALB ( F: CTACGCAGCCATTCGAGATA and R: CGGAGAATCTGTTCCCTGAA ) , RCHY1 ( F: CCGTGTTGTTGCTCATGTCT and R: CATCATCCAGCTGTCTCCAA ) , SASH1 ( F: TCCGAAAGAACCAGAAAGGA and R: TAGCTGAATCCGCTCCTCAT ) , SDC4 ( F: CCACCGAACCCAAGAAACTA and R: GCACAGTGCTGGACATTGAC ) , SERPINB5 ( F: CCCTATGCAAAGGAATTGGA and R: CAAAGTGGCCATCTGTGAGA ) , SMAD3 ( F: CTCCAAACCTATCCCCGAAT and R: CGCTGGTTCAGCTCGTAGTA ) , SMARCA4 ( F: CCTGAATGAGGAGGAAACCA and R: GCAGACATGTCGCACTTGAT ) , SMARCC1 ( F: GCGGATGCTCCTACCAATAA and R: CACTTTGCAGGGAGTTTGGT ) , STAT3 ( F: GGCCATCTTGAGCACTAAGC and R: CGGACTGGATCTGGGTCTTA ) , TP53 ( F: GAAGACCCAGGTCCAGATGA and R: CTGGGAAGGGACAGAAGATG ) , WASF3 ( F: CAGCTGAGCAGTCTGAGCAA and R: CTGGGTGACTTTGACAGCAA ) , WDR77 ( F: GTCTTGAGCTCTGGCACACA and R: CAGCATGAGCTCGGTATGAA ) , and ZIC2 ( F: GGCACCTTGTGATCATGTTG and R; CAAAGACTCCGGAAGGGATA ) . For ChIP assays , primers were CAMKK2 ( F: AGAACACTGTAGCTCACACAGGCA and R: GGGCACTTCCCAACCTTTCTTACT ) , GNB4 ( F: TATGAGTCCGTCTCAGTGTTG and R: TTTGAATGCACCTAATCAGCC ) , MEF2A ( F: TTGTTCTGTTTCTAGTGCTGTG and R: GCCAAATCTTTCCAAGTAGC ) , NET1 ( F: CCGAAAGTCAGCTCAGATCA and R: TTGCCTGTTCCTTCTCTCTGA ) , RAB27A ( F: TCCTGACCACAATCATAGGTTA and R: CGTTAAAAGCAAAGTCAAGGTC ) , RALB ( F: TAGGTGGTGGTGCTTGAGTG and R: TCTTCAGTCACAATCCTTGGAA ) SASH1 ( F: CATTTCAGAACAACAGGCTCAG and R: TTGTTCATTGAGGTCAACGTG ) and PSA ( F: ACAGACCTACTCTGGAGGAAC and R: AAGACAGCAACACCTTTTT ) . For site-directed mutagenesis of p53 , primers used were: T125M ( F: GCCAAGTCTGTGACTTGCATGTACTCCCCTGCCCTCAACAAG and R: CTTGTTGAGGGCAGGGGAGTACATGCAAGTCACAGACTTGGC ) , F134L ( F: CCTGCCCTCAACAAGATGTTATGCCAACTGGCCAAGAC and R: GTCTTGGCCAGTTGGCATAACATCTTGTTGAGGGCAGG ) , C135Y ( F: GCCCTCAACAAGATGTTTTACCAACTGGCCAAGACCTGCC and R: GGCAGGTCTTGGCCAGTTGGTAAAACATCTTGTTGAGGGC ) , P152L ( F: TGGGTTGATTCCACACCCCTGCCCGGCACCCGCGTCCG and R: CGGACGCGGGTGCCGGGCAGGGGTGTGGAATCAACCCA ) , R174W ( F: ACATGACGGAGGTTGTGTGGCGCTGCCCCCACCATGAG and R: CTCATGGTGGGGGCAGCGCCACACAACCTCCGTCATGT ) , V272L ( F: GGACGGAACAGCTTTGAGTTGCATGTTTGTGCCTGTCCTG and R: CAGGACAGGCACAAACATGCAACTCAAAGCTGTTCCGTCC ) , R273C ( F: GAACAGCTTTGAGGTGTGTGTTTGTGCCTGTCCTGGG and R: CCCAGGACAGGCACAAACACACACCTCAAAGCTGTTC ) , N239T ( F: CTACAACTACATGTGTACCAGTTCCTGCATGGGCGGCATG and R: CATGCCGCCCATGCAGGAACTGGTACACATGTAGTTGTAG ) , and P359S ( F: TGCCCAGGCTGGGAAGGAGTCAGGGGGGAGCAGGGCTCAC and R: GTGAGCCCTGCTCCCCCCTGACTCCTTCCCAGCCTGGGCA ) . All experiments were performed at least twice . | Prostate cancer is the second leading cause of cancer deaths in men in the Western world . Almost all of these deaths happen when the main treatment for advanced prostate cancers stops working . The treatment , known as androgen deprivation therapy , targets a protein called the androgen receptor . This receptor is activated when it binds to signaling molecules and , once active , it switches on genes that encourage the cancer cells to grow . Androgen deprivation therapy blocks the androgen receptor from interacting with the signaling molecules; however , this treatment eventually fails because the receptor finds other ways to remain active in prostate cancer . Increasing the survival of patients with prostate cancer will depend on new treatments that can inhibit androgen receptors that no longer respond to androgen deprivation therapy . The androgen receptor’s ability to switch on genes could be another target for prostate cancer therapy – though not enough was known about the way this ability is regulated and how it controls the progression of prostate cancer . Liu , Kumari et al . set out to better define how this ability drives the growth of prostate cancer . The androgen receptor needs to interact with other proteins , known as coregulators , to work , and Liu , Kumari et al . developed an assay that examines , all at the same time , how important 18 such coregulators are for more than 400 genes that are regulated by the androgen receptor . This revealed that the coregulators did not all affect the same genes and that each coregulator tended to help activate sets of genes associated with a specific aspect of the biology of prostate cancer cells . Liu , Kumari et al . also discovered previously unknown interactions between androgen receptors , coregulators and other proteins that were responsible for the specific associations between genes and corregulators . The most important of these new interactions was one between the androgen receptor , the coregulator WDR77 , and a protein called p53 . These interactions are enriched in prostate cancers , including those that do not respond to androgen deprivation therapy , where they promote cancer growth . These findings lay the foundation to develop new drugs that interfere with the interactions between the androgen receptor and other proteins that are most important for the progression of advanced prostate cancers . Other researchers have already shown that it is possible to develop such drugs – though further testing is needed before any new treatments begin to help prostate cancer patients who no longer respond to androgen deprivation therapy . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"methods"
] | [
"cancer",
"biology"
] | 2017 | A comprehensive analysis of coregulator recruitment, androgen receptor function and gene expression in prostate cancer |
Despite marked advances in breast cancer therapy , basal-like breast cancer ( BBC ) , an aggressive subtype of breast cancer usually lacking estrogen and progesterone receptors , remains difficult to treat . In this study , we report the identification of MELK as a novel oncogenic kinase from an in vivo tumorigenesis screen using a kinome-wide open reading frames ( ORFs ) library . Analysis of clinical data reveals a high level of MELK overexpression in BBC , a feature that is largely dependent on FoxM1 , a master mitotic transcription factor that is also found to be highly overexpressed in BBC . Ablation of MELK selectively impairs proliferation of basal-like , but not luminal breast cancer cells both in vitro and in vivo . Mechanistically , depletion of MELK in BBC cells induces caspase-dependent cell death , preceded by defective mitosis . Finally , we find that Melk is not required for mouse development and physiology . Together , these data indicate that MELK is a normally non-essential kinase , but is critical for BBC and thus represents a promising selective therapeutic target for the most aggressive subtype of breast cancer .
Breast cancer is a heterogeneous disease with a high degree of diversity in histology , therapeutic response , and treatment outcomes . Transcriptional profiling analyses have reproducibly identified at least five major ‘intrinsic’ subtypes of breast cancer: normal breast-like , luminal A , luminal B , HER2/Neu-enriched , and basal-like breast cancer ( BBC ) ( Perou et al . , 2000; Sorlie et al . , 2001 ) . These molecular subtypes have recently been confirmed in a comprehensive characterization of human breast tumors at the genomic , epigenetic , transcriptomic , and proteomic levels ( Cancer Genome Atlas Network , 2012 ) . Among these subtypes , basal-like breast cancer ( BBC ) is strongly associated with an aggressive phenotype and poor prognosis ( Rakha et al . , 2008 ) . Unlike their luminal counterparts , BBC cells lack expression of estrogen receptor ( ER ) and progesterone receptor ( PR ) . Most BBC tumors also lack expression of HER2 and thus this subtype largely overlaps with the clinically defined ‘triple-negative’ breast cancer ( TNBC ) , which is also characterized by the lack of ER , PR , and HER2 expression ( Rakha et al . , 2008; Foulkes et al . , 2010 ) . The lack of these molecular targets renders BBC or TNBC cells relatively unresponsive to the targeted therapies that are highly effective in the treatment of luminal or HER2 positive breast cancer . Thus , establishing the molecular pathogenesis of this subtype and identifying potential targets for treatment remains a key challenge for BBC/TNBC . Kinases comprise a large family of proteins that is frequently involved in tumor pathogenesis . Indeed , a large number of mutations , alterations in copy number , and/or expression level have been observed in genes encoding kinases across multiple types of human cancers . In addition , kinases have proven to be pharmacologically tractable , making inhibition of kinase activity with small molecules a highly effective strategy for cancer treatment ( Zhang et al . , 2009 ) . Therefore , identifying kinases critical for the growth and survival of BBC cells could not only provide valuable insights into the pathogenesis of BBC , but also define potential druggable targets for therapeutic interventions . Kinases that regulate progression through mitosis , including Aurora A , Aurora B and PLK1 , are essential for cell proliferation . Inhibiting them in cancer cells causes mitotic arrest and/or abnormalities in chromosome segregation and cytokinesis , which in turn trigger apoptosis ( Taylor and Peters , 2008; Lens et al . , 2010 ) . Inhibitors of these kinases are effective at eradicating human cancer cells in culture and in mouse xenograft models , but their efficacy in the clinic has been limited by killing of normal proliferating cells especially the bone marrow ( Dar et al . , 2010 ) . If a kinase exists that is required for mitosis in a specific type of cancer cell , but not other tumor cells or in normal cells , inhibitors of that kinase might make highly effective and safe drugs . To date , this type of cancer-specific mitotic kinase has not been identified for any cancer . In this study , we report the identification of MELK as a novel oncogenic kinase that emerged from an in vivo tumorigenesis screen . Analyses of breast cancer patient data according to subtypes revealed a remarkable overexpression of MELK in BBC . We further demonstrate that MELK is directly regulated by the FoxM1 transcription factor , a master mitotic regulator also found to be overexpressed in BBC . We discover that MELK is essential in basal-like , but not in luminal breast cancer cells . Notably , mice in which MELK has been genetically ablated display normal development and hematopoiesis . Together , our data establish MELK as a mitosis-regulating kinase involved in the pathogenesis of BBC and a promising molecular target for patients with basal-like breast malignancy .
Transformation of primary human cells with defined genetic elements is a powerful method for identifying specific genes or pathways that are involved in oncogenic transformation ( Hahn et al . , 1999; Zhao et al . , 2004 ) . To this end , we first developed an in vivo tumorigenesis system that models the pathogenesis of human breast cancer , using a previously established human mammary epithelial cell ( HMEC ) -based transformation system ( Zhao et al . , 2003 ) . To further optimize this system , we engineered telomerase-immortalized HMECs to express a dominant negative form of p53 ( p53DD ) , NeuT and PI3KCA H1047R . The resulting cells , termed HMEC-DD-NeuT-PI3KCA , were fully transformed as evaluated by their ability to form orthotopic tumors in the mammary fat pads of mice ( Figure 1—figure supplement 1 ) . Our model recapitulates the concurrent activation of HER2/Neu and PI3KCA that is prevalent in breast cancer ( Stephens et al . , 2012 ) . The HMEC transformation system described above provided us with a platform to identify novel oncogenic events capable of replacing the mutant PIK3CA in cooperating with NeuT to drive HMECs to form tumors in mice . To this end , we infected HMEC-DD-NeuT cells ( lacking the mutant PIK3CA ) with subpools of a kinome-wide retroviral library consisting of 354 human kinases and kinase-related open reading frames ( Boehm et al . , 2007 ) . The library was screened as a series of subpools of 10–12 kinase ORFs in HMEC-DD-NeuT cells . The infected cells were injected into the inguinal mammary fat pads of mice , and recipient mice were followed for tumor formation . Kinases in 12 pools induced tumor formation with latencies of 2–4 months . Genomic DNA was extracted from harvested tumor specimen as well as HMECs infected with matched pools of kinases prior to injection . We then used quantitative PCR to determine the relative abundance of each kinase in these paired samples . In total , 26 kinases were found specifically enriched in the tumors in vivo ( Figure 1 , Figure 1—figure supplement 2 ) . Several candidate kinases that scored in the screen have previously been implicated as proto-oncogenes or cancer-associated genes , such as the inhibitor of nuclear factor kappa-B kinase subunit epsilon ( IKBKE ) ( Boehm et al . , 2007 ) , rearranged during transfection ( RET ) ( Takahashi et al . , 1985 ) , casein kinase 1 epsilon ( CSNK1E ) ( Kim et al . , 2010 ) , NIMA-related serine/threonine kinase 6 ( NEK6 ) ( Nassirpour et al . , 2010 ) , and polo-like kinase 1 ( PLK1 ) ( Liu et al . , 2006 ) . At least three of them , PLK1 ( Golsteyn et al . , 1994 ) , NEK6 ( Yin et al . , 2003 ) , and MELK ( maternal embryonic leucine zipper kinase ) ( Le Page et al . , 2011 ) , have been previously implicated in regulating mitotic progression . 10 . 7554/eLife . 01763 . 003Figure 1 . An in vivo kinome-wide screen identifies MELK as a potential oncogenic kinase . Pools of retroviral vectors encoding 354 human kinases and kinase-related proteins ( 37 pools in total , each consisting of 10–12 unique open reading frames ) were transduced into HMED-DD-NeuT cells . After selection with neomycin , cells were transplanted into mammary fat pads of nude mice . Tumors that formed from HMECs infected with 12 pools of kinases were harvested , and genomic DNA was extracted . qPCR was performed on genomic DNA from the tumor specimens and cells infected with matched pools of kinases before injection . The relative fold enrichment was calculated from the differences in Ct value . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 00310 . 7554/eLife . 01763 . 004Figure 1—figure supplement 1 . Development of an in vivo tumorigenesis model . In telomerase-immortalized human mammary epithelial cells expressing p53DD ( HMEC-DD ) , expression of two potent oncogenes ( such as NeuT , PIK3CA [H1047R] ) is required to induce these cells to form orthotopic tumors with 100% penetrance . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 00410 . 7554/eLife . 01763 . 005Figure 1—figure supplement 2 . Screen hits and their gene description . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 005 One of the top-scoring hits from our genetic screen was MELK ( Figure 1 ) , an atypical member of AMPK serine/threonine kinase family ( Lizcano et al . , 2004 ) . While little is known about the exact biological functions of MELK , this kinase has been reported to be overexpressed in a variety of tumors ( Gray et al . , 2005 ) . When we analyzed MELK expression in the breast cancer data set of The Cancer Genome Atlas ( TCGA ) ( Cancer Genome Atlas Network , 2012 ) , a large cohort consisting of 392 invasive ductal breast carcinomas and 61 samples of normal breast tissues , the level of MELK transcript was approximately eightfold higher in breast tumors compared to their normal counterparts ( Figure 2A ) . The p value for this differential expression ( 4 . 6 × 10−54 ) places MELK in the top 1% overexpressed genes in breast cancer ( Figure 2A ) . The overexpression of MELK in breast tumors relative to normal breast tissues was further confirmed by analyzing two other independent data sets ( Figure 2—figure supplement 1A; Ma et al . , 2009; Richardson et al . , 2006 ) . 10 . 7554/eLife . 01763 . 006Figure 2 . MELK is highly overexpressed in breast cancer and its overexpression strongly correlates with poor prognosis . ( A ) MELK expression levels are significantly higher in breast carcinoma ( n = 392 , red circles ) than in normal breast tissues ( n = 61 , blue circles ) in the TCGA breast cancer cohort ( Cancer Genome Atlas Network , 2012 ) . Black lines in each group indicate median with interquartile range . p=4 . 6 × 10−54 ( Student's t test ) . ( B ) Expression level of MELK tightly correlates with the pathological grade of breast tumors in the three independent cohorts for which these data are available . Black lines in each group indicate median with interquartile range . p values were calculated with one-way ANOVA . ( C ) Kaplan–Meier analysis of metastasis-free survival of breast cancer patients in two independent cohorts . Samples were divided into two groups with high and low expression levels of MELK . p values were obtained from the log-rank test . Hazard ratio ( HR ) was calculated using GraphPad Prism . ( D ) Kaplan–Meier analysis of overall survival in the van de Vijver cohort breast cancer patients . Samples were divided as in ( C ) . Log-rank p value and hazard ratio ( HR ) are shown . ( E ) MELK expression among the molecular subtypes of breast cancer . Samples in each cohort were classified into five distinct molecular subtypes using PAM50 ( Parker et al . , 2009 ) . Black lines in each group indicate median with interquartile range . ( F ) MELK expression inversely correlates with that of estrogen receptor ( ER ) or progesterone receptor ( PR ) . Linear regression was determined using GraphPad Prism . The linear regression Pearson's correlation coefficient ( R2 ) and its p value are indicated . ( G ) ER/PR− breast tumors have higher abundance of MELK protein than ER/PR+ ones . Lysates of primary human tumors were subjected to immunoblotting using the indicated antibodies . ( H ) Expression of ER/PR determines MELK expression within HER2+ breast cancer . Samples with molecular HER2+ status were classified into ER/PR+ and ER/PR− groups . Black lines in each group indicate median with interquartile range . ( I ) MELK expression in subtypes of breast cancer that are defined by ER/PR , and HER2 expression . Note that HER2+ tumors were divided into ER/PR+ and ER/PR− groups . *p<0 . 05 , ****p<0 . 0001 ( Student's t test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 00610 . 7554/eLife . 01763 . 007Figure 2—figure supplement 1 . MELK is a top-ranking overexpressed gene in breast cancer and a strong prognostic indicator . ( A ) MELK expresses at a higher level in breast tumors than in normal breast tissues . ( B ) MELK expression is positively correlated with the histologic grade of disease . The indicated p values rank 7th ( of total 19 , 574 genes measured , Bittner cohort ) and 3rd ( of total 12 , 624 genes measured , Hatzis cohort ) . ( C ) MELK expression predicts metastasis . Samples in the indicated cohorts were divided into groups of MELK high and MELK low , which represent the top 60% and bottom 40% in the descending order of MELK expression . Kaplan–Meier curves are shown , with log-rank p values and hazard ratios ( HR ) . ( D ) High MELK expression predicts inferior overall survival of breast cancer patients . Samples were grouped into MELK high and low as in ( D ) . p values were obtained from log-rank test , hazard ratio ( HR ) was calculated using GraphPad Prism . ( E ) MELK expression in subtypes of breast cancer that are defined by gene expression profiling . Samples were divided into subtypes based on PAM50 gene signature ( Parker et al . , 2009 ) . ‘ns’ denotes not significant . ****p<0 . 0001 . ( F ) MELK expression is reversely correlated with that of luminal marker ( ER/PR ) . Linear regression was determined using GraphPad Prism . The linear regression Pearson's correlation coefficient ( R2 ) and its p value are indicated . ( G ) Triple-negative breast cancer exhibits higher expression of MELK than ER/PR+ tumors . Patients were classified into groups with subtypes of diseases based on expression of ER/PR and HER2 . All the data were downloaded from Oncomine ( Rhodes et al . , 2004 ) , and were re-analyzed . The black lines in each panel ( A , B , E , F ) indicate median with interquartile range . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 00710 . 7554/eLife . 01763 . 008Figure 2—figure supplement 2 . Correlation of MELK expression with breast cancer subtypes and the histologic grade of disease . ( A ) Data of MELK expression and the histologic grade of disease among 1556 invasive ductal breast carcinoma ( Curtis et al . , 2012 ) were analyzed . Black lines in each group indicate median with interquartile range . ****p<0 . 0001 ( Student's t test ) . Note that statistical analysis was not performed in grade 1 group , due to the limited number of HER2+ and basal-like breast cancer samples . ( B ) Data were analyzed in respect to the grade of disease . p values were calculated with one-way ANOVA . For the analysis of basal-like breast cancer , a Student's t test between grade 2 and grade 3 was used instead , due to a single sample characterized as grade 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 008 To gain insights into the potential relevance of MELK overexpression in breast cancer , we asked whether MELK expression correlates with the status of disease . By analyzing gene expression data across five independent cohorts totaling more than 1500 patients ( Desmedt et al . , 2007; Hatzis et al . , 2011; Schmidt et al . , 2008; Wang et al . , 2005b; Supplementary file 1 ) , we found that higher expression of MELK was strongly associated with higher histologic grade in breast cancer ( Figure 2B , Figure 2—figure supplement 1B ) ; the p values for this correlation rank in the top 1% of a total 12 , 624 or more genes measured in all these cohorts . We also examined whether MELK expression is correlated with metastatic recurrence . We analyzed three independent cohorts in which patients with early-stage breast cancer were followed for metastasis-free survival and had not received adjuvant systemic treatment after surgery ( van 't Veer et al . , 2002; Wang et al . , 2005b; Schmidt et al . , 2008; Supplementary file 1 ) . In all three cohorts , higher MELK expression levels were strongly associated with earlier metastasis in women initially diagnosed with lymph-node-negative tumors ( all p values<0 . 001 , hazard ratios >2; Figure 2C , Figure 2—figure supplement 1C ) . We further analyzed two cohorts , where a majority of patients had high grade and lymph-node-positive breast cancer and nearly all patients received neoadjuvant chemotherapy and/or hormone therapy ( Hatzis et al . , 2011; Loi et al . , 2007; Supplementary file 1 ) . Again , high expression level of MELK robustly correlates with metastasis in breast cancer patients ( both p values<0 . 001 , hazard ratios >2; Figure 2C ) . Thus MELK overexpression appears to have a strong predictive value for breast cancer metastasis irrespective of tumor grade or treatment regimen . We next asked if MELK expression also correlates with the survival of breast cancer patients . In five independent large cohorts in which more than 1100 total patients were followed for overall survival ( Desmedt et al . , 2007; Esserman et al . , 2012; Kao et al . , 2011; Pawitan et al . , 2005; van de Vijver et al . , 2002; Supplementary file 1 ) , high expression level of MELK strongly correlated with increased rates of mortality ( all p values<0 . 05 , hazard ratios >2 ) ( Figure 2D , Figure 2—figure supplement 1D ) . Together , these data show that MELK may serve as a prognostic indicator in predicting breast cancer patients' likelihood of metastasis and overall survival rate . Given the heterogeneity of breast cancer , we analyzed MELK expression in different subtypes of breast cancer as defined by gene expression profiling ( Perou et al . , 2000; Sorlie et al . , 2001 ) . We categorized samples in multiple breast cancer data sets by PAM50 gene signature ( Parker et al . , 2009 ) . In five independent cohorts with more than 1500 patients in total , we observed a strikingly similar pattern of MELK expression among these different subtypes of breast tumors ( Figure 2E , Figure 2—figure supplement 1E ) . While luminal A and normal-like subtypes displayed the lowest expression of MELK , basal-like breast cancers ( BBC ) showed the highest expression level of MELK among all subtypes ( p<0 . 0001 ) . Given that there are more high-grade tumors in the BBC than the other subtypes , we sought to determine the correlation of MELK with subtypes of breast tumors within the same grade . We performed statistical analysis of a large cohort of breast cancer for grade 1 , 2 and 3 across all subtypes , respectively , and found that MELK is most highly expressed in BBC ( Figure 2—figure supplement 2A ) , suggesting that MELK expression is most pronounced in basal-like breast tumors with the same pathological grade . Moreover , a significant association of MELK expression with disease status also exists within the subtype of BBC ( Figure 2—figure supplement 2B ) , suggesting that MELK expression is associated with tumor aggressiveness and poor prognosis in this disease . Consistent with this observation of MELK overexpression in BBC , we found that MELK expression in breast tumors has a significant inverse correlation with the expression of luminal markers , including estrogen and progesterone receptors ( ER , PR ) ( Figure 2F , Figure 2—figure supplement 1F ) . To confirm this observation at the protein level , we analyzed primary tumors samples for MELK expression . Strikingly , all the four ER/PR+ tumor samples lacked detectable signal of MELK expression . In contrast , ER/PR-negative tumors had abundant MELK protein ( Figure 2G ) . Given that ER/PR expression varies within the molecular HER2+ subtype , we analyzed MELK expression within this subtype . We found that MELK expression was significantly higher in ER/PR− tumors than in those with ER/PR+ status ( Figure 2H ) . An alternate categorization of breast cancers uses the expression of ER/PR and HER2 . Triple-negative breast cancer ( TNBC ) , a subtype lacking ER/PR and HER2 expression , largely overlaps with basal-like breast cancer ( Rakha et al . , 2008; Foulkes et al . , 2010 ) . Because this subtype-categorization has been routinely used in the clinic for diagnosis and selection of treatment strategies , we also examined whether MELK expression correlates with this alternate subtype categorization . In two independent cohorts , the expression level of MELK is the highest in TNBC ( Figure 2I , Figure 2—figure supplement 1G ) . Again , within the HER2+ sub-group , ER/PR− tumors have much higher MELK expression than ER/PR+ ones ( Figure 2I ) . Together , these data indicate that MELK expression is highly elevated in breast tumors lacking the expression of ER and PR luminal markers . To investigate the mechanism underlying MELK overexpression in BBC , we first analyzed the copy number of MELK in breast cancer . Gene amplification of MELK occurs in both primary tumors and human breast cancer cell lines , especially in ER-negative samples ( Figure 3—figure supplement 1 ) . These tumors or cells with increased copy number of MELK also exhibit high level of MELK expression , suggesting that gene amplification contributes to the overexpression of MELK . However , gene amplification of MELK occurs at low frequency , and does not explain the widespread overexpression of MELK in BBC . Recent comprehensive profiling of breast cancer suggests a role for FoxM1 activation in the transcriptional maintenance of BBC ( Cancer Genome Atlas Network , 2012 ) . We found that like MELK , FoxM1 is most highly expressed in the BBC or TNBC subtypes ( Figure 3A , Figure 3—figure supplement 2A , B ) . Moreover , an extremely tight correlation between FoxM1 and MELK expression was observed in multiple large-sized cohorts ( Figure 3B , Figure 3—figure supplement 2C ) . FoxM1 downregulation via gene silencing or a chemical inhibitor , thiostreptoin ( Hegde et al . , 2011 ) , reduced MELK expression ( Figure 3C , D , Figure 3—figure supplement 2D ) . Furthermore , we found that the promoter of MELK contains a putative FoxM1 binding motif ( Wierstra and Alves , 2007 ) , and chromatin immunoprecipitation assays using a FoxM1-specific antibody recovered a MELK promoter region that included the putative binding site ( Figure 3E ) . Together these data suggest that FoxM1 is a transcription factor that is enriched in BBC and regulates MELK expression , providing a molecular mechanism underlying the overexpression of MELK in BBC . 10 . 7554/eLife . 01763 . 009Figure 3 . FoxM1 is overexpressed in BBC and regulates the expression of MELK . ( A ) High expression of FoxM1 in BBC . Samples in the indicated data sets were grouped into subtypes based on the PAM50 gene signature ( Parker et al . , 2009 ) . ns , denotes not significant . *p<0 . 05 , ****p<0 . 0001 . ( B ) FoxM1 and MELK expression are tightly correlated . Expression of MELK was plotted against that of FoxM1 . Each circle represents an individual sample of human breast carcinoma ( n = 392 for TCGA dataset; n = 261 for Bittner dataset ) . Red and green circles represent basal-like breast tumors and all other subtypes of breast tumors , respectively . Correlation analysis was performed by GraphPad Prism . ( C ) FoxM1 knockdown suppresses MELK expression . Cells were transfected with either control siRNA or siRNA-targeting FoxM1 . Lysates were harvested 3 days after transfection and subjected to immunoblotting . Aurora kinase A ( AURKA ) , a known transcriptional target of FoxM1 ( Lefebvre et al . , 2010 ) , was used as a positive control . ( D ) FoxM1 inhibition downregulates the expression of MELK . MDA-MB-231 cells were treated for the indicated time with vehicle or thiostrepton . Protein lysates were subjected to immunoblotting analysis of MELK and AURKA as indicated . ( E ) A putative FoxM1 binding site in the MELK promoter , and the FoxM1 consensus binding site ( left ) . Numbers for the nucleotides are relative to the transcription start site ( +1 ) of MELK . Chromatin immunoprecipitation assay of the MELK promoter in MDA-MB-468 cells ( right ) . Control rabbit IgG and an antibody against FoxM1 were used . Primers for the promoter region of CDC25B were used as a positive control . ( F ) Cell cycle-dependent expression of MELK . MDA-MB-231 cells were treated with nocodazole ( 100 ng/ml ) for 18 hr or not treated ( Asynchronized , As ) . Nocodazole-arrested mitotic cells ( M ) were isolated by shake-off , and the attached cells enriched in G2 phase ( G2 ) were harvested . A part of the mitotic cells were released into G1 phase after 4 hr of incubation ( M + 4 hr ) . The left panel shows the flow cytometry analysis of cell cycle , and the right panel shows immunoblotting analysis of MELK and other cell cycle-specific proteins as indicated . ( G ) Expression of MELK and other mitotic factors during cell cycles in basal-like ( MDA-MB-231 ) vs luminal ( MCF7 ) breast cancer cells . Cell lysates were prepared as in ( F ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 00910 . 7554/eLife . 01763 . 010Figure 3—figure supplement 1 . Gene amplification of MELK in BBC . Gene expression of MELK among ER-positive and ER-negative invasive ductal breast carcinoma ( A , n = 389; TCGA , 2012 ) and breast cancer cell lines ( B , n = 55; Barretina et al . , 2012 ) . Red circles indicate samples with MELK gene amplified ( defined as copy number >3 ) . Note that samples with MELK amplification tend to have high expression level of MELK . Black lines in each group indicate median with interquartile range . p values from two-tailed Student's t test are shown . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 01010 . 7554/eLife . 01763 . 011Figure 3—figure supplement 2 . FoxM1 is overexpressed in BBC and transcriptionally regulates MELK . ( A ) FoxM1 expression in subtypes of breast cancer that are defined by gene expression profiling . Samples in the two indicated cohort was grouped into subtypes based on PAM50 gene signature ( Parker et al . , 2009 ) . ****p value <0 . 0001 . ( B ) Expression of FoxM1 is significantly higher in triple-negative than in ER/PR+ subtypes . Samples were classified into subtypes based on the protein expression of ER/PR and HER2 . The indicated p values were from comparing MELK expression in triple-negative with that in ER/PR+ breast cancer . ( C ) FoxM1 and MELK expression are tightly correlated with each other . Expression of MELK was plotted against that of FoxM1 . Each circle represents an individual human breast tumor sample ( n = 295 for van de Vijver dataset; n = 508 for Hatzis dataset ) . Correlation analysis was performed by GraphPad Prism . ( D ) FoxM1 inhibition by thiostrepton decreases the transcription of MELK . Cells were treated with vehicle or thiostrepton for 16 hr , and total RNA was extracted followed by cDNA synthesis . Quantitative PCR was performed using the primers for the indicated genes . The error bars indicate standard deviation . *p value <0 . 01 . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 01110 . 7554/eLife . 01763 . 012Figure 3—figure supplement 3 . MELK expression in different cell cycle of BBC cells . The indicated BBC cells were left untreated ( Asynchronized , As ) or treated with 100 ng/ml nocodazole for 18 hr . Mitotic cells ( M ) were harvested by shake-off , with the attached cells harvested as those enriched in G2 phase ( G2 ) . A part of the mitotic cells was washed off nocodazole , and incubated for 4 hr before the attached cells ( M + 4 hr ) were harvested . Lysates from the prepared cells were prepared and subjected to immunoblotting using the indicated antibodies . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 012 Given that FoxM1 is a master transcription factor for many genes that are essential for mitosis ( Laoukili et al . , 2005; Wang et al . , 2005a ) , our finding also suggests that MELK is a mitotic factor in BBC cells . Proteins required for mitotic progression typically accumulate during G2 and M phase , and are destroyed by ubiquitin-dependent proteolysis at the end of cytokinesis . A previous study reported that MELK is stabilized in mitosis and partially degraded upon mitotic exit in HeLa cells and Xenopus embryos ( Badouel et al . , 2010 ) . We found that , in BBC cells , MELK was highly expressed during mitosis , and its protein abundance decreased dramatically when mitotic cells progressed into G1 phase ( Figure 3F , Figure 3—figure supplement 3 ) . This expression pattern of MELK , which is similar to that of Cyclin B1 and Aurora kinases , indicates that MELK is a mitotic kinase in BBC cells . Interestingly , while luminal breast cancer cells have a similar pattern of MELK expression during cell cycle , their MELK protein levels in the M phase are much lower than those of BBC cells ( Figure 3G ) . The expression levels of other mitotic factors including Cyclin B1 and Aurora A are comparable between basal-like and luminal cancer cells ( Figure 3G ) , suggesting that MELK may play a unique role during mitosis in BBC cells . MELK was scored in our kinase library screen and is overexpressed in breast cancer , particularly in basal-like breast tumors . Therefore , we sought to further determine the potential oncogenic role of MELK . To this end , we re-engineered HMEC-DD-NeuT cells to express wild type ( WT- ) or myristoylated ( myr- ) MELK ( the kinases in our initial screen were myristoylated , Boehm et al . , 2007 ) . While HMEC-DD-NeuT cells expressing the empty vector failed to form tumors in mice , overexpression of either WT- or myr-MELK in these cells drove tumor formation with 100% penetrance within 2 months ( Figure 4—figure supplement 1 ) , demonstrating that overexpression of MELK was able to confer the tumorigenicity of HMEC-DD-NeuT cells . MELK expression strongly correlates with cell proliferation ( Venet et al . , 2011 ) , indicating a functional role of MELK for cell growth . Indeed , we found that MELK overexpression in non-transformed HMEC-DD cells resulted in increased cell proliferation in suspension culture ( Figure 4—figue supplement 2A ) . While oncogenic PIK3CA , Ras , or NeuT alone can induce colony formation of HMEC-DD cells in soft agar , two oncogenic events ( e . g . , PIK3CA plus NeuT ) , are usually required to fully transform HMEC-DD cells to form tumors in mice ( Zhao et al . , 2003 , 2004 ) . Similar to these oncogenes , over-expression of MELK alone can also promote anchorage-independent growth of HMEC-DD and MCF10A cells ( Figure 4—figure supplement 2B–E ) . Likewise , wild-type MELK cooperates with a second oncogene , for example NeuT , to induce tumor formation in vivo . In contrast to the transformation of HMEC-DD cells , one oncogenic event is sufficient to transform Rat1 rodent fibroblasts expressing p53DD ( Rat1-DD ) cells , as indicated by both anchorage-independent growth in vitro and tumor formation in vivo ( Ni et al . , 2012 ) . To determine whether MELK has a transforming activity as a single event in this system , we engineered Rat1-DD cells expressing MELK ( Rat1-DD-MELK ) , or PI3KCA H1047R ( Rat1-DD-PI3KCA H1047R ) as a positive control ( Figure 4A ) . As expected , Rat1-DD cells transduced with an empty vector failed to grow as colonies in soft agar or to form tumors in mice . Strikingly , Rat1-DD-MELK cells displayed a robust transformed phenotype comparable to Rat1-DD-PI3KCA H1047R cells , as evidenced by both colony growth in vitro and tumor formation in vivo ( Figure 4B , C ) . 10 . 7554/eLife . 01763 . 013Figure 4 . Overexpression of wild-type MELK induces oncogenic transformation . ( A ) Immunoblotting analysis of Rat1-DD cells expressing vector , wild-type ( WT ) allele of human MELK , or an oncogenic allele of PIK3CA ( H1047R ) . Expression of PIK3CA ( H1047R ) enhances Akt phosphorylation . β-tubulin serves as a loading control . ( B ) Overexpression of MELK confers anchorage-independent growth of Rat1-DD cells . The left panel shows representative bright-field images of the anchorage-independent growth of cells expressing MELK or PIK3CA H1047R . The bar graph represents means ± SD for three experiments . ( C ) Overexpression of MELK drives Rat1-DD cells to form tumors in vivo . Representative subcutaneous tumors arising from injected Rat1-DD cells expressing MELK or PIK3CA H1047R are shown ( left ) . The tumor weights for each group are shown as a dot chart ( right ) . ( D ) Immunoblotting analysis of Rat1-DD cells expressing vector , WT MELK or two kinase-inactive alleles of MELK: D150A or T167A . Note that MELK is c-terminally tagged with a Flag epitope . ( E ) Rat1-DD cells expressing kinase-inactive alleles of MELK ( D150A or T167A ) , fail to grow as colonies in soft agar . The bar graph represents means ± SD for three experiments . ( F ) Rat1-DD cells expressing kinase-inactive alleles of MELK ( D150A or T167A ) , fail to grow as tumors in vivo . The tumor weights for each group are shown as a dot chart ( right ) . ***p<0 . 001 , Student's t test . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 01310 . 7554/eLife . 01763 . 014Figure 4—figure supplement 1 . MELK overexpression promotes tumorigenesis . HMEC-DD-NeuT cells were transduced with empty vector , myristoylated ( myr- ) or wild type ( WT- ) MELK . Cells were transplanted into mammary fat pads of nude mice . The number of injections and tumors formed within 2 months is listed . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 01410 . 7554/eLife . 01763 . 015Figure 4—figure supplement 2 . MELK overexpression promotes oncogenic transformation in vitro . ( A ) HMEC-DD cells were seeded in 6-well plate coated with 0 . 6% agar ( 50 , 000 cell per well ) . Bright-field images were captured 4 days after seeding . Cells were also collected for trypin digestion and counting ( right histogram , mean ± SD ) . ( B ) Overexpression of MELK in HMEC-DD cells . Cells were stably transduced with either empty vector ( pWzl-blast ) or vector encoding human MELK . Cell lysates were subjected to immunoblotting using the indicated antibodies . ( C ) MELK overexpression induces anchorage-independent growth of HMEC-DD cells . Cells were seeded in 0 . 3% agar ( 4000 cell seeded , per well of a 12-well plate ) , and harvested after 4 weeks . Bright-field images are shown . ( D ) Overexpression of MELK in MCF-10A cells . Cells were stably transduced with either empty vector ( pWzl-blast ) or vector encoding human MELK . Cell lysates were subjected to immunoblotting using the indicated antibodies . ( E ) MELK overexpression confers anchorage-independent growth in MCF-10A cells . Cells were seeded in 0 . 3% agar ( 4000 cell seeded , per well of a 12-well plate ) , and harvested after 4 weeks . Bright-field images and quantification of colonies per field are shown . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 015 To determine whether the transforming ability of MELK requires its kinase activity , we introduced catalytically inactive alleles of MELK , D150A or T167A ( Lizcano et al . , 2004; Vulsteke et al . , 2004 ) , into Rat-DD cells . Unlike Rat1-DD-MELK cells , both Rat1-DD-MELK-D150A and Rat1-DD-MELK-T167A cells exhibited only limited growth in soft agar or in mice ( Figure 4D–F ) . Together , these studies indicate that MELK can be a potent oncogenic driver , when it is aberrantly overexpressed and that this oncogenic potential relies on its kinase activity . Since MELK is predominantly overexpressed in basal-like breast tumors , we sought to determine whether MELK plays a role in the proliferation of BBC cells . We first analyzed a set of breast cancer cell lines that mirror the molecular subtypes of clinical tumors ( Neve et al . , 2006 ) , and found that the expression level of MELK is much higher in the cohort of 23 BBC cell lines than in the cohort of 24 luminal breast cancer cell lines ( Figure 5A ) . This is consistent with the expression pattern of MELK in primary human breast tumors . We also confirmed that the protein abundance of MELK is much higher in BBC cells compared to luminal cells ( MCF7 and T47D ) ( Figure 5B ) . These cell lines thus , provide an excellent platform to assess potential roles of MELK in BBC . 10 . 7554/eLife . 01763 . 016Figure 5 . MELK is essential for the growth of basal-like breast cancer cells . ( A ) MELK expression levels are significantly higher in BBC cell lines than in luminal breast cancer cell lines . The MELK mRNA data in 23 established BBC and 24 luminal breast cancer cell lines were obtained from the Neve dataset ( Neve et al . , 2006 ) and are shown as a dot chart . ( B ) Immunoblotting analysis of MELK protein abundance in 6 basal-like and 2 luminal breast cancer cell lines . α-tubulin was used as a loading control . ( C ) Effects of inducible shRNA-mediated MELK silencing ( tet-shMELK ) in one BBC cell line , BT549 . Immunoblotting analysis of MELK protein levels in the presence and absence of doxycycline is shown in the left panels . The middle and the right panels show the crystal violet staining of the plates and their respective quantification . The bar graphs indicate means ± SD for three experiments . ( D ) Effects of MELK knockdown on the proliferation of additional five BBC cell lines . Cells were treated as in ( C ) . The bar graph indicates means ± SD for three experiments . ( E ) Luminal breast cancer cells are insensitive to MELK knockdown . The indicated five luminal breast cell lines were treated as in ( C ) . Quantification of cell proliferation is shown ( means ± SD ) . ( F ) WT but not a kinase-inactive allele of MELK rescues the impaired cell proliferation of BBC cells induced by MELK knockdown . The left panel shows immunoblotting analysis of MELK protein level in MDA-MB-468 cells carrying tet-shMELK , and expressing either shMELK resistant WT MELK ( MELK-R ) or kinase-inactive MELK ( MELK-R , T167A ) in the presence and absence of doxycycline . Note that the exogenous MELK is tagged with Flag epitope . The middle and the right panels show , respectively , the crystal violet staining of the plates and their respective quantification . The bar graph indicates means ± SD for three experiments . *p<0 . 05 , **p<0 . 01 , Student's t test . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 01610 . 7554/eLife . 01763 . 017Figure 5—figure supplement 1 . MELK knockdown in basal and luminal breast cancer cells . ( A ) Conditionally knocking down MELK in five basal and five luminal breast cancer cell lines . Cells were exposed to doxcycyclin ( 100 ng/ml ) for 3 days . Cell lysates were subjected to immunoblotting using anti-MELK . α-tubulin was used as a loading control . ( B ) Quantification of MELK knockdown by q-PCR . Total RNA was extracted from control cells or cells treated with doxycycline ( 100 ng/ml , 3 days ) , followed by reverse transcription and quantitative PCR . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 01710 . 7554/eLife . 01763 . 018Figure 5—figure supplement 2 . MELK knockdown does not affect the proliferation of HMECs . ( A ) Immunoblotting analysis of MELK protein levels in HMECs in the presence and absence of doxycycline . ( B ) Quantification of MELK knockdown by q-PCR . ( C ) Quantification of the growth of HMECs . Cells were treated without or with doxycyline for 7 days before cells are fixed and stained with crystal violet . The staining was extracted to determine the absorbance . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 01810 . 7554/eLife . 01763 . 019Figure 5—figure supplement 3 . Generation of shMELK-resistant MELK cDNA ( MELK-R ) . Top , the 21-mer sequence targeted by shMELK2 is marked in bold . The silent mutations are indicated by the arrows . Bottom , the indicated shRNA ( scramble or shMELK ) were co-transfected with plasmid encoding GFP , or parental MELK , or MELK-R . Cell lysates were harvested for immuoblotting . Note that MELK-R , but not the parental wild type MELK is resistant to shMELK . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 019 To examine the role of MELK in the proliferation of these cells , we utilized a tetracycline-inducible gene knockdown technique in which shRNA transcription ( and consequently target gene silencing ) is induced upon exposure of the targeted cells to doxycycline ( Wiederschain et al . , 2009 ) . Among the multiple inducible shRNAs that we generated to target MELK , two shRNAs were found to efficiently reduce MELK expression in cells treated with doxycycline ( Figure 5C ) . We then stably introduced both shMELKs into basal or luminal breast cancer cell lines . Cell proliferation was measured upon induction of the shRNA in the presence of doxycycline . As hypothesized , MELK knockdown strongly impaired the growth of all six BBC cell lines tested , including BT549 , MDA-MB-468 , MDA-MB-231 , MDA-MB-436 , HCC70 , and HCC1954 ( Figure 5C , D , Figure 5—figure supplement 1A , B ) . In contrast , in five luminal breast cell lines , MELK knockdown with equivalent efficiency did not result in obvious inhibition on cell growth ( Figure 5E , Figure 5—figure supplement 1A , B ) . Similarly , MELK knockdown had little effect on non-transformed HMECs , which have low level of MELK expression ( Figure 4—figure supplement 2 ) . To further validate the essential role of MELK in BBC cells , we carried out rescue experiments with both WT and kinase inactive MELK . We found that the proliferation of MDA-MB-468 cells expressing shMELK was restored , when the MELK expression level in these cells was rescued by expression of a shMELK-resistant allele of MELK ( MELK-R ) ( Figure 5F , Figure 5—figure supplement 3 ) , confirming that the effects of shRNA are due to the specific knockdown of MELK . Notably , expression of a kinase inactive version of MELK-R , MELK-R ( T167A ) , failed to restore cell proliferation in these cells ( Figure 5F ) , indicating that kinase activity of MELK is critical for the proliferation of these BBC cells . To understand the mechanism ( s ) underlying the MELK function in BBC cells , we examined how inducible shRNA-mediated MELK depletion affects various cellular processes . In the presence of doxycycline , BBC cells underwent cell death indicated by increased apoptotic markers , including cleaved caspase 3 , cleaved PARP , and DNA fragmentation ( Figure 6A , B , Figure 6—figure supplement 1A ) . zVad , a pan-caspase inhibitor , was able to rescue cell death , indicating an active role of caspases in executing cell death upon MELK depletion ( Figure 6C , Figure 6—figure supplement 1B ) . In contrast , MELK knockdown has little effect on luminal tumor cells , such as MCF7 ( Figure 6D ) . To complement our RNAi-mediated MELK knockdown studies , we used a recently developed chemical inhibitor of MELK , OTSSP167 ( Chung et al . , 2012 ) , to evaluate the functional dependency on MELK by basal and luminal breast cancer cells . Consistently , OTSSP167 induced apoptotic cell death selectively in basal breast cancer cells ( Figure 6—figure supplement 2A–C ) . 10 . 7554/eLife . 01763 . 020Figure 6 . MELK downregulation induces apoptosis and impairs mitosis in BBC cells . ( A ) Immunoblotting analysis of MDA-MB-468 cells carrying a scrambled control or tet-shMELK in the presence and absence of doxycycline . Both cleaved PARP and Caspase-3 levels increased upon MELK downregulation . ( B ) MELK knockdown induces DNA fragmentation . MDA-MB-468 cells carrying scramble control or tet-shMELK were treated with or without doxcycline followed by fixation and staining with DAPI . The bright and punctate staining indicative of DNA fragmentation was only seen in cells carrying tet-shMELK in the presence of doxycycline ( lower right panel ) . ( C ) A caspase inhibitor prevents MELK knockdown-induced cell death . MDA-MB-468 cells stably transduced with tet-shMELK were either untreated or treated with doxycycline for 4 days , and further treated with with zVad-fmk ( 40 μM ) or vehicle during the last 2 days . Lysates from these cells were subjected to immunoblotting , with β-tubulin as a loading control . ( D ) MELK knockdown induces cell death selectively in BBC cells . The indicated cells were untreated or treated with doxycycline for 4 days followed by immunoblotting and imaging analyses . MELK knockdown induces increased level of cleaved PARP ( left ) and cell death ( right ) in BT549 but not in MCF7 cells . ( E ) MELK knockdown induces the accumulation of cells with 4n DNA content and G2/M arrest . MDA-MB-468 cells carrying tet-shMELK were treated or untreated with doxycycline for 5 days . Samples were prepared for cell cycle analysis and immunoblotting . The left panel shows representative cell cycle histograms; the middle indicates the quantification of % cells with 4n DNA content; and in the right panel , immunoblotting analysis shows that depletion of MELK increases the expression of G2/M specific proteins as indicated . ( F ) MELK knockdown induces bi- or multi-nucleated cells . MDA-MB-468 cells carrying tet-shMELK were treated or untreated with doxycycline for 4 days , followed by fixation and DAPI staining . Cells with mono- , bi- , or multi-nuclei were counted , and the data indicate % cells with two or more than two nuclei . Each circle in the histogram represents a single randomly selected field ( total number of cells counted >500 for each group ) . The black lines indicate mean ± SEM . ( G ) MELK inhibition induces defective cell division . Fluorescent images were obtained from MDA-MB-468 cells carrying tet-shMELK as described in ( F ) stained with anti-β-tubulin ( green ) and DAPI ( blue ) . ( H ) MDA-MB-468 cells stably transduced with tet-shMELK and Histone 2B-GFP were cultured in the presence or absence of doxycycline for 3 days , and then subjected to time-lapse imaging . Time is given in hours:minutes . In the absence of doxycycline , cells undergo normal mitosis ( top panels ) . In the presence of doxycycline , binucleated cells ( middle panels ) and cells in metaphase ( bottom panels ) undergo cell death . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 02010 . 7554/eLife . 01763 . 021Figure 6—figure supplement 1 . MELK inhibition induces cell death in MDA-MB-468 cells . ( A ) Bright-field images of indicated cells untreated or treated with doxycycline for the induction of MELK silencing . ( B ) DAPI staining of MDA-MB-468 cells with tet-shMELK . The cells were untreated or treated with doxycycline for 4 days , and further treated with zVad-fmk or vehicle during the last 2 days . Note that zVAD decrease cell death that is induced by MELK knockdown . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 02110 . 7554/eLife . 01763 . 022Figure 6—figure supplement 2 . MELK inhibition induces cell death selectively in basal-like breast cancer cells . ( A ) The indicated cells were treated with vehicle or MELK inhibitor ( OTSSP167 , 100 nM ) for 2 days . Bright-field images of cells were captured . ( B ) Cell viability were treated as in ( A ) and assayed by trypan blue staining and counting . ( C ) Cell lysates were harvested for immunoblotting using the indicated antibodies . The order of samples loaded is as following: control apoptotic cell lysates; basal ( MDA-MB-231 , MDA-MB-468; HCC70 , BT549 , HCC1197 ) ; luminal ( T47D , MDA-MB-415 , CAMA1 , ZR-75-1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 02210 . 7554/eLife . 01763 . 023Figure 6—figure supplement 3 . MELK inhibition in BBC cells induces cell death and defective mitosis . ( A ) Conditionally knocking down MELK in BT549 cells induces the accumulation of cells with 4n DNA content and G2/M arrest . BT549 cells with tet-shMELK were either treated without or with doxycycline for 5 days . Cells were subjected to cell cycle analysis by FACS and immunoblotting . The left , middle , and right panels show respectively immunoblotting , representative cell cycle distribution histograms , and the quantification of % cells with 4n DNA content . The black lines indicate median ± SD . ( B ) DAPI staining reveals cells with multiple nuclei . MDA-MB-468 cells with tet-shMELK were untreated or treated with doxycycline ( 100 ng/ml ) . Yellow arrows indicate cells with two or more nuclei . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 023 The finding that MELK is a mitotic kinase in BBC cells prompted us to hypothesize that the cell death observed upon MELK inhibition might be due to an altered cell cycle progression . MELK knockdown by doxycycline induced an accumulation of cells with 4n DNA content ( Figure 6E , Figure 6—figure supplement 3A ) , indicating an induction of G2/M arrest or failure of cytokinesis . By immunoblotting , we found that cells exposed to doxycycline exhibited an elevation of Cyclin B1 and Aurora A kinase , two markers of G2/mitosis ( Figure 6E , Figure 6—figure supplement 3A ) . We next used microscopy to define the cell division defect in more detail . Doxycycline induced a nearly twofold increase in the percentage of cells with two or more nuclei ( Figure 6F , G , Figure 6—figure supplement 3B ) , indicating a failure of cytokinesis . Indeed , our time-lapse microscopic analysis revealed binucleated cells forming after impaired cytokinesis ( Video 1 ) . Furthermore , cells in which MELK had been depleted displayed asymmetric division ( Figure 6G ) , characterized by an unequal allocation of cell mass into daughter cells . Interestingly , Caenorhabditis elegans with mutations in the MELK homologue , PIG-1 , demonstrate impaired asymmetric cell division ( Cordes et al . , 2006 ) , supporting a critical role of MELK in the late stage of cell division . 10 . 7554/eLife . 01763 . 024Video 1 . This representative time-lapse video , related to Figure 6 , shows a MDA-MB-468/tet-shMELK cell in the presence of doxycycline fails to undergo cytokinesis . The large frame indicates the initial position of the cell , and small frame its final position . Note that the cell progresses into mitosis , which ends with a double-nuclei cell following failed cytokinesis . Frame rate is five frames per second . Time is given in hours:minutes . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 024 We next used time-lapse microscopy of GFP-Histone 2B expressing cells ( Kanda et al . , 1998 ) to determine whether apoptosis and defective mitosis due to MELK knockdown are functionally associated . Cell death events were dramatically increased upon MELK knockdown ( 5 out of 235 cells in control , 151 out of 317 in doxycycline-treated cells during 10 hr of imaging ) . Moreover , cell death events were often preceded by division abnormalities in doxycycline-treated populations . Cells with double nuclei , which had presumably failed cytokinesis , often underwent cell death ( Figure 6H , middle panel; Video 2 ) . Some cells with an apparently normal metaphase plate were unable to progress towards anaphase , instead entering into the process of cell death directly from mitosis ( Figure 6H , bottom panel; Video 3 ) . Overall , following MELK knockdown , out of 27 examples we noted 16 failed mitoses among which 10 proceeded to cell death after the formation of metaphase plate and six gave rise to binucleated cells . By contrast , mitosis in 36 out of a total of 37 control cells appeared normal ( Figure 6H , top panel; Video 4 ) . The morphological events associated with failed cell division and ensuing cell death resembled the previous reports of the effects of inhibiting essential mitotic kinase such as Aurora B ( Keen and Taylor , 2009 ) . Together , these data suggest a model in which BBC cells rely on MELK for proper mitosis; inhibiting MELK in these cells causes impaired mitosis and consequent cell death . 10 . 7554/eLife . 01763 . 025Video 2 . This representative time-lapse video , related to Figure 6 , shows that in the presence of doxycycline , MDA-MB-468/tet-shMELK/GFP-H2B cells with double nuclei undergo cell death . The two frames indicate two such cells . Frame rate is five frames per second . Time is given in hours:minutes . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 02510 . 7554/eLife . 01763 . 026Video 3 . This representative time-lapse video , related to Figure 6 , shows that in the presence of doxycycline , MDA-MB-468/tet-shMELK/GFP-H2B cells undergo mitosis but ending with asymmetric cell division ( in the top frame ) or cell death ( in the bottom frame ) . Frame rate is five frames per second . Time is given in hours:minutes . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 02610 . 7554/eLife . 01763 . 027Video 4 . This representative time-lapse video , related to Figure 6 , shows that in the absence of doxycycline , MDA-MB-468/tet-shMELK/GFP-H2B cells demonstrate efficient mitosis . Frame rate is five frames per second . Time is given in hours: minutes . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 027 Since MELK is selectively required for the survival of BBC cells , we sought to determine whether MELK also supports the oncogenic growth of BBC cells using both in vitro colony formation and in vivo xenograft tumor growth assays . While MDA-MB-468 and MDA-MB-231 cells readily grew into macroscopic colonies in soft agar , MELK knockdown in these cells upon doxycycline treatment caused a nearly complete inhibition in colony formation ( Figure 7A ) . To determine whether MELK is also important for BBC cells to grow as tumors in vivo , we transplanted BBC cells expressing inducible shMELK into the mammary fat pads of athymic mice to allow orthotopic tumor formation . While all recipient mice in the control group without doxycycline treatment developed tumors within 2 months , mice treated with doxycycline immediately following transplantation failed to develop tumors ( Figure 7B ) , suggesting that MELK is required for the proliferation of these BBC cells in vivo . To further examine whether MELK is required for the maintenance of established tumors , we administered doxycycline to mice bearing xenograft tumors derived from basal-like or luminal breast cancer cells . Remarkably , down-regulation of MELK led to a substantial regression of tumors arising from BBC cells but had little effect on tumors derived from luminal cancer cells ( Figure 7C , D , Figure 7—figure supplement 1 ) . 10 . 7554/eLife . 01763 . 028Figure 7 . MELK is essential to sustain the oncogenic growth of BBC Cells . ( A ) Effects of MELK knockdown on anchorage-independent growth of BBC cells in soft agar . The left and middle panels show crystal violet staining , and bright-field images of the colonies respectively . The bar graphs indicate the means ± SD for three experiments . ( B ) Effects of MELK knockdown on the growth of BBC cells in vivo . MDA-MB-468 and MDA-MB-231 cells carrying tet-shMELK were orthotopically implanted into the mammary fat pads of nude mice . The recipient mice were divided into two groups: one group of mice was given doxycycline-supplemented drinking water on the second day of injection for the duration of the experiment , while the other group of mice was maintained without doxycycline . The histogram indicates tumor volume measured 7 weeks after treatment . Data are means ± SEM ( n ≥ 6 ) . ( C and D ) Effects of MELK knockdown on established tumors arising from implantation of basal ( C ) or luminal ( D ) breast cancer cells . Mice bearing orthotopic tumors arising from the indicated cells carrying tet-shMELK were divided into two groups , with one group of mice receiving doxycycline , and the other maintained without doxycycline . Tumor volumes were measured on the indicated days after the administration of doxycyline . Data are means ± SEM ( n ≥ 8 ) . ( E and F ) Effects of MELK inhibition on tumor growth . Mice with tumors developed from basal ( E ) or luminal ( F ) breast cancer cells , were treated once daily with vehicle ( 0 . 5% methycellulose ) or OTSSP167 ( 5 mg/kg ) . Tumor volumes were measured on the indicated days . Data are means ± SEM ( n ≥ 8 ) . ( G ) Knocking out Melk in mice . Indicated tissues were harvested from wild type or Melk−/− adult mice , and homogenized in RIPA lysis buffer . Lysates were subjected to immunoblotting . Total lysate of human breast cancer cell line MDA-MB-231 was used as a control . ( H ) Loss of Melk has no obvious impact on the development of immune system . Cells were isolated from bone marrow ( BM ) , spleen and thymus , and subjected to flow cytometric analysis . Note that CD11b+/Gr1+ is a marker for neutrophils , CD11b+/Gr1− for monocytes , B220 for B cells , CD3 , CD4 and CD8 for T cells . ( I ) Bone marrow was collected from wild type ( wt ) and Melk−/− ( KO ) mice and stained for the indicated cell populations . LSK: Lin−Sca1+ ckit+; LT-HSC ( long-term hematopoietic stem cells ) : LSK CD150+CD48−; ST-HSC ( short-term hematopoietic stem cells ) : LSK CD150−CD48−; CMP ( Common myeloid progenitor ) : Lin−cKit+Sca1−IL7Ra−CD34+FcRg−; GMP ( Granulocyte-macrophage progenitors ) : Lin−cKit+Sca1−IL7Ra−CD34+FcRg+; MEP ( Megakaryocyte-erythrocyte progenitors ) : Lin−cKit+Sca1−IL7Ra−CD34−FcRg−; CLP ( Common lymphoid progenitors ) : Lin−cKitmidSca1midIL7Ra+ . *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 , ****p<0 . 0001 , Student's t test . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 02810 . 7554/eLife . 01763 . 029Figure 7—figure supplement 1 . Efficient conditional MELK knockdown in vivo . Mice with mammary tumors derived from the indicated cells with stable tet-shMELK were untreated or treated with doxycycline-supplemented water for 4 days . Tumors lysates were used for immunoblotting , with α- or β-tubulin served as a loading control . DOI: http://dx . doi . org/10 . 7554/eLife . 01763 . 029 To determine if the pharmacological inhibition of MELK would recapitulate the effect of MELK knockdown in xenografts , we administered OTSSP167 or vehicle control to mice that have tumors derived from basal or luminal breast cancer cell lines . While the growth of luminal breast tumors were largely unaffected by the treatment of OTSSP167 , the chemical caused significant inhibition on the growth of basal breast tumors ( Figure 7E , F ) . Together , these data indicate the MELK is selectively required for the oncogenic growth of BBC cells , and suggest that MELK inhibition could be an effective approach in treating basal-like breast cancer . While MELK has a critical role in basal-like breast cancer , it is not clear whether this kinase is important for the proliferation of normal cells or tissue growth in vivo . This question is critical to the toxicity of any potential MELK-targeted therapy . To address this question , we generated mice with germ-line knockout ( KO ) of Melk ( Figure 7G ) . Notably , Melk-deficient mice are viable and appear normal without any noticeable phenotypes in the development of the embryos or adult mice . Both male and female mice are fertile and produce litters of normal size . A study by Hebbard et al . found a high activity of Melk promoter in mouse mammary progenitor cells ( Hebbard et al . , 2010 ) . Therefore , we anticipated an impairment of mammary gland development in Melk KO mice . However , mammary glands from mice with Melk KO appear normal in both morphology and function ( e . g . , lactation ) . Since bone marrow toxicity is a major side effect of most anti-cancer therapies and , in particular , of those drugs targeting mitotic kinases/machinery , we characterized the immune system in Melk-deficient mice . We isolated cells from bone marrow , spleen and thymus , and analyzed immune cell populations including monocytes , neutrophils , B and T cells . We also analyzed the hematopoietic stem cells and various progenitor cells in the bone marrow . In both cases , virtually no differences were found between wild-type and KO mice ( Figure 7H , I ) . Together , these data suggest that Melk is not essential for normal development and physiological functions in mice , providing compelling evidence for MELK as a highly selective target for therapeutic intervention of basal-like breast cancer .
Patients with basal-like breast cancer remain faced with limited treatment options due to the aggressive nature of the disease and the current lack of suitable molecular targets for therapeutic intervention . In this study , we report that MELK , a novel oncogenic kinase that emerged from an unbiased , in vivo tumorigenesis screen , may indeed be a therapeutic target in this tumor type . In a comprehensive analysis of databases with multiple cohorts of breast cancer , we find MELK to be highly overexpressed in breast cancer lacking the expression of ER/PR , including basal-like breast cancer . Remarkably , overexpression of wild-type MELK induces robust oncogenic transformation both in vitro and in vivo with a transforming potency comparable to that of the highly oncogenic mutant allele of PIK3CA . Even more striking is the finding that only basal-like , but not luminal breast cancer cells , depend on MELK for proliferation . In addition , the dispensable nature of Melk in normal development and hematopoiesis in mice underlines its selective role in BBC . Notably , the kinase activity of MELK is required for its transforming activity as well as for the survival and proliferation of BBC cells . Thus , MELK is potentially a novel oncogenic driver of basal-like breast carcinoma and a promising target for small molecule-based therapeutic intervention . Our data point to a potential role for MELK as a marker in predicting disease outcome . In multiple independent breast cancer cohorts analyzed , we found a strong association of high expression levels of MELK with a higher grade of malignancy and an unfavorable prognosis regardless of the treatment modality . While high MELK expression seems to be a unique phenomenon for BBC in breast cancer , MELK overexpression has been associated with tumor aggressiveness and poor outcome in a number of other cancer types , including glioblastoma ( Nakano et al . , 2008 ) , astrocytoma ( Marie et al . , 2008 ) , and prostate cancer ( Kuner et al . , 2013 ) . The prognostic feature of MELK expression is likely due to its correlation with cell proliferation . In fact , MELK and other proliferation-related genes are major components of multi-gene signature for predicting disease outcome . For example , a recent study developed a cell proliferation signature that consists of the top 1% genes whose expression is most positively correlated with that of proliferating cell nuclear antigen ( PCNA ) . The authors found that adjusting breast cancer expression data for this cell proliferation signature causes a dramatic reduction in outcome association of most published breast cancer signatures ( Venet et al . , 2011 ) . Notably , MELK expression strongly correlates with cell proliferation , and in fact is one of the top-ranking signature genes of cell proliferation that correlate with PCNA expression ( Venet et al . , 2011 ) . Previous studies demonstrated that , while MELK is a member of the AMPK family , it is not activated via phosphorylation by the tumor suppressor kinase LKB1 ( Lizcano et al . , 2004 ) . Recombinant MELK expressed in bacteria is catalytically active ( Davezac et al . , 2002; Lizcano et al . , 2004; Beullens et al . , 2005 ) . Consistent with these findings , overexpression of wild-type MELK readily drives transformation in vitro and in vivo . This behavior is similar to that of other established proto-oncoproteins , such ERBB2 ( Di Fiore et al . , 1987; Hudziak et al . , 1987 ) , and Aurora A kinase ( Bischoff et al . , 1998; Zhou et al . , 1998 ) , the transforming activity of which is driven by overexpression of the wild-type protein . While a number of substrates have been proposed for MELK , such as Bcl-G ( Lin et al . , 2007 ) , CDC25B ( Davezac et al . , 2002 ) , p53 ( Seong and Ha , 2012 ) , and PDK1 ( Seong et al . , 2012 ) , the substrates that mediate the oncogenic activity of MELK in breast cancer remain to be identified . An intriguing question is how the selective overexpression of MELK is achieved in BBC . Our finding that the mitotic transcription factor FoxM1 ( Laoukili et al . , 2005; Wang et al . , 2005a ) plays a major role in regulating MELK expression has shed some light on this enigma . Notably , the expression levels of FoxM1 and MELK demonstrate a striking correlation across all breast cancer samples and subtypes examined . Like MELK , FoxM1 is significantly overexpressed in BBC . Consistent with our results , FoxM1 was recently proposed as a transcriptional driver of proliferation-associated genes in BBC ( Cancer Genome Atlas Network , 2012 ) . However , why MELK is selectively required for cell division in BBC cells , but not in other types of breast cancer or normal cells , remains an open question . MELK was not observed as a hit in systematic screens for essential cell division proteins in HeLa cells ( Kittler et al . , 2004 ) . Both C . elegans and mice are tolerant of mutation or deletion of MELK ortholog ( Cordes et al . , 2006; Figure 7G–I ) . However , MELK can be essential in some circumstances . It is expressed in early frog embryos , where it seems to play some role in cell division ( Le Page et al . , 2011 ) , and we observed it accumulating in dividing cells ( Figure 3F ) , and playing an important role during cell division in BBC cells ( Figure 6 ) . To reconcile these apparently disparate findings , we propose that one or more MELK-related kinase is required for cell division in many , if not all vertebrate cells . In BBC cells , MELK must play this role uniquely and is selectively overexpressed , perhaps because redundant kinases are down-regulated . In other cells , MELK may function during division , but it is not essential due to redundancy with related kinases . Consistent with this hypothesis , the MELK-related kinase AMPK was recently shown to play a role in mitosis ( Vazquez-Martin et al . , 2009 ) . Perhaps AMPK , or other kinases in the same family , can substitute for MELK in some cells , but not in BBC cells , which seem to have become addicted to MELK for proper execution of cell division . Determining the precise function of MELK in cell division , and the reason this function is selectively required in BBC cells , will require further analysis . Nevertheless , our studies firmly establish MELK as a molecular target for the treatment of BBC . Unlike other mitotic factors like Aurora A , Aurora B , and PLK1 , that are normally essential , MELK presents a unique mitotic kinase that is only required by a subset of cancer cells , and is therefore an excellent therapeutic target . In summary , recent comprehensive characterization of basal-like breast cancer demonstrates that this subtype of disease has high genetic heterogeneity , but lacks commonly occurring genetic alterations , with the exception of the frequent inactivation of p53 ( Cancer Genome Atlas Network , 2012 ) . In contrast , the relative uniform overexpression of MELK in basal-like breast cancer makes it a potential common target in an otherwise heterogeneous disease . Thus our data on MELK provide important information for guiding the development of targeted therapies in basal-like breast cancer .
The human MELK was amplified using the template DNA deposited in the described kinase library , and cloned into pWZL retroviral vector ( Zhao et al . , 2003 ) , in which target gene expression is driven by the long terminal repeat of Moloney murine leukemia virus . The MELK mutants ( D150A , T167A , or shMELK-resistant MELK with silent mutations ) were generated via Quickchange XL Site-directed Mutagenesis ( Stratagene , La Jolla , CA ) . Primers were listed in Supplementary file 2 . To construct a tetracycline-inducible gene expression system , GFP or mutated MELK was amplified using the primers listed in Supplementary file 2 . The PCR products were digested with AgeI and PacI , and ligated with digested pLKO-TREX ( Wee et al . , 2008 ) . To construct pWzl-H2B-GFP , human Histone 2B was amplified using the genomic DNA of HEK293T cells as templates . Primers for cloning were listed in Supplementary file 2 . PCR products following digestion with BamHI and XhoI were ligated with digested pWzl-GFP . To generate pLKO-tet-on-shRNAs targeting human MELK , oligonucleotides were designed and synthesized ( IDT , Coralville , Iowa ) . Following annealination , double-stranded oligonucleotides were directly ligated with pLKO vector that was digested with AgeI and EcoRI . The sequences for scramble , shMELK1 , shMELK2 are listed in Supplementary file 2 . Retroviruses were generated by transfecting HEK293T cells with pWzl plasmids and packaging DNA . Typically 1 . 6 μg pWzl DNA , 1 . 2 μg pCG-VSVG and 1 . 2 μg pCG-gap/pol , 12 μl lipid of Metafectene Pro ( Biontex , Martinsried , Germany ) were used; DNA and lipid were diluted in 300 μl PBS respectively and mixed; and following 15 min of incubation , they were added to one 6-cm dish that was seeded with 3 million HEK293T cells 1 day earlier . Viral supernatant was collected 48 hr and 72 hr after transfection . After the supernatant was filtered through 0 . 45-μm membrane , it was added to target cells in the presence of 8 μg/ml polybrene ( Millipore , Billerica , MA ) . Lentiviruses were generated with a similar approach with the exception of HEK293T cells that were transfected with 2 μg pLKO DNA , 1 . 5 μg pCMV-dR8 . 91 , and 0 . 5 μg pMD2-VSVG . Cells were selected with antibiotics starting 72 hr after initial infection . Puromycin and blasticidin were used at the final concentrations of 1 . 5 μg/ml and 4 μg/ml respectively . Human mammary epithelial cells ( HMECs ) were maintained in DMEM/F-12 supplemented with EGF ( 10 ng/ml ) , insulin ( 10 μg/ml ) , and hydrocortisone ( 0 . 5 μg/ml ) under 5% CO2 and 37°C . Rat1 and HEK293T cells were maintained in DMEM supplemented with 10% FBS ( Invitrogen , Carlsbad , CA ) . All breast cancer cell lines ( MCF7 , T47D , MDA-MB-468 , MDA-MB-231 , MDA-MB-436 , HCC1197 , BT549 ) were cultured in RPMI 1640 medium supplemented with 10% FBS . For cells stably introduced with tetracyclin-inducible genes/shRNAs , Tet-approved FBS ( Clontech , Mountain View , CA ) was used . Typically , breast cancer cells were seeded in 12-well plates ( 1–2 × 104 ) in 1 ml medium . On the next day , wells were added with 110 μl medium without or with 1 μg/ml doxycycline ( to reach a final concentration of 100 ng/ml ) , which was repeated every 2 days . 6 days after the initial treatment , cells were fixed with formaldehyde , and stained with crystal violet ( 0 . 05% , wt/vol ) , a chromatin-binding cytochemical stain . The plates were washed extensively , and imaged with a flatbed scanner . For quantification of the staining , 1 ml 10% acetic acid was added to each well to extract the dye . The absorbance was measured at 590 nm with 750 nm as a reference . The assays were typically performed in a 12-well plate unless otherwise mentioned . Cells were suspended in medium containing 0 . 3% agar and plated onto a layer of 0 . 6% agar ( for each well , 4000 cell in 800 μl medium , 1 ml bottom agar ) . The wells were added with medium ( without or with 100 ng/ml doxcycycline ) on the next day . 3 weeks after seeding , the colonies were fixed with formaldehyde and imaged . The number of colonies in each well was quantified using ImageJ ( National Institutes of Health ) . All xenograft studies were conducted in accordance with the animal use guidelines from the National Institutes of Health and with protocols approved by the Dana-Farber Cancer Institute Animal Care and Use Committee . The recipient mice used were NCR-nude ( CrTac:NCr-Foxn1nu , Taconic , Hudson , NY ) . Cells were resuspended in 40% of Matrigel-Basement Membrane Matrix , LDEV-free ( BD Biosciences , San Jose , CA ) and sit on ice until injection . For transplanting human cell lines , mice were γ-irradiated with a single dose of 400 rads on the same day of injection . Mice were anesthesized by inhalation of isoflurane , and were injected with 150 μl cells ( 5 × 106 ) per site . Tumors were measured in two dimensions by a caliper . Tumor volume was calculated using the formula: V = 0 . 5 × length × width × width . All xenograft data are presented as mean ± SEM . Comparison between groups of treatment were conducted using two-tailed Student's t test . Calculations were performed using either Openoffice or GraphPad Prism version 5 . 0b . For tumorigenesis study , 5 × 106 HMEC cells were injected into the mammary fat pad , and 5 × 106 Rat1 cells subcutaneously . Tumor growth was monitored twice a week . Rat1 xenografts were harvested 3 weeks after injection . To study the impact of MELK knockdown on tumor growth , mice were randomly sorted into groups on the second day of injection , and were untreated or treated with doxycycline ( 2 mg/ml in 5% dextrose in drinking water , refreshed twice a week ) for the duration of the study . Tumor was measured twice a week . To study the roles of MELK in tumor maintenance , mice with established tumors ( ≥200 mm3 ) derived from orthotopic injections of MDA-MB-231 , or MDA-MB-468 , or MCF-7 , or T47D cells were randomly sorted into two groups , with one group receiving doxcycline in drinking water . Tumors were calipered twice per week to monitor the effect of MELK knockdown on tumor growth . Time-lapse imaging was performed on a Nikon Ti motorized inverted microscope , which was equipped with a perfect focus system and a humidified incubation chamber ( 37°C , 5% CO2 ) ( Nikon Imaging Center , Harvard Medical School ) . Cells stably expressing H2B-GFP were pre-seeded in 24-well glass-bottom plate , and either untreated or treated with doxycyline ( 100 ng/ml final ) . Images were captured every 5 min with a 20× objective lens , and a Hamamatsu ORCA-AG cooled CCD camera . Images were analyzed using ImageJ ( National Institutes of Health ) . Cells were seeded on No . 1 . 5 coverslips ( 12 mm round ) that were pre-placed into 24-well plates . Upon harvest , cells were fixed with 4% formaldehyde for 10 min . After washing , cells were permeablized with 0 . 1% Trition X-100 for 10 min . Cells were then washed and blocked with 1% bovine serum for 30 min before incubated with primary antibody ( anti-β-tubulin , #2128; Cell Signaling Technology , Beverly , MA ) prepared in PBS containing 1% bovine serum albumin . After overnight incubated at 4°C , the samples were washed and incubated with Alexa 488-conjugated secondary antibody ( Invitrogen ) for 1 hr at room temperature . After extensive washing , the samples were dried and mounted with ProLong Antifade reagent ( Invitrogen ) . The images were acquired with a Nikon 80i upright microscope at the Nikon Imaging Center ( Harvard Medical School ) , which is equipped with a Hamamatsu C8484-03 monochrome camera . ImageJ was used for analysis of the images , which includes merging channels with different colors and cropping . Cells were lysed with RIPA buffer ( 25 mM Tris , pH 7 . 4 , 150 mM NaCl , 1% Nonidet P-40 , 0 . 5% sodium deoxycholate , and 0 . 1% sodium dodecyl sulfate ) supplemented with protease inhibitors cocktail ( Roche ) and phosphatase inhibitors cocktail ( Thermo Scientific , Waltham , MA ) . Cleared lysates were analyzed for protein concentration using BCA kit ( Thermo Scientific ) . Equal amount of protein ( 10–20 μg ) was resolved on SDS-PAGE , and was subsequently transferred onto a nitrocellulose or polyvinylidene difluoride membrane . The membrane was blocked with 5% non-fat milk and was then incubated with primary antibodies overnight at 4°C . After washing , the membrane was incubated with fluorophore-conjugated secondary antibodies for 1 hr at room temperature . The membrane was then washed and scanned with an Odyssey Infrared scanner ( Li-Cor Biosciences , Lincoln , NE ) . Primary antibodies used in this study include anti-MELK , anti-α-tubulin ( Abcam , Cambridge , MA ) , anti-cyclin B1 ( Millipore ) , anti-Vinculin ( Sigma , St . Louis , MO ) , anti-FoxM1 ( Santa Cruz , Dalla , TX ) , anti-β-tubulin , anti-phopho-Akt ( S473 ) , anti-phospho-Akt ( T308 ) , anti-total Akt , anti-Flag , anti-cleaved PARP ( Asp214 ) , anti-cleaved Caspase-3 , anti-AURKA , anti-AURKB , anti-p27 , anti-Estrogen Receptor α ( all from Cell Signaling Technology ) . Secondary antibodies used were IRDye700-conjugated anti-rabbit IgG and IRDye800-conjugated anti-mouse IgG ( Rockland , Gilbertsville , PA ) . Primary human breast cancer samples were obtained from the Dana-Farber Cancer Institute with patients' consent and institutional review board approval . These samples were deidentified and are not considered human subject research . Samples were homogenized in RIPA buffer supplemented with protease/phosphatease inhibitors using Bullet blender ( Next advance , Averill Park , NY ) . After clearing , tissue lysates were subjected for protein concentration determination . 20 micrograms of lysates were used for immunoblotting . Mouse embryonic stem cells with one allele of Melk inserted with lacZ and neomycin-resistance genes between exon 2 and 3 were obtained from the Knockout Mouse Project ( KOMP; ID: CSD33136 ) . Cells with normal karyotype were injected into blastocysts isolated from C57BL/6 mice . The procedure of injection was performed at the Transgenic Core Facility , Brigham and Women's Hospital ( Boston , MA ) . Germline transmission was subsequently observed , and further cross was made to generate Melk homozygous knockout mice . Melk knockout was confirmed by long-rang PCR , qPCR , and immunoblotting . Cells were isolated from bone marrow , spleen , and thymus of mice , and stained with the following antibodies: B220 ( APC; BD Pharmingen ) , cKit ( PE-Cy7; BioLegend , San Diego , CA ) , CD3 ( PE-Cy7; BD Bioscience ) , CD4 ( APC-H7; BD Pharmingen ) , CD8 ( ECD; Beckman Coulter ) , CD11b ( PE; BD Bioscience ) , CD16/32 ( PE; eBioscience , San Diego , CA ) , CD34 ( FITC; BD Pharmingen ) , CD45 . 2 ( PerCP-Cy5 . 5; BD Pharmingen ) , CD48 ( APC-Cy7; BD Pharmingen ) , CD127 ( ECD; BD Pharmingen ) , CD150 ( PerCP-Cy5 . 5; BioLegend ) , Gr1 ( APC-Alexa700; BD Bioscience ) , Lineage Cocktail ( APC; BD Pharmingen ) , Sca1 ( Brilliant Violet 421; BioLegend ) . Dead cells were excluded using either DAPI or Vivid-Aqua ( Invitrogen ) staining . All data acquisition was performed on a LSRII ( BD ) flow cytometer , and results were analyzed using FlowJo v . 8 . 8 . 7 ( TreeStar ) . Cells were harvested by trypsinization , and repeatedly pipetted into single-cell suspension . After centrifugation , cells were fixed by adding 70% ethanol ( −20°C ) dropwise while vortexing . Cells were then stained with propidium iodide ( 50 μg/ml , Sigma ) solution containing 50 μg/ml DNase-free RNase A ( Sigma ) and 0 . 5% bovine serum albumin ( BSA ) . After 30 min of incubation , the samples were washed and resuspended in 0 . 5% BSA . The analysis was performed on a LSRFortessa ( BD Biosciences ) at the DFCI Flow Cytometry Core Facility . Single cells were gated via plotting FL3-A to FL3-H to exclude cell debris and doublets . At least 10 , 000 single cells were collected for each sample . Chromatin immunoprecipitation was performed as previously described ( Lee et al . , 2006 ) . Upon harvest , medium in cell culture dishes was added with 16% formaldehyde ( Electron Microscopy Sciences , Hatfield , PA ) to reach a final concentration of 1% , and quenched with glycine ( 125 mM final , 5 min incubation ) after incubation at room temperature for 10 min . Cells were harvested by scrapping into cold PBS , and centrifuged . Cell pellets were lysed with LB1 ( 50 mM HEPES , pH 7 . 5 , 140 mM NaCl , 1 mM EDTA , 10% glycerol , 0 . 5% NP-40 , 0 . 25% Triton-X-100 ) , then after centrifugation with LB2 ( 10 mM Tris–HCl pH 8 . 0 , 200 mM NaCl , 1 mM EDTA , 0 . 5 mM EGTA ) , and again after centrifugation resuspended in LB3 ( 10 mM Tris–HCl pH 8 . 0 , 100 mM NaCl , 1 mM EDTA , 0 . 5 mM EGTA , 0 . 1% Na-Deoxycholate , 0 . 5% N-lauroylsacosine ) . Samples were sonicated using a Q800R DNA Shearing Sonicator ( Qsonica , Newtown , CT ) at 50% amplitude for 10 min with a pulse of 30 s on and 30 s off . Samples were then supplemented with 10% Triton-X 100 to a final concentration of 1% , and centrifuged at 20 , 000×g for 10 min at 4°C . The cleared lysates were used for the following immunoprecipitation , with 50 µl of lysate saved as input . Protein G-conjugated Dynabeads ( Invitrogen ) were washed with block solution ( 0 . 5% bovine serum albumin in PBS ) and incubated overnight with 5 µg anti-FoxM1 ( SC-502 , Santa Cruz Biotechnology ) , or 5 µg rabbit IgG in block solution , and on the next day washed three times with block solution . Cell lysates were incubated with the antibody/magnetic bead , rotating at 4°C overnight . On the next day , the beads were collected with magnetic stand , and washed six times with RIPA buffer ( 50 mM HEPES pH 7 . 6 , 500 mM LiCl , 1 mM EDTA , 1% NP-40 , 0 . 7% Na-deoxycholate ) . After a single wash with Tris-EDTA buffer containing 50 mM NaCl , samples were resupsended with elution buffer ( 50 mM Tris–HCl pH 8 . 0 , 10 mM EDTA , 1% SDS ) for incubation at 65°C overnight . Also , the 50 µl input was mixed with 150 µl elution buffer and incubated at 65°C overnight for reverse crosslinking . On the next day , RNase A was added to the samples ( 0 . 2 µg/ml final ) , followed by incubation for 1 hr at 37°C . Samples were then treated with Proteinase K ( 0 . 2 µg/ml final ) and incubated at 56°C for 1 hr . DNA were purified with a QIAquick PCR purification kit ( Qiagen ) , and eluted with 30 µl water . PCR was performed using Quick-Load Taq 2X Master Mix ( New England BioLabs , Beverly , MA ) , using primers listed in Supplementary file 2 . Total RNA was extracted from cultured cells with RNeasy Mini kit ( Qiagen ) , with the use of QIAshredder spin column for homogenization and an on-column DNase digestion . 2 μg of the total RNA was reversely transcribed using a High Capacity RNA-to-cDNA Kit ( Applied Biosystems , Foster City , CA ) . cDNA were analyzed quantitatively using Power SYBR Green PCR Master Mix ( Applied Biosystems ) on an ABI7300 Real-time PCR system . Primers used were listed in Supplementary file 2 . Cycling conditions were 95°C for 15 min , 40 cycles of 15 s at 94°C , 30 s at 55°C and 30 s at 72°C . Ct values were generated using the default analysis settings . ΔCT was defined as Ct gene of interest − Ctβ-actin . ΔΔCT was defined as ΔCt treated sample − Ct control sample . Relative quantification ( RQ ) was calculated as 2−ΔΔCT . Statistical analysis was performed by Student's t test . Gene expression data were downloaded from Oncomine ( Rhodes et al . , 2004 ) . Information of the clinical data sets is listed in Supplementary file 1 . Analyses and figures were made in GraphPad Prism . In dot plot graphs , each dot indicates an individual sample , with results expressed as median with interquartile range . Independent cohorts of breast cancer patients with overall survival or metastasis-free survival data available were examined . Information of the cohorts is listed in Supplementary file 1 . Data of MELK expression and associated survival were downloaded from Oncomine ( Rhodes et al . , 2004 ) . For each cohort , patients were divided into top 60% ‘MELK high’ and bottom 40% ‘MELK low’ groups based on the expression of MELK . Kaplan–Meier curves , as well as the log-rank ( Mantel–Cox ) test and the hazard ratio were analyzed by GraphPad Prism . Two-tailed Student's t test and ANOVA ( Analysis of Variance ) were used for differential comparison between two groups and among three groups , respectively . Survival and correlation analysis were performed in GraphPad Prism . | Not all cancers are the same . There are , for example , at least five types of breast cancer . Different types of cancer can have different mutations and express different genes that determine how aggressively the tumors grow and how well they respond to different therapies . By exploiting these differences , scientists have developed therapies that target specific tumor types , and these targeted therapies have proven useful against most breast cancers . One type of breast cancer , however , has proven hard to treat . Basal-like breast cancer grows rapidly and there are few treatment options for women with this type of cancer . One reason for this is that , unlike other forms of breast cancer , these cancers do not have the hormone receptors that are the targets of existing therapies . Enzymes called kinases are promising alternate targets , and many kinase-inhibiting drugs can kill tumor cells in mice . Nevertheless , it has proven difficult to develop kinase inhibitors that are safe for use in humans because these drugs can also kill normal cells . To avoid this side effect , cancer researchers have been searching for a kinase that is active in cancer cells but not in normal cells . Wang et al . tested a large collection of kinases and found that one called MELK caused tumors to grow in the mammary glands of mice . Further examination of tumor samples collected from hundreds of women in previous clinical studies revealed that MELK expression was increased in basal-like breast cancers and other breast cancer tumors that lack the usual hormone receptor targets . When Wang et al . treated tumor cells and mice with tumors with a chemical that stops MELK working , basal-like breast cancer cells stopped multiplying and died . On the other hand , tumor cells that had the usual hormone receptors continued to multiply . To see if MELK is important in healthy mice , Wang et al . genetically engineered mice to delete the MELK gene and found that these mutant mice appear normal . The next challenge will be to test if drugs that inhibit MELK can kill basal-like breast cancer cells without having the side effect of harming normal cells . | [
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] | 2014 | MELK is an oncogenic kinase essential for mitotic progression in basal-like breast cancer cells |
Toll-like receptors ( TLRs ) detect microbial infections and trigger innate immune responses . Among vertebrate TLRs , the role of TLR13 and its ligand are unknown . Here we show that TLR13 detects the 23S ribosomal RNA of both gram-positive and gram-negative bacteria . A sequence containing 13 nucleotides near the active site of 23S rRNA ribozyme , which catalyzes peptide bond synthesis , was both necessary and sufficient to trigger TLR13-dependent interleukin-1β production . Single point mutations within this sequence destroyed the ability of the 23S rRNA to stimulate the TLR13 pathway . Knockout of TLR13 in mice abolished the induction of interleukin-1β and other cytokines by the 23S rRNA sequence . Thus , TLR13 detects bacterial RNA with exquisite sequence specificity .
Toll-like receptors are evolutionarily conserved transmembrane proteins that detect microbial components on the cell surface or within the endosomes ( Takeuchi and Akira , 2010 ) . All TLRs contain extracellular leucine-rich repeats ( LRRs ) and an intracellular Toll-interleukin-1 receptor ( TIR ) domain that recruits MyD88 and other adaptor proteins to activate signal transduction cascades , which culminate in the production of inflammatory cytokines and other antimicrobial molecules . Vertebrate TLRs comprise 6 major families , TLR1 , TLR3 , TLR4 , TLR5 , TLR7 and TLR11 ( Roach et al . , 2005 ) . The TLR1 family includes TLR1 , TLR2 , TLR6 , TLR10 and TLR14 , which are localized on the plasma membrane . Within this family , TLR2 forms a heterodimer with other member of the family ( e . g . , TLR1 , TLR6 or TLR10 ) to detect microbial lipopeptides and peptidoglycans . TLR4 and TLR5 also reside on the plasma membrane and detect bacterial lipopolysaccharide ( LPS ) and flagellin , respectively . TLR4 , after binding to LPS , can also traffic to endosomal membrane where it launches a signaling cascade leading to the production of type-I interferons ( IFNα and IFNβ ) . Members of the remaining TLR families , TLR3 , TLR7 and TLR11 are localized on the endosomal membrane . TLR3 detects double-stranded RNA and induces inflammatory cytokines and interferons . The TLR7 subfamily consists of TLR7 , TLR8 and TLR9 . TLR7 and TLR8 detect single-stranded RNA whereas TLR9 binds unmethylated CpG DNA . The TLR11 family consists of TLR11-13 in mice and TLR21-23 in fish and frogs . TLR11 has been shown to recognize a profilin-like protein from the parasite Toxoplasma gondii and an unknown ligand from uropathogenic E . coli . The ligands and roles of TLR12 and TLR13 are unknown . To date , 10 TLRs ( TLR1–10 ) have been identified in humans and 12 ( TLR1–9 , TLR11–13 ) in mice . In addition to TLRs , the innate immune system in vertebrate animals consists of other microbial pattern recognition receptors , including RIG-I like receptors ( RLRs ) , NOD-like receptors ( NLRs ) and C-type lectin receptors ( CLRs ) . RLRs , which include RIG-I , MDA5 and LGP2 , detect viral double-stranded RNA in the cytoplasm and activate a signaling cascade that leads to the production of type-I interferons and other antiviral molecules ( Yoneyama and Fujita , 2009 ) . Recently , we found that RNA from commensal bacteria could also stimulate the RIG-I pathway ( Li et al . , 2011 ) . Mice lacking the mitochondrial protein MAVS ( also known as IPS-1 , VISA or CARDIF ) , which is an essential adaptor protein in the RLR pathway , are highly sensitive to experimental colitis in part because of defective immune response to commensal bacterial RNA . Interestingly , whereas IFNβ induction by commensal bacterial RNA depends on MAVS , the induction of proinflammatory cytokines , including interleukin-1β ( IL-1β ) , depends on MyD88 ( Li et al . , 2011 ) . In the course of investigating the mechanism of MyD88-dependennt induction of IL-1β by bacterial RNA , we found that TLR13 was responsible for detecting the 23S ribosomal RNA of both gram-negative and gram-positive bacteria . Remarkably , a short sequence of 13 residues within domain V of 23S rRNA , which is known to be the catalytic center of peptide bond formation ( Nissen et al . , 2000 ) , is both necessary and sufficient to trigger the TLR13 pathway . Point mutations within the sequence , herein termed ISR23 ( Immune Stimulatory RNA of 23S rRNA ) , abolished the ability of the 23S rRNA to induce IL-1β . Mouse macrophages lacking TLR13 failed to induce IL-1β and other cytokines in response to ISR23 . Thus , TLR13 is distinct from all other nucleic acid sensing pattern recognition receptors in that it recognizes a specific RNA sequence .
Our previous studies showed that transfection of RNA from Lactobacillus salivarius ( LAB ) , a gram-positive commensal bacterium commonly found in the gastrointestinal tract , strongly induced IL-1β in mouse bone marrow derived macrophages ( BMDM ) and Raw264 . 7 , a mouse macrophage cell line ( Li et al . , 2011 ) . Interestingly , even when LAB RNA was added to the culture media of BMDM and Raw264 . 7 without the transfection reagent FuGENE , it still strongly induced IL-1β ( Figure 1A ) , which suggests that the RNA detection probably does not involve a cytoplasmic RNA sensor . To determine what type of RNA was responsible for the activity , LAB RNA was treated with RNase III or RNase T1 , which digests double-stranded ( dsRNA ) or single-stranded RNA ( ssRNA ) , respectively . RNase T1 but not RNase III destroyed the IL-1β inducing activity of LAB RNA ( Figure 1B ) . RNase V1 , which digests both dsRNA and ssRNA at high concentrations , also destroyed the activity . Thus , ssRNA from LAB was responsible for IL-1β induction . This induction was abolished in BMDM from MyD88−/− mice but not Mavs−/− , TLR2−/−TLR4−/− , or TLR7−/− mice ( Figure 1C , D ) . To determine if the detection of LAB RNA occurs in the endosome , we used BMDM from the 3d mouse , which harbors a loss of function mutation in Unc93b1 , a protein essential for the trafficking of endosomal TLRs from the ER to the endosomal membrane ( Tabeta et al . , 2006 ) . The induction of IL-1β by LAB RNA was abolished in the 3d BMDM ( Figure 1E ) . As controls , IL-1β induction by the TLR7 ligand R848 , but not the TLR4 ligand LPS , was dependent on Unc93b1 . 10 . 7554/eLife . 00102 . 003Figure 1 . IL-1β induction by bacterial RNA depends on MyD88 and UNC93b1 , but not MAVS , TLR2 , TLR4 or TLR7 . ( A ) L . salivarius total RNA ( LAB RNA; 2 μg ) was treated with or without RNase V1 , then added to Raw264 . 7 cell culture in the presence or absence of FuGENE . IL-1β RNA was measured by qPCR . ( B ) LAB RNA was digested with indicated amounts of RNase III , RNase T1 , RNase V1 or mock treated before adding to BMDM cell culture . 8 hr after incubation , total cell RNA was extracted to measure IL-1β expression by qPCR ( upper panel ) . The efficiency of RNase treatment was verified by agarose gel electrophoresis ( lower panel ) . ( C ) BMDM of the indicated genotypes was growing in the presence of LAB RNA at different concentrations for 8 hr , followed by the measurement of IL-1β RNA by qPCR . ( D ) BMDM of the indicated genotypes was growing in the presence of LAB RNA , R848 or LPS for 8 hr , then IL-1β induction was measured by qPCR . ( E ) Similar to ( D ) , except that BMDM from Unc93b1 mutant mice ( 3d ) was used . ( F ) BMDM from WT or TLR11−/− mice was incubated with LAB RNA or LPS followed by measurement of IL-1β RNA by qPCR . Error bars represent standard error of triplicate assays . N . D: not detected . DOI: http://dx . doi . org/10 . 7554/eLife . 00102 . 003 Previous studies have suggested that members of the TLR11 family are localized on the endosomal membrane ( Brinkmann et al . , 2007; Pifer et al . , 2011 ) . Because IL-1β induction by LAB RNA depends on MyD88 and Unc93b1 , but not other TLRs known to be involved in ssRNA detection , we investigated the role of TLR11 family members in detecting bacterial RNA . TLR11−/− BMDM induces IL-1β normally in response to LAB RNA ( Figure 1F ) . To explore the role of TLR13 , we constructed two lentiviral shRNA vectors targeting distinct regions of TLR13 coding sequences and used the lentiviruses to generate Raw264 . 7 cell lines with stable knock down of TLR13 expression ( Figure 2A , D ) . A lentiviral vector targeting GFP was used as a negative control . The knockdown of TLR13 by both shRNA vectors significantly reduced IL-1β induction by LAB RNA , but not by the TLR7 ligand R848 ( Figure 2A , B ) . Importantly , expression of an RNAi-resistant TLR13 cDNA in the TLR13-shRNA cells rescued IL-1β induction by LAB RNA ( Figure 2C ) . In fact , the IL-1β expression level in the TLR13-rescued cells was even higher than that in the WT cells , probably because of TLR13 overexpression ( Figure 2D ) . These results indicate that TLR13 is required for IL-1β induction by the bacterial RNA . 10 . 7554/eLife . 00102 . 004Figure 2 . TLR13 is required for detection of bacterial RNA . ( A ) Raw264 . 7 cells stably expressing two distinct pairs of shRNA against TLR13 ( TLR13a and TLR13b ) or an shRNA against GFP ( as a control ) were growing in the presence of R848 ( 1 μg/ml ) or LAB RNA at indicated concentrations for 8 hr . IL-1β induction was measured by qPCR . ( B ) Similar to ( A ) except that cells were growing in the presence of 2 μg LAB-RNA at the indicated times before harvest for qPCR analysis . ( C ) Similar to ( B ) except that an RNAi-resistant TLR13 cDNA was stably expressed in shTLR13a Raw264 . 7 cells . The TLR13 rescued cells were compared to shTLR13 and shGFP cells for IL-1β induction by LAB RNA . ( D ) The expression of TLR13 in the cells used in ( C ) was measured by qPCR . DOI: http://dx . doi . org/10 . 7554/eLife . 00102 . 004 To identify the ligand that activates the TLR13 pathway , we separated LAB RNA using formaldehyde denatured agarose gel . As the ribosomal RNAs are the dominant bands on the gel , we isolated these bands , extracted the RNAs and measured their activity . To avoid potential complications from other microbial ligands that might stimulate TLR2 or TLR4 , we used TLR2−/−TLR4−/− macrophages to measure IL-1β induction . Strikingly , 23S , but not 16S or 5S , rRNA potently stimulated IL-1β production ( Figure 3A ) . This activity was not limited to gram-positive bacterial RNA , because 23S rRNA from the E . coli strain DH5α , a gram-negative bacterium , also strongly induced IL-1β ( Figure 3B ) . To determine if the stark contrast in the IL-1β inducing activity of 23S vs 16S rRNA was due to their chemical modifications or their distinct sequences , we used T7 RNA polymerase to transcribe the E . coli rRNA in vitro ( Figure 3C ) . Remarkably , the in vitro-transcribed 23S , but not 16S , rRNA induced IL-1β , indicating that 23S rRNA contains unique sequences capable of activating the TLR13 pathway . 10 . 7554/eLife . 00102 . 005Figure 3 . 23S rRNA stimulates IL-1β production in mouse macrophages . ( A ) LAB RNA was separated by denatured agarose gel electrophoresis ( left panel ) and ribosomal RNA was extracted . 2 μg of purified RNA was added to TLR2−/−TLR4−/− BMDM culture and incubated for 8 hr . IL-1β mRNA expression was measured by qPCR ( right panel ) . ( B ) Similar to ( A ) except using gel-purified rRNA from DH5α . ( C ) 23S and 16S DH5α rRNA was synthesized in vitro using T7 RNA polymerase and then gel purified . Indicated amounts of the purified RNA was added to Raw264 . 7 cell culture and incubated for 8 hr before IL-1β mRNA was measured by qPCR . DOI: http://dx . doi . org/10 . 7554/eLife . 00102 . 005 Using in vitro transcription by T7 RNA polymerase , we carried out a systemic deletion analysis of 23S rRNA ( Figure 4 ) . Deletion of 520 nucleotides ( nt ) from the 3′ end ( nt 1–2384 based on E . coli sequence ) did not impair the ability of the RNA to induce IL-1β , but further deletion of another 490 nt ( 1–1894 ) did , suggesting that a sequence located between nt 1894 and 2384 is important ( Figure 4A , B ) . Further deletion analysis narrowed down the stimulatory RNA sequence to nt 2035–2074 ( Figure 4C–H ) , which was still fully capable of inducing IL-1β . To confirm and extend this result , we used chemically synthesized RNA corresponding to nt 2035–2074 , 2035–2050 and 2054–2068 of 23S rRNA . This analysis showed that the 15 nt RNA sequence ( 2054–2068 ) , ACGGAAAGACCCCGU , was a strong inducer of IL-1β ( Figure 5A ) . Further deletion of one residue ( A ) from the 5′ end reduced the IL-1β stimulatory activity by about eightfold ( Figure 5B; compare 2054–2068 with 2055–2068 ) , and deletion of another residue ( C ) from the 5′ end abolished the activity ( 2056–2068 ) . Deletion of two nucleotides ( 2054–2066 ) from the 3′ end was tolerable , but further deletion of another nucleotide ( 2054–2065 ) decreased the activity dramatically ( Figure 5B ) . Titration experiments using varying concentrations of different RNA oligos suggest that the optimal immune stimulatory RNA sequence resides in 2054–2066 of 23S rRNA ( ACGGAAAGACCCC ) ( Figure 5C ) . For simplicity , we refer to this 13-nt sequence as ISR23 ( Immune Stimulatory RNA from 23S rRNA ) . 10 . 7554/eLife . 00102 . 006Figure 4 . Structural and functional analysis of DH5α 23S rRNA . ( A ) Schematic summary of 23S rRNA deletion fragments and their IL-1β inducing activity . ( B ) – ( H ) DNA templates encoding full-length or truncation fragments of E . coli ( DH5α ) 23S rRNA were used for in vitro transcription using T7 RNA polymerase . The RNA products were purified and then incubated with Raw264 . 7 cells for 8 hr . IL-1β RNA levels were measured by qPCR . DOI: http://dx . doi . org/10 . 7554/eLife . 00102 . 00610 . 7554/eLife . 00102 . 007Figure 5 . A specific sequence in domain V of 23S rRNA activates the TLR13 pathway . ( A ) and ( B ) Chemically synthesized RNA corresponding to the indicated region of 23S rRNA was added to Raw264 . 7 cells followed by measurement of IL-1β RNA by qPCR . ( C ) Similar to ( B ) except that different concentrations of the RNA oligos were tested for IL-1β induction . ( D ) RNA oligo corresponding to 2054–2068 of 23S rRNA and those containing the indicated mutations were tested for their ability to induce IL-1β . ( E ) Full-length 23S rRNA and that containing point mutations at the indicated positions were in vitro transcribed by T7 RNA polymerase and then measured for their ability to induce IL-1β in Raw264 . 7 cells . ( F ) Full-length 23S rRNA or the RNA oligo corresponding to 2054–2068 of 23S was added to Raw264 . 7 cell lines stably expressing shRNA against TLR13 or GFP . IL-1β induction was measured by qPCR . ( G ) Secondary structure of the domain V of E . coli 23S rRNA , with the ISR23 sequence highlighted in red . The invariant catalytic residues ( G2061 and A2451 ) are shown in bold and indicated by an asterisk . ( H ) RNA oligos corresponding to the ISR23 sequence of different bacterial strains as indicated were added to Raw264 . 7 cell cultures followed by measurement of IL-1β by qPCR . DOI: http://dx . doi . org/10 . 7554/eLife . 00102 . 007 To determine the sequence specificity of ISR23 , we introduced point mutations in the sequence 2054–2068 . Point mutations at each residue from position 2055 to 2064 abolished the activity of the RNA , whereas an A>G mutation at position 2054 was tolerable ( Figure 5D ) . Notably , a mutation of A at position 2058 to any of the other three nucleotides abolished the activity , but 2′-O-methylation of A at this position had no detrimental effect ( Figure 5D ) . We also introduced point mutations at positions 2058 , 2060 and 2061 in the full-length 23S rRNA and found that each of these mutations abolished IL-1β induction ( Figure 5E ) . The induction of IL-1β by both full-length 23S rRNA and nt 2054–2068 was markedly reduced by two distinct TLR13 shRNAs , indicating that ISR23 also engaged TLR13 to induce IL-1β ( Figure 5F ) . Taken together , these results demonstrate that TLR13 detects the ISR23 sequence within 23S rRNA with exquisite sequence specificity . The ISR23 sequence is located in domain V of 23S rRNA , which is the catalytic center of the ribozyme responsible for peptide bond formation ( Cech , 2000; Nissen et al . , 2000 ) ( Figure 5G; also see Discussion ) . The ISR23 sequence is highly conserved among gram-negative and gram-positive bacteria , including E . coli , Salmonella , Listeria monocytogenes and LAB ( Figure 5H ) . However , Haloarcula marismortui 23S rRNA , for which the crystal structure has been solved ( Ban et al . , 2000 ) , contains 4 nucleotides that diverge from the conserved sequence of ISR23 . Interestingly , this ISR23 variant sequence from H . marismortui failed to stimulate IL-1β ( Figure 5H ) . Thus , some bacteria might escape immune surveillance by TLR13 through mutations in the ISR23 sequence . We generated TLR13 knockout mice using a targeted ES cell line generated by the knockout mouse project ( KOMP ) , which deleted almost the entire open reading frame in exon 2 and exon 3 of the mouse Tlr13 locus on the X chromosome ( Figure 6A ) . PCR of the mouse tail DNA and quantitative RT-PCR of spleen total RNA confirmed that the Tlr13 gene was deleted in the knockout mice ( Figure 6B , C ) . The Tlr13 KO mice were born and developed normally . BMDM derived from the WT and Tlr13 KO mice were stimulated with the bacterial 23S rRNA sequence 2054–2068 or the TLR7 ligand R848 ( Figure 6D , E ) . The deletion of Tlr13 in the macrophages completely abolished IL-1β induction by the 23S rRNA sequence , but not by R848 . To determine if stimulation of TLR13 triggers the secretion of mature IL-1β protein , which involves activation of the inflammasome , we immunoblotted bone marrow dendritic cell ( BMDC ) extracts and culture supernatants using an antibody against IL-1β . Stimulation of WT BMDC with the 23S rRNA sequence led to accumulation of pro-IL-1β , but not mature IL-1β ( Figure 6F , lane 5 ) . When the cells were stimulated with the 23S rRNA sequence and then transfected with the DNA poly[dA:dT] , which activates the AIM2 inflammasome ( Schattgen and Fitzgerald , 2011 ) , matured IL-1β was detected ( lane 6 ) . Neither pro-IL-1β nor mature IL-1β was detected in Tlr13 KO cells stimulated by the 23S rRNA sequence ( lanes 11 and 12 ) . In contrast , LPS and poly[dA:dT] treatment induced pro-IL-1β and mature IL-1β even in the absence of TLR13 ( lanes 9 and 10 ) . These results indicated that TLR13 was essential for the induction of pro-IL-1β by bacterial ribosomal RNA but this receptor alone was insufficient to trigger inflammasome activation . TLR13-deficient macrophages were also completely defective in inducing other cytokines , including IL6 , IL10 , TNFα and MCP1 , in response to the 23S rRNA sequence ( Figure 6G–J ) . Similar results were obtained using macrophages derived from the spleen ( data not shown ) . 10 . 7554/eLife . 00102 . 008Figure 6 . TLR13-deficient macrophages failed to induce cytokines in response to 23S rRNA . ( A ) Depiction of mouse Tlr13 locus and gene targeting strategy . PCR primers and predicted sizes of the amplified fragments from WT and disrupted Tlr13 loci are indicated . ( B ) Genotyping of one WT and two Tlr13 knockout ( KO ) mice by PCR of tail genomic DNA . ( C ) qPCR of Tlr13 RNA amplified from spleen total RNA . ( D ) BMDM from WT and Tlr13 KO mice were incubated with the 23S rRNA sequence ( 2054-2068; 1 μg/ml ) or R848 ( 1 μg/ml ) and then total RNA was isolated for qPCR analyses of IL-1β . The results are representative of two independent experiments . ( E ) Similar to ( D ) except that BMDM was stimulated with the 23S rRNA sequence for different lengths of time as indicated . ( F ) WT and Tlr13 KO BMDC were incubated with LPS ( 100 ng/ml ) or the 23S rRNA sequence ( 1 μg/ml ) for 8 hr followed by transfection in the presence or absence of poly[dA:dT] ( 1 . 5 μg/ml ) for 5 hr . Cell lysates ( upper ) and culture supernatants ( lower ) were immunoblotted with an antibody against IL-1β . ( G ) – ( J ) BMDM from WT and Tlr13 KO mice were stimulated with the 23S rRNA sequence and then the expression of the indicated cytokines was measured by qPCR . Error bars represent standard errors of triplicate assays . N . D: not detected . DOI: http://dx . doi . org/10 . 7554/eLife . 00102 . 008
Innate immune sensors invariably detect conserved microbial patterns that are indispensable for the life cycle of the microorganisms . Here we present evidence that TLR13 detects a highly conserved sequence at the catalytic center of the 23S ribosomal RNA of both gram-positive and gram-negative bacteria . Notably , G2061 ( based on E . coli sequence ) is hydrogen bonded to A2451 , the catalytic residue of peptide bond synthesis ( Nissen et al . , 2000 ) . Both G2061 and A2451 are completely conserved in the large ribosomal RNA subunits of all three kingdoms , suggesting a universal mechanism of peptide bond synthesis . Another residue of the ISR23 sequence , C2063 , forms a base pair with G2447 , which in turn forms a hydrogen bond with A2451 ( Nissen et al . , 2000 ) . This hydrogen bonding increases the pKa of A2451 , allowing it to serve as a general base to catalyze peptide bond synthesis . Remarkably , point mutations of several residues in ISR23 , including G2061 and C2063 , completely destroyed the ability of this RNA to induce IL-1β ( Figure 5D , E ) . Thus , TLR13 targets the most conserved and essential feature of bacteria , namely peptide bond formation . This antibacterial mechanism of TLR13 is analogous to that of many antibiotics , which target the catalytic center of bacterial ribosomes ( McCusker and Fujimori , 2012 ) . Similar results have recently been published by Oldenburg et al . ( 2012 ) , who showed that the conserved 23S rRNA sequence ‘CGGAAAGACC’ is a ligand for TLR13 . Through an independent and systemic analysis , we identified the optimal sequence that activated TLR13 as a 13-nucleotide sequence located in the active site of 23S rRNA ribozyme ( ACGGAAAGACCCC; Figure 5C ) . Interestingly , Oldenburg showed that N6 methylation at A2085 of S . aureus ( corresponding to A2058 of E . coli 23S rRNA ) , which conferred antibiotic resistance , abolished TLR13 stimulation . Complementing these results , we showed that point mutations of the TLR13 recognition sequence in full-length 23S rRNA , including those expected to impair peptide bond synthesis , destroyed its ability to induce IL-1β ( Figure 5E ) . Importantly , we have now provided the genetic evidence that knockout of TLR13 in mouse macrophages prevented the induction of IL-1β and other cytokines by 23S rRNA ( Figure 6 ) . The extremely high degree of sequence specificity of TLR13 is unprecedented for nucleic acid sensing receptors . TLR3 , TLR7 , TLR8 and TLR9 detect RNA or DNA in the endosomal lumen without significant sequence specificity; instead , they recognize the structures of RNA or DNA ( e . g . , dsRNA for TLR3 and unmethylated CpG DNA for TLR9 ) . RIG-I detects viral and bacterial RNA in the cytosol through the recognition of dsRNA bearing 5′-triphosphate . MDA5 detects viral dsRNA and perhaps some other unknown features . AIM2 binds dsDNA in the cytoplasm to trigger inflammasome activation . None of these cytosolic nucleic acid sensors display significant sequence specificity . The high degree of RNA sequence specificity of TLR13 may allow rodents and other organisms that carry this gene to effectively combat some bacterial pathogens that threaten their survival while minimizing potential autoimmune attacks to the host . However , such sequence specificity also presents at least two liabilities to the innate immune system . First , although bacteria cannot mutate the invariant residues in 23S rRNA that are important for peptide bond synthesis ( e . g . , G2061 and C2063 ) , they could mutate or modify other residues in the ISR23 sequences to render the bacterium invisible to TLR13 , as shown for H . marismortui ( Figure 5H ) . Second , the expansion of the mammalian genomes makes it possible that the exact 13-nt sequence of ISR23 may be found in some of these genomes , posing a threat of autoimmune reactions . Although mammalian ribosomal RNAs do not have significant sequence homology to ISR23 , we found by a BLAST search that two human mRNAs , which encode the ribosomal subunit S14 ( RPS14 ) and a pancreatic lipase ( PNLIP ) , respectively , have the exact sequence match to ISR23 . Since total RNA from human cells do not trigger IL-1β production in murine macrophages ( data not shown ) , the ISR23-like sequence in these mRNAs may be folded into secondary structures that cannot be detected by TLR13 . Nevertheless , it remains possible that unfolded RNA or fragments of these RNA ( e . g . , in dying or dead cells ) containing the ISR23 sequence could stimulate TLR13 . Thus , human might have abandoned TLR13 and relied on other pathogen receptors including RLRs , NLRs and other TLRs to detect pathogenic bacterial infections while avoiding autoimmune attacks . The loss of TLR13 in human might have also helped the expansion of the large commensal bacterial communities in the gut , which is important for the development of the immune system . In this regard , it might be interesting to determine whether there is a selective pressure against bacterial species that carry the ISR23 sequence in mice but not in human . It is interesting to note that humans lack the entire TLR11 subfamily , including TLR11 , TLR12 and TLR13 ( Roach et al . , 2005 ) . TLR11 was shown to detect a profilin-like protein in Toxoplasma gondii and is important for the production of IL-12 in dendritic cells in mice ( Yarovinsky et al . , 2005 ) . Despite the lack of TLR11 protein ( due to a stop codon in the coding sequence ) , humans have highly effective innate and adaptive immune responses against T . gondii . As profilin is abundantly present in human cells , humans might have evolved to abandon TLR11 to avoid autoimmune attacks and rely on other innate immune sensors to detect the parasite infection ( Balenga , 2007 ) . Despite its apparent absence in humans , the discovery of TLR13 as a sequence-specific bacterial RNA sensor offers an opportunity to study the role of this receptor in immune defense against bacterial infections . Our data obtained from the TLR13 knockout macrophages show that TLR13 is essential for cytokine induction by the bacterial 23S rRNA , a phenotype that has not been observed with other TLR mutant mice , including those lacking TLR2 , 4 , 7 or 11 . However , this does not exclude the possibility that TLR13 may cooperate with other TLRs to detect bacterial infections . In fact , in the context of bacterial infections , multiple TLRs are likely to be activated by distinct ligands associated with bacteria , including peptidoglycans , flagellin , lipopolysaccharides , DNA and RNA . Thus , the relative contribution of TLR13 to immune response against pathogenic and commensal bacteria in vivo requires further investigation , which will now be facilitated by the availability of the TLR13 knockout mice . How bacterial ribosomal RNA gains exposure to TLR13 is another interesting question for future research . Perhaps macrophages could take up bacterial particles through endocytosis and the lysis of bacteria in the endolysomal compartments expose the 23S rRNA and the ISR23-containing remnants to TLR13 on the endosomal membrane . Our finding that the ISR23 RNA could stimulate mouse macrophages without transfection suggests that TLR13 might take up bacterial RNA ( i . e . , from dead or lysed bacteria ) on the cell surface and traffic to the endosome where it launches the signaling cascades . The TLR13 signaling cascade clearly engage MyD88 and Unc93b1 , but the details of the signaling pathway requires further dissection . Finally , although ISR23 may not be an adjuvant for the development of human vaccines , its potent activity in stimulating cytokine production may be employed to boost the production of antibodies in vertebrate animals that possess the TLR13 pathway .
TLR13 knockout mice were generated using ES cells produced by the KOMP Repository ( UC Davis ) ( http://www . velocigene . com/komp/detail/10438 ) . Two independently targeted ES cell clones were injected into Albino B6 blastocysts to produce chimeric mice , which were bred with Albino mice to obtain germline transmission . The heterozygous F1 progenies were intercrossed to obtain TLR13 knockout mice . For genotyping TLR13 knockout mice , tail genomic DNA was isolated from 7- to 10-day-old pups and then amplified by PCR using primers described in Supplementary file 1A . The PCR condition was: 95°C 3 min; 95°C 30 s; 60°C 30 s , 72°C 30 s , repeated for 35 cycles . Wild type ( WT ) , Mavs−/− , Myd88−/− , Myd88−/−Mavs−/− , Unc93b1 mutant ( 3d ) , TLR2−/−TLR4−/− and TLR7−/− mice were bred and maintained under specific pathogen-free conditions in the animal care facility of University of Texas Southwestern Medical Center at Dallas . These strains were maintained on C57BL/6J background . All mice were engineered , housed and used according to the experimental protocols approved by the Institutional Animal Care and Use Committee . Bone marrow cells were collected from femurs and tibiae of mice . Cells were cultured in DMEM containing 10% fetal bovine serum ( FBS ) , antibiotics , and conditional media from either L929 cell culture or GM-CSF producing cells . 24 hr later , non-adherent cells were transferred to a new plate and fresh conditional medium were added every other day up to the seventh day . Mature macrophages or DC were harvested and transferred to new plates for further experiments . Murine macrophage cell line Raw264 . 7 was grown in DMEM supplemented with 10% FBS and antibiotics including penicillin , streptomycin and Normocin ( InvivoGen , San Diego , USA ) . Stable TLR13 knockdown cells were generated using lentiviruses expressing shRNA against TLR13 and selected by puromycin ( 2 μg/ml ) . The efficiency of TLR13 knockdown or rescue in these stable cell lines was confirmed by either qRT-PCR or immunoblotting using FLAG ( M2 ) antibody ( Sigma-Aldrich , St Louis , USA ) . In all experiments except indicated otherwise , 1 or 3 µg bacterial RNA was added to the culture medium and incubated for 6–8 hr before cells were harvested for analyses . Lactobacillus salivarius ( LAB ) ( ATCC 11741 ) and DH5α were grown in Difco Lactobacilli MRS Broth or Luria Broth ( LB ) under either anaerobic chamber ( BD GasPak , Franklin Lakes , USA ) without shaking or normal aerobic with shaking at 250 rpm , respectively . After 16–18 hr , bacterial cells were collected into lysing Matrix B tubes ( MP Biomedicals , Santa Ana , USA ) and rapidly frozen down with liquid nitrogen . To isolate the total RNA , TriZol ( Invitrogen , Grand Island , USA ) was added and the tubes were vortexed at high speed with FastPrep ( Thermo Electron Corporation , Waltham , USA ) at 4°C . Crude RNA was further purified with RNeasy Mini Kit ( Qiagen , Valencia , USA ) . RNA was treated with DNase I ( Roche , South San Francisco , USA ) ( 1 hr; 37°C ) to remove potential DNA contamination . Total RNA isolated from either LAB or DH5α was separated by native or denatured 1% agarose gel electrophoresis . Each band containing ribosomal RNA was excised and soaked in RNase-free water overnight . On the following day , 5-butanol was used to remove extra water and then RNA was precipitated with 100% ethanol . MEGAscript T7 kit ( Ambion , Austin , USA ) was used for in vitro transcription according to manufacturer's instruction . DNA encoding full length bacterial 23S or 16S rRNA was obtained by PCR amplification from DH5α genomic DNA using Phusion DNA polymerase ( Finnzyms , Waltham , USA ) and subsequently cloned in pGEM-T vector ( Promega , Madison , USA ) . DNA templates for in vitro transcription were amplified by PCR using primers listed in Supplementary file 1B . RNA oligos were synthesized by Sigma and listed in Supplementary file 1C . Point mutations of full-length 23S rRNA were carried out with Phusion Site-Directed Mutagenesis Kit ( Finnzymes ) . Mouse TLR13 cDNA was purchased from InvivoGen and subcloned into a pTY lentiviral vector in which TLR13 was fused in frame with a C-terminal Flag tag . The primers for amplification of TLR13 is shown in Supplementary file 1A , so are the DNA oligos for construction of pTY lentiviral shRNA vectors that target distinct regions of TLR13 coding sequence . Transfection of RNA into Raw264 . 7 or BMDM was carried out using FuGENE ( Roche ) or Lipofectamine 2000 ( Invitrogen ) . For enzymatic treatments of nucleic acids , 1 . 0 μg of nucleic acids was treated with RNase III , RNase T1 or RNase V1 ( Ambion ) at 37°C for 1 hr . Enzyme-treated RNAs were purified with RNeasy Mini Kit ( Qiagen ) before adding to cell culture . To extract RNA , cells were first lysed in 1 . 0 ml of TRIzol ( Invitrogen ) . Lysate was mixed with chloroform , and the aqueous phase was applied to RNeasy columns to obtain total RNA ( Qiagen ) . The iScript cDNA synthesis kit ( BioRad , Hercules , USA ) was used to create cDNA from 0 . 15 μg of RNA . Quantitative RT-PCR was performed in Applied Biosystem Vii7 using SYBR Green and primers described in Supplementary file 1D . | A central feature of the immune system is the ability to detect bacteria , viruses and other pathogens so that they can be repelled or neutralized before they cause lasting damage to an organism . Cells employ a number of different receptors that can detect these pathogens or the molecules they produce . Many of these are called pattern recognition receptors because they recognize certain signatures of microorganisms such as nucleic acids or carbohydrates . An important class of pattern recognition receptor is the toll-like receptor: there are many different families of the receptors , each recognizing a unique feature of bacteria or viruses . ( The word toll , which means ‘great’ in German , refers to a gene whose mutations lead to striking phenotypes in flies , and has nothing to do with road and bridge tolls . ) Toll-like receptors have two parts that perform two different functions: when one part binds the relevant microbial molecules , the other part sends a signal that results in the production of effector proteins . These proteins include interleukin-1β , which helps to fight infection by causing the inflammation of tissue . To date , 12 different types of toll-like receptors have been found in mice , including three—known as TLR11 , TLR12 and TLR13—that are not present in humans . Very little is known about the functions of TLR12 and TLR13 . Humans , on the other hand , possess 10 different TLRs , only one of which , TLR10 , is not found in mice . Li and Chen have now discovered that TLR13 is responsible for detecting a certain type of ribosomal RNA called 23S ribosomal RNA that are present in bacteria but not in eukaryotic cells . Moreover , they have shown that a short sequence of 13 residues within the 23S ribosomal RNA triggers this pathway and leads to the production of interleukin-1β . The sequence of 13 residues is located at an active site in the RNA that catalyzes the synthesis of peptide bonds , and changing just one of these residues stops the production of interleukin-1β . Other forms of ribosomal RNA are unable to trigger the production of interleukin-1β . These results show that TLR13 differs from all other pattern recognition receptors because it is able to recognize a specific RNA sequence . Li and Chen went on to generate mice lacking TLR13 and showed that immune cells isolated from these mice failed to respond to bacterial RNA . These mice can be used to investigate the role of TLR13 in immune responses to bacterial infections in vivo . | [
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Understanding how our use of antimicrobial drugs shapes future levels of drug resistance is crucial . Recently , there has been debate over whether an aggressive ( i . e . , high dose ) or more moderate ( i . e . , lower dose ) treatment of individuals will most limit the emergence and spread of resistant bacteria . In this study , we demonstrate how one can understand and resolve these apparently contradictory conclusions . We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host . We extend our analysis to the community level , exploring the spectrum between strict inter-strain competition and strain independence . From this perspective as well , we find that the magnitude of effective competition between resistant and sensitive strains determines whether an aggressive approach or moderate approach minimizes the burden of resistance in the population .
The growing crisis of resistance to antimicrobial drugs has captured the attention of the global public health community as the harrowing reality of the loss of previously effective medicines combined with slow discovery of new agents threatens a post-antibiotic era of untreatable infectious diseases . Although the quality and completeness of surveillance is variable , current data are consistent with rising levels of resistance; this worrisome trend is not restricted to particular pathogens or specific geographic settings ( WHO , 2014 ) . While an accurate assessment of the current health and economic losses attributable to antibiotic resistance is elusive , the estimated numbers , ranging up to 2 million serious infections , 23 , 000 deaths , and 35 billion dollars in the United States alone , are staggering ( CDC , 2013 ) . Similar numbers of deaths have been attributed to antibiotic-resistant infections in Europe ( O'Neill , 2014 ) . Most recently , a projection of 10 million deaths and 100 trillion dollars in economic losses attributable to antimicrobial-resistant infections by 2050 has been circulated ( O'Neill , 2014 ) . Given that antimicrobial treatment cures infections while simultaneously selecting for antimicrobial resistance , it is crucial to understand how alternative treatment strategies affect the probability of resistance . The conventional wisdom guiding the rapidity and dosing of drugs , often attributed to Paul Ehrlich , ( Ehrlich , 1913 ) is that early and aggressive use of antimicrobial agents is most effective for optimizing cure and minimizing the risk of resistance ( Levin et al . , 1999 ) . Recently , there has been some debate as to the universality of the claim that these aggressive approaches are optimal for minimizing the risk of resistance , with some researchers suggesting that more moderate approaches may perform better ( Read et al . , 2011 ) and others defending the standard approach ( Ankomah and Levin , 2014 ) . A central rationale for an aggressive approach is that early high dose treatment will most rapidly reduce the size of the microbial population from which drug-resistant isolates appear and thus minimize the probability of the emergence of resistance during treatment ( Bonhoeffer et al . , 1997; Knudsen et al . , 2003; Wiuff et al . , 2003; Tam et al . , 2005 , 2007; D'Agata et al . , 2007; Ankomah and Levin , 2014; Kim et al . , 2014 ) . In contrast , the rationale for a more moderate approach is that higher doses of antibiotics impose stronger selective pressure which drives a more rapid emergence of resistance ( Read and Huijben , 2009; Read et al . , 2011; Kouyos et al . , 2014 ) , and that rapid suppression of drug-susceptible isolates may allow for competitive release of existing drug-resistant isolates ( de Roode et al . , 2004; Wargo et al . , 2007; Huijben et al . , 2010; Pena-Miller et al . , 2013; Pollitt et al . , 2014 ) . Recently , Kouyos et al . ( 2014 ) summarized the relevant , albeit limited , empirical evidence about dosing and risk of resistance , and described a ‘conceptual curve’ relating the strength of selection to the expected rate of resistance emergence , highlighting theoretical conditions under which aggressive and moderate approaches may be preferred . How one formulates the question about optimal antimicrobial dosing strategies to minimize resistance will depend on one's perspective . For example , a clinician will likely be most concerned with identifying the dosing regimen that produces the best health outcome for the patient ( i . e . , highest probability of cure accounting for toxicities and the risk of resistance ) . A public health practitioner will likely seek to identify which treatment practices produce the greatest health gains while minimizing the long-term levels of resistance in the community . The recent debate over aggressive and moderate approaches has mainly been centered on identifying an optimal strategy for the treatment of individual hosts to minimize the probability of resistance . However , the emergence and subsequent transmission of resistance in the population may be of even greater concern . From a theoretical perspective , optimal dosing strategies for the prevention of resistance in individuals are not necessarily optimal for limiting resistance at the population level ( Lipsitch and Samore , 2002; Mills et al . , 2013 ) . Here , we provide a modeling framework that unifies the individual-level and population-level perspectives and provides additional insight into the debate about aggressive and moderate approaches for antimicrobial treatment . We demonstrate that the extent of effective competition between drug-susceptible and drug-resistant isolates is a key determinant of whether an aggressive approach is better ( in terms of resistance prevention ) than a moderate approach for hosts being treated for disease . Most importantly , we find that even within a model that allows for very strong competition , different realistic combinations of parameter values can support the aggressive or moderate approach as optimal . We illustrate how it is possible that models can support two such different conclusions by carefully considering the dominant interactions between the strains . We extend our analysis to the population level , exploring a spectrum of inter-strain interactions ranging from strict competition to independence . We find that the same framework explains why either aggressive or moderate treatment approaches can minimize resistance .
We describe two populations of bacteria within a single host using a model based on Ankomah and Levin ( 2014 ) . The model includes both wild-type ( drug-sensitive; DS ) bacteria and drug-resistant ( DR ) bacteria which arise by some presumably rare mechanism from the drug-sensitive type . This mechanism could be single-point mutation , acquisition of resistance genes through horizontal gene transfer , or another mechanism ( Lipsitch and Samore , 2002; zur Wiesch et al . , 2011 ) . While we do not explictly model these differences , we note that the mechanisms of resistance and their probabilities affect the relative importance of de novo resistance compared to pre-existing resistance circulating in a population ( Lipsitch and Samore , 2002 ) . In our model , each strain initiates an immune response which follows density-dependent kinetics . Bacteria grow in a resource-dependent manner and have a death rate which increases under higher antibiotic concentrations . Antibiotics enter the system and degrade at a constant rate . The DR strain , by definition , has a higher minimum inhibitory concentration ( MIC ) than the DS strain and is assumed to have a slower growth rate reflecting fitness costs associated with mutation or acquisition of resistance genes . The phrase ‘minimum inhibitory concentration’ parallels the language in Ankomah and Levin ( 2014 ) ; the MIC is the value of antibiotic A at which the growth rate with the drug is half its baseline value ( when A = 0 ) . The strain interactions in the model are complex: strains compete for resources , and each strain can suppress the other by triggering a host immune response . Thus , we expect the strains to be under fairly strong competition . However , the DS strain also benefits the DR strain as DR is generated from the DS population through acquired resistance . The equations are: ( 1 ) dBsdt=GsBs−kpPBs−kiIBs−μBsdBrdt=GrBr−kpPBr−kiIBr+μBsdIdt=αIBs+BrBs+Br+σi−uiIdPdt=η ( Pmax−P ) Bs+BrBs+Br+σp−γPdRdt=w ( CR−R ) −e ( GsBs+GrBr ) dAdt=Ain− ( d+w ) A . The variables are the densities of DS and DR bacteria ( Bs , Br in cells/ml ) and the adaptive and innate immune cells ( I , P in cells/ml ) , and the concentration of the resource R ( μmg/ml ) and the antibiotic A ( μmg/ml ) . Bacterial growth is resource dependent ( Ankomah and Levin , 2014 ) with growth rate λs=ΛsRk+R and similarly for λr , where Λs and Λr are the maximum growth rates of the two strains when the resource is not limiting . The net growth rate Gs is Gs = λs − ( us + δs ( A ) ) and Gr = λr − ( ur + δr ( A ) ) , capturing death and the bactericidal effect of the antibiotic . We adopt the assumption that recruitment of activated effector cells of the innate system ( P ) is dependent on the density of the pathogen population ( Bs + Br ) and σp > 0 is a saturation constant . Similarly , we assume that the expansion of specific adaptive immune response I is dependent upon the density of the pathogen population , the maximal expansion rate α and σi , the pathogen density at which the increase in the adaptive immune response is half maximal . The resource is replenished at rate wCR and depleted at rate wR . In our baseline results we assume , as in previous work ( Ankomah and Levin , 2014 ) , that it is the net growth Gs and Gr that determine the extent of the depletion of resources . This means that if the net growth is negative , lysis of cells can replenish the resource . We assess the sensitivity of our findings to this assumption in Appendix 1 and find that the model's inter-strain dynamics and their dependence on the parameters are unaltered when the lysis effect is removed ( in which case the resource equation in ( 1 ) reads dR/dt = w ( CR − R ) − e ( ΛsBs + ΛrBr ) ) . To incorporate the possibility of stochastic die-off of the DR population when its density is very low , the growth rate is 0 when the density is less than 30 cells/ml . Not allowing for stochastic die-off might allow resistant strains to enjoy an unrealistic advantage in the model due to its deterministic nature . Here , 30 cells/ml is approximately 10−8 times the maximum bacterial load , though this varies with the parameter choice . Antibiotic concentration A speeds the death of bacteria according to a saturating mechanism δs ( A ) =λsA/MsA/Ms+1 , and similarly for R , where Ms and Mr are the minimum inhibiting concentrations of antibiotic for the DS and DR strains , respectively . A is introduced through dosage Ain and is removed at rate d + w ( Ankomah and Levin , 2014 ) . To explore these complex interactions , we drew 60 , 000 sets of parameters from ranges containing the values used previously ( Ankomah and Levin , 2014 ) ( see Table 1 ) , spanning a range of strengths of the immune system ( ki , kp , η , α ) , relative overall fitness of the DR strain ( Λr , Mr ) , pre-existing DR bacilli , the growth rate of the DS strain ( Λs ) , and the mutation parameter μ . Each parameter was chosen uniformly at random from the ranges given in Table 1 . All were held fixed , and the model was simulated under multiple dosages . This gave us an understanding of how treatment affected resistance at each set of parameters . We captured the relationship between increasing Ain and the maximum and total ( integrated over time in the simulations ) DR at each randomly drawn set of parameters . 10 . 7554/eLife . 10559 . 009Table 1 . Parameters and rangesDOI: http://dx . doi . org/10 . 7554/eLife . 10559 . 009SymbolDescriptionRangeUnitWithin-host model ΛsMax growth rate ( DS ) 0 . 4–0 . 8hr−1 ΛrMax growth rate ( DR ) ( 0 . 6–1 ) × Λshr−1 kHill coeffient in bacterial growth0 . 5–50 . 5μg/ml us , urDeath rates DS , DR0 . 2hr−1 mSMIC ( DS ) 1μg/ml mRMIC ( DR ) 1–8μg/ml kpRate of innate immune clearance10−7 − 10−5hr−1 kiRate of adaptive immune clearance10−5 − 10−3hr−1 μRate of DR mutation5 × 10−9 − 5 × 10−7#/division αRecruitment of adaptive immunity0 . 002–0 . 02hr−1 σi , σpHill parameter in I , P dynamics1000 , 10 , 000cells/ml uiLoss rate of I5 × 10−5 − 5 × 10−4hr−1 γLoss rate of P5 × 10−4 − 5 × 10−3hr−1 ηRecruitment of innate immunity10−5 − 9 × 10−4hr−1 wWashout rate0 . 2hr−1 CRResource reservoir concentration300–700μg/ml eUse of resource per unit growth5 × 10−7μg/cell AinAntibiotic treatment0–2 . 5μg/ ( ml × 24 hr ) dLoss rate of antibiotic0 . 1hr−1Between-host model βxTransmission parameter ( DS ) 1–4months−1 βyTransmission parameter ( DR ) 1 − βxmonths−1 κPartial immunity coefficient1none κtTreatment protection from DS1 − 0 . 3Tnone cSimilarity coefficient0–1none uDS clearance without treatment13βx − 23βxmonths−1 uyDR clearanceβy/R01 − βymonths−1 TIntensity of treatment0–1none rRelease of DS through treatment0–0 . 1months−1 umaxMax clearance DS under treatment1 . 05 βxmonths−1Ranges are indicated with a − separating lower and upper values . Where a single value is given the parameter was fixed . Within-host parameter ranges contain the values used in Ankomah and Levin ( 2014 ) . The relative growth of the resistant and sensitive strains ( without treatment ) can be modified either through k or Λ . We have chosen to vary the Λ parameters as the effect on relative fitness is linear . In the between-host model the value of u was chosen such that the DS strain has a basic reproductive number in Bonhoeffer et al . ( 1997 ) and Ankomah and Levin ( 2014 ) . Similarly , uy was chosen so that R02 ranges from 1 to R01 , to ensure that the DR strain has a smaller maximum growth rate than the DS strain . We determined whether ‘aggressive’ or ‘moderate’ therapy was the best approach according to which one minimized the overall ( maximum and total ) levels of resistance . If treatment is negatively correlated with resistance , then more treatment results in less resistance and an aggressive approach is best . Conversely , if the correlation is positive , then treatment drives increases in resistance , and a moderate approach is best ( from a resistance standpoint ) . Accordingly , parameter sets in which resistance levels were negatively ( S < −0 . 7 ) or positively ( S > 0 . 7 ) correlated with antibiotic dosage as determined by the Spearman correlation S were classed as ‘aggressive is best’ or ‘moderate is best’; other results were classed as neutral . We removed parameter sets in which treatment does not succeed to avoid unfair inclusion of those parameter sets in which the long-term selective pressure of unsuccessful treatment drives resistance . In the main analyses , we assume that the threshold for successful treatment is defined as causing a >80% reduction in the maximum DS population; in sensitivity analyses , we vary this threshold and provide results from the full set of simulations in which such a threshold is not imposed ( see Appendix 1 ) . To explore a wide range of inter-strain interactions at the population level , we developed a model with four host compartments: susceptible , infected with DS ( X ) , infected with DR ( Y ) and dually infected ( D ) . We envision a continuum of inter-strain interactions that in principle describe co-circulating pathogens . At one of the continuum , we posit that distinct pathogens may be entirely independent of each other , not interacting directly or indirectly ( e . g . , through immune modulation or resource competition ) . In this case , infection with one strain does not affect infection or recovery with the other strain . At the other end of the continuum , very similar strains of the same pathogen are likely to be competing for hosts . Figure 1 illustrates two models , one with strict competition and one with independence . 10 . 7554/eLife . 10559 . 010Figure 1 . Models at the two ends of the competition independence spectrum ( between hosts ) . In the competing model ( top row ) , decreasing the DS strain ( X ) paves the way for an increase in resistance ( Y ) by removing the DR strain's competitor , despite the fact that decreasing DS also removes a source of resistance . The bifurcation analysis illustrates competitive exclusion: whichever strain has the higher R0 excludes the other . In both cases , some resistance is present when the sensitive strain is present , due to acquisition of resistance , for example , through mutation . Consequently , the ‘1-only’ region of the plot has some strain 2 at very low prevalence . In contrast , if the strains are not competing ( bottom row ) , including not competing for hosts , hosts must be able to harbor both infections ( duals , D ) . In this case , reducing DS reduces DR by reducing a source of resistance . The bifurcation plot where strains are independent illustrates that strain 1 is present if R01 > 1 , strain 2 is present if R02 > 1 and both are present if they are both >1 . Our between-host model incorporates competition and independence , parameterized by a ‘similarity coefficient’ that smoothly moves from one to the other ( see Appendix 1 for details ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10559 . 010 We have previously described ‘neutral null’ models ( Lipsitch et al . , 2009 ) , in which biologically indistinguishable strains have sensible dynamics in models ( i . e . , outcomes do not depend on which strain a host has ) . Our model spans this continuum , which is parameterized by a ‘similarity coefficient’ c . When c = 1 the strains are highly similar and neutral in the sense of Lipsitch et al . ( 2009 ) if they are identical . When c = 0 , the two strains act independently; infection with one does not affect the spread of the other . See Appendix 1 for more details and a proof of these statements . The model equations are: ( 2 ) dXdt=FxS−κFyX+12κtcFxD−uxX+ ( 1−c ) uyD− ( μ+Tr ) XdYdt=FyS−κtκFxY+12κtcFyD−uyY+ ( 1−c ) uxD+12TrD+ ( μ+Tr ) XdDdt=κFyX+κtκFxY−12κtc ( Fx+Fy ) D− ( 1−c ) ( ux+uy ) D−12TrD−12c ( ux+uy ) Dux=u+ ( umax−u ) T , Fx=βxX+ ( 1−12c ) D , Fy=βyY+ ( 1−12c ) D . In this model , hosts may become infected with both strains and enter the dually infected class ( D ) ; the chance of this may be reduced by cross-immunity , which we assume is symmetric between resistant and sensitive strains ( κ ) . In models where dual infection cannot occur , there is an implicit assumption of very strong competition between strains . Dually infected individuals may also be again re-infected with a single strain ( Lipsitch et al . , 2009 ) . Clearance terms ( with recovery ux and uy ) are modulated with the similarity coefficient , c , to ensure that the model has independent interactions when c = 0 and neutral null dynamics when c = 1 ( see Appendix 1 ) . Transmission rates are βx and βy , recovery rates are ux and uy , and we assume that over the time frame of the simulation the population does not change; we scale it to 1 so that S = 1 − X − Y − D . The forces of infection Fx and Fy contain a contribution from both singly and dually infected hosts such that when the strains are different , dually infected hosts contribute as much as singly infected ones , and when they are very similar , each strain contributes half what a singly infected host would ( Lipsitch et al . , 2009 ) . Treatment T ranges from 0 to 1 ( where the DS strain is eliminated ) and has several effects . Primarily , it cures the sensitive strain by reducing its duration of infection 1/ux . Individuals with a resistant strain ( Y ) who are given treatment are partially protected ( κt ) from re-infection with the sensitive strain . Dually infected individuals given treatment have the drug-sensitive portion of their infection cured at an increased rate ux due to treatment , but their resistant infection is not cured . To capture the risk of releasing small sub-populations of resistant bacilli within such hosts , we include a parameter r which is a small rate at which resistance is uncovered by treatment . This parameter links the in-host and between-host models: in circumstances in which strong treatment drives increases in resistance , r would be high ( approaching the treatment rate ux of the sensitive strain ) . We use a range of parameters such that the basic reproductive numbers ( β/u ) of the strains , R01 ( DS ) and R02 ( DR ) , are greater than 1 , with R02 < R01 ( Table 1 ) . We draw parameters randomly and increase the treatment T . We explore the relationship between the strength of treatment and the long-term and maximum level of resistance . We classify the resulting optimal strategy as aggressive if the Spearman correlation is less than −0 . 7 and moderate if it is larger than 0 . 7 . The between-host model admits three possible steady states ( equilibria ) : a disease-free equilibrium ( X = Y = D = 0 stable when R01 and R02 are both less than 1 ) , a resistance-only steady state ( X = D = 0 , Y = 1 − uy/βy ) , and a steady state with X , Y , and D positive whose explicit form is not available . If there were no acquisition of resistance ( μ = 0 ) and no ‘competitive release’ term ( transition from X to Y ) , there would be an additional equilibrium with Y = D = 0 and X = 1 − ux/βx . We carried out an invasion analysis to determine the point at which the resistance-only equilibrium loses local stability as βx increases . When the two strains are independent , X should be prevalent if and only if R01 ≥ 1 . But if they are not , the values of the strain Y parameters ( and its prevalence ) affect the prevalence of X . We performed the invasion analysis as follows: we computed the Jacobian of model ( 2 ) and evaluated it at the resistance-only steady state for a given set of parameters including βy . We used matlab's nonlinear solver , fsolve , to determine the value of βx at which the resistance-only steady state loses stability . We repeated this for a range of values of βy to produce the colored lines in Figure 5C . We took several approaches to understand how the parameters of each model relate to whether aggressive or moderate treatment minimizes resistance . The most direct approach is simply to choose a set of parameters , vary the dosage , and examine how resistance changes ( Figure 2 ) . Naturally , the result depends strongly on the parameter choice . We also vary one parameter at a time , keeping others fixed , and examine the trajectories ( Appendix figures 2 , 3 ) . The next approach is to examine , over all simulations simultaneously , how the outcome depends on each parameter by stratifying the outcomes ( Figure 3 ) . Using heatmaps or scatter plots , it is also possible to explore how pairs of parameters determine an outcome ( Figure 4 ) . We take the same approach in the between-host model , with Figure 5 showing demonstrative trajectories under varying treatment strength , Appendix figure 4 showing a sensitivity analysis varying one parameter at a time , and Figures 6 , 7 showing the stratified dependence of the outcome on single and paired parameters while other parameters are allowed to vary . 10 . 7554/eLife . 10559 . 003Figure 2 . How treatment changes the trajectory of the in-host model . Parameters are ( A ) Λs = 0 . 5 , Λr = 0 . 45 , mR = 2 . 8 and ( B ) Λs = 0 . 6 , Λr = 0 . 53 , mR = 3 . 4 . Other parameters are k = 48 , kp = 2 . 2e−6 , ki = 7 . 2e−4 , μ = 4e−7 , α = 0 . 018 , σi = 1000 , σp = 10000 , γp = 0 . 001 , and the remainder are as in Table 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 10559 . 00310 . 7554/eLife . 10559 . 004Figure 3 . Frequency of best policies over key parameters . An aggressive policy ( dark blue ) is deemed best if the Spearman correlation S between treatment and resistance is S < −0 . 7 , moderate ( light blue ) is deemed best if S > 0 . 7 and the classification is neutral ( medium blue ) otherwise . When the DR strain has a lower growth rate ( LamR ) , an aggressive policy is more likely best because more of the DR strain's population arises through resistance acquisition from the DS population . In this case , reducing the DS strain also reduces DR . Conversely , when ΛR ( LamR ) is high the DR strain is a more robust competitor and a moderate policy is more frequently best . Similarly , when the DR strain has a low MIC ( mR ) , it is a less robust competitor . In this case , an aggressive policy is more frequently best than when mR is high ( second panel ) . The third panel shows that when the immune system is strong ( high kp ) , an aggressive policy is more frequently best , because again more of the DR population increases are driven by acquisition from DS , due to immune suppression of DR growth . A plot with η on the horizontal axis is very similar to this one . Finally , the right plot shows that when the DS growth rate ( LamS ) is low , an aggressive strategy is more often best to minimize resistance; this depends on the ability of therapy to prevent the emergence of resistance . DOI: http://dx . doi . org/10 . 7554/eLife . 10559 . 00410 . 7554/eLife . 10559 . 005Figure 4 . Heatmaps illustrating how best policies depend on key combinations of parameters . Color indicates the policy that minimizes resistance . Yellow: aggressive; green: neutral; blue: moderate . When the growth rate ΛR ( LamR ) is high , a moderate policy is more frequently best , but a strong immune system ( high kp ) can compensate by reducing DR growth . When the DR strain is a strong competitor , a moderate policy is frequently best; this can be achieved by either a high ΛR or a high DR MIC ( mR ) ( top left ) . Either a high kp or a high η can compensate ( bottom right ) , reducing the growth potential of the DR strain and leading to either a neutral outcome or an aggressive policy being best . DOI: http://dx . doi . org/10 . 7554/eLife . 10559 . 00510 . 7554/eLife . 10559 . 006Figure 5 . Trajectories of the between-host model under varying treatment . Treatment is introduced at 5 years . ( Left ) Parameters are βx = 1 . 5 , βy = 1 . 04 , c = 0 . 05 , r = 0 , μ = 0 . 001 . ( Middle ) Parameters are βx = 2 , βy = 1 . 1 , c = 0 . 3 , r = 0 . 05 , μ = 0 . 0001 . ( Right ) Invasion analysis ( bifurcation ) plot . The plot shows regions of stability of the disease-free equilibrium ( both R0 values less than one ) , the DR-only equilibrium ( top left region ) , and the equilibrium with both ( primarily DS , with low-level DR due to acquisition ) . The diagonal lines show the boundary at which the DR-only equilibrium loses stability . Lines move to the right as the similarity coefficient increases from 0 ( light blue vertical line ) to 1 ( pink ) . When it reaches 1 , the line is R01 = R02 . DOI: http://dx . doi . org/10 . 7554/eLife . 10559 . 00610 . 7554/eLife . 10559 . 007Figure 6 . Best policy over parameters in the population-level model . Light blue corresponds to Spearman correlation greater than 0 . 7 , dark corresponds to less than −0 . 7 and mid-range blue corresponds to all values in between . An aggressive policy is best when the DR strain is relatively unfit ( low R0 value; left panel ) . When the acquisition rate is high , treatment-driven reductions in DS decrease the DR prevalence ( second panel ) . When the two strains are more independent ( low similarity coefficient ) , competition is reduced , so that reductions in the DS strain do not much benefit the DR strain , leading to the aggressive policy being preferred ( third panel ) . When R01 is high , a moderate outcome results , because inter-strain competition is more effective . Vertical axes ( ‘count’ ) are the fraction of simulations in each category . DOI: http://dx . doi . org/10 . 7554/eLife . 10559 . 00710 . 7554/eLife . 10559 . 008Figure 7 . Best policy over parameter combinations in the population-level model . A moderate-is-best ( blue ) outcome occurs when R01 and the similarity coefficient are high , because the strains are competing ( left panel ) . When the DR strain is relatively unfit ( low R02 ) , an aggressive policy is likely best ( second and third panels ) ; we restricted R02 to be lower for the DR strain than the DS one . A high rate of acquisition can counter-balance a low R02 ( second panel ) . When the strains are more independent ( low similarity coefficient ) , an aggressive policy is more often best even over a much wider range of R02 ( third panel ) . DOI: http://dx . doi . org/10 . 7554/eLife . 10559 . 008 However , we cannot expect any one or two parameters to entirely determine which approach minimizes resistance . We used discriminant analysis of principal components ( DAPC , in the adegenet package in R [Jombart and Ahmed , 2011] ) to systematically identify which parameters contribute to each outcome ( Jombart et al . , 2010 ) . DAPC is related to principal component analysis ( PCA ) but instead of finding combinations of parameters to account for the variability in data ( as PCA does ) , DAPC finds combinations of parameters that best account for variability between groups . Here , we used whether aggressive or moderate treatment minimizes resistance to define the groups ( aggressive , neutral or moderate as above ) and used DAPC to find combinations of parameters that separate these groups from each other .
Figure 2 illustrates how treatment dosage may affect the level of resistance in the in-host model . Whether treatment increases or decreases resistance over the course of infection depends on many of the parameters in the model . We performed a sensitivity analysis to determine how the relationship between resistance and dosage changes with each parameter when the others are held fixed ( Appendix figures 2 , 3 ) . We found that the relationship is relatively robust to variation in each parameter alone , but that small changes in several parameters can have a radical effect . In Appendix figure 2 , increasing the dosage does not increase resistance and this result is fairly robust to variation in each parameter alone; Appendix figure 3 shows the contrasting robust scenario ( increased dosage increases resistance ) . The difference is that the baseline parameters in Appendix figure 3 have somewhat higher growth Λs and Λr of both strains and the resistant strain has a higher MIC . No single parameter determines whether aggressive or moderate treatment minimizes resistance . A relatively small change in several parameters can shift the model from one mode to the other . We separated parameter combinations in which aggressive vs moderate treatment minimizes resistance using a principled approach ( DAPC; Appendix figure 6 ) . We found that a linear combination of parameters ( one ‘principle component’ ) captures almost all of the difference between the three groups ( aggressive minimizes DR , moderate minimizes DR , or neutral , i . e . , in between ) . The ‘loadings’ ( e . g . , coefficients ) of the parameters in this linear combination correspond to the relative importance of the parameters in determining whether aggressive or moderate treatment minimizes resistance . The number of pre-existing resistant cells affects the outcome ( coefficient 0 . 79 ) . When pre-existing numbers are low ( <10 ) , parameters which lead to a ‘moderate’ policy to be best include higher growth rate of the DR and DS strains ( ΛR , 1 . 4 and ΛS , 0 . 92 ) , lower immune parameters kp and η ( −0 . 74; −0 . 66 ) and a higher MIC of the DR strain ( mR; 0 . 34 ) . Other parameters had loadings less than 0 . 1 and did not contribute much to the classification . In other words , we find that an aggressive approach is preferred when the immune system is relatively strong ( higher values of immune parameters kp , η ) , and when the DR strain has a relatively low growth rate ( ΛR ) and low MIC ( mr ) . Conversely , if there is pre-existing resistance , the immune system is weaker , and/or the growth rate or MIC higher , a moderate approach minimizes resistance . Pre-existing populations of DR pathogens ( i . e . , resistance that appears prior to exposure to treatment ) favor a moderate approach , since there is no possibility that a hard-and-fast approach will clear the infection before resistance can arise . Figure 3 illustrates how the best policy relates to individual model parameters , and Appendix figures 7 , 8 show the distributions of individual parameters where treatment increases or decreases resistance . No single parameter determines which policy is best; rather , the outcome depends on the combined effects of a set of complex interactions . This means that from a location in parameter space where aggressive therapy minimizes resistance , a relatively small change in several parameters ( e . g . , a slight decrease in kp , increase in mR and increase in Λr ) can result in a moderate policy being best . Figure 4 shows the best policies for key pairs of parameters whose values combine to influence whether an aggressive or moderate policy minimizes resistance . These figures reveal a few intuitive trade-offs: a higher DR growth rate ( ΛR ) generally leads to a moderate policy being best ( light blue; positive correlation between treatment and DR ) , but this can be offset with a strong immune system keeping both strains in check ( high η or high kp ) . A higher DR growth rate or a higher MIC ( mR ) make the DR strain a robust competitor and also consequently favors a moderate policy . Interestingly , while the mutation rate affects the overall numbers of DR bacteria ( particularly in cases when they are not present initially ) , it does not have a strong effect on the relationship between treatment and total resistance . If resistant cells are present initially , then they do not need to emerge by ( rare ) mutation or acquisition mechanisms from the sensitive strain . This simple observation has consequences for our analysis; a ‘moderate is best’ , or neutral conclusion is much more likely with pre-existing resistance , keeping everything else the same . When there is no pre-existing resistance , the DR strain must have a higher MIC , higher growth rate and face a weaker immune system in order to be a robust competitor than it does when it is present initially . Appendix figure 1 shows the heatmaps as in Figure 4 but stratified according to whether there is pre-existing resistance . To understand which policy minimizes resistance , one must be able to characterize the net effect that the presence of one strain has on the other strain . There is strong opportunity for competition between strains encoded in the model; competition plays out through shared resources which may be limiting as well as through the triggering of an immune response that suppresses both strains equally . Both of these effects occur when bacterial populations are large . However , the initial appearance of resistance also depends critically on the presence of drug-sensitive organisms . Altering both the strength of competition and the dependence of the DR strain on the DS progenitor population determines whether or not such competition is effective . In particular , effective competition naturally requires a DR strain that has the capacity to be a robust competitor to the DS progenitor . This can be achieved in two ways: it can maintain a strong growth capacity in the presence of antibiotic treatment or immune pressure , or it can face an immune system that is not particularly strong . Our exploration of the parameter space uncovered both of these mechanisms . These findings are not an artifact of the model structure , and indeed they will likely occur in any model that includes de novo appearance of resistant strains by mutation or acquisition of resistance determinants by drug-sensitive organisms , and where resistant strains can then compete for resources with their drug-sensitive cousins . Effective competition can be described as the net extent to which a decrease in one strain benefits the other . This should capture both direct and indirect effects . Quantifying effective competition is challenging because either strain may affect the other over short or long time frames and because different ways to decrease one strain have different effects . From the second equation of ( 1 ) , the direct effect of a decrease in Bs on the immediate growth of the resistant strain is simply the change in the acquisition term ( μBs ) . If the other terms are small , this term can make the difference between net growth and net decline of resistant cells . The indirect effects are more difficult to determine; a decrease in Bs will mean more available resource and reduced recruitment of immune cells which will have onward effects . We made a step towards quantifying effective competition as follows . We numerically solved the system , obtaining values of each variable through time . We decreased Bs and computed the predicted value of Br two time steps later , using Euler's method , the derivatives in ( 1 ) and the time steps defined by the adaptive ODE solver . We did this ( separately ) at each time point and averaged the fractional change in Br due to a 5% change in Bs over the portion of the trajectory where Bs > 105 . We do this only in the untreated case , for each value of the parameters we sampled . This captures only the immediate indirect effects ( occurring in the next two small time steps ) and the intervention we made ( reducing Bs when dosage is 0 ) is not the same as treatment . Nevertheless , this formulation of effective competition is a good predictor of whether treatment , when added , increases resistance . We used it to classify whether treatment would increase or decrease resistance , and found that the classification worked well , with an area under the receiver–operator characteristic curve ( AUC ) of 0 . 94 ( the theoretical maximum is 1 , and a classifier that guesses randomly has AUC of 0 . 5 ) . See Appendix figure 10 . Figure 5 illustrates the behavior of the between-host model at two demonstrative parameter sets , one illustrating resistance decreasing with treatment due to the dominant effect of acquisition of DR from the DS strain , and the other illustrating resistance increasing with treatment as a result of inter-strain competition . Appendix figure 4 shows the relationship between resistance and strength of treatment and its sensitivity to variation in single parameters . No one parameter defines whether treatment increases or decreases resistance . The model has the possibility of three distinct steady states ( equilibria ) : no disease , both DS and DR present , and only the DR strain present . If there were no acquisition of resistance , there would also be the possibility of an equilibrium with only the DS strain . Figure 5 shows that the stability region of the DR-only equilibrium changes as the similarity coefficient increases . When c = 0 , the DR-only equilibrium is invaded by the DS strain as soon as R01 > 1 ( vertical blue line ) . However , when the strains are more similar , R01 must be higher in order to invade , and when c = 1 , there is no equilibrium DS prevalence unless R01 ≥ R02 . Appendix figure 9 , Figures 6 , 7 illustrate how the best policy depends on the fitness of the DR strain and the other parameters . Treatment decreases resistance when the R0 values of both strains are relatively low , the rate of acquisition of resistance is high and the similarity coefficient is low . We find that the parameter groups where aggressive therapy minimizes resistance are well separated by those where moderate therapy is best , by a single DAPC function ( Appendix figure 6 ) . Here , the strongest driver of a moderate policy being best is a high similarity coefficient ( c , coefficient 0 . 93 ) . High R02 and R01 ( coefficients 1 . 01 , 0 . 65 ) contribute , as does a low acquisition rate ( coefficient −0 . 56 ) . Somewhat surprisingly , the rate of competitive release does not contribute to the DAPC weighting ( −0 . 02 ) . An aggressive strategy is more likely to minimize resistance when the DR strain is relatively unfit ( low R02 ) , and the DR population is supported by a high rate of acquisition . Furthermore , an aggressive strategy is likely to be best when the strains are more independent ( a low similarity coefficient ) . Independence means that even a relatively unfit strain can be transmitted in the population , despite the presence of a more fit strain , because when strains are independent they can each super-infect hosts already infected with the other strain , and they can be transmitted from those with dual infection ( if these cannot happen then the strains cannot be independent; rather , they would compete for hosts and/or for infectivity ) . We noted previously ( Colijn et al . , 2009 ) that such co-infection can , but does not always , allow DR strains to persist in the long term where they would not be able to do so otherwise; similar results were recently reported by Hansen and Day ( 2014 ) . Our current results clarify that these effects are a result of the level of competition and are not a consequence of co-infection . Co-infection can be present under high , low , or intermediate levels of competition . The factors that favor an aggressive policy—lower R02 , higher rates of resistance acquisition and increased independence between strains—have the net impact of reducing inter-strain competition . A low R01 also makes an aggressive approach more likely to be preferred ( see Appendix figure 9 ) ; competition for hosts is low when there are plenty of susceptible hosts , whether the model has strict competition mechanisms or not . This occurs when both R0 values are low . Low R02 means the DR strain is not a fit competitor , a high independence ( 1 − c ) explicitly reduces competition through protection from re-infection and through independent recovery , and a higher mutation rate increases the benefit the DR strain enjoys from DS . We used the same approach to quantify the short-term competitive interaction as in the within-host model . We reduced the population of the sensitive strain , iterated the model forward two time steps , and computed the effect on the resistant strain ( in the absence of treatment ) . We used the average proportional change in the resistance strain following this direct reduction in the sensitive strain to predict what would happen ( increase or decrease ) to resistance under treatment . As in the within-host model , this measure of effective competition is a good predictor of the effect of treatment ( AUC 0 . 94; Appendix figure 10 ) . The models are directly linked through the competitive release , r , which reflects a portion ( in the between-host model ) of treated sensitive infections that convert to resistant ones . Even where this does not occur at all ( r = 0 ) , successful treatment of individuals drives population-level resistance when strains are competing . Consider the within-host model in the regime where aggressive treatment minimizes resistance , for example , low DR growth Λr or a strong immune system , or high rate of acquisition of resistance . Individuals treated successfully are not susceptible to , or infectious with , the sensitive strain . At the between-host level , though , they may remain susceptible to a circulating resistant strain , which now has more susceptible hosts available than the sensitive strain . Treatment ( reducing R01 ) moves the system to the left on the invasion plot ( Figure 5C ) , increasing resistance if there is effective competition at the between-host level .
An aggressive policy for antibiotic treatment is preferred when the appearance and persistence of DR is driven by the existence of a sufficiently large DS population . In these settings , the benefits to the DR population which accrue from the acquisition of resistance from DS outweigh the costs of competition from a larger DS population . In contrast , a moderate dosing policy is preferred when the DR strain is a fit enough competitor that acquisition of resistance plays a sufficiently small role in the DR population dynamics . Here , the cost of competition from the DS population outweighs the benefit of additional DR bacteria appearing through acquired resistance . Both modes occur in models containing acquisition of resistance and competition , and a small change in parameters can shift models from one mode to the other . Understanding why previous models and theory have differed in support of aggressive ( Ehrlich , 1913; Knudsen et al . , 2003; Wiuff et al . , 2003; Tam et al . , 2005; D'Agata et al . , 2007; Ankomah and Levin , 2014; Kim et al . , 2014 ) and moderate ( de Roode et al . , 2004; Wargo et al . , 2007; Gullberg et al . , 2011; Huijben et al . , 2013 ) approaches requires evaluating both structural assumptions and parameter choices ( Spicknall et al . , 2013 ) as these together affect the strength of effective competition between DR and DS strains . We have used a comprehensive approach to exploring these assumptions and have begun the process of defining measures of effective competition to predict whether moderate or aggressive treatment minimizes resistance . Previous contradictory results on this question fit neatly into the framework we have presented . In Ankomah and Levin ( 2014 ) , the model structure incorporates complex interactions between strains , allowing for many facets of competition to be explored . At their chosen parameter values , however , there is little effective competition between DR and DS strains and they found an aggressive policy to be best . In a recent work by Kim et al . ( 2014 ) , DR strains had two alleles with no onward fitness evolution , little in-host competition , and low DR fitness; consequently , they also found that an aggressive approach would be best . Another recent model ( Gomes et al . , 2013 ) assumed that there was competition for resources at high bacterial populations , and concluded that this competition could play a role in suppression of resistant strains . Work by Geli et al . ( 2012 ) explored different ecological dynamics and found that strong immunity supports an aggressive policy , but that selection was most intensive at intermediate strengths of treatment in chronic infections ( Geli et al . , 2012; Kouyos et al . , 2014 ) . Gullberg et al . ( 2011 ) found that even low concentrations of antibiotic ( where the DR and DS fitness may not differ ) can rapidly enrich DR sub-populations . Huijben et al . ( 2013 ) found experimentally that competitive release of ( pre-existing and relatively fit ) resistant strains increased with increasing drug pressure . We conclude that both of these perspectives are reasonable . We find that even in a system where aggressive approaches are most frequently best ( the within-host model , based on Ankomah and Levin ( 2014 ) ) , a moderate approach can be preferred if the DR strain is slightly more fit and the environment is slightly more permissive; in this case , inter-strain competition mechanisms , which are always present , are more effective . Likewise , at the between-host level where we might expect herd-level competition effects to play out , we find that even where competition mechanisms are strong , an aggressive approach may be best of one or both strains have a low basic reproduction number or if the rate of acquisition of resistance is high . Both of these factors limit the effective competition . In most circumstances , we expect that DR and DS variants of a single pathogen compete quite strongly: they will be closely related , and so are likely to share antigenic properties and induce a similar host immune response . They are also likely to consume or be reliant on similar host resources ( Fiegna and Velicer , 2005 ) and occupy similar biological niches within hosts ( Dall'Antonia et al . , 2005; Lam and Monack , 2014 ) . The extent to which these aspects dominate the fact that resistance is also driven by de novo acquisition , and hence benefits from high DS population levels , will depend on the acquisition rate and mechanism ( Lipsitch and Samore , 2002 ) as well as on the degree of effective competition . Furthermore , models have typically reflected competition for host resources or via a carrying capacity ( Geli et al . , 2012; Gomes et al . , 2013; Ankomah and Levin , 2014 ) , such that competition takes place at high bacterial populations . If direct antagonistic ( Claverys and Håvarstein , 2007; Baquero and Lemonnier , 2009 ) or cooperative ( Diard et al . , 2013 ) interactions occur , they are likely to substantially alter the extent and timing of competition , with profound onward consequences for optimal treatment . We do want to emphasize some key differences between the problem of minimizing resistance among each individual receiving treatment and minimizing resistance of circulating pathogens in a community ( Lipsitch et al . , 2000; Lipsitch and Samore , 2002; Cohen et al . , 2006; zur Wiesch et al . , 2011; Geli et al . , 2012; Mills et al . , 2013 ) . Consider , for example , an immune-competent individual initially harboring a drug-susceptible infection free of any ( or many ) sporadically resistant isolates . An aggressive approach may well be preferred for this individual . However , if there is a fit resistant strain circulating in the community , then this policy can drive substantial resistance at the population level ( e . g . , by selectively suppressing the DS strain , making hosts susceptible to re-infections only with the DR strain ) , even if it does not increase the risk of acquiring resistance in any individual given treatment . Consequently , an aggressive approach might well be best at the individual level while still driving resistance over longer time frames . An aggressive approach may also diminish in utility over time if DR strains become fitter through selection , if they begin to circulate widely and compete with DS strains ( zur Wiesch et al . , 2011 ) . Co-infection of individual hosts by multiple strains or isolates has been observed for most pathogens in which it has been investigated ( Balmer and Tanner , 2011 ) , and we have incorporated it in both models . At the population level , models that fail to include co-infection assume that co-infections do not occur; this equates to a very strong assumption about competition for hosts , regardless of whether there is sufficient data to inform co-infection parameters . We have previously argued that diversity-promoting mechanisms in models should be explicit ( Lipsitch et al . , 2009 ) and that ‘neutral’ models are a useful framework for understanding implicit assumptions in multi-strain models . Here , we note that such neutrality is competition . The ‘no coexistence for free’ directive can be reframed: we expect identical strains to compete . The extent of competition is a key driver of how the balance between multiple strains changes in response to interventions; if we are to use models to understand these responses , we must be clear about the mechanisms and the extent of effective competition between strains . These results highlight the importance of identifying empirical data that reveal whether effective competition between DS and DR strains is present . Experimental approaches in which mixed bacterial populations are studied in vitro or in vivo may reveal mechanisms by which these sub-populations may exhibit interference competition through direct interaction ( Claverys and Håvarstein , 2007 ) or exploitative competition through shared dependence on a common resource ( Fiegna and Velicer , 2005 ) . These types of controlled experiments have been valuable for identifying conditions under which such direct competition effects are likely to manifest within individual hosts . Identifying data that would reveal the conditions under which we would expect competition between DS and DR strains at the community level is clearly more challenging . The scale and the timing at which we would expect to observe the effects of intraspecific competition will likely differ by pathogen type . Similar to studies of vaccines or other interventions in which indirect effects are important to consider , community-randomized trials are the most promising design , but the expense and logistics of such trials for considering different antibiotic dosing strategies may be prohibitive . In the absence of such trials , relating population-level antibiotic use data to surveillance data describing trends in resistance in the community may help to identify signals of such competition . Detailed analysis of the numbers and ages of treated cases , the population density , ‘drug-bug’ interactions , and the time since resistance first emerged ( Turnidge and Christiansen , 2005 ) could improve our ability to do this . Meanwhile , careful consideration of the level of effective competition is essential when using models to understand the relationship between antimicrobial use and resistance . | Antibiotics are chemical compounds used to treat bacterial infections . The discovery of antibiotics , starting with penicillin in 1929 , revolutionized medicine , making it possible to cure or prevent life-threatening infections such as tetanus and pneumonia . However , many bacteria have become resistant to one or more antibiotics and so can no longer be killed by these drugs . The emergence of antibiotic resistance reflects an evolutionary process that occurs during antibiotic treatment . While the antibiotic will kill most bacteria , some bacteria may naturally have a feature or genetic mutation that allows them to survive in the presence of the antibiotic . These bacteria then reproduce and pass on their resistant traits , eventually leading to the emergence of a new antibiotic-resistant strain of bacteria . Once a resistant strain exists it may be able to spread from one person to another . There is conflicting evidence about how best to prevent antibiotic-resistant bacteria from evolving and spreading . The results of some experiments suggest that treating bacteria with large doses of antibiotics early in an infection is the most effective way to optimize treatment and minimize the risk of an antibiotic-resistant strain developing . However , other studies suggest that exposing bacteria to high levels of antibiotics more efficiently selects for resistance; in this case a more moderate approach should be used when treating bacterial infections . Here , Colijn and Cohen present a mathematical model that suggests that the natural competition between the antibiotic-resistant and antibiotic-sensitive strains of bacteria influence which treatment strategy should be taken . Strains were modeled both within individual hosts and spreading in a community of individuals . In the models , aggressive antibiotic treatment is most effective ( in that it minimizes antibiotic resistance ) when the antibiotic-resistant strain either does not experience strong competition from the non-resistant strains of bacteria or is not fit enough to be a good competitor . However , a more moderate treatment is appropriate when the two strains are competing and the antibiotic-resistant strain is a fit competitor . Competition may mean that moderate treatment is best to avoid resistance at the community level , even in situations when aggressive treatment is likely best for individuals . Two important future challenges are to better understand the diversity of strains in bacterial infections , and to develop tools to measure to what extent strains are effectively competing with each other . | [
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] | 2015 | How competition governs whether moderate or aggressive treatment minimizes antibiotic resistance |
During development , neurons form synapses with their fate-determined targets . While we begin to elucidate the mechanisms by which extracellular ligand-receptor interactions enhance synapse specificity by inhibiting synaptogenesis , our knowledge about their intracellular mechanisms remains limited . Here we show that Rap2 GTPase ( rap-2 ) and its effector , TNIK ( mig-15 ) , act genetically downstream of Plexin ( plx-1 ) to restrict presynaptic assembly and to form tiled synaptic innervation in C . elegans . Both constitutively GTP- and GDP-forms of rap-2 mutants exhibit synaptic tiling defects as plx-1 mutants , suggesting that cycling of the RAP-2 nucleotide state is critical for synapse inhibition . Consistently , PLX-1 suppresses local RAP-2 activity . Excessive ectopic synapse formation in mig-15 mutants causes a severe synaptic tiling defect . Conversely , overexpression of mig-15 strongly inhibited synapse formation , suggesting that mig-15 is a negative regulator of synapse formation . These results reveal that subcellular regulation of small GTPase activity by Plexin shapes proper synapse patterning in vivo .
During nervous system development , various instructive and repulsive signaling cues cooperatively direct neurons to form chemical synapses with their appropriate targets . Studies have identified some molecules and elucidated their downstream mechanisms that instruct synaptogenesis such as FGF , Ephrin/Eph , Ig-family of cell adhesion molecules ( IgCAMs ) and synaptic cell adhesion molecules ( SynCAMs ) ( Shen and Bargmann , 2003; Shen et al . , 2004; Dabrowski and Umemori , 2016; Dabrowski et al . , 2015; Feng et al . , 2000; Kayser et al . , 2008; Terauchi et al . , 2010; Yamagata et al . , 2003 ) . Several axon guidance cues and their receptors also play critical roles to inhibit synapse formation ( Inaki et al . , 2007; Klassen and Shen , 2007; Poon et al . , 2008 ) . Semaphorins ( Sema ) and their receptors , Plexins , are two conserved families of molecules that have a well-established function to repel axons during development ( Kolodkin et al . , 1993 , 1992; Negishi et al . , 2005a; Tran et al . , 2007 ) and play prominent roles contributing to immune system , cardiovascular development and cancer regulation ( Epstein et al . , 2015; Neufeld et al . , 2005; Takamatsu and Kumanogoh , 2012 ) . In addition to its function as a long-range axon guidance cue during neuronal development , Sema/Plexin signaling plays a critical role as a negative regulator of synapse formation . The role of Sema/Plexin signaling to inhibit synapse formation was first observed in Drosophila , where ectopic expression of Sema2a causes elimination of specific neuromuscular junctions ( Matthes et al . , 1995 ) . In mammals , Sema3E/PlexinD1 specifies sensory-motor connections ( Pecho-Vrieseling et al . , 2009 ) . Secreted Sema3F locally inhibits spine development through its receptors PlexinA3 and Neuropilin-2 in hippocampal granule cells ( Tran et al . , 2009 ) . Sema5A/PlexinA2 signaling inhibits excitatory synapse formation in dentate granule cells ( Duan et al . , 2014 ) . Sema5B diminishes synaptic connections in cultured hippocampal neurons ( O'Connor et al . , 2009 ) . However , little is known about the intracellular mechanisms through which Sema/Plexin signaling inhibits synapse formation . The cytoplasmic domain of Plexin contains a GAP ( GTPase-activating protein ) domain that inactivates small GTPases ( Oinuma et al . , 2004; Rohm et al . , 2000 ) . Upon activation by Semaphorins , Plexins repel axon outgrowth by inhibiting R-Ras ( Negishi et al . , 2005b; Hota and Buck , 2012; Tasaka et al . , 2012 ) . Recent biochemical and structural analyses demonstrated that the GAP domain of mammalian PlexinA3 is specific for Rap GTPases , which belong to the Ras family of GTPases and regulate the actin cytoskeleton ( Wang et al . , 2012 , 2013 ) . PlexinA3 dimerization by Semaphorin activates its GAP domain , thereby inhibiting Rap1 from inducing neurite retraction . Drosophila PlexA and zebrafish PlexinA1 promote remodeling of epithelial cells by inhibiting Rap1 GTPase during wound healing ( Yoo et al . , 2016 ) . Another Rap GTPase , Rap2 , can inhibit neurite outgrowth ( Kawabe et al . , 2010 ) . Similar to Sema/Plexin signaling , Rap GTPases regulate synapse formation and function . Rap2 negatively regulate spine number in cultured hippocampal neurons ( Fu et al . , 2007 ) . Rap1 and Rap2 regulate synaptic activity by removing AMPA receptors from spines during long-term depression and depotentiation , respectively ( Zhu et al . , 2002 , Zhu et al . , 2005 ) . While the GAP domain of Plexin is critical to inhibit synapse formation ( Duan et al . , 2014; Mizumoto and Shen , 2013a ) , we still do not know whether Plexin regulates synapse patterning via Rap GTPases at presynaptic sites . In Caenorhabditis elegans , Sema/Plexin signaling functions in vulva formation and male ray development ( Dalpé et al . , 2005 , 2004; Fujii et al . , 2002; Ikegami et al . , 2004; Liu et al . , 2005; Nakao et al . , 2007; Nukazuka et al . , 2008 , 2011 ) . Using this model system , we previously reported that Sema/Plexin signaling in the nervous system mediates a critical inter-axonal interaction for the tiled synaptic innervation of two DA-class cholinergic motor neurons ( DA8 and DA9 ) ( Mizumoto and Shen , 2013a ) . Cell bodies of nine DA neurons in C . elegans reside in the ventral nerve cord , sending dendrites ventrally and axons dorsally to form en passant synapses onto the dorsal body wall muscles . Even though axons of DA neurons show significant overlap , each motor neuron forms synapses onto muscles within specific sub-axonal domains , which do not overlap with those from neighboring DA neurons . This unique synaptic innervation creates tiled synaptic patterns along the nerve cord ( White et al . , 1986 ) . Tiled synaptic innervation occurs within most motor neuron classes and may contribute to the sinusoidal locomotion pattern of C . elegans ( White et al . , 1986 ) . Using a combination of two fluorescent proteins ( GFP and mCherry ) fused with the presynaptic vesicle protein , RAB-3 , and two tissue specific promoters ( Figure 1 ) ( Mizumoto and Shen , 2013a ) , we can visualize this synaptic tiling between DA8 and DA9 neurons . We reported that PLX-1 localizes at the anterior edge of the DA9 synaptic domain in axon-axon interactions in a Semaphorin-dependent manner , where it locally inhibits formation of the presynaptic specialization via its GAP domain . Loss of Semas or plx-1 causes anterior expansion of DA9 synaptic domain and posterior expansion of DA8 synaptic domain . This result indicates loss of inter-axonal interactions between DA8 and DA9 neurons . Consistently , Tran et al . , also observed excess dendritic spine formation , specifically within the region close to the cell body , in the plexin knockout mouse ( Tran et al . , 2009 ) . These findings suggest a conserved mechanism by which Sema/Plexin locally inhibits synapse formation . We previously reported that let-60/KRas gain-of-function mutants showed very mild synaptic tiling defects . Since mammalian Plexin acts as a RapGAP , we hypothesized that Rap GTPase is the major downstream effector of PLX-1 to regulate synaptic tiling of DA neurons . Here , we report that rap-2 , a C . elegans ortholog of human Rap2A , and its effector kinase mig-15 ( TNIK: Traf2- and Nck-interacting kinase ) act genetically downstream of plx-1 to regulate synaptic tiling . PLX-1 delineates the border of synaptic tiling by locally inhibiting RAP-2 along the DA9 axon . We also discovered an unexpected role for mig-15 in inhibiting synapse formation . Our results reveal the mechanism underlying Plexin signaling to form fine synaptic map connectivity .
Three Rap genes exist in the C . elegans genome ( rap-1 , rap-2 and rap-3 ) . To delineate which Rap GTPase functions downstream of PLX-1 in synaptic tiling between DA8 and DA9 neurons , we first examined the expression patterns of all three rap genes ( Figure 1—figure supplement 1 ) . Among them , only rap-2 , an ortholog of mammalian Rap2a , was expressed in motor neurons including DA8 and DA9 , while rap-1 and rap-3 were not expressed in these cells ( Figure 1—figure supplement 1 ) . In wild type animals , synaptic domains of DA8 and DA9 neurons did not show significant overlap , creating tiled synaptic innervation ( Figure 1A and G ) . In the plx-1 ( nc36 ) null mutant , synaptic domains of DA8 and DA9 expanded posteriorly and anteriorly , respectively . As a result , synaptic domains of these neurons overlapped significantly ( Figure 1B and G ) . Since the intracellular domain of Plexin contains a RapGAP domain , we hypothesized that RAP-2 preferentially exists in a GTP-bound form in the plx-1 mutants . The G12V mutant is widely used as a constitutively GTP-form of small GTPases including mammalian Rap2A and C . elegans RAP-1 ( Kawabe et al . , 2010; Pellis-van Berkel et al . , 2005 ) . Expression of a constitutively GTP-bound form of rap-2 ( G12V ) under the A-type neuron specific promoter , Punc-4 , elicited a similar synaptic tiling defect as plx-1 mutants ( Figure 1C and G ) . Expression of wild type rap-2 under the unc-4 promoter did not affect the synaptic tiling pattern , suggesting that G12V mutation but not over-expression of rap-2 caused the synaptic tiling defect ( Figure 1G ) . We then generated rap-2 ( G12V ) mutants using CRISPR/Cas9 genome editing . We observed the same synaptic tiling defects in three independent rap-2 ( G12V ) mutant alleles ( miz16 , miz17 and miz18 ) as in plx-1 mutants ( Figure 1D and G and Figure 1—figure supplement 2 ) . We found a comparable level of gene expression among all three rap-2 ( G12V ) mutants to wild type rap-2 using RT-qPCR ( Figure 1—figure supplement 2 ) . These results confirm that the rap-2 ( G12V ) mutation itself , not changes in gene expression , underlie the synaptic tiling defect in rap-2 ( G12V ) mutants . Surprisingly , the null mutant of rap-2 ( gk11 ) also showed the same synaptic tiling defect , as did the constitutively GTP-form of rap-2 ( G12V ) mutants ( Figure 1E and G ) . This result suggests that synaptic tiling requires both the GTP- and GDP-bound forms of RAP-2 . Indeed , the constitutively GDP-bound form of rap-2 mutants ( S17A: miz19 , miz20 ) showed the identical synaptic tiling defect as rap-2 ( G12V ) and rap-2 ( gk11 ) mutants ( Figure 1F and G and Figure 1—figure supplement 2 ) . These results suggest that the cycling between GTP- and GDP-forms of RAP-2 is critical to regulate the spatial patterning of synapses . Similar to plx-1 mutants , the synaptic tiling defect in all rap-2 mutants is caused by both the posterior expansion of DA8 synaptic domain and the anterior expansion of the DA9 synaptic domain ( Figure 1—figure supplement 2 ) . However , none of rap-2 ( G12V ) , rap-2 ( S17A ) and rap-2 ( gk11 ) mutants enhanced or suppressed the synaptic tiling defect in plx-1 mutants ( Figure 1G ) . These results suggest that plx-1 and rap-2 function in the same genetic pathway . Consistent with the expression patterns of rap-1 and rap-3 , neither rap-1 ( pk2082 ) nor rap-3 ( gk3975 ) null mutants showed significant synaptic tiling defects by themselves and did not enhance the synaptic tiling defect in rap-2 ( gk11 ) null mutants ( Figure 1—figure supplement 1 ) . All mCherry::RAB-3 puncta co-localized with active zone markers , CLA-1 ( Xuan et al . , 2017 ) and UNC-10/Rim ( Wu et al . , 2013 ) in the DA9 neurons of plx-1 ( nc36 ) and rap-2 ( gk11 ) mutants , suggesting that RAB-3 puncta represent bona fide synapses ( Figure 1—figure supplement 3 ) . Taken together , these data indicate that plx-1 and rap-2 act in the same genetic pathway for synaptic tiling in DA neurons . We next determined the cellular location for rap-2 function . Since rap-2 ( gk11 ) null mutants showed a synaptic tiling defect , we conducted tissue specific rescue experiments using tissue-specific promoters as previously described ( Mizumoto and Shen , 2013a ) . Expression of rap-2 in the post-synaptic body wall muscle cells under the hlh-1 promoter or in another class of cholinergic motor neurons in the dorsal nerve cord ( DB neurons ) under the truncated unc-129 promoter did not rescue the synaptic tiling defect in rap-2 ( gk11 ) animals ( Figure 2A , B and G ) . However , DA neuron-specific expression using the unc-4c promoter strongly rescued the synaptic tiling defect ( Figure 2C and G ) . DA9-specific expression of rap-2 under the mig-13 promoter partially rescued the synaptic tiling defect ( Figure 2E and G ) . DA9-specific expression of rap-2 rescued the phenotype of anterior expansion of the DA9 synaptic domain but not the posterior expansion of DA8 synaptic domain ( Figure 2H and I ) . These results suggest that rap-2 regulates synapse patterning in a cell-autonomous manner . In contrast to the DA9-specific rescue experiment in rap-2 mutants , DA9-specific expression of plx-1 cDNA was sufficient to rescue synaptic defects in both DA9 and DA8 ( Mizumoto and Shen , 2013a ) ( see Discussion ) . We also observed that expression of human Rap2a in DA neurons rescued the synaptic tiling defect of rap-2 mutants , suggesting the function of rap-2 in synapse patterning is conserved across species ( Figure 2D and G ) . Previous work suggested a partial functional redundancy between rap-1 and rap-2 in C . elegans ( Pellis-van Berkel et al . , 2005 ) . However , we found that rap-1 expression in DA neurons did not rescue the synaptic tiling defect of rap-2 mutants , suggesting functional diversity between rap-1 and rap-2 ( Figure 2F and G ) . Taken together , we conclude that rap-2 functions cell autonomously in DA neurons to regulate synaptic tiling . Previously , we demonstrated that PLX-1::GFP is localized at the anterior edge of the DA9 synaptic domain , where it negatively regulates synapse formation through its cytoplasmic GAP domain ( Figure 3A and E ) ( Mizumoto and Shen , 2013a ) . In the rap-2 ( gk11 ) mutant background , we observed no change in PLX-1::GFP localization but did observe ectopic synapses in the axonal region anterior to the PLX-1::GFP domain ( Figure 3B and F ) . This result is consistent with our hypothesis that rap-2 acts downstream of plx-1 to regulate synaptic tiling . Together with our finding that synaptic tiling requires both GTP- and GDP-bound forms of RAP-2 , we speculate that PLX-1 acting at the anterior edge of the DA9 synaptic domain regulates the spatial activity of RAP-2 along the axon . We then sought to determine the spatial distribution of GTP-RAP-2 in DA9 axon . We conducted Fluorescence Lifetime Imaging Microscopy ( FLIM ) -based FRET ( Förster Resonance Energy Transfer ) measurements using EGFP-Rap2A ( human ) and mRFP-RalGDS ( RBD: Ras Binding Domain ) -mRFP ( Yasuda et al . , 2006 ) . As RalGDS-RBD specifically binds to GTP-Rap2 but not GDP-Rap2 ( Ohba et al . , 2000 ) , FRET from EGFP-Rap2A to mRFP-RalGDS ( RBD ) -mRFP can be used as a readout of Rap2 activity . We detected FRET signal as a change of GFP fluorescence lifetime ( Figure 4A ) . In HeLa cells , we observed a shorter lifetime of constitutively bound GTP construct EGFP-Rap2A ( G12V ) compared to GDP-bound EGFP-Rap2A ( S17A ) , indicating that the FRET sensor can detect the nucleotide state of Rap2A ( Figure 4B and C ) . Due to the low expression of C . elegans RAP-2 constructs in HeLa cells , we were not able to test whether the mammalian FRET sensor can detect C . elegans RAP-2 activity ( data not shown ) . We then expressed EGFP-Rap2A and mRFP-RalGDS ( RBD ) -mRFP FRET sensors in DA9 neurons in C . elegans . As human Rap2a rescued the synaptic tiling defect of rap-2 ( gk11 ) mutants ( Figure 2G ) , we reasoned that the activity pattern of human Rap2A should recapitulate that of endogenous RAP-2 . We indeed observed lower Rap2A activity at the anterior edge of the DA9 synaptic domain compared to within the synaptic domain ( Figure 4D and F ) . This observation is consistent with the localization of PLX-1::GFP at the anterior edge of DA9 synaptic domain ( Figure 3A ) ( Mizumoto and Shen , 2013a ) . Local inhibition of Rap2a activity was strongly diminished in the plx-1 mutant background ( Figure 4D and F ) . Higher Rap2 activity in the synaptic region could simply indicate the presence of synapses within the synaptic domain , rather than Rap2 inactivation by Plexin at the anterior edge of the synaptic domain . To exclude this possibility , we examined Rap2 activity in unc-104/Kif1A mutants , which show no synapses are formed in DA9 axon ( Ou et al . , 2010 ) . We showed previously that PLX-1::GFP localization to the synaptic tiling border was independent of synapses , since it was unaffected in unc-104/Kif1A mutants ( Mizumoto and Shen , 2013a ) . In unc-104 mutants , we observed the same local inhibition of Rap2A activity at the putative synaptic tiling border , but not in unc-104; plx-1 double mutants ( Figure 4E and G ) , indicating that Plexin controls local Rap2 activity independent of synapses . To understand that this local Rap2 inactivation at the synaptic tiling border depends on the localized RapGAP activity of PLX-1 , we examined the rescue activity of two PLX-1 mutant constructs , PLX-1 ( RA ) and PLX-1 ( ΔSema ) , neither of which rescued the synaptic tiling defect of plx-1 mutants ( Mizumoto and Shen , 2013a ) . PLX-1 ( RA ) is a GAP-deficient mutant but localizes normally at the anterior edge of the DA9 synaptic domain . PLX-1 ( ΔSema ) contains intact GAP domain but cannot be activated by the endogenous ligand and shows diffused localization due to deletion of the extracellular SEMA domain ( Mizumoto and Shen , 2013a ) . We observed no local Rap2 inactivation in plx-1 mutant animals expressing these mutant PLX-1 constructs in DA9 , while expression of wild type PLX-1 cDNA rescued local Rap2 inactivation at the anterior edge of the DA9 synaptic domain ( Figure 4H ) . While we do not fully exclude the possibility that PLX-1 indirectly regulates local Rap2 activity , these data taken together with the biochemical evidence that mammalian Plexin acts as RapGAP ( Wang et al . , 2013 , 2012 ) strongly suggests that Plexin localized at the anterior edge of the DA9 synaptic domain locally inactivates Rap2 GTPase to delineate the synaptic tiling border in DA9 . In mammals , TNIK ( Traf2 and Nck1-interacting kinase ) acts with Rap2 to regulate neurite extension , AMPA receptor trafficking in hippocampal neurons and microvilli formation in intestinal cells ( Hussain et al . , 2010; Kawabe et al . , 2010; Gloerich et al . , 2012 ) . In C . elegans , mig-15 is the sole ortholog of mammalian TNIK and its paralog MINK1 ( Misshapen-like kinase 1 ) , which also is an effector of Rap GTPase ( Nonaka et al . , 2008 ) . mig-15 can regulate various cellular processes , such as axon guidance and cell migration ( Chapman et al . , 2008; Poinat et al . , 2002; Shakir et al . , 2006; Teulière et al . , 2011 ) . We found that mig-15 ( rh148 ) hypomorphic mutants showed a severe synaptic tiling defect ( Figure 5A and D ) . Similar to plx-1 and rap-2 mutants , the synaptic tiling defect followed the anterior expansion of the DA9 synaptic domain and the posterior expansion of the DA8 synaptic domain ( Figure 5E and F ) . All RAB-3 puncta in mig-15 ( rh148 ) mutants co-localized with active zone markers , CLA-1 and UNC-10 ( Figure 1—figure supplement 3 ) , suggesting that these RAB-3 puncta represent bona fide synapses . We observed axon guidance defects ( 23% , n = 100 ) or ectopic branch formation ( 56% , n = 100 ) in DA9 of mig-15 mutant animals ( Figure 5—figure supplement 1 ) . These were excluded from our analysis of synaptic tiling phenotypes . While only half of the mig-15 mutant animals showed axon guidance defects or ectopic branch formation , the synaptic tiling defect of mig-15 mutants was almost fully penetrant ( Figure 5D ) . We did not observe significant synaptic tiling defects in cdh-4 ( rh310 ) mutants ( DA8/DA9 overlap: 4 . 6 ± 1 . 02 μm , n = 21 ) , which can exhibit an axon defasciculation phenotype in the dorsal nerve cord neurons ( Schmitz et al . , 2008 ) . These data suggest that the synaptic tiling defect in the mig-15 mutants is not a secondary effect of axon outgrowth and guidance . The other two nonsense alleles ( rh326: Q439Stop , rh80: W898Stop ) also showed identical synaptic tiling defects as mig-15 ( rh148 ) ( Figure 5—figure supplement 2 ) . mig-15 ( rh80 ) has a nonsense mutation within the highly conserved CNH ( citron/NIK homology ) domain , which is required to interact with Rap2 in both mammals and C . elegans ( Taira et al . , 2004 ) . This suggests a physical interaction between RAP-2 and MIG-15 for synaptic tiling . plx-1 or rap-2 mutants did not enhance the synaptic tiling defect in mig-15 mutants ( Figure 5B–F ) . This result is consistent with our hypothesis that mig-15 acts in the same genetic pathway as plx-1 and rap-2 . The PLX-1::GFP patch at the putative synaptic tiling border was unaffected in mig-15 mutants , even though the position of the PLX-1::GFP patch has shifted slightly posteriorly compared with wild type ( Figure 3C and G ) . Taken together , these results suggest that mig-15 acts downstream of plx-1 to regulate synaptic tiling . Interestingly , the degree of overlap between DA8 and DA9 synaptic domains was even larger in mig-15 mutants than those observed in plx-1 and rap-2 mutants ( compare Figures 1G and 5D ) , suggesting that mig-15 also acts downstream of additional signaling pathways ( see discussion ) . We then determined in which cells mig-15 functions by conducting tissue specific rescue experiments . Since several mig-15 isoforms ( wormbase and data not shown ) exist , we used the mig-15 genomic sequence for the rescue experiments . Expression of mig-15 under the DA neuron specific promoter ( Punc-4c ) strongly rescued the synaptic tiling defect of mig-15 ( rh148 ) mutants ( Figure 6A and F ) , consistent with our hypothesis that mig-15 acts in the same genetic pathway as plx-1 and rap-2 . Expression of mig-15 in both DA8 and DA9 rescued both posterior expansion of the DA8 synaptic domain and anterior expansion of the DA9 synaptic domain ( Figure 6G and H ) . DA9 specific expression of mig-15 under the mig-13 promoter rescued anterior expansion of the DA9 synaptic domain , suggesting that mig-15 functions cell autonomously in DA9 ( Figure 6B and G ) . We observed that Pmig-13::mig-15 weakly rescued the posterior expansion of the DA8 synaptic domain ( Figure 6H ) . This is likely due to the leaky expression of mig-15 in DA8 , as the mig-15 genomic fragment without promoter showed slight rescue of the synaptic tiling defect in mig-15 ( rh148 ) mutants ( Figure 6F ) . Kinase dead TNIK mutants act as a dominant-negative ( Mahmoudi et al . , 2009 ) . In DA neurons , expression of mutant mig-15 ( kd ) , which carries the same mutation at the corresponding amino acid of the dominant-negative TNIK ( Figure 6E ) , in DA neurons caused a severe synaptic tiling defect ( Figure 6C , D and I ) . Based on these results , we conclude that mig-15 functions cell autonomously in DA neurons . We observed that DA9-specific expression of mig-15 under the mig-13 promoter in mig-15 mutants often exhibited a shorter synaptic domain compared to wild type ( Figure 6B and G ) . So , we speculated that an excess amount of mig-15 inhibits synapse formation . We tested the effect of mig-15 overexpression in the wild type background . Strikingly , DA9-specific mig-15 overexpression in wild type ( mig-15 ( OE ) ) significantly reduced synapse number compared to wild type ( Figure 7A , C and D and Figure 7—figure supplement 1 ) . This reduction occurred without affecting the overall morphology of the DA9 neuron ( Figure 5—figure supplement 1 ) . Conversely , DA9 synapse number was significantly increased in the mig-15 ( rh148 ) mutants ( Figure 7B and D and Figure 7—figure supplement 1 ) . mig-15 overexpression also significantly reduced synapse number in DD-type GABAergic motor neurons ( Figure 7—figure supplement 2 ) . These results indicate that mig-15 is a negative regulator of synapse formation . Further , pan-neuronal expression of mig-15 under the rab-3 promoter caused severe uncoordinated locomotion in wildtype animals ( Figure 7—figure supplement 3 ) . These locomotor defects occurred concomitant with significantly reduced GFP::RAB-3 intensity in the dorsal nerve cord in mig-15 over-expressing animals and without causing significant axon guidance defects ( Figure 7—figure supplement 3 ) . Taken together , these data indicate that reduced synapse number by mig-15 overexpression disrupted proper functioning of the motor circuit . Importantly , we observed no significant increase in synapse numbers in plx-1 or rap-2 mutants ( Figure 7—figure supplement 1 ) , suggesting that the role of mig-15 in negatively regulating synapse number is independent of its role in PLX-1/RAP-2 -mediated synaptic tiling ( Figure 8G ) . Rap GTPase and TNIK are well-known actin cytoskeleton regulators ( Lin et al . , 2010 , 2008; Taira et al . , 2004 ) . Previous studies demonstrated that presynaptic development requires ARP2/3-dependent branched F-actin ( Chia et al . , 2012 , 2014 ) . Branched F-actin visualized by GFP::ut-CH ( utrophin calponin homology domain ) is enriched within the DA9 synaptic domain ( Figure 7E ) ( Chia et al . , 2012; Mizumoto and Shen , 2013a ) . We predicted that mig-15 negatively regulates synapse formation by re-organizing branched F-actin at the anterior edge of the synaptic domain . Consistently , we observed longer synaptic F-actin distribution in rap-2 ( gk11 ) and mig-15 ( rh148 ) mutants ( Figure 7F , G and I ) . While GFP::ut-CH was observed in the posterior asynaptic axonal region or in the dendrite of DA9 , synapse formation is likely inhibited by Wnt and Netrin signaling as previously reported ( Klassen and Shen , 2007; Poon et al . , 2008 ) . Conversely , overexpression of mig-15 in DA9 significantly decreased the length of synaptic F-actin ( Figure 7H and I ) . Overexpression of mig-15 also appeared to decrease the overall amount of synaptic F-actin ( Figure 7H ) . This result suggests that mig-15 inhibits synapse formation by negatively regulating the formation of synaptic F-actin . mig-15 ( OE ) reduced the number of synapses in DA9 . As a result , the length of the DA9 synaptic domain was significantly reduced in mig-15 ( OE ) animals than in wild type ( Figure 8A and E ) . Yet , synaptic tiling is maintained without a significant gap between DA8 and DA9 synaptic domains in mig-15 ( OE ) animals , suggesting that the position of synaptic tiling border shifted posteriorly in mig-15 ( OE ) animals . Indeed , the length of the posterior asynaptic domain of DA8 was significantly shorter in mig-15 ( OE ) animals , indicating that the DA8 synaptic domain expanded posteriorly ( Figure 8D ) . The PLX-1::GFP patch at the anterior edge of the DA9 synaptic domain also shifted posteriorly in mig-15 ( OE ) animals with reduced synapse number ( Figure 3D ) . These results strongly suggest that the PLX-1/RAP-2 signaling pathway may specify the position of the synaptic tiling border according to the available number of synapses in each DA neuron ( Figure 8G ) . We propose that synaptic tiling is a mechanism to maintain a uniform distribution of synapses from one class of motor neuron in the nerve cord . Consistently , we found that DA8 synaptic domain did not shift posteriorly when mig-15 was overexpressed in DA9 of the synaptic tiling mutants , plx-1 or rap-2 ( Figure 8B–E ) . This result suggests that DA8 no longer senses the reduction of DA9 synapse number in the synaptic tiling mutants . Synapse number was not different between mig-15 ( OE ) and in rap-2 ( gk11 ) ; mig-15 ( OE ) animals ( Figure 7—figure supplement 1 ) , suggesting that mig-15 is not dependent on the Plexin/Rap2 signaling pathway to inhibit synapse number ( Figure 8G ) . In summary , we demonstrate that synaptic tiling maintains a uniform distribution of synapses from one class of motor neurons along the nerve cord . Further , our results indicate plx-1 and rap-2 play critical roles in this process by coordinating the position of the synaptic tiling border .
Previously we showed that both smp-1 and plx-1 are necessary and sufficient in DA9 , which suggests that smp-1 and plx-1 act cell-autonomously in DA9 and non-autonomously in DA8 to regulate synaptic tiling . We proposed that Sema/PLX-1 in DA9 send a retrograde signal to DA8 through an unidentified signaling molecule ( X ) to induce the synaptic tiling pattern in DA8 ( Mizumoto and Shen , 2013a ) . However , we found that both rap-2 and mig-15 act cell autonomously , since our DA9-specific rescue experiment only rescued the DA9 phenotype , but not the DA8 phenotype . This conclusion is further supported since the synaptic tiling defects of these mutants were fully rescued when both neurons express functional rap-2 cDNA or mig-15 genomic DNA . We propose that each neuron utilizes a different set of cell surface proteins but share common intracellular mechanisms to specify synapse patterning . Diverse signaling and cell adhesion molecules , such as atrial natriuretic peptide receptor ( NPR ) and GPCRs , regulate Rap activity ( Gloerich and Bos , 2011; Birukova et al . , 2008; Weissman et al . , 2004 ) . Screening for these potential Rap regulators should identify novel molecules that interact with Sema/Plexin and act in DA9 . We showed that proper synapse patterning requires both GDP- and GTP-forms of RAP-2 . Considering that PLX-1 regulates the spatial distribution of RAP-2 activity and mig-15 acts genetically downstream of plx-1 in synaptic tiling , RAP-2 may also locally regulate MIG-15 ( TNIK ) . While we did not observe a specific subcellular localization of GFP-MIG-15 in DA9 ( data not shown ) , PLX-1 and RAP-2 may instead regulate MIG-15 activity rather than its spatial localization . Further biochemical characterization of MIG-15 regulation by GTP-RAP-2 or GDP-RAP-2 will elucidate the exact functions of RAP-2 in synapse patterning . Small GTPase activity is regulated by GAP and GEF ( Guanine nucleotide exchange factor ) proteins . Yet , we did not observe significant synaptic tiling defects in mutants of putative RAP-2 GEFs , which include pxf-1 ( RAPGEF2/6 ) and epac-1 ( RAPGEF3/4/5 ) ( data not shown ) ( Frische et al . , 2007; Pellis-van Berkel et al . , 2005 ) . We speculate that multiple RapGEFs act redundantly to activate RAP-2 in synaptic tiling . mig-15 mutants show a greater degree of overlap between DA8 and DA9 synaptic domains than plx-1 or rap-2 mutants . This effect partially occurs from excess synaptogenesis in the posterior asynaptic domain of both DA8 and DA9 neurons . Previously , we demonstrated that Wnt morphogens and their receptors , Frizzled , instruct synaptic topographic patterning by locally inhibiting synapse formation . Indeed , synaptic tiling defects in mig-15 mutants was somewhat similar to the combined effect of plx-1 and wnt mutants ( Mizumoto and Shen , 2013b ) . TNIK can act as a positive regulator of the canonical Wnt signaling pathway in colorectal cancer cells ( Mahmoudi et al . , 2009; Shitashige et al . , 2010 ) . While we do not know whether the canonical Wnt signaling pathway contributes to local inhibition of synapse formation , we propose that TNIK integrates multiple signaling pathways for precise synapse pattern formation . In addition to its role in synapse pattern formation , our data indicate that mig-15 also plays a role as a negative regulator of synapse number . Since neither plx-1 nor rap-2 mutants showed a significant increase in synapse number in DA9 , mig-15 may inhibit synapse formation in a different signaling pathway ( Figure 8G ) . As we observed a global reduction of synaptic actin staining in animals over-expressing mig-15 , we propose that mig-15 controls synapse number by regulating synaptic F-actin . The exact mechanisms of synaptic actin regulation by TNIK remain undetermined . TNIK could activate the JNK kinase pathway ( Taira et al . , 2004; Fu et al . , 1999 ) . The MIG-15/JNK-1 signaling pathway inhibits axonal branch formation in s C . elegans ensory neurons ( Crawley et al . , 2017 ) . In contrast to these well-established roles of MIG-15/TNIK as an activator of the JNK pathway , we did not observe any synaptic tiling defect nor change in synapse number in jnk-1 mutant animals ( Figure 5—figure supplement 2 and data not shown ) . Our results suggest mig-15 does not inhibit synapse formation through the JNK pathway . Due to the pleiotropic phenotype of the mig-15 mutants , our genetic and phenotypic analyses of mig-15 did not exquisitely reveal the mechanistic relationship between PLX-1 and MIG-15 in synaptic tiling regulation . Further biochemical studies of MIG-15 regulation by Plexin/Rap2 in synaptic tiling will elucidate the molecular mechanisms that underlie the role of MIG-15/TNIK in synapse pattern formation . Aberrant neuronal wiring underlies many neurological disorders . Not surprisingly , Semaphorin and Plexin genes are associated with various neurodevelopmental disorders and intellectual disabilities , including autism spectrum disorders ( ASD ) and schizophrenia ( Mah et al . , 2006; Gene Discovery Project of Johns Hopkins & the Autism Consortium et al . , 2009 ) . For example , PLXNB1 , SEMA3A , SEMA4D and SEMA6C are significantly upregulated in the prefrontal cortices of schizophrenic patients ( Eastwood et al . , 2003; Gilabert-Juan et al . , 2015 ) . On the other hand , non-synonymous variations in the Sema3D gene had a significant protective effect against developing schizophrenia ( Fujii et al . , 2011 ) . More recent work showed that loss of Sema5A/PlexA2 signaling induces excess excitatory synapse formation in granule cells , which caused ASD-like behavioural defects in mice ( Duan et al . , 2014 ) . Similar to Sema/Plexin signaling , TNIK is also associated with various neurological disorders , including schizophrenia and intellectual disabilities ( Anazi et al . , 2016; Potkin et al . , 2010 ) . TNIK can also physically bind and act with DISC1 ( Disrupted in Schizophrenia 1 ) to regulate synaptic composition ( Wang et al . , 2011 ) . So , we propose that the Sema/Plexin/Rap2/TNIK signaling pathway plays a critical role to precisely define synaptic connections and its disruption may induce serious neurological disorders . Interestingly , SNPs in Plexin genes are also associated with extremely high IQ ( Spain et al . , 2016 ) . Recent work suggests that loss of PlexinA1 confers better motor control in rodents due to increased synaptic connectivity in the corticospinal cord ( Gu et al . , 2017 ) . Further studies on the Plexin/Rap2/TNIK signaling pathway in synapse map formation , as presented here , will likely reveal the genetic basis of these disorders and conditions .
All C . elegans strains were derived from Bristol N2 and raised on OP50 Escherichia coli-seeded nematode growth medium ( NGM ) plates at 20C and maintained as described previously ( Brenner , 1974 ) . The following mutants were used in this study: unc-104 ( e1265 ) II , plx-1 ( nc36 ) IV , rap-1 ( pk2082 ) IV , rap-3 ( gk3975 ) IV , jnk-1 ( gk7 ) IV rap-2 ( gk11 ) V , rap-2 ( miz16 ) V , rap-2 ( miz17 ) V , rap-2 ( miz18 ) V , rap-2 ( miz19 ) V . rap-2 ( miz20 ) V , mig-15 ( rh148 ) X , mig-15 ( rh80 ) X , mig-15 ( rh326 ) X . rap-2 ( miz16 ) V , rap-2 ( miz17 ) V , rap-2 ( miz18 ) V , rap-2 ( miz19 ) V . rap-2 ( miz20 ) V were generated using Co-CRISPR method ( Kim et al . , 2014 ) . unc-22 or dpy-10 co-CRISPR markers were used for selecting candidate animals ( Kim et al . , 2014; Arribere et al . , 2014 ) . Vectors for sgRNA and Cas9 were obtained from Addgene ( Plasmid ID: 46169 and 46168 , respectively ) ( Friedland et al . , 2013 ) . The rap-2 guide RNA sequence ( 5’ – gTAGTGGAGGTGTCGGAAAAT-3’ ) was designed using MIT CRISPR design tool ( crispr . mit . edu:8079 ) and inserted into sgRNA vector using Q5 Site-Directed Mutagenesis kit ( NEB ) . Repair templates with either G12V ( miz17 and miz18 ) and S17A ( miz19 and miz20 ) mutation were generated by PCR with primer sets carrying corresponding mutations ( see supplemental Experimental Procesures ) . Synonymous mutations were also introduced in the sgRNA recognition sequence to avoid Cas9 recruitment to the edited genome . PCR products were cloned into EcoRI site of the pBluescript SK ( + ) vector . Synthesized double-stranded DNA ( GeneArt , Thermo Fisher ) was used as a repair template for generating rap-2 ( miz16 ) mutant . C . elegans expression clones were made in a derivative of pPD49 . 26 ( A . Fire ) , the pSM vector ( kind gift from S . McCarroll and C . I . Bargmann ) . Primer sets used in this study are listed in the Supplemental Experimental Procedures . The following constructs were used and transgenes were generated using standard microinjection method ( Mello et al . , 1991 ) : wyIs446 ( Punc-4::2xGFP-rab-3; Pmig-13::mCherry-rab-3; Podr-1::RFP ) , wyIs85 ( Pitr-1::GFP-rab-3; Podr-1::RFP ) , wyIs442 ( Pflp-13::2xGFP-rab-3; Pplx-2::mCherry-rab-3; Podr-1::RFP ) , wyIs320 ( Pitr-1::plx-1::GFP; Pmig-13::mCherry;;rab-3; Podr-1::GFP ) , wyIs329 ( Pmig-13::GFP-ut-CH; Pmig-13::mCherry::rab-3; Podr-1::GFP ) , wyIs524 ( Punc-4::2xGFP-rab-3; Pmig-13::mCherry-rab-3; Podr-1::RFP ) , wyIs685 ( Pmig-13::mCherry::rab-3; Pmig-13::GFPnovo2::cla-1; Podr-1::GFP ) mizIs1 ( Pitr-1::GFPnovo2-CAAX; Pvha-6::zif-1; Pitr-1::mCherry::rab-3; Podr-1::GFP ) , mizIs19 ( Pmig-13::eGFP::hRap2a; Pmig-13::mRFP-RalGDS ( RBD ) -mRFP; Podr-1::GFP ) , mizIs33 ( Prab-3::mig-15; Podr-1::GFP ) , jsIs682 ( Prab-3::GFP::rab-3; lin-15 ( + ) ) , wyEx5445 ( Prap-1::GFP; Punc-4::myr-mCherry; Podr-1::RFP ) , wyEx5464 ( Prap-2::GFP; Punc-4::myr-mCherry; Podr-1::RFP ) , mizEx194 ( Prap-3::GFP; Pmig-13::myr-mCherry; Podr-1::RFP ) , mizEx165 ( Phlh-1::rap-2; Podr-1::GFP ) , mizEx164 ( Punc-129::rap-2; Podr-1::GFP ) , mizEx174 ( Punc-4c::rap-2; Podr-1::GFP ) , mizEx157 ( Pmig-13::rap-2; Podr-1::GFP ) , mizEx156 ( Punc-4c::hRap2a; Podr-1::GFP ) , mizEx177 ( Punc-4c::rap-1; Podr-1::GFP ) , mizEx151 ( Pmig-13::mig-15; Podr-1::GFP ) , mizEx147 ( Punc-4c::mig-15; Podr-1::GFP ) , mizEx153 ( ΔpSMmig-15; Podr-1::GFP ) , mizEx178 ( Punc-4c::mig-15 ( K50A ) ; Podr-1::GFP ) , mizEx173 ( Punc-4::rap-2 ( G12V ) ; Podr-1::GFP ) , mizEx179 ( Pflp-13::mig-15; Podr-1::GFP ) , mizEx170 ( Pmig-13::mig-15; Podr-1::GFP ) , mizEx197 ( Pmig-13::mig-15; Podr-1::GFP ) , mizEx210 ( Pmig-13::mig-15; Podr-1::RFP ) , mizEx257 ( Prab-3::GFP; Prab-3::mCherry::rab-3 , Podr-1::mScarlet::CAAX ) , mizEx272 ( Podr-1::GFP; Pmig-13::unc-10::TdTomato ) ; mizEx309 ( Pmig-13::plx-1 ( RA ) ; Podr-1::RFP ) , mizEx312 ( Pmig-13::plx-1 ( Δsema ) ; Podr-1::RFP ) ; mizEx314 ( Pmig-13::plx-1; Podr-1::RFP ) . cDNAs of rap-1 and rap-2 were obtained from cDNA library prepared from N2 RNA . Trizol ( Invitrogen ) was used to purify total RNA from N2 , and the SuperScript III First-Strand Synthesis System for RT-PCR ( Invitrogen ) was used for the reverse-transcription . mig-15 genomic DNA was amplified from the N2 genomic DNA purified using GeneJET Genomic DNA Purification Kit ( Thermo Scientific ) . Phusion ( NEB ) or Q5 ( NEB ) DNA polymerases were used for all PCR reactions for amplifying cDNA and genomic DNA fragments . Amplified fragments were cloned into the AscI and KpnI sites of pSM vector using SLiCE method ( Motohashi , 2015 ) , Gibson assembly ( Gibson et al . , 2009 ) or T4 ligase ( NEB ) . List of primers used in this study is available in the Supplemental Experimental Procedures . Images of fluorescently tagged fusion proteins were captured in live C . elegans using a Zeiss LSM800 confocal microscope ( Carl Zeiss , Germany ) . Worms were immobilized on 2% agarose pad using a mixture of 7 . 5 mM levamisole ( Sigma-Aldrich ) and 0 . 225M BDM ( 2 , 3-butanedione monoxime ) ( Sigma-Aldrich ) . Images were analyzed with Zen software ( Carl Zeiss ) or Image J ( NIH , USA ) . Definition of each parameter is as follows ( Mizumoto and Shen , 2013a ) : DA8/9 overlap: a distance between the most anterior DA9 synapse and the most posterior DA8 synapse , DA8 asynaptic domain: a distance from commissure to the most posterior DA8 synapse , DA9 synaptic domain: a distance between the most anterior and posterior DA9 synapses . Middle L4 ( judged by the stereotyped shape of developing vulva ) animals were used for quantification . Averages were taken from at least 20 samples . For GFP::Utrophin-CH , we measured the length from the posterior end of dorsal axon to the anterior end of GFP::Utrophin-CH domain . For each marker strain , the same imaging setting ( laser power , gain pinhole ) and image processing were used for comparing different genotypes . Expression vector for cultured cells ( pCI-eGFP-hRap2a , pCI-eGFP-RAP-1 , pCI-eGFP-RAP-2 , pCI-eGFP-RAP-2 ( G12V ) , pCI-eGFP-RAP-2 ( S17A ) ) were generated by replacing Ras in pCI-eGFP-Ras ( Yasuda et al . , 2006 ) with hRap2a and rap-2 cDNAs with XhoI and BamHI . pCI-mRFP-RalGDS-mRFP plasmid is a kind gift from Dr . Yasuda . Rap2 and FRET sensor plasmids were mixed in 1:2 ratio and transfected into HeLa cells using Lipofectamine 3000 ( ThermoFisher ) . FLIM was conducted 24 hr after transfection . For expression of FRET sensor in the DA9 neuron , each fusion protein constructs were cloned into AscI and KpnI sites of the pSM vector containing mig-13 promoter using SLiCE method . A custom-made two-photon fluorescence lifetime imaging microscope was used as described elsewhere ( Murakoshi et al . , 2011 ) . Briefly , EGFP-Rap2a was excited with a Ti-sapphire laser ( Mai Tai; Spectra-Physics ) tuned to 920 nm . The X and Y scanning galvano mirrors ( 6210 hr; Cambridge Technology ) were controlled with ScanImage software ( Pologruto et al . , 2003 ) . EGFP photon signals were collected an objective lens ( 60× , 1 . 0 NA; Olympus ) and a photomultiplier tube ( H7422-40p; Hamamatsu ) placed after a dichroic mirror ( FF553-SDi01; Semrock ) and emission filter ( FF01-510/84; Semrock ) . A fluorescence lifetime curve was recorded by a time-correlated single-photon-counting board ( SPC-150; Becker and Hickl ) controlled with the software described previously ( Yasuda et al . , 2006 ) . For construction of a fluorescence lifetime image , the mean fluorescence lifetime values ( τm ) in each pixel were translated into a color-coded image . We quantified free EGFP-Rap2a and EGFP-Rap2a undergoing FRET ( binding fraction ) as described elsewhere ( Yasuda et al . , 2006 ) . Briefly , we calculated the proportion of EGFP undergoing FRET in individual ROIs using the following formula: ( 1 ) PFRET=τfree ( τfree−τm ) ( τfree−τFRET ) ( τfree+τFRET−τm ) PFRET=τfree ( τfree−τm ) ( τfree−τFRET ) ( τfree+τFRET−τm ) where τfree and τFRET are the fluorescence lifetime of free EGFP and EGFP undergoing FRET , respectively . Prism ( GraphPad ) software was used for statistical analysis . One-way ANOVA was done and corrected for multiple comparisons with posthoc Tukey's multiple comparisons tests done between all genotypes . Student’s t-test was used for pairwise comparison . Sample numbers were pre-determined before conducting statistical analyses . The method for qRT-PCR and sequences of primers and repair templates used in this study are available in Supplementary file 1 . | Genes do more than just direct the color of our hair or eyes . They produce proteins that are involved in almost every process in the body . In humans , the majority of active genes can be found in the brain , where they help it to develop and work properly – effectively controlling how we move and behave . The brain’s functional units , the nerve cells or neurons , communicate with each other by releasing messenger molecules in the gap between them , the synapse . These molecules are then picked up from specific receptor proteins of the receiving neuron . In the nervous system , neurons only form synapses with the cells they need to connect with , even though they are surrounded by many more cells . This implies that they use specific mechanisms to stop neurons from forming synapses with incorrect target cells . This is important , because if too many synapses were present or if synapses formed with incorrect target cells , it would compromise the information flow in the nervous system . This would ultimately lead to various neurological conditions , including Autism Spectrum Disorder . In 2013 , researchers found that in the roundworm Caenorhabditis elegans , a receptor protein called Plexin , is located at the surface of the neurons and can inhibit the formation of nearby synapses . Now , Chen et al . – including one author involved in the previous research – wanted to find out what genes Plexin manipulates when it stops synapses from growing . Knowing what each of those genes does can help us understand how neurons can inhibit synapses . The results revealed that Plexin appears to regulate two genes , Rap2 and TNIK . Plexin reduced the activity of Rap2 in the neuron that released the messenger , which hindered the formation of synapses . The gene TNIK and its protein on the other hand , have the ability to modify other proteins and could so inhibit the growth of synapses . When TNIK was experimentally removed , the number of synapses increased , but when its activity was increased , the number of synapses was strongly reduced . These findings could help scientists understand how mutations in Rap2 or TNIK can lead to various neurological conditions . A next step will be to test if these genes also affect the formation of synapses in other species such as mice , which have a more complex nervous system that is structurally and functionally more similar to that of humans . | [
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BubR1 is a key component of the spindle assembly checkpoint ( SAC ) . Mutations that reduce BubR1 abundance cause aneuploidization and tumorigenesis in humans and mice , whereas BubR1 overexpression protects against these . However , how supranormal BubR1 expression exerts these beneficial physiological impacts is poorly understood . Here , we used Bub1b mutant transgenic mice to explore the role of the amino-terminal ( BubR1N ) and internal ( BubR1I ) Cdc20-binding domains of BubR1 in preventing aneuploidy and safeguarding against cancer . BubR1N was necessary , but not sufficient to protect against aneuploidy and cancer . In contrast , BubR1 lacking the internal Cdc20-binding domain provided protection against both , which coincided with improved microtubule-kinetochore attachment error correction and SAC activity . Maximal SAC reinforcement occurred when both the Phe- and D-box of BubR1I were disrupted . Thus , while under- or overexpression of most mitotic regulators impairs chromosome segregation fidelity , certain manipulations of BubR1 can positively impact this process and therefore be therapeutically exploited .
Chromosomal instability ( CIN ) describes a condition where cells frequently acquire cytogenetic alterations and do not accurately segregate their chromosomes ( Giam and Rancati , 2015 ) . Aneuploidy , defined as a state in which there are alterations to whole chromosome copy number , results from CIN and is a feature of almost all tumors , but whether aneuploidy is a cause or consequence of transformation is the subject of much debate ( Ricke and van Deursen , 2013 ) . CIN is thought to allow pre-neoplastic cells to acquire genes that promote tumor progression and lose those which suppress transformation ( Baker et al . , 2009; Burrell et al . , 2013; Hanahan and Weinberg , 2011; Loeb , 2011 ) and there are multiple lines of evidence which support aneuploidy having a causative role for cancer . For instance , several human aneuploidy syndromes are characterized by increased susceptibility to cancer , including trisomies 8 , 18 ( Edwards syndrome ) and 21 ( Down Syndrome ) ( Ganmore et al . , 2009 ) , and mosaic variegated aneuploidy ( MVA ) ( Hanks et al . , 2004; Snape et al . , 2011 ) . Furthermore , bidirectional deviations in protein levels of various mitotic regulators , including Mad2 , Mad1 and Bub1 , cause aneuploidy and tumor predisposition in mice ( Iwanaga et al . , 2007; Jeganathan et al . , 2007; Michel et al . , 2001; Ricke et al . , 2011; Ryan et al . , 2012; Sotillo et al . , 2007 ) . Additionally , modulations to a spectrum of other proteins that participate in diverse cellular functions , such as the E2 ubiquitin-conjugating enzyme Ubch10 ( van Ree et al . , 2010 ) , the centromere-linked microtubule protein CENP-E ( Weaver et al . , 2007 ) , and the nuclear pore complex protein Nup88 ( Naylor et al . , 2016 ) result in aneuploidy and accelerated cancer progression . Finally , genome-wide screens of proteins that negatively ( STOP ) and positively ( GO ) regulate proliferation are recurrently and selectively lost and gained respectively in either focal regions or whole chromosomes ( Davoli et al . , 2013; Solimini et al . , 2012 ) . This suggests a model where changes in gene copy number are under selection rather than simply accompanying transformation , supporting genomic instability as a driver of cancer ( Davoli et al . , 2013; Solimini et al . , 2012 ) . On the other hand , several other mouse models of CIN have revealed inconsistent results regarding the relationship between aneuploidy and cancer , where some mouse models with elevated levels of aneuploidy do not show increased susceptibility to cancer ( Babu et al . , 2003; Kalitsis et al . , 2005; Ricke et al . , 2012 ) . Furthermore , proteotoxic stress from increased gene expression in cells with extra chromosome copies has adverse effects on cell growth and may thus counteract cancer progression ( Tang et al . , 2011; Williams et al . , 2008 ) . Aneuploidy results when cells fail to segregate chromosomes properly . To promote high-fidelity separation of duplicated chromosomes , cells have the machinery to safeguard against missegregation . One such mechanism is the spindle assembly checkpoint ( SAC ) . This surveillance system prevents activation of the E3 ubiquitin ligase anaphase-promoting complex/cyclosome ( APC/C ) by its co-activator , Cdc20 ( Peters , 2006 ) . This ensures chromosomal stability by preventing sister chromatid separation prior to bi-orientation of mitotic chromosomes at the metaphase plate ( Musacchio and Salmon , 2007 ) . An additional measure to promote accurate chromosome segregation is allowing sufficient time to form proper and correct erroneous kinetochore-microtubule ( MT-KT ) attachments prior to anaphase onset ( Meraldi et al . , 2004 ) . Merotely , a type of improper attachment in which a single kinetochore is attached to microtubules emanating from both spindle poles , is undetected by the SAC and can result in lagging chromosomes ( Cimini et al . , 2001; Rodriguez-Bravo et al . , 2014 ) . Mad1/2 , Mps1 , and BubR1 specify the minimum time in mitosis , and loss of these proteins reduces the duration of mitosis and increases the rates of missegregation ( Maciejowski et al . , 2010; Meraldi et al . , 2004; Rodriguez-Bravo et al . , 2014 ) . BubR1 , along with Mad2 and Bub3 , is a component of the mitotic checkpoint complex ( MCC ) , which mediates the SAC ( Kulukian et al . , 2009; Musacchio and Salmon , 2007; Sudakin et al . , 2001 ) . Once each chromosome has properly and stably attached to the mitotic spindle and sufficient inter-kinetochore tension is generated , the MCC dissociates , allowing Cdc20 to activate the APC/C ( Musacchio and Salmon , 2007 ) . Co-activation of APC/C by Cdc20 in metaphase results in the polyubiquitination and subsequent proteasomal degradation of cyclin B1 and securin , thereby triggering sister chromatid separation and anaphase onset ( Kapanidou et al . , 2015; Musacchio and Salmon , 2007 ) . BubR1 , encoded by the gene Bub1b in mice or BUB1B in humans , is a modular protein , with several known functional domains that together ensure mitotic fidelity and genome stability . BubR1 localizes to the kinetochore by interacting through its GLEBs-like motif with Bub3 ( Elowe et al . , 2010; Lampson and Kapoor , 2005 ) . In human cells , kinetochore-localized BubR1 was shown to be important for MT-KT stabilization through an internally located kinetochore attachment and regulatory domain ( KARD ) ( Suijkerbuijk et al . , 2012 ) . The KARD allows kinetochore localization of the phosphatase PP2A , which counteracts the MT-KT destabilizing activity of Aurora B kinase , a key mediator of error-correction ( Ruchaud et al . , 2007; Suijkerbuijk et al . , 2012 ) . Additional BubR1 functional domains include a putative kinase/pseudokinase domain that has been reported to reinforce the SAC and stabilize MT-KT attachments ( Elowe , 2011; Harris et al . , 2005; Suijkerbuijk et al . , 2012 ) and two Cdc20-binding domains , of which the N-terminal domain ( BubR1N ) is a critical APC/CCdc20 inhibitor essential for cell survival ( Malureanu et al . , 2009 ) . BubR1N contains two KEN-boxes , spanning amino acids 19–21 ( KEN1 ) and 298–300 ( KEN2 ) in mice , that in conjunction with a destruction ( D ) -box ( D1 ) just downstream of KEN2 permits BubR1 to behave as a pseudo-substrate inhibitor of APC/CCdc20 ( Burton and Solomon , 2007; Chao et al . , 2012; Han et al . , 2013; Izawa and Pines , 2015; Malureanu et al . , 2009 ) . Recent work has highlighted that BubR1 is capable of binding both soluble Cdc20 through KEN1 to prevent APC/C-Cdc20 association , and a second Cdc20 that has already bound to and activated the APC/C through a combination of KEN2 and D1 for even more dynamic APC/CCdc20 inhibition ( Izawa and Pines , 2015 ) . The second Cdc20-binding domain of BubR1 , BubR1I , is an internally located and functionally distinct region important for Cdc20 kinetochore recruitment , and has been proposed to serve a dual function in SAC activation and silencing ( Chao et al . , 2012; Di Fiore et al . , 2015; Diaz-Martinez et al . , 2015; Izawa and Pines , 2015; Lischetti et al . , 2014; Tang et al . , 2001 ) . Several conserved and somewhat redundant motifs within BubR1I were recently identified that are thought to promote the BubR1-Cdc20 interaction through complementary mechanisms: the ABBA motif , named for its conserved presence in Acm1 , Bub1 , BubR1 and Cyclin A ( Di Fiore et al . , 2015 ) ; the Phe box , a phenylalanine-containing region which is encompassed within the ABBA motif ( Diaz-Martinez et al . , 2015 ) ; and a D-box just downstream of the Phe Box ( D-box2 ) ( Diaz-Martinez et al . , 2015 ) . Whereas bidirectional changes to protein levels of Mad1 , Bub1 and Mad2 cause aneuploidy and tumorigenesis ( Ricke and van Deursen , 2013 ) , BubR1 is unique amongst mitotic regulators in that both under- and overexpression results in drastically different phenotypes ( Baker et al . , 2013; Baker et al . , 2004 ) . Complete loss of BubR1 causes early embryonic death ( Wang et al . , 2004 ) , and while BubR1 hypomorphic ( Bub1bH/H ) mice are viable , they develop a variety of premature aging phenotypes ( Baker et al . , 2004; Hartman et al . , 2007; Kyuragi et al . , 2015; Matsumoto et al . , 2007; North et al . , 2014 ) , progressive aneuploidy ( Baker et al . , 2004 ) , and are predisposed to carcinogen-induced cancers ( Baker et al . , 2006 ) . Additionally , in humans , mutations in BUB1B have been causally implicated in MVA , a rare clinical syndrome characterized by widespread aneuploidy , growth retardation , shortened lifespan , and cancer predisposition ( García-Castillo et al . , 2008; Hanks et al . , 2004; Matsuura et al . , 2006; Wijshake et al . , 2012 ) . Conversely , overexpression of BubR1 extends life- and healthspan of mice , decreases the tumor incidence , and provides protection against age-related phenotypes in tissues that are prone to increased aneuploidy rates with age ( Baker et al . , 2013 ) . Despite profound anti-tumor and anti-aneuploidization effects of BubR1 overexpression , the molecular mechanism ( s ) of how it prevents CIN and cancer remains unclear ( Baker et al . , 2013 ) . Here , we focus on the role of BubR1-Cdc20 binding , and explore how this interaction reinforces the SAC and error-correction machinery by using a series of transgenic mice overexpressing BubR1 mutants with disruptions in Cdc20-binding domains . We show that overexpression of a mutant BubR1 that includes disruptions of the internal Cdc20-binding domain ( BubR1∆I ) elicits a tumor-protective mechanism similar to that of full-length ( FL ) -BubR1 overexpression . Importantly , like in FL-Bub1b , overexpression of this mutant also safeguards against aneuploidization , likely by both strengthening SAC signaling and preventing improper KT-MT attachments . Thus , the internal Cdc20-binding domain is dispensable to mediate these protective effects , while the N-terminal Cdc20-binding domain is necessary , but not sufficient . BubR1∆I also provides distinct molecular properties unique to that of overexpression alone that also likely promote genetic stability and show no overt detrimental effects on cells or mice . This includes a more robust SAC that is more responsive to weak stimuli , and an increase in the normal length of mitosis . With further refined mutant Bub1b constructs , we demonstrate that a maximal SAC response can be achieved exclusively by the loss of the Phe box and D-box2 , and that the mitotic timing may be dependent on previously uncharacterized regions of BubR1 . Importantly , this work sheds light on the causal role of CIN in cancer by demonstrating that enhancing genomic stability fortifies the barriers of transformation , and may provide unique insights into the generation of new therapeutic strategies .
To determine the role of the N-terminal and internal Cdc20-binding domains in the protective effect of BubR1 overexpression on aneuploidy and cancer , we generated three distinct Flag-tagged mutant Bub1b transgenic mouse strains ( Figure 1A ) . The first two mutants lacked either residues 1–363 containing the N-terminal Cdc20-binding domain ( Bub1bΔN ) or residues 525–700 ( Bub1bΔI ) which disrupts the Phe box , and removes D-box2 and KARD . The third mutant contained only the N-terminal Cdc20-binding domain ( Bub1bN ) . Like FL-Bub1b , all three mutants were expressed under the control of the ubiquitously active CAAGS promoter ( Baker et al . , 2013 ) . Enhanced green fluorescent protein ( EGFP ) was co-expressed from an internal ribosome entry site ( IRES ) . Western blots of mouse embryonic fibroblasts ( MEFs ) and lung tissue from 5-month old mice revealed that each of the three BubR1 mutants was expressed at levels comparable to that of FL-BubR1 ( strain T23; Figure 1B , and Figure 1—figure supplement 1 ) . 10 . 7554/eLife . 16620 . 003Figure 1 . BubR1 transgenic mutant proteins are highly overexpressed in vitro and in vivo . ( A ) ( top ) Bub1b transgenic vector design . pCAGGS , promoter consisting of the CMV immediate enhancer and the chicken-actin promoter . FL , full-length . IRES , internal ribosome entry site . ( bottom ) Schematics of the Flag-Bub1b transgenic mouse constructs . KEN , KEN-box . D , destruction- ( D- ) box . GLEBs , GLEBs-binding motif . Phe , Phe box . KARD , kinetochore attachment regulatory domain . ( B ) Western blots of MEF ( left ) and lung tissue extracts ( right ) from wild-type ( WT ) and Flag-Bub1b transgenic mice . Blots were probed with the indicated antibodies . Ponceau S was used to normalize loading . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 00310 . 7554/eLife . 16620 . 004Figure 1—figure supplement 1 . Analysis of BubR1 overexpression in transgenic MEFs . Full scan and multiple exposures of Western blots of MEF lysates from Figure 1B . WT , wild-type . FL , full-length . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 004 BubR1 is unique among mitotic regulators in that its overexpression does not lead to chromosome missegregation and aneuploidization and actually protects cells against chromosomal instability and karyotypic abnormalities ( Baker et al . , 2013; Ricke et al . , 2011; Ryan et al . , 2012; Sotillo et al . , 2007 ) . To examine whether the Bub1b mutants we created negatively impacted karyotype integrity , we performed chromosome counts on metaphase spreads of MEFs derived from wild-type and Bub1b transgenic MEFs ( Table 1 ) . There was no significant difference in aneuploidy rates between FL-Bub1b , Bub1bN , Bub1bΔI and wild-type MEFs , whereas Bub1bΔN MEFs had increased aneuploidy . However , these aneuploidy-prone MEFs did not have higher rates of chromosome segregation errors as assessed by live cell imaging ( Table 2 ) . As expected , missegregation rates for the FL-Bub1b , Bub1bN , and Bub1bΔI mutants were normal . By interphase FISH , none of the transgenic mouse lines , including Bub1bΔN , showed evidence of elevated aneuploidy rates in a broad spectrum of mouse tissues and organs collected from 5-month-old mice ( Table 3 ) . Altogether , these data indicated that our transgenic mutant lines could provide the framework necessary to characterize the benefits of FL-BubR1 overexpression . 10 . 7554/eLife . 16620 . 005Table 1 . Karyotypes are stable in select BubR1 mutant overexpressing MEFs . Karyotype analysis of passage 5 MEFs of indicated genotype . n ≥ 3 lines , 50 cells per line . Data are mean ± s . d . WT , wild-type . FL , full-length . ( See associated Table 1— source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 00510 . 7554/eLife . 16620 . 006Table 1—source data 1 . Source file for MEF aneuploidy rate data . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 006Mitotic MEF genotype ( n ) Mitotic figuresAneuploid figures % ( s . d ) Karyotype with indicated chromosome number363738394041424344WT ( 5 ) 2509 ( 6 ) 11052279700FL-Bub1b ( 3 ) 15010 ( 3 ) 00121357500Bub1bΔI ( 3 ) 1508 ( 3 ) 00121389000Bub1bΔN ( 5 ) 25018 ( 6 ) *0021520423600Bub1bN ( 3 ) 1506 ( 2 ) 00131414100*p<0 . 05 . 10 . 7554/eLife . 16620 . 007Table 2 . Bub1b transgenic MEFs have normal missegregation rates . Live-cell imaging of chromosome segregation defects in primary H2B-RFP MEFs of indicated genotypes . n ≥ 3 lines , ≥ 20 cells per line . Data are mean ± s . e . m . WT , wild-type . FL , full-length . ( See associated Table 2—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 00710 . 7554/eLife . 16620 . 008Table 2—source data 1 . Source file for missegregation assay data . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 008MEF genotype ( n ) Mitotic cells inspectedCells with segregation defectsMetaphases with misaligned chromosomesAnaphases with lagging chromosomesAnaphases with bridges chromosome% ( s . e . m ) % ( s . e . m ) % ( s . e . m ) % ( s . e . m ) WT ( 3 ) 9416 ( 1 ) 0 ( 0 ) 6 ( 3 ) 13 ( 1 ) FL-Bub1b ( 3 ) 10517 ( 2 ) 3 ( 3 ) 3 ( 2 ) 11 ( 2 ) Bub1bΔI ( 3 ) 10512 ( 1 ) 1 ( 1 ) 1 ( 1 ) 10 ( 1 ) Bub1bΔN ( 3 ) 10119 ( 1 ) 1 ( 1 ) 7 ( 1 ) 11 ( 2 ) Bub1bN ( 3 ) 9517 ( 6 ) 3 ( 2 ) 2 ( 2 ) 12 ( 6 ) 10 . 7554/eLife . 16620 . 009Table 3 . Bub1b transgenic mice have normal rates of aneuploidy in vivo . Interphase FISH on specified tissues from mice of indicated genotypes . n = 3 animals , 100 cells per tissue per animal . Data are mean ± s . d . WT , wild-type . FL , full-length . ( See associated Table 3—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 00910 . 7554/eLife . 16620 . 010Table 3—source data 1 . Source file for tissue aneuploidy rate data . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 010Percentage of aneuploidy ( s . d ) Tissue TypeGenotypeChrom 4Chrom 7LungWT1 . 3 ( 0 . 6 ) 3 . 0 ( 0 ) FL-Bub1b2 . 3 ( 0 . 6 ) 2 . 0 ( 1 ) Bub1bΔI2 . 0 ( 0 ) 2 . 3 ( 0 . 6 ) Bub1bΔN3 . 0 ( 1 ) 2 . 3 ( 0 . 6 ) Bub1bN2 . 3 ( 0 . 6 ) 2 . 3 ( 0 . 6 ) HeartWT2 . 0 ( 1 ) 1 . 7 ( 0 . 6 ) FL-Bub1b1 . 3 ( 0 . 6 ) 2 . 0 ( 1 ) Bub1bΔI1 . 3 ( 0 . 6 ) 2 . 0 ( 0 ) Bub1bΔN1 . 7 ( 1 . 2 ) 2 . 0 ( 1 ) Bub1bN1 . 3 ( 0 . 6 ) 1 . 7 ( 0 . 6 ) EyeWT2 . 0 ( 0 ) 2 . 0 ( 1 ) FL-Bub1b2 . 0 ( 0 ) 2 . 3 ( 0 . 6 ) Bub1bΔI1 . 7 ( 0 . 6 ) 1 . 3 ( 0 . 6 ) Bub1bΔN2 . 0 ( 1 ) 2 . 3 ( 0 . 6 ) Bub1bN1 . 7 ( 0 . 6 ) 2 . 0 ( 0 ) KidneyWT2 . 0 ( 1 ) 2 . 0 ( 1 ) FL-Bub1b2 . 3 ( 0 . 6 ) 2 . 0 ( 0 ) Bub1bΔI2 . 0 ( 1 ) 1 . 3 ( 0 . 6 ) Bub1bΔN2 . 7 ( 0 . 6 ) 2 . 0 ( 0 ) Bub1bN2 . 0 ( 1 ) 1 . 3 ( 0 . 6 ) SpleenWT3 . 3 ( 0 . 6 ) 2 . 3 ( 1 . 2 ) FL-Bub1b3 . 0 ( 1 ) 2 . 0 ( 1 ) Bub1bΔI2 . 0 ( 1 ) 1 . 7 ( 0 . 6 ) Bub1bΔN3 . 0 ( 1 ) 2 . 7 ( 0 . 6 ) Bub1bN2 . 7 ( 0 . 6 ) 3 . 0 ( 0 ) Skeletal muscleWT2 . 3 ( 0 . 6 ) 2 . 6 ( 0 . 6 ) FL-Bub1b2 . 7 ( 0 . 6 ) 2 . 0 ( 0 ) Bub1bΔI2 . 0 ( 0 ) 2 . 0 ( 1 ) Bub1bΔN2 . 3 ( 1 . 2 ) 2 . 7 ( 0 . 6 ) Bub1bN2 . 0 ( 0 ) 2 . 3 ( 0 . 6 ) In earlier studies , we found that overexpression of FL-BubR1 markedly inhibits lung tumor formation in KrasLA1 mice , a genetically engineered strain carrying a conditional oncogenic Kras allele ( KrasG12D ) that becomes active upon intrachromosomal homologous recombination ( Baker et al . , 2013; Johnson et al . , 2001 ) . Given the robustness of this tumor protection , we used KrasLA1 mice to explore the role of the amino-terminal and internal Cdc20-binding domains in the tumor protective effect of BubR1 overexpression on cancer . Consistent with our previously published data ( Baker et al . , 2013 ) , overexpression of FL-BubR1 had a tumor-protective effect ( Figure 2A–C ) . Bub1bN and Bub1bΔN , however , were unable to ameliorate the tumor burden of KrasLA1 mice , indicating that binding of Cdc20 mediated by the N-terminal domain is necessary , but not sufficient , to protect against tumor formation . In contrast , Bub1bΔI fully retained the tumor-protective benefit of FL-Bub1b . In addition to adenomas , KrasG12D produces a hyperplastic epithelium throughout the lung , which is prone to aneuploidization as evidenced by FISH for chromosomes 4 and 7 ( Figure 2B , D ) ( Baker et al . , 2013 ) . Analysis of hyperplastic lung tissue from KrasLA1 mice containing the FL-Bub1b transgene revealed that BubR1 overexpression has the ability to counteract KrasG12D-mediated aneuploidization ( Baker et al . , 2013 ) . Using FISH analysis , mice expressing the various Bub1b mutant transgenes revealed that tumor protection tightly correlated with an ability to counteract KrasG12D-mediated aneuploidization ( Figure 2D ) . Taken together , these data are consistent with the idea that BubR1 exerts its anti-neoplastic actions by preserving genomic integrity and suggest that only a subset of functional domains of BubR1 are necessary to afford protection against aneuploidization , which includes the N-terminal domain required for potent inhibition of APC/CCdc20 ( Malureanu et al . , 2009 ) . 10 . 7554/eLife . 16620 . 011Figure 2 . Select BubR1 domain overexpression protects against aneuploidy and cancer . ( A ) Lung lobes of KrasLA1 mice and KrasLA1 mice expressing various BubR1 transgenic proteins sacrificed at 6 weeks of age . Entire lungs were inspected using a dissection microscope to quantitate the number of lung tumors ( adenomas ) per mouse . ( B ) Hematoxylin-eosin stained lung sections of representative normal ( WT ) lung and a KrasLA1 ( Kras ) hyperplastic tumor-bearing lung ( the dashed line marks the adenoma boundary ) . Insets highlight normal and hyperplastic lung architecture . ( C ) Quantification of the number of lung tumors from mice shown in A . n = 20 , except for full-length ( FL ) -Bub1b where n = 7 . Data are mean ± s . e . m . ***p<0 . 001 . ( D ) Interphase FISH on the lungs of wild-type and KrasLA1 with and without FL-BubR1 and mutant overexpression . n = 5 , ≥ 100 cells per animal . Data are mean ± s . d . *p<0 . 05 , **p<0 . 01 . ***p<0 . 001 . Scale bars: A , 2 mm; B , 200 µm ( main image ) and 50 µm ( insets ) . ( See associated Figure 2—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 01110 . 7554/eLife . 16620 . 012Figure 2—source data 1 . Source file for tumor incidence and tissue aneuploidy rate data . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 012 To understand the molecular mechanism ( s ) underlying BubR1’s protective qualities , we conducted an extensive comparative analysis between MEFs from transgenics expressing FL-BubR1 and our BubR1 mutants for their ability to engage pathways that safeguard against chromosome missegregation , including the SAC and the MT-KT attachment error correction machinery . First , we focused on BubR1-kinetochore localization , as this property has been shown to be important for its role in both error correction and SAC signaling ( Malureanu et al . , 2009 ) . By immunostaining with antibodies directed against the BubR1 N-terminus , we found that both FL-Bub1b and Bub1bΔI prometaphases had markedly increased amounts of BubR1 compared to wild-type MEFs ( Figure 3A , B ) . Due to antibody limitations , we were unable to distinguish the ratio of kinetochore-localized endogenous to transgenic BubR1 within the mutant MEFs , with the exception of Bub1bN , in which only endogenous protein can be detected . This illuminated that endogenous BubR1 was displaced from the kinetochore , but we cannot rule out that the other mutants also had lower endogenous levels at the kinetochore , which is likely due to increased abundance of mutant protein within the cell . By staining with a Flag antibody that recognizes only the transgenic BubR1 protein , we determined that the relative expression of BubR1ΔI was slightly higher than FL-BubR1 , while BubR1N was equivalent to FL-BubR1 ( Figure 3C , D ) . BubR1ΔN , which did not protect against cancer , failed to localize to kinetochores , consistent with a lack of the GLEBs motif ( Lampson and Kapoor , 2005 ) . Taken together , these data demonstrate a likely need for BubR1 to retain functionality , perhaps mediated through the N-terminal domain , at the kinetochore to prevent aneuploidy and tumorigenesis . 10 . 7554/eLife . 16620 . 013Figure 3 . Increased BubR1 localization to kinetochore corresponds to phenotypic benefits . ( A ) MEFs of indicated genotypes were stained for BubR1 ( red ) , centromeres ( cyan ) , and DNA ( blue ) . WT , wild-type . FL , full-length . ( B ) Quantification of immunostaining of BubR1 shown in A . Values were normalized to centromere stain and are relative to wild-type . n = 3 lines , ≥ 10 cells per line . Data are mean ± s . d . *p<0 . 05 , ***p<0 . 001 . ( C ) Same as in A except with anti-Flag antibody to detect transgenic BubR1 . ( D ) Quantification of immunostaining of Flag shown in C . Values were normalized to centromere stain and are relative to wild-type . Wild-type and Bub1bN represent background . n = 3 lines , ≥ 10 cells per line . Data are mean ± s . d . *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . Scale bar 10 µm . ( See associated Figure 3—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 01310 . 7554/eLife . 16620 . 014Figure 3—source data 1 . Source file for intensity of kinetochore-localized BubR1 and FLAG protein data . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 014 BubR1 , Mad2 , and Mps1 kinase set the speed limit for mitosis ( Meraldi et al . , 2004 ) , and perturbations of these proteins accelerate mitotic timing and promote erroneous chromosome segregation ( Rodriguez-Bravo et al . , 2014 ) . Therefore , we sought to determine the effect of FL-BubR1 and mutant BubR1 overexpression on mitotic timing by following MEFs from nuclear envelope breakdown ( NEBD ) to anaphase onset by time-lapse microscopy ( Figure 4A ) . Because errors such as misalignments that may be caused by unattached kinetochores can trigger the SAC to delay mitotic progression , only cells that proceeded through mitosis without missegregation defects were included . Overexpression of FL-BubR1 had no impact on mitotic timing and the same was true for BubR1N and BubR1∆N ( Figure 4B ) . In contrast , Bub1bΔI MEFs spent significantly more time in mitosis ( Figure 4A , B ) . The increase in mitotic timing was specifically attributed to the metaphase-to-anaphase transition , where Bub1bΔI MEFs spent more than twice as long . 10 . 7554/eLife . 16620 . 015Figure 4 . Bub1bΔI MEFs have an increased time in mitosis and duration of mitotic arrest . ( A ) Representative time-lapse images of live MEF cells of indicated genotypes progressing from prophase ( t = 0 ) to anaphase ( A ) . Time is indicated in min . WT , wild-type . P , prophase . M , metaphase . ( B ) Analysis of the time from nuclear envelope breakdown ( NEBD ) to anaphase onset in H2B-RFP MEFs of the indicated genotypes by live cell time-lapse imaging . n = 3 lines , ≥ 20 cells per line . Data are mean ± s . d . **p<0 . 01 . FL , full-length . ( C ) In a nocodazole challenge , H2B-RFP MEFs of indicated genotypes were treated with 100 ng/ml of nocodazole ( noc ) and monitored by live cell time-lapse imaging . The point of time in which 50% of cells are arrested in mitosis is plotted . n ≥ 3 lines , ≥ 20 cells per line . Data are mean ± s . d . *p<0 . 05 , **p<0 . 01 . ( D ) H2B-RFP wild-type and mutant transgenic MEFs were treated concurrently with 100 ng/ml nocodazole and indicated concentrations of the Mps1 kinase inhibitor , AZ3146 . The point of time in which 50% of cells are arrested in mitosis is plotted . n = 3 lines , ≥ 20 cells per line . Data are mean ± s . d . *p<0 . 05 . Scale bar , 10 µm ( See associated Figure 4—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 01510 . 7554/eLife . 16620 . 016Figure 4—source data 1 . Source file for mitotic timing and nocodazole arrest data . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 01610 . 7554/eLife . 16620 . 017Figure 4—figure supplement 1 . Bub1bΔI MEFs do not have persistent Mad2 signaling . ( A ) MEFs of indicated genotypes and cell stage were stained for Mad2 ( red ) , centromeres ( cyan ) , and DNA ( blue ) . White arrowhead depicts misaligned chromosome . WT , wild-type . FL , full-length ( B ) Table depicting the number of metaphases with at least one Mad2-positive kinetochore . n = 3 lines , 15 cells per line . ( C ) MEFs of indicated genotypes and cell stage were stained for Mad2 ( red ) , centromeres ( cyan ) , and DNA ( blue ) . Scale bar 10 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 017 To verify that the extended time in mitosis was not due to unattached chromosomes , we performed an immunostaining for Mad2 , which strongly localizes to unattached kinetochores ( Waters et al . , 1998 ) . We found that while prometaphases of wild-type , FL-Bub1b and Bub1bΔI MEFs had many Mad2-positive kinetochores , there were very rare incidences of Mad2-positive kinetochores in metaphase ( Figure 4—figure supplement 1 ) . This indicated that the increased time of the metaphase-to-anaphase transition was delayed independent of unattached chromosomes or otherwise persistent Mad2 signaling . The extension of mitotic timing is a feature that is not shared with FL-overexpression alone , indicating it likely has a minimal contribution to tumor protection in our Kras model . However , it is particularly intriguing as a mechanism to prevent aneuploidy , as KT-MT attachment errors that are not detected by the SAC , namely merotelic attachments , may perhaps be given extra time to allow for the error correction machinery to prevent missegregation ( Cimini et al . , 2001; Rodriguez-Bravo et al . , 2014 ) . Next , we examined whether overexpression of BubR1 and its variants impacted SAC sustainability . To do this , we added 100 ng/ml of the microtubule depolymerizing agent nocodazole and monitored the amount of time individual cells stayed arrested in mitosis . FL-BubR1 overexpression caused a slight but significant increase in duration of arrest , with transgenic cells arresting on average for 3 . 7 hr compared to 3 hr for wild-type MEFs ( Figure 4C ) . Bub1bN MEFs were unchanged from wild-type , while Bub1bΔN MEFs actually had a slight but significant reduction in arrest time . In contrast , Bub1bΔI MEFs showed a dramatic extension of checkpoint sustainability , with cells arresting for an average time of 6 . 2 hr . Thus , the two BubR1 alterations offering tumor protection , overexpression of FL-BubR1 and BubR1ΔI , improve checkpoint sustainability although the latter does so much more robustly . In complementary experiments , we challenged the SAC by inhibiting Mps1 , a kinase necessary both for the establishment and maintenance of the SAC ( Hewitt et al . , 2010; Liu and Winey , 2012 ) . Treatment of MEFs concomitantly with 100 ng/ml nocodazole and a high concentration of the Mps1 inhibitor AZ3146 ( 2 µM ) completely abolished SAC activity irrespective of the Bub1b transgene expressed ( Figure 4D ) . At a four-fold lower inhibitor concentration , wild-type , Bub1bN , Bub1bΔN MEFs were all capable of mounting a modest mitotic arrest . The relative extension of mitotic arrest in FL-Bub1b and Bub1bΔI MEFs , however , was considerably higher , with Bub1bΔI MEFs reaching a similar level of SAC signaling in the presence of 0 . 5 µM AZ3146 as wild-type MEFs in the absence of inhibitor ( Figure 4D ) . Thus , under normal SAC conditions and conditions where the SAC signaling is weakened , both BubR1ΔI and FL-BubR1overexpression seem capable of prolonging mitotic arrest , albeit to different degrees . Because the internal Cdc20-binding domain of BubR1 has been implicated in both initiating and silencing the mitotic checkpoint ( Diaz-Martinez et al . , 2015; Lischetti et al . , 2014 ) , we hypothesized that FL-Bub1b and Bub1bΔI MEFs may have a lower threshold of checkpoint activation or a difficulty silencing the checkpoint , or both . To examine whether FL-Bub1band Bub1bΔI MEFs might have a lower threshold for SAC activation , we challenged them with low concentrations of nocodazole and monitored time to anaphase onset ( Figure 5A ) . Based on the response of wild-type MEFs , we found that 20 ng/ml was the optimal dose to use in this experiment ( Figure 5A ) . At this dose , however , the time Bub1bΔI MEFs took to go through mitosis increased by 60% compared to increases of ~25% in wild-type and FL-Bub1b MEFs , suggesting that these MEFs had a lower threshold for SAC activation . 10 . 7554/eLife . 16620 . 018Figure 5 . Bub1bΔI MEFs have a lower threshold to checkpoint activation . ( A ) Analysis of the time from NEBD to anaphase onset in H2B-RFP MEFs of the indicated genotypes treated with either DMSO vehicle ( Veh ) or indicated concentration of nocodoazole ( Noc ) . n = 3 lines , ≥ 20 cells per line . Data are mean ± s . d . *p<0 . 05 , **p<0 . 01 . WT , wild-type . FL , full-length . ( B ) ( top ) Strategy for analyzing the checkpoint silencing efficiency . MEFs of indicated genotypes were treated with 100 ng/ml nocodazole for 1 . 5 hr before addition of either DMSO vehicle ( Veh ) or 2 µM AZ3146 , at which point cells were marked and monitored for time of escape ( time point zero ) . ( bottom ) Analysis of duration of mitotic arrest from time point zero as outlined in ( top ) . n = 3 lines , ≥ 20 cells per line . ( See associated Figure 5—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 01810 . 7554/eLife . 16620 . 019Figure 5—source data 1 . Source file for low-dose nocodazole challenge and SAC silencing data . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 01910 . 7554/eLife . 16620 . 020Figure 5—figure supplement 1 . PP2A localization is normal in Bub1bΔI MEFs . ( A ) Wild-type ( WT ) and Bub1bΔI MEFs were arrested in 100 ng/ml nocodazole and stained for PP2A ( red ) , centromeres ( cyan ) , and DNA ( blue ) . ( B ) Quantification of immunostaining of PP2A in A . Values were normalized to centromere stain . n = 3 lines , ≥ 10 cells per line . Data are mean ± s . d . Scale bar 10 µm . ( See associated Figure 5—figure supplement 1—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 02010 . 7554/eLife . 16620 . 021Figure 5—figure supplement 1—source data 1 . Source file for intensity of kinetochore-localized PP2A protein data . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 021 Next , we sought to determine whether these MEFs also had difficulty in silencing the SAC . To test this we used a live-cell imaging-based approach in which we cultured MEFs in 100 ng/ml nocodazole for 1 . 5 hr to activate the SAC and then monitored mitotically-arrested cells for time to mitotic exit following treatment with either vehicle ( DMSO ) or 2 µm AZ3146 as a stimulus for dissolving MCCs ( Figure 5B ) . In this assay , neither FL-BubR1 nor BubR1ΔI overexpression permitted an arrest longer than that observed in wild-type MEFs . Additionally , while BubR1-associated PP2A has been shown to be important for error correction in human cells ( Suijkerbuijk et al . , 2012 ) , it is also important for silencing the SAC ( Espert et al . , 2014 ) . As BubR1ΔI lacks the KARD region implicated in PP2A recruitment , its overexpression could potentially mislocalize PP2A and impede proper SAC silencing . We found PP2A localization to be normal in BubR1ΔI overexpressing cells , suggesting this branch of signaling is not impacted ( Figure 5—figure supplement 1 ) , and further supporting that silencing of the SAC is not disrupted . Taken together , these results suggest that the threshold to engage the SAC is instead lowered by the Bub1bΔI transgene . To explore the mechanism as to why Bub1bΔI and FL-Bub1b MEFs both had more robust checkpoint signaling when challenged with nocodazole , we determined whether the amount of Cdc20 bound to BubR1 was increased in these cells . To this end , we treated wild-type , Bub1bΔI and FL-Bub1b MEFs cells with nocodazole for 1 hr before harvesting them by mitotic shake-off . We found that overexpression of FL-BubR1 lead to increased interaction of BubR1 and Cdc20 , as had been previously shown ( Figure 6A ) ( Baker et al . , 2013 ) . This was confirmed by Cdc20 and Mad2 co-IPs and subsequent analysis of co-precipitating proteins ( Figure 6B , C ) that indicated there were an increased amount of complexes consisting of BubR1-Cdc20-Mad2 , a potent APC/C inhibitor . However , we found that while the total amount of BubR1 ( wild-type and mutant ) immunoprecipitated in Bub1bΔI MEFs is increased substantially over normal MEFs , BubR1 lacking the internal Cdc20-binding domain failed to pull-down excess Cdc20 and vice versa ( Figure 6A , C ) . This suggests that while BubR1ΔI can likely bind to Cdc20 , it is not a preferred partner . Surprisingly , the amount of Mad2 co-precipitated by Cdc20 was increased , indicating that a larger proportion of Cdc20 bound Mad2 than in wild-type MEFs , while retaining similar levels of BubR1-Cdc20-Mad2 complexes as wild-type . Immunoprecipitation of Mad2 and Cdc20 and analysis of co-precipitating MCC components confirmed this ( Figure 6B , C ) . These unique MCC compositions did not result from changes to total levels of these proteins ( Figure 6—figure supplement 1 ) . Thus , FL-BubR1 overexpression alone results in the ability for cells to form more mitotic checkpoint complexes compared to wild-type MEFs , which can likely fortify the SAC signaling potential . Bub1bΔI , however , exerts its impacts on the MCC through a different mechanism . The increased abundance of Mad2-Cdc20 complexes , albeit a weaker inhibitor of APC/C than a full complement of the MCC , in addition to wild-type levels of MCC could represent a state in which the cells are poised to quickly activate the SAC . This is supported by our data in which Bub1bΔI show increased sensitivity to a weak SAC-inducing stimulus ( Figure 5A ) . 10 . 7554/eLife . 16620 . 022Figure 6 . Composition of mitotic checkpoint complexes is unique in Bub1bΔI MEFs . ( A–D ) Immunoblots of mitotic wild-type ( WT ) and indicated mutant MEF extracts subjected to co-immunoprecipitation with the indicated antibodies . Each blot is a representative of at least 3 experiments . FL , full-length . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 02210 . 7554/eLife . 16620 . 023Figure 6—figure supplement 1 . Mitotic checkpoint components have a normal expression in Bub1b transgenic MEFs . Western blot analysis of mitotic MEF lysates of indicated genotypes . Blots were probed with indicated antibodies . Ponceau S was used to normalize loading . Blot is a representative of at least three experiments . WT , wild-type . FL , full-length . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 023 In complimentary experiments , we sought to determine if an MCC-independent mechanism could also contribute to the extended SAC arrest of FL-Bub1b and Bub1bΔI MEFs . In addition to being incorporated into the MCC , Cdc20 is subject to two regulatory phosphorylation events that disrupt its ability to activate the APC/C ( Jia et al . , 2016 ) . We examined the phosphorylation status of two residues of Cdc20 implicated in mediating this inhibition , S153 and S92 by Bub1 kinase and Plk1 kinase respectively , in wild-type , FL-Bub1b and Bub1bΔI MEFs by Western blot ( Figure 6D ) ( Jia et al . , 2016 ) . We found the levels of phosphorylation of both these residues to be equivalent to wild-type MEFs , suggesting this method of APC/C control is not hyperactive in our mutants . Next we investigated whether and how overexpressed FL-BubR1 and BubR1ΔI contributed to high-fidelity chromosome segregation under mitotic duress by reinforcing the attachment error correction machinery . To do so , we used the motor protein Eg5 inhibitor monastrol to induce syntelic attachments , a malattachment type that presents as misaligned chromosomes and is resolved by the attachment error correction machinery ( Lampson et al . , 2004 ) . Because error correction is highly efficient in wild-type MEFs , we challenged the machinery in our experimental system by limiting Aurora B kinase activity with 10 nM of the small molecule inhibitor AZD1152 ( Ricke et al . , 2012 ) . FL-BubR1 and BubR1ΔI both significantly blunted the increase in syntelic attachments caused by hypoactive Aurora B ( Figure 7 ) . In contrast , no such corrective effects were observed in the Bub1bΔN or Bub1bN mutants . 10 . 7554/eLife . 16620 . 024Figure 7 . Overexpression of FL-BubR1 and BubR1ΔIimproves error correction rates . ( A ) Representative images of MEFs with aligned or misaligned chromosomes after monastrol washout . White arrowhead depicts misaligned chromosome . ( B ) Analysis of chromosome misalignment in MEFs expressing the indicated Bub1b transgenes . MEFs were treated with 100 µM monastrol for 1 hr and then with monastrol and 10 µM MG132 for 1 hr and released for 90 min into MG132 . Cells were treated with DMSO ( Vehicle ) or 10 nM AZD1152-HQPA ( AZD ) throughout the duration of the experiment . n = 6 lines ( ≥25 cells per line were analyzed ) . Data are mean ± s . d . *p<0 . 05 , **p<0 . 01 . Scale bar , 10 µm . WT , wild-type . ( See associated Figure 7—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 02410 . 7554/eLife . 16620 . 025Figure 7—source data 1 . Source file for monastrol washout data . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 025 At the time of transgenic design , BubR1 residues 525–700 were defined as the internal Cdc20 domain ( Malureanu et al . , 2009 ) . However , subsequent studies have revealed that this region includes at least three discrete functional units: the Phe-box , D-box2 and KARD , the first two of which function as non-redundant Cdc20-binding motifs ( Diaz-Martinez et al . , 2015; Lischetti et al . , 2014; Suijkerbuijk et al . , 2012 ) . This prompted us to study the extent to which BubR1 overexpression is able to preserve genomic stability when these functional units are deleted individually or in combination ( Figure 8 ) . These mutants were expressed in wild-type primary MEFs using a lentiviral expression system that allows for doxycycline inducible transgene expression . FL-BubR1 and BubR1∆I expressed in the same system were used as controls . We confirmed that each mutant was specifically and highly overexpressed in the presence of doxycycline ( Figure 8—figure supplement 1 ) . 10 . 7554/eLife . 16620 . 026Figure 8 . Schematics of pTripZ-Flag-Bub1b mutants . Schematics of the pTripZ-Flag-Bub1b constructs . D , destruction- ( D- ) box . GLEBs , GLEBs-binding motif . Phe , Phe box . KARD , kinetochore attachment regulatory domain . FL , full-length . Sequence alignment of the Phe box , D-box2 , and KARD region of human and mouse BubR1 . Residues characterized in human BubR1 are underlined , and homologous residues deleted in mouse Bub1b constructs are bold . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 02610 . 7554/eLife . 16620 . 027Figure 8—figure supplement 1 . Protein levels of Bub1b deletion constructs in wild-type MEFs . Western blots of wild-type ( WT ) MEFs infected with the indicated constructs with or without doxycycline ( Dox ) . Blots were probed with the indicated antibodies . Ponceau S was used to normalize loading . Asterisk indicates specific band for BubR1ΔI . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 027 Next , we examined the impact on mitotic timing by live cell imaging ( Figure 9A ) . Overexpression of BubR1∆I provided a similar extension to the metaphase-to-anaphase transition as MEFs derived from Bub1b∆I transgenic mice . However , none of the other deletion constructs changed the duration of mitosis when overexpressed . An additional deletion mutant lacking the Phe box , D-box2 and the KARD ( Bub1b∆PheD∆KARD ) was generated to test if combined deletion of all three motifs would mimic Bub1b∆I ( Figure 9—figure supplement 1 ) . Again , no extended mitotic timing was observed , implying that an unmapped domain within residues 525–700 regulates mitotic timing . 10 . 7554/eLife . 16620 . 028Figure 9 . BubR1 deletion constructs extend nocodazole arrest and positively impact error attachment machinery . ( A ) Analysis of the time from nuclear envelope breakdown ( NEBD ) to anaphase onset in H2B-RFP wild-type MEFs infected with the indicated constructs with and without the addition of doxycycline ( Dox ) by live cell time-lapse imaging . n = 1 line , ≥ 20 cells per treatment . Data are mean ± s . e . m . ***p<0 . 001 . FL , full-length . ( B ) In a nocodazole challenge , H2B-RFP wild-type MEFs infected with the indicated constructs with and without the addition of Dox were treated with 100 ng/ml of nocodazole and monitored by live cell time-lapse imaging . The point of time in which 50% of cells are arrested in mitosis is plotted . n = 1 line , ≥ 15 cells per treatment . *p<0 . 05 , **p<0 . 01 , ***p<0 . 001 . ( C ) Analysis of chromosome misalignment in wild-type MEFs infected with the indicated constructs with and without addition of Dox . MEFs were treated with 100 µM monastrol for 1 hr and then with monastrol and 10 µM MG132 for 1 hr and released for 90 min into MG132 . Cells were treated with DMSO ( Vehicle ) or 50 nM AZD1152-HQPA ( AZD1152 ) throughout the duration of the experiment . n = 3 lines , 50 cells per line per treatment . Data are mean ± s . d . *p<0 . 05 . ( See associated Figure 9—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 02810 . 7554/eLife . 16620 . 029Figure 9—source data 1 . Source file for mitotic timing , nocodazole challenge and monastrol washout data . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 02910 . 7554/eLife . 16620 . 030Figure 9—figure supplement 1 . Combined loss of Phe , D-box2 and KARD does not impact mitotic timing . ( A ) Schematic of the pTripZ-Flag-Bub1b∆PheD∆KARD construct . D , destruction- ( D- ) box . GLEBs , GLEBs-binding motif . Phe , Phe box . KARD , kinetochore attachment regulatory domain . FL , full length . ( B ) Western blot of wild-type ( WT ) MEFs infected with Bub1b∆PheD∆KARD with or without doxycycline ( Dox ) . Blot was probed with the indicated antibodies . Ponceau S was used to normalize loading . ( C ) Analysis of the time from nuclear envelope breakdown ( NEBD ) to anaphase onset in H2B-RFP WT MEFs infected with Bub1b∆PheD∆KARD with and without the addition of Dox by live cell time-lapse imaging . n = 1 line , ≥ 20 cells . Data are mean ± s . e . m . M , metaphase . ( See associated Figure 9—figure supplement 1—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 03010 . 7554/eLife . 16620 . 031Figure 9—figure supplement 1—source data 1 . Source file for mitotic timing data . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 031 As expected , BubR1∆I-expressing MEFs showed the most profound increase in the duration of nocodazole-mediated arrest , while FL-BubR1 overexpression caused a moderate , but significant increase ( Figure 4B ) . Of our newly generated mutants , Bub1b∆Phe , Bub1b∆D and Bub1b∆KARD behaved like overexpression of FL-BubR1 , while Bub1b∆PheD phenocopied Bub1b∆I . These findings indicate that none of the individual domains is required for SAC reinforcement by high levels of BubR1 and the combinatorial loss of both the Phe and D-box2 motifs is a requirement for robust checkpoint sustainability . When examined for the ability to improve microtubule-kinetochore attachment error correction in monastrol washout assays with and without Aurora B inhibition , all mutants did so to a similar extent as FL-BubR1 ( Figure 9C ) , indicating that this feature of BubR1 overexpression is not impacted by any functional units in the central portion of BubR1 . Altogether , our studies using refined BubR1ΔI mutants indicate that individual domains within the 525–700 region are not required for SAC and error correction reinforcement by BubR1 overexpression , and that maximal SAC reinforcement is achieved when both internal Cdc20-binding motifs are absent . Overexpression of FL-BubR1 and BubR1∆I reduces tumor burden and aneuploidization in KrasLa1 mutant mice . To better understand the underlying mechanisms , we determined the type of mitotic errors caused by oncogenic Ras in wild-type MEFs . We found that ectopic expression of KrasG12V had no impact on timing of mitosis and SAC signaling ( Figure 10A–C ) . On the other hand , in monastrol washout assays , KrasG12V-expressing MEFs produced significantly higher rates of misaligned chromosomes over empty vector alone ( Figure 10D ) . In complementary experiments in which we monitored chromosome segregation errors by live cell imaging , KrasG12V-expressing MEFs showed a remarkable increase in misaligned chromosomes ( Figure 10E ) . Collectively , these data suggest that oncogenic Kras cells may be particularly prone to syntelic attachments . 10 . 7554/eLife . 16620 . 032Figure 10 . Oncogenic Kras increases microtubule-kinetochore malattachment . ( A ) Western blot of wild-type ( WT ) MEFs infected with pBABE-Puro-KRas ( G12V ) or empty vector ( EV ) . Blot was probed with the indicated antibody . Ponceau S was used to normalize loading . ( B ) Analysis of the time from nuclear envelope breakdown ( NEBD ) to anaphase ( A ) onset in H2B-RFP wild-type MEFs infected with KrasG12V or EV by live cell time-lapse imaging . n = 1 line , ≥ 19 cells . Data are mean ± s . d . *p<0 . 05 . M , metaphase ( C ) In a nocodazole challenge , H2B-RFP wild-type MEFs infected with KrasG12V or EV were treated with 100 ng/ml of nocodazole and monitored by live cell time-lapse imaging . The point of time in which 50% of cells are arrested in mitosis is plotted . n = 1 line , ≥ 20 cells . *p<0 . 05 . ( D ) Analysis of chromosome misalignments in wild-type MEFs infected with KrasG12V or EV . MEFs were treated with 100 µM monastrol for 1 hr and then with monastrol and 10 µM MG132 for 1 hr and released for 90 min into MG132 . n = 1 line , ≈200 cells . *p<0 . 05 . ( E ) Live-cell imaging of chromosome segregation defects in primary H2B-RFP wild-type MEFs infected with Kras or EV . n = 1 line , ≈40 cells . *p<0 . 05 . ( See associated Figure 10—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 03210 . 7554/eLife . 16620 . 033Figure 10—source data 1 . Source file for mitotic timing , nocodazole challenge , missegregation assay and monastrol washout data . DOI: http://dx . doi . org/10 . 7554/eLife . 16620 . 033
BubR1 is different from other mitotic regulators in that supranormal expression improves numerical chromosomal integrity . Although it will be impractical to overexpress BubR1 for therapeutic purposes , increased insight into the molecular mechanisms underlying the positive effects of BubR1 overexpression might create entry points for development of novel anti-cancer treatments based on small molecules that complement current therapies . As a first step , we focused on the roles of BubR1’s Cdc20-binding domains . Here we show that overexpression of the BubR1 N-terminal region is necessary , but not sufficient to prevent Kras-mediated aneuploidy and tumorigenesis , implying that reinforcing pseudo-substrate inhibition of Cdc20-bound APC/C by BubR1 is a requirement but entirely ineffective in isolation . In contrast to the N-terminal Cdc20-binding domain , a region spanning residues 525–700 that includes the elements of the internal Cdc20-binding domain and KARD , is dispensable for the aneuploidy and tumor suppressing effects . Overexpression of BubR1 lacking this region reinforced genomic stability and cancer protection in mice , despite dramatically altering metaphase duration of regularly dividing , unchallenged , MEF cells . We find that key shared characteristics of overexpressed FL-BubR1 and BubR1ΔI include increased kinetochore localization , increased error correction ability and a more robust SAC , though at differing magnitudes . BubR1ΔI overexpression was most extreme in altering the SAC in that it appeared to lower the threshold for checkpoint activation and maintenance . By analyzing the mitotic phenotypes of more refined deletion mutants , we determined that the profound length of nocodazole-induced mitotic arrest in this mutant was likely provided by a combined loss of the Phe box and D-box2 , which is in alignment to a previous report showing that this region normally serves to shorten mitotic arrest times ( Diaz-Martinez et al . , 2015 ) . However , whether the increased duration of mitotic arrest is actively participating in attenuation of Kras-mediated tumorigenesis in either FL-Bub1b or Bub1b∆I KrasLa1 mice is unclear , as oncogenic Kras alone did not negatively impact SAC signaling in MEFs . Another unique attribute of BubR1∆I overexpression was its impact on normal mitotic timing . In pursuing this phenotype , we found that while overexpression of BubR1∆PheD recapitulated the robust nocodazole arrest seen in Bub1b MEFs , it did not reproduce the extension of the metaphase-to-anaphase transition . We further explored this with a mutant BubR1 lacking a combination of the Phe box , D-box2 and KARD ( Bub1b∆Phe∆KARD ) , but also did not see changes to mitotic timing . This suggests that the loss of a region within BubR1 between residues 525 and 700 , either alone or in combination with the aforementioned domains , might be responsible for influencing the duration of mitosis . Further expansion of this notion could assign new functions related to timing to previously unmapped regions of BubR1 . Our studies in MEFs which had been infected with oncogenic Kras illuminated that while they had normal mitotic timing and SAC signaling , they had challenges with proper error correction machinery . Taken altogether , these data suggest that specific manipulations of BubR1 and subsequent protection against aneuploidy can occur through distinct complimentary mechanisms that result in tumor protection . In FL-Bub1b and Bub1bΔI , parameters that positively influence genomic stability such as a robust SAC and improved error correction correlate with loss of Kras-mediated aneuploidy and tumorigenesis . Therefore , it is tempting to speculate that the strongest mechanism preventing aneuploidization in our Kras system might be a strengthening of the error correction machinery . Given the heterogeneous nature of lung tumors compared to MEFs , however , it is possible that distinct challenges and defects can arise in Kras lung tumors that are not overtly evident in MEFs . Lung tissues with activated oncogenic Kras proceed through several morphological stages , including regions of mild hyperplasia/dysplasia that have increased aneuploidy relative to normal lung tissue , to small alveolar adenomas and finally culminating into overt carcinoma ( Baker et al . , 2013; Johnson et al . , 2001 ) . We proposed two distinct but non-mutually exclusive mechanisms of tumor protection . In the first mechanism , the impact of a hypersensitive SAC combined with an ability to promote proper attachments and prevent misalignments by FL-BubR1 and BubR1ΔI simply prevents genetic heterogeneity that facilitates cancer progression . Thus , while hyperplasia is still a feature of the lungs , there is a block to full neoplastic transformation because necessary losses of tumor suppressors and gains of cancer promoting genes are prevented . The second scenario is that survival of pre-neoplastic cells within the hyperplastic tissue is impacted by BubR1 overexpression , such that unstable cells have an increased propensity to die . The competing-networks model proposes that two independent yet competing cell fates oppose each other during mitotic arrest: death by caspase-mediated apoptosis or mitotic slippage resulting from persistent cyclin B1 degradation due to incomplete SAC inhibition ( Brito and Rieder , 2006; Gascoigne and Taylor , 2008; Topham et al . , 2015 ) . Cells with prolonged mitotic arrest have a greater chance and more time to accumulate death signals ( Gascoigne and Taylor , 2008 ) . Both mutants have an impact on one arm of the competing network branches , as evidenced by their increase in arrest time in mitosis , though BubR1ΔI appears to have a more significant contribution . This could shift a given cell population towards death rather than survival . However , this hypothesis remains to be rigorously tested in our model especially since oncogenic Kras-infected MEF cells do not show evidence of a weakened mitotic checkpoint . We argue that taking advantage of an extended arrest independent of whether or not the machinery functions normally could still be used to promote cancer cell death , and that BubR1 overexpression would be an entry point to such therapies . Along these lines , microtubules have been targeted in anti-cancer chemotherapy with much success ( Dumontet and Jordan , 2010 ) . Furthermore , the use of Wee/Chk1 inhibitors which cause cells to bypass the G2 checkpoint , force reliance on the SAC ( Mc Gee , 2015 ) . This could be exploited and we would predict an increased susceptibility to microtubule poisons and cytotoxicity , in particular with a system mimicking BubR1ΔI or BubR1ΔPheD overexpression . Previously we reported a non-significant increase in the mitotic arrest time of MEFs overexpressing FL-BubR1 when challenged with nocodazole relative to wild-type ( Baker et al . , 2013 ) . Here , we conclude , based on more in depth and sophisticated studies to test this aspect of SAC signaling , that there is a modest but significant increase in SAC potency in these cells ( Baker et al . , 2013 ) . Furthermore , overexpression of BubR1∆Phe , BubR1∆D , and BubR1∆KARD deletion constructs in wild-type MEFs also provided a slight but significant increase in mitotic arrest time compared to controls , indicating that they were analogous to FL-BubR1 overexpression alone . It is important to note that the data provided by the extensive analysis of Bub1b∆I and Bub1b∆KARD MEFs are in disagreement with previous work in human cells , where the KARD was proposed to provide the MT-KT attachment function of BubR1 ( Suijkerbuijk et al . , 2012 ) . In these studies , introducing exogenous BubR1 with a mutated KARD into systems depleted of endogenous BubR1 have decreased PP2A kinetochore localization , and subsequent chromosome alignment defects ( Suijkerbuijk et al . , 2012 ) . In Bub1bΔI MEFs , however , we do not see increased alignment errors or decreased PP2A kinetochore localization ( Table 2 , Figure 5—figure supplement 1 ) and both overexpressed BubR1∆I and BubR1∆KARD actually have increased error correction capabilities ( Figure 7B , Figure 9C ) . We emphasize that unlike the studies by Suijkerbuijk and colleagues , our transgene and deletion construct are on the background of a full complement of endogenous BubR1 , which may still provide adequate kinetochore docking of PP2A . Furthermore , our data are also in alignment with our previous work in which BubR1∆I expressed on a BubR1–/– background does not show overt increases in misalignments ( Malureanu et al . , 2009 ) . This could represent species-specific differences in the reliance of the KARD for attachment , or on the dependency of BubR1 to recruit PP2A to the kinetochore . Overexpression of BubR1ΔN or its complimentary fragment BubR1N does not recapitulate benefits observed with FL-BubR1 and BubR1ΔI overexpression . Instead , BubR1ΔN may be imposing detrimental effects on the cells , as its overexpression results in a slight increase in aneuploidy and a decreased ability to sustain a checkpoint arrest in MEFs , though in vivo aneuploidy rates do not change . Interestingly , however , while the BubR1 N-terminus appears necessary for physiological benefits , it alone did not exert an anti-tumor effect . FL-Bub1b and Bub1bΔI MEFs have increased BubR1 expression at kinetochores ( Figure 2B ) , corresponding to phenotypic benefits . BubR1N cannot localize to kinetochores , in addition to lacking several functional domains such as the putative kinase/pseudokinase domain , the internal Cdc20-binding domain , and KARD . As we have determined from the studies herein that the internal binding domain and KARD are dispensable , kinetochore localization and subsequent action of BubR1 there might be key to beneficial phenotypes , in particular , error attachment ( Lampson and Kapoor , 2005 ) . This is in agreement with our previous study in which we found BubR1 that is unable to localize to kinetochores due to disruptions of the Bub3-binding domain cannot fully rescue spindle assembly checkpoint or mediate complete corrective effects on misalignment in cells depleted of endogenous BubR1 ( Malureanu et al . , 2009 ) . The use of transgenic strains in which BubR1N is artificially tethered to kinetochores and evaluating the impact on SAC and error correction signaling could shed light on this in future studies ( Maldonado and Kapoor , 2011 ) . Other transgenic models could parse out the specific contribution of kinetochore-localized overexpression to tumor prevention with a domain mutant that does not permit BubR1 to localize to the kinetochore , as well as overexpression of a kinase-dead BubR1 . Whether or not aneuploidy causes cancer or is simply a feature is a longstanding question ( Giam and Rancati , 2015 ) . Here , we offer a unique perspective by showing the reinforcement of genomic stability through several complimentary mechanisms , with an emphasis on error correction machinery , attenuates tumors in a Kras mouse model of lung cancer . Tumor cells often have compromised DNA repair pathways , and are therefore sensitized to chemotherapies such as topoisomerase inhibitors and alkylating agents that promote cellular damage ( Calderón-Montaño et al . , 2014 ) . There are few , if any , cancer treatments that revolve around promoting chromosomal stability and reinforcing known checkpoint pathways . Thus , by forcing reliance on the SAC and death in mitosis by damaging or bypassing other cell-cycle checkpoints , a more potent anti-tumor therapy could be designed .
All protocols were reviewed and approved by the Mayo Clinic Institutional Animal Care and Use Committee . Mice used in these studies were housed in a pathogen-free barrier and are maintained on a mixed 129SV/E × C57BL/6 genetic background . Full-length Flag-mBub1btransgenic mice have been described previously ( Baker et al . , 2013 ) , and the generation of Flag-mBub1bN , Flag-mBub1bΔI , and Flag-mBub1bΔN was performed using a similar strategy . The development of constructs for these fragments of BubR1 has been described in detail ( Malureanu et al . , 2009 ) . Tumor studies of KrasLA1 mice were performed as previously described ( Baker et al . , 2013 ) . KrasLA1 mice were obtained from the MMHCC ( NCI Frederick ) ( Johnson et al . , 2001 ) . Mice were sacrificed at 6 weeks of age and surface adenomas were counted using a dissection microscope . Formalin-fixed , paraffin-embedded lung samples were stained for histological analysis using routine haematoxylin and eosin staining . pTripz-Flag-FL-Bub1b was created from pTripz-PKG-puro-loxp ( GE Dharmacon , Layfayette , CO ) . The loxp sites were removed and a multiple cloning site ( MCS ) was inserted . Flag-FL-Bub1b was removed from pMSCV-IRES-GFP ( Malureanu et al . , 2009 ) and cloned into the MluI site of the MCS . Deletion constructs were generated using the Q5 Site-Directed Mutagenesis Kit ( New England Biolabs , Ipswich , MA; #E0554S ) following manufacturer’s instructions from template pTripz-Flag-FL-Bub1b . The following primers were used to create the following deletion constructs: pTripZ-Flag-Bub1b∆I , Fwd 3’- GACGGGGCAGAAAATGCT-5’ , Rev 3’- AAAAATGGAGAAAGGCATACTG-5’; pTripZ-Flag- Bub1b∆KARD , Fwd 3’- TCTGGCTTCTCCAGGTCTT-5’ , Rev 3’- GAGGGCCTGGTGATGAAC-5’; pTripZ-Flag-Bub1b∆Phe , Fwd 3’- TCTCTTTCAGACAAAAAGGAC-5’; Rev 3’- ACTGGAACCTTTAGAATCAG-5’; pTripZ-Flag-Bub1b∆D , Fwd 3’- AAAACTACAGAAGTGGGC-5’ , Rev 3’- CTGGGCATTGAGAACCTG-5’; pTripZ-Flag-Bub1b∆PheD , Fwd 3’- AAAACTACAGAAGTGGGC-5’ , Rev 3’- CTGGAACCTTTAGAATCAG-5’ . We used a similar approach to create pTripZ-Flag-Bub1b∆PheD∆KARD using pTripZ-Flag-Bub1b∆PheD forward and reverse primers and pTripZ-Flag-Bub1b∆KARD as a template . Cloned plasmids were transfected into HEK-293T cells using the Trans-Lentiviral shRNA packaging kit with calcium phosphate ( GE Dharmacon; #TLP5912 ) and virus was harvested 48 hr post-transfection . Primary wild-type p3 MEFs were infected twice every 8 hr and selected with 2 µg/ml puromycin ( InvivoGen , San Diego , CA ) 48 hr post initial infection . At this time , 1 µg/ml doxycycline ( Clontech , Mountain View , CA ) was added and 48 hr later , cells were processed for western blotting or monastrol washout , or infected with H2B-RFP for live cell imaging . Wild-type MEFs were infected with pBABE-Puro-KRas ( G12V ) ( Addgene plasmid #46746 ) or empty vector ( Addgene plasmid #1764 ) and selected with 2 µg/ml puromycin 48 hr post-infection with cells for live-cell imaging being infected with H2B-RFP at this time . After 48 hr of selection , cells were processed for western blotting , monastrol washout , or live cell imaging . Wild-type and Bub1b transgenic MEFs were generated and cultured as described previously ( Baker et al . , 2004 ) . At least three independently generated MEF lines per genotype were used unless otherwise stated . Asynchronous and mitotic shake-off MEF lysates were created as described previously ( Baker et al . , 2013 ) . Western blot analysis was performed as previously described ( van Ree et al . , 2010 ) . Lung tissue lysates were prepared as previously described ( Baker et al . , 2013 ) . Briefly , the lung tissue was snap-frozen in liquid nitrogen and then ground into powder with a mortar and pestle . Ten milligrams of the powder was resuspended in 100 μl of PBS , boiled for 10 min at 100°C after the addition of 100 μl Laemmli lysis buffer ( Bio-Rad , Hercules , CA ) , and loaded into Tris-HCl polyacrylamide gels ( Bio-Rad ) . Primary antibodies used were mouse anti-BubR1 ( BD Transduction , San Jose , CA; 612503 , 1:1 , 000 ) , rabbit anti-mouse BubR1 ( aa382-420 ) ( [Baker et al . , 2004]; 1:1000 ) , rabbit anti-human BubR1 ( aa1-350 ) ( [Baker et al . , 2004]; 1:1000 ) , rabbit anti-Flag ( Sigma-Aldrich , St . Louis , MO; F7425 , 1:1000 ) , rabbit anti-Flag ( Cell Signaling , Danvers , MA; 2368S , 1:1000 ) , rabbit anti-Cdc20 ( Santa Cruz , Dallas , TX; sc-8358 , 1:1000 ) , mouse anti-Kras ( Santa Cruz; sc-30 , 1:1000 ) and rabbit anti-pCdc20S92 and rabbit anti-pCdc20S153 ( generous gifts from Hongtau Yu ) . All antibodies were detected with secondary HRP-conjugated goat anti-mouse or anti-rabbit antibodies ( Jackson Immunoresearch , West Grove , PA; 1:10 , 000 ) . Ponceau S staining ( 1% glacial acetic acid , 1 . 1 g/ml Ponceau S [Sigma-Aldrich] ) served as a loading control for blots . All western data are representative for two or three independent experiments . Co-IP was performed with mitotic MEFs that were immortalized by expression of SV40 large T antigen as previously described ( Baker et al . , 2013 ) . Primary antibodies used were mouse anti-BubR1 ( BD Transduction; as above ) , rabbit anti-mouse BubR1 ( aa382-420 ) ( [Baker et al . , 2004]; as above ) , rabbit anti-Cdc20 ( Santa Cruz; as above ) , mouse anti-Mad2 ( BD Transduction , 610679 , 1:1000 ) , rabbit anti-Mad2 ( [Ricke et al . , 2011]; 1:1000 ) . All antibodies were detected with secondary HRP-conjugated goat anti-mouse or anti-rabbit antibodies ( Jackson Immunoresearch; as above ) except when Cdc20 immunoblot was performed from CDC20 IP , in which Rabbit TrueBlot Anti-Rabbit IgG HRP ( Rockland , Limerick , PA; 18-8816-33 1:1000 ) was used . MEF karyotype analyses were performed as previously described on at least n = 3 individual MEF lines per genotype ( Babu et al . , 2003 ) . Interphase FISH analysis on single cells isolated from various fresh tissues from 3-mo-old mice and KrasLA1 hyperplastic lungs was performed as described previously ( Baker et al . , 2008 ) , and were analyzed in the Mayo Clinic Cytogenetics Core Facility . At least 100 cells were analyzed per sample . At least n = 3 individual mice per genotype per tissue were used . Chromosome segregation analysis was performed on MEFs stably expressing H2B-RFP , as previously described ( Malureanu et al . , 2009 ) . In mitotic timing experiments , the time interval between nuclear envelope breakdown ( NEBD ) and anaphase onset was measured in H2B-mRFP positive cells by monitoring unchallenged mitoses . Briefly , cells undergoing NEBD were marked and monitored at two minute intervals until anaphase onset . For SAC sensitivity experiments , cells were treated with nocodazole ( Sigma-Aldrich ) at a final concentration of either 20 or 10 ng/ml and then monitored from NEBD to anaphase onset . Nocodazole challenge experiments were performed as previously described ( Malureanu et al . , 2009 ) . Briefly , nocodazole was added to a final concentration of 100 ng/ml . Cells undergoing NEBD were marked and monitored at 10 min intervals to determine when they decondensed their chromosomes . The duration of arrest in mitosis , which is defined as the interval between NEBD ( onset of mitosis ) and chromatin decondensation ( exit from mitosis without cytokinesis ) , was then calculated and plotted . For checkpoint silencing and sensitivity experiments , 500 nM or 2 µM AZ3146 ( Sigma-Aldrich ) was added either concurrently or in sequence with nocodazole . All experiments were performed on at least three independently generated MEF lines unless stated . Monastrol washout was performed as previously described ( Ricke et al . , 2012 ) . Briefly 100 µM monastrol ( Enzo Life Sciences , Famingdale , NY ) was added to cells for 60 min , after which , 10 µM MG132 ( Sigma-Aldrich ) was added for 60 min . Cells were then released for 90 min into 10 µM MG132 alone before fixation ( 4% PFA for 10 min ) and staining with Hoechst . Cells treated with Aurora B inhibitor were cultured in medium with 10 nM or 50 nM AZD1152-HQPA ( ChemieTek , Indianapolis , IN ) , as specified for each experiment . Cells in which one or more chromosome was misaligned were considered misaligned . All experiments were performed on at least three independently generated MEF lines unless otherwise stated . Immunofluorescence was performed and quantified as previously described ( Kasper et al . , 1999 ) . In all cases , DNA was visualized with Hoechst and centromeres were visualized with human anti-centromeric antibody ( Antibodies , Inc , Davis , CA; 15-234-001 , 1:100 ) . Primary antibodies used were mouse anti-BubR1 ( BD Transduction; 612503 , 1:250 ) , rabbit anti-Flag ( Cell Signaling; 2368S , 1:100 ) , rabbit anti-Mad2 ( [Ricke et al . , 2011]; 1:500 ) , and mouse-anti-PP2A-B56α ( BD Transduction; 610615 , 1:200 ) . A laser-scanning microscope ( LSM 880; Carl Zeiss ) with an inverted microscope ( Axiovert 100 M; Carl Zeiss ) was used to capture images . For quantification , we used ImageJ software ( National Institutes of Health , Bethesda , MD ) as previously described ( Ricke et al . , 2012 ) . All confocal microscopic images are representative of at least three independent experiments . All experiments were performed on at least three independently generated MEF lines . Prism software ( GraphPad Software ) was used for all statistical analyses . A two-tailed Mann-Whitney test was used for pairwise significance analysis in Figure 2C; Figure 9A; Figure 9—figure supplement 1C; Figure 10B . A log-rank Mantel-Cox test was used for significance analysis in Figure 5B; Figure 9B; Figure 10C . A two-tailed unpaired t -test was used for comparisons in the following figures: Figure 2D; Figure 3B and D; Figure 4B–D; Figure 5A; Figure 5—figure supplement 1B; Figure 7B; Tables 1–3 . A two-tailed paired t-test was used for significance analysis in Figure 9C . A Fischer’s exact two-tailed test was used for significance analysis in Figure 10D and E . For consistency in these analyses , significance is indicated as follows: *p<0 . 05; **p<0 . 01; and ***p<0 . 001 . Sample sizes were chosen based on previously published studies where differences were observed . No samples were excluded . | Human DNA is organized into 46 chromosomes , which must be duplicated before a cell divides and are then shared equally between the two new cells . When this process goes awry , the new cells either have too many or too few chromosomes . This situation – known as aneuploidy – frequently occurs in cancer cells , and is thought to cause cells to gain extra copies or lose copies of genes that promote or prevent cancer , respectively . Cells have several ways to prevent aneuploidy . One of these safeguards , known as the spindle assembly checkpoint ( SAC ) , involves a protein called BubR1 , which acts at the stage when the duplicated chromosomes need to be equally divided into each daughter cell . Mouse models show that low levels of the BubR1 protein result in aneuploidy and increased predisposition to cancer . High levels of BubR1 , on the other hand , allow the mice to stay healthier for longer and can stop tumors from forming . However , it was not known exactly how high amounts of BubR1 protect against cancer . To address this question , Weaver et al . set out to determine which parts , or domains , of the BubR1 protein protect against cancer . Mice with high levels of the full-length BubR1 protein were compared with mice that made mutant versions of BubR1 lacking certain domains . These experiments revealed that a small portion of the beginning of the protein was necessary to protect against tumor formation , but removing a large region in the middle of BubR1 still protected mice against lung cancer and aneuploidy . Additional experiments performed on mouse cells grown in the laboratory revealed that whole BubR1 protein and the mutant protein lacking the middle region might prevent aneuploidy in multiple ways . For example , both systems had stronger SAC signaling , which could serve to make segregating the chromosomes more accurate . In the future , it will be important to find out whether BubR1 acts in the same way in human cells and cancers . Lastly , since it is not possible to over-produce BubR1 in humans , other methods will need to be investigated to use this knowledge to treat cancer . | [
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] | 2016 | BubR1 alterations that reinforce mitotic surveillance act against aneuploidy and cancer |
In many songbird species , males sing to attract females and repel rivals . How can gregarious , non-territorial songbirds such as zebra finches , where females have access to numerous males , sustain monogamy ? We found that the dopaminergic reward circuitry of zebra finches can simultaneously promote social cohesion and breeding boundaries . Surprisingly , in unmated males but not in females , striatal dopamine neurotransmission was elevated after hearing songs . Behaviorally too , unmated males but not females persistently exchanged mild punishments in return for songs . Song reinforcement diminished when dopamine receptors were blocked . In females , we observed song reinforcement exclusively to the mate’s song , although their striatal dopamine neurotransmission was only slightly elevated . These findings suggest that song-triggered dopaminergic activation serves a dual function in social songbirds: as low-threshold social reinforcement in males and as ultra-selective sexual reinforcement in females . Co-evolution of sexually dimorphic reinforcement systems can explain the coexistence of gregariousness and monogamy .
Many species of highly gregarious and colonial birds form long-term monogamous pairs ( Goodson et al . , 2012; Goodson and Kingsbury , 2011; Griffith et al . , 2010; Zann , 1994 ) . Pair bonding and flocking behaviors are regulated by neuropeptides and dopaminergic reward system ( Goodson et al . , 2012; Goodson and Kingsbury , 2011 ) . However , for an animal to be highly social and at the same time monogamous , it must possess two distinct reinforcement systems: one with low selectivity for social stimuli to promote aggregation , and another highly selective for sexual stimuli to promote monogamy . But many communicative stimuli , including birdsong , may serve both social and sexual functions . In such cases , reinforcement may depend on stimulus context: for example , in many solitary songbird males , producing the same song may either attract females or repel rival males ( Kroodsma and Byers , 1991; Slater , 2003 ) . In social songbirds , however , many females and males live in close proximity , which gives females immediate access to numerous males whose songs may sexually attract them . What is it , then , that allows gregariousness and monogamy to coexist ? We investigated this question in zebra finches , which are highly social , yet monogamous songbirds ( Griffith et al . , 2010; Zann , 1994 ) . Male zebra finches produce a single stereotyped song that can be female-directed or undirected ( Jarvis et al . , 1998; Scharff and Nottebohm , 1991; Sossinka and Böhner , 1980; ten Cate , 1985; Woolley and Doupe , 2008 ) . Males typically tolerate the singing behavior of their neighbors even when housed in crowed cages , although the song is occasionally used in an aggressive context too ( Ihle et al . , 2015 ) . Female zebra finches are attracted to male songs ( Holveck and Riebel , 2007 ) , but do not sing ( Nottebohm and Arnold , 1976 ) . The zebra finch striatal dopaminergic reward circuitry is activated in both social and sexual context ( Banerjee et al . , 2013; Ihle et al . , 2015; Iwasaki et al . , 2014; Sasaki et al . , 2006 ) . In general , there are more dopamine-producing neurons in social than in territorial songbirds ( Goodson et al . , 2009 ) . In zebra finches , gregariousness is correlated with the level of activity in dopaminergic neurons ( Kelly and Goodson , 2015 ) . Striatal dopamine increases in social situations , e . g . , when adult males interact with females ( Ihle et al . , 2015; Sasaki et al . , 2006 ) , or juvenile males with adult male tutors , and importantly , even without singing in either of these contexts ( Ihle et al . , 2015 ) . During pair formation striatal dopamine levels increase in both sexes ( Banerjee et al . , 2013; Iwasaki et al . , 2014 ) . In the context of song learning , striatal dopaminergic input is modulated during singing ( Gadagkar et al . , 2016; Hoffmann et al . , 2016; Simonyan et al . , 2012 ) . However , although song is an important sexual stimulus in songbirds ( Kroodsma and Byers , 1991; Slater , 2003 ) , there is no direct evidence that hearing songs may affect striatal dopamine in either sexual or affiliative ( Hausberger et al . , 1995 ) context . Here we performed in vivo imaging and behavioral experiments that show the forebrain dopaminergic system response to song stimulation in zebra finches across sexes and breeding states , in order to distinguish between social and sexual components of song reinforcement in social songbirds . We developed two complementary experimental approaches . First , we used a delayed positron emission tomography ( PET ) procedure ( Patel et al . , 2008 ) in order to measure dopamine neurotransmission ( Laruelle , 2000 ) in awake and unrestrained birds . Zebra finches were injected with [11C]raclopride radiotracer , which binds to dopamine type 2 ( D2 ) receptors . Instead of acquiring PET immediately , we first stimulated them with song playbacks for 20 min while awake and behaving and scanned them just after the stimulation under general anesthesia ( delayed PET , Figure 1 , see protocol in Materials and methods ) . Second , we developed an apparatus for assessing song reinforcement behaviorally . This approach is a variant on drug addiction experiments , which typically measure how much rodents are willing to work , or exchange mild punishment , in return for access to dopaminergic stimulants such as cocaine ( Shaham et al . , 2000 ) ( Figure 2 ) . We used a song stimulus instead of the drug and measured the extent to which birds were willing to receive mildly aversive air puffs ( Tokarev and Tchernichovski , 2014 ) in exchange for hearing song playbacks . Finally , in order to test for causality between dopamine neurotransmission and song reinforcement behavior , we blocked dopamine neurotransmission with a selective antagonist of D2 receptors L-741 , 626 ( Li et al . , 2010; Watson et al . , 2012 ) . We used PET to determine the localization of dopaminergic blockage , and then tested behaviorally if blocking of dopamine D2 receptors was sufficient to diminish reinforcing effect of songs .
We first tested if our delayed PET technique could detect changes in striatal dopamine neurotransmission after hearing song playbacks . We scanned eight unmated female zebra finches , where we expected to find higher levels of dopamine neurotransmission after song playbacks ( i . e . , lower levels of [11C]raclopride binding ) , and eight unmated males , where we expected to find a weaker effect , if any . Each bird was scanned twice: after stimulation with a variety of unfamiliar songs ( both female-directed and undirected ) over 20 min , and after silence over the same duration ( Figure 1 ) . As expected from the distribution of dopamine receptors in the songbird brain ( Kubikova et al . , 2010 ) , the averaged PET map showed that the striatum was the major site of [11C]raclopride binding in both conditions in males ( Figure 3a ) and in females ( Figure 3b ) . However , against our expectations , lower level of [11C]raclopride binding after hearing songs ( suggesting increased striatal dopamine neurotransmission ) was detected only in the male group . In males , the song minus silence parametric difference map showed that song stimulation resulted in significantly lower level of [11C]raclopride binding in a part of the striatum ( Figure 3c; cluster-level pcorrected = 0 . 024 , paired t-test corrected for multiple comparisons ) . Exploratory analysis of individual changes ( within the cluster of significant change ) showed that [11C]raclopride binding was at lower levels in all males after hearing songs by 29 ± 8% ( mean ± s . e . m . hereafter; Figure 3d; p=0 . 015 , pair-wise t-test ) . These results , based on PET of D2 receptors , are comparable to the 26 . 5 ± 8 . 4% increase in dopamine detected with microdialysis in a study where male zebra finches were presented with females ( Ihle et al . , 2015 ) , confirming that [11C]raclopride binding at D2 receptors is a robust indicator of the overall striatal dopamine neurotransmission . Surprisingly , females lacked any brain areas with significant change in [11C]raclopride binding in response to song playbacks . Nevertheless , we produced a mask image from the cluster of significant change in males ( Figure 3c ) and used it as a volume of interest to assess for a possible effect in females . Exploratory analysis of individual changes in females showed no apparent change in striatal [11C]raclopride binding in response to song playbacks ( Figure 3e; 0 . 4 ± 6% , p=0 . 737 , pair-wise t-test ) . A direct comparison between males and females showed statistically significant differences in striatal [11C]raclopride binding after hearing songs ( Figure 3—figure supplement 1; p=0 . 015 , t-test ) . Note , however , that the difference in the magnitude of change between males and females is , at least partially , driven by the low baseline ( silence ) [11C]raclopride binding in females ( Figure 3e ) . The sexually dimorphic striatal response to songs could reflect behavioral or anatomical differences between sexes not related to reinforcement . First , as striatal dopamine neurotransmission correlates with movement ( Cousins and Salamone , 1996; Gadagkar et al . , 2016; Howe and Dombeck , 2016 ) , we tested if birds tended to move more when hearing song playbacks , in a manner that could explain our results . We analyzed movement in eight males and eight females , in similar conditions to those in our experiments before PET scan: injection of raclopride followed by 20 min of silence or song playbacks . We observed very little of such body movements as flying , hopping and wing-whirring , and also quantitatively tracked the whole body movement ( analyzed every 0 . 3 s for the center of body mass ) , but there were no significant differences between conditions or sexes ( Figure 3—figure supplement 2; Table 1 ) . Tracking head movement , we observed a significant trend to move the head more during song playbacks in most birds ( Figure 3—figure supplement 2 ) . However , there was no significant difference between males and females in this respect ( Table 2 ) . Therefore , mere movement is unlikely to explain our finding of male-specific dopamine response to songs . Another concern is that our results could simply reflect anatomical dimorphism in the basal ganglia pathway of the premotor song system: in particular , Area X , which has high density of dopamine D2 receptors ( Kubikova et al . , 2010 ) and receives dopamine during female-directed singing ( Sasaki et al . , 2006 ) , exists only in zebra finch males . However , Area X was mostly excluded from the cluster of significant change ( Figure 3c and Figure 3—figure supplement 3 ) , suggesting that its contribution was small , if any . This is in line with the finding that Area X does not respond to auditory stimulation in awake songbirds , except for error signals during singing ( Gadagkar et al . , 2016 ) . Given that the expectation of reward is only one of several scenarios that could explain the unanticipated pattern of striatal dopamine neurotransmission that we observed ( Cousins and Salamone , 1996; Gadagkar et al . , 2016; Hoffmann et al . , 2016; Howe and Dombeck , 2016; Kubikova and Kostál , 2010; Riters , 2011; Salimpoor et al . , 2011; Schultz , 2002; Stuber et al . , 2008 ) , we developed an independent method for assessing the effect of song reinforcement in male and female zebra finches . In order to directly estimate song reinforcement we paired the song stimulus with a mild punishment . We presented the same birds that had been scanned earlier for dopamine with video of a perching male ( Figure 2 ) . Each bird was presented with two daily sessions of videos over ten days ( 20 sessions , 20 min each ) . In ten sessions the video was played in silence , and in the alternating ten sessions , it was accompanied by song playbacks ( the same mix of initially unfamiliar songs as in the PET experiments ) . When a bird perched next to the window facing the video display , it would occasionally receive a mildly aversive air puff , in random intervals and without warning . We assessed reinforcement by measuring the number of air puffs the bird was willing to tolerate in return for the stimulus , comparing the silent sessions to the song playback sessions . We found that males voluntarily received many more air puffs during song playback sessions compared to silent sessions ( Figure 4; p=0 . 001 , paired t-test ) ; they appeared attentive during the sessions but did not show any aggressive behavior . Females , on the other hand , showed little motivation to hear song playbacks: their tendencies to receive air-puffs were moderate and did not differ significantly across song playback and silent sessions ( Figure 4; p=0 . 267 , paired t-test ) . To test whether the song reinforcement we observed in males was dependent on dopamine neurotransmission , we used the D2 receptor antagonist L-741 , 626 to interfere with D2 receptors . First , we performed a whole brain PET after injections of L-741 , 626 in order to determine the localization of dopaminergic blockage: as expected , changes in [11C]raclopride binding were observed exclusively in the striatum ( Figure 5 ) . We found substantially lower levels of the striatal binding of [11C]raclopride after L-741 , 626 injection compared to saline ( Figure 5—figure supplement 1 ) . Therefore , L-741 , 626 blocks D2 receptors in the songbird striatum as it does in rodents ( Li et al . , 2010; Watson et al . , 2012 ) and primates ( Achat-Mendes et al . , 2010 ) . We then tested song reinforcement in four males with our air-puff apparatus as described before , but after injections of either L-741 , 626 or saline on alternate sessions . On the days of L-741 , 626 injections , the animals were still active and approached the video , but stimulation with song playbacks no longer increased the number of air puffs they were willing to receive , while on the alternate days of saline injections , song reinforcement was similar to that of untreated males ( Figure 6; see Table 3 for statistics ) . How is it that song stimuli are reinforcing in unmated males but not in unmated females ? We hypothesized that the non-selective dopamine neurotransmission by unfamiliar songs in males might reflect a social function , but in females , song reinforcement might be exclusively sexually driven , as a part of the mate choice ( Riebel , 2009 ) . A possible explanation to those counterintuitive results is that reinforcement could be much more selective in females . We therefore measured song reinforcement in six mated females that were ready to breed ( Figure 7—figure supplement 1 ) . We compared song reinforcement in three conditions: video accompanied with the songs of their mates , video accompanied with songs of other , unfamiliar mated males , and video alone . The mated females showed little interest in the videos and minimal motivation to tolerate air puffs in return to hearing non-mate songs . However , they were willing to receive many air puffs in return for hearing their mates’ songs ( Figure 7; see Table 4 for statistics ) . Based on these behavioral results , we tested if the pattern of striatal dopamine neurotransmission would be also mate-selective in these females . Using delayed PET , we compared two sets of stimuli: playbacks of mates’ songs versus playbacks of songs produced by other mated males ( in both conditions we played a mix of both female-directed and undirected songs ) . We detected a cluster of voxels with lower [11C]raclopride binding in response to mate song in a small part of the medial dorsal striatum ( Figure 8a , b ) ; however , the difference across those voxels did not survive correction for multiple comparisons ( Figure 8b ) . An exploratory post-hoc analysis of individual differences in the same area found that [11C]raclopride binding was 12 ± 4% lower in response to mate song compared to non-mate song ( Figure 8c; p=0 . 042 , paired t-test ) . These differences suggested a weak trend for higher levels of dopamine transmission in response to mates’ songs in females , but this borderline effect should be treated with caution and validated in future studies .
We found in the zebra finch an unanticipated pattern of sexual dimorphism in dopaminergic responses to song . In males , stimulation with songs resulted in higher levels of striatal dopamine neurotransmission compared to silence condition . Behaviorally too , unfamiliar song playbacks were strongly reinforcing in males . Blocking striatal dopamine D2 receptors extinguished song reinforcement , suggesting involvement of the striatal dopaminergic reward system . In unmated females , hearing songs did not affect dopamine neurotransmission , and playbacks were not reinforcing behaviorally either . In mated females , mate song was strongly reinforcing , with high specificity , but we observed only slightly higher levels of dopamine neurotransmission in response to mate song compared to non-mate song . Thus , in males , both striatal dopamine neurotransmission and behavioral responses to song playbacks indicate low-threshold and non-specific positive reinforcement . This is consistent with a social , perhaps affiliative function of birdsong to promote aggregation ( Hausberger et al . , 1995 ) . In females , both behavioral and dopaminergic responses to song were high-threshold and mate-selective , consistent with a sexual function to promote monogamy . However , even though behaviorally mated females showed strong reinforcement to mate song , their striatal dopaminergic responses to mate song were barely detectable . This discrepancy will require further assessment in future studies . Note that there are several open questions about the receptor mechanisms that could account for the sexual dimorphism we observed , including different receptors expression levels , different densities of dopaminergic cells , different reuptake mechanisms and different ratios of D1/D2 receptors . For example , it should be tested whether D1 receptors , which are known to be important for reinforcement ( Robbins and Everitt , 1996 ) , are also crucial in the reward mechanism of song in zebra finches . A simple evolutionary scenario can explain the pattern of sexual dimorphism we observed . Territorial songbird males respond aggressively to intruders and are easy to irritate with conspecific song playbacks ( Kroodsma and Byers , 1991; Slater , 2003 ) . Females may show strong preference to certain male song features but are generally attracted to conspecific songs ( Kroodsma and Byers , 1991; Slater , 2003 ) . Monogamy could be sustained during an evolutionary transition from the territorial to gregarious behavior if male evolved high tolerance to song while female simultaneously co-evolved highly selective reinforcement threshold to songs . Our results are consistent with such a scenario . Future studies could test this hypothesis further by systematic examination of sexual dimorphism across territorial and social species of songbirds , and in species where both sexes sing . We would expect to see a lack of song reinforcement in non-social territorial songbirds , at least outside the breeding period . But possibly , aggressive reaction might also increase brain dopamine , and one should try to carefully dissect such effects . For example , it was shown that fighting cocks ( Thompson , 1964 ) and Siamese fighting fish ( Thompson , 1963 ) may perceive seeing a potential opponent as a reinforcing stimulus; so , either they may look forward to the fight , or it is an anticipation of reward after winning the fight . In Siamese fighting fish , it was shown that dominant males are more likely to use such stimuli than subordinate ( Baenninger , 1970 ) . Avian species demonstrate a wide range of social structures , so the reinforcement value of social clues may vary greatly among them . In sum , a sexually dimorphic activation of the dopaminergic reward circuitry that we observed in our study could provide a joint mechanism for aggregation and pair-bonding , two seemingly conflicting characteristics of the social structure of zebra finches and other gregarious yet monogamous species .
This study was conducted in accordance with the guidelines of the US National Institutes of Health and was approved by the Institutional Animal Care and Use Committees of Hunter College of the City University of New York ( protocol 'OT imaging 10/18–01' ) and Weill Cornell Medical College ( protocol #2010–0003 ) . Eleven adult male and seventeen adult female zebra finches ( Taeniopygia guttata ) bred at Hunter College ( room temperature 19–24˚C , 12:12 hr light/dark cycle ) were used in the neuroimaging experiments . The animals were raised by both parents until adulthood and spent their life , except for the time of experiments , in the colony room with possibility to engage in social interactions with other zebra finches . All males and nine of the females were non-mated , eight other females were mated in breeding pairs . The concept of our work was similar to a human study , where favorite musical pieces were shown to increase striatal dopamine levels ( Salimpoor et al . , 2011 ) , but we employed a modification in PET protocol that allowed to obtain measurements that reflected changes in dopamine release in awake songbirds . Before imaging , the non-mated animals were injected [11C]raclopride and then either exposed to recorded songs of unfamiliar male zebra finches or kept in quiet conditions for 20 min ( Figure 1 ) . This time interval was chosen according to the 11C half-life of 20 min and its detectability with the current PET technique . PET and anatomical X-ray computed tomography ( CT ) images were acquired immediately afterwards using an Inveon Research Workplace ( Siemens ) . Delayed PET scans for dopamine are well established in several animal species ( Marzluff et al . , 2012; Patel et al . , 2008 ) , but since this is a novel method for measuring striatal responses to birdsong , we describe it in detail as a protocol in the next section . Eight mated female zebra finches were tested in a similar experiment , but with songs of either their own mate or another mated male; they were also synchronized in their breeding cycle so that during stimulation and PET they would be in similar hormonal states ( Figure 7—figure supplement 1 . The females were kept together with their mates for the first week after hatching of the offspring but then were moved ( together with offspring ) to the nursery room in the absence of adult males until post-hatch day 30 , after which they would reunite with their mates . This cycle is routinely performed in the laboratory to produce juvenile zebra finches not exposed to adult male song , which we use in other studies . For this experiment , we used females that had gone through this cycle several times , and stimulation/scanning took place shortly before their return to the mates ( Figure 7—figure supplement 1 ) . Scanning procedures were the same as in the previous experiment and are described in more detail in the next section . Eight of the males , four unmated females and four of the mated females were also tested in a behavioral paradigm for preferences to the auditory stimuli that had been used in the PET experiments ( Figure 2 ) . We modified our socially-reinforced auditory discrimination paradigm ( Tokarev and Tchernichovski , 2014 ) , so that after a period of isolation the zebra finches were attracted to a video of a male ( Ljubičić et al . , 2016 ) . The video was played either in silence ( 20 min ) or with the same auditory stimuli as in the PET experiments: a mix of songs of unfamiliar male zebra finches for the males and unmated females , and songs of unfamiliar males or mates for the mated females ( 20 min ) . The order of auditory accompaniment ( silence/songs ) in each session was random; each animal was tested in 10 sessions . In order to see the video and be closer to source of auditory stimulation , the animals had to sit on a perch that produced air puff in a random manner controlled by Bird Puffer software ( http://soundanalysispro . com/bird-puffer ) . We previously determined that random air puffs with a probability of ~2/minute are well tolerated by the birds . Our software automatically registered the bird’s perching activity , delivered the air puffs , and kept continuous records of air puffs that each bird received . We then analyzed during which stimulation the animals were willing to receive more air puffs . We also tested whether the movement might account for observed differences in striatal dopamine release . If dopamine level changes were due to movement , then movement should differ across treatments: higher in zebra finch males but not females when hearing songs compared to when they are kept in silence . To test if this were the case , we performed an additional control experiment with a new group of 8 males and eight females , where we simulated the song vs . silence pre-PET conditions ( including transfer to the same room and raclopride injection ) , and also video tracked birds’ movement . We monitored for such body movements as flying , hopping and wing-whirring , as well as quantitatively analyzed videos for Euclidian distances every 0 . 3 s for the center of body mass and beak to continuously track changes in position of body and head , respectively . To detect whether dopamine neurotransmission was necessary for the observed behavioral effects in males , four of them were injected with L-741 , 626 ( Sigma-Aldrich , Saint Louis , MO , USA ) , a very selective antagonist of D2-receptors , which had been used to study the function of D2-receptors in rodents ( Dai et al . , 2016; Li et al . , 2010; Watson et al . , 2012 ) and primates ( Achat-Mendes et al . , 2010 ) . We injected L-741 , 626 intraperitoneally at 3 . 33 µg/g body weight , within the range described for rodents ( Li et al . , 2010; Watson et al . , 2012 ) , diluted in saline ( acetic acid was added to increase solubility at first , then pH was neutralized by caustic soda solution ) . The L-741 , 626 injections were administered 30 min before each test with at least 48 hr between treatments , 5 times for each animal , with an intra-individual control of sham injections ( saline ) of the same volume . We established a minimally invasive method for in vivo imaging in zebra finches to measure dopamine neurotransmission in four awake unrestrained animals simultaneously; these measurements may be taken multiple times allowing for intra-subject comparisons ( Figure 1 ) . Due to their small size compared to the available imaging volume of our micro-PET , we were able to scan four birds simultaneously . Thus , the experiments were done in tetrads , with two animals in one condition , and two animals in another , and then the conditions were reversed for them in the subsequent PET scan . [11C]raclopride was delivered via intravenous ( i . v . ; ulnar vein ) or intraperitoneal ( i . p . ) bolus injections that lasted around 1 min or less; radioactivity doses were ~300 µCi or less , in solutions of 150 µl for i . p . injections and 100 µl for i . v . injections with [11C]raclopride mass at ~0 . 3 nmol/g ( body weight ) . Usage of [11C]raclopride to track changes in dopamine levels has been validated in studies with simultaneous microdialysis ( Morris et al . , 2008; Normandin et al . , 2012 ) . When dopamine is released , decrease in radioactive [11C]raclopride signal is mediated through direct competition between these two molecules for D2 receptors ( Fisher et al . , 1995 ) and as a result of D2 receptors switching from low to high affinity for dopamine but not raclopride ( Fisher et al . , 1995; Seeman et al . , 1994 ) ; also , the striatal [11C]raclopride signal does not rebound after its decline once dopamine is released ( Endres et al . , 1997 ) . Therefore , differences in dopamine neurotransmission between zebra finches exposed to song playbacks and silence observed in our work were likely due to experimental conditions , even though imaging was performed after stimulation ( Yoder et al . , 2008 ) . This method of delayed PET ( aka ‘awake uptake’ ) was first used to detect changes in dopamine levels in freely moving rats ( Patel et al . , 2008 ) . A similar protocol was also used in songbirds ( crows ) , but with [18F]−2-fluoro-2-deoxy-D-glucose to detect general brain activation in response to visual stimuli ( Marzluff et al . , 2012 ) . The animals were let to recover after handling for 1–2 min and then were kept individually either in quiet conditions ( 20 min ) or were presented with recordings of various zebra finch songs ( one novel song every 15 s during 20 min ) , thus providing stimulation almost immediately after radioligand injection , similarly to previous studies ( Marzluff et al . , 2012; Patel et al . , 2008 ) . Food and water were provided ad libitum . None of the birds sang or attempted to sing during the 20 min of the experiment ( in all conditions ) . Their behavioral activity was at minimum during the experiment with no drinking or feeding , and only occasional perching . This suggested that the difference in experimental conditions ( song playbacks or silence ) would be the sole factor in possible differences in dopamine neurotransmission . Immediately after the experiment , the animals were sedated ~2 min under 3% isoflurane in O2 , 2 L/min , and transferred into a custom-made plexiglass chamber with 4 head holders made from vinyl tubes; their bodies were additionally fixed with a surgical tape to reduce spontaneous movements during scanning . Animal placement ( 2 in radial , 2 in axial direction; heads facing towards the center of the imaging volume ) was chosen to maximize image quality ( Siepel et al . , 2010 ) . The chamber was then placed in the micro-PET scanner , and anesthesia was reduced to 2% isoflurane . Acquisition of the radioactive signal lasted 60 min and was followed by an anatomical CT scan of 10 min duration . Differences in radioactive signal acquired during the PET scan were expected to reflect dopamine release during auditory stimulation , as after [11C]raclopride is displaced by dopamine its level does not rebound within this time frame despite clearance of dopamine and even with continuous infusion of [11C]raclopride ( Endres et al . , 1997 ) , while we performed single bolus injection . We were able to inject a sufficient amount of radiotracer to obtain images of [11C]raclopride uptake , and all animals recovered quickly after the scan . We established that both i . v . and i . p . injections of [11C]raclopride produced a radioactive signal in striatum that was detectable by micro-PET , and the data from birds after i . v . and i . p . injections of [11C]raclopride overlapped and therefore were combined . Thus , both injection methods appeared to be effective for detection of dopamine level changes . We recommend i . p . injections for future research , as they are faster and easier to perform , require less handling and thus are less stressful for animals ( and experimenters ) . We also performed an additional PET scan on four males that had been tested with the D2 receptor antagonist , L-741 , 626 , to confirm that it blocked binding at the receptor . Two of them were injected L-741 , 626 solution and two others saline 30 min before [11C]raclopride injection . The rest of the procedure was the same . The radiotracer [11C]raclopride was synthesized on-site immediately before each experiment at the Citigroup Biomedical Imaging Center , Weill Cornell Medical College , following standard procedures ( Broft et al . , 2015; Mawlawi et al . , 2001 ) . The average specific activity of [11C]raclopride was 6046 mCi/μmol . [11C]raclopride was isolated and formulated into an isotonic solution containing 5–7% ethanol , with concentration of 0 . 13 µg/mL . Although alcohol could potentially influence behavioral state of the animal , the amount injected in our experiments ( ~0 . 3 g/kg ) was substantially lower than that causing an intoxicated stupor in a previous study ( 2–3 g/kg ) ( Olson et al . , 2014 ) and importantly was similar across all experimental conditions . PET imaging data were first processed in PMOD software ( http://www . pmod . com ) . As four animals were scanned simultaneously at each experiment , raw images were separated into four zones around each brain and cropped accordingly in PMOD software . PET data were summed across 6 evenly distributed time points for each scan . Further , PET data were processed and analyzed in SPM12 software ( http://www . fil . ion . ucl . ac . uk/spm ) . Anatomical CT images were transformed into standardized stereotaxic space and aligned with a 3D magnetic resonance imaging atlas of the zebra finch brain , which also references common brain areas ( Poirier et al . , 2008 ) . All PET images were corrected for volume-to-volume motion by inter-frame realignment and then co-registered to the subject's anatomical CT image . All alignment transformations were visually inspected to ensure that there was no mismatch with the template brain image . Datasets of three males , one unmated and two mated females were discarded because of difficulties with alignment of the images due to motion during scans . Data from the remaining 22 animals were analyzed further . [11C]raclopride binding potential for dopamine D2 receptors in each voxel was calculated using a simplified reference region method ( Gunn et al . , 1997; Lammertsma et al . , 1996; Patel et al . , 2008 ) , with the cerebellum as the reference region , since it does not contain detectable D2 receptors and is traditionally used for determination of nonspecific binding and free radiotracer in the brain ( Lammertsma et al . , 1996; Litton et al . , 1994 ) : ( CSt–CCb ) /CCb , where CSt is radioactivity concentration in striatal ( St ) voxels ( or anywhere else outside the reference region ) , and CCb is averaged radioactivity concentration in cerebellum ( Cb ) . Therefore , [11C]raclopride binding potential was represented by a striatal-cerebellar ratio ( SCR ) of radioactive concentrations ( Patel et al . , 2008 ) . As [11C]raclopride and dopamine compete for D2-receptors , decrease in [11C]raclopride binding potential indicates an increase of dopamine concentration ( Endres et al . , 1997; Fisher et al . , 1995 ) and thus reflects increased dopamine neurotransmission ( Laruelle , 2000; Martinez et al . , 2003 ) . Statistical parametric maps of [11C]raclopride binding potential change were produced by comparing the parametric SCR maps of the two scan sessions ( song playbacks and quiet condition , or mate’s and unfamiliar songs ) ; comparisons between two conditions were performed with paired t-tests , with two-tailed probability value of p<0 . 05 chosen as statistically significant ( Urban et al . , 2012 ) . Clusters of significant change were identified in xjView ( http://www . alivelearn . net/xjview ) at p<0 . 05; p values corrected for multiple comparisons were calculated for each cluster of contiguous voxels at a t threshold of 3 . 56 within a search volume equal to the whole brain and an effective spatial resolution of 1 . 4 mm full-width at half maximum ( FWHM ) ( Salimpoor et al . , 2011 ) . Mean binding potential values were extracted from the significant cluster for each individual , and the normalized percent change in dopamine level was calculated as Δ = ( SCRsilence–SCRsong ) ×100/SCRsilence . | While monogamy is rare within the animal kingdom , some species – including humans and many birds – can be highly social and yet sustain monogamous relationships . Zebra finches , for example , are among a number of species of songbirds in which numerous males and females live closely together but maintain monogamous partnerships . Male songbirds use their songs to attract females , who do not themselves sing . But if female birds are attracted to any male song , how do they manage to remain monogamous when surrounded by potential suitors ? In songbirds , and in humans too , a region of the brain called the striatum regulates both social and sexual behaviors . It does this by modulating the release of a molecule called dopamine , which is the brain’s reward signal . Tokarev et al . show that hearing songs triggers dopamine release within the striatum of unattached male zebra finches , but has no such effect in single females . Unattached male songbirds will also put up with irritating puffs of air in exchange for being able to watch videos of singing birds , whereas unattached females will not . Female songbirds with partners will tolerate the air puffs , but only if the videos are accompanied with the songs of their own mate . These findings suggest that song serves as a generic social stimulus for zebra finch males , helping large numbers of birds to live together . By contrast , for a female zebra finch , the song of her partner is a highly selective sexual stimulus . These sex-specific responses to the same socially-relevant stimuli may explain how gregarious animals are able to maintain monogamous pair bonds . More generally , these results are a step towards understanding how brain reward systems regulate social interactions . Studying these mechanisms in songbird species with different social and mating systems could ultimately provide insights into social and sexual behavior in people . | [
"Abstract",
"Introduction",
"Results",
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"neuroscience"
] | 2017 | Sexual dimorphism in striatal dopaminergic responses promotes monogamy in social songbirds |
CIDE-N domains mediate interactions between the DNase Dff40/CAD and its inhibitor Dff45/ICAD . In this study , we report that the CIDE-N protein Drep-2 is a novel synaptic protein important for learning and behavioral adaptation . Drep-2 was found at synapses throughout the Drosophila brain and was strongly enriched at mushroom body input synapses . It was required within Kenyon cells for normal olfactory short- and intermediate-term memory . Drep-2 colocalized with metabotropic glutamate receptors ( mGluRs ) . Chronic pharmacological stimulation of mGluRs compensated for drep-2 learning deficits , and drep-2 and mGluR learning phenotypes behaved non-additively , suggesting that Drep 2 might be involved in effective mGluR signaling . In fact , Drosophila fragile X protein mutants , shown to benefit from attenuation of mGluR signaling , profited from the elimination of drep-2 . Thus , Drep-2 is a novel regulatory synaptic factor , probably intersecting with metabotropic signaling and translational regulation .
Caspase family proteases regulate cellular pathways by cleavage of target proteins . They are best known for their roles during programmed cell death . One of their targets is the DNase Dff40/CAD , which degrades DNA during apoptosis ( Enari et al . , 1998 ) . Dff40 is a member of the DNA fragmentation factor ( Dff ) family of proteins , characterized by CIDE-N domains that mediate protein–protein interactions ( Wu et al . , 2008 ) . In Drosophila , four CIDE-N domain proteins were identified and named Dff related protein Drep-1 to Drep-4 ( Inohara and Nuñez , 1999 ) . Caspase-regulated Drep-4 is the ortholog of mammalian Dff40/CAD ( Yokoyama et al . , 2000 ) . Drep-4 is inhibited by Drep-1 , the ortholog of Dff45/ICAD , which is also cleaved by caspases ( Mukae et al . , 2000 ) . The two other Drosophila family members , Drep-2 and -3 , were suggested to be additional regulators of apoptosis , solely based on in vitro interactions ( Inohara and Nuñez , 1999; Park and Park , 2012 ) . We show here that Drep-2 , contrary to the expectations , is a novel synaptic protein . We have generated drep-2 mutants , which display learning and memory deficits . Further analyses suggest that Drep-2 regulates these processes by intersecting with metabotropic glutamate receptor signaling .
The gene coding for Drep-2 is located on chromosome IIR and contains five exons ( Figure 1A ) . While the last two exons are used in all known isoforms , the first three exons are included alternatively within the four isoforms drep-2-RA to -RD . 10 . 7554/eLife . 03895 . 003Figure 1 . Expression and mutants of drep-2 . ( A ) Genetic scheme of the drep-2 locus on chromosome IIR . The neighboring genes mad-1 and myd88 extend beyond the sequence displayed . The cDNA labeled drep-2-RA was used for rescue experiments . Blue: untranslated regions; green: exons; black lines: deleted regions in the mutants . ( B ) In situ hybridization of drep-2 reveals a neuronal expression pattern ( stage 17 ) . ( C ) Western blot of adult fly head extracts using the anti-Drep-2C-Term antibody . Drep-2 isoforms are predicted to run at 52 and 58 kDa . The signal is absent in both the drep-2ex13 and the drep-2ex27/Dfw45-30n mutant . DOI: http://dx . doi . org/10 . 7554/eLife . 03895 . 00310 . 7554/eLife . 03895 . 004Figure 1—figure supplement 1 . Drep protein alignment . Sequence alignment of all four Drosophila Dff proteins , as well as human ( HS ) and murine ( MM ) Dff40 . Drep-4 has the strongest similarity to Dff40 , yet Drep-2 also shows conserved motifs in addition to the CIDE-N domain . The alignment was created using Geneious v5 . 3 . 6 . ( http://www . geneious . com ) DOI: http://dx . doi . org/10 . 7554/eLife . 03895 . 00410 . 7554/eLife . 03895 . 005Figure 1—figure supplement 2 . Reduced lifespan of drep-2ex13 mutants . Comparison to isogenic w1118 control flies: 50% of mutant flies were dead after 21 . 5 days . Mutant: n = 10 vials ( each containing 25 flies ) , control: n = 11 . DOI: http://dx . doi . org/10 . 7554/eLife . 03895 . 005 High-throughput RT-PCR data indicate a strong enrichment of both drep-2-RA and drep-3 transcripts in the central nervous system ( CNS ) , while drep-4 and -1 are expressed ubiquitously ( Graveley et al . , 2011 ) . We conducted in situ hybridization using drep-2-RA constructs in order to confirm that the expression of drep-2 is nervous system specific ( Figure 1B ) . Next , we produced polyclonal antibodies against a fusion protein containing the C-terminal half of the protein , which is part of all isoforms ( Drep-2C-Term ) . Western blots from wild-type fly head extracts probed with Drep-2C-Term showed a double band ( Figure 1C ) of the size expected for Drep-2 isoforms ( McQuilton et al . , 2012 ) . We generated drep-2 mutants by FLP-mediated excision between FRT site-bearing transposons to explore the function of Drep-2 . The transposons P ( XP ) d00223 and PBac ( RB ) e04659 were used for the deletion allele drep-2ex13 ( Figure 1A ) . A second deletion allele , drep-2ex27 , was established using transposon lines PBac ( RB ) e02920 and PBac ( RB ) e04659 . All drep-2 exons are deleted in homozygous drep-2ex13 animals , while no other annotated transcription unit is affected . In the smaller intragenic deletion drep-2ex27 , all drep-2 exons apart from the very small ( 12 bp ) first exon are eliminated . Both Drep-2C-Term antibody bands were absent in head extracts of both mutants ( Figure 1C ) , confirming the complete elimination of Drep-2 expression in these deletion alleles . Flies lacking drep-2 were viable and fertile but shorter-lived than the isogenic controls ( Figure 1—figure supplement 2 ) . Subsequently , we used the Drep-2C−Term antibody for wholemount immunostainings of Drosophila brains . The synaptic neuropil was strongly labeled throughout the brains of larvae ( not shown ) and adults ( Figure 2 ) . In both drep-2 mutants , Drep-2C−Term staining in the CNS was completely abolished ( drep-2ex13: Figure 2A , B ) . 10 . 7554/eLife . 03895 . 006Figure 2 . Synaptic Drep-2 staining in the CNS . ( A–B ) Confocal frontal sections of adult Drosophila brains . Anti-Drep-2C-Term and BrpNc82 immunostaining; the latter marks all synaptic active zones . Synaptic Drep-2C-Term signal is visible throughout the brain of wild-type flies ( A ) . Complete loss of the anti-Drep-2C-Term staining can be observed in drep-2ex13 mutants ( B ) . Scale bars: 20 µm . ( C–E ) Frontal sections of wild-type brains , anti-Drep-2C-Term , and BrpNc82 staining . Scale bars: 5 µm . ( C ) Posterior–dorsal detail showing strong Drep-2 staining in MB calyces ( arrow ) . ( D ) Anterior frontal section with antennal lobes and MB lobes . ( E ) Ellipsoid body in the central complex and bulbs ( lateral triangles ) ( E′: magnification of strong Drep-2 staining in bulbs ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03895 . 006 As a Dff family member , Drep-2 has been suggested to be involved in the apoptotic regulation of DNA degradation ( Inohara and Nuñez , 1999; Park and Park , 2012 ) . However , neuronal cell bodies lacked Drep-2C−Term staining ( Figure 2 ) . We produced synaptosome-like preparations by fractionation of the adult Drosophila CNS to biochemically confirm the association of Drep-2 with synapses ( Owald et al . , 2012; Depner et al . , 2014 ) . Drep-2 was strongly enriched in fractions containing synaptic membranes ( Figure 3A ) . By contrast , no enrichment of Drep-2 could be observed in the nuclear fraction . 10 . 7554/eLife . 03895 . 007Figure 3 . No evidence for a role of Drep-2 in regulation of apoptosis . ( A ) Synaptosome-like preparation of adult wild-type head extracts ( Depner et al . , 2014 ) , probed with Drep-2C-Term . Drep-2 is concentrated in fractions containing synaptic membranes . S = supernatant , P = pellet , L = ( after ) lysis . Please see the protocol by Depner et al . ( 2014 ) for a more detailed explanation of the fractionation procedure . ( B ) Mutants ( drep-2ex13 ) did not show a rough eye phenotype . The facet eyes of flies , highly ordered structures , are often affected in apoptosis mutants . By contrast , the eyes of drep-2 mutants appeared normal . ( C ) The number of mb247-positive KCs does not differ between drep-2ex13 mutants and controls . GFP was expressed using the MB KC driver mb247-Gal4 . GFP-positive cell bodies were counted and compared between genotypes . No significant difference was found between mean cell body counts ( Mann–Whitney U test , p = 0 . 886 ) . Average cell body counts were in the expected range: control = 651 , mutant = 669 , published = 700 ( Schwaerzel et al . , 2002 ) . ( D ) Purified Drep-2 does not degrade linearized plasmid DNA . Left: SDS-PAGE of the final elusion profile of purified Drep-2 , loaded onto a HighLoad Superdex S200 16/60 column . Right: Nuclease activity assay of purified Drep-2 analyzed by 1% ( wt/vol ) agarose gel . Drep-2 was incubated in a time course experiment with linearized plasmid DNA . No nuclease activity could be detected . Instead , Drep-2 seemed to precipitate DNA , as evidenced by high-molecular DNA not entering into the agarose gel when incubated with Drep-2 ( arrow ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03895 . 007 Mutants lacking drep-2 showed normal exterior morphology , including their facet eyes ( Figure 3B ) . Fly facet eyes are highly ordered structures , already displaying abnormal patterns in cases of moderate misregulation of apoptosis ( Wolff and Ready , 1991; Song et al . , 2000 ) . The regular facet eyes of drep-2 mutants , therefore , argue against a major function of the protein in apoptosis . Wholemount brain stainings of wildtypes and drep-2ex13 mutants also showed no apparent morphological differences between either genotype ( Figure 2A , B; Figure 5—figure supplement 1 ) . If apoptosis was , nevertheless , misregulated in the CNS of drep-2 mutants in vivo , an altered count of cell bodies should be expected in adult flies . Drep-2 staining was especially prominent at KC synapses in the mushroom body ( MB ) calyx of wildtypes ( Figure 2C ) . We , therefore , quantified the numbers of cell bodies of a subset of MB-intrinsic neurons ( KCs ) . No differences between drep-2 mutants and controls could be observed ( Figure 3C ) . Drep-2 was reported to degrade DNA in vitro ( Park and Park , 2012 ) . This supposed nuclease activity was observed if purified Drep-2 was incubated in vitro with plasmid DNA at a molar ratio of protein:DNA 80:1 ( Park and Park , 2012 ) . However , we ( Figure 3D ) and another previous report ( Inohara and Nuñez , 1999 ) found no evidence of a nuclease activity of Drep-2 , even at high concentrations . Instead , Drep-2 appeared to precipitate DNA , as evident by plasmid DNA no longer entering the agarose gel when incubated with Drep-2 . This precipitation might have generated the previous impression that DNA is degraded in the presence of Drep-2 . Taken together , we could not find evidence for an in vivo role of Drep-2 in regulating apoptosis in the CNS . While a function of the protein related to apoptosis can still not be fully excluded , we decided rather to examine the synaptic functions of Drep-2 . In order to narrow down a potential site of action of the protein , we examined Drep-2 expression in the adult CNS in more detail . While Drep-2C-Term stained synapses throughout the brain , including optic lobes , antennal lobes , and the central complex ( Figure 2 ) , the immunoreactivity was particularly pronounced in the MB calyx ( Figures 2C and Figure 4A ) . 10 . 7554/eLife . 03895 . 008Figure 4 . Drep-2 is enriched at KC postsynapses . ( A–B ) Drep-2C-Term and BrpNc82 staining in animals expressing the construct mb247::Dα7GFP that marks acetylcholine receptors in MB KCs . ( A ) Detailed image of the MB calyx . Scale bar: 2 µm . ( B ) Detail of a single microglomerulus in the calyx . Drep-2C-Term overlaps with postsynaptic mb247::Dα7GFP and not with presynaptic Brp . Scale bars: 1 µm . ( C ) Localization of Drep-2 relative to choline acetyltransferase ( ChAT , presynaptic cytosol , C ) , the postsynaptic ACh receptor subunit Dα7 ( antibody staining , C′ ) , and the postsynaptic scaffolding protein Discs large ( Dlg , C″ ) . Drep-2 colocalizes with postsynaptic markers . Scale bars: 1 µm . ( D ) Post-embedding immunoelectron microscopy of Drep-2C-Term in the calyx . Arrows: Clusters of postsynaptic Drep-2C-Term . Scale bars: 100 nm . DOI: http://dx . doi . org/10 . 7554/eLife . 03895 . 00810 . 7554/eLife . 03895 . 009Figure 4—figure supplement 1 . Drep-2 localizes to postsynaptic membranes of KCs in the calyx . ( A ) STED microscopy superresolution recording of Drep-2C-Term; the BrpNc82 channel is in normal confocal mode . The Drep-2 signal does not overlap with presynaptic Brp . Scale bar: 1 µm . ( B–E ) Expression of drep-2 constructs in KCs yields a label resembling the Drep-2 antibody staining . Comparison to BrpNc82; all scale bars: 1 µm . ( B ) Pan-neural overexpression . Elavc155-Gal4 and UAS-Drep-2mStrawberry; mStrawberry signal is shown . ( C ) KC-specific overexpression . C305a-Gal4 , UAS-Drep-2mStrawberry , and UAS-Dα7GFP; mStrawberry and GFP signals are shown . D-2 = Drep-2mStrawberry , D7 = Dα7GFP . ( D ) PN-specific overexpression . Gh146-Gal4 and UAS-Drep-2mStrawberry; diffuse mStrawberry is shown . ( E ) KC-specific expression of UAS-Drep-2 in the drep-2ex13-mutant background . Mb247-Gal4 and untagged UAS-Drep-2; Drep-2C-Term staining is shown . DOI: http://dx . doi . org/10 . 7554/eLife . 03895 . 009 The principle circuitry processing olfactory information in Drosophila is highly similar to mammals ( Davis , 2004 ) . Signals are transferred in the fly's antennal lobe to projection neurons ( PNs ) , which target the mushroom body ( MB ) calyx and the lateral protocerebrum . PNs synapse in the calyx onto MB-intrinsic Kenyon cells ( KCs ) by forming large cholinergic presynaptic boutons . These presynaptic specializations are tightly encircled by acetylcholine ( ACh ) receptor-expressing postsynaptic densities ( PSDs ) of KC dendritic claws ( Yasuyama et al . , 2002; Leiss et al . , 2009a ) . In the MB calyx , the Drep-2C-Term signal overlapped with Dα7 ACh receptor subunits expressed in KC PSDs ( Figure 4A , B , C′ ) , which surrounded Bruchpilot ( Brp ) -positive ( but Drep-2-negative ) PN presynapses ( Figure 4A , B , Figure 4—figure supplement 1A ) . Furthermore , Drep-2 colocalized with the postsynaptic protein Discs large , but clearly segregated from presynaptic choline acetyltransferase ( Figure 4C ) . Consistent with endogenous Drep-2 being present at KC-derived PSDs , overexpression of UAS-Drep-2mStrawberry with either pan-neural ( elavc155-Gal4 ) or KC-specific drivers ( c305a-Gal4 ) resulted in an mStrawberry signal equivalent to that of the Drep-2C-Term antibody ( Figure 4—figure supplement 1B , C ) . Overexpression with a PN driver ( gh146-Gal4 ) , however , produced only a weak , diffuse expression pattern that bore no similarity to endogenous Drep-2 staining ( Figure 4—figure supplement 1D ) . Moreover , re-expression of UAS-Drep-2 in KCs of drep-2 mutants produced a distinctive label at KC PSDs ( Figure 4—figure supplement 1E ) . Finally , we confirmed the presence of Drep-2 at postsynaptic membranes of PN-KC synapses by immunoelectron microscopy ( Figure 4D ) . Thus , Drep-2 accumulates at postsynaptic specializations of KCs within the MB calyx . Kenyon cells have an essential function in olfactory learning ( Davis , 2011; Dubnau and Chiang , 2013 ) . Here , conditioned stimuli ( odors ) get associated with unconditioned stimuli ( e . g . , electric foot shock ) . Because Drep-2 was strongly enriched at PSDs of KCs , we wondered whether the protein might contribute to olfactory learning . In aversive olfactory conditioning , flies are trained to learn the difference between a punished and an unpunished odor . We measured short-term memory ( STM ) directly following training and intermediate-term memory ( ITM ) after 3 hr . All flies used for behavioral experiments were outcrossed into our isogenic background ( w1118 ) for at least five generations . Performance of controls allowed for the detection of differences in either direction ( Figure 5B , C ) . 10 . 7554/eLife . 03895 . 010Figure 5 . Drep-2 is required in KCs for olfactory short- and intermediate-term memory . ( A ) Flies mutant for drep-2 sense electric shock and the odors 4-methyl-cyclohexanol ( 4-MCH ) and 3-octanol ( 3-OCT ) normally; there is no difference in mean performance indices between mutants and isogenic w1118 control flies ( Mann–Whitney U tests ( MWU ) ) . Sample sizes n are indicated with white numbers; grey bars show SEMs . ( B ) Both mutants drep-2ex13 and drep-2ex27/Dfw45-30n are deficient in aversive olfactory conditioning , 3 min STM in a T-maze . The graph shows mean learning indices and SEMs . Mutants performed significantly worse than isogenic controls ( MWU: p = 0 . 00001 for both comparisons , Bonferroni-corrected significance level α = 0 . 0167 , 3 tests ) . ( C ) Re-expression of drep-2 cDNA with elavIII-Gal4 ( pan-neural ) , 30y-Gal4 ( MB KCs ) , or mb247-Gal4 ( MB KCs ) restores the deficit to normal levels . Heterozygous drep-2ex13 mutants do not display a significant deficit . MWU for individual comparisons showed a significant difference between these groups ( α = 0 . 0042 , 12 tests ) : w1118 and drep-2ex13 ( p < 0 . 00001 ) , drep-2ex13/drep-2ex13 and drep-2ex13/+ ( p < 0 . 00001 ) , drep-2ex13 and drep-2ex13;uas-drep-2/elavIII-gal4 ( p < 0 . 00001 ) , drep-2ex13 and drep-2ex13;uas-drep-2/30y-gal4 ( p < 0 . 00001 ) , drep-2ex13 and drep-2ex13;uas-drep-2/mb247-gal4 ( p < 0 . 00001 ) . None of the differences indicated as not significant had a p < 0 . 12 , except for w1118 and drep-2ex13/+ ( p = 0 . 03851; not significant in the case of α = 0 . 0042 ) . ( D ) Intermediate-term memory ( ITM = ASM + ARM ) performance . Mutants ( drep-2ex13 ) are defective in ASM , but not in ARM . The defect can be restored with elavIII-Gal4 or mb247-Gal4 ( 30y-Gal4 was not used here ) . Statistical tests were run separately for ITM and ARM . For ITM , MWU for individual comparisons showed a significant difference between these groups ( α = 0 . 00625 , 8 tests ) : w1118 and drep-2ex13 ( p < 0 . 0001 ) , drep-2ex13 and drep-2ex13;uas-drep-2/elavIII-gal4 ( p < 0 . 0001 ) , drep-2ex13 and drep-2ex13;uas-drep-2/mb247-gal4 ( p < 0 . 0001 ) . For assessing differences in ARM , ITM and ARM performances of each genotype were compared with MWU . The following genotypes showed a significant difference between ITM and ARM ( α = 0 . 0071 , 7 tests ) : w1118 ( p < 0 . 0001 ) , drep-2ex13;uas-drep-2/elavIII-gal4 ( p = 0 . 0002 ) , drep-2ex13;uas-drep-2/mb247-gal4 ( p = 0 . 0006 ) . None of the differences indicated as not significant had a p < 0 . 11 . DOI: http://dx . doi . org/10 . 7554/eLife . 03895 . 01010 . 7554/eLife . 03895 . 011Figure 5—figure supplement 1 . PN-KC synapses appear morphologically normal in drep-2 mutants . ( A ) Absence of major neuroanatomical defects in drep-2ex13 mutant brains . MB lobes , Fasciclin II ( FasII ) staining , maximum intensity projections . Scale bar: 10 μm . ( B ) Antibody staining of w1118 control and drep-2ex13 mutant brains , using antibodies against the postsynaptic ACh receptor subunit Dα7 and presynaptic BrpN−Term . Focus on microglomeruli of PN-KC synapses in the MB calyx . Microglomeruli of mutants appear structurally normal . Scale bar: 1 µm . ( C ) Electron microscopy of w1118 control and drep-2ex13 mutant brains . Microglomeruli and postsynaptic KC profiles of mutants appear structurally normal . Scale bars: 100 nm . ( D ) The number of synapses ( active zones ) in the MB calyx does not significantly differ between drep-2ex13 mutants and w1118 controls . Syd-1-positive spots were counted and compared between genotypes as described ( Kremer et al . , 2010 ) . No significant difference was found between the number of spots ( MWU , p = 0 . 62 ) . Average synapse counts were in the range expected ( 28 , 000–30 , 000 [Kremer et al . , 2010] ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03895 . 011 Mutants lacking drep-2 showed normal naïve sensory acuity , with innate olfactory responses and shock avoidance behavior not significantly different from controls ( Figure 5A ) . However , the STM performance scores of both mutants ( drep2ex13 and drep2ex27 ) were significantly lower than the scores of isogenic w1118 controls ( Figure 5B , C ) . Re-expression of drep-2 cDNA by using either the pan-neural driver elavIII-Gal4 or two different KC-specific drivers ( 30y-Gal4 and mb247-Gal4 ) was sufficient to rescue STM scores ( Figure 5C ) . Drep-2 is , therefore , required in KCs for normal olfactory STM . ITM is composed of two memory components , consolidated anesthesia-resistant memory ( ARM ) and labile anesthesia-sensitive memory ( ASM ) ( Quinn and Dudai , 1976; Tully et al . , 1994 ) . ASM and ARM rely on different molecular and neuronal mechanisms ( Folkers et al . , 1993; Tully et al . , 1994; Scheunemann et al . , 2012 ) . Amnestic cooling abolishes the labile ASM and is , thus , used to separate both components . The drep-2 mutants exhibited regular ARM but were deficient in ASM ( Figure 5D ) , which was evidenced by memory scores not differing between cooled and untreated drep-2 mutants . This loss of ASM was rescued by re-expression of drep-2 using elavIII-Gal4 or KC-specific mb247-Gal4 . We conclude that Drep-2 is required in KCs for both STM and ASM , but not for ARM . Concurrently , morphologies of drep-2-mutant MBs and PN-KC synapses appeared normal ( Figure 5—figure supplement 1 ) . Furthermore , the number of synapses in the MB calyx did not differ between either genotype ( Figure 5—figure supplement 1D ) . Thus , drep-2-mutant phenotypes should not be caused by gross structural developmental aberrations . Instead , given the synaptic localization of the protein , it appeared likely that Drep-2 might intersect with signaling pathways involved in synaptic plasticity . PN-KC synapses , as is the case for many excitatory synapses in the Drosophila CNS , use ACh as the main fast neurotransmitter ( Gu and O'Dowd , 2006; Yasuyama et al . , 2002 ) . Drep-2 colocalized here with nicotinic ACh receptor subunits ( Figure 4 ) . In addition , several types of metabotropic receptor are also expressed in the calyx , including GABAB , dopamine , octopamine , and metabotropic glutamate receptors ( Enell et al . , 2007; Devaud et al . , 2008; Busch et al . , 2009; Mao and Davis , 2009; Kanellopoulos et al . , 2012 ) . DmGluRA is the single functional metabotropic glutamate receptor ( mGluR ) in Drosophila ( Parmentier et al . , 1996 ) , orthologous to mammalian group II/III mGluRs . DmGluRA and the mGluR-associated protein Homer show a characteristic expression throughout the Drosophila brain , with strong expression in the MB calyx ( Ramaekers et al . , 2001; Diagana et al . , 2002; Hamasaka et al . , 2007; Urizar et al . , 2007; Devaud et al . , 2008; Kahsai et al . , 2012; Kanellopoulos et al . , 2012 ) . We examined different receptor types and related proteins for colocalization with Drep-2 ( not shown ) . Drep-2 colocalized with both mGluR and Homer throughout the brain ( not shown ) , with very prominent co-labeling in the MB calyx ( Figure 6A ) . This made mGluR signaling a prime candidate for a pathway interacting with Drep-2 function . 10 . 7554/eLife . 03895 . 012Figure 6 . Functional overlap between Drep-2 and mGluR in olfactory conditioning . ( A ) Wildtype adult MB calyces stained with Drep-2C-Term and DmGluRA7G11 ( first row ) or with Drep-2C-Term and anti-Homer ( second row ) . Drep-2 colocalizes tightly with both proteins . The insets show single microglomeruli . Scale bars: 2 µm . ( B ) Flies carrying the mutation dmGluRA112b are deficient in aversive olfactory conditioning STM when compared to isogenic dmGluRA2b controls that do express DmGluRA; MWU: p = 0 . 043 , α = 0 . 05 . The graph shows mean learning indices and SEMs; sample sizes n are indicated with white numbers . ( C ) The drep-2ex13 phenotype in olfactory STM can be rescued by raising animals on food containing the DmGluRA agonist 1S , 3R-ACPD ( ACPD ) . Food was supplemented throughout development and adulthood with either the DmGluRA receptor antagonist MPEP ( 9 . 7 µM ) or the agonist ACPD ( 72 . 2 µM ) diluted in H2O ( label: dev+ad ) . Control animals received only H2O . One group of animals was transferred to food supplemented by ACPD only after eclosion and not during development; the corresponding experiments are indicated by the label +ACPD adult . MPEP lowered the w1118 performance significantly ( MWU p = 0 . 0003 ) . MPEP did not alter drep-2ex13 indices ( p = 0 . 8772 ) and ACPD did not change w1118 performance ( p = 0 . 1145 ) . ACPD rescued the drep-2 mutant phenotype to control levels if fed during both development and adulthood ( comparison of drep-2ex13 +ACPD dev+ad to untreated drep-2ex13: p < 0 . 00001; comparison to untreated w1118: p = 0 . 0945 ) . ACPD did not rescue the mutant phenotype if fed only during adulthood ( +ACPD adult , no significant difference to untreated drep-2ex13 ( p = 0 . 2281 ) , significant difference to mutants treated with ACPD during both development and adulthood ( p < 0 . 00001 ) ) . The difference between untreated w1118 and drep-2ex13 flies was also significant ( p < 0 . 00001 ) . Significance level α = 0 . 005 ( 10 tests ) . ( D ) Phenotypes of drep-2ex13; dmGluRA112b double mutants were non-additive . Both drep-2ex13 and dmGluRA112b single mutants showed significantly lower olfactory STM than isogenic controls ( MWU , p = 0 . 00008 for both comparisons ) . Double mutants showed similar learning indices ( comparison to w1118: p = 0 . 00018 ) . The two single mutants and the double mutant did not significantly differ from each other ( p > 0 . 178 ) . α = 0 . 0083 ( 6 tests ) . ( E ) Loss of drep-2 antagonizes dfmr1 phenotypes in olfactory conditioning STM . Both homozygous drep-2ex13 mutants and heterozygous dfmr1B55/+ mutants are deficient in olfactory learning STM , but double mutants carrying both alleles do learn . The graph shows mean learning indices and SEMs . MWU for individual comparisons ( α = 0 . 01 , 5 tests ) : w1118 and drep-2ex13 p < 0 . 00001 , w1118 and dfmr1B55/+ p = 0 . 00069 , w1118 and drep-2ex13; dfmr1B55/+ p = 0 . 83751 , drep-2ex13 and drep-2ex13; dfmr1B55/+ p < 0 . 00001 , dfmr1B55/+ and drep-2ex13; dfmr1B55/+ p = 0 . 00071 . DOI: http://dx . doi . org/10 . 7554/eLife . 03895 . 012 Recently , mGluRs have been found to be important for olfactory conditioning in flies . In that work , a decrease of mGluR levels provoked by RNA interference improved performance scores of the olfactory learning mutant dfmr1 ( Kanellopoulos et al . , 2012 ) . The same effect was observed upon administration of the mGluR antagonist 2-methyl-6- ( phenylethynyl ) pyridine ( MPEP ) . We assayed the STM performance of the mGluR mutant dmGluRA112b to independently confirm a function of mGluRs in olfactory learning ( Bogdanik et al . , 2004 ) . Indeed , a significant reduction in learning scores was observed in these mutants ( Figure 6B ) . In addition to the mGluR antagonist MPEP ( McBride et al . , 2005; Bolduc et al . , 2008; Tauber et al . , 2011; Kanellopoulos et al . , 2012 ) , the agonist 1S , 3R-1-amino-1 , 3-cyclopentanedicarboxylate ( ACPD ) has also been shown to be effective in flies ( Parmentier et al . , 1996; Hamasaka et al . , 2007 ) . We , therefore , tested the effects of both MPEP and ACPD on olfactory learning scores of drep-2 mutants ( Figure 6C ) . To this end , we raised flies on food containing either of the two components throughout development and adulthood . In agreement with our result for dmGluRA mutants ( Figure 6B ) , the antagonist ( MPEP ) significantly decreased learning scores of wild-type flies when fed throughout development ( Figure 6C ) . By contrast , MPEP did not further reduce the learning ability of drep-2 mutants . However , feeding the mGluR agonist ACPD during development effectively rescued the drep-2ex13 phenotype ( Figure 6C ( +ACPD dev+ad ) ) . At the same time , feeding the agonist to controls did not alter their learning scores . When ACPD was fed only to adult animals after eclosion , it had no discernible effect on the performance of drep-2 mutants ( Figure 6C ( +ACPD adult ) ) . In summary , artificial activation of mGluR receptors starting during development can compensate for the olfactory learning deficits of drep-2 mutants . In order to further investigate a potential relationship between Drep-2 and mGluRs , we produced drep-2; dmGluRA double mutants . These double mutants showed learning indices that were very similar to the scores of both single mutants ( Figure 6D ) . As the learning deficits of both mutants did not add up to a stronger impairment in double mutants , it is likely that both proteins converge , at least partially , into a common regulatory pathway . Activation of mGluR signaling is thought to stimulate local synaptic translation through a signaling cascade involving Homer . By contrast , the RNA-binding fragile X mental retardation protein FMRP was shown to repress translation , thereby counteracting mGluR-mediated synaptic translation ( Bhakar et al . , 2012 ) . Loss of FMRP function causes fragile X syndrome ( FXS ) , the most frequent monogenic intellectual disorder . Pharmacological treatment with allosteric inhibitors of mGluRA was demonstrated to attenuate phenotypic deficits in rodent models of FXS and in FXS patients ( Krueger and Bear , 2011; Bhakar et al . , 2012; Gross et al . , 2012 ) . In Drosophila , FMRP function and mGluR signaling also behave antagonistically . Importantly , learning phenotypes of dfmr1 mutants lacking FMRP can be rescued by pharmacological inhibition of mGluRs ( McBride et al . , 2005; Bolduc et al . , 2008; Tauber et al . , 2011; Kanellopoulos et al . , 2012 ) . By contrast , we describe here that drep-2 mutants profited from pharmacological stimulation of mGluRs ( Figure 6C ) . We wondered , therefore , whether drep-2 and dfmr1 mutants would behave antagonistically . To this end , we generated drep-2; dfmr1 double mutants . Single and double dfmr1 mutants lacking both copies of FMRP ( dfmr1B55/dfmr1Δ50M ) only hatched in small numbers , insufficient for olfactory conditioning experiments . However , Kanellopoulos et al . ( 2012 ) have shown that heterozygous dfmr1 mutants are also deficient in olfactory learning . We , therefore , compared homozygous drep-2 single mutants to heterozygous dfmr1B55/+ single and drep-2ex13/drep-2ex13; dfmr1B55/+ double mutants regarding aversive olfactory conditioning performance . Both single mutants showed decreased olfactory learning ( Figure 6E ) , confirming the published olfactory learning phenotype of dfmr1/+ heterozygotes . Notably , however , performance of drep-2; dfmr1/+ double mutants was indistinguishable from the controls ( Figure 6E ) , despite the deficit of both single mutants . This suggests that the absence of Drep-2 functionally compensates for the loss of FMRP . Our experiments thus provide first evidence that Drep-2 and FMRP display a functional antagonism . Finally , we began exploring the molecular basis of the behavioral connections between Drep-2 and both mGluR signaling and FMRP . We first examined whether Drep-2 might regulate the protein levels or localization of either mGluR or Homer . However , mGluR and Homer levels appeared unaltered in drep-2 mutants ( not shown ) . Therefore , it appeared more likely for Drep-2 to intersect with signaling processes downstream of the mere metabotropic glutamate receptor complex . We used quantitative affinity purification in combination with mass spectrometry to learn about Drep-2 in vivo interaction partners ( Vermeulen et al . , 2008; Paul et al . , 2011 ) . Unfortunately , the Drep-2C-Term antibody did not precipitate the endogenous Drep-2 protein sufficiently to allow for such an analysis . Thus , a Drep-2GFP fusion protein showing a localization pattern identical to endogenous Drep-2 ( Figure 7—figure supplement 1 ) was expressed pan-neurally using elavc155-Gal4 . We isolated protein complexes of this pan-neurally overexpressed Drep-2GFP from fly heads using anti-GFP beads ( Figure 7 ) . Parallel pulldowns , using either plain beads or Drep-2GFP-negative lysates , were performed as controls for nonspecific binding ( Figure 7A ) . All three pulldowns were conducted in triplicate and processed and analyzed by high-resolution shotgun proteomics . Proteins were quantified by label-free quantification and specific interaction partners were extracted using t-test statistics ( Hubner et al . , 2010 ) . 10 . 7554/eLife . 03895 . 013Figure 7 . Quantitative mass spectrometry: Drep-2 and FMRP were found in a common protein complex . ( A ) Strategy for the identification of Drep-2 interactors by quantitative mass spectrometry . UAS-Drep-2GFP was overexpressed using the pan-neural driver line elavc155-Gal4 . ( B ) Volcano plot showing proteins from Drep-2GFP flies binding to anti-GFP and/or plain control beads . A hyperbolic curve ( set at an FDR of 1% ) separates GFP-enriched proteins ( light pink ) from background ( grey ) . Proteins enriched in the control are shown in blue . Proteins that were significantly enriched , both in Drep-2GFP flies and in independent control experiments with wild-type flies , are colored magenta ( n = 35 ) . Drep-2 and GFP are shown as green dots . ( C ) Classification of the 35 core network proteins; multiple counts were allowed . ( D ) Network of the 35 proteins that were significantly and reproducibly enriched in GFP pulldown experiments ( at an FDR of 1% , magenta-colored dots in B ) . Additional putative interactors of the core network ( FDR set at 10% ) are shown in white ( Supplementary file 2 ) . The circle ( node ) and font size correspond to the rank within the results ( indicated in Supplementary files 1 and 2 ) . The line ( edge ) width and shade correspond to the number of interactions each of the significantly enriched proteins has with others . The line/edge length is arbitrary . ( E ) Anti-FMRP probing confirmed the specific presence of FMRP in Drep-2GFP complexes . Head extracts of flies expressing Drep-2GFP or the presynaptic protein Syd-1GFP were processed in parallel . FMRP was only enriched in preparations of Drep-2GFP extracts . Immunoprecipitations were performed using either GFP-Trap-A beads ( lanes labeled IP ) or blocked agarose beads as binding control ( labeled Blank beads ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03895 . 01310 . 7554/eLife . 03895 . 014Figure 7—figure supplement 1 . Drep-2GFP colocalizes with endogenous Drep-2 . ( A ) Pan-neural overexpression of UAS-drep-2GFP by elavc155-Gal4 . MB calyx stained with anti-GFP , BrpN-Term and Drep-2C-Term . The Drep-2GFP label does not differ from the Drep-2C-Term antibody staining , compare also to Figure 4 and Figure 4—figure supplement 1 . Scale bars: 10 µm and 1 µm ( insets ) . ( B ) KC-specific expression of UAS-drep-2GFP by mb247-Gal4 in drep-2ex13 mutants . Staining and scale bars as in ( A ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03895 . 014 On top of the bait proteins Drep-2 and GFP ( the latter because the fusion protein Drep-2GFP was expressed ) , 35 proteins were robustly enriched over both controls at a false-positive discovery rate ( FDR ) of 1% ( Figure 7B , Supplementary file 1 ) . In order to visualize which of these 35 core proteins are part of a larger grid of putative interactors , an extended protein network was generated ( Figure 7D ) . Proteins that DroID ( Murali et al . , 2011 ) or Flybase ( McQuilton et al . , 2012 ) lists as ( putative ) interactors of any of the 35 core proteins ( FDR 1% ) were added if they fitted the following three conditions: ( i ) enriched in the pulldowns ( elavc155; uas-drep-2GFP flies , GFP beads vs plain beads ) at an FDR of 10% , ( ii ) not enriched in the control experiment ( GFP beads , wild-type flies vs elavc155; uas-drep-2GFP flies ) at an FDR of 10% ( to eliminate false-positives ) , and ( iii ) a ( predicted ) interaction with at least two of the 35 core proteins ( Supplementary file 2 ) . Among the proteins found to be significantly enriched were Drep-2 , GFP , and Drep-3 , a cognate binding partner of Drep-2 ( Inohara and Nuñez , 1999; Park and Park , 2012 ) . Thus , we were successful in precipitating proteins interacting with Drep-2 and not merely peptides binding unspecifically . 14 of the 35 putative core interacting proteins are associated with membranes ( Figure 7C , Supplementary file 1 ) , consistent with our observation that Drep-2 localizes to the postsynaptic plasma membrane . However , neither DmGluRA nor Homer could be identified in the preparation . Both mGluR and FMRP regulate local synaptic translation ( Bhakar et al . , 2012 ) . Interestingly , 10 of the 35 proteins have been implicated in the control of mRNA translation and stability . Network analysis of putative interacting proteins ( Figure 7D ) underlined a strong connection of Drep-2 with RNA-associated proteins as well . Among them , several proteins regulating translation were found: for example , the eIF4G-related , cap-independent translation initiation factor NAT1/p97/DAP5 ( Levy-Strumpf et al . , 1997; Hundsdoerfer et al . , 2005 ) and Caprin , a dendritic translational repressor ( Shiina et al . , 2005 ) . Notably , Argonaute-2 , involved in RNA interference ( Ketting , 2011 ) , was also among the most highly enriched proteins ( Supplementary files 1 and 2 ) . It is interesting in this context that both Caprin and Argonaute-2 bind to FMRP ( Ishizuka et al . , 2002; Papoulas et al . , 2010 ) . Finally , FMRP was identified within Drep-2 complexes at an FDR cutoff of 5% ( Supplementary file 2 ) . In fact , we could directly confirm the presence of FMRP in Drep-2GFP immunoprecipitates by immunoblotting ( Figure 7E ) . FMRP , by contrast , could not be detected in an identically treated control experiment conducted in parallel , in which we precipitated the presynaptic protein Syd-1GFP ( Owald et al . , 2010 ) ( Figure 7E ) . Thus , FMRP appears to be present specifically within Drep-2GFP complexes . Since Drep-2 could be detected in protein complexes containing FMRP , translational control processes constitute a possible place of action of Drep-2 . However , further mechanistic analysis will have to work out the details of the regulatory functions executed by the novel synaptic protein Drep-2 .
We here identify the CIDE-N family protein Drep-2 as a novel synaptic protein expressed in the Drosophila CNS ( Figure 2 ) and important for learning and memory ( Figure 5 ) . Loss of Drep-2 did not cause transmission deficits at neuromuscular junctions and photoreceptor synapses ( not shown ) and we did not observe any structural deficits at synapses ( Figure 5—figure supplement 1 ) . Thus , Drep-2 is most likely less important for maintaining either base-line transmission or fundamental synaptic architecture . Instead , it might be involved in the regulation of synaptic signaling and plasticity . Expression of Drep-2 is particularly strong at postsynaptic densities ( PSDs ) of synapses between projection neurons ( PNs ) and Kenyon cells ( KCs ) in the mushroom body ( MB ) calyx ( Figure 4 ) . At these synapses , acetylcholine is released from PNs upon transmission of odor signals ( Busto et al . , 2010; Gu and O'Dowd , 2006 ) . While elimination of Drep-2 in the whole animal severely impaired olfactory short-term and anesthesia-sensitive intermediate-term memory ( Figure 5 ) , re-expression of Drep-2 restricted to KCs was sufficient to fully rescue these learning deficits . As sensation of electric shock , the unconditioned stimulus in aversive olfactory learning , is mediated via dopamine in the MB lobes ( Aso et al . , 2012 ) , Drep-2 most likely plays its role at PN::KC synapses during reception of the odor , the conditioned stimulus . Several types of metabotropic receptors are expressed in the calyx: GABAB , dopamine , octopamine , and metabotropic glutamate receptors ( Enell et al . , 2007; Devaud et al . , 2008; Busch et al . , 2009; Mao and Davis , 2009; Kanellopoulos et al . , 2012 ) . Which exact role this metabotropic signaling plays in synaptic plasticity processes at PN::KC synapses is essentially unknown . Motivated by the close match between Drep-2 and DmGluRA localization on the level of individual PSDs of the PN-KC synapse ( Figure 6A ) , we started to address a potential functional connection between Drep-2 and DmGluRA-dependent signaling and behavior . We observed that the learning deficits of dmGluRA and drep-2 single mutants did not add up to a stronger phenotype in double mutants ( Figure 6D ) . Moreover , the drep-2 memory deficit was effectively rescued by pharmacological stimulation of DmGluRA ( Figure 6C ) . Drep-2 might , therefore , affect plasticity processes downstream of DmGluRA . In this pathway , the protein could be required for extracting relevant aspects of the olfactory information ( odor discrimination vs generalization ) and thereby influence subsequent learning . Interestingly , preliminary experiments indicate that the learning deficits of drep-2 mutants might involve decreased Ca2+ responses in KCs ( not shown ) . How might Drep-2 interfere with metabotropic signaling ? We were unable to biochemically detect DmGluRA in complexes containing Drep-2 , making a direct interaction between both proteins unlikely . Neither could we find any indication of an influence of Drep-2 on mGluR protein levels or localization ( not shown ) . However , we did confirm the presence of the fragile X mental retardation protein FMRP in complexes containing Drep-2 ( Figure 7E ) . In addition to FMRP , several other additional RNA-associated proteins and translational regulators were found by quantitative affinity purification experiments of Drep-2GFP complexes , followed by mass spectrometry-based protein identification and quantification ( Figure 7C , D , Supplementary files 1 and 2 ) . It is , therefore , a probable scenario that Drep-2 indirectly regulates local synaptic protein synthesis . Several studies have demonstrated that FMRP antagonizes mGluR-mediated synaptic translation ( Bhakar et al . , 2012 ) . Notably , we found evidence of a functional antagonism between Drep-2 and FMRP-mediated plasticity: both drep-2 and heterozygous dfmr1 single mutants were deficient in olfactory conditioning , but drep-2ex13/drep-2ex13; dfmr1B55/+ double mutants showed normal performance ( Figure 6E ) . Thus , Drep-2 might be required downstream of mGluR signaling , probably in the context of synaptic translation , counteracting translational repression executed by FMRP . We observed that only chronic activation of mGluRs starting during development could improve the learning scores of drep-2 mutants ( Figure 6C ) . This result could be explained by chronic misregulation of neuronal translation in the mutants , rendering the synapses insensitive to enhanced mGluR signaling later in life . At first glance , it might appear surprising that drep-2ex13/drep-2ex13 and dfmr1B55/+ mutants cancel each other out . However , evidence for a tightly balanced control over synaptic translation has been provided: mutations in the gene tsc2 cause tuberous sclerosis , a disease phenotypically similar to fragile X syndrome ( FXS ) . Impaired long-term depression ( LTD ) in tsc2-mutant mice could be rescued by the application of mGluR agonists ( Auerbach et al . , 2011 ) . Moreover , murine fmr1 mutants showed exaggerated LTD , while the double mutant exhibited normal LTD . This demonstrated that synaptic proteins , if misregulated by either impaired or excessive mGluR-induced translation , impede appropriate LTD . In this manner , misregulation of opposing effectors can cause similar phenotypes , as is the case for the olfactory learning performance of drep-2 , dmGluRA , and dfmr1 mutants . Fragile X associated tremor/ataxia syndrome ( FXTAS ) is a late-onset neurodegenerative disorder occurring in carriers of fragile X premutation repeats which is distinct from FXS . The presence of fragile X rCGG premutation repeats in flies activates the microRNA miR-277 , which causes neurodegeneration ( Tan et al . , 2012 ) . One of the targets negatively regulated by miR-277 is Drep-2 . Notably , a putative drep-2 hypomorph was shown to enhance the FXTAS neurodegenerative phenotype . While Drep-2 function per se was not investigated in this study and the mechanistic details remain rather elusive , this independent connection between Drep-2 and a scenario related to FXS is also suggestive . Additional experiments will be required to both examine whether Drep-2 plays a role during translational regulation and to further explore its relationship to metabotropic signaling . Nevertheless , based on our findings , a function of Drep-2 in regulating mGluR-mediated local translation is a possible scenario , which is now open for further investigation . In mammals , Dff-related CIDE proteins have , up to now , mainly been studied in fat tissue ( Yonezawa et al . , 2011 ) . However , CIDEc is highly expressed in the mammalian brain ( Li et al . , 2009 ) . It is now an intriguing possibility that Dff family proteins might play non-apoptotic neuronal and synaptic roles in mammals as well .
All fly strains were reared under standard laboratory conditions ( Sigrist et al . , 2003 ) at 25°C and 65–70% humidity , with a constant 12/12 hr light/dark regimen . Flies were fed standard semi-defined cornmeal/molasses medium . Bloomington stock collection strain #5905 , w1118 , was used as background for both the generation of transgenic animals ( Bestgene , Inc . , Chino Hills , CA ) and for behavioral assays . The following fly stocks were used: 30y-Gal4 ( Yang et al . , 1995 ) , c305a-Gal4 ( Krashes et al . , 2007 ) , elavc155-Gal4 ( Lin and Goodman , 1994 ) , elavL3-Gal4 ( elavIII-Gal4 ) ( Luo et al . , 1994 ) , gh146-Gal4 ( Stocker et al . , 1997 ) , mb247-Gal4 ( Zars et al . , 2000 ) , mb247::Dα7GFP ( Kremer et al . , 2010 ) , UAS-Dα7GFP ( Leiss et al . , 2009b ) , Dfw45−30n ( Bloomington stock #4966 ) , dmGluRA112b and its control dmGluRA2b ( Bogdanik et al . , 2004 ) , fmr1B55 ( Inoue et al . , 2002 ) , and w1118 ( Hazelrigg et al . , 1984 ) . All flies used for behavioral experiments were outcrossed to w1118 for more than five generations in order to generate an isogenic genetic background . Drep-2ex13 mutants were generated using FLP–FRT recombination between the two stocks drep-2d00223 and drep-2e04659 , as previously described ( Parks et al . , 2004 ) . Drep-2ex27 mutants were created in an analogous manner , using the transposon lines drep-2e02920 and drep-2e04659 . In short , one of the elements containing a FRT site was combined with a line expressing the FLP recombinase under a heat shock promoter . These flies were crossed with the strain containing the second element to place both FRT sites in trans . Expression of FLP recombinase was triggered by a heat shock to 37°C . Offspring were collected and mutant candidates were validated by genomic PCR ( forward primer: 5′-GCT GCT TGA GTA TGG GTG CA-3′; reverse primer: 5′-GGA GAC ATC CTC TCA AAG C-3′ ) . We generated transgenic flies expressing either plain drep-2 cDNA or eGFP- or mStrawberry-tagged drep-2 constructs , all under the UAS enhancer . The drep-2 cDNA LD32009 was amplified using the forward primer 5′-CAT GCC ATG GCA ATG GCC AGA GAG GAG TCT CGC-3′ and the reverse primer 5′-CGG GGT ACC AAT TCT GTC CTC CTC ATC CTC TTC C-3′ . The amplicon was inserted into the pEnter vector using NcoI and KpnI restriction sites . Invitrogen gateway cloning was used to create the expression constructs from pEnter . The vectors pTWG and pTGW ( Carnegie Institution of Washington , Washington , DC ) were used for generation of eGFP constructs; eGFP was replaced by mStrawberry by PCR for mStrawberry constructs . Lack of dmGluRA in drep-2ex13; dmGluRA112b double mutants was validated by single-fly PCR . The sequence of the primers for this PCR was as follows: forward primer: 5′-GGT GCC CCT TGC GGA CCA AA-3′; reverse primer: 5′- TTG TCG TCT GCG GCA CTG GG-3′ . Lack of drep-2 was confirmed by stainings . In order to assay the life span , male flies were placed in groups of 25 animals in small food vials and transferred to fresh vials at least twice a week . Flies were kept at standard conditions . After each transfer , the number of dead and remaining live flies was counted . The number of days for each vial was determined at which 50% of flies were dead . All experiments were conducted with three- to 5-day-old animals and carried out in a w1118 genetic background . Flies were raised at 24°C and 60% relative humidity with a 14/10 hr light–dark cycle on cornmeal-based food prepared according to the Würzburg recipe ( Guo et al . , 1996 ) . Flies were transferred to fresh food vials for up to 48 hr before behavioral experiments . Behavioral experiments were performed in dim red light at 70% relative humidity with 3-octanol ( 1:150 dilution in mineral oil presented in a 14 mm cup ) and 4-methyl-cyclohexanol ( 1:100 dilution in mineral oil presented in a 14 mm cup ) serving as olfactory cues and 120V AC current serving as a behavioral reinforcer . Associative training was carried out following the single-cycle training procedure previously described ( Tully and Quinn , 1985 ) . Electric foot shock was applied after 10 s of odor presentation; afterwards twelve shock/odor pairings were conducted within 50 s . Odors and electric shock were applied in the same manner during conditioning as when testing for sensory acuity . STM was tested immediately after the end of the training session , 3 min after the onset of training . Performance of ITM and ARM was determined 3 hr after training; flies were transferred to neutral containers without food for the resting period . Two groups of flies were separately trained for separation of consolidated ARM and labile ASM , and one group was cooled in an ice-bath ( 0°C ) for 90 s , 2 . 5 hr after training . Odor memory of this group was tested after a 30-min recovery period , that is , 3 hr after onset of training . Since labile ASM is erased by this procedure , performance of the cooled group is solely due to ARM . Pharmaceutical components ( MPEP ( ab120008 , Abcam , Cambridge , MA ) and 1S , 3R-ACPD ( #0284 , Tocris Bioscience , Bristol , United Kingdom ) ) were supplemented to liquid fly food , as previously described ( McBride et al . , 2005; Tauber et al . , 2011 ) . 1S , 3R-ACPD was used in a concentration of 72 . 2 µM and MPEP at 9 . 7 µM ( Parmentier et al . , 1996; McBride et al . , 2005 , 2010 ) . Both compounds were diluted in H2O . The same amount of H2O lacking any additional compounds was added to the control food . Flies were either raised throughout their entire development and adulthood on this food or , where indicated , were raised on control food lacking ACPD and only transferred to food containing ACPD after eclosion . Calculation of behavioral indices was carried out as previously published ( Tully and Quinn , 1985 ) . ASM can be calculated by subtracting the performance of the cooled group from an uncooled group . Non-parametric tests ( Mann–Whitney U test or Kruskal–Wallis ) were used because of the small sample sizes . The significance level α was set to 5% . Asterisks are used to indicate significance in figures ( * = p < 0 . 05; ** = p < 0 . 01; *** = p < 0 . 001; ns = p ≥ 0 . 05 ) . If several genotypes were compared , α and * symbols were adjusted by dividing the significance level by the number of comparisons ( Bonferroni correction ) . Experimental data were analyzed using Microsoft Office 2011 and OriginLab ( Northampton , MA ) Origin Pro 9 . 0 . Graphs were created using Gnuplot v4 . 6 ( http://www . gnuplot . info ) and Adobe Illustrator CS4 . In situ hybridizations of whole mount embryos were performed as described by the Berkeley Drosophila Genome Project ( http://www . fruitfly . org ) . The plasmid LD32009 was cut using BamHI and in vitro transcribed using Sp6 RNA polymerase to prepare antisense RNA probes . The plasmid was cut with SmaI and transcribed with T7 RNA polymerase to prepare sense probes . The drep-2 cDNA LD32009 was amplified using the forward primer 5′-GAC CGT CGA CGT GGG TGT GGG AGC TGT CCA-3′ and the reverse primer 5′-GAC CCT CGA GTG AAT TCT GTC CTC CTC ATC CTC-3′ . The amplicon was inserted into the pENTR4 vector ( Invitrogen , Life Technologies , Carlsbad , CA ) using SalI and XhoI restriction sites . Invitrogen gateway cloning was used to create a 6xHis-tagged construct in pDEST17 ( Invitrogen ) . A rabbit serum against this 6xHis-tagged C-terminal Drep-2 fusion protein ( amino acids 252-483 of Drep-2-PA ) was produced ( Seqlab , Göttingen , Germany ) and affinity-purified with the same fusion protein . Antibody concentrations were as follows: mouse anti-BrpNc82 ( Wagh et al . , 2006 ) 1:100 , guinea pig anti-BrpN-Term 1:800 , mouse anti-ChAT4B1 ( Yasuyama and Salvaterra , 1999 ) 1:1000 , rat anti-Dα7 ( Fayyazuddin et al . , 2006 ) 1:2000 , mouse anti-Dlg4F3 ( Parnas et al . , 2001 ) 1:500 , mouse anti-DmGluRA7G11 ( Panneels et al . , 2003 ) 1:100 , rabbit anti-Drep-2C-Term 1:500 , mouse anti-Fasciclin-II1D4 ( Lin and Goodman , 1994 ) 1:50 , mouse anti-FMRP5A11 ( Okamura et al . , 2004 ) 1:100 , mouse anti-GFP3E6 ( Molecular Probes , Life Technologies ) 1:500 , rabbit anti-GFP ( A11122 , Life Technologies ) 1:1000 , guinea pig anti-Homer ( Diagana et al . , 2002 ) 1:200 , rabbit anti-Syd-1 ( Owald et al . , 2010 ) 1:500 , mouse anti-α-TubulinDM1A ( Sigma-Aldrich , St . Louis , MO ) 1:100000 , goat anti-mouse Alexa 488 ( A11001 , Invitrogen ) 1:500 , goat anti-rabbit Cy3 ( 111-167-003 , Dianova , Hamburg , Germany ) 1:500 , goat anti-guinea pig Cy3 ( 106-166-003 , Dianova ) 1:500 , donkey anti-rat Cy3 ( 712-165-153 , Dianova ) 1:250 , goat anti-rabbit Atto 647N ( 40839 , Sigma-Aldrich ) 1:200 , and goat anti-rabbit HRP ( 111-035-144 , Dianova ) 1:5000 . Adult brains were dissected in ice-cold hemolymph-like saline ( HL3 ) solution , fixed for 20 min in 4% paraformaldehyde ( PFA ) in 1x phosphate-buffered saline ( PBS ) , pH 7 . 2 , and then blocked in 5% normal goat serum ( NGS ) in PBS with 0 . 3% Triton X-100 ( PBT ) for 30 min . The brains were incubated with primary antibodies together with 5% NGS for 48 hr at room temperature ( RT ) and then washed in PBT for 3 hr , followed by overnight incubation with secondary antibodies at RT . The brains were then washed for 3 hr with PBT and mounted in VectaShield ( Vector Laboratories , Burlingame , CA ) on slides . 3- to 7-day-old female flies were used for dissections . Conventional confocal images were acquired at 21°C with a Leica Microsystems ( Wetzlar , Germany ) TCS SP5 confocal microscope using a 63× , 1 . 4 NA oil objective for detailed scans and a 20× , 0 . 7 NA oil objective for overview scans . Lateral pixel size was set to values around 90 nm for detailed scans . Exact values varied , depending on the situation . Typically , 1024 × 1024 images were scanned at 100 Hz using 4× line averaging . All images were acquired using the Leica LCS AF software . Confocal stacks were processed using ImageJ software ( http://rsbweb . nih . gov/ij ) . Deconvolution of images was conducted using MediaCybernetics ( Rockville , MD ) AutoQuant X2 . 1 . 1 . Contrast was adapted for visualization , where necessary , using the levels tool in Adobe Photoshop CS4 . Images shown in a comparison or quantified were processed with exactly the same parameters . Images were not post-processed before quantification , but exclusively afterwards and only for visualization . Cell body and active zone counts were quantified similarly as described previously ( Kremer et al . , 2010; Christiansen et al . , 2011 ) ; the area of interest was segmented in ImageJ and then analyzed in Bitplane ( Zürich , Switzerland ) Imaris v6 . 23 using the surface tool . The cell body counts are comparable to the published number of 700 KCs in mb247-Gal4 ( Schwaerzel et al . , 2002 ) . Active zone numbers were assessed via an anti-Syd-1 staining; counts were similar to the published number of 28 , 000–30 , 000 synapses in the calyx ( Kremer et al . , 2010 ) . STED microscopy was performed using a Leica Microsystems TCS STED setup equipped with a 100× , 1 . 4 NA oil immersion STED objective , as previously described ( Waites et al . , 2011 ) . The depletion laser ( Mai Tai Ti:Sapphire; Spectra Physics , Newport , Santa Clara , CA ) was set to 760 nm . 1024 × 1024 STED images were scanned at 10 Hz using 2× line averaging . STED images were processed using linear deconvolution software integrated into the Imspector software ( Max-Planck-Innovation , München , Germany ) . Brains were dissected in HL3 solution and fixed for 20 min at RT with 4% paraformaldehyde and 0 . 2% glutaraldehyde in a buffer containing 50 mM sodium cacodylate and 50 mM NaCl at pH 7 . 5 . Afterwards , brains were washed twice in the buffer and dehydrated through a series of increasing alcohol concentrations . Samples were embedded in LR-Gold resin by incubation in ethanol/LR-Gold 1:1 solution overnight at 4°C , followed by ethanol/LR-Gold 1:5 solution for 4 hr at RT and , finally , 3× with LR-Gold/0 . 2% benzil once overnight , then for 4 hr and again overnight . Thereafter , the brains were placed in BEEM ( West Chester , PA ) capsules covered with LR-Gold/0 . 2% benzil resin and placed under a UV lamp at 4°C for 5 days to allow for polymerization of the resin . Following embedding , 70–80 nm sections were cut using a Leica Ultracut E ultramicrotome equipped with a 2-mm diamond knife . Sections were collected on 100 mesh nickel grids ( Plano , Wetzlar , Germany ) coated with 0 . 1% Pioloform resin and transferred to a buffer solution ( 20 mM Tris–HCl , 0 . 9% NaCl , pH 8 . 0 ) . Prior to staining , sections were blocked for 10 min with 0 . 04% BSA in buffer . Sections were incubated with the primary antibody in blocking solution overnight at 4°C . After washing 4× in buffer , sections were incubated in buffer containing the secondary antibody ( goat anti-rabbit 10 nm colloidal gold , BBI Solutions , Cardiff , United Kingdom , 1:100 ) for 2–3 hr at RT . Finally , the sections were washed 4× in buffer and 3x in distilled water . Contrast was enhanced by placing the grids in 2% uranyl acetate for 30 min , followed by 3× washing with water and , afterwards , incubation in lead citrate for 2 min . The grids were then washed 3× with water and dried . Images were acquired on a FEI ( Hillsboro , OR ) Tecnai Spirit , 120 kV transmission electron microscope equipped with a FEI 2K Eagle CCD camera . Fly head protein extraction was performed as follows: flies were decapitated and 20 heads of each genotype were sheared manually in 40 µl of 2% SDS aqueous solution using a micropistil fitting tightly into a 1 . 5-ml cup . An amount of 4 µl of a 10% Triton-X 100 aqueous solution and 40 µl of 2× sample buffer ( Laemmli , 1970 ) was added , and samples were heated at 95°C for 10 min . After centrifugation for 5 min at 16 , 000×g , in order to pellet the debris , 8 . 4 µl of the sample ( equivalent to two fly heads ) was subjected to denaturing SDS-PAGE using an 8% Tris-Cl gel . Proteins were transferred onto a nitrocellulose membrane , blocked with 5% skim-milk in 1× PBS supplemented with 0 . 1% Tween-20 and probed with affinity-purified rabbit anti-Drep-2C-Term ( #7183; 1:5000 ) diluted in 5% skim-milk in 1x PBS , supplemented with 0 . 1% Tween-20 , followed by washing steps . Secondary anti-rabbit IgG horseradish peroxidase ( HRP ) -conjugated antibodies ( Dianova ) and an enhanced chemoluminescence detection system ( RPN 2232 , GE Healthcare ( Little Chalfont , United Kingdom ) ECL Prime ) with Hyperfilm ECL ( GE Healthcare ) were used for detection . After NaN3 treatment , membranes were reprobed for α-Tubulin as a loading control , using the monoclonal antibody DM1A ( Sigma; 1:100000 ) . The Drosophila head fractionation protocol has recently been published ( Depner et al . , 2014 ) . It is based on protocols from mammalian subcellular preparations ( Huttner et al . , 1983; Ahmed et al . , 2013 ) . In brief , Drosophila wild-type heads were sheared mechanically in the absence of detergents and differential centrifugation was applied to separate particles according to their size and density . Analytical samples from the fractions obtained were taken and the protein concentration determined . An amount of 10 μg total protein from each fraction was subjected to SDS-PAGE ( 10% polyacrylamide Tris-Tricine gel ( Schägger , 2006 ) ) , followed by immunoblotting for Drep-2C-Term . HRP-conjugated goat anti-rabbit antibodies ( 111-035-144 , Dianova , 1:5000 ) were used for ECL detection . Signals were recorded using an ImageQuant LAS 4000 image reader ( GE Healthcare ) . Films were scanned in transmission mode ( Epson ( Long Beach , CA ) V770 scanner ) . The drep-2 cDNA LD32009 was fused to an N-terminal His-tagged maltose-binding protein for expression and purification of Drep-2 . Drep-2 was transformed in Escherichia coli BL21 Rosetta2 ( DE3 ) pLys cells ( NEB , Ipswich , MA ) . Drep-2 was cultured in TB medium at 37°C until an OD of ∼1 . 0 was reached and subsequently cooled down to 20°C . Protein expression was induced by the addition of 0 . 5 mM IPTG . Cells grew overnight and were harvested by centrifugation ( 6 min , 6000 rpm at 4°C ) . The Drep-2 pellet was resuspended in 20 mM Tris/HCl pH 7 . 4 , 250 mM NaCl , 8 mM imidazole , and 1 mM DTT . Cells were lysed by sonication at 4°C and the supernatant was cleared by 45 min centrifugation ( 21 , 500 rpm at 4°C ) . A Ni2+-NTA ( cv ∼1 ml; GE Healthcare ) was equilibrated with 20 mM Tris/HCl pH 7 . 4 , 250 mM NaCl , 1 mM MgCl2 , 8 mM imidazole , and 1 mM DTT . Drep-2 was loaded onto the column and washed with 3 cv of equilibration buffer . Drep-2 was eluted in a linear gradient to 20 mM Tris/HCl pH 7 . 5 , 250 mM NaCl , 400 mM imidazole , and 1 mM DTT . The maltose-binding protein was cleaved by TEV proteases , yielding untagged Drep-2 protein , during dialysis in 20 mM Tris/HCl pH 7 . 4 , 100 mM NaCl , and 1 mM DTT and loaded onto a MonoQ 10/100 column ( GE Healthcare ) equilibrated with 20 mM Tris/HCl pH 7 . 4 and 1 mM DTT . Drep-2 was eluted in a linear gradient from 100 to 1000 mM NaCl . Size exclusion chromatography was performed with a HighLoad Superdex S200 16/60 column ( GE Healthcare ) equilibrated with 20 mM Tris/HCl pH 7 . 5 , 250 mM NaCl , and 1 mM DTT . The actual nuclease activity assay was conducted as follows: an amount of 10 μg of Drep-2 was incubated with 0 . 3 μg linearized pUC19 plasmid DNA in 15-μl reaction buffer ( 20 mM Hepes-NaOH pH 7 . 4 , 50 mM NaCl , 5 mM MgCl2 ) at 37°C in limited digestion experiments . Aliquots were taken at different time intervals and the reaction was stopped by DNA loading dye containing 10 mM EDTA . Samples were electrophoretically separated over a 1% ( wt/vol ) agarose gel containing ethidium bromide . Drep-2 in vivo interaction partners were identified using affinity purification and mass spectrometry ( q-AP-MS; Figure 7A ) . The major challenge in such experiments is to distinguish true interaction partners from non-specific contaminants . Q-AP-MS can solve this problem by comparing the abundance of identified proteins with a control ( Vermeulen et al . , 2008; Paul et al . , 2011 ) . Drep-2GFP was expressed using elavc155-Gal4 . We purified Drep-2GFP from fly heads using a single chain anti-GFP antibody coupled to agarose beads . We performed parallel pulldowns on the same lysates using control agarose beads to control for unspecific binding . As an additional control , GFP-negative lysate from wild-type flies was included into the experiment . We identified a total of 3284 proteins in the pulldown experiments . 202 proteins were significantly enriched in GFP pulldowns compared to plain bead controls . 35 of these proteins could be confirmed in comparative analysis with GFP pulldowns of wild-type lysates and were , therefore , defined as robust interactors/core proteins ( permutation-based FDR = 1%; S0 = 1 , Figure 7B ) . Pulldown experiments for the quantitative affinity purification were conducted in the following manner: an amount of 500 µl of fly heads was immersed in liquid nitrogen and pulverized mechanically with a BioPulverizer ( Biospec Products , Bartlesville , OK ) . Powdered tissue was homogenized in 500 µl cold lysis buffer ( 50 mM Tris–HCl ( pH 7 . 6 ) , 150 mM NaCl , 1 mM MgCl2 , 1 mM EDTA , 10% glycerol , 0 . 4% DOC , and protease inhibitors ( Complete Mini , Roche Diagnostics , Indianapolis , IN ) ) . After incubation on ice for 30 min , 500 µl lysis buffer without DOC and Triton were added to a final concentration of 1% . Samples were centrifuged at 14 , 000×g for 15 min at 4°C to remove insoluble material . The supernatant was transferred to a fresh tube for pull-down experiments . Immunoprecipitations of GFP-tagged bait proteins were performed in triplicate using GFP-Trap agarose beads ( Chromotek , Planegg-Martinsried , Germany ) according to the manufacturer's instructions . Soluble protein fractions were incubated with either 25 μl of GFP-Trap or plain control beads for 60 min at 4°C under constant rotation . The beads were washed twice with washing buffer ( 50 mM Tris–HCl ( pH 7 . 6 ) , 150 mM NaCl , 1 mM MgCl2 , 1 mM EDTA , 10% glycerol ) and once with PBS . Proteins bound to the beads were eluted by applying 50 μl elution buffer ( 6 M urea/2 M thiourea ) twice and proceeded to in-solution digestion followed by LC-MS/MS analysis . Liquid chromatography MS/MS analysis was performed as follows: protein eluates were reduced for 30 min at RT in 10 mM dithiothreitol solution , followed by alkylation by 55 mM iodacetamide for 20 min in the dark at RT . The endoproteinase LysC ( Wako , Osaka , Japan ) was added following a protein:enzyme ratio of 50:1 and incubated for 4 hr at RT . After dilution of the sample with 4x digestion buffer ( 50 mM ammonium bi-carbonate in water ( pH 8 . 0 ) ) , sequence grade modified trypsin ( Promega , Madison , WI ) was added ( same protein:enzyme ratio as for LysC ) and digested overnight . Finally , trypsin and Lys-C activity was quenched by acidification of the reaction mixtures with TFA to pH ∼2 . Afterwards , peptides were extracted and desalted using StageTips ( Rappsilber et al . , 2003 ) . Peptide mixtures were separated by reversed phase chromatography using the EASY-nLC system ( Thermo Scientific , Waltham , MA ) on in-house manufactured 20 cm fritless silica microcolumns with an inner diameter of 75 µm . Columns were packed with ReproSil-Pur C18-AQ 3 µm resin ( Dr . Maisch GmbH , Ammerbuch-Entringen , Germany ) . Peptides were separated on an 8–60% acetonitrile gradient ( 214 min ) with 0 . 5% formic acid at a nanoflow rate of 200 nl/min . Eluting peptides were directly ionized by electrospray ionization and transferred into a Q Exactive mass spectrometer ( Thermo Scientific ) . Mass spectrometry was performed in the data-dependent positive mode with one full scan ( m/z range = 300-1700; R = 70 , 000; target value: 3 × 106; maximum injection time = 120 ms ) . The ten most intense ions with a charge state greater than one were selected ( R = 35 , 000 , target value = 5 x 105; isolation window = 4 m/z; maximum injection time = 120 ms ) . Dynamic exclusion for selected precursor ions was set to 30 s . MS/MS data were analyzed by MaxQuant software v1 . 2 . 2 . 5 as described ( Cox et al . , 2011 ) . The internal Andromeda search engine was used to search MS/MS spectra against a decoy D . melanogaster UniProt database ( DROME . 2016-06 ) containing forward and reverse sequences . The search included variable modifications of methionine oxidation and N-terminal acetylation , and fixed modification of carbamidomethyl cysteine . Minimal peptide length was set to six amino acids and a maximum of two missed cleavages was allowed . The FDR was set to 0 . 01 for peptide and protein identifications . If the identified peptide sequence set of one protein was equal to or contained another protein's peptide set , these two proteins were grouped together and the proteins were not counted as independent hits . Label-free quantification ( LFQ ) was performed in MaxQuant as described ( Hubner et al . , 2010 ) . Unique and razor peptides were considered for quantification with a minimum ratio count of 1 . Retention times were recalibrated based on the built-in nonlinear time-rescaling algorithm . MS/MS identifications were transferred between LC-MS/MS runs with the ‘Match between runs’ option , in which the maximal retention time window was set to 2 min . For every peptide , corresponding total signals from multiple runs were compared to determine peptide ratios . Median values of all peptide ratios of one protein then represent a robust estimate of the protein ratio . LFQ intensity values were logarithmized and missing values were imputed with random numbers from a normal distribution whose mean and standard deviation were chosen to best simulate low abundance values below the noise level ( width = 0 . 3; shift = 1 . 8 ) . GFP pull-down samples and plain-bead control samples were selected as individual groups of three technical replicates each; significantly enriched proteins were determined by a volcano plot-based strategy , combining standard two-sample t-test p-values with ratio information . Significance corresponding to an FDR of 1 , 5 , or 10% was determined by a permutation-based method ( Tusher et al . , 2001 ) . The network of biochemical interactions ( Figure 7D ) was created using Microsoft Excel 2011 , Cytoscape v2 . 8 . 3/v3 . 0 . 0 ( http://www . cytoscape . org ) and Adobe Illustrator CS4 , following the protocol available at http://protocols . andlauer . net/cytoscape . pdf . Flies of the genotype elavc155-Gal4; uas-drep-2GFP were used for preparations containing Drep-2GFP and flies of the genotype elavc155-Gal4; uas-syd-1GFP ( Owald et al . , 2010 ) were used for preparations containing Syd-1GFP . For each experiment , 500 µl of adult fly heads were mechanically homogenized in 500 µL lysis buffer ( 50 mM Tris pH 8 . 0 , 150 mM KCl , 1 mM MgCl2 , 1 mM EGTA , 10% glycerol-containing protease inhibitor cocktail ( Roche Diagnostics ) ) . Sodium deoxycholate ( 10% ) was added to achieve a final concentration of sodium deoxycholate of 0 . 4% and the lysate was incubated for 30 min on ice . The lysate was diluted 1:1 with sodium deoxycholate-free lysis buffer , then 10% Triton X-100 was added for a final concentration of 1% Triton X-100 , and the lysate was rotated at 4°C for 30 min . After centrifugation for 15 min at 16 , 000×g , the supernatant was used in immunoprecipitations with GFP-Trap-A beads and blocked agarose beads as binding control ( Chromotek ) . After incubation at 4°C overnight , the beads were washed in buffer without detergent and glycerol . Proteins were eluted from the beads with SDS sample buffer . The samples were separated by one-dimensional SDS-PAGE gradient gel ( TGX 4–12% precast , Bio-Rad , Hercules , CA ) . Proteins were transferred onto a nitrocellulose membrane and probed with mouse anti-FMRP5A11 ( 1:100 ) . A secondary anti-mouse IgG horseradish peroxidase ( HRP ) –conjugated antibody ( Dianova ) and an enhanced chemoluminescence ( ECL ) detection system with Hyperfilm ECL ( GE Healthcare ) were used for detection . After NaN3 treatment , the membranes were re-probed with rabbit anti-Drep2C-term ( 1:2000 ) and rabbit anti-GFP ( 1:1000 ) ( Life technologies , A11122 ) . Films were scanned in transmission mode ( Epson V770 ) and images were imported to Adobe Photoshop . | Synapses are specialized structures that connect nerve cells to one another and allow information to be transmitted between the cells . Synapses are essential for learning and storing memories . Many proteins that regulate how signals are transmitted at synapses have already been studied . In this manner , much has been learned about their function in learning and memory . Cells can commit suicide by a process called apoptosis , also known as programmed cell death . Apoptosis is not only triggered in damaged cells but is also necessary for an organism to develop correctly . In fruit flies , the protein Drep-2 is a member of a family of proteins that degrade the DNA of cells that undergo apoptosis . Andlauer et al . found no evidence that Drep-2 plays a role in apoptosis , but have now found Drep-2 at the synapses of the brain of the fruit fly Drosophila . Drep-2 could be observed in close proximity to another type of protein called metabotropic glutamate receptors . Metabotropic glutamate receptors and their signaling pathways are important for regulating certain changes to the synapses that mediate learning processes . Indeed , Andlauer et al . found that flies that have lost the gene that produces Drep-2 were unable to remember smells when these were paired with a punishment . Stimulating the regulatory glutamate receptors with drugs helped to overcome learning deficits that result from the lack of Drep-2 . Alterations in the production of a protein called FMRP cause fragile X syndrome in humans , the most common form of hereditary mental disability originating from a single gene defect . Flies lacking the FMRP protein show learning deficits that are very similar to the ones seen in flies that cannot produce Drep-2 . However , Andlauer et al . observed that flies lacking both Drep-2 and FMRP can learn normally . Exactly how Drep-2 works in synapses to help with memory formation remains to be discovered , although there are indications that it boosts the effects of signaling from the glutamate receptors and counteracts FMRP . Further research will be needed to establish whether the mammalian proteins related to Drep-2 perform similar roles in the brains of mammals . | [
"Abstract",
"Introduction",
"Results",
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"methods"
] | [
"neuroscience"
] | 2014 | Drep-2 is a novel synaptic protein important for learning and memory |
Apart from glucose , fatty acid-derived ketone bodies provide metabolic energy for the brain during fasting and neonatal development . We investigated the evolution of HMGCS2 , the key enzyme required for ketone body biosynthesis ( ketogenesis ) . Unexpectedly , we found that three mammalian lineages , comprising cetaceans ( dolphins and whales ) , elephants and mastodons , and Old World fruit bats have lost this gene . Remarkably , many of these species have exceptionally large brains and signs of intelligent behavior . While fruit bats are sensitive to starvation , cetaceans and elephants can still withstand periods of fasting . This suggests that alternative strategies to fuel large brains during fasting evolved repeatedly and reveals flexibility in mammalian energy metabolism . Furthermore , we show that HMGCS2 loss preceded brain size expansion in toothed whales and elephants . Thus , while ketogenesis was likely important for brain size expansion in modern humans , ketogenesis is not a universal precondition for the evolution of large mammalian brains .
Periods of fasting are a common event for many animals ( Secor and Carey , 2016 ) . Fasting occurs due to natural food scarcity or as part of the life history strategy , for example during hibernation or migration . During fasting , the organism relies on stored sources of energy such as glucose in the form of glycogen and fatty acids ( Secor and Carey , 2016 ) . In addition , ketone bodies become an alternative fuel source that is important for many mammals to survive episodes of fasting or starvation ( Baird et al . , 1972; Bouchat et al . , 1981; Sicart et al . , 1978 ) . For example , ketone bodies are used as an energy source in hibernating ground squirrels or elephant seal pups during their post-weaning fasting period ( Krilowicz , 1985; Castellini and Costa , 1990 ) . Notably , while the brain cannot metabolize fatty acids , ketone bodies can cross the blood-brain barrier and provide fuel under conditions of low blood glucose levels . For example , after starving for 3 days , the human brain takes 25% of its energy from ketone bodies and if fasting continues , ketone bodies replace glucose as the predominant fuel for brain metabolism ( Owen et al . , 1967; Hasselbalch et al . , 1994 ) . During the neonatal period , the developing human brain has high energy requirements and also relies on ketone bodies as a major fuel ( Cunnane and Crawford , 2003; Cahill , 2006 ) . Given their importance in fueling large , energetically expensive brains , it has been posited that ketone bodies do not only have an important role during fasting , but have also been crucial for brain expansion during human evolution ( Cunnane and Crawford , 2003; Wang et al . , 2014 ) . Ketone bodies comprise acetoacetate , acetone , and d-β-hydroxybutyrate ( Figure 1A ) and are mainly produced in the liver by ketogenesis . This metabolic process occurs in the mitochondria and uses fatty acid-derived acetyl-CoA to generate the water-soluble , acidic ketone bodies , which are secreted into the blood . The rate limiting step of ketogenesis is the production of 3-hydroxy-3-methylglutaryl-CoA ( HMG-CoA ) by HMG-CoA synthase ( Hegardt , 1999 ) . Mammals possess two HMG-CoA synthases that originated by gene duplication . While the cytosolic enzyme , encoded by HMGCS1 , is broadly expressed and is necessary to produce cholesterol ( Hegardt , 1999 ) , the mitochondrial HMG-CoA synthase , encoded by HMGCS2 , is primarily expressed in the liver and is only used for ketone body production . HMGCS2 is required for ketogenesis , as mutations in the human gene and mouse gene-knockdown experiments abolish or greatly reduce ketogenesis ( Bouchard et al . , 2001; Ramos et al . , 2013; Thompson et al . , 1997; Wolf et al . , 2003; Pitt et al . , 2015; Cotter et al . , 2014 ) . HMG-CoA synthase-2 deficiency in human can lead to coma after fasting for more than 22 hours due to low glucose levels ( Thompson et al . , 1997; Morris et al . , 1998 ) . Human individuals with HMGCS2 mutations therefore require regular carbohydrate intake but show no other symptoms , suggesting that this deficiency is probably underdiagnosed . Here we investigated the evolution of HMGCS2 in mammals . Unexpectedly , we identified three independent losses of this gene in cetaceans ( dolphins and whales ) , pteropodids ( Old World fruit-eating bats ) and Elephantimorpha ( elephants and mastodons ) . Remarkably , these species have relatively large brains , suggesting that , unlike in humans , ketone bodies are not strictly required for fueling complex brains . Furthermore , we show that in the cetacean and Elephantimorpha clades HMGCS2 was lost before brain size expansion happened , suggesting that the lack of ketogenesis did not prohibit the evolution of large brains in these lineages . While strong conservation of HMGCS2 in other mammals indicates that ketogenesis is a crucial metabolic process , the recurrent loss of this gene highlights an unexpected flexibility in mammalian energy metabolism .
To investigate the evolution of HMGCS2 , we used a previously published whole genome alignment to inspect the gene sequence and the surrounding locus across 70 placental mammals ( Sharma and Hiller , 2017 ) . Surprisingly , we discovered that three independent lineages ( cetaceans , pteropodids and the African savanna elephant ) exhibit large deletions that remove HMGCS2 exons or gene-inactivating mutations that shift the HMGCS2 reading frame and destroy conserved splice site dinucleotides ( Figure 1B ) . All three lineages have a deletion of exon one that encodes the mitochondrial targeting domain; such a deletion causes HMG-CoA synthase-2 deficiency in human individuals ( Pitt et al . , 2015 ) . Other mutations affect exons encoding key residues required for HMG-CoA synthase catalytic activity and leave little of the coding sequence intact . Together with the deletion of the promoter region in pteropodids , the elephant and the sperm whale ( Figure 1—figure supplement 1 ) , this shows that three mammalian lineages lost the enzyme that is required for ketogenesis . In cetaceans and pteropodids , the remnants of the once-functional HMGCS2 gene are located in a conserved genomic context with REG4 upstream and PHGDH downstream . In elephant , the three remaining HMGCS2 exons also occur in the same genomic locus adjacent to the conserved PHGDH gene , but inversions that already happened in the ancestor of elephants and the closely related manatees rearranged the locus upstream of HMGCS2 ( Figure 1—figure supplement 2 ) . These rearrangements were succeeded by a large deletion in the elephant lineage that removed the first five HMGCS2 exons together with the REG4 gene . To rule out that the gene-inactivating mutations are sequencing or genome assembly errors , we validated all smaller mutations and exon deletions with unassembled sequencing reads from the SRA and TRACE archives using blastn . All 22 mutations in cetaceans were confirmed by at least 30 reads , with no support for the non-gene-inactivating allele ( Figure 1B , Supplementary file 1 ) . This includes the deletion of exon one that exhibits shared breakpoints in the toothed and baleen whale lineages ( Figure 1—figure supplement 3 ) , which strongly suggests that this deletion and thus HMGCS2 loss already occurred before the split of the main cetacean lineages ( Figure 1C ) . This is further supported by the 2 bp frameshifting insertion in exon two that is shared between killer whale and minke whale , and was later deleted in dolphin and sperm whale . In pteropodid bats , the ~4 . 5 kb deletion that removed coding exon two is validated by sequencing reads and is shared between both flying foxes ( Figure 1B ) , suggesting that HMGCS2 was already lost in their common ancestor . Using the HMGCS2 sequence of the David’s myotis bat , we detected no evidence for the presence of the deleted HMGCS2 exons in unassembled sequencing reads of both flying fox species , while we readily found all exons of the HMGCS1 paralog , showing that the search is sufficiently sensitive . In the Egyptian fruit bat , HMGCS2 is entirely removed by a large deletion between the REG4 and PHGDH genes , which we validated with an independent PacBio assembly ( Figure 1—figure supplement 4 ) . Consistent with ongoing gene erosion , the 1 bp deletion in exon three is heterozygous in the black flying fox ( Figure 1B ) . To rule out that the partial gene deletion in the African savanna elephant is an assembly error , we used the manatee HMGCS2 sequence . Sensitive blastn searches found no significant hits for the deleted HMGCS2 exons or the deleted neighboring REG4 gene in the unassembled sequencing reads of two different savanna elephant individuals ( Cortez et al . , 2014 ) . In contrast , the three remaining HMGCS2 exons as well as all exons of the paralogous HMGCS1 could be recovered . We also investigated related elephant species , making use of recently published sequence data from the African forest elephant and the Asian elephant ( Palkopoulou et al . , 2018; Reddy et al . , 2015 ) . Further , we queried sequence data from two American mastodons , extinct Elephantimorpha that split from elephants 28–24 Mya ( Rohland et al . , 2007 ) . As for the savanna elephant , the three remaining HMGCS2 exons and entire HMGCS1 gene were found in all three species , while the deleted HMGCS2 exons and the REG4 gene were not found ( Figure 1B ) . Parsimony suggests that the deletion , which removed large parts of HMGCS2 , occurred prior to the divergence of mastodons and the elephant species . We further found that the remaining HMGCS2 sequence evolves under relaxed selection in cetaceans , pteropodids and Elephantimorpha ( p<3e-3 , Supplementary file 2 ) . Together with the conserved genomic context , the lack of any evidence of a remaining functional HMGCS2 in unassembled reads and the validated gene-inactivating mutations , we conclude that the main ketogenesis enzyme is lost in three independent mammalian lineages . Finally , we considered the possibility that HMGCS1 , the cytosolic HMG-CoA synthase , may compensate for HMGCS2 loss , which would require HMGCS1 to be localized in the mitochondria , where ketogenesis happens in other species . We found that the HMGCS1 protein of cetaceans , pteropodids and elephant does not possess a mitochondrial targeting domain . Furthermore , an analysis of available liver RNA-seq data from the minke whale and Egyptian fruit bat provides no indication of alternative or novel exons in HMGCS1 that could encode such a targeting signal . Thus , HMGCS1 does not seem to be capable of compensating for the loss of HMGCS2 , suggesting that ketogenesis is lost in cetaceans , pteropodids and Elephantimorpha . Next , we investigated whether the loss of HMGCS2 is associated with the loss of other enzymes in the ketogenesis pathway ( Figure 1A ) . ACAT1 and HMGCL do not exhibit inactivating mutations in cetaceans , pteropodids and the elephant , likely because the respective enzymes are not only required for the production of ketone bodies but are also involved in leucine and isoleucine metabolism . In contrast to these two pleiotropic genes , BDH1 is only involved in converting acetoacetate into the ketone body d-β-hydroxybutyrate ( Figure 1A ) . We found that BDH1 exhibits several inactivating mutations and evolved under relaxed selection in cetaceans and pteropodids ( Figure 1—figure supplement 5 , Supplementary file 2 ) . Overall , this suggests that the loss of HMGCS2 is only associated with the loss of non-pleiotropic genes in the ketogenesis pathway . The 59 other mammals , for which the genome assembly fully covered the HMGCS2 locus ( Figure 1B ) , do not exhibit inactivating mutations in this gene . Consistent with the presence of a functional gene , we further estimated an average non-synonymous/synonymous ( dN/dS ) ratio of 0 . 16 , which indicates that HMGCS2 evolves under strong purifying selection in other mammals . The observation that HMGCS2 is well-conserved in the majority of mammals is consistent with ketogenesis being an important metabolic process . However , the recurrent loss of HMGCS2 raises the question of which energy source is used by the brain during fasting . Consistent with the loss of ketogenesis in cetaceans , bottlenose dolphins do not produce ketone bodies after fasting for 3 days but are nevertheless able to maintain high blood glucose levels over this entire period ( Ridgway , 2013 ) . It was suggested that dolphins maintain high glucose levels by synthesis of glucose from non-carbohydrates ( gluconeogenesis ) , in particular from glucogenic amino acids that are abundant in their diet ( Ridgway , 2013 ) . This suggests that ketogenesis became dispensable in dolphins and that HMGCS2 was lost as a consequence of relaxed or no selection to maintain this gene . Similarly , the loss of ketogenesis in pteropodid fruit bats may be a consequence of the relatively constant availability of fruit year-round , which provides large quantities of glucose . This is in agreement with molecular dating , which estimates that the loss of HMGCS2 happened rather late in the lineage leading to the fruit bat clade and may even have occurred independently after the split of the frugivorous flying foxes and the Egyptian fruit bat ( Figure 2A ) . Consistent with lack of ketone bodies as alternative fuel , Egyptian fruit bats that were fasted for more than 24 hours in captivity frequently died ( van der Westhuyzen , 1978 ) . Thus , like HMG-CoA synthase-2 deficient human individuals , these bats are sensitive to starvation . Hence , while ketogenesis may have been lost under ancestral conditions of constantly available , glucose-rich food , the loss of HMGCS2 may now represent a disadvantage , which will be of interest to ongoing conservation efforts for ecologically and economically important species in the pteropodid family . In contrast to cetaceans and fruit bats , little is known about how elephants respond to fasting; however , the following observation is consistent with the loss of ketogenesis . During musth , when elephant males experience longer periods of fasting and can lose 10% of their body weight , their blood becomes slightly more alkaline ( Rasmussen and Perrin , 1999 ) . This is contrary to an increased blood acidity that would be expected from an increasing production of acidic ketone bodies . Given the importance of ketogenesis to provide energy to the brain during starvation , it is noteworthy that species in all three HMGCS2-loss lineages generally have large relative brain sizes ( Stephan et al . , 1981; Boddy et al . , 2012 ) . For example , the encephalization quotient ( EQ ) , measuring the ratio between the observed brain size and the size expected for a mammal of the same body weight , is 3 . 7 for the bottlenose dolphin ( Montgomery et al . , 2013 ) . Compared to human , dolphins and elephants are also among the few mammals that have a higher degree of neocortex folding , a measure that positively correlates with neuron number ( Manger et al . , 2012; Lewitus et al . , 2014 ) . Furthermore , while powered flight imposes a constraint on body and brain size in bats , pteropodid fruit bats exhibit a well-developed visual brain system and have brains nearly twice as large as that of insectivorous vesper bats of equal body weight ( Stephan et al . , 1981 ) . Species in all three lineages also exhibit cognitive behaviors that are regarded as a sign of intelligence , exemplified by vocal learning and , in dolphins and elephants , by complex social structures , tool use and self-recognition ( Krützen et al . , 2005; Foerder et al . , 2011; Poole et al . , 2005; Prat et al . , 2015; Plotnik et al . , 2006 ) . Thus , the loss of HMGCS2 in independent large-brained species suggests that ketone bodies are not strictly required to fuel large mammalian brains during fasting . Finally , the timing of HMGCS2 loss has implications for understanding the general preconditions for brain size expansion during the evolution of mammals . While the loss of HMGCS2 in pteropodids likely happened after brain size expansion in this lineage ( Figure 2A ) , shared inactivating mutations show that HMGCS2 was already inactivated in the cetacean ancestor , and thus prior to a period of brain size expansion that resulted in the large brains of dolphins ( Boddy et al . , 2012; Montgomery et al . , 2013 ) ( Figure 2B ) . For the elephant lineage , we used molecular dating to estimate that HMGCS2 was lost around 45–42 Mya ( Supplementary file 3 ) . Thus , like in toothed whales , the loss of this gene likely occurred prior to the period that led to large relative brain sizes in modern elephants ( Shoshani et al . , 2006 ) ( Figure 2C ) . Consequently , while ketogenesis was likely a crucial factor for brain size increase in humans ( Cunnane and Crawford , 2003; Wang et al . , 2014 ) , the loss of ketogenesis has not prohibited drastic evolutionary brain size expansion in two other mammalian lineages . In conclusion , we have identified three independent losses of HMGCS2 in placental mammals . While this may contribute to starvation sensitivity in fruit bats , cetaceans and elephants can withstand periods of fasting . Hence , alternative strategies to fuel large brains during fasting have evolved at least twice , revealing flexibility in the energy metabolism of mammals . Finally , the timing of HMGCS2 loss indicates that ketogenesis is not a universal precondition for the evolution of large mammalian brains . More generally , our results further highlight the potential of comparative gene analyses ( Emerling and Springer , 2014; Meredith et al . , 2009; Castro et al . , 2014; Albalat and Cañestro , 2016; Lopes-Marques et al . , 2017; Hecker et al . , 2017; Gaudry et al . , 2017; Sharma et al . , 2018a; Sharma et al . , 2018b; Meyer et al . , 2018; Emerling et al . , 2018 ) to reveal novel insights into the evolution of metabolic , physiological or morphological phenotypes .
To investigate the HMGCS2 sequence across mammals , we used a whole genome alignment between the human reference genome ( hg38 assembly ) and 69 other placental mammal genomes ( Sharma and Hiller , 2017 ) . In addition to these assemblies , we downloaded the genome assembly of the Hippopotamus ( Árnason et al . , 2018 ) ( NCBI GCA_002995585 . 1 ) and updated genome assemblies of the Large flying fox ( NCBI GCF_000151845 . 1 ) , the Egyptian fruit bat ( NCBI GCF_001466805 . 2 ) and the African savanna elephant ( ftp://ftp . broadinstitute . org/distribution/assemblies/mammals/elephant/loxAfr4/ ) . For these four assemblies , we computed pairwise alignment chains to the human hg38 genome by applying lastz ( Harris , 2007 ) with parameters K = 2400 , L = 3000 and the default scoring matrix , axtChain ( Kent et al . , 2003 ) and chainCleaner ( Suarez et al . , 2017 ) ( both with default parameters ) . Collinear alignment chains were visualized in the UCSC genome browser ( Casper et al . , 2018 ) and inspected for conserved synteny with adjacent genes . All analyzed genome assemblies are listed in Supplementary file 4 . We used the gene loss detection approach ( Sharma et al . , 2018a ) to search across all mammals for mutations that could inactivate HMGCS2 . This approach considers large deletions that cover exons , frameshifting insertions and deletions , mutations that disrupt donor ( GT/GC ) or acceptor ( AG ) splice site dinucleotides , and nonsense mutations . To exclude false inactivating mutations caused by alignment ambiguities , this method only considers those putative inactivating mutations that were confirmed by CESAR ( Sharma et al . , 2016; Sharma et al . , 2017 ) , a method trained to output an intact exon alignment whenever possible . Furthermore , exon deletions or exonic regions that do not align between human and another species were only considered if the respective locus did not overlap an assembly gap in the other genome ( Hiller et al . , 2012 ) . For the proboscis monkey and lesser Egyptian jerboa , greater than 20% of the HMGCS2 protein-coding region was ambiguous bases due to assembly gaps . These species were classified as ‘missing’ , as it is not possible to unambiguously determine presence or absence of HMGCS2 . Exon losses and inactivating mutations identified were manually validated using unassembled sequencing read data from the TRACE and Sequence Read Archives . To validate exon losses , we used sensitive blastn runs ( word size = 7 ) to search read data of HMGCS2 loss species . As queries , we used HMGSC1 and HMGCS2 exon sequences from a closely related species with an intact HMGCS2 gene . Specifically , we used the cow sequence to search cetacean read data , and the sequence of David’s myotis bat to search pteropodid read data . Read data from elephants and mastodon was searched using the manatee HMGSC1 , HMGCS2 and REG4 exonic sequence . To validate smaller inactivating mutations ( stop codon , frameshift and splice site mutations ) and exon deletions , we extracted the genomic context 50 bp up- and downstream of each inactivating mutation in an HMGCS2 loss species and determined the number of sequencing reads that support the derived ( inactivating ) and ancestral ( non-inactivating ) allele , as described in ( Hecker et al . , 2017 ) . SRA accessions are provided in Supplementary file 5 . We generated a multiple sequence alignment of the HMGCS2 coding sequence from the CESAR alignments and replaced in-frame stop codons with ‘NNN’ . Using MACSE v2 ( Ranwez et al . , 2018 ) , we added to this alignment the Chinese Horseshoe bat ( Rhinolophus sinicus , XM_019730577 ) and the Hippopotamus amphibius HMGCS2 coding sequence as well as the inferred exonic sequences of the Asian elephant , the African forest elephant and the mastodon . The alignment was then refined using MACSE v2 prior to visual inspection and further refinement . RELAX ( Wertheim et al . , 2015 ) was applied to test for relaxation of selection . First , we designated all branches within the cetacean , pteropodid and elephant/mastodon subtrees as foreground and designated all other branches as background . Second , we tested each subtree separately against the background branches , removing the other two HMGCS2 loss lineages . We also tested the elephant lineage including only the African savanna elephant . To date the loss of HMGCS2 along the putative loss branches in the phylogenetic tree , we used the method described in ( Meredith et al . , 2009; Gaudry et al . , 2017 ) , which estimates the portion of the loss branch where the gene evolved under selection and the portion where it evolved neutrally . Since synonymous positions do not entirely evolve neutrally due to constraints on splicing and translation , this approach assumes that the synonymous mutation rate of a functional gene is 70% of the fully-neutral synonymous mutation rate of an inactivated gene . Upper and lower bounds of species divergence times , the estimated length of the loss branch and respective sources are given in Supplementary file 3 . The branch model in PAML ( Yang , 2007 ) was fit , with five dN/dS classes , one for each of the three loss branches , one for the subsequent pseudogene branches and a final class for all functional branches . Pseudogene branches were assumed to evolve with a dN/dS of 1 for the dating calculations . We also fit models for each loss lineage individually and further tested the elephant lineage including only the African savanna elephant . We tested the amino acid sequences of the annotated or CESAR-inferred HMGCS1 protein from all HMGCS2-loss species for the presence of a potential mitochondrial target peptide ( mTP ) using TargetP ( Emanuelsson et al . , 2007 ) . This revealed no evidence for the presence of an mTP in any species . To investigate the possibility that an mTP is provided by a novel or alternative first coding exon , we inspected gene predictions from Augustus that were available for all species . Those predicted gene models that contained an alternative first exon were found to not have an mTP . Furthermore , we used RNA-seq data from liver , the primary site of ketogenesis in other species , which was available for the Egyptian fruit bat ( SRA SRR2914059 , SRR2914369 ) and the minke whale ( SRR919296 ) . RNA-seq reads were mapped to the genome using HISAT2 ( Kim et al . , 2015 ) , SAM files were sorted and converted to BAM files using SAMtools ( Li et al . , 2009 ) prior to visualization in the UCSC genome browser . For both species , we found no evidence of alternative or novel exons that could result in a different HMGCS1 N-terminus . Three other genes , ACAT1 , HMGCL and BDH1 , which encode components of the ketogenesis pathway were investigated for potential inactivating mutations using the same gene loss pipeline and mutation validation strategy described above . These genes were also tested for signs of relaxed selection in the three HMGCS2-loss lineages using RELAX ( Supplementary file 2 ) . All data analyzed during this study is publicly available on NCBI , SRA and the Trace Archive . The multiple sequence alignment of the mammalian HMGCS2 coding sequences is provided as Figure 2—source data 1 . | Our brain requires a lot of energy to work properly . Sugars are usually the main type of fuel for the body , but when they run low – for example during a food shortage – fat , in the form of fatty acids , can be used instead . However , the brain cannot directly process these molecules; instead , fatty acids need to go through ketogenesis , a process that turns fat into ketone bodies , which the organ can then burn . Scientists believe that the ability to create ketone bodies was essential for us to evolve large brains . Yet , it is still unclear if all mammals can transform fatty acids into ketone bodies . One way to look into this question is to track whether other species have HMGCS2 , the main enzyme that drives ketogenesis . Jebb and Hiller examined the genomes of 70 different species of mammals for the gene that codes for HMGCS2 . The comparisons revealed that cetaceans ( whales , dolphins and porpoises ) , Old World fruit bats and the African savanna elephant have all independently lost their working version of HMGCS2 . Yet , many members of these three groups have evolved brains that are large for their body size . The genetic analyses showed that dolphins and elephants developed big brains after the enzyme became inactive , challenging the idea that HMGCS2 – and by extension ketogenesis – is always required for the evolution of large brains . These results may also be useful for conservation efforts . Many fruit bats across the world are severely threatened , and their lack of ketogenesis could explain why these animals are highly sensitive to starvation and quickly die when food becomes scarce . | [
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] | 2018 | Recurrent loss of HMGCS2 shows that ketogenesis is not essential for the evolution of large mammalian brains |
Protein modification by SUMO helps orchestrate the elaborate events of meiosis to faithfully produce haploid gametes . To date , only a handful of meiotic SUMO targets have been identified . Here , we delineate a multidimensional SUMO-modified meiotic proteome in budding yeast , identifying 2747 conjugation sites in 775 targets , and defining their relative levels and dynamics . Modified sites cluster in disordered regions and only a minority match consensus motifs . Target identities and modification dynamics imply that SUMOylation regulates all levels of chromosome organization and each step of meiotic prophase I . Execution-point analysis confirms these inferences , revealing functions for SUMO in S-phase , the initiation of recombination , chromosome synapsis and crossing over . K15-linked SUMO chains become prominent as chromosomes synapse and recombine , consistent with roles in these processes . SUMO also modifies ubiquitin , forming hybrid oligomers with potential to modulate ubiquitin signaling . We conclude that SUMO plays diverse and unanticipated roles in regulating meiotic chromosome metabolism .
Meiosis precisely halves the chromosome complement enabling parents to contribute equally to their progeny while maintaining a stable ploidy through successive generations ( Hunter , 2015 ) . Ploidy reduction occurs by appending two rounds of chromosome segregation to a single round of replication to produce haploid gametes from diploid germline cells ( Figure 1A ) . The key events of meiosis that ensure accurate segregation include the connection of homologous chromosomes by crossovers ( Hunter , 2006 ) , monopolar orientation of sister-kinetochores during meiosis I ( Watanabe , 2012 ) , and the stepwise loss of sister-chromatid cohesion , first from chromosome arms at anaphase I and then from sister centromeres at anaphase II ( McNicoll et al . , 2013 ) . Crossover formation is the culmination of a complex series of interdependent chromosomal events during meiotic prophase I that include programmed homologous recombination , and the intimate pairing and synapsis of homologs ( Zickler and Kleckner , 2015 ) . Meiotic recombination is initiated by programmed DNA double-strand breaks ( DSBs ) , some 200–300 DSBs per nucleus in budding yeast , mouse , and human ( Lam and Keeney , 2015 ) . Ensuing recombinational interactions promote chromosome pairing and the assembly of synaptonemal complexes ( SCs ) , densely packed transverse filaments with a zipper-like morphology that connect homologs during the pachytene stage ( Fraune et al . , 2012; von Wettstein et al . , 1984; Zickler and Kleckner , 1999 ) . Within the context of the SCs , selected recombinational interactions mature into crossovers such that each pair of chromosomes attains at least one crossover despite a low number of events per nucleus . Homologs then desynapse and prepare for the meiosis-I division . The connections created by crossovers enable the stable bipolar orientation of homologs on the meiosis-I spindle , and thus accurate segregation during meiosis I . By creating new combinations of gene alleles , crossing over and independent chromosome segregation during meiosis fuels natural selection . Orchestrating the elaborate events of meiosis are regulatory networks that function at the transcriptional , post-transcriptional , translational , and post-translational levels ( Bose et al . , 2014; Brar et al . , 2012; Cahoon and Hawley , 2016; Cheng et al . , 2018; Crichton et al . , 2014; Gao and Colaiácovo , 2018; Govin and Berger , 2009; Gray and Cohen , 2016; Jin and Neiman , 2016; Nottke et al . , 2017; Otto and Brar , 2018; Tresenrider and Ünal , 2018 ) . The post-translational , SUMO ( Small Ubiquitin-like MOdifier ) protein-modification system ( SMS ) is now recognized as an essential regulator of meiotic prophase ( Cheng et al . , 2007; de Carvalho and Colaiácovo , 2006; Lake and Hawley , 2013; Nottke et al . , 2017; Rodriguez and Pangas , 2016; Sakaguchi et al . , 2007; Vujin and Zetka , 2017; Watts and Hoffmann , 2011 ) . Like ubiquitin , SUMO is conjugated to lysine ( K ) side-chains on target proteins via a cascade of enzymes that activate ( E1 ) and conjugate ( E2 ) SUMO , and provide target specificity ( E3 ligases ) ( Jürgen Dohmen , 2004; Gareau and Lima , 2010; Jentsch and Psakhye , 2013; Johnson , 2004; Zhao , 2018 ) . SUMOylation can also be reversed by the action of dedicated proteases ( Kunz et al . , 2018 ) . The consequences of SUMOylation are varied and target specific ( Zhao , 2018 ) , but include conformational changes , creating and masking binding interfaces to mediate protein interactions , and competing with other lysine modifications such as ubiquitylation and acetylation ( Almedawar et al . , 2012; Flotho and Melchior , 2013; Liebelt and Vertegaal , 2016; Papouli et al . , 2005; Steinacher and Schär , 2005 ) . To date , only a handful of meiotic SUMO conjugates have been identified and studied in any detail . In budding yeast , these include the SC component Ecm11 ( Humphryes et al . , 2013; Zavec et al . , 2008 ) , SUMO E2 conjugase , Ubc9 ( Klug et al . , 2013 ) , core recombination factor , Rad52 ( Sacher et al . , 2006 ) , chromosome-axis protein Red1 ( Cheng et al . , 2013; Eichinger and Jentsch , 2010; Lin et al . , 2010; Zhang et al . , 2014 ) , and type-II topoisomerase Top2 ( Zhang et al . , 2014 ) . Also , in Caenorhabditis elegans , SUMO targets components of the chromosome congression/segregation ring complexes ( Davis-Roca et al . , 2018; Pelisch et al . , 2017 ) . This paucity of examples underscores how understanding meiotic SUMOylation has been impeded by inefficient piecemeal approaches to identifying targets and mapping the sites of SUMO conjugation . To overcome this impediment , we developed an efficient proteomics regimen to map SUMO-conjugation sites proteome-wide during meiosis in budding yeast . In combination with label-free quantitation ( LFQ ) and highly synchronous meiotic time courses , this approach allows SUMOylation at protein and site levels to be monitored during the key transitions of homologous recombination , chromosome pairing and synapsis . The resulting mass spectrometry ( MS ) datasets provide a comprehensive and unprecedented view of the SUMO landscape , revealing dynamic waves of modification coincident with the major events of meiotic prophase I . Functional classes of SUMO targets imply roles in basic cellular functions including metabolism , chromatin organization , transcription , ribosome biogenesis , and translation . In addition , meiosis-specific aspects of chromosome metabolism are strongly represented pointing to roles for SUMO in regulating recombination , chromosome synapsis , and segregation . These inferences were explored by acutely inactivating de novo SUMOylation at different times during meiotic prophase . This analysis reveals distinct execution points for SUMO modification and identifies roles in the onset of S-phase , DSB formation , crossing over , and chromosome synapsis . Together , our analysis delineates a diverse and dynamic meiotic SUMO-modified proteome and provides a rich resource toward a mechanistic understanding of how SUMO regulates the complex events of meiosis .
Standard meiotic time-courses in budding yeast have relatively poor temporal resolution of the key events of meiotic prophase I . To sharpen culture synchrony , we employed the method of Berchowitz et al . in which cells synchronize in G0 before meiosis is triggered by inducing expression of the master regulator , IME1 , which is under control of the copper-inducible CUP1 promoter ( PCUP1-IME1; Figure 1A; Berchowitz et al . , 2013 ) . Five hr after induction of meiosis , cells were synchronized for a second time during pachytene , when homologs are fully synapsed . This was achieved by reversibly arresting cells using an estradiol inducible NDT80 gene ( PGAL-NDT80 ) ( Benjamin et al . , 2003 ) , which encodes a meiosis-specific transcription factor required for progression beyond pachytene . Upon PGAL-NDT80 expression , double-Holliday junction intermediates ( dHJs ) are rapidly resolved into crossovers , SCs disassemble and cells progress to MI ( Allers and Lichten , 2001; Chu and Herskowitz , 1998; Clyne et al . , 2003; Sourirajan and Lichten , 2008 ) . Cell samples from synchronized cultures were harvested and processed for SUMO proteomics at six different time-points that capture the key transitions of meiotic prophase I ( Figure 1A–D and Figure 1—figure supplement 1 ) . Cells prior to PCUP1-IME1 induction were sampled as a pre-meiotic control ( G0 ) . 1 . 5 hr after PCUP1-IME1 induction ( S ) , cells were in meiotic S-phase , but DSB formation had not begun . By 2 . 5 hr ( DSB ) , DNA replication was complete and DSB formation was ongoing . 3 . 5 hr ( SI ) , captures the events of DNA strand-invasion and accompanying SC formation . By 5 hr ( dHJ ) , cells were arrested in pachytene with fully synapsed chromosomes and unresolved dHJs . PGAL-NDT80 expression was then induced and cells harvested 1 hr later ( CO ) , as dHJs were being resolved into crossovers but meiotic divisions had not yet begun . To obtain the highest quality peptide samples for SUMO proteomics , we addressed three major impediments: ( i ) proteases are hyper-activated in meiotic cells ( Klar and Halvorson , 1975 ) ; ( ii ) the stoichiometry of SUMOylation is typically very low ( the ‘SUMO paradox’ ) ( Hay , 2005 ) ; ( iii ) the native branched SUMO remnant produced by trypsin digestion ( K-ε-GGIQE ) is not amenable to efficient MS-based identification ( Wohlschlegel et al . , 2006 ) . Thus , strains were generated in which the native SMT3 locus was engineered to express hexa-histidine tagged Smt3 with an I96K mutation ( 6His-Smt3-I96K; Figure 1E; functionality of this construct is reflected in the 96% spore viability of 6His-Smt3-I96K/6His-Smt3-I96K homozygotes and the timing and efficiency of meiosis in these strains , Figure 1B–D and Figure 1—figure supplement 1; Tammsalu et al . , 2015; Wohlschlegel et al . , 2006; Xu et al . , 2010 ) . This construct enabled a two-step purification regimen to yield samples that were highly enriched for peptides with a K-ε-GG di-glycine SUMO-conjugation remnant , which is readily detected by tandem MS . SUMO-conjugated proteins were initially enriched using immobilized metal-affinity chromatography under denaturing conditions ( 6M guanidine ) , thereby limiting proteolysis . Eluted samples were then split and digested with LysC , or a combination of LysC plus GluC , to yield peptides with di-glycine branched SUMO-remnants that are amenable to further affinity purification using anti-di-glycyl-lysine antibodies ( Xu et al . , 2010 ) . Eluted peptides were subjected to LC-MS/MS over a 90 min acetonitrile gradient on a Q-Exactive Orbitrap ( Thermo Scientific ) with data-dependent acquisition; and data were processed using MaxQuant and Perseus software ( Max Planck Institute ) ( Cox et al . , 2014; Cox and Mann , 2008; Tyanova et al . , 2016 ) . Critically , this method unambiguously distinguishes between conjugation sites for SUMO and ubiquitin because LysC ( ± GluC ) digestion does not yield a di-glycine remnant for ubiquitin ( for ubiquitin , K-ε-GG is a product only when trypsin is employed; Tammsalu et al . , 2015; Wohlschlegel et al . , 2006; Xu et al . , 2010 ) . To obtain biological replicates with high correlation scores ( r ≥ 0 . 8 Pearson correlations , Figure 2—figure supplement 1A ) for quantitative analysis of SUMOylation dynamics , samples were collected from three independent time courses , processed in parallel , and then LC-MS/MS was performed in tandem with a randomized sample order and identical run conditions . In order to maximize the identification of target proteins and their conjugation sites , data from additional time courses were included in a separate analysis . Parallel analysis of samples digested separately with LysC and LyC + GluC enhanced peptide coverage , thereby increasing the number of SUMOylated proteins identified by 11% and the number of conjugation sites mapped by 27% . Combined analysis of all samples identified 2747 SUMOylation sites in 775 proteins . By comparison , the most comprehensive analysis of SUMO targets in vegetative yeast to date identified 244 targets and 257 sites ( Esteras et al . , 2017 ) . Of these targets , 166 were also identified in our analysis indicating that meiotic and mitotic SUMO targets show some overlap ( Supplementary file 1 summarizes previous yeast SUMO MS/MS studies in vegetative cells ) . For individual time-point samples in the three parallel time courses , between 505 and 553 target proteins , and 1147 and 1362 sites were identified , with a majority of proteins being identified in multiple time points ( Figure 2A and B ) . The relatively narrow numerical range of proteins identified across samples is one indication that sample preparation was consistent over the entire set . Also , of the 2414 conjugation sites identified in a single MaxQuant run , 1866 could be assigned with high confidence , with localization probabilities of ≥0 . 96 ( Figure 2C , Supplementary file 2 ) . Moreover , a majority of lower confidence sites remain viable for functional analysis because decreased probabilities typically stemmed from ambiguity between two adjacent lysines in the same peptide . Ubc9 binds directly to consensus SUMOylation peptide , ψ-K-x-E/D ( ψ = large hydrophobic residue ) , to favor modification at these sites ( Bernier-Villamor et al . , 2002; Rodriguez et al . , 2001; Sampson et al . , 2001 ) . However , only 14 . 26% of identified conjugation sites conformed to this consensus , with an additional 0 . 50% displaying the hydrophobic variant ( ψψψ -K-x-E/D ) and 3 . 23% having the reverse consensus sequence ( E/D-x-K-ψ; Figure 2D ) . Around a fifth of sites comprised partial-consensus acidic ( K-x-E/D , 11 . 57% ) and reverse-acidic ( E/D-x-K , 10 . 32% ) motifs . Di-lysine was the only other recognizable motif ( 7 . 55% ) . Thus , a majority of modified sites ( 52 . 57% ) did not have a recognizable motif . Identified sites were compared to those predicted by GPS-SUMO software ( Zhao et al . , 2014 ) . Even when the search threshold setting was ‘low’ , less than 19% ( 512 of 2745 ) of identified sites were predicted , emphasizing the limited utility of such software and the value of our proteomics dataset ( Supplementary file 3 ) . Two or more conjugation sites were identified in 465 ( 60% ) of the 775 identified SUMOylated proteins and the distribution of site numbers had a long tail , with 178 ( 23% ) proteins containing five or more sites and three proteins with ≥30 sites ( Ulp1 , Red1 and Sir4; Figure 2E ) . When multiple conjugation sites were present in a single protein , they tended to cluster , with a 47% probability of adjacent SUMO sites being less than five residues apart ( Figure 2F ) . This clustering effect implies that target features such as local secondary structure , solvent exposure and targeting by E3 ligases are more important determinants of SUMOylation site specificity than the presence a consensus conjugation motif , which is absent from a majority of sites . Consistently , SUMO sites were depleted from regions of predicted globular , buried or transmembrane structure , and were enriched in regions of moderate disorder ( Figure 2G and H ) . For each target protein identified in our analysis , we generated a diagram detailing ( i ) the locations of SUMO sites relative to non-SUMOylated lysines , ( ii ) predicted SIMs , ( iii ) PFAM domains , and ( iv ) protein secondary structure ( Supplementary file 5 ) . The diversity of targets identified by our analysis points to roles for SUMOylation throughout meiotic prophase I and at each step of HR . To test this assertion and define execution points for SUMOylation , we employed an auxin-induced degron ( AID ) allele of the E1 subunit Aos1 to acutely block de novo SUMOylation at four key transition points ( Figure 3A , B and Figure 3—figure supplement 1; very similar results were obtained using a Uba2-AID degron allele , Figure 3—figure supplements 2 and 3 ) . In each case , meiotic cultures were split at the appropriate time point , and auxin was added to one sub-culture to induce degradation of Aos1-AID with the other sub-culture serving as a positive control . When Aos1-AID degradation was induced 30 min after entry into meiosis ( experiment 1 , Figure 3B and C ) , the onset of S-phase , as assessed by FACS analysis , was delayed by ≥30 min and meiotic divisions were completely blocked ( Figure 3D and E ) . Identified SUMOylation targets involved in DNA replication , cell cycle , and metabolic regulation could be responsible for these phenotypes ( Supplementary file 4 ) . To uncover post S-phase functions of SUMO that could account for the block to meiotic divisions , cells were synchronized using an analog-sensitive allele of the Cdc7 kinase ( cdc7-as; condition 2 , Figure 3F , G; Wan et al . , 2006 ) . Treatment of cdc7-as cells with the ATP analog PP1 causes meiotic cultures to reversibly arrest after S-phase , but prior to the initiation of recombination by DSB formation . The DNA events of HR were monitored using Southern blot assays at a well-characterized DSB hotspot ( Figure 3—figure supplement 1A–E ) ; and synapsis was analyzed by immunostaining chromosome spreads for the major SC component Zip1 ( Figure 3—figure supplement 1F ) . Degradation of Aos1-AID immediately following release from cdc7-as arrest blocked DSB formation for ~2 hr indicating an unanticipated role for SUMO in the initiation of HR . Identified targets that could affect DSB formation include cohesin , Hop1 , Red1 , Spp1 , Mer2 , Rec114 , and Dbf4 ( discussed below; Supplementary file 4 ) . Condition 2 also resulted in a complete block to crossing over and meiotic divisions ( Figure 3G ) , suggesting additional roles for SUMO in HR and/or the progression of meiosis . Therefore , we also determined the effects of degrading Aos1-AID later , just after DSBs were formed following release from cdc7-as arrest ( condition 3 , Figure 3H , I ) . In control cells ( no auxin ) , initial DSB levels were high and continued to rise for 1 hr , before being repaired to yield high levels of crossovers and noncrossovers . When Aos1-AID was degraded , initial DSB levels were also high , but instead of continuing to rise , levels immediately decreased . These observations are consistent with the role for SUMO in DSB formation defined above , with later degradation of Aos1-AID preventing only late-forming DSBs . Joint molecule ( JM ) strand-exchange intermediates appeared to form efficiently following Aos1-AID degradation but reached peak levels with a ~ 1 hr delay relative to control cells suggesting slower formation . Subsequent resolution of JMs was severely delayed and cells again failed to divide . Consistent with a JM resolution defect , crossover and non-crossover products were reduced by 70% and 48% , respectively ( Figure 3I ) . For condition 3 , we also analyzed chromosome synapsis by immunostaining for the major SC component Zip1 and quantifying four classes of nuclei: no staining , foci only , partial synapsis with both lines and foci of Zip1 , and full synapsis with extensive linear staining ( Figure 3J and Figure 3—figure supplement 1F ) . Both synapsis and , unexpectedly , de-synapsis were defective when Aos1-AID was degraded . Without auxin , synapsis levels peaked at 10 hr ( 1 hr after the culture was split ) , with partial synapsis in 53% of nuclei and full SCs in 19% . De-synapsis rapidly ensued and by 11 hr , 75% of cells had no Zip1 staining . By comparison , Aos1-AID degradation appeared to stall synapsis , with levels remaining largely unchanged between 9 and 10 hr ( p=0 . 09 , G-test; Figure 3J ) . However , by 11 hr , very high levels of synapsis were achieved; 72% of cells had partial synapsis and 14% had full SCs . At 12 hr , synapsis levels were almost unchanged suggesting defective de-synapsis . Thus , de novo SUMOylation has both a post-DSB function to promote the timely formation of JMs and SCs , and a post-synapsis function in JM resolution and de-synapsis . These roles of SUMO may be mediated by the numerous targets identified in processes such as HR , SC formation , the DNA damage response , and transcription ( discussed below in Figures 5-7; Supplementary file 4 ) . Finally , late roles of de novo SUMOylation were determined by degrading Aos1-AID as cells were released form pachytene arrest ( condition 4; Figure 3A ) using the PGAL-NDT80 allele ( see above and Figure 1A; Benjamin et al . , 2003 ) . In contrast to the other conditions , meiotic divisions occurred efficiently when Aos1-AID was degraded at this late stage , suggesting that de novo SUMOylation is not essential for MI and MII ( Figure 3K , L ) . However , when a degron allele of Uba2 was degraded , a reproducible delay in MI was observed and spore viability was reduced to 74% compared to 93% in the no auxin control ( Figure 3—figure supplement 2I ) . This may be a consequence of more acute inactivation of SUMOylation due to faster and more complete degradation , and/or the fact that Uba2 is the catalytic subunit of E1 . Although divisions occurred efficiently when Aos1-AID/Uba2-AID were degraded , JM resolution and crossover formation were delayed by ~30 min , and final crossover levels were reduced by ~18% ( Figure 3K , L and Figure 3—figure supplement 2H , I ) . These HR defects were accompanied by a delay in de-synapsis ( Figure 3M and Figure 3—figure supplement 2J ) . Without auxin , only 25% of cells still had partial or full synapsis 1 hr after NDT80-IN expression , compared to 57% when Aos1-AID was degraded . After 2 hr , SCs had completely disassembled in 82% of control cells without auxin , compared to 47% following Aos1-AID degradation . Late defects caused by E1 inactivation could reflect SUMO targets such as Sgs1 , Top3 , Slx4 , Smc5/6 , Chd1 , the ZMM proteins and components of SCs ( discussed below , Figures 6 and 7 ) . Collectively , real-time inactivation of the SUMO E1 enzyme confirms that SUMOylation regulates the major transitions of meiotic prophase and identifies roles in S-phase , DSB formation , the formation and resolution of joint molecules , synapsis and de-synapsis , and the progression of meiotic prophase I . Proteins involved in all aspect of meiotic chromosome metabolism were SUMOylated , including DNA replication and repair , the DNA damage response , chromatin , transcription , telomeres , homologous recombination , synapsis , and chromosome segregation . Below , we analyze subsets of these targets pertinent for regulation the SUMO and ubiquitin modification systems , chromatin , chromosome structure , and homologous recombination . SUMO targets also revealed cross-talk with the ubiquitin-proteasome system ( UPS; Figure 4A ) . UPS targets included Ubc4 and Ubc5 , paralogous E2 conjugases involved in protein quality control , stress response , and cell-cycle regulation by the APC/C ( Finley et al . , 2012 ) . Notably , Ubc4/5 work with SUMO-targeted ubiquitin ligases ( STUbLs ) that target poly-SUMOylated substrates ( Sriramachandran and Dohmen , 2014; Uzunova et al . , 2007 ) . SUMO also modified five ubiquitin E3 ligases , Ubr1 , Ufo1 , Uls1 , Gid7 , and Ufd4 , involved in a variety processes including the N-end rule , catabolite-degradation of fructose-1 , 6-bisphosphatase , transcription , cell cycle , and genome maintenance ( Bao et al . , 2015; Baranes-Bachar et al . , 2008; Finley et al . , 2012; Kramarz et al . , 2017; Lin et al . , 2015; Sriramachandran and Dohmen , 2014; Varshavsky , 1997 ) . Notable is Uls1 , a putative STUbL that is thought to compete with a second STUbL , Slx5-Slx8 , to displace from DNA poly-SUMOylated proteins that have been rendered defective or inactive ( Sriramachandran and Dohmen , 2014; Tan et al . , 2013; Wei et al . , 2017 ) . Inferred Uls1 targets , all of which were SUMOylated in meiosis , include Top2 ( Wei et al . , 2017 ) , H2A . Z ( Takahashi et al . , 2017 ) , Rap1 ( Lescasse et al . , 2013 ) , Srs2 ( Kramarz et al . , 2017 ) , Rad51 ( Chi et al . , 2011 ) and possibly Dmc1 ( Dresser et al . , 1997; Figure 4A ) . The ubiquitin-dependent segregase , Cdc48/p97 , can target proteins co-modified by ubiquitin and SUMO ( Bergink et al . , 2013; Nie et al . , 2012 ) . Both Cdc48 and Ufd1 , the cofactor implicated in SUMO binding , were SUMOylated ( Figure 4A ) . Intriguingly , ubiquitin itself was SUMOylated on six lysines that are also sites for ubiquitin chain formation ( K6 , 11 , 27 , 29 , 48 , and 63; Figure 4K ) . Clear differences in site intensities were detected , with K48 and K11 being the most abundant followed by K27 . Interestingly , K48 SUMOylation , which has the potential to modulate targeting to proteasomes , peaked during DSB formation and dipped thereafter . Ubiquitin is expressed from four loci in budding yeast , as a head-to-tail poly-ubiquitin precursor from the UBI4 locus , and as ubiquitin fusions to the ribosomal proteins Rps31 and Rpl40A/Rpl40B . Whether ubiquitin SUMOylation reflects modification of ubiquitin precursors , free ubiquitin and/or ubiquitin conjugates is unclear . However , this observation further corroborates evidence for a unique class of mixed ubiquitin-SUMO chains with potential for novel signaling functions ( Esteras et al . , 2017; Hendriks et al . , 2014 ) . Autophagy is particularly important for the initiation of meiosis ( Wen et al . , 2016 ) , but also functions in nuclear architecture and chromosome segregation ( Matsuhara and Yamamoto , 2016 ) . Three autophagy factors were SUMOylated: the ubiquitin-like Atg8 protein that undergoes lipidation via conjugation to phosphatidylethanolamine , its cognate E1 enzyme Atg7 , and the dual receptor Cue5 , which simultaneously binds ubiquitylated cargo and Atg8 ( Supplementary file 4 ) . These conjugates raise the possibility that SUMO modulates the targeting of aggregated proteins and inactivated proteasomes to phagophores ( Wen and Klionsky , 2016 ) . Meiotic HR is physically and functionally linked to homolog axes and synaptonemal complexes ( summarized in Figure 6A; Zickler and Kleckner , 2015 ) , and our functional analysis implies that each step of meiotic HR is regulated by SUMO ( Figure 3 ) .
Full genotypes are shown in the strain table . The Auxin-Induced Degron ( AID ) system designed for use during meiosis has been described ( Tang et al . , 2015 ) . Minimal AID fusions to Aos1 and Uba2 were constructed as described using plasmid p7aid-9m as a template for PCR-mediated allele replacement ( Morawska and Ulrich , 2013; Tang et al . , 2015 ) . The estradiol-inducible GAL4-ER IN-NDT80 system has been described ( Benjamin et al . , 2003; Carlile and Amon , 2008; Louvion et al . , 1993; Tang et al . , 2015 ) . Each 20–25 g cell pellet was resuspended in 60 mL fresh ice-cold 0 . 25 M NaOH , 1% β-mercaptoethanol lysis solution and incubated on ice for 20 min . Proteins were precipitated with 10 mL 100% trichloroacetic acid on ice for 20 min and pelleted by centrifugation at 5000 x g for 15 min . The pellet was broken up , thoroughly washed twice with 5 ml ice-cold acetone and air dried . Proteins were dissolved in 6 M Guanidine buffer ( 6 M Guanidine , 100 mM Tris pH 8 . 0 , 500 mM NaCl , 10 mM imidazole pH 8 . 0 ) by vigorous shaking at 30°C . Lysates were clarified by centrifugation at 10 , 000 x g for 30 min and filtration through a Whatman filter paper . A 1 mL HisTrap FF column mounted on an Äkta Avant FPLC system ( GE Lifesciences ) was equilibrated with 5 mL of 6 M Guanidine buffer and then the cell lysate was passed through , followed by washes with 20 mL 6 M Guanidine buffer , 10 mL each of Urea buffer pH 6 . 3 ( 6 M urea , 100 mM tris pH 6 . 3 , 500 mM NaCl ) , and Urea buffer pH 8 . 0 ( 6 M urea , 100 mM Ammonium Bicarbonate pH 8 . 0 , 150 mM NaCl , 20 mM Imidazole ) . Bound proteins were eluted with 0 . 5 M Imidazole ( 6 M urea , 100 mM Ammonium Bicarbonate pH 8 . 0 , 150 mM NaCl , 500 mM Imidazole pH 8 . 0 ) and collected in 2 mL fractions . Fractions covering the elution peak were pooled and final protein concentrations were determined by the Bradford assay . Proteins were reduced with 4 mM TCEP ( Pierce ) for 30 min at room temperature , alkylated with 10 mM iodoacetamide ( Sigma ) for 30 min in the dark and then quenched with 10 mM DTT . There is some concern about iodoacetamide producing artifacts that can be mistaken for a diGly modification ( Nielsen et al . , 2008 ) . However , in a control experiment where a second anti-diGly immuno-enrichment step was not employed , we failed to detect any diGly signatures ( data not shown ) , showing that this concern is not warranted in this case . A total of 2 mg protein from each sample was transferred to a new tube , samples were diluted with elution buffer so that all samples had the same final volume and then diluted with twice that volume of 100 mM ammonium bicarbonate pH 8 . 0 . Samples were digested with 0 . 4 AU Lys-C ( Wako Chemicals ) overnight at 37°C on a shaker at 225–250 rpm . The next day , samples were split and one half was diluted with 100 mM ammonium bicarbonate to 0 . 8 M urea and digested for another 6 hr with 10 µg Glu-C . Digestion was stopped by acidification with 10% trifluoroacetic acid ( TFA ) to pH ≤3 , and samples were the desalted on Sep-Pak tC18 reversed phase columns ( Waters ) . The desalted samples were lyophilized for at least 48 hr and then dissolved in 500 µL 1X immunoaffinity purification buffer ( IAP buffer , Cell Signaling Technologies ) . Samples were sonicated for 30 min at room temperature in a water-bath sonicator and then clarified by centrifugation . Peptides containing di-glycyl lysine ( K-ε-GG ) residues were enriched using the UbiScan kit ( Cell Signaling technologies ) according to the manufacturer’s instructions with some modifications . Each tube of immunoaffinity beads was equilibrated with the IAP buffer and then split evenly into six tubes . Each set of six tubes was used for one set of timepoints ( i . e . one tube per time course ) to reduce variation . Peptide solutions were incubated with UbiScan beads at 4°C for 90 min , washed twice with chilled IAP buffer and twice with chilled HPLC grade water ( Fisher ) . Bound peptides were then eluted twice with 55 µL 0 . 15% TFA for 5–10 min at room temperature , eluates were pooled , flash frozen and then dried by vacuum centrifugation . K-ε-GG enriched peptide mixtures were analyzed with a Q Exactive Orbitrap tandem MS system ( Thermo Scientific ) with an upstream in-line Proxeon Easy-nLCII HPLC system ( Thermo Scientific ) . Peptides were resuspended in 0 . 2% TFA and loaded on to a 25 mm Magic C18 RPLC column and eluted over a 90 min acetonitrile gradient at 300 nl/min . MS1 spectra were sampled with a top-20 cutoff and 5 s dynamic exclusion and subjected to high-energy collision dissociation to obtain MS2 spectra . For the triplicate set that was used for quantitative analysis , all samples were run back-to-back on the same LC column , with the same instrument settings to minimize variation . Additionally , the run-order of samples was randomized to eliminate any effect on the final data . Cells were cultured as for cdc7-as3 time courses . After washes were completed at 8 hr , cultures were split and at 9 hr , copper and auxin were added to one subculture . At 9 . 5 hr , auxin was added again ( 1:2000 dilution of 2 M stock ) . Cell samples were then collected to assay protein depletion , meiotic divisions , and recombination intermediates . Cells were incubated in SPM as previously described ( Oh et al . , 2009 ) . At 6 . 5 hr , copper was added , and at 7 hr auxin was added to one subculture , and 1 μM estradiol ( Sigma E2758 in DMSO ) was added to both subcultures to induce IN-NDT80 . At 7 . 5 hr , auxin was added again ( 1:2000 dilution of 2 M stock ) . Cell samples were collected to assay protein depletion , meiotic divisions , and recombination intermediates . Detailed protocols for meiotic time courses and DNA physical assays at the HIS4::LEU2 recombination hotspot have been described ( Oh et al . , 2009 ) . Error bars show averages ( ± SD ) from three experiments . Whole cell extracts were prepared using a TCA extraction method , as described ( Johnson and Blobel , 1999; Tang et al . , 2015 ) . Following SDS-PAGE and western blotting , an anti-c-Myc mouse monoclonal antibody ( Roche; 11667149001 ) was used to detect Aos1-AID-9myc and Uba2-AID-9Myc . Anti-Arp7 goat polyclonal antibody ( Santa Cruz Biotechnology; y-C20 ) was used to detect Arp7 as a loading control . Donkey anti-goat ( IRDye 680; LI-COR Biosciences; 926–68074 ) and donkey anti-mouse ( IRDye 800; LI-COR Biosciences; 926–32212 ) were used as secondary antibodies . Membranes were imaged using a LI-COR Odyssey system . Surface spreading of meiotic nuclei was performed as described by Grubb et al . , 2015 . During spheroplasting , 20 μl of dithiothreitol ( DTT ) was used instead of the published 40 μl . Fixation was achieved with 4% PFA/sucrose solution . Spreads from Figure 3J , M were stained with anti-Zip1 guinea pig polyclonal antibody ( a gift from Scott Keeney , 1:500 dilution ) , and then with goat anti-guinea pig polyclonal secondary antibody ( Alexa Fluor 555; ThermoFisher; A-21435 , 1:200 dilution ) . Spreads from Figure 3—figure supplement 2J were stained with anti Zip1 goat polyclonal antibody ( Santa Cruz Biotechnology; y-N16 , 1:50 dilution ) , and then with donkey anti-goat polyclonal secondary antibody ( Alexa Fluor 555; ThermoFisher; A-21432 , 1:1000 dilution ) . Slides were mounted in antifade with added DAPI ( Prolong Gold , Invitrogen; P36930 ) and images were captured using a Zeiss AxioPlan II microscope , Hamamatsu ORCA-ER CCD camera and Volocity software . Synapsis was quantified by characterizing four classes of Zip1 staining pattern: no Zip1 , foci only , a mixture of foci and lines , and lines only ( as previously described by Chen et al . , 2015 ) . Cell pellets from 200 μl of meiotic culture were fixed in 70% ethanol . Cells were then washed in 1 ml of 50 mM sodium citrate ( pH 7 . 5 ) and resuspended in 1 ml of 50 mM sodium citrate ( pH 7 . 5 ) with 130 μg RNaseA and incubated at 37°C for 1 hr . 0 . 52 mg of Proteinase K was added and samples were incubated at 65°C for an additional hour . 100 μl of sodium citrate ( pH 7 . 5 ) containing a 1:10 , 000 dilution of SYBR Green ( Invitrogen S7563 ) and 25 μl of 10% Triton-X 100 were added , and cells were sonicated with a probe sonicator on setting 1 . 5 for 10 s . Cells were scanned on a FACScan ( BD Biosciences ) and data was acquired with CellQuest Pro , using the FL1 ( green ) detector to trigger doublet discrimination . Data was analyzed using FlowJo to gate live , single-cell events measuring SYBR Green signal . Histograms were plotted modally , or as the percent of the maximum . All proteomics data were processed with Perseus ( Max Planck Institute ) ( Tyanova et al . , 2016 ) . Text files titled ‘proteinGroups’ and ‘GlyGly ( K ) Sites’ were loaded into a Perseus workspace . Protein and modification matrices were filtered to remove reverse matches and potential modifications . SUMOylated proteins were identified by filtering protein matrices for ‘GlyGly’ modification . The list of Uniprot IDs of SUMOylated proteins was loaded on Panther Gene Ontology search ( http://www . pantherdb . org/ ) and a statistical over-representation test was run for GO-slim Biological Processes and GO-slim cellular Component , with S . cerevisiae proteome as the background . To identify SUMOylation consensus sequence motifs , the motif configuration file of Perseus was edited to add the known SUMOylation consensus sequences , and motifs were added to the GlyGly-site matrix with ‘sequence window’ as the search target . To calculate the number of proteins and SUMO sites identified at each timepoint , protein and SUMO-site matrices from the triplicate dataset were exported to Microsoft Excel and the entries in identification type columns were converted to numbers . Numbers for each triplicate sample were added up and rows containing valid values were counted to give number of identified proteins and sites respectively . Quantitative analysis was carried out in Perseus , with label-free quantitation generated by MaxQuant . For proteins , the ‘LFQ’ values were used . For SUMO sites , ‘intensity x_y’ values were used , where x is the timepoint and y is the replicate number . To analyze the temporal changes in SUMOylation states of proteins and SUMO sites , a new categorical annotation called ‘time’ was applied to the protein and site matrices , and individual replicates were labeled to identify the timepoint they belonged to . This categorical annotation was used to average the quantitative data for triplicate samples at each timepoint . These averages were used for further analysis . To visualize the changes in protein SUMOylation profiles across the time course , the averaged LFQ profiles were loaded into Morpheus ( https://software . broadinstitute . org/morpheus ) and a similarity matrix was generated . To generate hierarchical clustering , proteins were filtered to isolate those that were quantifiable for at least one timepoint . LFQ values were transformed to Log ( 2 ) . Invalid values were imputed from a separate normal distribution for each column . All values were then normalized by Z-scoring to fit them in a range of −2 to +2 . These normalized LFQs were loaded into Morpheus and the rows were subjected hierarchical clustering by 1- Pearson correlation to generate clustering tree and heatmap . The cumulative intensities of all identified sites were retrieved from the ‘intensity’ values from the GlyGly site matrix of a full search of all ( 63 ) samples . For all proteins with at least one identified SUMOylation site , each lysine residue was scored for whether or not it was predicted to reside in different structural contexts . Globular domains , long disordered regions , and short disordered regions were predicted using IUPred ( Dosztányi , 2018 ) with default settings . An additional ‘Disordered’ class was defined as any region predicted to be disordered , that is not inside a predicted globular domain . Solvent exposed and buried regions were predicted using ACCpro from the Scratch package ( Magnan and Baldi , 2014 ) . ‘Globular-Exposed’ and ‘Globular-Buried’ classes were then defined based on the combined IUPred and ACCpro predictions . Coiled-coil regions were predicted using ncoils with default parameters ( Lupas et al . , 1991 ) . Transmembrane regions were predicted using HMMTOP version 2 . 1 with default settings ( Tusnády and Simon , 1998 ) . ‘Disordered Not CC’ was defined from the Disordered and coiled coil predictions . ‘Disordered Terminus’ was defined as a disordered region that extends to the N- or C-terminus of the protein . ‘Disorder in 100 AA of Terminus’ was defined as being in a Disordered Not CC region and being within 100 amino acids of either the N- or C-terminus of the protein . For each structure class , the fold-enrichment was computed as the percentage of detected lysine SUMOylation sites falling within the structure class divided by the percentage of all lysines ( in proteins with at least one detected SUMOylation site ) falling within the same structure class . For all proteins with at least one identified SUMOylation site , the fraction of lysines detected as SUMOylated was computed as a function of the distance in amino acids from either a SUMOylated lysine ( distance from SUMO-K in Figure 4F ) or from a lysine ( distance from K ) . To compute these curves , for every SUMOylation site in our data set we computed the distance to each other lysine in the protein and annotated whether or not the lysine was detected as a SUMOylation site . We then pooled the data for all SUMOylation sites and computed the fraction of lysines that were detected as SUMOylation sites as a function of distance in amino acids ( rounded to the nearest 10 amino acids ) . We then completed an analogous computation using all lysines rather than just the SUMOylation sites . We measured the distribution of quantitative ‘Short Disorder’ scores from IUPred for lysines detected as SUMOylation sites and for lysines that were not detected as SUMOylation sites . For all proteins with at least one identified SUMOylation site , we generated an image annotating the positions of all lysines ( upper track , black lines ) , all detected SUMOylation sites ( upper track , red lines with residue numbers annotated ) , all predicted SUMO-interacting motifs ( SIMs ) defined using the GPS-SUMO algorithm with either the high ( upper track , dark gray rectangles ) , medium ( upper track , gray rectangles ) , or low ( upper track , light gray rectangles ) score threshold ( Zhao et al . , 2014 ) . In the middle track , protein domains detected with HMMER version 3 . 1b2 and Pfam-A version 29 set of models are shown ( Eddy , 1998; Finn et al . , 2016 ) . In the bottom track , globular domains predicted by IUPred ( blue rectangles ) , coiled-coil regions predicted by ncoils ( red rectangles ) , and transmembrane regions predicted by HMMTOP ( black rectangles ) are shown . The short disorder score from IUPred is also indicated by the width of light red region . | Most mammalian , yeast and other eukaryote cells have two sets of chromosomes , one from each parent , which contain all the cell’s DNA . Sex cells – like the sperm and egg – however , have half the number of chromosomes and are formed by a specialized type of cell division known as meiosis . At the start of meiosis , each cell replicates its chromosomes so that it has twice the amount of DNA . The cell then undergoes two rounds of division to form sex cells which each contain only one set of chromosomes . Before the cell divides , the two duplicated sets of chromosomes pair up and swap sections of their DNA . This exchange allows each new sex cell to have a unique combination of DNA , resulting in offspring that are genetically distinct from their parents . This complex series of events is tightly regulated , in part , by a protein called the 'small ubiquitin-like modifier' ( or SUMO for short ) , which attaches itself to other proteins and modifies their behavior . This process , known as SUMOylation , can affect a protein’s stability , where it is located in the cell and how it interacts with other proteins . However , despite SUMO being known as a key regulator of meiosis , only a handful of its protein targets have been identified . To gain a better understanding of what SUMO does during meiosis , Bhagwat et al . set out to find which proteins are targeted by SUMO in budding yeast and to map the specific sites of modification . The experiments identified 2 , 747 different sites on 775 different proteins , suggesting that SUMO regulates all aspects of meiosis . Consistently , inactivating SUMOylation at different times revealed SUMO plays a role at every stage of meiosis , including the replication of DNA and the exchanges between chromosomes . In depth analysis of the targeted proteins also revealed that SUMOylation targets different groups of proteins at different stages of meiosis and interacts with other protein modifications , including the ubiquitin system which tags proteins for destruction . The data gathered by Bhagwat et al . provide a starting point for future research into precisely how SUMO proteins control meiosis in yeast and other organisms . In humans , errors in meiosis are the leading cause of pregnancy loss and congenital diseases . Most of the proteins identified as SUMO targets in budding yeast are also present in humans . So , this research could provide a platform for medical advances in the future . The next step is to study mammalian models , such as mice , to confirm that the regulation of meiosis by SUMO is the same in mammals as in yeast . | [
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] | 2021 | SUMO is a pervasive regulator of meiosis |
Executive function develops during adolescence , yet it remains unknown how structural brain networks mature to facilitate activation of the fronto-parietal system , which is critical for executive function . In a sample of 946 human youths ( ages 8-23y ) who completed diffusion imaging , we capitalized upon recent advances in linear dynamical network control theory to calculate the energetic cost necessary to activate the fronto-parietal system through the control of multiple brain regions given existing structural network topology . We found that the energy required to activate the fronto-parietal system declined with development , and the pattern of regional energetic cost predicts unseen individuals’ brain maturity . Finally , energetic requirements of the cingulate cortex were negatively correlated with executive performance , and partially mediated the development of executive performance with age . Our results reveal a mechanism by which structural networks develop during adolescence to reduce the theoretical energetic costs of transitions to activation states necessary for executive function .
Executive function is essential for a wide range of cognitive tasks , and is strongly associated with both overall intelligence ( Arffa , 2007 ) and academic performance ( Best et al . , 2011 ) . Executive function undergoes protracted maturation during adolescence ( Best and Miller , 2010; Gur et al . , 2012 ) , and its development is linked to the expansion of the cognitive and behavioral repertoire . Notably , executive deficits are linked to both increased morbidity associated with risk-taking behaviors ( Romer et al . , 2009 ) as well as a wide range of neuropsychiatric disorders ( Shanmugan et al . , 2016 ) , such as attention deficit hyperactivity disorder ( ADHD ) and psychosis ( Barkley , 1997; Wolf et al . , 2015 ) . Prior studies have consistently established that executive function relies on activity in a distributed network of fronto-parietal regions , including the dorsolateral prefrontal cortex , cingulate cortex , superior parietal cortex , and frontopolar cortex ( Alvarez and Emory , 2006; Mansouri et al . , 2017; Niendam et al . , 2012; Rottschy et al . , 2012; Satterthwaite et al . , 2013 ) . Notably , both functional ( Fair et al . , 2007; Grayson and Fair , 2017; Gu et al . , 2015; Power et al . , 2010 ) and structural ( Baum et al . , 2017; Hagmann et al . , 2010; Huang et al . , 2015 ) connectivity among these regions undergoes active remodeling during adolescence , with increased connectivity among executive regions , and diminished connectivity between executive regions and other systems such as the default mode network . As structural white matter networks are known to constrain both intrinsic connectivity and patterns of task-related activation ( Hermundstad et al . , 2013; Honey et al . , 2009 ) , it is possible that white matter networks develop during adolescence to facilitate dynamic transitions to fronto-parietal system activation states with lower theoretical energetic cost . However , research that seeks to relate developing white matter networks to the functional dynamics of the fronto-parietal executive system remains sparse . Network control theory provides a powerful framework to address this gap in our knowledge . Previous work has shown that the brain becomes more theoretically controllable to all possible brain states ( on average ) through the control of individual brain regions during adolescence ( Tang et al . , 2017 ) . Network control theory has the potential to provide novel insights regarding mechanisms needed to transition to executive states , as executive function exerts top-down control on other brain systems in a manner akin to control points in a dynamic network ( Gu et al . , 2015; Tang et al . , 2017 ) . Capitalizing on recent developments in network control theory ( Betzel et al . , 2016; Gu et al . , 2017; Stiso et al . , 2019 ) , here we examine how the developing brain structural network supports the transition to a specific state necessary for executive function through the distributed control of multiple brain regions . Specifically , this new framework allows one to integrate information regarding network topology and patterns of brain activation within one mathematical model , in order to specify how theoretical neural dynamics are constrained by the structural connectome ( Tang and Bassett , 2018 ) . Such models assume that the activation state of the brain at a given time is a linear function of the previous state , the underlying white matter network , and any additional control energy injected into the system ( Betzel et al . , 2016; Gu et al . , 2017 ) . From this paradigm , one can calculate the optimal energy cost to move the brain from one state to another given a structural network topology ( Betzel et al . , 2016; Gu et al . , 2017; Kim et al . , 2018 ) . In the present work , we apply this new technique to a large sample of youth . Specifically , we investigated how the energetic cost of transitions to a fronto-parietal system activation state necessary for the executive function changes in response to the maturation of structural brain network . We hypothesized that maturation of structural brain networks would allow for the target activation state of the fronto-parietal executive system to be reached at a lower energetic cost . To test this hypothesis , we capitalized on a large sample of youth ( 8–23 years ) who completed neuroimaging as part of the Philadelphia Neurodevelopmental Cohort ( PNC ) ( Satterthwaite et al . , 2014 ) . We examined how white matter networks ( estimated using diffusion imaging ) support the transition to a fronto-parietal system activation state . As described below , we demonstrate that the energy required to reach this state declines with age , especially within the fronto-parietal control network . Furthermore , we find that the whole-brain control energy pattern contains sufficient information to predict individuals’ brain maturity across development . Finally , participants with better performance on executive tasks require less energetic cost in the bilateral cingulate cortex to reach this activation target , and the energetic cost of this region mediates the development of executive performance with age . Notably , these results could not be explained by individual differences in general network control properties , and were not present in alternative activation target states . Together , these results suggest that structural brain networks become optimized in development to minimize the energetic costs of transitions to activation states necessary for executive function through the distributed control of multiple brain regions .
In this study , we included 946 youths aged 8–23 years who were imaged as part of the PNC ( Figure 1—figure supplement 1 ) . Structural white matter networks were reconstructed for each participant from diffusion imaging data using probabilistic tractography and a standard parcellation of 232 regions . Capitalizing on recent advances in network control theory , we modeled how structural networks facilitate state transitions from an initial baseline state to the target state . In the initial state , all regions had an activity magnitude of 0 . In the target state , regions in the fronto-parietal system had activity magnitude of 1 , with all other regions having an activity magnitude of 0 . Specifically , we defined the trajectory of a neural system to be the temporal path that the system traverses through diverse states , where the item state was defined as the vector of neurophysiological activity across brain regions at a single time point . Based on each participant’s unique network topology , we estimated the regional energetic cost required for the brain to transition from the baseline to the fronto-parietal activation target state ( Betzel et al . , 2016; Gu et al . , 2017; Stiso et al . , 2019; Figure 1—figure supplement 2 and Figure 1a ) . Formally , this estimation was operationalized as a multi-point network control optimization problem , where we aimed to identify the optimal trajectory between baseline and the fronto-parietal activation target state that minimizes both the energetic cost and the distance between the final state and the target state . Results of this linear dynamical model indicate that the trajectory distance ( i . e . , the distance between current and target states ) decreases with time until the desired target state is reached ( Figure 1—figure supplement 3a and b ) . For each network node , we calculated the control energy cost , which provides an indication of where energy must be injected into the network to achieve the transition to the target state . Consistent with a recent methodological study ( Karrer et al . , 2019 ) , and several recent empirical studies ( Betzel et al . , 2016; Gu et al . , 2017; Stiso et al . , 2019 ) , the trajectory distance ( Figure 1—figure supplement 3b ) was inversely related to the time-dependent energy cost within subject ( Figure 1—figure supplement 3c ) . We calculated the trajectory distance at each time point , which was defined as the Euclidean distance between the current brain state and the target brain state . A small distance suggests that the current vector of brain activity is similar to the target vector of brain activity . Across all subjects , we found the total trajectory distance of all time points was positively correlated with total control energy of all time points ( r = 0 . 97 , p<2 × 10−16 , Figure 1—figure supplement 3d ) , suggesting that subjects whose state transition trajectory is long require more energy input to reach the target state . Prior literature has demonstrated that control energy cost is lower in human brain than in the brains of Drosophila and mouse to support diverse network dynamics ( Kim et al . , 2018 ) , is related to network topology ( Betzel et al . , 2016; Kim et al . , 2018 ) and reflects the magnitude of focal electrocorticography stimulation required to drive the brain to a target memory state in patients with medically refractory epilepsy ( Stiso et al . , 2019 ) . Accordingly , here we used the control energy as a metric to summarize the optimal trajectory . We calculated the mean control energy of each system; the highest control energy was observed in systems involved in executive function ( Figure 1b and c ) , including the fronto-parietal and ventral attention/cingulo-opercular systems ( see Figure 1—figure supplement 2; Yeo et al . , 2011 ) . Based on recent evidence that network control properties depend appreciably on the topological structure of the network ( Kim et al . , 2018; Wu-Yan et al . , 2017 ) , we next sought to demonstrate that the topological structure of brain networks facilitates this transition . We therefore compared the energetic cost of this transition in empirical brain networks to the energetic cost observed in null model networks . Specifically , we randomly permuted ( 100 times per participant ) the placement of edge weights , while preserving the network degree and strength distribution . The mean whole brain energetic cost of the null networks was significantly higher ( p<2 × 10−16 ) than that of the empirical networks ( Figure 1d ) , indicating that structural brain networks are topologically optimized to reduce the energetic costs of the transition to a fronto-parietal activation state . Having shown that the topology of structural brain networks facilitates transitions to a fronto-parietal activation state , we next investigated how the energetic costs of this transition evolve in youth . We hypothesized that the energy required to make this transition would decline as networks were remodeled in development . Prior studies have demonstrated that the developmental changes of both brain structure and function could be either linear ( Hagmann et al . , 2010; Wierenga et al . , 2016 ) or non-linear ( Grayson and Fair , 2017; Mills et al . , 2016; Vandekar et al . , 2015 ) . Therefore , we used generalized additive models ( GAM ) with penalized splines , which allowed us to rigorously characterize both linear and nonlinear effects while avoiding over-fitting . Age associations with control energy were examined at multiple scales , including the level of the whole brain , cognitive systems , and individual nodes . For all analyses , we included sex , handedness , in-scanner head motion , total brain volume , and total network strength as covariates . These analyses revealed that the whole-brain average energetic cost of the transition to the fronto-parietal activation state declined with age ( Z = −5 . 12 , p=3 . 06 × 10−7 , Partial r = −0 . 17 , 95% confidence interval ( CI ) = [−0 . 23 , –0 . 10]; Figure 2a ) . Notably , analyses of cognitive systems indicated that age effects were heterogeneously distributed ( Figure 2b ) , with the largest declines in control energy occurring in fronto-parietal ( Z = −5 . 30 , PFDR = 4 . 54 × 10−7 , Partial r = −0 . 17 , CI = [−0 . 23 , –0 . 11]; Figure 2c ) , visual ( Z = −4 . 25 , PFDR = 5 . 71 × 10−5 , Partial r = −0 . 14 , CI = [−0 . 20 , –0 . 08] ) , and motor ( Z = −3 . 20 , PFDR = 2 . 70 × 10−3 , Partial r = −0 . 09 , CI = [−0 . 15 , –0 . 03] ) systems . In contrast , energetic costs within the limbic ( Z = 8 . 69 , PFDR <2 × 10−16 , Partial r = 0 . 29 , CI = [0 . 23 , 0 . 35] ) and default mode ( Z = 2 . 86 , PFDR = 5 . 66 × 10−3 , Partial r = 0 . 10 , CI = [0 . 04 , 0 . 17] ) systems increased with age ( see Figure 2—figure supplement 1 ) . These system-level results aligned with analyses of individual network nodes; we found that the control energy of 49 regions decreased significantly with age ( PFDR <0 . 05 ) , including regions in the fronto-parietal control , visual , and motor systems . Furthermore , the control energy significantly increased with development in 30 regions ( PFDR <0 . 05 ) , which were mainly situated in limbic and default mode systems ( Figure 2d ) . Having found associations between age and control energy , we next conducted a series of eight additional analyses . First , we found that our results held true for a range of baseline initial states and a range of fronto-parietal activation target states . When 100 different initial baseline states were evaluated , we found that in all cases both the whole brain and the fronto-parietal system showed a significant decline in control energy with age ( Figure 2—figure supplement 2a ) . Similarly , when 100 different target states of fronto-parietal activation were evaluated , we found that in all cases both the whole-brain and the fronto-parietal system showed a significant decline in control energy with age ( Figure 2—figure supplement 2b ) . Second , we evaluated whether age effects could be due to non-topological network properties by evaluating the presence of age effects in null networks where degree and strength distributions were preserved . We found that the significance level of age effects in null networks were smaller than those observed in the real network ( p<0 . 01 , 100 permutations ) , suggesting that the empirically measured developmental effects were indeed driven by changes in the network topology ( Figure 2—figure supplement 2c ) . Third , it should be noted that we only constrained the state of regions in the fronto-parietal system . Therefore , the distance travelled by these off-target regions outside the fronto-parietal system were not included in our cost function for calculating optimal control energy . This choice also serves to ensure that our calculation of control energy is largely robust to both the initial and target states of other regions . To demonstrate the robustness of our results to our definition of the matrix S , we calculated the control energy cost using the same initial and target states as in the main analyses but constraining the whole brain . Results showed that there is a high correlation ( r = 0 . 94 , p<2 × 10−16 ) between the whole-brain control energy cost when constraining the whole brain and that when constraining the fronto-parietal system only ( Figure 2—figure supplement 2d ) . Fourth , we assessed whether the structural network optimized the transition to an a priori motor system activation target ( Figure 1—figure supplement 2; Yeo et al . , 2011 ) . Results indicated that the mean whole brain energetic cost of the null networks was significantly higher ( p<2 × 10−16 ) than that of the empirical networks ( Figure 2—figure supplement 2e left ) , suggesting that the lower energetic cost of activtivating thefronto-parietal system was not unique , but was present when activating other systems as well . We further evaluated the age effects of control energy cost to activate the motor system . As the age range of 8–23 years is a critical period in the development of executive function rather than motor function , we expected weaker age effects in the motor system . We found that the whole-brain control energy required to transition to the motor system activation did not significantly change over the age range studied ( Z = 1 . 48 , p=0 . 14 , Partial r = 0 . 05 , CI = [−0 . 02 , 0 . 11]; Figure 2—figure supplement 2e right ) . Fifth , we evaluated whether our developmental results could be explained by modal controllability . Modal controllability reflects the extent to which all dynamic modes of a system will change in response to small changes at a single node ( Gu et al . , 2015 ) . If an individual has high modal controllability , it suggests that the underlying brain structural network was optimized to support efficient state transitions to diverse states . In line with this intuition , modal controllability increases with development in youth as flexible switching between patterns of brain activity becomes more common ( Tang et al . , 2017 ) . Controlling for modal controllability did not alter our results ( Figure 2—figure supplement 3a ) . Specifically , while controlling for modal controllability , average control energy of the whole-brain and fronto-parietal system both significantly declined with age ( whole-brain: Z = −6 . 00 , p=2 . 09 × 10−9 , Partial r = −0 . 19 , CI = [−0 . 25 , –0 . 13]; fronto-parietal: Z = −9 . 95 , PFDR <2 × 10−16 , Partial r = −0 . 32 , CI = [−0 . 37 , –0 . 26] ) . Sixth , because the modularity of brain networks evolves with age , one could ask whether that evolution impacts the observed assocations with control energy ( Baum et al . , 2017; Hagmann et al . , 2010; Huang et al . , 2015 ) . However , we found that results remained consistent after controlling for network modularity in all analyses ( Figure 2—figure supplement 3b ) . For example , average control energy of the whole brain and of the fronto-parietal system both significantly declined with age after controlling for network modularity ( whole-brain: Z = −3 . 95 , p=7 . 73 × 10−5 , Partial r = −0 . 13 , CI = [−0 . 19 , –0 . 07]; fronto-parietal: Z = −4 . 31 , PFDR = 6 . 46 × 10−5 , Partial r = −0 . 14 , CI = [−0 . 20 , –0 . 08] ) . We further assessed whether the increasing segregation of fronto-parietal system during youth ( Baum et al . , 2017 ) could explain the age effect of control energy . Results remained consistent after controlling for the average participation coefficient within the fronto-parietal system when examining age-related differences in the average control energy of the fronto-parietal system ( Z = −4 . 64 , p=3 . 51 × 10−6 , Partial r = −0 . 15 , CI = [−0 . 21 , –0 . 09] ) . Seventh , we assessed whether connectivity within the fronto-parietal system or between the fronto-parietal and other systems could explain observed associations between age and control energy . Specifically , we calculated the sum of all the connections within fronto-parietal system and also the sum of all the connections between the fronto-parietal system and other systems . While controlling for within fronto-parietal connectivity strength , the control energy in the fronto-parietal system still significantly declined with development ( Z = −3 . 53 , p=0 . 0004 , Partial r = −0 . 12 , CI = [−0 . 18 , –0 . 06] ) . Similarly , while controlling for the connectivity strength between the fronto-parietal system and other systems , the control energy in the fronto-parietal system still significantly declined with development ( Z = −4 . 88 , p=1 . 06 × 10−6 , Partial r = −0 . 16 , CI = [−0 . 22 , –0 . 10] ) . Finally , in our main analyses , we specified the target state as regions within the fronto-parietal system , with each region having a magnitude of 1 . As a final step , we also considered a biologically recorded target state defined as the average activation pattern elicited by an n-back working memory task that reliably recruits the fronto-parietal system ( Figure 2—figure supplement 4a ) . Using this alternative target state , we found that the control energy cost of the real network was significantly lower than null networks ( Figure 2—figure supplement 4b ) . As in the main analyses , the control energy cost was highest in the fronto-parietal system ( Figure 2—figure supplement 4c ) . Similarly , the whole-brain average control energy cost ( Z = −7 . 59 , p=3 . 26 × 10−14 , Partial r = −0 . 25 , CI = [−0 . 30 , –0 . 18]; Figure 2—figure supplement 4d ) and average control energy in the fronto-parietal system ( Z = −5 . 26 , PFDR = 2 . 92 × 10−7 , Partial r = −0 . 17 , CI = [−0 . 23 , –0 . 11]; Figure 2—figure supplement 4e ) both significantly declined with age . Nodal analyses provided convergent results , revealing that the control energy in nodes within the fronto-parietal system significantly declined with age ( Figure 2—figure supplement 4f ) . Having established that the control energy required to reach the fronto-parietal activation state changes with age on a regional and system-level basis using mass-univariate analysis , we next evaluated the developmental changes of control energy using multivariate pattern analysis . Multivariate pattern analysis complements mass-univariate analysis , as mass-univariate analysis investigates each feature ( i . e . , control energy of one brain region ) in isolation . In contrast , multivariate pattern analyses are sensitive to the spatially distributed pattern of features ( Davatzikos , 2004; Haynes , 2015; Haynes and Rees , 2006; Norman et al . , 2006 ) . To provide an integrated view of this high-dimensional data , we used multivariate pattern analysis to determine whether spatially distributed patterns of control energy could accurately predict participant age . Specifically , we applied ridge regression with nested two-fold cross validation ( 2F-CV , see Figure 3—figure supplement 1 ) to identify an individual participant’s age in an unbiased fashion using the multivariate pattern of regional control energy . Specifically , we divided all subjects into two subsets based on age , with the first subset used as a training set and the second subset used as a testing set . Within the training set , we used inner 2F-CV to select an optimal regularization parameter ( λ ) . Then , we trained a model using the training data and predicted the brain maturity ( i . e . , ‘brain age’ ) of participants in the testing set ( Dosenbach et al . , 2010; Franke et al . , 2010 ) . The significance of the model was evaluated using permutation testing , where the correspondence between a subject’s control energy features and their age was permuted at random . This analysis revealed that the multivariate pattern of control energy could predict an unseen individual’s age ( Figure 3a and Figure 3—figure supplement 2a and b ) : the correlation between the predicted ‘brain age’ and chronological age was 0 . 63 ( p < 0 . 001 ) after controlling for the covariates , and the mean absolute error ( MAE ) was 2 . 16 years ( p < 0 . 001 ) . For completeness , we also repeated this procedure while reversing the training and test sets , which yielded very similar results ( partial r = 0 . 58 , p < 0 . 001; MAE = 2 . 27 , p < 0 . 001; Figure 3a and Figure 3—figure supplement 2c and d ) . We further examined model weights at the level of individual network nodes . The regions that contributed the most to the prediction of brain maturity aligned with mass-univariate analyses , and included the dorsolateral and ventrolateral prefrontal cortex , the cingulate cortex , superior parietal cortex , and lateral temporal cortex ( Figure 3b ) . In order to ensure that our initial split of the data was representative , we repeated this analysis with 100 random splits , which returned highly consistent results ( mean partial r = 0 . 61 , mean MAE = 2 . 21 years ) . Lastly , we investigated the cognitive implications of individual differences in control energy . Specifically , we expected that participants with higher executive performance on a standardized cognitive battery would require reduced control energy to activate the fronto-parietal system . In order to ensure that associations were present above and beyond the observed developmental effects , we controlled for linear and nonlinear effects of age in addition to the other covariates described above . While we did not find effects at the whole-brain or systems level , two regions survived after FDR correction at nodal level . Specifically , reduced control energy within two regions in the fronto-parietal control system -- the left and right middle cingulate cortex -- was associated with higher executive function ( Left: Z = −3 . 65 , PFDR = 0 . 032 , Partial r = −0 . 13 , CI = [−0 . 19–0 . 06]; Right: Z = −4 . 49 , PFDR = 0 . 002 , Partial r = −0 . 15 , CI = [−0 . 21–0 . 08]; Figure 4a and b ) . Given that control energy reflects the topology of diffusion network ( Kim et al . , 2018 ) and prior study showed that diffusion network properties mediated the age-related development of executive function ( Baum et al . , 2017 ) , we conducted mediation analyses to investigate the extent to which control energy accounted for the association between age and executive function . Using a bootstrapped mediation analysis while adjusting for the covariates described above ( See Materials and methods ) , we found that control energy in both the left ( β = 0 . 03 , p=0 . 001 , 95% confidence interval = [0 . 01 , 0 . 04]; Figure 4c ) and right middle cingulate cortex ( β = 0 . 03 , p<0 . 001 , 95% confidence interval = [0 . 02 , 0 . 05]; Figure 4d ) mediated the development of executive function with age .
Using a large sample of youths ( 8–23 years ) and a generative model of brain network function , we demonstrated that the control energy theoretically required to transition to a fronto-parietal activation state declines with age in youth . Furthermore , the multivariate pattern of the whole-brain control energy predicted the brain maturity of unseen individual participants . These results could not be explained by general network control property and were not observed in analyses stipulating alternative activation targets . Finally , we found that individuals who had higher executive function required lower control energy in the bilateral middle cingulate cortex to activate the fronto-parietal system , and the control energy of this region partially mediated the development of executive performance with age . These results suggest that maturation of structural brain networks may facilitate transitions to fronto-parietal activation states that support executive function . While prior work has consistently demonstrated similarities in the configuration of structural and functional connectivity ( Hagmann et al . , 2010; Honey et al . , 2009; Mollink et al . , 2019 ) , these studies did not evaluate how brain structural networks constrain functional dynamics ( Avena-Koenigsberger et al . , 2017 ) . Recently , using network control methods , several studies have modeled the structural network and brain activation in one framework to describe how the structural connectome may theoretically constrain dynamic transitions between two activation states ( Betzel et al . , 2016; Gu et al . , 2017; Kim et al . , 2018; Stiso et al . , 2019 ) . By extending this framework in a large population , we found that the theoretical energetic cost of brain state transition from a baseline to a fronto-parietal activation state was lower in real brain networks compared to null networks that preserved basic properties such as degree and strength distribution . This result was consistent with prior studies showing that human structural brain networks exhibit non-random topological properties , such as both high clustering and segregated modules ( Bullmore and Sporns , 2009; Gong et al . , 2009b; Hagmann et al . , 2008 ) . This non-random topological organization could support the energetically efficient activation of functional systems ( Avena-Koenigsberger et al . , 2017 ) , and we specifically demonstrate that it supports the efficient control of transitions to the precise activation states required for executive function . Structural connectivity remodels during youth , with increasing segregation ( Huang et al . , 2015 ) , and greater integration ( Hagmann et al . , 2010 ) . Together , these impose a stronger constraint on functional dynamics ( Hagmann et al . , 2010 ) . However , prior work had not explored how the development of structural connectivity supports the emergence of functional activation relevant to executive function . Our results indicate that the control energy theoretically required to transition to the fronto-parietal activation state declines with development , and suggest that the topological organization of structural connectivity supports more energetically efficient signal transformation to activate the fronto-parietal network . Consistent with our results , prior work has demonstrated that metabolism costs declined in youth at both resting-state ( Jog et al . , 2016; Takahashi et al . , 1999 ) and during working memory tasks ( Jog et al . , 2016 ) . Examination of individual cognitive systems revealed that this decline in whole-brain energy was driven by reduced energetic costs within the fronto-parietal system . In particular , substantial negative associations between age and control energy were observed in lateral prefrontal cortex and middle cingulate cortex , which are responsible for preparation , execution , monitoring and switching of tasks ( i . e . , working memory , attention , inhibitory control , etc . ) ( Alvarez and Emory , 2006; Apps et al . , 2013; Niendam et al . , 2012; Rottschy et al . , 2012 ) . Such reduced regional energetic cost suggests that structural brain networks may mature to allow for neural events that occur in these regions to impact the broad activation state of the entire network more efficiently ( Baum et al . , 2017; Hagmann et al . , 2010 ) , and more easily drive the brain towards the fronto-parietal activation state associated with demanding executive tasks ( Satterthwaite et al . , 2013; Thomason et al . , 2009 ) . In contrast , the energetic cost of regions within the limbic and default mode systems increased with age . This localization of costs suggests that these regions become less able to move the brain to a fronto-parietal activation state as development progresses . This result was consistent with previous studies using both structural and functional connectivity data , which have shown that the fronto-parietal system becomes more segregated in development from other systems in association cortex ( Grayson and Fair , 2017; He et al . , 2019; Huang et al . , 2015; Lee and Telzer , 2016; Sherman et al . , 2014 ) , including the default mode ( Sherman et al . , 2014 ) and limbic ( Lee and Telzer , 2016 ) systems . This maturation may potentially allow for functional specialization and a reduction of interference . It should be noted that in prior work we demonstrated that another network control property – modal controllability – increased with age ( Tang et al . , 2017 ) . However , modal controllability quantifies the general controllability necessary to reach all possible states , and therefore is not sufficient to answer questions about specific patterns of activity . Given the importance of executive function in youth to academic achievement ( Best et al . , 2011 ) , risk taking behaviors ( Romer et al . , 2009 ) , and psychopathology ( Shanmugan et al . , 2016 ) , here we sought to understand how structural networks develop to facilitate the transition to fronto-parietal activation states that are necessary for executive function . We therefore leveraged recent advances in network control theory that allow for examining the controllability of a specific biologically meaningful target state and using multi-point control – rather than examining the general controllability and using the single point control methods employed previously ( Tang et al . , 2017 ) . We have previously demonstrated the validity of this new control framework using mathematics , animal data , brain stimulation data , and human brain imaging data ( Betzel et al . , 2016; Kim et al . , 2018; Stiso et al . , 2019 ) . As the brain is constantly receiving input from multiple sensory modalities and top down cognitive processes , we believe that multi-point control is substantially more biologically plausible for understanding executive function than single-point control frameworks . Using this framework , we found that the energetic costs to transition to a specific fronto-parietal activation target state decline with age , and models trained using control energy accurately encode brain maturity in unseen data . Importantly , we demonstrate that our current results are not simply a result of increasing single-point modal controllability: when modal controllability was included as a model covariate , our results remained unchanged . Critically , the increase of general controllability ( i . e . , modal controllability ) with development does not provide information regarding control of the transition to a specific brain state transition . While the brain becomes more controllable on average as shown in Tang et al . ( 2017 ) , the control energy required to arrive a specific activation state could decrease , not change , or even possibly increase . Consistent with this intuition , we demonstrated that the control energy cost to activate the motor system did not significantly change during development . The result accords with prior evidence indicating that motor development precedes executive development , and is largely complete by late childhood or early adolescence ( Andersen , 2003; Gogtay et al . , 2004 ) and suggests that the observed developmental changes in control energy may be specific for transitions to activation states recruiting higher-order cognitive systems , which undergo protracted maturation . Additionally , we found null networks that preserved degree and strength distribution did not represent similar developmental changes of control energy , suggesting our results were driven by topological structure of the brain network . Our results suggest that neither the overall modularity of the structural network nor the segregation of the fronto-parietal system , which both mature during youth ( Baum et al . , 2017 ) , could explain the association between age and control energy . This result was consistent with our recent study showing that the modularity can be positively , non-monotonically , and non-significantly related to control energy depending on the architecture of the structural adjacency matrix ( Patankar et al . , 2020 ) . We found that connectivity strength within the fronto-parietal system partially contributed to reduced energy requirements for activating the fronto-parietal areas in development . The energy required for a state transition depends upon a system’s response to an energetic perturbation . The simplest such perturbation engenders an impulse response ( Karrer et al . , 2020 ) . In Srivastava et al . , 2020 , we show that the impulse response of the system is formally related to the network communicability , suggesting that paths of all lengths -- not just direct connections -- contribute to the control energy . However , the precise relationship between network topology and control energy is unknown . In prior theoretical work , we made some progress in linking the underlying graph architecture to the observed control energy ( Kim et al . , 2018 ) . Specifically , given we have the ability to control a subset of nodes in the network , we showed that the more strongly and more diversely the control nodes are connected to the non-control nodes , the less energy is required to control the network on average . We provided an analytical derivations of the expressions relating a network’s minimum control energy to its connectivity between control and non-control nodes , as well as offering an intuitive geometric representation to visualize this relationship , and rules for modifying edges to alter control energy in a predictable manner ( Kim et al . , 2018 ) . However , this theory is not applicable to our current study because the theory requires that only a set of brain regions be used for control , whereas here we set the control set to be the entire brain . More formal theoretical work is necessary to identify the topological properties that explain control energy . Our main results regarding brain development ( as well as the supplementary analyses described above ) used a mass-univariate analysis approach , where the association between the control energy of each region was modeled separately . Complementary analyses sought to identify distributed multivariate patterns of control energy , which could be used to predict the brain maturity of unseen individuals . Such an approach is similar to prior studies that have used structural ( Franke et al . , 2010 ) , functional ( Dosenbach et al . , 2010 ) , or diffusion ( Erus et al . , 2015 ) based imaging to predict brain development . Here , we used a rigorous split half validation framework with nested parameter tuning . We found that the complex pattern of control energy could be used to predict individual brain maturity . The feature weights from this multivariate model were generally consistent with findings from mass-univariate analyses , underscoring the robustness of these results to the methodological approach . In this context , control energy could have potential to determine whether individuals display either precocity or delay in specific dynamic aspects of brain maturation , which may be relevant to studying developmental disorders and neuropsychiatric syndromes ( Dosenbach et al . , 2010; Erus et al . , 2015 ) . Furthermore , while controlling for age , we observed a significant negative correlation between control energy of both the bilateral middle cingulate cortex and executive function performance . The middle cingulate is a component of the fronto-parietal control system ( Fair et al . , 2007; Yeo et al . , 2011 ) and is critical for executive tasks such as performance monitoring , error detection , and task switching ( Apps et al . , 2013; Vogt , 2016 ) . This result suggests that individuals with better executive function may be able to transition to the fronto-parietal activation state more easily . This result is consistent with prior studies showing that executive function training improves efficiency in activating the executive network ( Kozasa et al . , 2012; Rueda et al . , 2012 ) , and that subjects with higher cognitive performance have lower brain cerebral metabolism cost in resting-state ( Bastin et al . , 2012 ) and have improved metabolic control ( Ryan et al . , 2006 ) . Moreover , the decline of control energy of the bilateral middle cingulate cortex partially mediated the observed improvement of executive function with age . This result is consistent with prior literature suggesting that the optimization of the structural network is associated with better executive function ( Baum et al . , 2017; Wen et al . , 2011 ) , as the decline of control energy cost reflects the optimization of the structural brain network ( Kim et al . , 2018 ) . However , it should be noted that this mediation effect was small , as the direct effect was much larger than the indirect effect . While brain network dynamics are known to be nonlinear , we used a linearization model here . It should be acknowledged that this linearization constrains the model’s predictive power to short time-scales and to states in the immediate vicinity of the operating point . Nonetheless , it has been consistently demonstrated that linearization offers fundamental insights into nonlinear dynamics . For example , Honey et al . ( 2009 ) shows that predictions of function from structure can be obtained with both linear and nonlinear models . Second , if the linearized system is locally controllable along a specific trajectory in state space , then the original nonlinear system is also controllable along the same trajectory ( Coron , 2007; Yan et al . , 2017 ) . Finally , linear controllers are often used to control nonlinear systems through gain scheduling in flight and process control ( Leith and Leithead , 2000 ) . Accordingly , while the linear control trajectories in our work suffer from consequences due to linearization , they also serve as a natural and informative prior for future work in principled neural stimulation and nonlinear control when more is known about the exact nature of the brain’s nonlinearity . Several limitations should be noted . First , all data presented here were cross-sectional , which precludes inference regarding within-individual developmental effects . Ongoing follow-up of the PNC will yield informative longitudinal data , as will other large-scale studies such as the Adolescent Brain and Cognitive Development Study . Second , it should be noted that probabilistic tractography methods remain limited in their ability to fully resolve the complex white matter architecture of the human brain . However , these methods are currently considered state-of-the-art , and may be superior to tensor-based tractography in resolving crossing fibers ( Behrens et al . , 2007 ) . Third , it should be noted that motion artifact is a major potential confound for any study of brain development , and prior studies by our group and others have shown that motion artifact can bias estimates of tractography and confound developmental inference ( Baum et al . , 2018 ) . However , to limit the impact of this confound , we conducted rigorous quality assurance and included in-scanner motion as a covariate in all analyses . Fourth , it should be noted that most univariate effect sizes at system level reported in our work were small . However , prior work has consistently demonstrated that small samples systematically inflate the apparent effect size ( Yarkoni , 2009 ) , whereas large samples ( such as this one , n = 946 ) provide a much more accurate estimate of the true effect size . Furthermore , in contrast to the small univariate effects observed at system level , some univariate effects at nodal level were at medium size and results from the multivariate analyses yielded large effect sizes in unseen data . Finally , the differences between the developmental effects with the motor system target state and that with the fronto-parietal system target state could be due to the differences in size or in spatial congruence between the two target states . However , the non-significant developmental effect with motor target state is consistent with the evidence that motor development is largely complete by late childhood or early adolescence ( Andersen , 2003; Gogtay et al . , 2004 ) . These potential limitations notwithstanding , we demonstrated that the topological structure of white matter networks is optimized during development to facilitate transitions to a fronto-parietal activation state . Moving forward , this framework may be useful for understanding the developmental substrates of executive dysfunction in diverse psychiatric disorders including psychosis and ADHD . Improved knowledge regarding both normal network development and abnormalities associated with psychopathology is a prerequisite for developing individualized interventions to alter disease trajectories and improve patient outcomes . In the future , advances in non-invasive neuromodulatory therapies may allow for targeted stimulation of specific brain regions that are optimally situated within the brain’s control architecture to facilitate transitions to specific target states ( Medaglia et al . , 2018 ) . Such advances could potentially aid in the treatment of the wide range of neuropsychiatric disorders marked by executive dysfunction ( Braun et al . , 2018 ) .
All subjects or their parent/guardian provided informed consent , and minors provided assent . The Institutional Review Boards of both Penn and CHOP approved study procedures . Overall , 1601 participants were enrolled ( Satterthwaite et al . , 2014 ) . However , 340 subjects were excluded owing to clinical factors including medical disorders that could affect brain function , current use of psychoactive medications , prior inpatient psychiatric treatment , or an incidentally encountered structural brain abnormality . Among the 1261 subjects eligible for inclusion , 54 subjects were excluded for a low quality T1-weighted image or errors in the FreeSurfer reconstruction . Of the remaining 1207 subjects with a usable T1 image , 128 subjects were excluded because of the lack of a complete diffusion scan . Of the 1079 subjects with complete diffusion data , 110 subjects failed quality assurance as part a rigorous quality assurance protocol for diffusion MRI ( Roalf et al . , 2016 ) . Additionally , 20 subjects were excluded because they had no field map for distortion correction . Finally , of the remaining 949 subjects , three subjects were excluded due to incomplete image coverage during brain parcellation , yielding a final sample of 946 participants ( Figure 1—figure supplement 1 ) . The Penn computerized neurocognitive battery ( Penn CNB ) was administered to all participants during a separate session from neuroimaging . The CNB consists of 14 tests adapted from tasks applied in functional neuroimaging to evaluate a broad range of cognitive domains ( Gur et al . , 2012 ) . These domains include executive control ( abstraction and mental flexibility , attention , working memory ) , episodic memory ( verbal , facial , spatial ) , complex cognition ( verbal reasoning , nonverbal reasoning , spatial processing ) , social cognition ( emotion identification , emotion differentiation , age differentiation ) and sensorimotor and motor speed . Accuracy and speed for each test were z-transformed and summarized into an efficiency score . A factor analysis was used to summarize these efficiency scores into four factors ( Moore et al . , 2015 ) , including executive function , complex reasoning , memory , and social cognition . Here , we focused on the executive function factor score . Of the sample of 946 participants with complete imaging data that passed quality assurance , two participants had incomplete cognitive data . Accordingly , 944 participants were used in the analysis examining the association between cognition and control energy . As previously described ( Satterthwaite et al . , 2014 ) , all MRI scans were acquired on the same 3T Siemens Tim Trio whole-body scanner and 32-channel head coil at the Hospital of the University of Pennsylvania . Each of the 232 nodes in our network was assigned to a standard set of 7 functional systems originally defined by Yeo et al . ( 2011 ) in a whole-brain clustering analysis . To make this assignment , we calculated the purity index for the 7-system parcellation and brain regions from the Lausanne 232 parcellation atlas as in prior work ( Baum et al . , 2017 ) . This measure quantifies the maximum overlap of cortical Lausanne labels and functional systems defined by Yeo et al . ( 2011 ) . Each cortical Lausanne label was assigned to a functional system by calculating the non-zero mode of all voxels in each brain region ( Figure 1—figure supplement 2 ) . Subcortical regions were assigned to an eighth , subcortical module . We investigated how a structural brain network composed of white matter fiber tracts constrains the brain in transitioning from a baseline state ( i . e . , 1 × 232 zero vector ) to a fronto-parietal activation state , which was defined as regions in the fronto-parietal system that had activity magnitude equal to one while other regions had activity magnitude equal to 0 . According to previous studies ( Betzel et al . , 2016; Gu et al . , 2017; Kim et al . , 2018; Stiso et al . , 2019 ) , we employed a simplified noise-free linear continuous-time and time-invariant network model: ( 1 ) x˙ ( t ) =Ax ( t ) +Bu ( t ) Here , x ( t ) is a 1 × N vector that represents the brain state at a given time , where N is the number of ROIs ( N = 232 ) . The initial sate x ( 0 ) is a 1 × 232 zero vector , and the target state xT is a 1 × 232 vector of fronto-parietal activation . The matrix A encodes the connection probability weighted network , where A has been scaled by its largest eigenvalue and had the identity matrix subtracted to assure that it is stable ( Betzel et al . , 2016; Gu et al . , 2017; Karrer et al . , 2020; Stiso et al . , 2019 ) . The matrix B is a N × N input matrix that identifies the nodes in the control set . Here , B is an identity matrix because all 232 regions in the whole brain were control nodes . The input u ( t ) denotes the control energy injected for each node at a given time . This work aims to model the control process necessary to activate the fronto-parietal system , which is critical to executive function . We set the baseline state to zero , because we sought to model the contrast in activation between an executive task and the resting state . This comparison is motivated by a long history of task fMRI experiments that explicitly contrast executive tasks to the resting state , resulting in robust activation of the fronto-parietal cortex ( Cohen et al . , 1997; Forsyth et al . , 2014; Nagel et al . , 2009; Ragland et al . , 2002; Rowe et al . , 2000 ) . We set the values of regions in the fronto-parietal system to one to represent the fact that these regions were activated . We were interested in a control task where the system transitions from initial state x ( 0 ) to target state xT with minimum-energy input , which is an optimal control problem . We first defined a cost function as the weighted sum of the energy cost of the transition and the integrated squared distance between the transition states and the target state . ( 2 ) minu∫0T ( xT−x ( t ) ) TS ( xT−x ( t ) ) +ρu ( t ) Tu ( t ) dt , s . t . x˙ ( t ) =Ax ( t ) +Bu ( t ) , x ( 0 ) = x0 , and x ( T ) =xT , where xT is the target state , ( xT-x ( t ) ) T ( xT-x ( t ) ) is the distance between the state at time t and the target state xT , T is a free parameter that defines the finite amount of time given to reach the target state , and ρ is a free parameter that weights the energy constraint . Because the time of each step was defined as 0 . 001 , there were 1 , 000 steps from initial to target state if we set T=1 . S is 0-1 diagonal matrix of size N×N that selects only the nodes that we wish to control . Here , we only constrain the activity of the fronto-parietal system . Specially , ( xT-x ( t ) ) TS ( xT-x ( t ) ) constrains the trajectories of all nodes in fronto-parietal system by preventing the system from traveling too far from the target state , and utTu ( t ) constrains the amount of energy used to reach the target state . To compute an optimal u* that induces a transition from the initial state x ( 0 ) to the target state xT , we define a Hamiltonian as: ( 3 ) H ( p , x , u , t ) = xT-xTS ( xT-x ) +ρuTu+p ( Ax+Bu ) From the Pontryagin minimum principle ( Boltyanskii et al . , 1960 ) , if u* is a solution to the minimization problem with corresponding trajectory x* , then there exists p* such that: ( 4 ) ∂H∂x=−2S ( xT−x∗ ) +ATp∗=−p∗˙ , ( 5 ) ∂H∂u=2ρu∗+ BTp∗=0 . From Equation ( 5 ) and Equation ( 1 ) , we derive that ( 6 ) u∗=−12ρBTp∗ , ( 7 ) x˙∗=Ax∗−12ρBBTp∗ . Then , we rewrite Equations ( 4 ) and ( 7 ) as ( 8 ) [x˙∗p˙∗]=[A−12ρBBT−2S−AT][x∗p∗]+[02S]xT , We denote:A~=[A−12ρBBT−2S−AT] , x~=[x∗p∗] , b~=[02S]xT , Then , Equation ( 8 ) can be reduced as:x~˙=A~x~+b~ , Which can be solved as: ( 9 ) x~ ( t ) =eA~tx~ ( 0 ) +A~−1 ( eA~t−I ) b~ . Then , by fixing t = T , we rewrote Equation ( 9 ) as ( 10 ) x~ ( T ) =eA~Tx~ ( 0 ) +A~−1 ( eA~T−I ) b~ . Letc=A~−1 ( eA~T−I ) b~ , eAT~=[E11E12E21E22] . We can then rewrite Equation ( 10 ) as:[x∗ ( T ) p∗ ( T ) ]=[E11E12E21E22][x∗ ( 0 ) p∗ ( 0 ) ]+[c1c2] , from which we can obtain x*T=E11x*0+E12p*0+c1 , which can be rearranged to p*0=E12-1x*T-E11x*0-c1 . Now that we have obtained p* ( 0 ) , we can use it and x ( 0 ) to solve for x~ via forward integration according to Equation ( 9 ) . To solve for u* , we take p* from our solution of x~ and plug it into Equation ( 6 ) . To quantify differences in trajectories , and the ease of controlling the system , we calculated a single measure of energy for every trajectory . Particularly , the energy of each control node i was defined as:Ei=∫t=0Tui*t2 . In order to determine whether the topology of brain networks specifically facilitated transitions to the fronto-parietal activation target state , we compared the energetic cost to that of null model networks . Specifically , for each participant we constructed 100 null model networks where the degree and strength distribution was preserved ( Rubinov and Sporns , 2010 ) . We compared the control energy cost of the transition to the fronto-parietal activation target state estimated from the empirical networks to the average energy cost estimated in these null networks using a paired t-test . Prior studies demonstrated that the developmental changes of brain structure and function could be either linear ( Hagmann et al . , 2010; Wierenga et al . , 2016 ) or non-linear ( Grayson and Fair , 2017; Mills et al . , 2016; Vandekar et al . , 2015 ) . Accordingly , for our developmental analyses we used generalized additive models ( GAMs ) in order to simultaneously model linear and nonlinear relationships with age using penalized splines ( Wood , 2004 ) . We evaluated associations between control energy and age at multiple resolutions , including the whole brain , cognitive systems , and network nodes . Similarly , we evaluated associations between control energy and executive performance while controlling for age . For all models , we included sex , handedness , total brain volume , total network strength , and in-scanner head motion during the diffusion scan as model covariates . Multiple comparisons were accounted for using the False Discovery Rate ( q < 0 . 05 ) . For developmental effect of control energy , the GAM model was: We used the gam command in the R package ‘mgcv’ to implement the model . The spline term estimates a nonparametric smooth function for age-related differences in control energy , which can include linear or nonlinear effects depending on the structure of the data . Restricted maximum likelihood is used to penalize non-linearity in order to prevent overfitting ( Wood , 2004 ) . Furthermore , for regions that displayed the associations between control energy and both age and cognition , we evaluated whether regional control energy might mediate the relationship between age and executive function . Specifically , we regressed out the effects of nuisance covariates ( i . e . , sex , handedness , total brain volume , total network strength , and in-scanner head motion ) on the independent ( X , age ) , dependent ( Y , executive efficiency ) and mediating ( M , control energy ) variables using a linear model . The resultant normalized residuals were used in our mediation analysis . We then evaluated the significance of the indirect effect using bootstrapped confidence intervals within the R package lavaan . Then , we examined: 1 ) path c: the total effect of age on executive performance; 2 ) path a: the relationship between age and the control energy; 3 ) path b: the relationship between control energy and executive performance; and 4 ) path c’: the age effect of executive function controlling for the mediator/control energy . The mediation/indirect effect a*b is the effect size of the relationship between age and executive performance that was reduced after controlling for the mediator/control energy . For each path , we calculated the beta coefficient , which reflected the changes of the outcome for every one-unit change in the predictor . A bootstrap analysis ( i . e . , resampled 10 , 000 times ) was implemented to estimate the confidence intervals for the indirect effect . As a complement to the mass-univariate analyses described above , we also sought to predict individual brain maturity using the multivariate pattern of control energy ( Dosenbach et al . , 2010; Erus et al . , 2015; Franke et al . , 2012 ) . We used ridge regression with nested two-fold cross validation ( 2F-CV ) . We conducted several additional supplementary analyses to assess the sensitivity and specificity of our results . First , in order to evaluate the robustness of our results to variation in target states , we additionally generated 100 new initial states and 100 new target states with noise added . In this distribution of initial states , the activation value of regions in the fronto-parietal system is Gaussian with a mean value of 0 and a standard deviation of 0 . 1 , while in the distribution of target states , the activation value of regions in the fronto-parietal system is Gaussian with a mean value of 1 and a standard deviation of 0 . 1 . Second , to ensure the observed associations with age were driven by the topological structure of real brain networks , we tested whether age effects existed using null networks that preserved the degree and strength distribution . We created 100 null networks and calculated the one-tailed P value for effect size of whole-brain and fronto-parietal system , which was the portion of null networks that showed a lower negative effect size value than the actual value for the real network . Third , we assessed whether the structural network also contributed to other cognitive functions as well by comparing the control energy cost required to reach a motor activation state for real networks and null networks . We further evaluated the age effects of control energy cost needed to activate the motor system . Fourth , the present work explored a specific transition of the brain from a baseline state to a state of fronto-parietal activation by enacting multi-point control . In contrast , modal controllability quantifies the difficulties of transitioning to all possible states via single-node control ( Gu et al . , 2015 ) . Modal controllability identifies brain areas that can push the brain into difficult-to-reach states; our prior work has shown that modal controllability increases with age in youth ( Tang et al . , 2017 ) . Accordingly , it is important to establish whether our present results were driven by developmental changes in modal controllability . As in Tang et al . ( 2017 ) , before calculating controllability , we scaled the matrix by 1+ξ0 , where ξ0 is the largest eigenvalue value of the matrix . Next , we conducted sensitivity analyses where we controlled for modal controllability by including it as a covariate in the regression equation at each resolution of analysis ( e . g . , whole brain , functional system , network nodes ) . Specifically , we controlled for nodal modal controllability in nodal analysis of control energy , controlled for the average modal controllability of each system for system-level analysis , and controlled for the whole-brain average modal controllability for whole-brain analysis . Fifth , one might expect that the modular organization of the brain’s structural network could potentially change the control energy cost of brain state transitions ( Avena-Koenigsberger et al . , 2017 ) . Prior work has reported age-related increases in brain network modularity during youth ( Baum et al . , 2017; Hagmann et al . , 2010; Huang et al . , 2015 ) . Here , we evaluated if observed developmental associations with control energy might be driven by changes in network modularity . We calculated network modularity quality ( Q ) using the community structure defined by the functional atlas ( Yeo et al . , 2011 ) as in Baum et al . ( 2017 ) . For comparability with analyses of control energy , we scaled the matrix by the maximum eigenvalue before calculating Q . However , for this specific analysis , we did not subtract the identity matrix because it would lead to ( uninterpretable ) negative values of Q . We controlled for Q by including it as a model covariate in sensitivity analyses , which were conducted at all resolutions ( whole brain , functional systems , and network nodes ) . Further , we evaluated the possibility that the segregation of the fronto-parietal system during youth ( Baum et al . , 2017 ) could explain the age effect of control energy . We calculated the average participation coefficient of the fronto-parietal system , and evaluated if developmental associations with control energy in the fronto-parietal system remained while controlling for the average participation coefficient in this system alongside with other covariates . Finally , we evaluated an alternative , biologically recorded target state that was defined using the activation pattern from a working memory task that reliably recruits the fronto-parietal network and executive system . Specifically , the target state was defined as the average participants’ 2-back > 0-back contrast from a fractal n-back working memory task ( Ragland et al . , 2002 ) ; task design and image processing was as previously detailed ( Satterthwaite et al . , 2013 ) . For each participant , we calculated the control energy cost to transition from the baseline ( zero ) state to this target activation state defined by the average pattern of activation recruited by the working memory task . As in the main analyses , we compared the control energy cost from real brain networks and that from null networks . Furthermore , we calculated the developmental association between control energy and age at multiple scales , including the whole brain , each cognitive system , and for each network node ( with covariates as prior ) . | Adolescents are known for taking risks , from driving too fast to experimenting with drugs and alcohol . Such behaviors tend to decrease as individuals move into adulthood . Most people in their mid-twenties have greater self-control than they did as teenagers . They are also often better at planning , sustaining attention , and inhibiting impulsive behaviors . These skills , which are known as executive functions , develop over the course of adolescence . Executive functions rely upon a series of brain regions distributed across the frontal lobe and the lobe that sits just behind it , the parietal lobe . Fiber tracts connect these regions to form a fronto-parietal network . These fiber tracts are also referred to as white matter due to the whitish fatty material that surrounds and insulates them . Cui et al . now show that changes in white matter networks have implications for teen behavior . Almost 950 healthy young people aged between 8 and 23 years underwent a type of brain scan called diffusion-weighted imaging that visualizes white matter . The scans revealed that white matter networks in the frontal and parietal lobes mature over adolescence . This makes it easier for individuals to activate their fronto-parietal networks by decreasing the amount of energy required . Cui et al . show that a computer model can predict the maturity of a person's brain based on the energy needed to activate their fronto-parietal networks . These changes help explain why executive functions improve during adolescence . This in turn explains why behaviors such as risk-taking tend to decrease with age . That said , adults with various psychiatric disorders , such as ADHD and psychosis , often show impaired executive functions . In the future , it may be possible to reduce these impairments by applying magnetic fields to the scalp to reduce the activity of specific brain regions . The techniques used in the current study could help reveal which brain regions to target with this approach . | [
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] | 2020 | Optimization of energy state transition trajectory supports the development of executive function during youth |
Premature fusion of the cranial sutures ( craniosynostosis ) , affecting 1 in 2000 newborns , is treated surgically in infancy to prevent adverse neurologic outcomes . To identify mutations contributing to common non-syndromic midline ( sagittal and metopic ) craniosynostosis , we performed exome sequencing of 132 parent-offspring trios and 59 additional probands . Thirteen probands ( 7% ) had damaging de novo or rare transmitted mutations in SMAD6 , an inhibitor of BMP – induced osteoblast differentiation ( p<10−20 ) . SMAD6 mutations nonetheless showed striking incomplete penetrance ( <60% ) . Genotypes of a common variant near BMP2 that is strongly associated with midline craniosynostosis explained nearly all the phenotypic variation in these kindreds , with highly significant evidence of genetic interaction between these loci via both association and analysis of linkage . This epistatic interaction of rare and common variants defines the most frequent cause of midline craniosynostosis and has implications for the genetic basis of other diseases .
The cranial sutures are not fused at birth , allowing for doubling of brain volume in the first year of life and continued growth through adolescence ( Persing et al . , 1989 ) . The metopic suture normally closes between 6 and 12 months , while the sagittal , coronal , and lambdoid sutures typically fuse in adulthood ( Persing et al . , 1989; Weinzweig et al . , 2003 ) . Premature fusion of any of these sutures can result in brain compression and suture-specific craniofacial dysmorphism ( Figure 1 ) . Studies of syndromic forms of craniosynostosis , each with prevalence of ~1/60 , 000 to 1/1 , 000 , 000 live births and collectively accounting for 15–20% of all cases , have implicated mutations in more than 50 genes ( Twigg and Wilkie , 2015; Flaherty et al . , 2016 ) . For example , mutations that increase MAPK/ERK signaling ( e . g . FGFR1-3 ( Twigg and Wilkie , 2015; Flaherty et al . , 2016 ) , ERF [Twigg et al . , 2013] ) cause rare syndromic coronal or multisuture craniosynostosis , while mutations that perturb SMAD signaling ( e . g . TGFBR1/2 [Loeys et al . , 2005] , SKI [Doyle et al . , 2012] , RUNX2 [Mefford et al . , 2010; Javed et al . , 2008] ) cause rare syndromes involving the midline ( sagittal and metopic ) sutures . While the detailed pathophysiology of premature suture fusion has not been elucidated , aberrant signaling in cranial neural crest cells during craniofacial development has been suggested as a common mechanism ( Mishina and Snider , 2014; Komatsu et al . , 2013 ) . 10 . 7554/eLife . 20125 . 003Figure 1 . Phenotypes of midline craniosynostosis . ( a ) Normal infant skull with patent sagittal ( S ) and metopic ( M ) sutures . ( b ) Three-dimensional reconstruction of computed tomography ( 3D CT ) demonstrating premature fusion of both the sagittal and metopic sutures . ( c ) A three-month-old boy with sagittal craniosynostosis featuring scaphocephaly ( narrow and elongated cranial vault ) , and frontal bossing . ( d ) 3D CT reconstruction of a one-month-old boy found to have sagittal craniosynostosis . ( e ) A six-month-old boy presenting with trigonocephaly ( triangulation of the cranial vault , with prominent forehead ridge resulting from premature fusion of the metopic suture ) and hypotelorism ( abnormally decreased intercanthal distance , also a result of premature fusion of the metopic suture ) . 3D CT reconstruction demonstrated metopic craniosynostosis . ( f ) 3D CT reconstruction demonstrating premature fusion of the metopic suture with characteristic trigonocephaly and hypotelorism . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 003 Despite success in identifying the genes underlying rare syndromic craniosynostosis , mutations in these genes are very rarely found in their non-syndromic counterparts ( Boyadjiev and International Craniosynostosis Consortium , 2007 ) . Non-syndromic craniosynostosis of the midline sutures account for 50% of all craniosynostosis ( Slater et al . , 2008; Greenwood et al . , 2014 ) . A GWAS of non-syndromic sagittal craniosynostosis has implicated common variants in a segment of a gene desert ~345 kb downstream of BMP2 , and within an intron of BBS9; these risk alleles have unusually large effect ( odds ratios > 4 at each locus ) ( Justice et al . , 2012 ) . Nonetheless , rare alleles with large effect have not been identified to date in non-syndromic sagittal or metopic craniosynostosis . We considered that the often sporadic occurrence of non-syndromic craniosynostosis might frequently be attributable to de novo mutation or incomplete penetrance of rare transmitted variants .
We recruited a cohort of 191 probands with non-syndromic midline craniosynostosis , including 132 parent-offspring trios and 59 probands with one parent , along with selected extended family members ( see Materials and methods ) . All probands had undergone reconstructive surgery for either isolated sagittal ( n = 113 ) , metopic ( n = 70 ) or combined sagittal and metopic ( n = 8 ) craniosynostosis . Seventeen kindreds had 1 to 3 additional affected family members , including 7 parents , 12 siblings , 3 aunts/uncles and 4 more distant relatives of probands . DNA was prepared from buccal swab samples . Exome sequencing was performed as described in Materials and methods; summary data are shown in Supplementary file 1A . Variants were called using the GATK pipeline ( see Materials and methods ) and de novo mutations in parent-offspring trios were called using TrioDeNovo ( Wei et al . , 2015 ) . The impact of identified missense variants on protein function was inferred using MetaSVM ( Dong et al . , 2015 ) . All de novo calls were verified by in silico visualization of aligned reads ( Figure 2—figure supplement 1 ) , and all calls contributing to significant results for individual genes were verified by direct Sanger sequencing . We identified a total of 144 de novo mutations , providing a de novo mutation rate of 1 . 64 × 10–8 per base pair , and 1 . 09 de novo mutations in the coding region per offspring , consistent with prior experimental results and expectation ( Ware et al . , 2015; Homsy et al . , 2015 ) ( Table 1 ) . Comparison of the observed distribution of mutation types to the expected from the Poisson distribution demonstrated significant enrichment of protein-altering mutations , predominantly accounted for by an excess of damaging missense mutations ( MetaSVM D-mis; 28 observed D-mis compared to 14 . 5 expected , p=1 . 0 × 10–3 , 1 . 93-fold enrichment ) , with a corresponding paucity of silent mutations ( 21 compared to 40 . 4 expected , p=3 . 0 × 10–4 ) . From the difference in the observed vs . expected number of de novo protein-altering mutations per subject , we infer that these de novo mutations contribute to ~15% of non-syndromic midline craniosynostosis . 10 . 7554/eLife . 20125 . 004Table 1 . Enrichment of protein-altering de novo mutations in 132 subjects with sagittal and/or metopic craniosynostosis . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 00410 . 7554/eLife . 20125 . 005Table 1—source data 1 . De novo mutations in 132 trios with sagittal and/or metopic craniosynostosis . Mutations highlighted in orange are likely loss of function mutations , those highlighted in blue are likely damaging missense mutations ( D-mis ) as called by MetaSVM , and those without highlight are predicted to be tolerated ( T-mis ) or are synonymous ( syn ) . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 005ObservedExpectedEnrichmentp-valueClass##/subject##/subjectAll mutations1441 . 09142 . 81 . 081 . 010 . 47Synonymous210 . 1640 . 40 . 310 . 523 . 0 × 10−4Protein altering1230 . 93102 . 40 . 781 . 170 . 03Total missense1100 . 8389 . 70 . 681 . 230 . 02T-mis820 . 6275 . 20 . 571 . 090 . 23D-mis280 . 2114 . 50 . 111 . 931 . 0 × 10−3Loss of function ( LOF ) 130 . 1012 . 70 . 101 . 030 . 50LOF + D-mis410 . 3127 . 10 . 211 . 517 . 8 × 10−3# , number of de novo mutations in 132 subjects; #/subject , number of de novo mutations per subject; Damaging and tolerated missense called by MetaSVM ( D-mis , T-mis respectively ) ; Loss of function denotes premature termination , frameshift , or splice site mutation . For mutation classes with enrichment compared to expectation , p-values represent the upper tail of the Poisson probability density function . For mutation classes in which we observed a paucity of mutations compared to expectation , p-values represent the lower tail . Analysis of de novo mutation burden revealed that a single gene , SMAD6 , harbored three de novo mutations , including two inferred loss of function ( LOF ) mutations ( p . Q78fs*41 and p . E374* ) and one D-mis mutation ( p . G390C ) . All three were heterozygous and occurred in families in which the proband was the sole affected member . Two de novo mutations occurred in probands and one occurred in an unaffected mother of a proband ( Figure 2 ) . All three de novo mutations were confirmed by Sanger sequencing of PCR amplicons containing the putative mutation ( Figure 2—figure supplement 2 ) . TTN , which encodes the largest human protein , was the only other gene with more than one protein altering de novo mutation , and both of these were predicted by MetaSVM to encode tolerated variants ( p . I3580M , p . T19373S ) . From the prior probability of de novo mutation of each base in SMAD6 and the impact on the encoded protein ( Samocha et al . , 2014 ) , the probability of seeing at least two de novo LOFs and one missense mutation by chance in a cohort of this size was 3 . 6 × 10–9 ( Table 2 ) . Similarly , observing two or more de novo LOF mutations in any gene in this cohort was not expected by chance ( p=8 . 4 × 10–3 , see Materials and methods ) . Lastly , SMAD6 is not unusually mutable , as we found no de novo SMAD6 mutations in 900 control trios comprising healthy siblings of individuals with autism ( Iossifov et al . , 2014; O'Roak et al . , 2011; Sanders et al . , 2012 ) . These findings provide highly significant evidence implicating damaging mutations in SMAD6 as a cause of midline suture craniosynostosis . 10 . 7554/eLife . 20125 . 006Figure 2 . Segregation of SMAD6 mutations and BMP2 SNP genotypes in pedigrees with midline craniosynostosis . ( a ) Domain structure of SMAD6 showing location of the MH1 and MH2 domains . The MH1 domain mediates DNA binding and negatively regulates the functions of the MH2 domain , while the MH2 domain is responsible for transactivation and mediates phosphorylation-triggered heteromeric assembly with receptor SMADs . De novo or rare damaging mutations identified in craniosynostosis probands are indicated . Color of text denotes suture ( s ) showing premature closure . ( b ) Pedigrees harboring de novo ( denoted by stars within pedigree symbols ) or rare transmitted variants in SMAD6 . Filled and unfilled symbols denote individuals with and without craniosynostosis , respectively . The SMAD6 mutation identified in each kindred is noted above each pedigree . Below each symbol , genotypes are shown first for SMAD6 ( with 'D' denoting the damaging allele ) and for rs1884302 risk locus downstream of BMP2 , ( with 'T' conferring protection from and 'C' conferring increased risk of craniosynostosis ) . All 17 subjects with craniosynostosis have SMAD6 mutations , and 14/17 have also inherited the risk allele at rs1884302 , whereas only 3 of 16 SMAD6 mutation carriers without the rs1884302 risk allele have craniosynostosis . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 00610 . 7554/eLife . 20125 . 007Figure 2—source data 1 . Variants identified in SMAD6 . Highlighted variants indicate de novo mutations; 'D' and 'T' respectively denote damaging and tolerated missense variants called by MetaSVM . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 00710 . 7554/eLife . 20125 . 008Figure 2—source data 2 . PCR primer sequences for Sanger sequencing of reported variants . Source Data for Figure 2—figure supplement 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 00810 . 7554/eLife . 20125 . 009Figure 2—figure supplement 1 . Plots of independent Illumina sequencing reads in a parent-offspring trio showing de novo SMAD6 mutation . The reference sequence of a segment of SMAD6 that includes base 15:67073502 ( denoted by arrow ) is shown in the top row , with red , blue , green and yellow squares representing A , C , G , T , respectively . Below , all independent reads that map to this interval are shown . The results show that the proband has 23 reads of reference ‘G’ , and 10 reads of non-reference ‘T’ . Only the reference ‘G’ is seen in both parents , providing evidence of a de novo mutation . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 00910 . 7554/eLife . 20125 . 010Figure 2—figure supplement 2 . Confirmation of SMAD6 mutations by Sanger sequencing of PCR products . Sanger sequencing traces of PCR amplicons containing SMAD6 mutations identified by exome sequencing are shown . Above each trace or set of traces , the kindred ID , mutation identified in the DNA sequence and its impact on SMAD6 protein is indicated . Above sequence traces , the inferred DNA sequence is shown , along with the inferred amino acid sequence ( shown in single letter code ) . Heterozygous mutations are indicated beneath the wild-type sequence and non-reference amino acid sequences are shown in red . Deleted and inserted bases are denoted , and result in an overlap of wild-type and mutant sequences . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 01010 . 7554/eLife . 20125 . 011Table 2 . Probability of observed de novo mutations in SMAD6 and Sprouty genes occurring by chance in 132 subjects using gene-specific mutation probabilities . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 011Gene ( s ) MutationsNumber of observed mutationsNumber of expected mutationsp valueSMAD6Loss of function20 . 000263 . 31 × 10−8SMAD6Missense10 . 00464 . 67 × 10−3SPRY1 , SPRY2 , SPRY3 , SPRY4Nonsense , splice site , frameshift20 . 0011937 . 11 × 10−7Probabilities calculated from the Poisson distribution using DenovolyzeR . The probability of observing at least 2 LOF and 1 missense mutation in SMAD6 was 3 . 6 ×10−9 via Fisher’s method . We next considered the total burden of rare ( prospectively specified allele frequency in ExAC database <2 × 10–5 ) LOF and D-mis mutations in each gene in probands . Among 191 probands , we found 1135 rare LOF and 3156 rare damaging ( LOF + D-mis ) alleles . The probability of the observed number of rare variants in each gene occurring by chance was calculated from the binomial distribution after adjusting for the length of each gene; Q-Q plots comparing the observed and expected P-value distributions are shown in Figure 3 . The observed distribution conforms closely to expected with the exception of SMAD6 . The expected number of rare LOF alleles in SMAD6 in probands was 0 . 05 , and the observed number was 8 ( p=1 . 1 × 10–15 , 156-fold enrichment ) . Similarly , there were 13 rare damaging variants in SMAD6 compared to 0 . 14 expected ( p=1 . 3 × 10–21 , 91 . 4-fold enrichment ) . All of these SMAD6 variants were confirmed by direct Sanger sequencing ( Figure 2—figure supplement 2 ) . All were heterozygous and different from one another ( Figure 2—source data 1 ) ; 11 were absent among >105 alleles in the ExAC database , while two were previously seen , each once in ExAC ( p . E407* and p . R465C , ExAC allele frequencies 9 . 0 × 10–6 and 9 . 4 × 10–6 respectively ) . The results for SMAD6 remain highly significant after excluding de novo mutations and only analyzing transmitted variants ( Figure 3—figure supplement 1 ) , demonstrating a significant contribution of both de novo and transmitted variants ( Table 3 ) . The fact that eight of the 13 rare heterozygous damaging variants in SMAD6 seen in our cohort are frameshift ( n = 5 ) or premature termination ( n = 3 ) mutations , which are distributed throughout the encoded protein ( Figure 2a ) , strongly supports haploinsufficiency as the mechanism of the genetic contribution of SMAD6 to craniosynostosis . 10 . 7554/eLife . 20125 . 012Figure 3 . Quantile-quantile plots of observed versus expected p-values comparing the burden of rare LOF and damaging ( LOF + D-mis ) variants in protein-coding genes in craniosynostosis cases . Rare ( allele frequency <2 × 10–5 in the ExAC03 database ) loss of function ( LOF ) and damaging missense ( D-mis ) variants were identified in 191 probands . The probability of the observed number of variants in each gene occurring by chance was calculated from the total number of observed variants and the length of the coding region of each gene using the binomial test . The distribution of observed P-values compared to the expected distribution is shown . ( a ) Q-Q plot for rare LOF variants in each gene from a total of 1135 LOF variants identified in probands . The distribution of observed p-values closely conforms to expectation with the exception of SMAD6 , which shows p=1 . 1 × 10–15 and 156-fold enrichment in cases . ( b ) Q-Q plot for rare damaging ( LOF + D-mis ) variants in each gene from a total of 3156 damaging variants in probands . Again , SMAD6 deviates greatly from the expected distribution , with p<10–20 and 91-fold enrichment . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 01210 . 7554/eLife . 20125 . 013Figure 3—source data 1 . Source data for Figure 3—figure supplement 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 01310 . 7554/eLife . 20125 . 014Figure 3—figure supplement 1 . Quantile-quantile plots comparing all transmitted , damaging variants in protein-coding genes in 191 probands with midline craniosynostosis to the expected binomial distribution . De novo variants were excluded from this analysis , leaving 1122 rare ( ExAC allele frequency < 2 x10−5 ) , transmitted LOF variants and 3115 transmitted damaging ( LOF + D-mis ) variants . All genes closely matched expectation , with the exception of SMAD6 . ( a ) There were 6 transmitted SMAD6 LOF mutations , a 118-fold enrichment compared to the expected 0 . 05 ( p=2 . 2 × 10–11 ) . ( b ) Similarly , there were 10 transmitted damaging SMAD6 variants , a 71-fold enrichment compared to the expected 0 . 14 ( p=7 . 0 × 10–16 ) . The results demonstrate genome-wide significance of rare transmitted variants in SMAD6 independent of de novo mutations . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 01410 . 7554/eLife . 20125 . 015Figure 3—figure supplement 2 . Principal-component analysis of 191 probands and 3337 European autism controls . ( a ) Principal component analysis of exome sequence genotypes from 191 probands with sagittal , metopic , or combined sagittal and metopic craniosynostosis clustered along with HapMap subjects . Results identify 172 craniosynostosis subjects that cluster with HapMap European subjects . ( b ) Principal component analysis of genotypes from exome sequencing data of European autism parent controls ( n = 3337 ) showing clustering with HapMap subjects . In both panels , subjects considered to be of European ancestry are circled . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 01510 . 7554/eLife . 20125 . 016Figure 3—figure supplement 3 . Quantile-quantile plot of observed versus expected p-values comparing the burden of damaging ( LOF + D-mis ) variants in protein-coding genes in craniosynostosis cases and controls . The frequency of rare ( allele frequency < 2 × 10–5 in the ExAC03 database ) loss of function and D-mis variants in each gene was compared in 172 European probands with midline craniosynostosis and 3337 European controls . The distribution of observed p-values conforms to expectation with the exception of SMAD6 , which deviates significantly from expectation . Because exon 1 of SMAD6 was poorly captured using the V2 capture reagent ( used in control samples ) , 3 damaging variants in exon 1 in cases were excluded from this analysis . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 01610 . 7554/eLife . 20125 . 017Table 3 . Enrichment of de novo and transmitted damaging variants in SMAD6 in craniosynostosis . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 017ObservedExpectedEnrichmentp-valueDe novo LOF and D-mis30 . 00496123 . 6 × 10−9Transmitted LOF and D-mis100 . 140471 . 27 . 0 × 10−16Total130 . 145389 . 51 . 4 × 10−22LOF , loss of function; D-mis , damaging missense variants per MetaSVM; The total number of SMAD6 variants expected in this cohort was calculated by summing the expected number of de novo and transmitted variants . P-value combining probabilities from de novo and transmitted protein damaging SMAD6 variants was determined by Fisher’s method . Lastly , we compared the frequency of rare ( allele frequency <2 × 10–5 in the ExAC database ) damaging ( LOF + D-mis ) variants in all genes in 172 European probands and 3337 unrelated European controls , who were parents of autism probands sequenced to a similar depth of coverage and analyzed in a similar fashion ( see Materials and methods , Supplementary file 1A ) . European ancestry was determined by principal component analysis of exome sequence data ( Figure 3—figure supplement 2 ) . Q-Q plots showed that the observed distribution of Fisher’s exact statistics comparing the frequency of damaging variants in cases and controls closely corresponded to the expected distribution , again with the exception of SMAD6 , in which cases showed enrichment of damaging variants ( p=6 . 3 × 10–8 ) and LOF variants ( p=5 . 7 × 10–6 ) ( Figure 3—figure supplement 3 ) . Significant enrichment was also seen in comparison to European NHLBI and ExAC controls ( Figure 3—source data 1 ) . The odds ratios for association of all damaging variants in SMAD6 in cases vs . controls was consistent across control cohorts , ranging from 26 . 9 to 35 . 1; the odds ratios for LOFs ranged from 102 . 6 to infinity ( owing to zero LOF’s in autism controls ) . Aside from SMAD6 , no other single gene approached genome-wide significance in these analyses of dominant alleles . Analysis of recessive genotypes , considering alleles with frequency <10–3 , identified no genes with more than one rare recessive genotype . Collectively , the significant burden of both de novo and rare transmitted mutations , along with significant association results in case-control analysis provide extremely strong evidence that rare damaging SMAD6 alleles impart large effects on risk of non-syndromic midline craniosynostosis . SMAD6 mutations were significantly more frequent in kindreds with any metopic craniosynostosis ( 10 of 78 , 12 . 8% ) compared to those with isolated sagittal craniosynostosis ( 3 of 113 , 2 . 7%; p=8 . 1 × 10–3 by Fisher’s exact test , odds ratio 5 . 3 ) . These results suggest that mutations in SMAD6 confer greater risk for metopic suture closure . We found no significant correlation between the type of mutation ( LOF vs . D-mis ) or location within the gene of SMAD6 mutation and phenotypic class ( Table 4 ) . 10 . 7554/eLife . 20125 . 018Table 4 . Distribution of suture involvement in kindreds with and without rare ( allele frequency < 2 × 10−5 ) de novo and transmitted damaging ( LOF + D-mis ) variants in SMAD6 . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 018Total # kindredsTotal # SMAD6 mutations ( % ) # LOF ( % ) Sagittal1133 ( 2 . 7 ) 2 ( 1 . 8 ) Metopic707 ( 10 ) 3 ( 3 . 9 ) Sagittal and Metopic83 ( 37 . 5 ) 3 ( 37 . 5 ) Total19113 ( 6 . 8 ) 8 ( 4 . 2 ) Interestingly , transmitted SMAD6 mutations were significantly enriched in kindreds with familial craniosynostosis , accounting for 4 of 17 kindreds with more than one affected subject ( p=0 . 02 , Fisher’s exact test; odds ratio 5 . 6 ) . In these kindreds , all four additional affected subjects carried the SMAD6 mutation found in the proband ( Figure 2 ) . SMAD6 is a member of the inhibitory-SMAD family . Activation of BMP receptors leads to phosphorylation of receptor SMADs , which can complex with SMAD4 , translocate to the nucleus and partner with RUNX2 to induce transcription of genes that promote osteoblast differentiation ( Javed et al . , 2008; Hata et al . , 1998 ) ( Figure 4a ) . This process is inhibited by SMAD6 binding to phosphorylated receptor SMADs , forming an inactive complex . SMAD6 also inhibits BMP signaling by complexing with the ubiquitin ligase SMURF1 , which ubiquitylates BMP receptors , receptor SMADs and RUNX2 , leading to their proteasomal degradation ( Figure 4b ) ( Murakami et al . , 2003 ) . This pathway plays a well-established role in the development of the cranial vault and closure of cranial sutures . In mice , constitutive activity of BMPR1A in cranial neural crest results in SMAD-dependent development of metopic craniosynostosis ( Komatsu et al . , 2013 ) , and genetic deficiency for the SMAD inhibitor SMURF1 causes midline craniosynostosis ( Shimazu et al . , 2016 ) . Similarly , duplication of RUNX2 causes syndromic metopic craniosynostosis in humans ( Mefford et al . , 2010 ) . Lastly , SMAD6 knockout mice are born with domed skulls and show anomalous bone deposition in the metopic suture; they also show an augmented and prolonged response to BMP2 stimulation ( Estrada et al . , 2011; Retting , 2008 ) . These findings are consistent with haploinsufficiency as the mechanism of SMAD6 mutations in craniosynostosis , with loss of the inhibitory effect of SMAD6 promoting increased BMP signaling and premature closure of sutures . 10 . 7554/eLife . 20125 . 019Figure 4 . SMAD6 inhibits osteoblast differentiation by inhibiting BMP-mediated SMAD signaling ( Salazar et al . , 2016 ) . ( a ) BMP ligands activate BMP receptors , leading to phosphorylation of receptor-regulated SMADs ( R-SMADs ) , which complex with SMAD4 and enter the nucleus , cooperating with RUNX2 to induce osteoblast differentiation . SMAD6 inhibits this signal by competing with SMAD4 for binding to R-SMADs , preventing nuclear translocation . ( b ) SMAD6 also cooperates with SMURF1 , an E3 ubiquitin ligase , to induce ubiquitin-mediated proteasomal degradation of R-SMADs , BMP receptor complexes , and RUNX2 . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 019 We explored our kindreds for other mutations in this signaling pathway . Interestingly , we identified one de novo D-mis mutation in SMURF1 in a proband with sporadic metopic craniosynostosis ( Figure 5 ) . 10 . 7554/eLife . 20125 . 020Figure 5 . A de novo variant identified in SMURF1 . ( a ) Sanger sequence electropherogram of a PCR product amplified from the genomic DNA of a proband with metopic craniosynostosis , confirming a de novo R468W mutation in SMURF1 , a SMAD6 binding partner . ( b ) Patient photographs of the proband , who presented with trigonocephaly and mild orbital abnormalities . 3D CT reconstruction demonstrates metopic craniosynostosis , trigonocephaly , and a patent sagittal suture . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 020 Within the 13 kindreds harboring rare damaging SMAD6 variants , all 17 affected subjects had the SMAD6 mutation found in the proband ( Figure 2 ) . Nonetheless , SMAD6 mutations showed striking incomplete penetrance . In particular , zero of 10 parental SMAD6 mutation carriers had a diagnosis of , or showed evidence of craniosynostosis . Examination of Illumina read counts and Sanger sequence traces provided no suggestion that the mutations were mosaic in unaffected parents ( mean/median of 52 . 9%/52 . 4% variant reads in transmitting parents , respectively; range 37 . 3% to 71 . 4% ) . There was no significant effect of gender on penetrance . From the data in these kindreds , the penetrance of SMAD6 mutations is estimated at 24% following exclusion of probands , who were ascertained for the presence of disease ( 57% if probands are included ) . The striking absence of craniosynostosis among transmitting parents suggests the possibility of purifying selection , with subjects having craniosynostosis less likely to have offspring . We considered whether inheritance at other genetic loci might account for the striking incomplete penetrance of SMAD6 mutations in these kindreds . A previous GWAS of non-syndromic sagittal craniosynostosis implicated common variants ~345 kb downstream of the closest gene , BMP2 ( encoding bone morphogenetic protein 2 ) , with unusually large effect size ( e . g . , rs1884302 , with risk allele frequency of 0 . 34 and odds ratio of 4 . 6 ) . BMP2 is a ligand for BMP receptors upstream of SMAD signaling ( Salazar et al . , 2016 ) and is an inducer of osteogenesis . We posited that risk alleles at this locus might increase the penetrance of SMAD6 mutations by increasing BMP2 levels and further increasing SMAD signaling . Genotypes for rs1884302 are shown in Figure 2 and provide strong evidence of epistatic interaction between SMAD6 and BMP2 alleles . Fourteen individuals had both a SMAD6 mutation and the rs1884302 risk allele; 100% of these had craniosynostosis . In contrast , 16 subjects had a SMAD6 mutation but no rs1884302 risk allele; only 3 of these individuals ( 19% ) had craniosynostosis . Lastly , 0 of 18 members of these kindreds who had only the rs1884302 risk allele had craniosynostosis . The relationship of these two genotypes to craniosynostosis in these kindreds was highly significant ( p=1 . 4 × 10–10 by the Freeman-Halton extension of Fisher’s exact test; Table 5 ) . 10 . 7554/eLife . 20125 . 021Table 5 . Risk of craniosynostosis in SMAD6 mutation carriers in the presence or absence of a BMP2 risk allele . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 021SMAD6/BMP2 GenotypesCraniosynostosis ( + ) Craniosynostosis ( − ) SMAD6 ( + ) / BMP2 risk allele ( + ) 140SMAD6 ( + ) / BMP2 risk allele ( − ) 313SMAD6 ( − ) / BMP2 risk allele ( + ) 018All members of kindreds found to have a mutation in SMAD6 were included . SMAD6 ( + ) indicates the presence of a heterozygous LOF or D-mis allele . The reported BMP2 risk allele is ‘C’ at risk locus rs1884302 , found within a gene desert ~345kb downstream of BMP2 . p=1 . 4 × 10−10 by the Freeman-Halton extension of Fisher’s exact test . Odds ratio in favor of disease was incalculable due to the absence of craniosynostosis in SMAD6 ( − ) individuals in these kindreds . Confining analysis just to subjects with SMAD6 mutation , there was dramatically increased occurrence of craniosynostosis among those with the rs1884302 risk allele compared to those without ( p=4 . 8 × 10–6 by Fisher’s exact test ) . The presence of the rs1884302 risk allele increased the risk of craniosynostosis >5-fold with a very high odds ratio that includes infinity owing to 100% penetrance among those with risk genotypes at both loci . This two locus contribution is further supported by significant transmission disequilibrium , with rs1884302 risk alleles transmitted from heterozygous parents to affected offspring in 11 of 13 transmissions ( p=0 . 013 by Chi-square , Supplementary file 1B ) . In sum , inheritance at rs1884302 explains nearly all of the variation in phenotype among subjects with SMAD6 mutations and demonstrates two locus transmission of craniosynostosis . In contrast , common variants at the BBS9 locus that also showed strong association with midline craniosynostosis in case-control analysis ( OR > 4 ) ( Justice et al . , 2012 ) showed no significant interaction with SMAD6 ( TDT p=0 . 89; Supplementary file 1B ) , demonstrating specificity of the observed interaction of rare variants in SMAD6 and a common variant near BMP2 . Lastly , we compared the joint segregation of rare damaging SMAD6 and common BMP2 risk alleles to the segregation of craniosynostosis in a parametric two locus linkage model in these kindreds ( see Materials and methods , Supplementary file 1C ) . The results provided extremely strong evidence supporting linkage under a two locus model , with a maximum lod score of 7 . 37 ( odds ratio 2 . 3 × 107:1 in favor of linkage compared to the null hypothesis; family-specific lod scores are shown in Supplementary file 1D ) . The maximum likelihood model specified 100% penetrance of craniosynostosis when risk alleles at both loci are present , 9% penetrance when only a damaging SMAD6 allele is present , 0 . 08% or 0 . 32% penetrance when only one or two BMP2 risk alleles are present , and a 0 . 02% phenocopy rate , with zero recombination between trait and both marker loci . This two locus model was 1410 – fold more likely than than the best single locus model , in which damaging SMAD6 variants had penetrance of 20% . These results provide extremely strong statistical support for the two locus model by linkage and extends the genetic evidence beyond simple association to linkage within pedigrees , which is not susceptible to potential confounders such as population stratification , and is insensitive to misspecification of allele frequency . Previous research has implicated increased activity of the MAP kinase/ERK pathway in craniosynostosis ( Twigg et al . , 2013; Shukla et al . , 2007 ) . We identified one de novo LOF in both SPRY1 and SPRY4 , developmental regulators of the MAP kinase/ERK pathway; these variants comprised two of only 11 de novo LOFs other than those in SMAD6 . The SPRY4 mutation ( p . E160* ) arose de novo in a proband with sagittal craniosynostosis and no family history ( Figure 6a ) . The SPRY1 mutation ( p . Q6fs*8 ) was de novo in a woman with mild cranial dysmorphism who did not undergo surgery , and was transmitted to both of her children , who both had sagittal craniosynostosis ( Figure 6b ) . The probability of observing two or more de novo LOF mutations in any of the 4 Sprouty genes by chance in this cohort surpassed genome-wide significance ( p=7 . 1 × 10–7 , Table 2 ) . Consistent with a role for SPRY1 haploinsufficiency , a de novo microdeletion that included SPRY1 has previously been reported in a child with sagittal craniosynostosis ( Fernández-Jaén et al . , 2014 ) . Moreover , in mice with TWIST1 haploinsufficiency , a model of syndromic craniosynostosis , overexpression of SPRY1 prevents suture fusion ( Connerney et al . , 2008 ) . Lastly , protein altering de novo mutations were also identified in other regulators and mediators of MAP kinase signaling , including RASAL2 , DUSP5 , MAP3K8 , KSR2 , RPS6KA4 , and RGS3 . 5 of 6 occurred in probands with sagittal craniosynostosis ( Table 1—source data 1 ) . Determining the significance of these findings will require further study . 10 . 7554/eLife . 20125 . 022Figure 6 . De novo loss-of-function mutations in Sprouty genes . ( a ) Pedigree and Sanger sequencing traces for kindred SAG150 , demonstrating a de novo nonsense mutation in SPRY4 ( p . E160* ) in the proband . ( b ) Pedigree and Sanger sequencing traces in a kindred with a de novo SPRY1 frameshift mutation ( p . Q6fs*8 ) that was transmitted to two affected offspring . DOI: http://dx . doi . org/10 . 7554/eLife . 20125 . 022
These findings implicate a two locus model of inheritance in non-syndromic midline craniosynostosis via epistatic interactions of rare heterozygous SMAD6 mutations and common risk alleles near BMP2 . There is extremely strong evidence implicating each locus independently , along with highly significant evidence from both analysis of association and linkage that the risk of craniosynostosis is markedly increased in individuals carrying risk alleles at both loci compared to those with only a single risk allele at either locus . Rare damaging variants in SMAD6 alone impart very large effects on disease risk with low penetrance , with inheritance at BMP2 explaining nearly all of the variation in occurrence of craniosynostosis seen among SMAD6 mutation carriers . The results support a threshold effect model , with quantitative increases in SMAD signaling resulting from reduced inhibition of SMAD signaling by SMAD6 , owing to haploinsufficiency ( strongly supported by a plethora of LOF variants distributed across SMAD6 ) , along with a putative increase in SMAD signaling owing to increased BMP2 expression via the risk SNP rs1884302 leading to accelerated closure of midline sutures . Consistent with this model , as articulated previously , substantial prior evidence in mouse and human has implicated BMP signaling via SMADs in closure of the midline sutures , and SMAD6 in inhibiting this pathway . Moreover , consistent with the common variant near BMP2 modifying BMP2 expression , duplication of a nearby limb-specific enhancer increased BMP2 expression , leading to a Mendelian limb defect ( Dathe et al . , 2009 ) . While the genetic data provide unequivocal support for the role of these two loci in midline craniosynostosis , and for haploinsufficiency as the mechanism of SMAD6 contribution , further studies will be necessary to delineate the precise mechanism by which the risk genotypes cause disease . SMAD6 mutations with and without BMP2 risk alleles account for ~7% of probands in this cohort of non-syndromic midline craniosynostosis . This frequency is much greater than any other genotype causing syndromic midline craniosynostosis ( e . g . , TGFBR1/2 , SKI , RUNX2 ) , which are sufficiently rare that their prevalence has not been well-established . Moreover , because non-syndromic sagittal and metopic craniosynostosis comprise half of all craniosynostoses ( Slater et al . , 2008; Greenwood et al . , 2014 ) , SMAD6/BMP2 genotypes are inferred to account for ~3 . 5% of all craniosynostosis , and are likely rivaled in frequency only by mutations in FGFR2 as the most frequent cause of all craniosynostoses ( Twigg and Wilkie , 2015 ) . These findings will be of immediate utility in clinical diagnosis and genetic counseling . The combination of a rare damaging SMAD6 mutation plus a common BMP2 risk allele conferred 100% risk of craniosynostosis in our cohort , while those with a SMAD6 mutation but no BMP2 risk allele were at markedly lower risk . Interestingly , these SMAD6 mutation-only cases thus far have all had isolated metopic craniosynostosis ( Figure 2 ) . Rare damaging SMAD6 mutations were found in nearly 25% of kindreds with recurrent midline craniosynostosis , and 37 . 5% of patients with combined sagittal and metopic craniosynostosis in our cohort . The precision of penetrance estimates and prevalence in specific disease subsets will improve with larger sample sizes . Given the suggestion of a threshold for phenotypic effect , we also considered whether there might be additional SMAD6 alleles that impart phenotypic effect that have a higher frequency than the 2 × 10–5 threshold that we used . We found only one additional SMAD6 damaging allele among parents in our cohort with allele frequency <0 . 001 . Interestingly , this allele , p . E287K , ( ExAC frequency 3 . 3 × 10–5 ) was transmitted to a proband with sagittal and metopic craniosynostosis along with two doses of the BMP2 risk allele , each inherited from a heterozygous parent . Considering neurodevelopmental outcomes , 11 of 15 subjects with rare damaging SMAD6 variants who are more than one year of age ( and hence can have neurodevelopmental evaluation ) had some form of developmental delay ( Supplementary file 1E ) . While early surgical intervention provides the best neurological outcomes ( Patel et al . , 2014 ) , more than a third of patients with non-syndromic midline craniosynostosis have subtle learning disability ( Magge et al . , 2002; Shipster et al . , 2003; Sidoti et al . , 1996 ) . BMP signaling plays an essential role in vertebrate brain development ( Bier and De Robertis , 2015 ) , raising the possibility that aberrant BMP signaling could contribute to neurodevelopmental outcome independent of its effect on craniosynostosis . The only other clinical finding observed in more than one subject with SMAD6 mutation was a congenital inguinal hernia in 3 patients ( 16 . 7%; Supplementary file 1E ) . While SMAD6 was the only single gene showing genome-wide significant burden of de novo mutation , de novo protein-altering mutations are estimated to contribute to ~15% of cases . This estimated fraction is similar to estimates for autism and congenital heart disease , other diseases in which large-scale studies have shown a role for de novo mutations ( Homsy et al . , 2015; Iossifov et al . , 2014; Sanders et al . , 2012; Zaidi et al . , 2013; De Rubeis et al . , 2014 ) . Also like autism and congenital heart disease , few individual genes were implicated after sequencing modest numbers of trios , implying that de novo mutation in a large number of genes are likely to contribute to sagittal and metopic craniosynostosis . This observation strongly supports sequencing substantially larger numbers of non-syndromic patients , an approach that has proved highly productive for discovery of genes and pathways underlying autism and CHD . Lastly , these results provide a clear example of the epistatic ( non-additive ) interaction of very rare mutations at one locus with a common variant at a second , unlinked locus . This observation adds to the small number of two locus phenotypes that have been defined with robust genetic data ( Lupski , 2012 ) , and suggest that other common variants , particularly those with relatively large effect , may combine with rare alleles at one or more loci to produce genotypes with high penetrance that together may account for a substantial fraction of disease risk .
Participants were ascertained from either the Yale Pediatric Craniofacial Clinic or by responding to an invitation posted on the Cranio Kids- Craniosynostosis Support and Craniosynostosis-Positional Plagiocephaly Support Facebook pages . All participants or their parents provided written informed consent to participate in a study of the genetic causes of craniosynostosis in their family . Inclusion criteria included a diagnosis of sagittal and/or metopic craniosynostosis in the absence of known syndromic forms of disease by a craniofacial plastic surgeon or pediatric neurosurgeon . All probands had undergone reconstructive surgery . Participating family members provided buccal swab samples ( Isohelix SK-2S buccal swabs ) , craniofacial phenotype data , medical records , operative reports , and imaging studies . Written consent was obtained for publication of patient photographs . The study protocol was approved by the Yale Human Investigation Committee Institutional Review Board ( IRB ) . Control cohorts comprised 3337 previously studied healthy European parents of probands with autism , Europeans found in the NHLBI Exome Sequencing Project database ( NHLBI ) , and Non-Finnish Europeans found in the Exome Aggregation Consortium v0 . 3 database ( ExAC ) . DNA was prepared from buccal swab samples according to the manufacturer’s protocol . Exome sequencing was performed by exon capture using the Roche MedExome or Roche V2 capture reagent followed by 74 base paired-end sequencing on the Illumina HiSeq 2000 instrument as previously described ( Zaidi et al . , 2013 ) . Samples were barcoded then captured and sequenced in multiplex . Quality metrics are shown in Supplementary file 1A . Sequence reads were aligned to the GRCh37/hg19 human reference genome using BWA-Mem . Local realignment and quality score recalibration were performed using the GATK pipeline , after which variants were called using the Genome Analysis Toolkit Haplotype Caller . A Bayesian algorithm , TrioDeNovo , was used to call de novo mutations ( Wei et al . , 2015 ) . VQSR "PASS" variants with ExAC allele frequency ≤0 . 001 sequenced to a depth of 8 or greater in the proband and 12 or greater in each parent with Phred-scaled genotype likelihood scores >30 and de novo quality scores ( log10 ( Bayes factor ) ) >6 were considered . Independent aligned reads at variant positions were visualized in silico to remove false calls ( Figure 2—figure supplement 1 ) . For de novo calls passing visual inspection , variants receiving the highest de novo genotype quality score ( 100 ) were deemed valid . Forty of these de novo mutations were selected at random for validation by bidirectional Sanger sequencing of the proband and both parents; 100% of these tests confirmed de novo mutation in the proband . The observed number of de novo variants identified per trio closely matched the expected Poisson distribution ( Supplementary file 1F ) . All de novo variants named in the main text were confirmed by Sanger sequencing . Transmitted variants were similarly aligned and called as per above . All de novo and transmitted variants were annotated using ANNOVAR ( Wang et al . , 2010 ) . Allele frequencies of identified variants were taken from the ExAC database . The impact of nonsynonymous variants was predicted using the MetaSVM rank score , with scores greater than 0 . 83357 serving as a threshold for predicting that the mutation was deleterious ( MetaSVM 'D' , D-mis ) ( Dong et al . , 2015 ) . For case control burden analysis of all protein coding genes , all GATK VQSR 'PASS' variants were considered . In assessing the association of known risk alleles near BMP2 and within BBS9 with craniosynostosis in SMAD6 mutation carriers , genotypes for rs1884302 and rs10262453 were determined by direct Sanger sequencing . All mutations reported in SMAD6 , SMURF1 , SPRY1 , and SPRY4 were confirmed by direct bidirectional Sanger sequencing of the products of PCR amplification of segments containing putative mutations ( Figure 2—figure supplement 2 , Figure 5 , Figure 6 ) . PCR primers are listed in Figure 2—source data 2 . Sanger sequencing electropherograms were manually inspected using Geneious after alignment to the reference genome sequence in GRCh37/hg19 . The observed distribution of mutation type was compared to pre-computed expected values across the exome using Poisson statistics as described ( Ware et al . , 2015; Homsy et al . , 2015 ) . Pre-calculated gene-specific mutation probabilities were used to determine the probability of the observed number and type of de novo mutations in SMAD6 occurring by chance using denovolyzeR ( Ware et al . , 2015; Samocha et al . , 2014 ) . To assess the probability of observing 3 protein damaging mutations , P values for observing 2 de novo LOF’s and one de novo missense mutation were combined using the Fisher’s combined probability test . To assess the burden of de novo mutation in Sprouty genes , a gene set was curated in denovolyzeR including: SPRY1 , SPRY2 , SPRY3 , SPRY4 . The probability of observing 2 de novo LOF mutations in this gene set was calculated by comparing this number to expectation in denovolyzeR . The probability of observing more than one de novo LOF mutation in any gene in our cohort was determined using a permutation function- denovolyzeMultiHits ( ) ( Ware et al . , 2015 ) . In total , 13 de novo LOF variants were observed in our cohort . The probability of observing >1 de novo LOF in any gene by chance with a set of 13 mutations was determined by 1 million iterations in which these 13 ‘hits’ were sampled from all genes given the probability of de novo mutation in each ( Homsy et al . , 2015 ) . The number of times any gene had more than one hit in an iteration was counted , and that number divided by 1 million represented the probability of observing more than one de novo LOF mutation in our cohort . We infer that the number of probands with protein altering de novo mutations ( n = 123 ) in excess of expectation by chance ( n = 102 . 4 ) represents the number of subjects in whom these mutations confer craniosynostosis risk ( n = 20 . 6 ) . Comparing this fraction to the total number of trios ( 20 . 6/132 ) yields the fraction of patients in our cohort in whom we expect these mutations contribute to disease: ~15 . 6% . The observed distribution of rare ( ExAC frequency <2 × 10–5 ) LOF and D-mis alleles was compared to the expected distribution using the binomial test . The total number of LOF and D-mis alleles in 191 probands was tabulated , totaling 1135 LOF alleles and 2021 D-mis alleles ( 3156 total damaging alleles ) . The expected number of variants in each gene was calculated from the proportion of the exome comprising the coding region of each gene multiplied by the total number of alleles identified in cases . Enrichment was calculated as the number of observed mutations divided by the expected number . We used a transmission disequilibrium test to compare the transmission ( M1 ) and non-transmission ( M2 ) of BMP2 and BBS9 risk alleles ( rs1884302 and rs10262453 respectively ) to affected offspring in kindreds with SMAD6 mutations . We tested for deviation from the expected transmission value of 50% by the binomial Chi-square test with 1 Df . A fisher exact test was used to compare the prevalence of LOF or LOF+D-mis variants in 172 craniosynostosis probands and 3337 controls , the latter comprising the unaffected parents of offspring with autism . Controls were exome sequenced on the Roche V2 capture reagent followed by sequencing on the Illumina platform ( Sanders et al . , 2012; Krumm et al . , 2015; Iossifov et al . , 2012 ) . All control BAM files were processed with sequences aligned and variants called in parallel to aforementioned cases . Cases and controls , on average , both had ~94–95% of targeted bases read 8 or more times ( Supplementary file 1A ) . We restricted cases and controls to European ( CEU ) ancestry using the EIGENSTRAT program , which compared single nucleotide polymorphism ( SNP ) genotypes from case and control subjects with individuals of known ancestry in HapMap3 ( Frazer et al . , 2007 ) ( Figure 3—figure supplement 2 ) . To avoid bias , exons analyzed were restricted to those that intersected between the Roche V2 and MedExome capture reagents . For genes with more than 1 LOF or D-mis variant in cases , aligned reads were visualized in silico at all variant positions in both cases and controls . For genes displaying p<0 . 005 , we compared the burden of mutated alleles in cases to European subjects in the NHLBI ESP and ExAC databases . The total number of alleles evaluated per gene was taken as the median of the allele numbers reported for all positions across a gene in NHLBI and ExAC respectively ( Figure 3—source data 1 ) . Parametric two locus linkage analysis was performed comparing the segregation of rare damaging SMAD6 alleles and common BMP2 risk alleles at rs1884302 to the segregation of craniosynostosis in kindreds harboring rare damaging SMAD6 variants . Risk alleles at both loci were specified as showing zero recombination with underlying trait loci; risk/penetrance of each two locus genotype was estimated from their values at the maximum likelihood ( Supplementary file 1C ) . A phenocopy rate of 0 . 02% was specified from estimates of disease prevalence and the fraction of disease attributable to risk genotypes . Likelihood ratios of the observed results occurring under the specified model vs . the alternative of chance were calculated for each kindred , converted to lod scores ( Supplementary file 1D ) and the sum of lod scores across all kindreds was calculated . For kindreds ( n = 2 ) with missing parental genotypes , the likelihood ratio of observed genotypes in offspring occurring under the parametric model compared to chance was estimated from ethnic-specific BMP2 genotype frequencies and frequencies of rare damaging SMAD6 alleles in missing parental genotypes . The likelihood ratio of the best two locus model was compared to that of the best single locus model considering only the segregation of rare damaging SMAD6 variants . Kinship analysis was performed for all probands and controls using Plink . All trio structures were confirmed with parent-offspring pairs having PiHat values of 0 . 45–0 . 55 . GATK: ( https://www . broadinstitute . org/gatk ) ; TrioDeNovo: ( http://genome . sph . umich . edu/wiki/Triodenovo ) ; DenovolyzeR: ( http://denovolyzer . org ) ; Plink: ( http://pngu . mgh . harvard . edu/~purcell/plink ) ; MetaSVM/ANNOVAR: ( http://annovar . openbioinformatics . org ) ; NHLBI ESP: ( http://evs . gs . washington . edu/EVS ) ; ExAC03: ( http://exac . broadinstitute . org ) ; Geneious: ( www . geneious . com ) . Isohelix Buccal Swab DNA isolation: ( http://www . isohelix . com/wp-content/uploads/2015/09/BuccalFixIsoKit . pdf ) . Whole-exome sequencing data have been deposited in the database of Genotypes and Phenotypes ( dbGaP ) under accession phs000744 . NCBI RefSeq accessions for all named genes are listed in Table 1—source data 1 . | The bones in the front , back and sides of the human skull are not fused to one another at birth in order to allow the brain to double in size during the first year of life and continue growing into adulthood . However , one in 2 , 000 infants is born with a condition called craniosynostosis in which some of these bones have already fused . This fusion prevents the skull from growing properly , and can lead to the brain becoming compressed . As such , surgeons routinely undo the fusion in these infants to allow the brain and skull to grow normally . Eighty-five percent of craniosynostosis cases occur in infants with no other abnormalities , ( called non-syndromic cases ) and most have no other affected family member . It has therefore been unclear whether these infants have craniosynostosis due to a genetic or non-genetic cause . If the cause is genetic , it is also not clear whether a mutation in a single gene , the combined effects of many genes , or something in between is responsible . Now , by focusing on a group of 191 infants with premature fusion of bones joined at the midline of the skull , Timberlake et al . asked if any of the approximately 20 , 000 genes in the human genome were altered more frequently in these infants than would be expected by chance . This search revealed that rare mutations that disable one copy of a gene called SMAD6 in combination with a common DNA variant near another gene called BMP2 account for about 7% of infants with midline forms of craniosynostosis . These genes are both known to regulate how bones form , which explains how the mutation of these genes could lead to craniosynostosis . In all cases , the parents of these children were unaffected . This was typically because one parent had only the SMAD6 mutation while the other had only the common BMP2 variant; the transmission of both to their offspring resulted in craniosynostosis . The finding that a rare mutation’s effect is strongly modified by a common variant from another site in the genome is unprecedented . These findings will allow doctors to counsel families about the risk of having additional children with craniosynostosis . Timberlake et al . next plan to study more patients with craniosynostosis to identify additional genes that contribute to this disease . They will also look at other diseases to see whether the combination of rare mutation and common DNA variant could be behind other unexplained disorders . | [
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] | 2016 | Two locus inheritance of non-syndromic midline craniosynostosis via rare SMAD6 and common BMP2 alleles |
Serotonin receptors ( 5-HT3AR ) play a crucial role in regulating gut movement , and are the principal target of setrons , a class of high-affinity competitive antagonists , used in the management of nausea and vomiting associated with radiation and chemotherapies . Structural insights into setron-binding poses and their inhibitory mechanisms are just beginning to emerge . Here , we present high-resolution cryo-EM structures of full-length 5-HT3AR in complex with palonosetron , ondansetron , and alosetron . Molecular dynamic simulations of these structures embedded in a fully-hydrated lipid environment assessed the stability of ligand-binding poses and drug-target interactions over time . Together with simulation results of apo- and serotonin-bound 5-HT3AR , the study reveals a distinct interaction fingerprint between the various setrons and binding-pocket residues that may underlie their diverse affinities . In addition , varying degrees of conformational change in the setron-5-HT3AR structures , throughout the channel and particularly along the channel activation pathway , suggests a novel mechanism of competitive inhibition .
Cancer treatment by radiation or chemotherapy triggers the release of excess serotonin from the mucosal enterochromaffin cells in the upper gastrointestinal tract ( Schwörer et al . , 1991 ) . Serotonin binds to serotonin ( 3 ) receptors ( 5-HT3Rs ) , a pentameric ligand-gated ion channel ( pLGIC ) , on the vagal afferent nerve in the gut and on the chemoreceptor trigger zone in the brainstem leading to severe nausea and vomiting in patients receiving cancer treatments . These common side effects of cancer treatments take a significant physical and psychological toll on cancer patients . Without management , these side effects can reduce patient compliance , undermining treatment success ( Gilmore et al . , 2018 ) . Furthermore , uncontrolled debilitating side effects result in secondary complications such as dehydration and anorexia that require additional hospitalization and increase overall healthcare costs . Current antiemetic therapies include a 5-HT3R antagonist treatment regimen , which is considered a major advancement in improving patient quality of life during cancer treatment . Setrons , competitive antagonists of 5-HT3R , are effective in the prevention of chemotherapy-induced nausea and vomiting ( CINV ) , radiation therapy- induced nausea and vomiting ( RINV ) , and postoperative nausea and vomiting ( PONV ) ( Spiller , 2011; Hsu , 2010 ) . Notably , CINV occurs in acute and delayed phases . The first generation of FDA approved setrons are effective for treating acute but not delayed phase nausea due to their short plasma half-lives . They belong to the following major classes based on their chemical structures: carbazole ( e . g . ondansetron ) , indazole ( e . g . granisetron ) , indole ( e . g . dolasetron , tropisetron ) , and pyridoindole ( e . g . alosetron ) . Although setrons share the same fundamental mechanism of action , they have varying efficacies , dose-response profiles , duration of action , and off-target responses . These differences perhaps underlie variable patient response , particularly in the context of acute and refractory emesis ( de Wit et al . , 2005 ) . The isoquinoline derivative palonosetron , the only FDA approved second generation setron , is shown to have a longer half-life , improved bioavailability , and efficacy . In addition , palonosetron is implicated in causing receptor internalization , which further improves antiemetic properties . Beyond their role in controlling emesis , setrons are used to treat GI disorders including irritable bowel syndrome ( IBS ) , obesity , and several inflammatory , neurological and psychiatric disorders such as migraine , drug abuse , schizophrenia , depression , anxiety , and cognitive disorders . However , in some cases , toxicity and adverse side effects have hampered their use . For example , the FDA approved treatment of diarrhea-predominant IBS with alosetron led to severe ischemic colitis in many patients ( Friedel et al . , 2001 ) . Given the broad therapeutic potential of 5-HT3AR antagonists , the prospect of substantial therapeutic gains by probing the setron pharmacophore as well as developing novel pharmaceuticals with higher efficacy and reduced side effects is encouraging . At the physiological level , the 5-HT3Rs play an important role in gut motility , visceral sensation , and secretion ( Engel et al . , 2013; Lummis , 2012; Kia et al . , 1995; Bétry et al . , 2011; Thompson and Lummis , 2006; Gershon , 2004 ) , and are also implicated in pain perception , mood , and appetite . 5-HT3Rs are the only ion channels ( Maricq et al . , 1991 ) among the large family of serotonin receptors , the rest being G-protein coupled receptors ( GPCRs ) . 5-HT3Rs are expressed as homopentamers of subunit A or heteropentamers of subunit A , in combination with B , C , D , or E subunits ( Niesler et al . , 2007 ) . Compositional and stoichiometric differences lead to differential responses to serotonin , gating kinetics , permeability , and pharmacology ( Davies et al . , 1999; Kelley et al . , 2003; Thompson and Lummis , 2013 ) . This functional diversity , tissue specific expression patterns , and distinct pathophysiology of 5-HT3R isoforms establish a need for subtype specific drugs to address diverse clinical needs ( Hammer et al . , 2012 ) . Of note , granisetron , palonosetron , ondansetron , and alosetron have slightly different affinities for various receptor subtypes ( Gregory and Ettinger , 1998 ) . Ondansetron , in addition to binding to 5-HT3Rs , also binds to several GPCRs , such as 5-HT1BR , 5-HT1CR , α1-adrenergic receptors and μ-opioid receptors ( Kovac , 2016 ) . Granisetron binds to all subtypes of 5-HT3R , but has little or no affinity for 5-HT1R , 5-HT2R and 5-HT4R receptors . Palonosetron is highly selective for 5-HT3AR and dolasetron for 5-HT3ABR ( Smith et al . , 2012 ) . Structural insights into setron-binding poses came initially from crystal structures of the acetylcholine binding protein ( AChBP ) , bound to granisetron , tropisetron , or palonosetron ( Kesters et al . , 2013; Hibbs et al . , 2009; Price et al . , 2016 ) and more recently from 5-HT3AR complexed with tropisetron and granisetron ( Polovinkin et al . , 2018; Basak et al . , 2019 ) . While some of the basic principles of setron-binding are now clear , there is still limited understanding of differing pharmacodynamics among setrons and the associated clinical relevance . In the present study , we have solved cryo-EM structures of the full-length 5-HT3AR in complex with palonosetron , ondansetron , and alosetron at the resolution range of 2 . 9 Å to 3 . 3 Å . Together with our previously solved structures of 5-HT3AR in complex with granisetron ( 5-HT3AR-Grani ) and serotonin-bound ( 5-HT3AR-serotonin , State 1-preopen ) , as well as ligand-free 5-HT3AR ( 5-HT3AR-Apo ) , we provide details of various setron-binding modes and the ensuing conformational changes that lead to channel inhibition . Using molecular dynamics ( MD ) simulations and electrophysiology , we have further validated setron-binding modes and interactions within the conserved binding pocket . Combined with abundant functional , biochemical , and clinical data , these new findings may serve as a structural blueprint of drug-receptor interactions that can guide new drug development .
Structures of the full-length 5-HT3AR in complex with setrons were solved by single-particle cryo-EM . Detergent solubilized 5-HT3AR was incubated with 100 µM of palonosetron , ondansetron , or alosetron for 1 hr prior to vitrification on cryo-EM grids . Iterative classifications and C5 symmetry-imposed refinement produced a final three-dimensional reconstruction at a nominal resolution of 3 . 3 Å for 5-HT3AR-Palono ( with 91 , 163 particles ) , 3 . 0 Å for 5-HT3AR-Ondan ( 67 , 333 particles ) , and 2 . 9 Å for 5-HT3AR-Alo ( 42 , 065 particles ) ( Figure 1—figure supplement 1a and b ) . The local resolution of the map was estimated using ResMap and in the range of 2 . 5–3 . 5 Å for each of these reconstructions ( Figure 1—figure supplement 1c ) . Structural models were built using refined maps containing density for the entire extracellular domain ( ECD ) , transmembrane domain ( TMD ) , and the structured regions of the intracellular domain ( ICD ) ( Figure 1a and Figure 1—figure supplement 2 ) . Overall , each of the setron-bound 5-HT3AR complexes has an architecture similar to previously solved 5-HT3A receptors ( Polovinkin et al . , 2018; Hassaine et al . , 2014; Basak et al . , 2018a; Basak et al . , 2018b ) . Three sets of peripheral protrusions corresponding to N-linked glycans are bound to the Asn82 , Asn148 , and Asn164 in each subunit ( Figure 1a , right ) . A strong , unambiguous density is seen at each of the intersubunit interfaces , corresponding to individual setrons ( Figure 1b ) . Besides this site , no additional densities for setrons were found under these conditions , although there have been predictions that palonosetron may act as both an orthosteric and allosteric ligand ( Del Cadia et al . , 2013 ) . The map quality was particularly good at the ligand-binding site allowing us to model sidechains and the setron orientation . Setrons bind within the canonical neurotransmitter-binding pocket and are lined by residues from Loops A , B , and C on the principal ( + ) subunit and Loops D , E , and F from the complementary ( - ) subunit ( Figure 2 and Figure 2—figure supplement 1 ) . Residues within 4 Å of setron include Asn101 in Loop A , Trp156 in Loop B , Phe199 , and Tyr207 in Loop C , Trp63 and Arg65 in Loop D , and Tyr126 in Loop E . These residues are strictly conserved , and perturbations at each of these positions impact efficacy of setrons and serotonin ( Yan et al . , 1999; Duffy et al . , 2012; Thompson et al . , 2005 ) . In each setron-5-HT3AR complex , the essential pharmacophore of the setron is placed in a similar orientation: the basic amine is at the deep-end of the pocket in the principal subunit; the defining aromatic moiety interacts with residues in the complementary subunit; and the carbonyl-based linker , between the two groups , is essentially coplanar with the aromatic ring . The basic amine of the setron is in a bicyclic ring in granisetron and palonosetron , and a diazole ring in ondansetron and alosetron . The amine is within 4 Å of Trp156 ( Loop B ) , Tyr207 ( Loop C ) , Trp63 ( Loop D ) and Tyr126 ( Loop E ) , and is likely to be involved in polar interactions with these residues . In particular , the carbonyl oxygen of Trp156 is close to the amine group of setron , and in the 5-HT3AR-Alo , it forms a hydrogen bond with the amine group in the diazole ring . The relative orientation of the tertiary nitrogen and Trp156 is conducive for a cation-pi interaction , as seen in the AChBP-5-HT3 chimera structure ( Kesters et al . , 2013 ) . A similar interaction is also predicted for the primary amine group of serotonin ( Beene et al . , 2002 ) . The aromatic end of the molecule is an indazole in granisetron , isoquinoline in palonosetron , carbazole in ondansetron , and pyridoindole in alosetron . It is oriented toward the complementary subunit , and lies parallel to the membrane . In this orientation , the aromatic moiety is stabilized by a number of hydrophobic interactions with Ile44 , Trp63 , Tyr64 , Ile201 , and Tyr126 ( shown by gray color in surface representation Figure 2—figure supplement 1 ) . The setron molecule is within 4–5 Å and potentially makes π-π interactions ( edge-to-face or face-to-face ) with Trp63 , Tyr126 , Trp156 , and Tyr207 . These interactions are also consistent with our MD simulations ( discussed below ) . While most interactions with setrons observed in these structures are apolar in nature , it is to be noted that water molecules were not modeled into the structures . Interactions mediated through water molecules are inferred from MD simulations ( discussed below ) . In addition , the planar aromatic rings lie beneath Arg65 , and are in close proximity to the positively charged nitrogen in the guanidinium group of Arg65 , revealing a potential cation-pi interaction ( Figure 2—figure supplement 2 ) . This interaction was also observed in the AChBP-5-HT3 chimera ( Kesters et al . , 2013 ) and 5-HT3AR-Grani structures ( Basak et al . , 2019 ) . As previously noted in 5-HT3AR-Grani , the setron position causes reorientation of Arg65 ( β2 strand or Loop D ) and Trp168 ( β8-β9; Loop F ) ( Basak et al . , 2019 ) . Earlier reports also predicted large orientational differences for Trp168 when the binding site was occupied by agonist or antagonist ( Thompson et al . , 2006 ) . In this position , Arg65 is in a network of interactions involving Asp42 ( β1 ) , Try126 ( β6 ) , Trp168 ( β8-β9; Loop F ) , Arg169 ( β8-β9; Loop F ) , and Asp177 ( β8-β9; Loop F ) ( Figure 2—figure supplement 2b ) . Glu102 ( Loop A ) which is in the vicinity of the ligand-binding site is in a hydrogen-bond network with Thr133 and Ala134 carbonyl ( β6 strand ) . Interestingly , both of these networks are also present in serotonin-bound 5-HT3AR , but absent in 5-HT3AR-Apo , indicating the ligand-induced formation of the interaction network ( Basak et al . , 2018a; Basak et al . , 2018b ) . To understand the dynamics of ligand-receptor interactions , 100 ns MD simulations were carried out for 5-HT3AR-Grani , 5-HT3AR-Palono , 5-HT3AR-Ondan and 5-HT3AR-Alo , structures embedded in a 1-palmitoyl-2-oleoyl phosphatidyl choline ( POPC ) membrane and encased in water with 150 mM NaCl . The analysis also included simulations of the un-liganded ( 5-HT3AR-Apo ) and the serotonin-bound structures under the same conditions . In the presence of serotonin , two conformational states were resolved for 5-HT3AR by previous cryo-EM studies; one was partially open ( referred to State 1 ) and the other was open ( referred to State 2 ) ( Basak et al . , 2018b ) . Although the two states had major differences in the TMD and the ICD , they were identical at the level of the serotonin-binding site and Loop C orientation . Given the better resolution of State 1 ( referred to as 5-HT3AR-Serotonin here and throughout ) , we used this structural model for comparison with setron-bound structures . While the cryo-EM density for the setrons allowed precise orientation of the ligand in the pocket , accurate modeling of the serotonin was limited by the cryo-EM resolution in combination with the smaller size of the molecule ( Basak et al . , 2018b ) . Upon evaluating the various docking poses for serotonin acquired using an initial pose placement with GlideSP followed by an in place refinement with GlideXP ( Schrödinger Release 2019–2: Glide , Schrödinger , LLC , New York , NY , 2019 ) , we found that the identified top-scored pose was essentially identical to that in the cryo-EM model . To assess the stability of each ligand binding-pose modeled from cryo-EM density , we quantified the root mean square deviation ( RMSD ) of each pose relative to its starting conformation , assessed for each subunit independently every 500 ps of each ligand-5HT3AR 100 ns simulation for a total of 200 simulation frames ( Figure 3a ) . We also quantified the average RMSD of each pose relative to its starting conformation by averaging over 1000 simulation snapshots ( 200 frames sampled every 500 ps for each of the five subunits treated as replicates ) extracted from the 100 ns simulations for each ligand-5HT3AR complex . Among the ligands studied , the serotonin molecule exhibited considerable fluctuations ( RMSD up to 5 . 4 Å with an average value of 1 . 9 Å , across five subunits ) within the binding pocket . In contrast , most setron molecules maintained a low RMSD ( RMSD average values of 1 . 1 Å , 1 . 0 Å , and 1 . 1 Å for granisetron , ondansetron , and alosetron , respectively ) , with palonosetron demonstrating the largest RMSD ( 1 . 9 Å ) among all the simulated setrons ( Figure 3a ) . During the simulation , the palonosetron’s bicyclic ring displayed fluctuation and positional reorientation . In these orientations , palonosetron had distinct interactions with binding-site residues , in particular with Asn101 in the ‘down’ position and Trp156 in the ‘up’ position ( Figure 3b and c ) . A recently reported structure of 5-HT3AR in complex with palonosetron ( published during revision of this manuscript ) shows an overall similar conformation of the protein as seen here ( Figure 3—figure supplement 1; Zarkadas et al . , 2020 ) . Interestingly , the bicyclic ring orientation reported in this structure is similar to that captured in our MD simulations . To evaluate the types of interactions that these ligands maintained with protein residues during MD simulation , we calculated 5-HT3AR-ligand interaction fingerprints ( see figure legend or methods for full interaction type definitions ) averaged across each protomer in the complex ( Figure 3—figure supplement 2 ) . In the 5-HT3AR-Serotonin simulations , the indole ring of serotonin makes pi-pi stacking interactions ( edge-to-face or face-to-face ) with key aromatic residues Trp63 , Tyr126 , and Trp156 , and Tyr207 . Among these only interactions with Trp156 and Tyr126 occur with probability larger than 50% . Mutations to each of these aromatic position affects serotonin-binding and is reflected in increase in EC50 for activation ( Beene et al . , 2002; Spier and Lummis , 2000; Beene et al . , 2004 ) . The indole nitrogen of serotonin forms water-mediated interactions with Asp42 and Arg169 , albeit with a 25% probability whereas the ligand’s hydroxyl group forms a hydrogen-bond with the amine nitrogen of Trp156 with a 50% probability . The indole nitrogen is also involved in an extended water network with Glu173 on Loop F , particularly in simulation frames where Loop F orients toward serotonin where this network is sampled as a two water -mediated hydrogen bond interaction . The primary amine nitrogen in serotonin makes multiple water-mediated interactions with the side chains of Glu209 and Thr154 , and occasionally with Asn101 ( less than 25% of the simulation time ) . In addition , serotonin forms relatively stable apolar interactions ( >50% probability ) with Ile44 , Phe199 , and Ile201 . The structural fingerprint analysis shows that all setrons form a number of very stable ( probability >75% ) , mostly apolar , interactions with the following residues: Asp42 , Val43 , Ile44 , Trp63 , Tyr64 , Arg65 , Tyr126 , and Trp156 . While interactions between serotonin and residues Ile44 , Trp63 , Arg65 , Tyr126 , and Trp156 occurred with similar probability , those with Val43 and Tyr64 did not form at all and those with Asp42 were reduced , albeit complemented by hydrogen bond and water-mediated interactions . In 5-HT3AR-Grani , the bicyclic nitrogen makes water-mediated hydrogen bonds with the sidechains of Thr154 , Ser155 , and Glu209 . The indazole nitrogen occasionally interacts with Asp202 through a water-mediated hydrogen bond . Reduced affinity for granisetron is noted upon mutations at these positions ( Yan et al . , 1999 ) and notably W63A , W156A , and Y207A do not bind granisetron . In 5-HT3AR-Palono , the tertiary amine on the ligand’s bicyclic ring makes water-mediated and direct polar contacts with the backbone oxygen of Trp156 or the sidechain of Asn101 depending on the orientation of bicyclic ring . The carbonyl group of the isoquinoline moiety interacts with the amine nitrogen of Trp156 and carbonyl oxygen of Tyr64 through a water molecule . Mutations of Trp156 or Tyr64 cause large or small increases of palonosetron-induced inhibition , respectively ( Price et al . , 2016; Del Cadia et al . , 2013; Beene et al . , 2002 ) . Interestingly , while N101Q preserves palonosetron-induced inhibition similar to wild-type receptor , a mutation to N101A ( no H-bond with side chain ) increases the potency of palonosetron ( Price et al . , 2016 ) . Palonosetron forms an apolar interaction with Arg169 with higher probability compared to the other ligands , potentially due to palonosetron’s pose forming weak interactions with loop C in favor of loop F . In 5-HT3AR-Ondan , the ondansetron molecule forms highly probable edge-to-face stacking interactions with Tyr126 and Tyr207 . The secondary nitrogen on the diazole ring forms a hydrogen-bond interaction with Glu209 through a water molecule . These water-mediated interactions are also occasionally seen with Thr154 and Asn101 . In 5-HT3AR-Alo , one of the secondary amine nitrogen on the diazole ring forms a water-mediated hydrogen-bond interaction with the carbonyl oxygen of Trp156 . The amide oxygen of Trp156 forms a water-mediated hydrogen-bond contact with the amine group of the ligand’s imidazole . The backbone oxygen of Tyr64 interacts with the carbonyl group of the ligand’s pyridoindole ring through a water molecule . Interestingly , water-mediated interactions with Glu209 are absent in the alosetron fingerprint when compared to the ondansetron , granisetron , and serotonin fingerprints . It is to be noted that many of the ligand-receptor interactions identified as important by the MD simulations are not directly evident from the Cryo-EM structures particularly since many of these interactions are mediated through water molecules ( not modeled in the structural coordinates ) . In addition , MD simulation captures several transient interactions arising from side-chain flexibility and drug mobility within the pocket . We previously showed that instead of being in a 5-HT3AR-Apo like conformation , 5-HT3AR-Grani revealed a counter-clockwise twist of beta strands in the ECD leading to a small inward movement of Loop C ( connecting β9-β10 strands ) closing-in on granisetron ( Basak et al . , 2019 ) . The Loop C conformation has been correlated to the agonistic nature of the ligand in the binding site . The AChBP-ligand complexes have shown that agonist binding induces a ‘closure’ of Loop C , capping the ligand-binding site ( Hansen et al . , 2005 ) . The 5-HT3AR and other pLGIC structures solved thus far , in the apo and agonist-bound states , also follow this general trend ( Polovinkin et al . , 2018; Basak et al . , 2018a; Basak et al . , 2018b; Du et al . , 2015; Kumar et al . , 2020 ) . This conformational change may be part of a conserved pLGIC mechanism that couples ligand binding to channel opening through the ECD-TMD interfacial loops . However , studies have shown that unliganded pLGIC gating kinetics remain unaffected by Loop C truncation ( Purohit and Auerbach , 2013 ) , raising ambiguity over the role of Loop C closure in channel opening . Antagonist-bound AChBP structures show that Loop C further extended outward ( Hansen et al . , 2005 ) , while partial agonists seem to induce partial Loop C closure but not to the level achieved by agonists ( Hibbs et al . , 2009 ) , suggesting a correlation between the degree of Loop C closure and the level of agonism . However , in the crystal structure of AChBP in complex with antagonist dihydro-β-erythroidine , Loop C appears to move inward ( Shahsavar et al . , 2012 ) . In comparison to the 5-HT3AR-Apo structure , Loop C adopts varying degrees of an inward conformation , and in the 5-HT3AR-Alo structure the orientation is similar to 5-HT3AR-serotonin ( Basak et al . , 2019; Figure 4a ) . The twisting inward movement does not pertain to Loop C alone , but it is also shared by adjoining β7 , β9 and β10 strands forming the outer-sheets of the β-sandwich core , with notable deviations from the corresponding regions in the 5-HT3AR-Apo structure ( Figure 4b ) . In contrast , only minimal changes are observed in the β-strands of the inner sheets ( β1 , β2 , β6 ) ( Figure 4b and c ) . These conformational changes approach those seen in the 5-HT3AR-Serotonin structure ( Basak et al . , 2018b ) . Although these results appear to diverge from the classical view that competitive antagonists either cause steric hindrance to agonist binding or evoke structural changes that are opposite to those caused by agonist , they are generally consistent with previous findings from dynamics studies in pLGIC using voltage-clamp fluorometry ( VCF ) . In VCF , ligand-induced conformational changes and channel function are simultaneously monitored . When reporter-groups were introduced in Loop C of 5-HT3AR , similar fluorescence changes were recorded from binding either serotonin or setrons ( Munro et al . , 2019 ) . In GlyR , both glycine ( agonist ) and strychnine ( a competitive antagonist ) produced identical fluorescence responses from labels in certain regions of Loop C ( Pless and Lynch , 2009 ) , and a similar trend was also observed in ρ1GABAR ( Chang and Weiss , 2002 ) . These findings implied that the local structural changes induced by these ligands were indiscriminate to the functional response from the channel . Overall , these findings underscore the complexity of Loop C movement and its role in coupling channel opening . Interestingly , the inner β-sheet regions ( particularly Loop E , contributing to the binding pocket from the complementary subunit ) undergo distinct movements depending on the nature of the bound ligand in GlyR , 5-HT3AR , and GABAAR ( Munro et al . , 2019; Pless and Lynch , 2009; Chang and Weiss , 2002; Muroi et al . , 2006 ) suggesting that this region maybe a better reporter for ligand discrimination . Some of the differences in ligand-receptor interaction fingerprints across the different systems , particularly those involving residues Tyr207 , Phe199 , and Glu209 arise from differential positioning of Loop C . To characterize the flexibility of Loop C in the liganded and unliganded states of 5-HT3AR , we monitored two structural parameters during the MD simulation runs . First , we assessed the RMSD of Loop C for each protomer in each 5-HT3AR simulation by evaluating the distances of Cα , carbonyl carbon , and backbone nitrogen atoms of residues Ser200 through Asn205 with respect to their initial cryo-EM conformations ( Figure 4—figure supplement 1a ) . Second , we defined a custom dihedral formed by the Cα atoms of residues Ala208 , Phe199 , Glu198 , and Ile203 that measured the orientation of the loop relative to the complementary subunit binding site ( Figure 4—figure supplement 1b ) . This dihedral was defined in such a way that a large angle would denote that the loop is oriented away from the binding site and small or negative angles indicate that the loop is oriented toward the binding site . Comparing these two parameters across the different systems suggest that Loop C is stable in its initial cryo-EM conformation in the case of 5-HT3AR-Alo and 5-HT3AR-Serotonin simulations where it predominantly adopts a ‘closed’ conformation . In other setron complexes , Loop C is observed to switch to an alternate ‘open’ conformation where it extends away from the binding-site surface . In comparison to all the ligand-bound structures , Loop C exhibited a much larger flexibility in 5-HT3AR-Apo as evidenced by large RMSD values and a wide-range of dihedral angles . These multiple Loop C ‘opening’ and ‘closing’ events in the 5-HT3AR-Apo structure contrasts ligand-bound states of the channel and suggest that the presence of a ligand in the binding-site forces the loop C to remain ‘closed’ . Such enhanced flexibility in the unliganded state of 5-HT3AR has also been reported in a previous 20 μs simulation study ( Guros et al . , 2020 ) . To assess the impact of Loop C movement on the relative size of each setron-binding pocket , we quantified the average number of water molecules found within each binding site assessed separately for each protomer chain . This was evaluated by counting water oxygen atoms within 3 Å of any ligand atom . Since each ligand maintained its overall binding mode during simulation , these measurements represent an approximation of binding-site volume . These data show that alosetron , ondansetron , and serotonin have a relatively lower number of water molecules within their binding sites ( Figure 5 ) . To further investigate the motion of Loop C between the ‘closed’ cryo-EM structure and the MD sampled ‘open’ conformation we evaluated the minimum polar side-chain atom distance between Arg65 and Asp202 , residues known to form a hydrogen-bond interaction that may effectively rigidify Loop C in a ‘closed’ conformation ( Guros et al . , 2020 ) . We hypothesized that in our MD simulations Loop C would not adopt an ‘open’ conformation if an Arg65-Asp202 interaction was formed . We find that the 5-HT3AR-Alo and 5-HT3AR-Serotonin MD simulations maintained an interaction between Arg65 and Asp202 more often than in any other setron-bound structure , and that most setron-bound simulations did not appreciably form this stabilizing interaction ( Figure 6a and b ) . Thus , our mechanistic hypothesis is such that when Arg65 is interacting with Asp202 , Loop C is in a stable ‘closed’ conformation , which in turn reduces the accessibility of the binding pocket to water , and incidentally contributes to the higher stability of the ligand binding pose . Our MD simulations suggest that Arg65 may have a differential effect on the binding of various setrons . In agreement , mutations at the Arg65 position in human 5-HT3AR abolish granisetron binding but tropisetron binding is only reduced ( Ruepp et al . , 2017 ) . To further assess the role of Arg65 in binding various setrons , we measured the extent of inhibition of serotonin-induced currents . Since for competitive antagonists the extent of inhibition depends on agonist concentration , the serotonin concentration in each case was kept close to the EC50 value for wild type ( 2 μM ) and R65A ( 10 μM ) ( Figure 6c ) . Granisetron and palonosetron inhibition was measured at 1 nM; ondansetron and alosetron inhibition was measured at 0 . 1 nM ( these concentrations were chosen to achieve a 50% inhibition for wild type upon co-application with serotonin ) ( Figure 6d and e ) . Of note , co-application of setron in some cases has ~100 fold lower effect than pre-application due to slow on-rates ( Lummis and Thompson , 2013 ) . Mutational perturbation at Arg65 has a significant effect on inhibition by each setron , albeit to varying extents . While revealing a functional effect on serotonin and setron binding , the R65A mutational studies do not provide conclusive evidence for differential effects of various setrons . We think that R65 plays a role in concert with neighboring residues . Additional mutagenesis and combination of mutations may be needed to understand the proposed mechanism better . Analysis of the ion permeation pathway along the pore axis shows a slight constriction in the middle of the ECD lined by residues Lys108 and Asp105 in the β4-β5 loop . The Asp105 position is conserved among most cation-selective pLGICs and mutations at this position affect single-channel conductance and open probability in pLGICs ( Livesey et al . , 2011; Sine et al . , 2010; Chakrapani et al . , 2003 ) . The ECD constriction is narrower in the 5-HT3AR-Apo structure and widens in the serotonin-bound structures . 5-HT3AR-setron structures show varying extents of widening at this position . However , previous studies assessing permeation of water molecules and of ions with imposed membrane potential have shown that this constriction point does not impede ion permeation in the 5-HT3AR-Apo and 5-HT3AR-Serotonin structures ( Basak et al . , 2018b ) . Conformational changes are also present in the TMD and may arise from small twisting movements in the ECD . Interestingly , in each of the 5-HT3AR-setrons structures , the pore-lining M2 helices are positioned away from the central axis , and are in a more-expanded conformation than in the 5-HT3AR-Apo structure ( Figure 7a ) . At positions Val264 ( 13′ ) , Leu260 ( 9′ ) , Ser253 ( 2′ ) , and Glu250 ( −1′ ) , the pore radii in 5-HT3AR-setron structures are larger than in the 5-HT3AR-Apo structure . However , in all these structures , the pore is constricted to below the hydrated Na+ radii ( Marcus , 1988; Figure 7b ) . Local dynamics of the permeation pathway were monitored during the aforementioned 100 ns MD simulations of 5-HT3AR-Apo , 5-HT3AR-Serotonin , and each of the 5-HT3AR-setron structures embedded in a POPC membrane encased in water and 150 mM NaCl ( Figure 7—figure supplement 1 ) . As expected , no major changes to the overall pore profile of the channel were observed during simulations , which were carried out in the absence of positional restraints . The side-chain movements of the residues lining the permeation pathway caused only slight fluctuations of the pore radius as shown by the standard deviations from across eight equidistant simulation frames for each system . Most notably , in each case , the pore remained constricted at the Leu260 ( 9′ ) to under 2 . 3 Å ( below the hydrated Na+ radius ) . As also reported in earlier simulation studies , the Leu260 ( 9′ ) position is the major barrier in the TMD to ion permeation in the 5-HT3AR-Apo and 5-HT3AR-Serotonin structures ( Basak et al . , 2018b ) . While there are small conformational changes in the ICD , the post-M3 loop occludes the lateral portals at the interface of the TMD and ICD which are predicted to be the ion exit paths . The extent of occlusion is similar to that seen in the 5-HT3AR-Apo structure ( Basak et al . , 2018a ) suggesting that the ICD exits are closed in these conformations . Interestingly , in muscle-type nAChR , the ICD portals appear to be open even in the Apo conformation , highlighting the mechanistic differences among pLGIC members ( Rahman et al . , 2020 ) . Overall , these analyses suggest that although there are different extents of conformational changes in the 5-HT3AR-setron structures that lie between those of 5-HT3AR-Apo and 5-HT3AR-Serotonin , each of these structures appears to be non-conducting to ions . A limitation of these standard , shorter MD simulation timescales is that dynamic transitions between multiple states or allostery between ligand-binding at the ECD and pore-opening at the TMD are not expected to be captured during runs . The purpose of these simulations was to verify ligand stability in the pocket and the overall stability of the cryo-EM conformation rather than ligand-induced conformational rearrangements . Future studies with enhanced MD simulations may provide insights into transitions between different conformational states and the mechanistic details of coupling across domains . We further explored the question of whether setrons discriminate between homomeric 5-HT3AR and the heteromeric assemblies of subunit A in combination with either B , C , D , or E subunits . Among the different heteromeric 5-HT3R assemblies , the most studied is the 5-HT3ABR . A sequence alignment of mouse and human 5-HT3R subunits ( Figure 7—figure supplement 2 ) shows that two key residues that interact with serotonin and setrons , Trp156 ( on the principal side ) and Arg65 ( on the complementary side ) , are present exclusively in subunit A . This suggests that the setron- binding site may be limited to A-A interfaces both in the 5-HT3AR homomeric and heteromeric assemblies . A similar conclusion has also been drawn from earlier studies that have shown that setrons do not significantly differ in potency between 5-HT3AR and 5-HT3ABR , and that mutations to binding-site residues in subunit A had more dramatic effects on antagonist-binding affinity and an increased serotonin EC50 than mutations to equivalent positions in subunit B ( Del Cadia et al . , 2013; Lochner and Lummis , 2010 ) . A comprehensive structural analysis of multiple high-resolution structures of setron-bound 5- HT3AR complexes reveal several features of competitive antagonism that were not fully evident from the previous structural findings . Serotonin binds within a partially solvent-exposed cavity at the subunit interface and elicits Loop C closure and twisting of the β-strands within the ECD . The setron-binding pocket , while involving overlapping residues , extends further into the complementary subunit . Setron-binding evokes varying degrees of Loop C closure and in some cases , almost to the same degree as in the serotonin-bound state . The Loop C movements are associated with varying degrees of structural changes in the inner and outer β-strands that translate to small changes in the pore-lining M2 helices . Overall , setrons stabilize 5-HT3AR conformational states that are non-conductive , but appear to lie between the apo and serotonin-bound states . These findings therefore suggest that competitive antagonism in 5-HT3AR , and potentially in other pLGIC , may involve stabilizing intermediates along the activation pathway . With new emerging uses of setrons to treat psychiatric disorders , inflammation , substance abuse , and Alzheimer’s disease , these studies lay the foundation for the design of novel therapeutics that may have higher treatment efficacy and potentially fewer off-target effects .
Mouse 5-HT3AR gene ( purchased from GenScript ) and mutant genes were inserted into pTLN plasmid . The plasmids were linearized with MluI restriction enzyme by digesting overnight at 37°C . The mMessage mMachine kit ( Ambion ) was used to make mRNA as per the manufacturer’s protocol and cleanup using RNAeasy kit ( Qiagen ) . 3–10 ng of mRNA was injected into X . laevis oocytes ( stages V–VI ) , and incubated for 2–5 days , after which current recordings were performed . Water injected oocytes were used as a control to verify that no endogenous currents were present . Female X . laevis were purchased from Nasco and kindly provided by W . F . Boron . The Institutional Animal Care and Use Committee ( IACUC ) of Case Western Reserve University approved the animal experimental procedures . Oocytes were maintained in OR3 medium ( GIBCO-BRL Leibovitz medium containing glutamate , 500 units each of penicillin and streptomycin , pH adjusted to 7 . 5 , osmolarity adjusted to 197 mOsm ) at 18°C . Warner Instruments Oocyte Clamp OC-725 was used to perform two-electrode voltage-clamp experiments at a holding potential of −60 mV . Currents were sampled and digitized at 500 Hz with a Digidata 1332A . Clampfit 10 . 2 ( Molecular Devices ) was used to analyze experimental data . Perfusion solution consisted of 96 mM NaCl , 2 mM KCl , 1 . 8 mM CaCl2 , 1 mM MgCl2 , and 5 mM HEPES ( pH 7 . 4 , osmolarity adjusted to 195 mOsM ) was used at a flow rate of 6 ml/min . Chemical reagents ( serotonin hydrochloride , alosetron hydrochloride , ondansetron hydrochloride , and palonosetron hydrochloride ) were purchased from Sigma-Aldrich . The mouse 5-HT3AR ( NCBI Reference Sequence: NM_001099644 . 1 ) gene was codon-optimized for Spodoptera frugiperda ( Sf9 ) cells and purchased from GenScript . The construct consists of the 5-HT3AR gene along with a C-terminal 1D4-tag ( MacKenzie et al . , 1984 ) and four strep-tags ( WSHPQFEK ) at the N terminus , each separated by a linker sequence ( GGGSGGGSGGGS ) and followed by a TEV-cleavage sequence ( ENLYFQG ) . Sf9 cells ( Expression System ) were grown in ESF921 medium ( Expression Systems ) at 28°C without CO2 exchange and in absence of antibiotics . Cellfectin II reagent ( Invitrogen ) was used for transfection of recombinant 5-HT3AR bacmid DNA into sub-confluent Sf9 cells . After 72 hr of transfection , the progeny 1 ( P1 ) recombinant baculoviruses were obtained by collecting the cell culture supernatant . The P1 was then used to infect Sf9 cells which produced P2 viruses , and subsequently P3 viruses from the P2 virus stock . The P3 viruses were used for recombinant protein expression . Sf9 cells were grown to approximately 2 . 5 × 106 per ml followed by infection with P3 viruses . After 72 hr post-infection , the cells were centrifuged at 8 , 000 g for 20 min at 4°C to separate the supernatant from the pellet . The cell pellet was resuspended in 20 mM Tris-HCl , pH 7 . 5 , 36 . 5 mM sucrose supplemented with 1% protease inhibitor cocktail ( Sigma-Aldrich ) . Cells were sonicated on ice . Non-lysed cells were pelleted down by centrifugation ( 3 , 000 g for 15 min ) and the supernatant was collected . The membrane fraction was separated by ultracentrifugation ( 167 , 000 x g for 1 hr ) and solubilized in 50 mM Tris pH 7 . 5 , 500 mM NaCl , 10% glycerol , 0 . 5% protease inhibitor and 1% C12E9 for 2 hr at 4°C . Non-solubilized material was removed by ultracentrifugation ( 167 , 000 x g for 15 min ) . The solubilized membrane proteins containing 5-HT3A receptors were bound with 1D4 beads pre-equilibrated with 20 mM HEPES pH 8 . 0 , 150 mM NaCl and 0 . 01% C12E9 for 2 hr at 4°C . The non-bound proteins were removed by washing beads with 100 column volumes of 20 mM HEPES pH 8 . 0 , 150 mM NaCl , and 0 . 01% C12E9 ( buffer A ) . The protein was then eluted with 3 mg/ml 1D4 peptide ( TETSQVAPA ) which is solubilized in buffer A . Eluted protein was deglycosylated with PNGase F ( NEB ) by incubating 5 units of the enzyme per 1 μg of protein for 2 hr at 37°C under gentle agitation . Deglycosylated protein was then purified using a Superose six column ( GE healthcare ) equilibrated with buffer A . Purified protein was concentrated to 2–3 mg/ml using 50 kDa MWCO Millipore filters ( Amicon ) for cryo-EM studies . 5-HT3AR protein ( ~2 . 5 mg/ml ) was filtered and incubated with 100 μM drugs ( Alosetron , Ondansetron , and Palonosetron ) for 1 hr . Fluorinated Fos-choline-8 ( Anatrace ) was added to the protein sample to a final concentration of 3 mM . The protein was then blotted onto Cu 300 mesh Quantifoil 1 . 2/1 . 3 grids ( Quantifoil Micro Tools ) two times with 3 . 5 μl sample each time , and the grids were plunge frozen immediately into liquid ethane using a Vitrobot ( FEI ) . The grids were imaged using a 300 kV FEI Titan Krios G3i microscope equipped with a Gatan K3 direct electron detector camera . Movies containing ~50 frames were collected at 105 , 000 × magnification ( set on microscope ) in super-resolution mode with a physical pixel size of 0 . 848 Å/pixel , dose per frame 1 e-/Å ( Gilmore et al . , 2018 ) . Defocus values of the images ranged from −1 . 0 to −2 . 5 µm ( input range setting for data collection ) as per the automated imaging software SerialEM ( Mastronarde , 2005 ) . MotionCor2 ( Zheng et al . , 2017 ) was used to correct beam-induced motion using a B-factor of 150 pixels ( Gilmore et al . , 2018 ) . Super-resolution images were binned ( 2 × 2 ) in Fourier space , making a final pixel size of 0 . 848 Å . Entire data processing was conducted in RELION 3 . 1 ( Fernandez-Leiro and Scheres , 2017 ) . CTF of the motion-corrected micrographs were estimated using Gctf software ( Mindell and Grigorieff , 2003 ) . Auto-picked particles from total micrographs ( Table 1 ) from individual datasets ( each drug ) were subjected to 2D classification to remove suboptimal particles . An initial 3D reference model was generated from the 5-HT3AR-apo cryo-EM structure ( RCSB Protein Data Bank code ( PDB ID ) : 6BE1 ) . The model was low-pass filtered at 60 Å using EMAN2 ( Tang et al . , 2007 ) . Iterative 3D classifications , 3D auto-refinements , and Bayesian polishing generated density model of Alosetron , Ondansetron and Palonosetron bound 5-HT3AR with 42 , 065 particles , 67 , 333 particles , and 91 , 163 particles , respectively . During 3D classifications each of the classes was investigated carefully and particles appeared to belong to a single conformation . Per-particle contrast transfer function ( CTF ) refinement and beam tilt correction were applied followed by a final 3D-autorefinement . A soft mask was generated in RELION and used during the post-processing step , which resulted in an overall resolution of 3 . 32 Å , 3 . 06 Å , and 2 . 92 Å for , 5-HT3AR-Palono , 5-HT3AR-Ondan , and 5-HT3AR-Alo respectively ( calculated based on the gold-standard Fourier shell coefficient ( FSC ) = 0 . 143 criterion , Table 1 ) . B-factor estimation and map sharpening were performed in the post-processing step in RELION . The ResMap program was used to calculate local resolutions ( Kucukelbir et al . , 2014 ) . The final refined models have clear density of residues Thr7–Leu335 and Leu397–Ser462 . The unobserved density at the region of ( 336–396 ) is comprised of an unstructured loop which links the amphipathic MX helix and the MA helix . The 5-HT3AR-apo cryo-EM structure ( PDB ID: 6BE1 ) was used as an initial model and refined against its EM-derived map using PHENIX software package ( Adams et al . , 2002 ) , using rigid body , local grid , NCS , and gradient minimization parameters . COOT is used for manual model building ( Emsley and Cowtan , 2004 ) . Real space refinement in PHENIX yielded the final model with a final model to map cross-correlation coefficient of 0 . 834 ( 5-HT3AR-Palono ) , 0 . 846 ( 5-HT3AR-Ondan ) , and 0 . 848 ( 5-HT3AR-Alo ) . Stereochemical properties of the model were validated by Molprobity ( Chen et al . , 2010 ) . The pore profile was calculated using the HOLE program ( Smart et al . , 1996 ) . Figures were prepared using PyMOL v . 2 . 0 . 4 ( Schrödinger , LLC ) . The cryo-EM-derived structures of 5-HT3AR in the apo conformation or bound to palonosetron , alosetron , ondansetron , or serotonin were prepared for MD simulations with the Protein Prep Wizard in the Schrödinger scientific software suite 2019–2 using default settings ( Small-Molecule Drug Discovery Suite 2019–2 , Schrödinger , LLC , New York , NY , 2019 ) . This protocol adds missing hydrogen atoms to the initial protein-ligand complex . After the initial preparatory steps and protonation assignment of side chains , a brief restrained energy minimization in vacuo using the OPLS3 force field ( Harder et al . , 2016 ) was carried out to finalize system setup for each protein-ligand complex . Each setron-5-HT3AR complex was then embedded into a POPC bilayer using the Membrane Builder tool of the CHARMM-GUI webserver ( Jo et al . , 2008 ) . The system was then solvated with TIP3P water , and 150 mM NaCl was added to the simulation system by replacing random water molecules . Excess sodium ions were added to neutralize the charge of each protein-ligand complex . The resulting simulation systems had initial dimensions of ~130 × 130×207 Å3 and consisted of the unliganded 5-HT3AR pentamer , or the pentamer bound to the setron or serotonin at each 5-HT3AR subunit , ~400 POPC molecules , ~83 , 000 water molecules , ~240 sodium ions , and ~220 chloride ions , for a total of ~330 , 000–346 , 000 atoms . Throughout this work we reference data from our previously published simulation of granisetron-bound 5-HT3AR ( Basak et al . , 2019 ) in comparison to these three new setron-bound 5-HT3AR complexes , as well as the 5-HT3AR-serotonin complex and the 5-HT3AR-Apo structure . The CHARMM36m forcefield ( MacKerell et al . , 1998 ) was used to parameterize the protein and lipid atoms within each simulation system . Initial parameters for palonosetron , alosetron , and ondansetron were obtained from the ParamChem webserver using the CHARMM general force field ( Vanommeslaeghe et al . , 2010 ) ( https://cgenff . parmchem . org ) . Parameters were validated according to the procedure described previously ( Vanommeslaeghe et al . , 2010 ) . Said validation required quantum calculations performed with Gaussian 16 ( Gaussian 16 , Revision C . 01 , Gaussian , Inc , Wallingford CT , 2016 ) to finalize the charges and dihedrals defined within our setron molecule models . These parameter refinement steps were not conducted for serotonin as the default ParamChem parameters were found to be sufficient as described in Vanommeslaeghe et al . , 2010 . MD simulations were run using GROMACS 2018 . 6 ( Berendsen et al . , 1995 ) software with a timestep of 2 fs , following a steepest descent energy minimization run for 5000 steps , as well as 100 ps isothermal-isovolumetric ( NVT ) and 52 ns isothermal-isobaric ( NPT ) equilibration runs . The NVT equilibration was performed to initially heat the model systems after the steepest descents minimization . This step was performed with restraints on protein , membrane , and ligand molecule heavy atoms ( when ligand was present ) relative to their starting conformation . The NPT equilibration runs were performed in 5 steps of 10 ns each , within which the system was allowed to relax with gradually released restraints until finally the system was allowed to equilibrate for 2 ns of unrestrained NPT equilibration . This was followed by a 100 ns production run in isothermal-isobaric conditions . System temperature and pressure were maintained at 300 K and 1 bar , respectively , using velocity rescale ( Bussi et al . , 2007 ) for temperature coupling and Parrinello-Rahman barostat for pressure coupling during equilibration . Semi-isotropic pressure coupling and the Nose-Hoover thermostat ( Hoover , 1985 ) were applied during production runs . All bonds involving hydrogens were constrained using the LINCS algorithm ( Hess et al . , 1997 ) . Short-range nonbonded interactions were cut at 12 Å . Long-range electrostatic interactions were computed using the Particle Mesh Ewald summation with a Fourier grid spacing of 1 . 2 Å . Trajectory analyses were performed using a combination of Visual Molecular Dynamics ( VMD ) ( Humphrey et al . , 1996 ) and the GROMACS analysis toolkit ( Van Der Spoel et al . , 2005 ) over equidistant frames of our production simulations using a 500 ps stride . In particular , all RMSD measurements and Loop C orientations were obtained after aligning simulation frames onto the coordinates of the initial cryo-EM structure by comparing Cα atoms in the helices and β-sheets of the ECD . RMSD calculations were assessed for each ligand by evaluating the difference in heavy atoms of the ligands between each simulation frame and the initial cryo-EM structure conformation . Similarly , Loop C RMSD’s were calculated by comparing the Cα , backbone carbonyl carbon , and backbone nitrogen atoms of residues Ser200 through Asn205 relative to their conformation in the initial cryo-EM resolved structures . To measure the orientation of Loop C , we defined a custom Loop C dihedral as being drawn between the alpha carbons of residues Ala208 , Phe199 , Glu198 , and Ile203 . To determine whether Loop C adopted a ‘closed’ or ‘open’ conformation we evaluated the distance between the Arg65 and Asp202 side chains , measured by a minimum distance of their respective polar side-chain atoms for each analyzed simulation frame . To evaluate how well solvated the setron-binding sites were throughout our simulations , we counted the number of water oxygen atoms within 3 Å of any setron atoms for each simulation frame averaged across all five subunits . Structural interaction fingerprints were calculated with an in-house python script that monitored 5-HT3AR interactions with each setron . Specifically , for each residue of 5-HT3AR , ligand-protein interactions with both sidechain and backbone heavy atoms were calculated as a 9-bit representation based on the following 9 types of interactions: apolar ( van der Waals ) , face-to-face aromatic , edge-to-face aromatic , hydrogen-bond interactions with the protein either as a donor or acceptor , electrostatic with either the protein acting as a positive or negative charge , one-water-mediated hydrogen bond , and two-water-mediated hydrogen bonds . A distance cutoff of 4 . 5 Å was used to identify apolar interactions between two non-polar atoms ( carbon atoms ) , while a cutoff of 4 Å was used to evaluate aromatic and electrostatic interactions . Interaction probabilities were averaged across simulation frames as well as across all five 5-HT3AR binding sites and errors for each interaction type were estimated using a two-state Markov model , sampling the transition matrix posterior distribution using standard Dirichlet priors for the transition probabilities ( Trendelkamp-Schroer et al . , 2015 ) . Pore radii of 5-HT3AR systems were assessed over equidistant simulation frames with a stride of 12 . 5 ns using HOLE ( Smart et al . , 1996 ) . The coordinates of the 5-HT3AR-setron structures and the corresponding Cryo-EM maps have been deposited in wwPDB and EMDB with the following accession numbers . PDB ID: 6W1Y; EMBD ID: EMD-21518 for 5-HT3AR-Palono , PDB ID: 6W1M; EMBD ID: EMD-21512 for 5-HT3AR-Ondan and PDB ID: 6W1J; EMBD ID: EMD-21511 for 5-HT3AR-Alo . | Serotonin is perhaps best known as a chemical messenger in the brain , where it regulates mood , appetite and sleep . But as a hormone , serotonin works in other parts of the body too . Serotonin is predominantly made in the gut , where it binds receptor proteins that help to regulate the movement of substances through the gastrointestinal tract , aiding digestion . However , a surge in serotonin release in the gut induces vomiting and nausea , which commonly happens as a side effect of treating cancer with radiotherapy and chemotherapy . Anti-nausea drugs used to manage and prevent the severe nausea and vomiting experienced by cancer patients are therefore designed to target serotonin receptors in the gut . These drugs , called setrons , work by binding to serotonin receptors before serotonin does , essentially neutralising the effect of any surplus serotonin . Although they generally target serotonin receptors in the same way , some setrons are more efficient than others and can provide longer lasting relief . Clarifying exactly how each drug interacts with its target receptor might help to explain their differential effects . Basak et al . used a technique called cryo-electron microscopy to examine the interactions between three common anti-nausea drugs ( palonosetron , ondansetron and alosetron ) and one type of serotonin receptor , 5-HT3AR . The experiments showed that each drug changed the shape of 5-HT3AR , thereby inhibiting its activity to varying degrees . Further analysis identified a distinct ‘interaction fingerprint’ for the three setron drugs studied , showing which of the receptors’ subunits each drug binds to . Simulations of their interactions also showed that water molecules play a crucial role in the process , exposing the binding pocket on the receptor’s surface where the drugs attach . This work provides a structural blueprint of the interactions between anti-nausea drugs and serotonin receptors . The structures could guide the development of new and improved therapies to treat nausea and vomiting brought on by cancer treatments . | [
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] | 2020 | High-resolution structures of multiple 5-HT3AR-setron complexes reveal a novel mechanism of competitive inhibition |
The incubation period for typhoid , polio , measles , leukemia and many other diseases follows a right-skewed , approximately lognormal distribution . Although this pattern was discovered more than sixty years ago , it remains an open question to explain its ubiquity . Here , we propose an explanation based on evolutionary dynamics on graphs . For simple models of a mutant or pathogen invading a network-structured population of healthy cells , we show that skewed distributions of incubation periods emerge for a wide range of assumptions about invader fitness , competition dynamics , and network structure . The skewness stems from stochastic mechanisms associated with two classic problems in probability theory: the coupon collector and the random walk . Unlike previous explanations that rely crucially on heterogeneity , our results hold even for homogeneous populations . Thus , we predict that two equally healthy individuals subjected to equal doses of equally pathogenic agents may , by chance alone , show remarkably different time courses of disease .
The discovery that incubation periods tend to follow right-skewed distributions originally came from epidemiological investigations of incidents in which many people were simultaneously and inadvertently exposed to a pathogen . For example , at a church dinner in Hanford , California on March 17 , 1914 , ninety-three individuals became infected with typhoid fever after eating contaminated spaghetti prepared by an asymptomatic carrier known to posterity as Mrs . X . Using the known time of exposure and onset of symptoms for the 93 cases , Sawyer , 1914 found that the incubation periods ranged from 3 to 29 days , with a mode of only 6 days and a distribution that was strongly skewed to the right . Similar results were later found for other infectious diseases . Surveying the literature in 1950 , Sartwell noted a striking pattern: the incubation periods of diseases as diverse as streptococcal sore throat ( Sartwell , 1950 ) ( Figure 1a ) , measles ( Stillerman and Thalhimer , 1944 ) , polio , malaria , chicken pox , and the common cold were all , to a good approximation , lognormally distributed ( Sartwell , 1950 ) . On a time scale of years instead of days , the incubation periods for bladder cancer ( Goldblatt , 1949 ) ( Figure 1b ) , skin cancer , radiation-induced leukemia , and other cancers were also found to be approximately lognormally distributed ( Armenian and Lilienfeld , 1974 ) . Two natural questions arise: Why should incubation periods be distributed at all , and why should they be distributed in the same way for different diseases ? Previous explanations rest on the presumed heterogeneity of the host , the pathogen , or the dose ( Sartwell , 1950; Nishiura , 2007; Horner and Samsa , 1992 ) . To see how this works , return to the typhoid outbreak at the Hanford church dinner ( Sawyer , 1914 ) . Every person who ate that spaghetti presumably had a different level of overall health and immune function , and every plate of spaghetti was likely contaminated with a different dose and possibly even strain of typhoid . Suppose the typhoid bacteria proliferated exponentially fast within the hosts and triggered symptoms when they reached a fixed threshold . Then , if the bacterial dose , growth rate , or triggering threshold were normally distributed across the hosts , one can show that the resulting distribution of incubation periods would have been either exactly or approximately lognormal ( see Results , ‘Influence of heterogeneity’ ) . On the other hand , there is counter-evidence that lognormal distributions can occur even if some of these sources of heterogeneity are lacking . For example , Sartwell , 1950 reanalyzed data from a study ( Bodian et al . , 1949 ) in which identical doses and strains of polio virus were injected into the brains of hundreds of rhesus monkeys . The incubation period , defined as the time from inoculation to the onset of paralysis , was still found to be approximately lognormally distributed , even though the route of infection and the viral dose and strain were held constant . Moreover , the lognormal distributions commonly observed for human diseases have a particular shape , with a dispersion factor ( Sartwell , 1950 ) around 1 . 1-1 . 5 , which previous models cannot explain without special parameter tuning . ( See Box 1 for the definition of dispersion factors . ) Here , we propose a new explanation for the skewed distribution of incubation periods . Instead of heterogeneity , it relies on the stochastic dynamics of the incubation process , as the pathogen invades , multiplies , and competes with itself and the cells of the host in a structured network topology . The theory predicts that under a broad range of circumstances , incubation periods should follow a right-skewed distribution that resembles a lognormal , but is actually a Gumbel , one of the universal extreme value distributions ( Kotz and Nadarajah , 2000 ) . Heterogeneity is not required , but it is allowed; it does not qualitatively alter our results when included .
We model the incubation process using the formalism of evolutionary graph theory ( Lieberman et al . , 2005; Nowak , 2006; Ohtsuki et al . , 2006; Ashcroft et al . , 2015 ) . A network of N≫1 nodes is used to represent an environment within a host where a pathogenic agent , such as a harmful bacterium or a cancer cell , is invading and reproducing . The network could represent several plausible biological scenarios , for example the intestinal microbiome , where harmful typhoid bacteria are competing against a benign resident population of gut flora in a mixing system ( modeled as a complete graph ) ; or it could represent mutated leukemic stem-cells vying for space against healthy hematopoietic stem cells within the well-organized three-dimensional bone marrow space ( modeled as a 3D lattice ) ; or a flat epithelial sheet with an early squamous cancer compromising and invading nearby healthy cells ( modeled as a 2D lattice ) . For the sake of generality , we will refer to the two types of agents as healthy residents and harmful invaders . While Sartwell’s law has been applied to many different types of diseases with diverse etiologies , the model we propose makes the most sense for asexually reproducing invaders , like cancer cells or bacteria . Viruses , on the other hand , often reproduce with a ‘one-to-many’ dynamic , which is not faithfully captured in this model . So , while the general phenomenon of network invasion seems to apply to viruses as well , the model in its present form is not well suited to describe their dynamics . Considering asexually reproducing and competing invaders , then , we choose to model the invasion dynamics as a Moran process ( Moran , 1958; Williams and Bjerknes , 1972; Lieberman et al . , 2005; Nowak , 2006 ) . Invaders are assigned a relative fitness r ( suggestively called the carcinogenic advantage by Williams and Bjerknes , 1972 ) . The fitness of residents is normalized to 1 . We consider two versions of the Moran process . In the Birth-death ( Bd ) version ( Figure 2a ) , a random node is chosen , with probability proportional to its fitness . It gives birth to a single offspring . Then , one of its neighbors is chosen uniformly at random to die and is replaced by the offspring ( Figure 2b ) . We also consider Death-birth ( Db ) updates ( Figure 2c , d ) . In this version of the model , a node is randomly selected for death , with probability proportional to 1/r; then a copy of a uniformly random neighbor replaces it . To test the robustness of our results , we study both versions of the Moran model on various networks: complete graphs , star graphs , Erdős-Rényi random graphs , one- , two- , and three-dimensional lattices , and small-world , scale-free , and k-regular networks . We also vary the invader fitness r and the model criterion for the onset of symptoms . These extensions are presented in the Materials and methods , Figures 5 , 6 . Box 2 discusses other variants of the Moran model . Here we focus on the simplest cases to elucidate the basic mechanisms . Our simulations start with a single invader placed at a random node in a network of otherwise healthy residents . The update rule is applied at discrete time steps . In the long run , either the invaders replace all the residents , or vice versa . If symptoms are triggered when the entire network has been taken over by invaders , then the incubation period is the number of time steps between the introduction of the invader and its fixation . On the other hand , if the invaders die out and the healthy cells take over , then the process is stopped and no observable symptoms manifest . Later , in the paper , we consider a generalization from complete to partial takeovers , but for now the incubation period will refer to a complete takeover . Our notion of time in this model is linked directly to the biology of invasion of a reproducing asexual pathogen that divides and replaces other cells sequentially . Instead of considering divisions as a rate , and therefore linking the dynamics to real time , we consider time steps to be individual division events . This is more akin to the standard methods of modeling chemical interactions , as in the Gillespie algorithm ( Gillespie , 1977 ) . This focus on the biology of the individual pathogen ( or cancer cell ) also provides a simple explanation for how diseases with very different natural histories can have the same analytic distribution of incubation time . As each different disease would have a different characteristic mean doubling time , while the shape of the distributions might be the same , the physical time taken would scale with the characteristic proliferation time . Future iterations of this model could consider deriving an exact scaling between physical time and this biological event-based updating of time . First , consider what happens if the invaders have infinite fitness ( r→∞ ) in the Birth-death model . While an exaggeration , this case is instructive and is a reasonable approximation for aggressive cancers and infections . In this limit , the dynamics simplify enormously: only the invaders reproduce . But because they give birth and replace their neighbors blindly , they waste time whenever they compete between themselves and one invader replaces another . These random self-replacements slow down the incubation process , and make it highly variable . In fact , the level of in-fighting is what determines the incubation period in this case . Beyond fitness , the topology of the network matters too . For low-dimensional networks , exemplified by a two-dimensional lattice ( Figure 3a , red circles ) , the growth rate of the invader population remains roughly constant as takeover occurs . This leads to a normal distribution of incubation periods ( Figure 3a , red circles; and see Methods and Materials , ‘Birth-death , other solvable networks’ ) . However , on very high-dimensional networks like the complete graph ( Figure 3a , blue circles ) , the distribution becomes right skewed . Intuitively , this happens because every invader now has a chance of replacing any healthy node or any other invader . It is as if at every time step a candidate node for replacement gets blindly drawn from a bag , relabeled as an invader , and returned to the bag . At the start of the incubation process , almost every draw adds another invader to the population and the infection progresses rapidly . But near the end , it will take many , many draws to blindly fish out the last remaining healthy node , as needed to terminate the incubation period . This slowing-down phenomenon near the end should feel familiar to anyone who has tried to complete a collection of baseball cards , stamps , or coupons , since they are all manifestations of the coupon collector's problem , a well-studied concept in probability theory ( Pósfai , 2010; Feller , 1968; Erdős and Rényi , 1961 ) . Because of those frustratingly long waits to collect the final healthy node , the incubation period distribution gets skewed to the right . In the infinite-N limit ( see Methods and Materials , ‘Birth-death , complete graph’ ) , the coupon collector’s process returns a Gumbel distribution , which resembles a lognormal and can be mistaken for it ( Read , 1998 ) . Indeed , when a Gumbel and a lognormal are fit to the same real data , as in Figure 1 , it is hard to tell them apart . All this analysis can easily be repeated for the Death-birth model with minimal changes . At the other extreme , suppose the invaders have no selective advantage ( r=1 ) . Then a different stochastic mechanism skews the distribution of incubation periods to the right ( Figure 3b and Methods and Materials , ‘Random Walk Skewness’ ) . For many networks , the dynamics reduce to an unbiased random walk on the number of invaders , with waiting times at each population level . There are two absorbing states , corresponding to both 00 and N invaders for the two kinds of fixation . However , we only care about random walks that successfully hit N , as these represent disease processes that manifest symptoms , so we must always condition on its success . This demands that the invader experience early success and growth , pushing it away from probable extinction . This conditioning introduces a bias that makes short incubation times probable , but long walks may still occasionally occur , driving the mean time above the median . In short , a conditioned random walk will introduce a right skew in the distribution of incubation periods . This effect holds for both high- and low-dimensional networks ( Figure 3b ) , and for Birth-death and Death-birth dynamics . Right-skewed distributions typically persist in the face of various perturbations to the model , but some perturbations can turn them into normal distributions . For example , suppose we allow symptoms to occur when invaders take over only a fraction f of the whole network . This is a reasonable consideration as leukemic cells need not take over all the bone marrow before leukemia becomes evident , nor does typhoid need to overwhelm all the cells in the microbiome before causing fever; indeed it is likely far fewer in both cases . Figure 4 contrasts what happens for Birth-death and Death-birth dynamics under these assumptions . When r=∞ , the Gumbel distribution of Figure 3a persists for f=1 ( Figure 4a ) , but turns into a normal distribution ( Baum and Billingsley , 1965 ) when f=0 . 9 ( Figure 4b ) or f=0 . 1 ( Figure 4c ) . Yet under Death-birth dynamics , the distribution stays Gumbel for all nonzero values of f ( Figure 4d , e , f ) . The fact that birth-death dynamics returns a normal for 0<f<1 whereas Death-birth still returns a Gumbel can be rationalized via various convergence theorems ( Baum and Billingsley , 1965; Ottino-Löffler et al . , 2017; Pósfai , 2010 ) . However , the fact that similar update rules behave so differently under a reasonable perturbation should caution us to be mindful of our choice of models . Historically , the distribution of incubation periods has been ascribed to heterogeneity ( Sartwell , 1950; Nishiura , 2007; Horner and Samsa , 1992 ) in the fitness ( growth rate , say ) or dose of the pathogen , or in host factors like immune response . To see how these potential sources of heterogeneity could account for the skewed and approximately lognormal distribution of incubation periods , consider a pathogen growing exponentially with rate r from an initial population N0 , so that its population at time t is given by N ( t ) =N0ert . If an immune response or other detectable symptoms are triggered when N reaches a threshold population θ , then the incubation time T satisfies N ( T ) =N0erT=θ . Solving for T yields ( 1 ) T=1r ( logθ-logN0 ) . So if either the threshold θ or the inoculum N0 are normally distributed across the host population , the incubation period T will be lognormally distributed . Likewise , but in a more qualitative sense , a normal distribution of pathogen growth rates r will also produce a skewed distribution that resembles a lognormal ( Nishiura , 2007 ) . However , if there is no randomness in any of those sources , this model predicts a single deterministic value of T for the incubation period . In contrast , the stochastic model proposed here does not need these sources of heterogeneity to produce right-skewed distributions . But if they happen to be present , as they likely are for many real diseases , our model can accommodate them . Indeed , when any of the three sources of heterogeneity are included in our model , they only serve to make the predicted distributions even more right-skewed , as we now show . First , to emulate the heterogeneity of the strength of the pathogen , we assume heterogeneity in the parameter r ( which , in our model , governs the fitness of the invading cells relative to those of the host ) . In particular we randomly draw a different r>0 in each simulation , to simulate different hosts being infected with different pathogenic strains . The resulting distribution of invader fixation times depends on the distribution of the r’s , but our investigations demonstrate they consistently produce right-skewed distributions ( Figure 5a ) . Second , to emulate the heterogeneity of host factors like immune response , we allow variability in the parameter f , which quantifies the fraction of the network that needs to be invaded before symptoms appear . Let Tf denote the time it takes for N⋅f of the original resident nodes to be replaced by invaders . If we draw f randomly from some distribution , then essentially each host has a different threshold at which symptoms appear . In contrast to Figure 4b , where we saw that repeated simulations for a host population with a single , fixed , deterministic f can cause skewed distributions to turn into normal distributions , that is no longer the case when heterogeneity is included , as Figure 5b indicates . In fact , the heterogeneity actually causes even more right-skew than before . Third , emulating variable doses is also straightforward . Instead of always starting with a single invader cell , we choose the initial number of invaders according to some distribution . Again , this modification does not remove the right-skewed behavior established in the Moran model ( Figure 5c ) . Finally , we can apply all these sources of heterogeneity at once , and remain with a right-skewed distribution ( Figure 5d ) . In summary , although our main results were obtained by analyzing stochastic models of homogeneous host and pathogen populations , allowing for heterogeneity makes the predicted right-skewed distributions more , not less , prominent .
The evolutionary dynamical model presented here is intended to mimic the within-host development of certain cancers and bacterial infections . It is not well suited to the dynamics of viruses . Thus , explaining why Sartwell's law also holds for so many viral diseases remains an open question . Our model suggests two basic mechanisms underlie the observed right-skewed , approximately lognormal distributions of incubation periods . When the fitness of the pathogen is high , the skew comes from coupon collection; when the pathogen fitness is neutral or low , the skew comes from conditioned random walks; and at intermediate fitnesses , a combination of the two creates skew . Neither of these effects demand any heterogeneity from the invader or the host . However , the model can accommodate such heterogeneity , either by having the invader fitness r be randomly drawn , or by having symptoms occur when a random fraction f of the host network has been invaded . Our simulations show that both sources of heterogeneity only exaggerate the level of right-skewness we would have seen without them ( See Results , ‘Influence of heterogeneity’ , Figure 5 ) . Beyond accounting qualitatively for the distributions of incubation periods , our model accounts for a quantitative feature that has never been explained before . As shown in Methods and Materials , Table 1 , the distributions generated by highly fit pathogens and mutants are predicted to have dispersion factors ( also known as geometric standard deviations; see Box 1 ) of about 1 . 1-1 . 4 , close to the actual values of 1 . 1-1 . 5 observed for various infectious diseases ( Sartwell , 1950;Sartwell , 1966;Nishiura , 2007 ) . Moreover , the model also helps to explain why so few infectious diseases yield dispersion factors greater than 1 . 5 . Such high dispersion factors arise only for r≈1 , corresponding to pathogens or mutants that are only slightly more fit than the resident populations against which they are competing . On the other hand , it is tempting to speculate that this regime of nearly neutral fitness may be more relevant to cancer development . While it is likely that tumor cells late in the disease process have much higher fitness than healthy cells secondary to continued selection ( Scott and Marusyk , 2017 ) , there is ample evidence that most cancers have long latency periods , for example in genetic data from pancreatic cancers ( Yachida et al . , 2010 ) . One could speculate that during this early period , which accounts for the majority of the cancer’s time in the patient , the fitness is nearly neutral . For the cancer data reviewed by ( Armenian and Lilienfeld , 1974 ) , the observed distributions typically had dispersion factors around 1 . 4-1 . 9 . In our model , these high dispersion factors tend to arise when the invader is only slightly more fit than residents . This is also consistent with the suggestion of ( Williams and Bjerknes , 1972 ) ; the shape of tumors in the model most closely resembled that of real tumors when the fitness of the invaders was only slightly above neutral . In 1546 , Fracastorii , 1930 described the incubation of rabies after a bite from an rabid dog as ‘stealthy , slow , and gradual . ’ Today , nearly five centuries later , the dynamics of incubation processes remain stealthy and slow to yield their secrets . We have tried to shed light on their patterns of variability with the help of a new conceptual tool , evolutionary graph theory . This approach provides a possible solution to the longstanding question of why so many disparate diseases show such similarly-shaped distributions of incubation periods . What remains is to quantify the dynamics of incubation processes experimentally with high-resolution measurements in time and space . Aside from their possible application to incubation processes , our results also shed light on a broader theoretical question in evolutionary dynamics: when a mutant invades a structured population of residents , how does the distribution of mutant fixation times depend on the network structure of the population ? Early work in evolutionary graph theory ( Lieberman et al . , 2005; Nowak , 2006; Ohtsuki et al . , 2006 ) concentrated on the network’s impact on the probability of mutant fixation and the mean time to fixation . More recent studies have gone beyond the mean time to consider the full distribution of fixation times ( Ashcroft et al . , 2015 ) , as we have also done here . We hope that our exact results for disparate topologies and dynamics will stimulate further investigations of these important questions in evolutionary biology .
The population of cells is represented by a network of N nodes . Edges between nodes indicate which cells can potentially interact with each other . There are two types of cells: harmful invaders with fitness r , and healthy residents with fitness 1 . All simulations are initialized with a single invader placed at a random node . The Moran Birth-death ( Bd ) update rule has two steps . First , a node is randomly selected out of the total population , with probability proportional to its fitness . Second , a neighbor of the first node is chosen , uniformly at random , and takes on the type of the first node . In a complete graph , all nodes are adjacent . Therefore , the probability of adding a new invader , given there are currently m invaders , ispm:=P ( Choose an invader ) ⋅P ( Neighbor is resident ) =mrmr+ ( N-m ) ⋅N-mN-1 . In the limit of infinite fitness , ( r→∞ ) , the first term approaches one and we getpm:=N-mN-1 , and the probability of the invader population ever decreasing is 0 . So the time T to invader fixation is sum of all the transition times m→m+1 for m=1 , 2 , … , N-1 . These transition times can be calculated as follows . For the population to take t steps to go from m to m+1 invaders , nothing must have happened for t-1 steps before advancing on the t’th step . The probability of this happening is exactlypm ( 1-pm ) t-1 . In other words , the time to add a new invader is exactly a geometric random variable . Therefore , the total fixation time is justT=∑m=1N-1Geo ( pm ) =∑k=1N-1Geo ( kN-1 ) . This random variable T describes a process identical to that of the coupon collector’s problem ( Pósfai , 2010; Feller , 1968 ) . In both , we have a collection of N-1 nodes , and draw a random one with replacement at each time step . If we pick a healthy node , we relabel it and toss it back , and repeat until there are no healthy nodes left . By adapting classic results ( Erdős and Rényi , 1961; Baum and Billingsley , 1965 ) , we show in the Appendix that it is straightforward to find the asymptotic distribution of T as N gets large . To normalize this distribution , note that its mean is μ=∑mpm-1≈Nlog ( N ) +Nγ . Then we find ( 2 ) T-μN→𝑑Gumbel ( -γ , 1 ) . Here γ≈0 . 5772 is the Euler-Mascheroni constant , →𝑑 denotes convergence in distribution , and a Gumbel ( α , β ) random variable has a density given by ( 3 ) h ( x ) =β-1e- ( x-α ) /βexp ( -e- ( x-α ) /β ) . This prediction for the normalized distribution of the incubation period T agrees with simulations on large networks ( Figure 6a ) . A Gumbel distribution of incubation periods has previously been obtained for a variant of this model . Instead of working with the large-N limit of a complete graph , it assumed a continuous-time birth-death model of an invading microbial population whose dynamics were governed by differential equations ( Williams , 1965 ) . The analysis of the finite-N complete graph sets up an important framework that can be applied to more complicated networks . For example , in the Appendix we prove that the distribution of fixation times T for a star network also converges to a Gumbel for N≫1 , specifically: ( 4 ) T-N2log ( N ) - ( γ-1 ) N2N2→𝑑Gumbel ( -γ , 1 ) . This prediction matches simulations ( Figure 6b ) . The same framework also applies to a one-dimensional ( 1D ) ring lattice , but instead of using the coupon-collector framework , we need to cite the Lindeberg-Feller central limit theorem ( Durrett , 1991 ) . As shown in the Appendix , this gives us ( 5 ) T- ( N2-N ) /2 ( 2N3-3N2+N ) /6→𝑑Normal ( 0 , 1 ) . This prediction agrees with simulations ( Figure 6c ) . For a two-dimensional square lattice , it is more difficult to produce analytical results that are both rigorous and exact . But by making an approximation based on the geometry of the lattice , and using the fact that the population growth rate is proportional to its surface area ( see the Appendix , ”Normally distributed fixation times for 2D lattice’ ) , we can make a non-rigorous analytical guess about the distribution of the fixation times T . Via these arguments , and given μ=E[T] and σ2=Var ( T ) , we predict ( 6 ) T-μσ→𝑑Normal ( 0 , 1 ) . Despite the approximation , this prediction works well ( Figure 6d ) . By similar arguments , we predict that lattices of dimension d≥3 have right-skewed asymptotic distributions of fixation times . Specifically , given η:=1-1/d , we predict ( 7 ) Skew ( T ) :=E[ ( T-μ ) 3]σ3=2ζ ( 3η ) ζ ( 2η ) 3/2 , where ζ is the Riemann zeta function . The methods used to derive that can also be used to create approximate finite-size distributions for the lattices ( Figure 6e ) . In particular , we predict positive skew for all d≥3 and for the skew to increase monotonically with dimension ( see the Appendix ) . Meanwhile , both 1D and 2D lattices have normal asymptotic distributions , and therefore no skew . This establishes d=2 as a critical dimension in these dynamics , transitioning from zero skew to positive skew . Incidentally , these arguments also suggest that appropriate infinite-dimensional networks will asymptotically have a Gumbel distribution . This is numerically true for the Erdős-Rényi random graph ( Figure 6f ) . For more complex networks , such as the Watts-Strogatz small-world network , the k-regular random graph , and the Barabasi-Albert scale-free network , we currently lack theory to predict the asymptotic distributions analytically . However , numerical simulations produce simulations that are all well-approximated by a noncentral lognormal , obeying Sartwell’s law ( Sartwell , 1950 ) ( Figure 7a , c , e ) . Table 1 shows that geometric standard deviations of the incubation period distributions for all of these networks fall around 1 . 1-1 . 4 , in agreement with the dispersion factors of 1 . 1-1 . 5 observed for many infectious diseases ( Sartwell , 1950; Horner and Samsa , 1992 ) . So far we have focused on infinitely fit invaders ( r→∞ ) . Now we consider the opposite extreme , where invaders have nearly neutral fitness ( r≈1 ) relative to the residents . We will show that right-skewed distributions of incubation periods occur in this limit as well , but for a completely different reason than coupon collection . The analysis is again simplest for the complete graph , so we return to that case . As before , the probability of an invader replacing a resident in the next time step ispm+:=mrmr+ ( N-m ) ⋅N-mN-1 . Similarly , the probability of an invader being replaced by a resident in the next time step ispm-:=N-mmr+ ( N-m ) ⋅mN-1 . So the probability of the next replacement adding a new invader isq:=pm+pm++pm-=rr+1 . This defines a random walk with drift q on the invader population . Only a special subset of these walks are relevant to the computation of the incubation period distribution . For the incubation period to be well-defined , the invader population must not go extinct . Therefore , we need to condition on the fact that the invader population m hits N before it ever hits 0 . For the limiting case r=1 , corresponding to a perfectly neutral invader , we can show with martingale methods that the resulting distribution of incubation periods will be strongly skewed to the right as N gets large ( see the Appendix ) . This is to be expected: there are only a few ways to walk from one to N quickly , while there are many ways to have a long , meandering excursion before finally getting there . The variance from this conditioned random walk process tends to drown out the effects of network topology . The distribution of incubation periods ends up looking similar for diverse networks ( Figure 8 ) , including complex networks ( Figure 7b , d , f ) . So even though no coupon collection happens at low finesses r≈1 , the effect of the conditioned random walk is more than enough to generate right-skewed distributions of incubation periods . In fact , this conditioned random walk mechanism at low r produces an even higher dispersion factor ( ≈1 . 7 ) than coupon collection does at high r ( see Table 1 ) . In many diseases , it is unlikely that the total network size would remain constant in time . For example , targeted radiation and chemotherapy leads to a loss of mass in both the tumor and the substrate tissue . Depending on the specific physical case , the population levels of invaders and residents can have many nontrivial time dependencies . As a first-order examination of the effects of time-varying populations , three simple cases were considered on the complete graph for the intermediate fitness of r=10 . As a baseline , the distribution for a constant population was measured in Figure 9a . We considered a case when the resident population was growing . At every time step , a new resident node was added with probability 1/N , which was chosen so that takeover would happen in finite time . Even still , the majority of the run will be spent when the resident population is small , with takeovers and new additions occurring at a roughly even pace . This led to an accentuated level of right skew in Figure 9b . We then considered a case where the resident population was constantly shrinking . Again , the probability of change was 1/N every time step , but this time it decreased the resident population by 1 . While there is still a visible right skew in Figure 9c , it was somewhat lessened due to the global shrinkage speeding up the coupon collecting process . Finally , we considered a randomly varying resident population . Here , the resident population increases or decreases by one every time step , each with probability 1/2 . This random-walking population level also leads to an extreme level of skew in Figure 9d . | When one child goes to school with a throat infection , many of his or her classmates will often start to come down with a sore throat after two or three days . A few of the children will get sick sooner , the very next day , while others may take about a week . As such , there is a distribution of incubation periods – the time from exposure to illness – across the children in the class . When plotted on a graph , the distribution of incubation periods is not the normal bell curve . Rather the curve looks lopsided , with a long tail on the right . Plotting the logarithms of the incubation periods , however , rather than the incubation periods themselves , does give a normal distribution . As such , statisticians refer to this kind of curve as a “lognormal distribution" . Remarkably , many other , completely unrelated , diseases – like typhoid fever or bladder cancer – also have approximately lognormal distributions of incubation periods . This raised the question: why do such different diseases show such a similar curve ? Working with a simple mathematical model in which chance plays a key role , Ottino-Löffler et al . calculate how long it takes for a bacterial infection or cancer cell to take over a network of healthy cells . The model explains why a lognormal-like distribution of incubation periods , modeled as takeover times , is so ubiquitous . It emerges from the random dynamics of the incubation process itself , as the disease-causing microbe or mutant cancer cell competes with the cells of the host . Intuitively , this new analysis builds on insights from the “coupon collector’s problem”: a classical problem in mathematics that describes the situation where a person collects items like baseball cards , stamps , or cartoon monsters in a videogame . If a random item arrives every day , and the collector’s luck is bad , they may have to wait a long time to collect those last few items . Similarly , in the model of Ottino-Löffler et al . , the takeover time is dominated by dramatic slowdowns near the start or end of the infection process . These effects lead to an approximately lognormal distribution , with long waits , as seen in so many diseases . Ottino-Löffler et al . do not anticipate that their findings will have direct benefits for medicine or public health . Instead , they believe their results could help to advance basic research in the fields of epidemiology , evolutionary biology and cancer research . The findings might also make an impact outside biology . The term “contagion” has now become a familiar metaphor for the spread of everything from computer viruses to bank failures . This model sheds light on how long it takes for a contagion to take over a network , for a variety of idealized networks and spreading processes . | [
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] | 2017 | Evolutionary dynamics of incubation periods |
Touch is encoded by cutaneous sensory neurons with diverse morphologies and physiological outputs . How neuronal architecture influences response properties is unknown . To elucidate the origin of firing patterns in branched mechanoreceptors , we combined neuroanatomy , electrophysiology and computation to analyze mouse slowly adapting type I ( SAI ) afferents . These vertebrate touch receptors , which innervate Merkel cells , encode shape and texture . SAI afferents displayed a high degree of variability in touch-evoked firing and peripheral anatomy . The functional consequence of differences in anatomical architecture was tested by constructing network models representing sequential steps of mechanosensory encoding: skin displacement at touch receptors , mechanotransduction and action-potential initiation . A systematic survey of arbor configurations predicted that the arrangement of mechanotransduction sites at heminodes is a key structural feature that accounts in part for an afferent’s firing properties . These findings identify an anatomical correlate and plausible mechanism to explain the driver effect first described by Adrian and Zotterman .
A diverse array of touch receptors allows animals to discern object shapes , to explore surface textures and to detect forces impinging upon the skin . In mammals , distinct classes of mechanosensory afferents are tuned to extract specific features of a tactile stimulus and then to encode them as trains of action potentials , or spikes , with unique firing properties ( Johnson , 2001 ) . A common feature of mechanosensory neurons is specialized anatomical structures , termed end organs , that shape their neuronal outputs ( Chalfie , 2009 ) . For example , recent studies show that mouse hair follicles are innervated by at least three molecularly ( Li et al . , 2011 ) and 10 anatomically ( Wu et al . , 2012 ) distinct types of cutaneous afferents . A key unanswered question is: how does a tactile afferent’s peripheral architecture govern its neuronal response to touch stimuli ? Due to their unusual architecture , somatosensory neurons do not initiate action potentials at axon initial segments , as do neurons of the central nervous system . Instead , sensory stimuli act at peripheral terminals to produce receptor potentials , which locally sum to trigger spikes that travel to central terminals up to 1 m away . For myelinated tactile afferents , a landmark study of Pacinian corpuscles established that spikes initiate at the heminode , the most distal node of Ranvier ( Loewenstein and Rathkamp , 1958 ) . A Pacinian corpuscle is innervated by an un-branched afferent; however , most tactile end organs comprise branching afferents with multiple sites of sensory transduction . The question of how spike trains arise in branched sensory neurons has fascinated neurobiologists since Adrian and Zotterman ( Adrian and Zotterman , 1926a ) . In the simplest configuration , which is observed in crustacean stretch receptors and frog muscle spindles , receptor potentials from all branches integrate at a single spike initiation zone ( Adrian and Zotterman , 1926b ) . As stimulus intensity increases , additional transduction sites are recruited , producing larger receptor potentials to reach spike threshold . Thus , this configuration results in firing rates proportional to the number of transduction sites recruited . Alternatively , sensory afferents can have multiple spike initiation zones , each driven by inputs from one or a few branches ( Horch et al . , 1974 ) . Support for this model comes from studies of mammalian muscle spindles and tendon organs , which have multiple myelinated branches and heminodes where spikes might initiate ( Fukami , 1980; Quick et al . , 1980; Banks et al . , 1997 ) . When a stimulus excites multiple branches , a spike produced by one zone is thought to propagate antidromically into other branches , activating other spike initiation zones and thereby suppressing firing during their refractory period . As a consequence of this resetting mechanism , the spike initiation zone with the highest firing rate is thought to act as a driver for firing in the afferent as a whole . Electrophysiological studies provide strong support for this model ( Lindblom and Tapper , 1966; Horch et al . , 1974; Fukami , 1980; Peng et al . , 1999 ) ; however , the structural principles that govern spike initiation and integration in mammalian tactile afferents are unknown . To elucidate the origin of firing patterns in branched tactile receptors , we examined slowly adapting type I ( SAI ) afferents in mouse skin . These mechanoreceptors localize to skin regions specialized for high tactile acuity , including fingertips , whisker follicles and touch domes . SAI afferents represent fine spatial details with high fidelity; therefore , they are thought to encode object features such as edges and curvature ( Johnson , 2001 ) . The SAI afferent’s end organ is a cluster of Merkel cell-neurite complexes , which are required to produce canonical SAI firing patterns in mouse touch-dome afferents ( Maricich et al . , 2009 ) . Because the essential processes of mechanotransduction and spike initiation occur in tactile end organs , we analyzed the impact of end-organ architecture on touch-evoked responses . As it is not yet possible to directly record from tactile end organs embedded in mammalian skin , we employed a combined experimental and computational modeling approach to identify simple structural principles that can account for the SAI afferent’s mechanosensory coding properties .
SAI afferents are myelinated Aβ afferents that innervate Merkel cells located in the epidermis . Although dermal segments are thickly myelinated , SAI afferents lose their myelin sheaths just below the dermal–epidermal junction . Unmyelinated branches , which are here termed ‘neurites’ , then traverse the basal lamina to contact Merkel cells ( Figure 1A; Iggo and Muir , 1969 ) . To identify structural domains in mouse SAI afferents , we first sought to localize nodes of Ranvier , which are sites of spike integration and propagation , as well as heminodes , which are the anatomical substrates of spike initiation . 10 . 7554/eLife . 01488 . 003Figure 1 . Morphometry of touch-dome afferents reveals diverse end-organ architectures . ( A ) Schematic of the SAI afferent’s end organ . ( B and C ) SAI afferents , labeled with antibodies against Neurofilament-H ( NFH; cyan ) and Myelin Basic Protein ( MBP; magenta ) , were identified by their connection to Keratin 8-positive Merkel cells ( K8; blue ) in touch-dome cryosections . The voltage-gated sodium channel NaV1 . 6 ( yellow ) localized to heminodes ( B ) and nodes of Ranvier ( C ) . Scale bar in C ( 10 µm ) applies to B . ( D–F ) Projections of touch domes labeled in whole mount . ( D ) NFH ( red ) , MBP ( green ) and K8 ( blue ) labeled Merkel cells contacted by a single myelinated afferent ( see also Video 1 ) or ( E ) two afferent branches whose point of convergence was not identified ( see also Video 2 ) . Arrows: examples of heminodes; arrowheads: examples of nodes of Ranvier . ( F ) NaV1 . 6 ( green ) identified heminodes and nodes in an NFH-positive afferent ( red ) innervating K8-positive Merkel cells ( blue ) . Inset shows an expanded view of an NaV1 . 6-positive node . Scale bar in F ( 50 µm ) applies to D–F’ . ( D’–F’ ) Projections of 3D reconstructions of end organs shown above: afferent ( black ) , Merkel cells ( green ) , heminodes ( red half-circles ) and nodes ( red circles ) . ( E’ ) A non-converging branch is marked in gray . Note that this branch is thinner than other myelinated branches . ( G ) The highest branching order found in each SAI afferent arbor was independent of the number of Merkel cells contacted . ( H ) Morphometric quantification of reconstructed touch domes innervated by single afferents . ( I ) More than 80% of Merkel cells were contacted by neurites and a similar proportion of terminal neurites contacted Merkel cells ( N = 15 touch domes from five mice in G–I ) . Red lines represent median values in H and I . DOI: http://dx . doi . org/10 . 7554/eLife . 01488 . 003 We surveyed conserved node proteins in cryosections of adult mouse hairy skin ( 8–10 weeks of age ) . We identified SAI afferents by their immunoreactivity to Neurofilament H ( NFH; a myelinated-neuron marker ) and by their contacts with Keratin-8-positive Merkel cells in touch domes , which are specialized skin regions that surround tylotrich ( guard ) hairs ( Figure 1B , C ) . Myelin Basic Protein ( MBP ) antibodies were used to visualize myelin end points and gaps , which are the sites of heminodes and nodes of Ranvier , respectively ( Figure 1B , C ) . We identified intense , punctate immunoreactivity for the voltage-activated sodium channel NaV1 . 6 at myelin end points and myelin gaps in cutaneous afferents . In SAI afferents , NaV1 . 6 puncta localized to 93% of observed myelin end points ( N = 28/30 , Figure 1B ) and 100% of myelin gaps ( N = 9/9 , Figure 1C ) . These data demonstrate that nodes and heminodes in SAI afferents can be reliably identified by visualizing either MBP or NaV1 . 6 . Moreover , they identify NaV1 . 6 as a principal node component in these cutaneous afferents . We did not observe immunoreactivity against voltage-activated sodium or potassium channels in unmyelinated neurites juxtaposed to Merkel cells ( N = 201 Merkel cell-neurite complexes ) , although it is possible that these channels are present at levels below detection threshold . Based on the strong enrichment of NaV1 . 6 at heminodes , we infer that spikes likely initiate at these sites , as they do in Pacinian corpuscles ( Loewenstein and Rathkamp , 1958 ) , rather than initiating in SAI-afferent terminals . We next sought to quantify the arrangement of afferent branches , nodes and Merkel cells in complete tactile end organs . We employed confocal microscopy and whole-mount skin immunostaining to visualize the entirety of the SAI afferent’s end organ ( Figure 1D–F; Li et al . , 2011 ) . Myelin end points were capped with NaV1 . 6-positive heminodes ( Figure 1F ) . Unmyelinated neurites that extended from these heminodes branched to contact Merkel cells . In myelinated branches , nodes of Ranvier localized to myelin gaps at every branch point and along un-branching afferent lengths ( Figure 1D , E ) . These reconstructions demonstrate that SAI afferents have complex axonal arbors with extensive branching and multiple heminodes and nodes of Ranvier . Thus , we conclude that spikes have the potential to initiate at multiple domains and then to integrate downstream at branch-point nodes in the arbor . We next traced SAI afferents in three dimensions to quantify structural parameters ( Figure 1D’–F’ ) . Distributions of nodes identified by MBP and NaV1 . 6 were indistinguishable , so datasets were pooled for quantitative analysis ( Figure 1G–I ) . In 83% of touch domes surveyed , Merkel cells were innervated by branches of a single SAI afferent ( N = 18 , Figure 1D–D’ , F–F’; Video 1 ) . In three reconstructions , two afferents projecting from different nerve trunks contacted Merkel cells within a single touch dome ( Figure 1E–E’; Video 2 ) . It is possible that these branches converged beyond the field of view . Alternatively , two distinct afferents might innervate Merkel cells in a minority of touch domes , as previously observed in rat ( Yasargil et al . , 1988; Casserly et al . , 1994 ) . We focused quantitative analysis on touch domes with single-afferent innervation ( N = 15 ) . Afferents displayed five to seven nested orders of branches . Arbor complexity , as represented by the highest branching order , did not correlate with Merkel-cell number ( Figure 1G ) , which ranged almost fivefold ( Figure 1H ) . Total branch number varied more than twofold between touch domes , and unmyelinated neurites accounted for most of this variation ( Figure 1H ) . Quantities of myelinated branches and heminodes were more restricted and were independent of Merkel-cell counts ( linear regression p=0 . 56 and 0 . 55 , respectively ) . Most Merkel cells ( >85% ) were directly contacted by neurites , suggesting that they were incorporated into afferent arbors ( Figure 1I ) . Similarly , ≥80% of terminal neurites were occupied by Merkel cells in most touch domes ( Figure 1I ) . This quantitative analysis reveals a surprising degree of structural diversity in SAI-afferent end organs , particularly in the abundance of Merkel cell–neurite complexes . Given that the number of complexes exceeded heminodes within each arbor , we reasoned that individual heminodes must receive inputs from multiple Merkel cell–neurite complexes . 10 . 7554/eLife . 01488 . 004Video 1 . Three-dimensional reconstruction and Neurolucida tracing of the touch dome in Figure 1D , which is innervated by a single SAI afferent . DOI: http://dx . doi . org/10 . 7554/eLife . 01488 . 00410 . 7554/eLife . 01488 . 005Video 2 . Three-dimensional reconstruction and Neurolucida tracing of the touch dome in Figure 1E . This touch dome was innervated by three major branches , one of which did not converge within the imaging field . Note that this unbranched afferent is thinly myelinated and has a finer axonal diameter than typical SAI afferents . DOI: http://dx . doi . org/10 . 7554/eLife . 01488 . 005 To determine how these complexes are arranged within an afferent’s arbor , we analyzed the distribution of Merkel cell–neurite complexes among terminal neurites and heminodes . The number of terminal neurites emanating from each heminode was broadly distributed ( Figure 2A ) . Most Merkel cells were arranged individually on terminal neurites ( 70% , N = 165 , Figure 2B , C ) , although chains of three or more Merkel cells along individual neurites were occasionally observed ( Figure 2B , D; Ebara et al . , 2008 ) . As with terminal neurites , the number of Merkel cell–neurite complexes per heminode was broadly distributed ( Figure 2E ) . To quantify the degree of structural asymmetry within an arbor , heminodes were ordered by the size of their Merkel-cell clusters ( Figure 2F ) . For each arbor , a plot of the number of complexes at each heminode was fitted with a linear regression , the slope of which captures the skewness of the cluster distribution ( median = 2 . 3 complexes per heminode , interquartile range = 1 . 2–4 . 4; R2 = 0 . 6–1 . 0 ) . The degree of skew did not correlate with total number of Merkel cell–neurite complexes in the arbor ( Figure 2—figure supplement 1 ) . Together , these data indicate that spike initiation zones within each arbor integrate inputs from a variable number of mechanotransduction sites . We hypothesize that this asymmetric distribution accounts for features of the SAI afferent’s physiological output . 10 . 7554/eLife . 01488 . 006Figure 2 . Merkel cell–neurite complexes are asymmetrically distributed between heminodes . ( A ) Distribution of terminal neurites per heminode ( N = 219 neurites ) . ( B ) Histogram of the number of Merkel cells contacted by each terminal neurite ( N = 226 Merkel cells ) . Red: Gaussian fit ( R2 = 0 . 99 ) . ( C ) Confocal projection of six terminal neurites contacting individual Merkel cells ( asterisks ) . ( D ) A projection of a single terminal neurite contacting a chain of four Merkel cells ( asterisks ) . Arrowheads denote heminodes and scale bar ( 25 µm ) applies to C and D . ( E and F ) The distribution of Merkel cell–neurite complexes per heminode from pooled touch-dome afferents ( E; N = 51 heminodes from 15 touch domes ) and within individual tactile arbors ( F ) . In F , number of Merkel cells at each heminode from three touch domes is plotted from the largest , or primary ( 1° ) , cluster to the smallest , quaternary ( 4° ) , clusters . Representative touch domes across the skew range are shown and linear regressions are plotted ( slopes = −0 . 6 , −2 . 3 and −4 . 5 , R2 = 0 . 6 , 0 . 99 , 1 . 0 ) . See also Figure 2—figure supplement 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 01488 . 00610 . 7554/eLife . 01488 . 007Figure 2—figure supplement 1 . Skew values for each touch dome plotted vs the number of Merkel cell-neurite complexes in the arbor . Orange symbols denote the skew values for modeled arbors in Figure 5B , which were chosen to encompass the skew range observed for most SAI arbors . DOI: http://dx . doi . org/10 . 7554/eLife . 01488 . 007 Tactile afferents can differ in their touch-evoked response properties , including firing rate and mechanical sensitivity , which is the steepness of the stimulus–response relation . During sustained touch , SAI afferents produce a biphasic spike train characterized by high-frequency firing during stimulus onset ( ramp phase ) and low-frequency firing with a highly variable interspike interval ( ISI ) during sustained displacement ( static phase ) . Touch-evoked firing rates and mechanical sensitivity vary considerably between individual SAI afferents ( Mountcastle et al . , 1966; Goodwin et al . , 1995; Wellnitz et al . , 2010 ) . To determine whether the number of Merkel cells in a receptive field can account for variability in firing properties , we measured SAI responses over a range of displacements ( Figure 3A , B ) . By using a GFP-expressing Merkel-cell reporter strain ( Lumpkin et al . , 2003 ) , we visualized the number of Merkel cells within each touch dome , which represents an upper bound on the number of Merkel cell–neurite complexes in the SAI-afferent arbor ( N = 4 SAI afferents; Figure 3B , C ) . We first tested for a relationship between the total number of Merkel cells and the latency of first spikes , a measure that reliably conveys information about dynamic tactile stimuli ( Johansson and Birznieks , 2004 ) . For the first spike , which is independent of active zone resetting , the latency to reach firing threshold is expected to be inversely proportional to the number of transduction units activated by a given stimulus . Thus , touch domes with large Merkel-cell complements should have short first spike latencies compared with small touch domes . We grouped large touch domes ( 20 and 22 Merkel cells ) and small touch domes ( 12 and 13 Merkel cells ) to account for the possibility that up to 15% of Merkel cells were not innervated ( Figure 1I ) . As predicted , first spike latencies were significantly shorter in large touch domes ( mean ± SEM , 10 . 9 ± 1 . 6 ms , N = 57 ) compared with small touch domes ( 40 . 0 ± 14 . 5 ms , N = 60; p = 0 . 027; Student’s t test , one-tailed ) for suprathreshold stimuli . These data suggest that having more Merkel cell–neurite complexes in a touch dome results in a faster response during dynamic indentation . We also noted that the variance of first spike latencies was significantly higher in small touch domes ( p<0 . 0001; two-sample F test , two-tailed ) , which suggests that SAI afferents with fewer transduction units display less reliable spike timing during dynamic stimulation . 10 . 7554/eLife . 01488 . 008Figure 3 . Physiological response properties vary between mouse SAI afferents . ( A ) Extracellular recordings from an SAI afferent stimulated at two displacement magnitudes demonstrates the biphasic SAI response , which is characterized by high-frequency firing during the ramp phase , as well as slow adaptation and variable spike timing during the static phase . ( B ) Displacement–response relations from individual SAI afferents . Legend indicates the number of Merkel cells in each touch dome quantified based on GFP fluorescence . Responses from receptive fields with large end organs ( blue ) and small end organs ( orange ) are shown . Firing rates during the static phase are plotted ( mean ± SD , N = 3–12 stimuli per displacement magnitude ) . Data were fitted with single exponentials to estimate mechanical sensitivity ( κ ) and threshold firing rate ( Y0; R2 = 0 . 63–0 . 99 ) . ( C ) Merkel cells ( green ) from Atoh1/nGFP transgenic mice selectively express GFP . The receptive field of the SAI afferent in A is shown ( dotted line ) . ( D ) Force-displacement relations measured during the recordings shown in B . Skin mechanics were indistinguishable between these recordings . DOI: http://dx . doi . org/10 . 7554/eLife . 01488 . 008 We next analyzed displacement–response relations , which were fitted with single-exponential regressions . The time constant of the exponential fit , κ , was used to estimate an afferent’s mechanical sensitivity and Y0 , the y-intercept , to estimate threshold firing rate ( N = 4 afferents , Figure 3B ) . This analysis confirmed that mechanical sensitivity differed significantly between SAI afferents innervating mouse touch domes ( κ = 2 . 1–14 . 2 mm−1; p < 0 . 0001 , Extra sum-of-squares F test ) ; however , this measure did not scale with total Merkel-cell number . SAI afferents have been reported to innervate more than one touch dome in cats and neonatal mice ( Tapper , 1965; Iggo and Muir , 1969; Woodbury and Koerber , 2007 ) . As our 3-mm probe tip is large enough to cover several touch domes , it is possible that mechanical sensitivity scales with the number of touch domes innervated by an individual afferent . To rule out this possibility , we manually probed the skin’s surface to identify all receptive fields for each SAI afferent . For computational modeling , we analyzed SAI afferents whose receptive fields were limited to single touch domes ( Figure 3 ) . To determine whether single touch-dome innervation is typical of SAI afferents in adult mice , we analyzed a larger dataset of SAI afferent recordings that was not biased for receptive field structure ( N = 27 afferents ) . We found that 19 SAI afferents innervated individual touch domes , six innervated two touch domes each and two afferents innervated three touch domes each . Thus , the percentage of SAI afferents that innervate multiple touch domes in the hindlimb of adult mice ( 30% ) is much lower than that reported in cats ( >60%; Tapper , 1965; Iggo and Muir , 1969 ) or mouse neonatal back skin ( 3/4 SAI afferents; Woodbury and Koerber , 2007 ) . We considered two additional factors that might contribute to the observed differences in SAI-afferent sensitivity . First , skin mechanics did not account for these differences because displacement–force relations were indistinguishable between these recordings ( Figure 3D ) . A second possibility is that a touch dome might be innervated by multiple SAI afferents . In that case , the number of Merkel cells contacted by each afferent would be lower , resulting in reduced firing rates and mechanical sensitivities . This scenario is likely to apply to only a minority of mouse touch domes because >80% of reconstructed touch domes were innervated by a single myelinated afferent ( Figure 1 ) ; therefore , we sought to identify additional structural features that might account , in part , for differences in touch-evoked firing . We focused on the grouping of Merkel cell–neurite complexes to heminodes because this feature varied substantially between SAI afferents . We used predictive computational modeling to test functional consequences of the asymmetric distribution of mechanotransduction sites in SAI afferents . This approach affords the ability to analyze the effects of neuronal architecture on predicted firing patterns by systematically manipulating potential end-organ configurations . Our models assume that each Merkel cell–neurite complex serves as a mechanotransduction unit capable of producing receptor currents and that resulting signals sum to initiate spikes at heminodes . To represent the SAI afferent’s end organ in the skin , we constructed a novel network model comprising three modules , as detailed in ‘Materials and Methods’ ( Figure 4A , B ) . First , a finite element model ( FEM ) of skin mechanics transformed skin displacement into strain energy density ( SED ) at the location of mechanotransduction units . Second , a sensory transduction module transformed SED values into receptor currents . To account for the biphasic SAI response , the transduction function contained a dynamic component proportional to the rate of change in SED and a static component proportional to SED . A noise term accounted for the SAI afferent’s characteristic ISI variablility during the static phase of stimulation ( Figure 4C ) . The transduction function predicted an adapting receptor current , I ( t ) , whose form is consistent with those recorded from a wide range of mechanosensory receptor cells , including inner-ear hair cells ( Eatock et al . , 1987 ) , Drosphila bristle neurons ( Walker et al . , 2000 ) , and somatosensory neurons in vitro ( Lechner et al . , 2009 ) . Third , a neural dynamics module , consisting of an array of leaky integrate-and-fire ( LIF ) models that represent spike initiation zones , summed receptor currents and , at threshold , produced spike times . A unique feature of this network model is that it allows for reconfigurable transduction functions that represent asymmetrically grouped Merkel cell–neurite complexes at spike initiation zones . 10 . 7554/eLife . 01488 . 009Figure 4 . Computational modeling recapitulates characteristic features of the SAI response . ( A ) The network model configuration for the reconstructed SAI afferent in Figure 1D’ . ( B ) Data flow through computational models and example outputs from each module: a finite element model ( FEM ) produces strain energy density ( SED ) at transduction units , transduction functions ( Trans <# merkel cell–neurite complexes> ) predict transduction currents ( I ( t ) ) and a leaky integrate-and-fire ( LIF ) array produces spike times . ( C ) The model’s predicted spike-timing variability , assessed by the distribution of normalized ISIs during static-phase responses ( black bars: N = 1 , 591 intervals ) , corresponded to the skewed Gaussian distribution previously reported for mouse SAI afferents ( orange bars: N = 3 , 348 intervals from 11 afferents; Wellnitz et al . , 2010 ) . To compare ISIs across a range of displacement magnitudes , each ISI was normalized to the mean interval for its stimulus . ( D ) Simulated firing rates ( black symbols ) from the model configuration in A were fitted to linear regressions of ramp-phase ( blue dotted line: ramp acceleration = 20 mm·s−2 , pink dotted line: ramp acceleration = 1143 mm·s−2 ) and static ( orange dotted line ) responses pooled from the SAI afferents shown in Figure 3B . Goodness of fit = 0 . 96 ( fractional sum of squares ) . ( E ) Displacement–response relations from models configured with different primary cluster sizes . All configurations had 17 total transduction units and four spike initiation zones . Mean firing rates during the static phase of displacement are plotted ( mean ± SD , N = 15 simulations per displacement ) . Displacement-response curves were compared by fitting with exponential regressions ( R2 ≥ 0 . 99 ) . Increasing or decreasing primary cluster size by two transduction units significantly changed the best fits ( 8 vs 10: p=0 . 004; 6 vs 8: p=0 . 017 , extra sum-of-squares F test ) . Legend indicates the distribution of transduction units at spike initiation zones . DOI: http://dx . doi . org/10 . 7554/eLife . 01488 . 00910 . 7554/eLife . 01488 . 010Figure 4—figure supplement 1 . Linear regression analysis of pooled responses from the four SAI afferents in Figure 3B ( denoted by symbols; N = 3–12 stimuli per displacement magnitude ) . Displacements were ramped into the skin at three accelerations , which were analyzed separately ( purple: 1143 mm·s−2; black: 81 mm·s−2; cyan: 20 mm·s−2 ) . Static firing rates ( orange ) were pooled for regression analysis as they did not differ significantly between ramp accelerations . These regressions were used for model fitting . DOI: http://dx . doi . org/10 . 7554/eLife . 01488 . 010 We first created a model of a reconstructed SAI arbor ( Figure 1D’ ) containing four heminodes with clusters of eight , five , three and one Merkel cell–neurite complexes . The resulting model had four spike initiation zones with transduction-unit groupings of {8 , 5 , 3 , 1} ( Figures 1A and 4A ) . The model’s spike-timing predictions were fitted to a prototypical mouse SAI response ( Figure 4C , D ) . To derive the prototypical SAI response , we performed regressions of ramp- and static-phase responses from four mouse SAI afferents analyzed in aggregate ( Figure 4—figure supplement 1 ) . The model produced spike times that reproduced the dynamics of SAI responses over a range of stimulus conditions , including different displacement magnitudes and ramp accelerations . Firing properties that were well-fitted by the model included a high-frequency response at displacement onset , an adapted firing rate during the static phase and higher firing rates with increasing displacement magnitude and acceleration ( Figure 4D ) . Static- and ramp-phase response profiles were fitted with an R2 of 0 . 96 , as measured by fractional sum of squares ( Figure 4D ) . Taking advantage of the reconfigurable computational model , we tested the hypothesis that the relative distribution of transduction units at spike initiation zones influences touch-evoked firing . We compared predicted firing rates during sustained displacement for different end-organ configurations with 17 Merkel cells ( Figure 4E ) . From the initial configuration of {8 , 5 , 3 , 1} , we found that moving only two transduction units , to yield groupings of {10 , 5 , 1 , 1} or {6 , 5 , 3 , 3} , were sufficient to significantly alter the shape of simulated displacement–response relations . Increasing the primary cluster to 10 units increased firing rates for suprathreshold stimuli by 20% . Conversely , moving two transduction units from the largest group to the smallest decreased predicted firing rates for supra-threshold displacements by 25% . Similar results were observed for three additional modeled arbors configured to represent the range of anatomical features observed in reconstructions ( Table 1 ) . These models differed in their numbers of spike initiation zones ( 3–5 ) and transduction units ( 13–20 ) . Simulated firing rates were also enhanced , though to a lesser extent , when largest clusters were held constant and secondary clusters were increased ( Table 1 ) . On average , firing rates increased 7 . 2% per transduction unit added to a primary cluster and 2 . 8% per transduction unit added to a secondary cluster . Collectively , these models predict that touch-evoked firing is increased when Merkel cell–neurite complexes are arranged in a skewed distribution among heminodes . 10 . 7554/eLife . 01488 . 011Table 1 . Effects of primary and secondary cluster size on firing rateDOI: http://dx . doi . org/10 . 7554/eLife . 01488 . 011Model arbor #Merkel-cell numberGrouping 1Grouping 2ΔPrimary groupΔSecondary group% Firing Rate Δ117{6 , 5 , 3 , 3}{10 , 5 , 1 , 1}4–39117{8 , 3 , 3 , 3}{8 , 7 , 1 , 1}–415220{6 , 6 , 4 , 2 , 2}{9 , 6 , 3 , 1 , 1}3–18220{7 , 4 , 4 , 3 , 2}{7 , 7 , 4 , 1 , 1}–39313{4 , 4 , 3 , 2}{6 , 4 , 2 , 1}2–12313{5 , 3 , 3 , 2}{5 , 5 , 2 , 1}–24413{5 , 4 , 4}{7 , 4 , 2}2–14413{6 , 4 , 3}{6 , 6 , 1}–25Bold values indicate the group whose number was changed in the computational experiment . Although these studies predict that the arrangement of transduction units can in part set the coding properties of tactile afferents , we reasoned that the number of transduction units must also impact firing rate , since activating additional units will more readily bring the membrane potential to spike threshold . The interaction of these two parameters was examined by systematically adding transduction units to four prototypical models to increase end-organ size . For each arbor , two strategies were used to ‘fill up’ clusters until they equaled the size of the primary cluster . In a first set of simulations , transduction units were progressively added to secondary clusters . Alternatively , transduction units were added to smallest clusters ( Figure 5A ) . The first strategy , which skewed the distribution of transduction units , boosted firing rates more than equalizing the distribution with the second strategy ( Figure 5A ) . For example , increasing transduction units from 17 to 24 augmented responses on average by 39% when they were added to secondary clusters but only by 21% when they were more evenly distributed ( Figure 5A , Arbor 1 ) . This effect was consistently observed across prototypical models ( Figure 5A , Arbors 2–4 ) . Thus , our simulations predict both the number of transduction units and their arrangement within the arbor regulate SAI afferent firing properties . 10 . 7554/eLife . 01488 . 012Figure 5 . A survey of computational parameter space predicts that the number and arrangement of mechanosensory transduction units modulates SAI-afferent firing properties . ( A ) Two strategies for adding transduction units to an SAI-afferent arbor were tested in four independent model end organs ( Arbors 1–4 ) . Arbor configurations differed in number of spike initiation zones ( 3–5 ) and initial end-organ sizes ( 13–20 ) . Transduction units were added progressively to either secondary ( solid lines ) or smallest clusters ( dashed lines ) . Orange symbols highlight examples from the two strategies after adding multiple transduction units . Example cluster arrangements are indicated in brackets . Clusters changed from the initial configuration are indicated in orange font . The percent change in firing rate from baseline configuration is plotted . ( B ) Comparison of displacement–response relations ( mean ± SD , N = 15 stimuli per displacement magnitude ) for two model configurations indicated in brackets: a skewed distribution of 12 transduction units among three spike initiation zones ( gray ) and an equal distribution of 20 transduction units among five spike initiation zones ( black ) . Simulation results were fitted with single exponential equations ( R2 ≥ 0 . 99 ) . The mechanical sensitivity of the small end organ was predicted to be significantly greater than that of the large end organ ( κ = 7 . 7 and 5 . 0 , respectively , p=0 . 005 , extra sum-of-squares F test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 01488 . 012 Finally , we asked whether a small arbor with few transduction units can display a heightened mechanical sensitivity compared with a large arbor , as we observed in electrophysiological recordings ( Figure 3B ) . Two arbor configurations were computationally compared ( Figure 5B ) . The first had 12 transduction units asymmetrically grouped at three spike initiation zones ( skew = 4 . 5 , {10 , 1 , 1} ) . The second had 20 transduction units evenly distributed among five spike initiation zones ( skew = 0; {4 , 4 , 4 , 4 , 4} ) . Despite having 40% fewer transduction units , the skewed grouping strategy produced a significantly higher mechanical sensitivity than the evenly distributed end organ ( κ = 7 . 7 and 5 . 0 , respectively , p = 0 . 005; Figure 5B ) . This was not simply due to increased firing rates for suprathreshold stimuli , as firing rates at threshold were significantly lower for the small end organ compared with the large one ( Y0 = 1 . 48 and 2 . 92 Hz , respectively; p = 0 . 016 ) . Thus , these simulations demonstrate that a tactile afferent with few transduction units can achieve high touch sensitivity by unevenly grouping transduction units to action potential initiation sites .
Morphometric analysis of SAI afferents revealed multiple heminodes , the anatomical correlates of spike initiation zones in myelinated tactile afferents ( Loewenstein and Rathkamp , 1958 ) . Our results are the first to localize NaV1 . 6 in tactile end organs , which extends previous reports that identified this isoform in unmyelinated nociceptors and as the principal sodium channel at central and peripheral nodes ( Caldwell et al . , 2000; Black et al . , 2002 ) . The observation that NaV1 . 6 localizes to almost all heminodes and nodes within an end organ suggests that this ion channel plays an important role in spike initiation and integration in tactile afferents; however , other NaV isoforms might also be found at these sensory endings . SAI afferents display unusually high instantaneous firing frequencies exceeding 1000 Hz ( Iggo and Muir , 1969 ) . Thus , it is notable that NaV1 . 6 confers rapid sodium channel kinetics and mediates resurgent currents , which facilitate high-frequency firing , in DRG neurons ( Herzog et al . , 2003; Cummins et al . , 2005 ) . Our results also reveal how epidermal Merkel cells are incorporated into tactile arbors . Our quantification of Merkel cells in adult touch domes is consistent with that reported at E18 . 5 ( Lesko et al . , 2013 ) . Although most terminal neurites innervate single Merkel cells , the Gaussian distribution of these connections suggests that they are formed through probabilistic rather than deterministic mechanisms . Interestingly , touch-dome afferents display exuberant terminal branching in Atoh1 knockout mice , which lack Merkel cells ( Maricich et al . , 2009 ) . These findings suggest that targeting and maintenance of touch-dome innervation is independent of Merkel cell-derived signals . Instead , we propose that Merkel-cell contacts are required for appropriate sprouting and/or pruning of touch-dome arbors . Additional studies are needed to determine whether neurites induce Merkel-cell differentation or whether Merkel cell-derived signals establish stable neuronal connections . Although neural dynamics and skin mechanics are tied together in vivo , tactile afferent neural dynamics and skin mechanics models have largely been used in isolation . Thus , the models presented here are the first to computationally represent the sequence of key events in tactile encoding: the conversion of touch at the skin’s surface to mechanical distortion at tactile receptors , mechanoelectrical transduction and spike initiation at heminodes . This representation was achieved by combining three model sub-components , each of which extends previous efforts to model tactile responses . Previous studies have taken one of three general modeling approaches . First , empirical models such as those of Goodwin and Wheat use simple regression functions to abstract away the roles of skin and mechanoreceptors . These models focus on the role of noise and receptor co-variance in predicting population responses that align with psychophysical studies ( Goodwin and Wheat , 1999 , 2002 ) . Second , skin mechanics models use finite elements and continuum mechanics to represent how surface forces propagate to tactile end organs , but abstract away neural dynamics by using scaling functions to predict firing rates . A limitation is that these models only predict firing rates for steady-state stimuli as opposed to spike times . Finally , neural dynamics models convert receptor currents to spike timing but disregard the skin’s role in shaping end-organ output . For vibratory stimuli delivered with a skin-attached probe , this simplification is reasonable as the skin’s role is minimal when it follows probe movement closely . By contrast , viscoelastic skin relaxation occurs during sustained touch stimuli , such as those encoded by SAI afferents ( Cohen et al . , 1999 ) . In this study , we modeled skin mechanics using hyper- and visco-elastic material models with parameters fitted to values from mammalian tissues . Material models were validated against force-displacement data measured during ex vivo skin-nerve recordings and extend a previous study that used a linear elastic model ( Lesniak and Gerling , 2009 ) . Although parameter values were chosen within reasonable ranges for mouse skin , future models could be refined by employing recent compressive measurements of mouse skin ( Wang et al . , 2013 ) . It is also possible that the material properties of the touch dome itself differ from surrounding epidermis . In addition to combining skin and neuron models , the network model presented here extends previous neural dynamics models . Prior models have employed rate-sensitive transduction functions and LIF functions to make spike timing predictions by calculating SAI membrane potential as a function of vibration frequency and magnitude ( Freeman and Johnson , 1982a; 1982b; Kim et al . , 2009; Kim et al . , 2010 ) . Previous neural dynamics models have not accounted for end-organ size , neuronal branching or multiple sites of spike initiation . In this study , we utilized multiple , resettable LIF models in conjuction with transduction functions parameterized by the number of Merkel cell–neurite complexes . We introduced noise at the level of current within the transduction functions . Another approach to recreate the SAI afferent’s irregular interspike intervals could be to introduce probabilistic firing and adaptive thresholds at spike initiation zones , similar to that done for vibratory stimuli ( Jahangiri and Gerling , 2011; Dong et al . , 2013 ) . By employing an array of spike initiation zones , our model allows zone resetting upon action potential firing , consistent with that observed for SAI afferents and other myelinated somatosensory afferents ( Adrian and Zotterman , 1926a; Horch et al . , 1974 ) . Although Merkel cells make one-to-one connections with neurites , we found that these complexes were asymmetrically distributed between heminodes . Primary clusters of Merkel cell–neurite complexes , converging on a single heminode , sometimes contained ≥50% of Merkel cells in the entire arbor . In computational simulations , changing the primary cluster size by as few as two Merkel cell–neurite complexes significantly altered afferent firing . In this study , anatomical reconstructions were performed on freshly excised tissue to ensure that tissue morphology was well preserved for quantitative morphometry . Thus , anatomical reconstructions and electrophysiological recordings were achieved with different SAI afferents . Future studies to record and reconstruct individual sensory afferents are needed to directly test the model’s predictions . Nonetheless , our findings provide an anatomical correlate and a plausible biological mechanism to explain the driver effect observed in branched sensory afferents ( Lindblom and Tapper , 1966; Horch et al . , 1974; Fukami , 1980 ) . Theoretically , the most sensitive receptor configuration consists of a single cluster of transduction sites connected to a single heminode , as found in invertebrate stretch receptors ( Edwards and Ottoson , 1958 ) . What is the biological advantage of distributing transduction complexes among multiple heminodes , as we observed for SAI afferents ? First , given that the skin is our body’s protective covering , this arrangement could serve as a safety feature by increasing robustness to injury . Second , for cutaneous afferents with large receptive fields , multiple spike initiation zones ensure high-fidelity signal propagation from branches located millimeters apart ( Li et al . , 2011; Wu et al . , 2012 ) . For example , an SAI afferent can innervate 2–5 touch domes , each spaced ∼0 . 7 mm apart ( Tapper , 1965; Iggo and Muir , 1969; Woodbury and Koerber , 2007; Wellnitz et al . , 2010; Li et al . , 2011 ) . We also noted that heminodes were located within 19–41 µm of lanceolate endings innervating hair follicles ( N = 7 end organs ) . As individual rapidly adapting afferents can innervate tens to hundreds of hair follicles ( Li et al . , 2011; Wu et al . , 2012 ) , this observation suggests that spike initiation zones in close proximity to end organs is a general feature of myelinated tactile afferents . Third , distinct clusters might extend the receptor’s range of sensory coding ( Eagles and Purple , 1974 ) . For example , individual muscle-spindle afferents are proposed to encode both dynamic and static stimuli by innervating distinct structures called bag and chain fibers ( Quick et al . , 1980; Banks et al . , 1997 ) . Although our models assume equivalent transduction units , it is possible that populations of Merkel cell–neurite complexes are likewise tuned to different stimulus features . Our reconstructions suggest that some touch domes might be innervated by multiple sensory afferents , and this likely contributes to the wide range of firing properties observed for SAI afferents . SAI afferents with overlapping receptive fields have been described in rat touch domes located at dermatome borders ( Yasargil et al . , 1988; Casserly et al . , 1994 ) . Moreover , human touch domes are proposed to be innervated by distinct types of sensory afferents ( Reinisch and Tschachler , 2005 ) . In two touch domes , we observed both a typical SAI afferent and a thinly myelinated , unbranched afferent that contacted Merkel cells . We speculate that these are Aδ afferents based on their thin myelin sheaths and axonal diameters . The development of selective molecular markers is needed to understand how signals from distinct touch-dome neurons are integrated in the central nervous system . In monkey and human fingerpads , SAI afferents have non-uniform receptive fields with multiple hot spots that display higher firing rates than surrounding areas ( Phillips et al . , 1992; Vallbo et al . , 1995 ) . These hot spots can explain why the resolution of primate SAI afferents is smaller than their receptive field sizes ( Phillips et al . , 1992 ) . Johnson and colleagues hypothesized that hot spots correlate with the locations of individual Merkel cell–neurite complexes; however , their observations of 3–5 hot spots per receptive field in primates ( Phillips et al . , 1992 ) coincides well with our finding of 2–6 heminodes per touch-dome SAI afferent . Thus , we propose that the structural basis of a receptive-field hot spot is a cluster of Merkel cell–neurite complexes at a heminode . As SAI-afferent receptive field sizes and skin structure differ markedly between primate plantar skin and mouse touch domes , these observations suggest an organizing principle for SAI-afferent end organs across species and skin sites . Confirming the anatomical basis of hot spots will require the development of new transgenic mice to visualize individual SAI afferent branches during intact electrophysiological recordings , as well as microstimulation techniques to deliver controlled punctate stimuli to individual Merkel cells or Merkel cells clustered at single heminodes . To model such punctate stimuli will require building , validating and experimentally constraining new finite element models with a finer discretized mesh than the one used here . Individual SAI afferents are capable of representing shapes , edges and curvature with high fidelity ( Johnson and Lamb , 1981; Phillips and Johnson , 1981; Johnson and Hsiao , 1992 ) . Nonetheless , tactile qualities are conveyed to the central nervous system by an array of afferents distributed across the skin . To faithfully encode spatial features at the population level , one would expect SAI afferents to display uniform firing properties . Instead , touch-evoked firing rates , first spike latencies and mechanical sensitivity varied widely between SAI afferents in mouse touch domes , which corroborates previous reports of SAI afferent-to-afferent variability in monkey and human fingerpads ( Phillips and Johnson , 1981; Goodwin et al . , 1995 , 1997; Goodwin and Wheat , 1999 ) . Thus , this variability is likely to be a general feature of mammalian SAI afferents . How might the central nervous system cope with this large variation in firing properties among a single class of tactile afferents ? Simulated population responses predict that such differences will distort the representation of an object’s spatial features ( Goodwin and Wheat , 1999 ) . It is possible that the central nervous system introduces a scaling factor to compensate for peripheral distortion ( Goodwin et al . , 1995; Goodwin and Wheat , 1999 ) . Alternatively , the nervous system could take advantage of this variability to efficiently transfer information . For example , having a variety of SAI-afferent sensitivities might extend the dynamic range of the SAI-population response to sustained pressure . Moreover , since some SAI afferents innervate two or more touch domes , it is possible that variations in end-organ structure confer different firing properties to individual touch domes ( Lindblom and Tapper , 1966 ) . A moving stimulus will sequentially activate such receptive fields . In that case , one could envisage that distinct firing patterns arising from these receptive fields could provide a mechanism for tracking movement at the single-afferent level . We propose that variability in SAI end-organ structure observed in this study is the outcome of homeostatic mechanisms engaged during normal skin remodeling . Merkel cells renew within touch domes and whisker follicles ( Van Keymeulen et al . , 2009; Woo et al . , 2010; Doucet et al . , 2013 ) . Moreover , hair-growth cycles are accompanied by innervation changes ( Peters et al . , 2001; Shimomura and Christiano , 2010 ) . We speculate that SAI-afferent arbors with their Merkel-cell complements are likewise dynamic throughout adulthood . Our simulations predict that altering the number or arrangement of Merkel cells changes touch-evoked firing . Thus , our findings raise the possibility that the nervous system employs homeostatic mechanisms to achieve reliable signaling from individual touch receptors . This work sets the stage to identify molecular mechanisms that cutaneous afferents use to maintain signaling fidelity during normal tissue remodeling and in the context of repair .
Animal use was conducted according to guidelines from the National Institutes of Health’s Guide for the Care and Use of Laboratory Animals and was approved by the Institutional Animal Care and Use Committees of Baylor College of Medicine and Columbia University Medical Center . Skin was depilated ( Surgi-cream; Ardell , Los Angeles , CA ) and dissected from the proximal hind limb of female Atoh1/nGFP transgenic mice ( 8–10 weeks of age ) . This location was chosen to match the site of electrophysiological recordings in ex vivo skin-saphenous nerve preparations . Tissue was fixed in 4% paraformaldehyde ( PFA ) or , for staining with NaV1 . 6 antibodies , in 2% PFA in a sodium-acetate buffer ( pH 6 ) . For section staining , the skin was cryopreserved in 30% sucrose , frozen and cryosectioned at 25 µm . The sections were incubated overnight at room temperature in primary antibodies: rat anti-K8 ( TROMA-I; Developmental Studies Hybridoma Bank , Iowa City , Iowa ) , chicken anti-NFH ( AB5539; Millipore , Billerica , MA ) , rabbit anti-MBP ( ab40390; Abcam , Cambridge , MA ) and rabbit anti-NaV1 . 6 ( from MNR ) . The specificity of NaV1 . 6 antibodies was previously validated as described ( Rasband et al . , 2003 ) and control experiments lacking primary antibody demonstrated the specificity of immunoreactive puncta at nodes . Secondary goat Alexa Fluor-conjugated antibodies ( Invitrogen , Carlsbad , CA ) directed against rat ( Alexa Fluor 594; A11007 ) , chicken ( Alexa Fluor 647; A21449 ) or rabbit ( Alexa Fluor 488; A11008 ) IgG were incubated for 1 h at room temperature . Whole-mount immunostaining was performed as reported ( Li et al . , 2011 ) with antibodies listed above . Tissue was incubated at room temperature with primary antibodies for 72–96 h and secondary antibodies for 48 h . The tissue was imaged on a Zeiss Exciter confocal microscope with 20X , 0 . 8 NA or 40X , 1 . 3 NA objective lenses . Confocal image stacks were imported into Neurolucida ( MBF Bioscience , Williston , VT ) and traced in three dimensions . Images were prepared for publication in ImageJ ( Schneider et al . , 2012 ) or Photoshop ( Adobe , Mountain View , CA ) . Two independent observers quantified heminodes , nodes , branches and Merkel cell-neurite complexes by stepping though optical sections in each reconstruction . Single-unit SAI afferent recordings from mouse ex vivo skin-saphenous nerve preparations were performed as previously described ( Wellnitz et al . , 2010 ) . Recordings were made from adult Atoh1/nGFP transgenic mice to visualize Merkel cells within the intact skin . Mechanical stimuli were delivered via a ceramic cylindrical probe ( 3-mm tip diameter ) mounted on a displacement-controlled indenter . Stimuli were 5-s displacements ranging from 0 . 01 to 0 . 36 mm and applied in a randomized order . The skin’s reactive force was monitored with a load cell mounted on the indenter . Ramp-phase firing rates were calculated by dividing the number of spikes during the ramp phase by the ramp duration ( i . e . , the time period from probe contact with skin to final displacement ) . This calculation differs from a previous study that analyzed dynamic firing during the first 200 ms of stimulation , including the ramp phase and the period of rapid adaptation ( Wellnitz et al . , 2010 ) . Static firing rate was defined as the number spikes per second calculated during a 2 . 5-s window after the stimulus probe had reached its commanded depth . This time window excludes the period of rapid adaption that follows the dynamic phase of the SAI response . Fitting each end-organ model to the mean SAI afferent response involved three free parameters in the transduction function: β , α , and λ . These were selected with gradient free response surface methodology using Latin hyper-cube space filling designs , where each design was composed of 20 trial points ( sampled using the LHS package in R ) . The start point was informed by a domain search utilizing 50 points in a space filling design . Skin-mechanics models were fitted as described above , and the leaky integrate-and-fire parameters were fixed at values of 5 ms , 1 × 10−8 mF , and 30 mV for τ , C , and v¯ , respectively . Each end-organ model configuration was fitted to a prototypical mouse SAI response . The prototypical SAI response was derived from linear regressions of ramp-phase and static-phase firing rates recorded from mouse SAI afferents as described above ( N = 4 units ) . Fits maximized the combined goodness of fit , measured as fractional sum of squares ( Equation 3 ) , between biological data and the model’s simulated firing rates . This combined goodness of fit has a value of 2 for a model that perfectly matches the biological response profile . For stimulus i , hfri¯ and hfri represents the biologically observed and simulated static ( hold ) firing rate , respectively , and rfri¯ and rfri are the biologically observed and simulated dynamic ( ramp ) firing rate , respectively . The index i spanned from 1 to 75 to include five displacement depths and three accelerations giving 15 unique stimulations , each of which was simulated five times for a given set of model parameters . ( 3 ) fsscombined= ( 1−∑i=175 ( hfr¯i−hfri ) 2∑i=175 ( hfr¯i ) 2 ) + ( 1−∑i=175 ( rfr¯i−rfri ) 2∑i=175 ( rfr¯i ) 2 ) After fitting , β took the value of 5 . 643 × 10−8 , 5 . 648 × 10−8 , 5 . 669 × 10−8 , and 5 . 672 × 10−8 mA for model configurations of {8 , 5 , 3 , 1} , {7 , 6 , 4 , 2 , 1} , {6 , 4 , 3} , and {5 , 4 , 3 , 1} , respectively . Values for α were 2 . 539 × 10−14 , 2 . 386 × 10−14 , 2 . 612 × 10−14 , and 2 . 641 × 10−14 mA/Pa for {8 , 5 , 3 , 1} , {7 , 6 , 4 , 2 , 1} , {6 , 4 , 3} , and {5 , 4 , 3 , 1} . Finally , λ values were 5 . 833 × 10−11 , 4 . 994 × 10−11 , 6 . 211 × 10−11 , and 6 . 491 × 10−11 mA∙ms/Pa . These values were used for the first two computational experiments ( Figures 4E and 5A ) , where results were generated for each prototypical end organ . By contrast , model parameters for the two end-organ configurations in the third computational experiment ( Figure 5B ) were set as β = 5 . 658 × 10−8 mA , α = 2 . 545 × 10−14 mA/Pa , and λ = 5 . 882 × 10−11 mA∙ms/Pa , which represent the averages of all previous configuration parameters . To compare firing rates of configurations in Figure 5A and Table 1 , the change in firing rate was defined as the difference in summed firing rates across all stimulations divided by the lowest summed firing rate of the two configurations . This is described by Equation 4 , where frai and frbi are firing rates generated by the two configurations for stimulation i . ( 4 ) % fr change=∑i=175frai−∑i=175frbi∑i=175frbi , where ∑i=175frai>∑i=175frbi Statistical analyses were performed in Prism 5 ( Graphpad Software , La Jolla , CA ) . Data were fitted either with linear regressions or single exponentials as indicated . Significant differences between best–fit curves were assessed by comparing κ and Y0 of the exponential fits with extra sum-of-squares F tests . The distribution of Merkel cells to terminal neurites was fitted with a Gaussian distribution ( R2 > 0 . 99 ) . | Sensory receptors in the skin supply us with information about objects in the world around us , including their shape and texture . These receptors also detect pressure , temperature , and pain , enabling us to respond appropriately to stimuli that could be potentially harmful . The activation of a touch receptor—for example , due to the movement of a hair—causes ions to flow into the cell , changing the electric charge inside it . When the charge exceeds a threshold value , the cell fires action potentials , which travel along its axon to the central nervous system . The patterns of these action potentials from a population of touch receptors carry all the information about a touch stimulus to the brain . Different types of sensory receptors have unique anatomical structures and distinct signaling patterns; however , little is known about how the structures of sensory receptors influence action potential firing . Now Lesniak and Marshall et al . have revealed that structure determines function in a type of mammalian touch receptor called the slowly adapting type I receptor , which is concentrated in fingertips and other areas of high tactile acuity . With the aid of high-resolution microscopy , the complex branching structure of the receptor and its network of nerve endings were mapped in three dimensions . Experiments revealed highly variable structures and firing patterns between individual touch receptors , and computational modeling showed that changing either the number or the arrangement of receptor endings influenced the neuron’s firing properties . This is the first computational model that captures touch encoding by combining skin properties , sensory transduction , and spike initiation . As well as providing new information on how structure permits function , this work opens up new possibilities for exploring how the skin maintains its sensory capabilities during routine maintenance and after injury . | [
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] | 2014 | Computation identifies structural features that govern neuronal firing properties in slowly adapting touch receptors |
During meiosis homologous chromosomes undergo crossover recombination . Sequence differences between homologs can locally inhibit crossovers . Despite this , nucleotide diversity and population-scaled recombination are positively correlated in eukaryote genomes . To investigate interactions between heterozygosity and recombination we crossed Arabidopsis lines carrying fluorescent crossover reporters to 32 diverse accessions and observed hybrids with significantly higher and lower crossovers than homozygotes . Using recombinant populations derived from these crosses we observed that heterozygous regions increase crossovers when juxtaposed with homozygous regions , which reciprocally decrease . Total crossovers measured by chiasmata were unchanged when heterozygosity was varied , consistent with homeostatic control . We tested the effects of heterozygosity in mutants where the balance of interfering and non-interfering crossover repair is altered . Crossover remodeling at homozygosity-heterozygosity junctions requires interference , and non-interfering repair is inefficient in heterozygous regions . As a consequence , heterozygous regions show stronger crossover interference . Our findings reveal how varying homolog polymorphism patterns can shape meiotic recombination .
Sexual reproduction via meiosis is highly conserved within eukaryotes and allows recombination of genetic variation within populations ( Barton and Charlesworth , 1998 ) . During meiosis homologous chromosomes pair and undergo crossover recombination , which together with independent chromosome segregation and gamete fusion increases genetic diversity between progeny ( Barton and Charlesworth , 1998; Villeneuve and Hillers , 2001 ) . Meiotic crossovers form via the repair of DNA double-strand breaks ( DSBs ) generated by the SPO11 endonuclease ( Bergerat et al . , 1997; Keeney et al . , 1997 ) . Nucleolytic resection of DSBs generates 3′ single-stranded DNA ( ssDNA ) , which is bound by the RAD51 and DMC1 recombinases ( Bishop et al . , 1992; Shinohara et al . , 1992 ) . The resulting nucleoprotein filament then invades a homologous chromatid to form a heteroduplex intermediate ( Hunter and Kleckner , 2001 ) . The invading ssDNA 3′-ends undergo DNA synthesis using the homologous duplex as a template and after second-end capture forms double Holliday junctions ( dHJs ) ( Szostak et al . , 1983; Schwacha and Kleckner , 1995 ) . The dHJs can then be resolved as crossovers , which are cytologically evident as chiasmata ( Page and Hawley , 2003; Janssens et al . , 2012 ) . Chiasmata hold chromosomes together and ensure that homologous pairs segregate to opposite cell poles , so that gametes inherit a balanced chromosome number ( Page and Hawley , 2003 ) . Crossover numbers are under tight control , with many eukaryote species experiencing 1–2 per chromosome , despite large variation in genome size ( Villeneuve and Hillers , 2001; Smukowski and Noor , 2011; Henderson , 2012; Mercier et al . , 2014 ) . In Arabidopsis ∼200 DSBs form per meiosis and proceed to form strand invasion intermediates , of which ∼10 are repaired as crossovers , with the excess being repaired as non-crossovers , or via intersister recombination ( Giraut et al . , 2011; Ferdous et al . , 2012; Lu et al . , 2012; Sun et al . , 2012; Yang et al . , 2012; Drouaud et al . , 2013; Wijnker et al . , 2013; Qi et al . , 2014 ) . 80–85% of wild type crossovers are dependent on the ZMM pathway ( MSH4 , MSH5 , MER3 , HEI10 , ZIP4 , SHOC1 , PTD ) and show interference , that is , they are spaced further apart than expected at random ( Copenhaver et al . , 2002; Higgins et al . , 2004 , 2008a; Chen et al . , 2005; Mercier et al . , 2005; Chelysheva et al . , 2007 , 2010 , 2012; Macaisne et al . , 2008 ) . The remaining minority of crossovers are non-interfering and require MUS81 ( Berchowitz et al . , 2007; Higgins et al . , 2008b ) . However , as chiasmata are still observed in msh4 mus81 double mutants , additional crossover pathways must exist ( Higgins et al . , 2008b ) . The majority of interhomolog strand invasion intermediates are dissolved by the FANCM helicase , which acts with the MHF1 and MHF2 co-factors ( Crismani et al . , 2012; Knoll et al . , 2012; Girard et al . , 2014 ) . Mutations in FANCM , MHF1 and MHF2 cause dramatic increases in non-interfering crossovers ( Crismani et al . , 2012; Knoll et al . , 2012; Girard et al . , 2014 ) . It is presently unclear whether non-interfering crossovers occurring in fancm are generated by the same pathway as in wild type , as a direct test of MUS81 dependence is precluded by fancm mus81 lethality ( Crismani et al . , 2012; Knoll et al . , 2012 ) . Both crossovers and non-crossovers can be accompanied by gene conversion events , which in the case of non-crossovers form via the synthesis-dependent strand annealing pathway ( Allers and Lichten , 2001; McMahill et al . , 2007; Lu et al . , 2012; Sun et al . , 2012; Yang et al . , 2012; Drouaud et al . , 2013; Wijnker et al . , 2013; Qi et al . , 2014 ) . Meiotic recombination is sensitive to DNA polymorphism between homologous chromosomes , that is , heterozygosity . For example , insertion-deletion ( indel ) and single nucleotide polymorphisms ( SNPs ) suppress crossovers at the scale of hotspots ( kb ) in fungi , plants and mammals ( Dooner , 1986; Borts and Haber , 1987; Jeffreys and Neumann , 2005; Baudat and de Massy , 2007; Cole et al . , 2010 ) . This is thought to occur due to heteroduplex base-pair mismatches inhibiting recombination , following interhomolog strand invasion . Large scale chromosome rearrangements , such as inversions or translocations , also suppress crossovers ( Schwander et al . , 2014; Thompson and Jiggins , 2014 ) . Despite the inhibitory effects of polymorphism on crossovers , nucleotide diversity and population-scaled recombination estimates are positively correlated in many plant and animal genomes ( Begun and Aquadro , 1992; Hellmann et al . , 2003; Spencer et al . , 2006; Gore et al . , 2009; Paape et al . , 2012; Cutter and Payseur , 2013 ) . For example , linkage disequilibrium-based crossover estimates and sequence diversity ( π ) are positively correlated in Arabidopsis at varying physical scales ( Figure 1A and Table 1 ) ( Cao et al . , 2011; Choi et al . , 2013 ) . Multiple processes contribute to these relationships . For example , positive or negative directional selection can reduce diversity at linked sites , with a greater effect in regions of low recombination , known as hitchhiking and background selection ( Hill and Robertson , 1966; Hudson and Kaplan , 1995; Nordborg et al . , 1996; Smith and Haigh , 2007; Cutter and Payseur , 2013; Campos et al . , 2014 ) . These phenomena will cause regions of low recombination under selection to have low diversity , consistent with data in Drosophila ( Aguade et al . , 1989; Begun and Aquadro , 1992; Wiehe and Stephan , 1993; Campos et al . , 2014 ) . Recombination may also be mutagenic and increase diversity , for example via mismatch repair enzymes showing a mutational bias for A:T > G:C transversions ( Duret and Galtier , 2009; Webster and Hurst , 2012; Glémin et al . , 2014 ) . 10 . 7554/eLife . 03708 . 003Figure 1 . Testing for crossover modification by Arabidopsis natural variation . ( A ) Historical crossover frequency ( red , cM/Mb ) and sequence diversity ( π , blue ) along the physical length of the Arabidopsis thaliana chromosomes ( Mb ) ( Cao et al . , 2011; Choi et al . , 2013 ) . Mean values are indicated by horizontal dotted lines and centromeres by vertical dotted lines . The fluorescent crossover intervals analysed are indicated by solid vertical lines and coloured triangles . ( B ) Map showing the geographical origin of the Arabidopsis accessions studied , indicated by red points . ( C ) Genetic diagram illustrating the experimental approach with a single chromosome shown for simplicity . Fluorescent crossover reporters ( triangles ) were generated in the Col background ( black ) and crossed to accessions of interest ( red ) to generate F1 heterozygotes . Following meiosis the proportion of parental:crossover gametes from F1 heterozygotes was analysed to measure genetic distance ( cM ) between the fluorescent protein encoding transgenes . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 00310 . 7554/eLife . 03708 . 004Table 1 . Correlations between historical recombination and sequence diversity at varying physical scalesDOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 004Scale ( π ) Chr1Chr2Chr3Chr4Chr55 kb0 . 5210 . 3010 . 5450 . 5750 . 54110 kb0 . 5560 . 3050 . 5650 . 6020 . 56250 kb0 . 6570 . 3810 . 5790 . 6920 . 619100 kb0 . 6990 . 5630 . 6010 . 7440 . 646500 kb0 . 7410 . 5280 . 6150 . 8410 . 6531 Mb0 . 6390 . 5040 . 6830 . 8460 . 624Scale ( θ ) Chr1Chr2Chr3Chr4Chr55 kb0 . 5370 . 2980 . 5570 . 5850 . 55310 kb0 . 5690 . 3030 . 5760 . 6100 . 57250 kb0 . 6620 . 3820 . 5920 . 6990 . 623100 kb0 . 7100 . 5730 . 6170 . 7520 . 650500 kb0 . 7540 . 5340 . 6350 . 8440 . 6551 Mb0 . 6470 . 5040 . 6970 . 8490 . 635Spearman's rank correlation between historical crossover frequency estimates from LDhat and sequence diversity ( θ and π ) at varying physical scales ( Cao et al . , 2011; Choi et al . , 2013 ) . Adjacent windows of the indicated physical size were used for correlations . Here we use natural variation in Arabidopsis to directly investigate the influence of heterozygosity on meiotic recombination . Extensive evidence exists for cis and trans modification of crossover frequency by plant genetic variation ( Barth et al . , 2001; Yao and Schnable , 2005; Yandeau-Nelson et al . , 2006; Esch et al . , 2007; McMullen et al . , 2009; López et al . , 2012; Salomé et al . , 2012; Bauer et al . , 2013 ) . We define trans modifiers as loci encoding diffusible molecules that control recombination on other chromosomes , and elsewhere on the same chromosome , as exemplified by mammalian PRDM9 ( Baudat et al . , 2010; Berg et al . , 2010; Myers et al . , 2010; Parvanov et al . , 2010; Fledel-Alon et al . , 2011; Sandor et al . , 2012; Kong et al . , 2013 ) . We define cis modification as variation that influences recombination only on the same chromosome , for example , the inhibitory effects of high SNP density , inversions and translocations ( Dooner , 1986; Borts and Haber , 1987; Jeffreys and Neumann , 2005; Baudat and de Massy , 2007; Cole et al . , 2010; Schwander et al . , 2014; Thompson and Jiggins , 2014 ) . Regional patterns of chromatin and epigenetic information can also cause significant cis effects , for example loss of either H2A . Z deposition or DNA methylation alters crossover frequency in Arabidopsis ( Colomé-Tatché et al . , 2012; Melamed-Bessudo and Levy , 2012; Mirouze et al . , 2012; Yelina et al . , 2012; Choi et al . , 2013 ) . In this study we crossed Arabidopsis lines carrying fluorescent crossover reporters generated in a common background ( Col-0 ) to 32 diverse accessions . We observed extensive variation in F1 hybrid recombination rates , with both significantly higher and lower crossovers than homozygous backgrounds . We further analysed Col × Ct F2 recombinant populations using three independent crossover reporter intervals ( 420 , CEN3 and I2f ) . We did not detect trans modifiers in these crosses , but observed a novel cis modification effect caused by heterozygosity . Specifically , juxtaposition of heterozygous and homozygous regions is associated with increased crossover frequency in the heterozygous region and a reciprocal decrease in the homozygous region . To investigate this phenomenon mechanistically we repeated analysis in mutants where the balance of interfering and non-interfering crossover repair is altered ( fancm , zip4 and fancm zip4 ) . This analysis demonstrates that remodelling of crossovers across heterozygosity/homozygosity junctions is dependent on interference . We also show that the non-interfering repair is less efficient in heterozygous regions . As a consequence , interference measurements are stronger in heterozygous regions . Our findings show how varying polymorphism patterns can differentially influence meiotic recombination along chromosomes .
To test the effect of heterozygosity on meiotic recombination we crossed transgenic Arabidopsis with fluorescent crossover reporters generated in the Col-0 background to 32 diverse accessions that represent global genetic diversity within this species ( Figure 1 , Tables 2 , 3 ) ( Melamed-Bessudo et al . , 2005; Berchowitz and Copenhaver , 2008; Yelina et al . , 2013 ) . The 5 intervals tested ( I1b , I1fg , I2f , 420 and CEN3 ) range from 0 . 67–5 . 40 megabases ( Mb ) , represent 11 . 5% of the genome ( 14 . 34 Mb ) in total and are located in sub-telomeric , interstitial and centromeric regions ( Figure 1A and Table 2 ) . The intervals vary in experimental recombination rate , with the centromeric interval CEN3 being the lowest ( 2 . 11 cM/Mb ) and the sub-telomeric interval I2f being the highest ( 13 . 02 cM/Mb ) ( Table 2 ) . As Arabidopsis male meiosis shows elevated sub-telomeric recombination , this likely contributes to the high I2f crossover frequency , which is measured in pollen ( Giraut et al . , 2011 ) . Low recombination in CEN3 is also expected , as the centromeric regions are heterochromatic and known to show suppressed crossover frequency ( Figure 1A ) ( Copenhaver et al . , 1999; Giraut et al . , 2011; Salomé et al . , 2012; Yelina et al . , 2012 ) . To asses relative heterozygosity levels we analysed pairwise sequence differences relative to Col-0 using the 19 genomes dataset , which was generated from a subset of the accessions used in our crosses ( Gan et al . , 2011 ) . CEN3 shows the highest heterozygosity levels , followed by the interstitial and sub-telomeric intervals ( Table 2 ) . Therefore , the regions analysed represent diverse chromosomal environments with varying levels of recombination and inter-accession sequence polymorphism . 10 . 7554/eLife . 03708 . 005Table 2 . Fluorescent crossover reporter intervalsDOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 005IntervalChrMethodT-DNA 1T-DNA 2MbLocationcM/Mb ( Col-0 ) cM/Mb ( F1 ) HeterozygosityI1b1Pollen3 , 905 , 441-YFP5 , 755 , 618-dsRed21 . 85Interstitial4 . 254 . 051 . 93 ( 3 . 16 ) I1c1Pollen5 , 755 , 618-dsRed29 , 850 , 022-CFP4 . 09Interstitial4 . 55N/A2 . 80 ( 3 . 16 ) I1fg1Pollen24 , 645 , 163-YFP25 , 956 , 590-dsRed21 . 31Interstitial6 . 206 . 022 . 52 ( 3 . 16 ) I2a2Pollen12 , 640 , 092-CFP13 , 226 , 013-YFP0 . 59Interstitial5 . 19N/A2 . 33 ( 3 . 30 ) I2b2Pollen13 , 226 , 013-YFP14 , 675 , 407-dsRed21 . 45Interstitial3 . 09N/A1 . 53 ( 3 . 30 ) I2f2Pollen18 , 286 , 716-dsRed218 , 957 , 093-YFP0 . 67Sub-telomeric13 . 0217 . 411 . 43 ( 3 . 30 ) 4203Seed256 , 516-GFP5 , 361 , 637-dsRed25 . 11Sub-telomeric3 . 702 . 931 . 19 ( 3 . 37 ) CEN33Pollen11 , 115 , 724-YFP16 , 520 , 560-dsRed25 . 40Centromeric2 . 112 . 386 . 69 ( 3 . 37 ) I3b3Pollen498 , 916-CFP3 , 126 , 994-YFP2 . 63Sub-telomeric5 . 99N/A1 . 11 ( 3 . 37 ) I3c3Pollen3 , 126 , 994-YFP4 , 319 , 513-dsRed21 . 19Sub-telomeric4 . 01N/A1 . 64 ( 3 . 37 ) I5c5Pollen2 , 372 , 623-CFP3 , 760 , 756-YFP1 . 39Interstitial4 . 01N/A1 . 01 ( 3 . 27 ) I5d5Pollen3 , 760 , 756-YFP5 , 497 , 513-dsRed21 . 74Interstitial3 . 20N/A1 . 56 ( 3 . 27 ) The interval name is listed together with chromosome , method of scoring and location of the flanking T-DNAs together with the fluorescent proteins they encode . Interval cM/Mb values from Col-0 homozygous are listed ( Col-0 ) , in addition to the mean cM/Mb observed across all F1 crosses ( F1 ) . Heterozygosity values were calculated using pairwise comparison of polymorphism data from the 19 genomes project to the Col reference ( Gan et al . , 2011 ) , and the mean value for the interval shown , in addition to the mean chromosome value in parentheses . 10 . 7554/eLife . 03708 . 006Table 3 . Genetic distance in F1 heterozygotesDOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 006AccessionLocationI1bI1fgI2f420CEN3TotalPTsu-0Tsushima , Japan6 . 66 . 36 . 914 . 59 . 443 . 7<2 . 00 × 10−16Hi-0Hilversum , Netherlands6 . 86 . 96 . 913 . 69 . 643 . 8<2 . 00 × 10−16Wil-2Vilnius , Lithuania6 . 16 . 96 . 115 . 910 . 145 . 0<2 . 00 × 10−16Kn-0Kaunas , Lithuania7 . 46 . 68 . 015 . 58 . 746 . 2<2 . 00 × 10−16Ler-0Gorzow , Poland6 . 68 . 27 . 612 . 311 . 946 . 6<2 . 00 × 10−16Ws-0Vassilyevichy , Belarus6 . 77 . 710 . 213 . 09 . 046 . 6<2 . 00 × 10−16No-0Nossen , Germany7 . 47 . 96 . 714 . 111 . 447 . 4<2 . 00 × 10−16Wu-0Wurzburg , Germany7 . 66 . 39 . 514 . 011 . 448 . 8<2 . 00 × 10−16Zu-0Zurich , Switzerland7 . 57 . 113 . 412 . 29 . 950 . 10 . 0438Po-0Poppelsdorf , Germany7 . 27 . 99 . 115 . 810 . 951 . 00 . 000484Ct-1Catania , Italy7 . 88 . 77 . 215 . 912 . 151 . 79 . 27 × 10−08Oy-0Oystese , Norway7 . 78 . 48 . 515 . 712 . 552 . 80 . 969Rsch-4Rschew , Russia7 . 96 . 810 . 715 . 212 . 453 . 10 . 505Col-0Columbia , USA8 . 08 . 28 . 818 . 011 . 554 . 5–Sf-2San Feliu , Spain8 . 28 . 87 . 418 . 612 . 355 . 30 . 724KasKashmir , India6 . 98 . 613 . 213 . 813 . 355 . 8<2 . 00 × 10−16KondPugus , Tajikistan7 . 18 . 115 . 813 . 711 . 456 . 2<2 . 00 × 10−16Edi-0Edinburgh , Scotland8 . 08 . 013 . 413 . 313 . 656 . 3<2 . 00 × 10−16Bay-0Bayreuth , Germany8 . 68 . 311 . 318 . 611 . 558 . 3<2 . 00 × 10−16Mt-0Martuba , Libya9 . 67 . 813 . 220 . 69 . 660 . 8<2 . 00 × 10−16ShaPamiro-Alaya , Tajikistan7 . 87 . 520 . 07 . 018 . 660 . 90 . 0012C24Columbia , USA8 . 88 . 518 . 512 . 114 . 161 . 9<2 . 00 × 10−16Bur-0Burren , Ireland6 . 79 . 121 . 914 . 717 . 870 . 2<2 . 00 × 10−16Cvi-0Cape Verde Islands9 . 110 . 011 . 312 . 627 . 670 . 7<2 . 00 × 10−16Can-0Las Palmas , Canary Isles7 . 88 . 522 . 112 . 431 . 482 . 2<2 . 00 × 10−16CoCoimbra , Portugal–––11 . 113 . 8––Nw-0Neuweilnau , Germany–––14 . 714 . 4––Mh-0Szczecin , Poland–––14 . 910 . 1––Wl-0Wildbad , Germany–––17 . 09 . 5––Bu-0Burghaun , Germany–––28 . 98 . 8––CIBC5Ascot , United Kingdom–––13 . 211 . 3––RRS7North Liberty , USA–––17 . 211 . 7––F1 cM mean7 . 67 . 911 . 515 . 012 . 954 . 8cM StDev0 . 80 . 94 . 83 . 64 . 99 . 3The accessions crossed to are listed with their geographic location . Genetic distance ( cM ) data is shown for the five fluorescent intervals , in addition to a summed total . Also shown are the mean and standard deviation for all F1s . A generalized linear model ( GLM ) was used to test for significant differences between total recombinant vs non-recombinant counts between replicate groups of Col-0 homozygotes and F1 heterozygotes . Tests were performed for genotypes where data from all five tested intervals had been collected . The crossover reporter systems utilize fluorescent proteins encoded by linked , heterozygous transgenes that are expressed from the pollen-specific LAT52 , or seed-specific NapA promoters ( Melamed-Bessudo et al . , 2005; Francis et al . , 2006; Yelina et al . , 2013 ) . Fluorescent measurements of gametes or progeny are used to asses segregation of the transgenes through meiosis and thereby measure crossover rates ( Melamed-Bessudo et al . , 2005; Berchowitz and Copenhaver , 2008; Yelina et al . , 2013 ) . Previously , we developed flow cytometry protocols to increase scoring-throughput using fluorescent pollen , allowing up to 80 , 000 gametes to be scored per individual plant ( Yelina et al . , 2012 , 2013 ) . To increase throughput when measuring fluorescent seed we adapted CellProfiler image analysis software , allowing us to rapidly score ∼2000 seed per individual ( Figure 2A–F ) ( Carpenter et al . , 2006 ) . This method gives recombination measurements not significantly different from manually collected data ( Figure 2F , Figure 2—source data 1 ) ( generalized linear model ( GLM ) , hereafter GLM , p = 0 . 373 ) . To test for significant differences between recombinant and non-recombinant counts using replicate groups we used a GLM assuming a binomial count distribution . Replicate heterozygous F1 individuals were analysed for each cross and 13 , 264 , 943 gametes were scored in total , to provide an extensive survey of the influence of polymorphism heterozygosity on crossover frequency ( Figure 3 and Table 3 ) . 10 . 7554/eLife . 03708 . 007Figure 2 . High-throughput measurement of crossover frequency using image analysis of fluorescent seed . ( A ) Combined red and green , red alone and green alone fluorescent micrographs of seed from a self-fertilized 420/++ plant . ( B ) CellProfiler output showing identification of seed objects by green lines and scoring of red and green fluorescence shown by shading . Blue shading shows an absence of colour . ( C–D ) Histograms of seed object fluorescence intensities , with coloured and non-coloured seed divided by vertical dotted lines . ( E ) Plot of seed object red vs green fluorescence intensities , with each point representing an individual seed . The red and green dashed lines show the colour vs non-colour divisions indicated in ( C–D ) . The formula used for cM calculation is printed below . ( F ) 420 cM measurements from replicate plants of the indicated genotypes ( Col/Col F1 , Col/Ler F1 , Col/Sha F1 ) are shown by black dots with mean values indicated by red dots . Data generated by automatic and manual scoring are plotted alongside one another . Measurements made by the different methods were not significantly different as tested using generalized linear model ( GLM ) . See Figure 2—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 00710 . 7554/eLife . 03708 . 008Figure 2—source data 1 . 420 crossover frequency measured via manual or automated scoring of seed fluorescence . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 00810 . 7554/eLife . 03708 . 009Figure 2—figure supplement 1 . Distinguishing 420 RFP-GFP/++ vs RFP-+/+-GFP recombinant individuals . ( A ) Genetic diagram illustrating generation of F2 plants heterozygous for the 420 fluorescent transgenes , annotated as in Figure 1C . F2 plants heterozygous for the fluorescent transgenes can occur via fertilization with recombinant or non-recombinant chromosomes . ( B ) Fluorescence micrographs of seed derived from self-fertilization of 420 RFP-GFP/++ vs RFP-+/+-GFP plants . ( C ) Plots of seed object red vs green fluorescence intensities , with each point representing an individual seed from either self-fertilized 420 RFP-GFP/++ or RFP-+/+-GFP plants . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 00910 . 7554/eLife . 03708 . 010Figure 3 . Variation in F1 hybrid crossover frequency . ( A–E ) Genetic distance ( cM ) measurements for fluorescent crossover intervals I1b , I1fg , I2f , 420 and CEN3 with individual replicates ( black dots ) and mean values ( red dots ) for the crosses labelled on the x-axis . See Figure 3—source data 1–5 . ( F ) Heatmap summarising crossover frequency data for F1 crosses with data from all five intervals . Accessions are listed as rows and fluorescent intervals listed as columns . The heatmap is ordered according to ascending ‘Total’ cM ( red = highest , blue = lowest ) , which is the sum of the individual interval genetic distances . GLM testing for significant differences between total recombinant vs non-recombinant counts between replicate groups of Col-0 homozygotes and F1 heterozygotes was performed , for genotypes where data from all five tested intervals were collected ( Table 3 ) . Col/Col homozygous data are labelled and highlighted with an arrow in each plot . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 01010 . 7554/eLife . 03708 . 011Figure 3—source data 1 . I1b F1 flow cytometry count data . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 01110 . 7554/eLife . 03708 . 012Figure 3—source data 2 . I1b F1 flow cytometry count data . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 01210 . 7554/eLife . 03708 . 013Figure 3—source data 3 . I1b F1 flow cytometry count data . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 01310 . 7554/eLife . 03708 . 014Figure 3—source data 4 . I1b F1 flow cytometry count data . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 01410 . 7554/eLife . 03708 . 015Figure 3—source data 5 . CEN3 F1 flow cytometry count data . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 015 We observed substantial variation in crossovers between F1 crosses , although the interstitial intervals varied less than those in sub-telomeric and centromeric locations ( Figure 3A–E , Figure 3—source data 1–5 ) . F1 heterozygotes showed both significantly higher and lower total recombination compared to Col homozygotes ( Figure 3 and Table 3 ) ( GLM with 113° of freedom p < 2 . 0 × 10−16 ) . F1 genetic distances and polymorphism levels within the intervals were poorly correlated , consistent with previous observations ( Table 4 ) ( Barth et al . , 2001; Gan et al . , 2011; Salomé et al . , 2012 ) . This weak correlation may be partially explained by unknown structural rearrangements . For example , the Shahdara ( Sha ) accession has a sub-telomeric inversion ( 3–5 . 1 Mb ) on chromosome 3 relative to Col ( Loudet et al . , 2002; Simon et al . , 2008; Salomé et al . , 2012 ) , and Col/Sha F1s show consistently low crossovers in 420 , which overlaps the inversion ( Figure 3D and Table 3 ) . Hence the contribution of unknown structural polymorphisms to variation in recombination rates could be significant . Further evidence of the complex effect of polymorphism is evident from the CEN3 interval , which spans the repetitive and structurally diverse centromeric region of chromosome 3 ( Figure 1A ) ( Copenhaver et al . , 1999; Clark et al . , 2007; Ito et al . , 2007; Cao et al . , 2011; Gan et al . , 2011; Horton et al . , 2012; Long et al . , 2013 ) , and showed high variability in F1 crossover frequency ( Figure 3E and Table 3 ) . Unexpectedly , some of the most diverged crosses , for example two accessions from Atlantic islands Cvi-0 and Can-0 , showed highest CEN3 crossovers ( Figure 3E and Table 3 ) ( Ito et al . , 2007 ) . 10 of 26 F1s showed significantly higher summed crossover frequency compared with Col homozygotes , consistent with previous reports that recombination can increase in heterozygous backgrounds in Arabidopsis ( Barth et al . , 2001 ) ( Figure 3F and Table 3 ) . Both cis and trans modification of crossovers by genetic variation has been observed in plants ( Barth et al . , 2001; Yao and Schnable , 2005; Yandeau-Nelson et al . , 2006; Esch et al . , 2007; McMullen et al . , 2009; López et al . , 2012; Salomé et al . , 2012; Bauer et al . , 2013 ) . Therefore , the variation in F1 crossover frequency observed here is likely caused by complex interactions between cis and trans modifying effects . 10 . 7554/eLife . 03708 . 016Table 4 . F1 heterozygosity levels relative to Col-0DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 016AccessionChr 1I1bI1fgChr 2I2fChr 3420CEN3Chr 4Chr 5Bur-03 . 351 . 863 . 623 . 601 . 513 . 581 . 576 . 203 . 893 . 16Can-03 . 752 . 993 . 513 . 920 . 923 . 981 . 028 . 275 . 344 . 24Ct-12 . 621 . 672 . 292 . 611 . 853 . 350 . 966 . 913 . 233 . 36Edi-03 . 301 . 913 . 643 . 260 . 913 . 051 . 345 . 483 . 423 . 81Hi-02 . 431 . 591 . 871 . 801 . 502 . 581 . 074 . 622 . 692 . 46Kn-03 . 151 . 782 . 853 . 352 . 183 . 581 . 496 . 693 . 763 . 40Ler-03 . 101 . 612 . 663 . 622 . 243 . 431 . 137 . 393 . 873 . 53Mt-03 . 021 . 771 . 163 . 491 . 573 . 171 . 075 . 703 . 952 . 71No-03 . 252 . 282 . 713 . 361 . 273 . 521 . 217 . 143 . 513 . 56Oy-03 . 481 . 682 . 103 . 050 . 582 . 941 . 236 . 162 . 952 . 72Po-02 . 451 . 781 . 192 . 360 . 672 . 870 . 795 . 992 . 532 . 59Rsch-42 . 941 . 841 . 173 . 361 . 223 . 091 . 055 . 373 . 893 . 22Sf-23 . 611 . 944 . 243 . 542 . 063 . 741 . 308 . 243 . 813 . 58Tsu-03 . 371 . 682 . 363 . 691 . 393 . 981 . 148 . 783 . 693 . 05Wil-23 . 561 . 992 . 453 . 772 . 113 . 811 . 567 . 554 . 443 . 34Ws-03 . 251 . 933 . 543 . 681 . 583 . 301 . 306 . 653 . 703 . 41Wu-03 . 132 . 531 . 953 . 140 . 673 . 501 . 227 . 413 . 363 . 15Zu-03 . 101 . 852 . 023 . 831 . 433 . 190 . 965 . 843 . 383 . 64Mean3 . 161 . 932 . 523 . 301 . 433 . 371 . 196 . 693 . 633 . 27Correlation ( cM ) –0 . 13 ( p = 0 . 61 ) 0 . 47 ( p = 0 . 05 ) –−0 . 29 ( p = 0 . 23 ) –0 . 06 ( p = 0 . 81 ) 0 . 28 ( p = 0 . 26 ) ––Accessions sequenced as part of the 19 genomes project were analysed ( Gan et al . , 2011 ) and heterozygosity calculated as the sum of SNPs and indel lengths divided by the length of region ( kb ) . Correlations were between heterozygosity within the interval measured and F1 cM measurements . To investigate the extent of cis and trans modification of crossover frequency by heterozygosity we generated a 420 Col × Ct recombinant F2 population ( n = 139 ) ( Figure 4A ) . We selected F2 individuals that were heterozygous for linked T-DNAs expressing red and green fluorescent proteins and Col/Ct heterozygous within 420 , but genetically mosaic elsewhere in the genome ( Figure 4A , E ) . The 420/++ Col/Ct F2 population showed significantly greater variation in recombination rates than Col/Col homozygotes ( F-test p = 0 . 0129 ) ( Figure 4D , Figure 4—source data 1 ) . We genotyped 51 Col/Ct markers throughout the genome and tested for their association with 420 crossover frequency using QTL analysis . We detected no association using markers on chromosomes 1 , 2 , 4 or 5 ( Figure 4B ) . However , on chromosome 3 itself homozygosity ( Col/Col or Ct/Ct ) outside of 420 was associated with high recombination ( FDR corrected chi-square test p = 3 . 29 × 10−31 ) ( Figure 4B , E–F and Table 5 ) . For each marker we used the heterozygous and homozygous counts in the hottest quartile vs the coldest quartile to construct 2 × 2 contingency tables and performed chi-square tests , followed by FDR correction for multiple testing ( Table 5 ) . 10 . 7554/eLife . 03708 . 017Figure 4 . Modification of crossover frequency by juxtaposition of heterozygosity and homozygosity . ( A ) Diagram illustrating chromosome 3 genotypes ( black = Col , red = Ct ) in RG/++ F1 individuals and their F2 progeny . A single chromosome is shown for simplicity . Gametes or progeny are analysed for patterns of fluorescence following meiosis to measure genetic distance . ( B ) The program Rqtl was used to test for association between Col/Ct genotypes and 420 cM in a 420/++ F2 population . The logarithm of odds ( LOD ) score is plotted along the 5 chromosomes with the positions of markers shown along the x-axis by ticks . The red horizontal line shows the 5% genome-wide significance threshold calculated with Hayley-Knott regression and by running 10 , 000 permutations . ( C ) As for ( B ) but analyzing Col/Ct markers on chromosomes 2 and 3 for an I2f/++ F2 population . ( D ) 420 cM measurements from Col/Ct 420/++ F2 ( black ) , Col/Col homozygotes ( red ) and Col/Ct F1 ( blue ) individuals . Mean values are indicated by horizontal dotted lines . See Figure 4—source data 1 . ( E ) Chromosome 3 genotypes shown for 420/++ F2 individuals ranked by crossover frequency . Each horizontal row represents a single F2 individual . X-axis ticks show marker positions , and which are coloured red when they showed significantly higher homozygosity in the hottest vs coldest quartiles ( FDR-corrected chi square test ) . Fluorescent T-DNAs are indicated by triangles , in addition to the centromere ( Cen ) . ( F ) Heterozygosity along chromosome 3 in the hottest ( red ) , coldest ( blue ) 420 F2 quartiles and the mean ( green ) . The locations of reporter T-DNAs and the centromeres are indicated by vertical dashed lines . ( G–I ) As for ( D–F ) but for interval I2f . See Figure 4—source data 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 01710 . 7554/eLife . 03708 . 018Figure 4—source data 1 . 420 Col/Ct F2 fluorescent seed count data . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 01810 . 7554/eLife . 03708 . 019Figure 4—source data 2 . I2f Col/Ct F2 fluorescent seed count data . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 01910 . 7554/eLife . 03708 . 020Figure 4—source data 3 . CEN3 Col/Ct F2 flow cytometry count data . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 02010 . 7554/eLife . 03708 . 021Figure 4—figure supplement 1 . Modification of crossover frequency by juxtaposition of heterozygosity and homozygosity . ( A ) CEN3 cM from Col/Ct CEN3/++ F2 ( black ) , Col/Col homozygotes ( red ) and Col/Ct F1 ( blue ) individuals . Horizontal dotted lines indicate mean value . See Figure 4—source data 3 . ( B ) Chromosome 3 genotypes shown for CEN3/++ F2 individuals ranked by crossover frequency . X-axis ticks show marker positions , and which are coloured red when they showed significantly higher homozygosity in the hottest vs coldest quartile ( FDR-corrected chi square tests ) . Fluorescent T-DNAs are indicated by triangles , in addition to the centromere . ( C ) Heterozygosity along chromosome 3 in the hottest ( red ) , coldest ( blue ) CEN3 F2 quartiles and the mean ( green ) . The locations of reporter T-DNAs and the centromeres are indicated by vertical dashed lines . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 02110 . 7554/eLife . 03708 . 022Table 5 . Chromosome 3 genotype counts from hot and cold quartile 420/++ Col/Ct F2 individualsDOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 022Marker coordinates ( bp ) Hot quartile HETHot quartile HOMCold quartile HETCold quartile HOMFDR p value25900034034012718000340340153520003403401637500021133404 . 36 × 10−04694800017173311 . 05 × 10−04767400015193312 . 12 × 10−05849500012223403 . 65 × 10−0794040008263313 . 79 × 10−08106950008263041 . 36 × 10−061164900011232774 . 36 × 10−041235600011232774 . 36 × 10−0415949000132123114 . 48 × 10−0219165000171721130 . 59123040000132117170 . 591The number of 420/++ Col/Ct F2 individuals showing Col homozygosity ( HOM ) or Col/Ct heterozygosity ( HET ) for the indicated marker positions , in either the hottest or coldest F2 quartile . The p value was obtained by performing a chi square test between homozygous and heterozygous marker genotype counts in the hottest and coldest quartiles ( 2x2 contingency table ) , followed by FDR correction for multiple testing . To test an additional chromosome for the effect of heterozygosity/homozygosity juxtaposition we measured recombination in an I2f Col × Ct F2 population ( n = 78 ) ( Figure 4G–I ) . The I2f interval is 0 . 67 Mb and located sub-telomerically on the long arm of chromosome 2 ( Figure 1A and Table 2 ) . The I2f/++ Col/Ct F2 population also showed significantly greater variation in recombination rates than Col/Col homozygotes ( F-test , p = 0 . 04 ) ( Figure 4G , Figure 4—source data 2 ) . We performed QTL analysis for Col/Ct markers on chromosomes 2 and 3 and again observed a significant effect on the same chromosome and no trans effect from chromosome 3 . An identical trend to that seen for 420 was observed , with the highest recombination F2 quartile showing significantly greater marker homozygosity ( both Col/Col and Ct/Ct ) outside I2f on chromosome 2 ( FDR corrected chi-square test p = 1 . 44 × 10−10 ) ( Figure 4C , G–I and Table 6 ) . The most distal marker showing a significant difference between hot and cold quartiles was of comparable megabase distance for 420 ( 10 . 60 Mb ) and I2f ( 10 . 12 Mb ) . 10 . 7554/eLife . 03708 . 023Table 6 . Chromosome 2 genotype counts from hot and cold quartile I2f/++ Col/Ct F2 individualsDOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 023Marker coordinates ( bp ) Hot quartile HETHot quartile HOMCold quartile HETCold quartile HOMFDR p value132 , 00091181212 , 346 , 00071381214 , 748 , 00081291116 , 789 , 0007131190 . 6311 , 443 , 0005152006 . 26 × 10−0513 , 036 , 0007132003 . 32 × 10−0414 , 117 , 0009112001 . 30 × 10−0315 , 240 , 0009112001 . 30 × 10−0316 , 909 , 0001372000 . 026217 , 439 , 0001642000 . 23818 , 287 , 000200200118 , 960 , 000200200119 , 311 , 0001822000 . 764The number of I2f/++ Col/Ct F2 individuals showing Col homozygosity ( HOM ) or Col/Ct heterozygosity ( HET ) for the indicated markers , in either the hottest or coldest F2 quartile . The p value was obtained by performing a chi square test between homozygous and heterozygous marker genotype counts in the hottest and coldest quartiles ( 2 × 2 contingency table ) , followed by FDR correction for multiple testing . To test whether the effect of heterozygosity/homozygosity juxtaposition is dependent on chromosomal location we measured crossovers in a CEN3 Col × Ct F2 population ( n = 121 ) ( Figures 4A and 5C , Figure 4—figure supplement 1 , Figure 4—source data 3 ) . As for 420 and I2f , CEN3 F2 recombination rates were significantly more variable than Col/Col homozygotes ( F-test p = 0 . 01268 ) ( Figure 4A , Figure 4—figure supplement 1 ) . We genotyped 9 Col/Ct markers on chromosome 3 and observed that 5 markers in proximity to CEN3 were significantly more homozygous in the hottest compared to the coldest F2 quartile ( FDR corrected chi-square test p = 1 . 14 × 10−07 ) ( Figure 4D–F , Figure 4—figure supplement 1 and Table 7 ) . The physical extent of the effect was less ( 2 . 62 Mb ) on the long arm of chromosome 3 for CEN3 than observed for 420 and I2f , potentially due to heterozygosity effects acting independently from both arms across the centromere . Together this shows that juxtaposition of heterozygous and homozygous regions in various chromosomal locations can modify local crossover frequency . 10 . 7554/eLife . 03708 . 024Figure 5 . Juxtaposition of heterozygous and homozygous regions triggers reciprocal crossover remodelling . ( A ) Schematic diagram illustrating the physical location of 420 and I3bc transgenes expressing fluorescent proteins in seed and pollen . Beneath are diagrams illustrating the locations of Col/Col homozygous ( red ) and Col/Ct heterozygous ( black ) regions along chromosome 3 . Positions of Col/Ct genotyping markers are indicated by blue ticks along the axis of the chromosome . Printed alongside are formulae for the calculation of genetic distance ( cM ) and crossover interference using I3bc . Counts of pollen with different combinations of fluorescence are indicated . For example , NBYR indicates the number of pollen with blue , yellow and red fluorescence . ( B ) I3b and I3c genetic distance ( cM ) measured in HOM-HOM and HET-HOM plants as illustrated in ( A ) . See Figure 5—source data 1 . ( C ) As for ( B ) but showing calculation of crossover interference ( 1-CoC ) . See Figure 5—source data 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 02410 . 7554/eLife . 03708 . 025Figure 5—source data 1 . Three colour I3bc FTL flow cytometry count data . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 02510 . 7554/eLife . 03708 . 026Figure 5—source data 2 . Three colour I3bc FTL flow cytometry count data–measurement of crossover interference . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 02610 . 7554/eLife . 03708 . 027Figure 5—figure supplement 1 . Analysis of I3bc recombination using three-colour flow cytometry . Flow cytometry plots are shown measuring pollen for the indicated colour of fluorescent protein . In the upper plot total hydrated pollen is divided into blue and non-blue populations using polygonal gates . Gated populations are then analysed separately in the lower plots for red and yellow fluorescence . The indicated polygon gates represent the labelled pollen fluorescent classes . Beneath the plots is a diagram indicating the physical location of the I3bc T-DNA insertions at the end of chromosome 3 . The T-DNAs are represented by coloured triangles . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 02710 . 7554/eLife . 03708 . 028Table 7 . Chromosome 3 genotype counts from hot and cold quartile CEN3/++ Col/Ct F2 individualsDOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 028Marker coordinates ( bp ) Hot quartile HETHot quartile HOMCold quartile HETCold quartile HOMFDR P259000161417131271800016141812153520001911171317674000201012180 . 129849500023713170 . 0389940400026416140 . 03081111572430030011652056030030012100800027314160 . 004772207600023712180 . 03082304000024610200 . 00477The number of CEN3/++ Col/Ct F2 individuals showing Col homozygosity ( HOM ) or Col/Ct heterozygosity ( HET ) for the indicated markers , in either the hottest or coldest quartile . The p value was obtained by performing a chi square test between homozygous and heterozygous marker genotype counts in the hottest and coldest quartiles ( 2 × 2 contingency table ) , followed by FDR correction for multiple testing . We reasoned that if heterozygous regions increase recombination when juxtaposed with homozygous regions , then the linked homozygous regions may show compensatory decreases , due to crossover interference ( Copenhaver et al . , 2002; Zhang et al . , 2014a ) . To test this idea we constructed a three-colour pollen FTL interval termed I3bc that overlaps the 420 seed interval on chromosome 3 ( Figure 5 and Table 2 ) . Three-colour FTL configurations allow simultaneous measurement of crossover frequency in adjacent intervals and measurement of crossover interference ( Berchowitz and Copenhaver , 2008; Yelina et al . , 2013 ) ( Figure 5—figure supplement 1 ) . To calculate interference , the observed double crossover ( DCO ) classes ( N-Y- + NB-R ) are compared to the number expected in the absence of interference: ( I3b cM/100 ) × ( I3c cM/100 ) × Ntotal ( Figure 5A ) . The Coefficient of Coincidence ( CoC ) is calculated by dividing Observed DCOs by Expected DCOs , and interference strength calculated as 1-CoC ( Figure 5A ) . I3bc wild type genetic distance was greater than that measured from 420 self-fertilization data , as expected due to increases observed in sub-telomeric regions in male meiosis ( Table 2—Figure 5—source data 1 ) ( Giraut et al . , 2011 ) . I3b crossover frequency was also higher than I3c , consistent with a telomeric gradient in male crossover frequency ( Figure 5B and Table 2 ) ( Giraut et al . , 2011 ) . We compared crossovers in plants that were entirely Col homozygous ( HOM-HOM ) vs plants that were Col/Ct heterozygous within I3b , but Col/Col homozygous in I3c and for the rest of chromosome 3 ( HET-HOM ) ( Figure 5A ) . Dense genotyping markers were used to confirm the location of homozygous and heterozygous regions ( Figure 5A ) . We observed that I3b crossovers significantly increased in HET-HOM compared to HOM-HOM plants , and there was a reciprocal decrease in I3c crossovers ( Figure 5B , Figure 5—source data 2 ) ( both GLM p < 2 . 0 × 10−16 ) . Together this is consistent with reciprocal crossover changes in juxtaposed heterozygous and homozygous regions being driven by crossover interference . The effect of heterozygosity/homozygosity juxtaposition on crossovers extends over megabase distances , which is similar to the scale of crossover interference in Arabidopsis ( Copenhaver et al . , 2002; Giraut et al . , 2011; Salomé et al . , 2012 ) . We therefore next used mutations in meiotic recombination pathways to analyse the genetic requirements of these effects . Specifically , we generated plants carrying the linked chromosome 3 fluorescent crossover reporters 420 and CEN3 ( 420-CEN3 ) , with varying Col/Ct genotype and that were wild type , fancm or fancm zip4 ( Figure 6—Figure 6—figure supplement 1 ) . Crossover frequency in 420 and CEN3 can be scored in the same individuals , as these intervals use fluorescent proteins expressed in seed and pollen respectively . In fancm DSBs that would normally be repaired as non-crossovers enter a non-interfering pathway leading to a substantial increase in crossovers , although the interfering pathway remains active ( Crismani et al . , 2012 ) . In fancm zip4 only non-interfering crossovers occur , due to mutation of the ZMM gene ZIP4 ( Chelysheva et al . , 2007; Crismani et al . , 2012 ) . In wild type , both interfering and non-interfering pathways are active , but interfering crossovers predominate and constitute ∼85% of total crossovers ( Copenhaver et al . , 2002; Higgins et al . , 2004; Mercier et al . , 2005 ) . Therefore , by comparing genetic distances in wild type , fancm and fancm zip4 , where the relative proportions of interfering and non-interfering repair vary dramatically , we can investigate the sensitivity of different recombination pathways to heterozygosity . 10 . 7554/eLife . 03708 . 029Figure 6 . Genetic requirements of crossover remodelling via juxtaposition of heterozygous and homozygous regions . ( A–D ) Replicate measurements of 420 ( red ) and CEN3 ( blue ) genetic distances ( cM ) are plotted in wild type , fancm and fancm zip4 . See Figure 6—source data 1 , 2 . Chromosome 3 genotypes of the plants analysed are indicated above the plots ( green = Col and red = Ct ) , for example , HET-HOM indicates heterozygous within 420 and homozygous outside . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 02910 . 7554/eLife . 03708 . 030Figure 6—source data 1 . 420 fluorescent seed count data from wild type , fancm and fancm zip4 individuals with varying heterozygosity . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 03010 . 7554/eLife . 03708 . 031Figure 6—source data 2 . CEN3 flow cytometry count data from wild type , fancm and fancm zip4 individuals with varying heterozygosity . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 03110 . 7554/eLife . 03708 . 032Figure 6—figure supplement 1 . Generation of wild type , fancm or fancm zip4 420-CEN3 individuals with varying patterns of Col/Ct heterozygosity . Diagram showing the crossing scheme used to generate plants to test the requirement of recombination pathways in crossover remodelling . At relevant points the genotype of chromosome 3 is illustrated graphically with black indicating Col and red indicating Ct . The circles represent the location of the centromere and the red and green filled triangles represent the fluorescent T-DNAs of both 420 and CEN3 . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 032 When chromosome 3 is Col/Col homozygous ( HOM-HOM ) genetic distance in the 420 interval significantly increased in fancm and fancm zip4 mutants compared with wild type ( both GLM p < 2 . 0 × 10−16 ) ( Figure 6A , Figure 6—source data 1 ) , consistent with repair of the majority of DSBs via a non-interfering crossover pathway ( Crismani et al . , 2012 ) . However , the CEN3 interval experienced a smaller yet significant increase in genetic distance in fancm and decreased in fancm zip4 ( both GLM p < 2 . 0 × 10−16 ) , indicating that non-interfering crossover repair is less efficient in this region ( Figure 6A , Figure 6—source data 2 ) . We next generated plants that were Col/Ct heterozygous ( HET-HET ) on chromosome 3 and observed that the previous increase in 420 crossovers was strongly suppressed in fancm and fancm zip4 ( GLM p = 1 . 24 × 10−06 and p < 2 . 0 × 10−16 ) , whereas wild type Col/Ct were slightly but significantly higher than wild type Col/Col ( GLM p = 0 . 0126 ) ( Figure 6A–B ) . CEN3 crossovers were also significantly suppressed by Col/Ct heterozygosity in fancm and nearly eliminated in fancm zip4 compared to Col/Col ( both GLM p < 2 . 0 × 10−16 ) ( Figure 6A–B ) . Together this indicates that the non-interfering crossover repair pathway that predominates in fancm and fancm zip4 is less efficient in heterozygous regions and particularly within the centromeric region , which shows high polymorphism levels ( Table 2 ) . We next tested the effect of juxtaposing heterozygous and homozygous regions in fancm and fancm zip4 mutants . We first generated lines that were Col/Ct heterozygous within 420 and Col/Col homozygous outside ( HET-HOM ) ( Figure 6—figure supplement 1 ) . As expected , wild type HET-HOM lines show a significant increase in 420 and a reciprocal decrease in CEN3 crossovers compared to wild type HOM-HOM ( both GLM p < 2 . 0 × 10−16 ) ( Figure 6A , C ) , indicating compensatory changes between the two intervals in the HET-HOM lines . As the HET-HOM lines are heterozygous within 420 , this again inhibited crossovers in fancm compared to fancm HOM-HOM ( GLM p = 2 . 38 × 10−15 ) ( Figure 6A , C ) . HET-HOM lines in fancm zip4 showed lower 420 crossovers than wild type HOM-HOM ( GLM p < 2 . 0 × 10−16 ) , which demonstrates that the interfering pathway is required for the heterozygosity-homozygosity juxtaposition effect ( Figure 6A , C ) . We also generated HOM-HET lines that were homozygous within 420 and heterozygous outside , which significantly reduced 420 crossovers compared to wild type HOM-HOM as expected ( GLM p = 7 . 60 × 10−11 ) ( Figure 6A , D ) . HOM-HET lines in fancm and fancm zip4 showed high 420 crossovers comparable to HOM-HOM , as the non-interfering crossover repair active in these backgrounds is efficient in homozygous regions ( Figure 6A , D ) . CEN3 genetic distance was again strongly suppressed in fancm and fancm zip4 HOM-HET lines compared with HOM-HOM ( both GLM p < 2 . 0 × 10−16 ) , consistent with heterozygosity inhibiting non-interfering crossover repair ( Figure 6A , D ) . Together these data demonstrate that juxtaposition of heterozygous and homozygous regions causes reciprocal changes in crossover frequency via interference . As we observed regional changes in crossover frequency with varying patterns of heterozygosity , we next sought to test whether total recombination events were different . When homologous chromosomes align on the metaphase-I plate , crossovers can be cytologically visualized as chiasmata ( Sanchez-Moran et al . , 2002 ) . To estimate the number of crossovers per meiotic nucleus we performed chromosome spreads of pollen mother cells ( PMCs ) , followed by fluorescence in situ hybridization using a 45S rDNA probe ( Figure 7 , Figure 7—source data 1 ) . We counted total chiasmata in metaphase-I nuclei in Col/Col homozygotes , Ct/Ct homozygotes and Col/Ct F1 heterozygotes . In addition , we counted chiasmata in recombinant 420-CEN3 lines showing high ( HET-HOM , 27 . 96 cM ) and low ( HOM-HET , 13 . 83 cM ) 420 crossover frequency ( Figure 7C , D ) . Adjacent chiasmata count categories were combined to give a minimum expected value of five for the purposes of a chi-square test with 8° of freedom . This test gave no significant differences in chiasmata between the genotypes ( p = 0 . 3365 ) ( Figure 7 ) . Together this is consistent with homeostatic maintenance of crossover numbers , despite local crossover changes caused by juxtaposition of heterozygous and homozygous regions . 10 . 7554/eLife . 03708 . 033Figure 7 . Total chiasmata frequencies are stable between Col , Ct and recombinant lines . ( A–E ) Metaphase-I chromosome spreads from anthers from ( A ) Col/Col 420 , ( B ) Ct/Ct , ( C ) Col × Ct F1 , ( D ) a Col × Ct 420 ( HOM-HET ) cold recombinant line and ( E ) a Col × Ct 420 hot ( HET-HOM ) recombinant line . DNA is stained with DAPI ( blue ) and labelled with a 45S rDNA probe ( green ) . Scale bars = 10 μM . ( F ) Boxplot showing total number of chiasmata per nucleus for each genotype . See Figure 7—source data 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 03310 . 7554/eLife . 03708 . 034Figure 7—source data 1 . Chiasmata count data . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 034 Our analysis of 420-CEN3 recombination rates implicated interference as driving crossover changes across homozygosity/heterozygosity junctions . We therefore sought to directly measure interference in lines with varying heterozygosity . We generated I3bc lines that varied in Col/Ct genotype and that were wild type , fancm , zip4 or fancm zip4 ( Figure 8—figure supplement 1 ) . We first compared I3bc plants that were Col/Col homozygous ( HOM-HOM ) with Col/Ct heterozygotes ( HET-HET ) . In wild type , genetic distances did not significantly change between HOM-HOM and HET-HET ( GLM p = 0 . 352 and p = 0 . 666 ) , but crossover interference significantly increased ( GLM p < 2 . 0 × 10−16 ) ( Figure 8A , B , Figure 8—source data 1 ) . Consistent with previous observations , fancm and fancm zip4 showed a significant reduction and an absence of interference respectively , in a HOM-HOM background ( GLM p < 2 . 0 × 10−16 and p = 4 . 94 × 10−16 ) ( Figure 8A , Figure 8—source data 2 ) ( Crismani et al . , 2012; Yelina et al . , 2013 ) . In HET-HET plants the crossover frequency increases seen in fancm and fancm zip4 were again greatly suppressed , or eliminated , relative to HOM-HOM , as observed previously for 420-CEN3 ( GLM both p < 2 . 0 × 10−16 ) ( Figure 8B ) . Unexpectedly , interference measurements significantly increased in both fancm and fancm zip4 mutants in a HET-HET background compared to HOM-HOM ( GLM p < 2 . 0 × 10−16 and p = 4 . 94 × 10−16 ) ( Figure 8B ) . We propose that in the absence of the ZMM pathway alternative repair pathways exist which are differentially sensitive to polymorphism and interference . Multiple , redundant repair pathways are consistent with the residual crossovers observed in msh4 mus81 double mutants ( Higgins et al . , 2008b ) . Finally , we measured I3bc cM in zip4 mutants alone ( HOM-HOM ) and observed significantly decreased crossovers compared with wild type HOM-HOM ( GLM p < 2 . 0 × 10−16 ) ( Figure 8E , Figure 8—source data 1 ) . Importantly , zip4 genetic distances were further significantly reduced when comparing HOM-HOM to HET-HET backgrounds ( GLM p = 1 . 79 × 10−10 and p = 1 . 53 × 10−9 ) ( Figure 8E ) . This provides additional evidence that the non-interfering repair pathway remaining in zip4 is inefficient in heterozygous regions . Interference measurements using I3bc are reliant on the relatively rare double crossover classes ( N-Y- + NB-R ) ( Figure 5A ) . Due to low zip4 fertility it was difficult to obtain sufficient DCO counts to make reliable interference measurements , although the observed counts are consistent with an absence of interference in this mutant ( Figure 8—source data 4 ) . 10 . 7554/eLife . 03708 . 035Figure 8 . Crossover interference increases when heterozygous and homozygous regions are juxtaposed . ( A–D ) Replicate measurements of I3b and I3c genetic distances ( cM ) , and I3bc crossover interference are plotted in wild type , fancm , fancm zip4 and zip4 . Black dots represent replicate measurements with mean values indicated by red dots . Chromosome 3 genotypes of the plants analysed are indicated above the plots ( green = Col and red = Ct ) , for example , HET-HOM indicates heterozygous within I3bc and homozygous outside . See Figure 8—source data 1 , 2 . ( E ) I3b and I3c genetic distances ( cM ) are plotted in wild type and zip4 mutants with varying patterns of heterozygosity , labelled as for ( A–D ) . Mean values between samples are connected with red lines . See Figure 8—source data 3 , 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 03510 . 7554/eLife . 03708 . 036Figure 8—source data 1 . I3bc fluorescent seed count data from wild type , fancm and fancm zip4 individuals with varying heterozygosity . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 03610 . 7554/eLife . 03708 . 037Figure 8—source data 2 . Calculation of I3bc interference from wild type , fancm and fancm zip4 individuals with varying heterozygosity . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 03710 . 7554/eLife . 03708 . 038Figure 8—source data 3 . I3bc fluorescent seed count data from wild type and zip4 individuals with varying heterozygosity . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 03810 . 7554/eLife . 03708 . 039Figure 8—source data 4 . Calculation of I3bc interference in wild type and zip4 . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 03910 . 7554/eLife . 03708 . 040Figure 8—figure supplement 1 . Generation of wild type , fancm , zip4 or fancm zip4 I3bc/++ plants with varying patterns of Col/Ct heterozygosity . Diagram showing the crossing scheme used to generate plants to investigate the impact of the Col/Ct heterozygosity on crossover interference . Genotypes differing in polymorphism pattern for crosses with I3bc lines were obtained as described in Figure 6—figure supplement 1 . The genotype of chromosome 3 is illustrated graphically with black indicating Col and red indicating Ct . The circles represent the location of the centromere and the red and green filled triangles represent the fluorescent T-DNAs for both 420 and CEN3 . DOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 040 To test the effects of heterozygosity/homozygosity juxtaposition we next generated lines that were Col/Ct heterozygous within I3bc and Col/Col homozygous outside ( HET-HOM ) . As expected , wild type I3b and I3c genetic distances both significantly increase in HET-HOM lines relative to HOM-HOM ( GLM both p < 2 . 0 × 10−16 ) , consistent with our previous 420 experiments , and this was associated with a significant increase in crossover interference ( GLM p < 2 . 0 × 10−16 ) ( Figure 8A , C ) . As shown earlier , we observed that Col/Ct ( HET-HOM ) heterozygosity suppressed the crossover increases seen in fancm and fancm zip4 ( GLM p < 2 . 0 × 10−16 ) , with the same significant increases in crossover interference strength ( GLM p < 2 . 0 × 10−16 and p = 4 . 94 × 10−16 ) ( Figure 8A , C ) . The reciprocal situation was observed in HOM-HET plants where I3bc was Col/Col homozygous and the rest of the chromosome Col/Ct heterozygous . I3b and I3c genetic distances were significantly decreased in wild type HOM-HET compared with wild type HOM-HOM plants ( GLM both p < 2 . 0 × 10−16 ) ( Figure 8A , D ) . HOM-HET fancm and fancm zip4 plants showed high crossovers , as the non-interfering pathway is efficient in the homozygous I3bc interval ( Figure 8A , D ) . We also generated HET-HOM zip4 lines , which unlike wild type showed significantly lower I3b and I3c cM than HOM-HOM zip4 ( GLM both P= p < 2 . 0 × 10−16 ) ( Figure 8E ) . This again demonstrates that crossover remodelling at heterozygosity/homozygosity junctions requires interference and that non-interfering repair is inefficient in heterozygous regions . As an independent test of the effect of heterozygosity on crossover interference we analysed four three-colour FTL intervals distributed throughout the genome ( Figure 1A and Table 2 ) . We measured crossover frequency and interference in Col/Col homozygotes vs Col/Ler F1 heterozygotes using meiotic pollen tetrads ( Tables 8 , 9 ) . This approach is possible as the FTL crossover reporter system was generated in the qrt1-2 mutant background , where sister pollen grains remain physically attached as meiotic tetrads ( Berchowitz and Copenhaver , 2008 ) . We scored a total of 49 , 801 tetrads for Col/Col ( an average of 6225 per interval ) and 42 , 422 tetrads for Col/Ler ( an average of 5302 per interval ) ( Tables 8 , 9 ) . Compared to Col/Col , genetic distance significantly decreased in Col/Ler for six of the eight intervals measured and the remaining two intervals were not significantly changed ( Table 8 ) . To calculate interference strength we compared cM values in each interval from tetrads that had a crossover in the adjacent interval , to the same intervals in tetrads lacking a crossover in the adjacent interval , and detected significant positive interference in all cases ( Table 9 ) ( Berchowitz and Copenhaver , 2008 ) . The resulting interference ratios were then compared between Col/Col and Col/Ler using Fisher's combined probability test , which revealed a significant increase in interference strength in Col/Ler ( χ2 . 001[16] = 39 . 26 ) ( Table 9 ) . Therefore , the effect of heterozygosity increasing the interference strength is evident in both Col × Ct and Col × Ler crosses . 10 . 7554/eLife . 03708 . 041Table 8 . Tetrad FTL cM data in Col/Col and Col/Ler backgroundsDOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 041Col/ColCol/LerIntervalPDNPDTcM*PDNPDTcM*1b397637428 . 05 ± 0 . 29439526526 . 58 ± 0 . 25†1c302211169518 . 62 ± 0 . 04315618189119 . 73 ± 0 . 042a678724303 . 06 ± 0 . 15592002832 . 28 ± 0 . 13†2b658226354 . 48 ± 0 . 18579604073 . 28 ± 0 . 16†3b436322255719 . 37 ± 0 . 3527582105613 . 99 ± 0 . 38†3c618557365 . 53 ± 0 . 21357622383 . 28 ± 0 . 22†5c535616665 . 58 ± 0 . 21545806765 . 51 ± 0 . 205d535816645 . 56 ± 0 . 21554025944 . 94 ± 0 . 20†*Map distance in cM ( ±S . E . ) . †Significant difference in map distance in the heterozygous Col/Ler background compared to the same interval in the Col/Col homozygous background . 10 . 7554/eLife . 03708 . 042Table 9 . Tetrad FTL crossover interference data in Col/Col and Col/Ler backgroundsDOI: http://dx . doi . org/10 . 7554/eLife . 03708 . 042Col/ColCol/LerIntervalW/o adj . CO*w/ adj . CO*R1†W/o adj . CO*w/ adj . CO*R2†1b10 . 69 ± 0 . 403 . 31 ± 0 . 30‡3 . 239 . 78 ± 0 . 371 . 22 ± 0 . 18‡8 . 04§1c20 . 61 ± 0 . 457 . 92 ± 0 . 76‡2 . 622 . 13 ± 0 . 463 . 52 ± 0 . 50‡6 . 29§2a3 . 20 ± 0 . 161 . 18 ± 0 . 30‡2 . 752 . 42 ± 0 . 140 . 37 ± 0 . 21‡6 . 552b4 . 65 ± 0 . 191 . 74 ± 0 . 44‡2 . 683 . 41 ± 0 . 160 . 53 ± 0 . 30‡6 . 443b20 . 84 ± 0 . 376 . 95 ± 0 . 82‡2 . 314 . 73 ± 0 . 402 . 92 ± 0 . 76‡5 . 053c7 . 65 ± 0 . 301 . 90 ± 22‡4 . 034 . 28 ± 0 . 300 . 66 ± 0 . 18‡6 . 465c5 . 87 ± 0 . 233 . 23 ± 0 . 47‡1 . 825 . 85 ± 0 . 222 . 35 ± 0 . 43‡2 . 495d5 . 85 ± 0 . 233 . 22 ± 0 . 48‡1 . 825 . 29 ± 0 . 222 . 07 ± 0 . 38‡2 . 56*Map distances in cM ( ±S . E . ) for intervals with and without adjacent crossovers ( CO ) . †Ratios of map distances for intervals with and without adjacent crossovers in homozygous Col/Col ( R1 ) and heterozygous Col/Ler ( R2 ) backgrounds . ‡Significant difference in map distances in intervals when adjacent interval does or doesn't have a CO . §Significant difference between R2 and R1 .
We demonstrate reciprocal crossover increases and decreases when heterozygous and homozygous regions are juxtaposed and further demonstrate that this process requires crossover interference . The mechanism of interference is presently unclear , but a Beam-Film model has been developed where crossovers are patterned via forces similar to mechanical stress and which predicts experimental data ( Kleckner et al . , 2004; Zhang et al . , 2014a , 2014b ) . In this model each chromosome begins with an array of precursor interhomolog strand invasion events , one of which becomes crossover designated via a stress-related force ( Designation Driving Force DDF ) . This causes a local reduction and redistribution of stress in both directions that dissipates with increasing distance ( Kleckner et al . , 2004; Zhang et al . , 2014a , 2014b ) . At the point where stress increases sufficiently precursor events can again become crossover designated . Any remaining precursors then mature into other fates including non-crossovers and non-interfering crossovers ( Kleckner et al . , 2004; Zhang et al . , 2014a , 2014b ) . We considered the effect of juxtaposition of heterozygous/homozygous regions in the context of the Beam-Film model ( Kleckner et al . , 2004; Zhang et al . , 2014a , 2014b ) . Detection of heterozygosity most likely occurs downstream of interhomolog strand invasion and the formation of base pair mismatches . Therefore , we assume that the initial distribution of meiotic DSBs is unchanged in homozygous or heterozygous states . Mismatches are observed to have a local inhibitory effect on meiotic crossovers ( Dooner , 1986; Borts and Haber , 1987; Jeffreys and Neumann , 2005; Baudat and de Massy , 2007; Cole et al . , 2010 ) . Therefore , one possibility is that mismatched precursors in heterozygous regions are slowed in maturation and trigger feedback mechanisms that cause further DSBs , for example via ATM/ATR kinase signalling ( Carballo et al . , 2008; Lange et al . , 2011; Zhang et al . , 2011; Kurzbauer et al . , 2012; Garcia et al . , 2015 ) . As a consequence , heterozygous regions would receive more ‘late’ DSBs , leading to more precursors and a higher chance of receiving a crossover designation event . An increased chance of crossover designation would lead to spreading of interference into adjacent homozygous regions causing reciprocal crossover decreases . An alternative model is that mismatched precursors are more sensitive to crossover designation and thus heterozygous regions have a higher chance of an interfering crossover , leading to similar effects . These potential models could be distinguished by measurement of non-crossover ( NCO ) levels , which should increase in heterozygous regions if more DSBs occur . Our data also demonstrate that non-interfering repair is less efficient in heterozygous regions , which will further contribute to the changes we see across homozygosity/heterozygosity junctions . Sequence polymorphism has been observed to suppress crossover recombination at the hotspot ( kilobase ) scale in diverse eukaryotes ( Dooner , 1986; Borts and Haber , 1987; Jeffreys and Neumann , 2005; Baudat and de Massy , 2007; Cole et al . , 2010 ) . For example , at the mouse A3 hotspot an indel polymorphism within an inverted repeat overlaps a crossover refractory zone ( Cole et al . , 2010 ) . However , this zone forms significant numbers of non-crossovers , indicating that the repeat/indel does not inhibit DSB formation , but inhibits downstream progression to crossover recombination ( Cole et al . , 2010 ) . In yeast addition of SNPs to the MAT-URA3 hotspot decreased crossovers and increased the frequency of gene conversions , further indicating that polymorphism can inhibit crossovers at fine-scale ( Borts and Haber , 1987 ) . Finally , intragenic mapping of the maize Bronze hotspot demonstrated that transposon insertions suppress crossovers more strongly than single nucleotide changes ( Dooner , 1986; Fu et al . , 2001; Dooner and He , 2008 ) , again consistent with progression to crossover repair being inhibited by local sequence polymorphisms . Several heteroduplex joint molecules with distinct properties form during meiosis , including displacement-loops and dHJs ( Keeney and Neale , 2006 ) . It is possible that these joint molecules and their interactions with recombinases are sensitive to base-pair mismatches . The mismatch repair protein MutS directly recognizes mismatched base-pairs and serves as a paradigm for this type of function ( Lamers et al . , 2000; Obmolova et al . , 2000 ) . The reciprocal crossover changes we observe when heterozygous regions are juxtaposed with homozygous regions are reminiscent of other homeostatic effects characterized during meiosis ( Hillers and Villeneuve , 2003; Martini et al . , 2006; Robine et al . , 2007; Libuda et al . , 2013; Thacker et al . , 2014 ) . For example , multiple levels of interference have been detected in mice ( de Boer et al . , 2006; Cole et al . , 2012 ) , Zip3 foci with distinct timing and properties are observed in budding yeast ( Serrentino et al . , 2013 ) , and ‘upstream’ DSB patterns are altered in ‘downstream’ ZMM mutants ( Thacker et al . , 2014 ) . As plants , fungi and mammals share the presence of interfering and non-interfering crossover repair pathways similar effects over heterozygosity/homozygosity junctions may be generally important ( Stahl et al . , 2004 ) . However , when assessing the significance of such effects it is also important to consider how outcrossing vs selfing will influence patterns of homozygosity and heterozygosity within different species . Together our data show how varying patterns of sequence polymorphism along chromosomes can have a significant effect on distributions of meiotic recombination .
Flow cytometry of pollen can be used to rapidly measure meiotic segregation of heterozygous transgenes encoding distinct colours of fluorescent protein ( Yelina et al . , 2012 , 2013 ) . cM were calculated from flow cytometry data using the formula:cM=100× ( R5/ ( R3+R5 ) ) , Where R5 is a number of green-alone fluorescent pollen grains and R3 is a number of green and red fluorescent pollen grains ( Yelina et al . , 2012 , 2013 ) . We previously observed that the number of red-alone pollen exceeded that of green-alone pollen when lines heterozygous for both eYFP and dsRed ( eYFPDsRed/++ ) were analysed ( Yelina et al . , 2012 , 2013 ) . Using pulse-width/SSC ( side scatter ) analysis and back-gating we demonstrated that the excess counts come primarily from non-hydrated pollen ( Yelina et al . , 2012 , 2013 ) . Therefore to avoid this artifact we multiply the green-alone counts by two to obtain the number of recombinant pollen . To increase measurement throughput using fluorescent seed we adapted CellProfiler image analysis software ( Carpenter et al . , 2006 ) ( Figure 2 ) . This program identifies seed boundaries in micrographs and assigns a RFP and GFP fluorescence intensity to each seed object ( Figure 2A–B ) . Three pictures of the seed are acquired at minimum magnification ( ×0 . 72 ) using a charge coupled device ( CCD ) camera; ( i ) brightfield , ( ii ) UV through a dsRed filter and ( iii ) UV through a GFP filter ( Figure 2A ) . As seed are diploid , there are nine possible fluorescent genotypes when a RFP-GFP/++ heterozygote is self-fertilized , in contrast to four possible states for haploid pollen ( Yelina et al . , 2013 ) ( Figure 2E ) . Histograms of seed fluorescence can be used to classify fluorescent and non-fluorescent seed for each colour ( Figure 2C–D ) . Although it is possible to distinguish seed with one vs two T-DNA copies , there is greater overlap between the groups ( Figure 2C–E ) . Therefore , we use fluorescent vs non-fluorescent seed counts for crossover measurement . Using this method it is possible to score 2000–6000 meioses per self-fertilized individual . When plants have been self-fertilized , genetic distance is calculated using the formula:cM=100× ( 1−[1−2 ( NG+NR ) /NT]1/2 ) , Where NG is a number of green-alone fluorescent seeds , NR is a number of red-alone fluorescent seed and NT is the total number of seeds counted . During generation of 420/++ F2 populations we selected for individuals that are heterozygous for transgenes expressing red and green fluorescent proteins ( RFP-GFP/++ ) . The majority of these individuals receive a chromosome with linked RFP and GFP transgenes over a non-transgenic chromosome ( RFP-GFP/++ ) ( Figure 2—figure supplement 1 ) . In a minority of cases F2 plants receive recombined RFP-+ and +-GFP chromosomes ( Figure 2—figure supplement 1 ) . In the progeny of these individuals the fluorescent seed classes representing parental and crossover genotypes are reversed ( Figure 2—figure supplement 1 ) . As R+/+G plants also have variable heterozygosity/homozygosity patterns within 420 depending on crossover positions we excluded these plants from further analysis . To test whether recombinant and non-recombinant counts were significantly different between replicate groups we used a GLM . We assumed the count data is binomially distributed:Yi∼B ( ni , pi ) , where Yi represents the recombinant counts , ni are the total counts , and we wish to model the proportions Yi/ni . Then:E ( Yi/ni ) =pi , andvar ( Yi/ni ) =pi ( 1−pi ) ni . Thus , our variance function is:V ( μi ) =μi ( 1−μi ) , and our link function must map from ( 0 , 1 ) → ( −∞ , ∞ ) . We used a logistic link function which is:g ( μi ) =logit ( μi ) =logμi1−μi=βX+εi , where ει∼N ( 0 , σ2 ) . Both replicates and genotypes are treated as independent variables ( X ) in our model . We considered p values less than 0 . 05 as significant . Measurements of interference within the I3bc interval were carried out as described previously with minor modifications ( Yelina et al . , 2013 ) . Inflorescences were collected in polypropylene tubes and pollen was extracted by vigorous shaking in 30 ml of freshly prepared pollen sorting buffer ( PSB: 10 mM CaCl2 , 1 mM KCl , 2 mM MES , 5% wt/vol sucrose , 0 . 01% Triton X-100 , pH 6 . 5 ) . The pollen suspension was filtered through a 70 µM cell strainer to a fresh 50 ml polypropylene tube and centrifuged at 450×g for 3 min . The supernatant was removed and the pollen pellet washed once with 20 ml of PSB without Triton . The pollen suspension was centrifuged at 450×g for 3 min and the supernatant discarded and the pollen pellet resuspended in 500 µl of PSB without Triton . A CyAn ADP Analyser ( Beckman Coulter , California , USA ) equipped with 405 nm and 488 nm lasers and 530/40 nm , 575/25 nm and 450/50 nm band-pass filters was used to analyse the samples . Polygons were used for gating pollen populations and for each sample eight pollen class counts were obtained ( Figure 5—figure supplement 1 ) . I3b and I3c genetic distances were calculated using the following formula:Ntotal= ( N-Y-+NB-R+N-YR+NB--+NBY-+N--R+NBYR+ N--- ) I3b cM= ( N-Y-+NB-R+N-YR+NB-- ) /NtotalI3c cM= ( N-Y-+ NB-R+NBY-+N--R ) /Ntotal , where N-Y- , NB-R , N-YR , NB-- , NBY- , N--R , NBYR , and N--- are pollen grain counts in each of the eight populations ( Figure 5—figure supplement 1 ) . For example , NBYR is the number of pollen that were blue , yellow and red fluorescent . Crossover interference was calculated using the following formulas:Observed DCOs= ( N-Y-+NB-R ) , Expected DCOs= ( I3b cM/100 ) × ( I3c cM/100 ) ×Ntotal , Coefficient of Coincidence=Observed DCOs/Expected DCOs , Interference=1−CoC . At least three biological replicates , constituting 3–5 individual plants were analysed for each sample ( Yelina et al . , 2013 ) . Statistical tests for genetic distances were performed as described above using a GLM . To test for significant differences in interference we compared observed and expected double crossovers using the same approach . Col-0 420 and Ct-1 lines were crossed to fancm-1 zip4-2 double mutant lines in the Col-0 background ( Crismani et al . , 2012 ) ( Figure 6—figure supplement 1 ) . The resulting F1 plants were crossed together and progeny identified that were fancm zip4 heterozygous , and 420/++ Col/Ct heterozygous on chromosome 3 . Chromosome 3 genotypes were tested in all cases using 13 Col/Ct indel markers ( Supplementary file 1 ) . These plants were self-fertilized and 420 homozygous individuals identified ( all seed were red and green fluorescent ) that were also Ct homozygous outside of 420 and that were fancm zip4 heterozygous ( Figure 6—figure supplement 1 and Figure 8—figure supplement 1 ) . These plants were then crossed to CEN3 or I3bc in wild type , fancm and fancm zip4 mutants to obtain scorable progeny with a HOM-HET genotype ( Figure 6—figure supplement 1 ) . The selfed progeny of 420/++ Col/Ct fancm zip4 heterozygous plants were also selected for plants with no fluorescent T-DNAs and either chromosome 3 in a Ct homozygous state , or with Ct homozygosity within 420 and Col homozygosity outside ( Figure 6—figure supplement 1 ) . These plants were crossed with doubly marked 420-CEN3 or I3bc lines in either wild type , fancm or fancm zip4 mutant backgrounds to obtain HET-HET and HET-HOM scorable plants respectively ( Figure 6—figure supplement 1 and Figure 8—figure supplement 1 ) . Equivalent genetic crosses were performed during analysis of I3bc ( Figure 8—figure supplement 1 ) . At least three independent lines were generated and analysed for each combination , apart from HOM-HET 420-CEN3 where two were analysed . To genotype zip4-2 ( Salk_068052 ) the following primers were used: zip4-2-F 5′-TTGCTACCTTGGGCTCTCTC-3′ zip4-2-R 5′-ATTCTGTTCTCGCTTTCCAG-3′ LBb1 . 3 5′-ATTTTGCCGATTTCGGAAC-3′ The resulting PCR products were ∼680 bp for wild type ( zip4-2-F + zip4-2-R ) and ∼340 bp for zip4-2 mutant ( zip4-2-F + Lbb1 . 3 ) ( Crismani et al . , 2012 ) . To genotype the fancm mutation we amplified using the following primers: fancm1dCAPsF1 5′-ACAATATATGTTTCGTGCAGGTAAGACATTGGAAG-3′ fancm1dCAPsR1 5′-CACCAATAGATGTTGCGACAAT-3′ The resulting PCR product was digested with MboII , which yields a ∼215 bp product for wild type and ∼180 bp for fancm ( Crismani et al . , 2012 ) . Chiasmata counting was performed as previously described ( Sanchez-Moran et al . , 2002 ) . | The genomes of plants and animals consist of several long DNA molecules that are called chromosomes . Most organisms carry two copies of each chromosome: one inherited from each parent . This means that an individual has two copies of each gene . Some of these gene copies may be identical ( known as ‘homozygous’ ) , but other gene copies will have sequence differences ( or be ‘heterozygous’ ) . The sex cells ( eggs and sperm ) that pass half of each parent's genes on to its offspring are made in a process called meiosis . Before the pairs of each chromosome are separated to make two new sex cells , sections of genetic material can be swapped between a chromosome-pair to produce chromosomes with unique combinations of genetic material . The ‘crossover’ events that cause the genetic material to be swapped are less likely to happen in sections of chromosomes that contain heterozygous genes . However , in a whole population of organisms , the exchange of genetic material between pairs of chromosomes tends to be higher when there are more genetic differences present . Here , Ziolkowski et al . sought to understand these two seemingly contradictory phenomena by studying crossover events during meiosis in a plant known as Arabidopsis . The plants were genetically modified to carry fluorescent proteins that mark when and where crossovers occur . Ziolkowski et al . cross-bred these plants with 32 other varieties of Arabidopsis . The experiments show that some of these ‘hybrid’ plants had higher numbers of crossover events than plants produced from two genetically identical parents , but other hybrid plants had lower numbers of crossovers . Ziolkowski et al . found that crossovers are more common between heterozygous regions that are close to homozygous regions on the same chromosome . The boundaries between these identical and non-identical regions are important for determining where crossovers take place . The experiments also show that the heterozygous regions have higher levels of interference—where one crossover event prevents other crossover events from happening nearby on the chromosome . In future , using chromosomes with varying patterns of heterozygosity may shed light on how this interference works . | [
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] | 2015 | Juxtaposition of heterozygous and homozygous regions causes reciprocal crossover remodelling via interference during Arabidopsis meiosis |
Disproportionate reactions to unexpected stimuli in the environment are a cardinal symptom of posttraumatic stress disorder ( PTSD ) . Here , we test whether these heightened responses are associated with disruptions in distinct components of reinforcement learning . Specifically , using functional neuroimaging , a loss-learning task , and a computational model-based approach , we assessed the mechanistic hypothesis that overreactions to stimuli in PTSD arise from anomalous gating of attention during learning ( i . e . , associability ) . Behavioral choices of combat-deployed veterans with and without PTSD were fit to a reinforcement learning model , generating trial-by-trial prediction errors ( signaling unexpected outcomes ) and associability values ( signaling attention allocation to the unexpected outcomes ) . Neural substrates of associability value and behavioral parameter estimates of associability updating , but not prediction error , increased with PTSD during loss learning . Moreover , the interaction of PTSD severity with neural markers of associability value predicted behavioral choices . These results indicate that increased attention-based learning may underlie aspects of PTSD and suggest potential neuromechanistic treatment targets .
Posttraumatic stress disorder ( PTSD ) is debilitating and characterized by excessive behavioral , psychological , and physiological responses to unexpected stimuli ( Pitman et al . , 2012 ) . In particular , clinical and empirical observations have documented the negative impact of salient cues on neural and behavioral functioning in PTSD , including heightened orienting to unexpected events , impaired extinction of learned fear , and unstable attention biases toward perceived threatening stimuli ( Aupperle et al . , 2012; Bar-Haim et al . , 2007; Blair et al . , 2013; Morey et al . , 2009; Naim et al . , 2015 ) . Together , these behavioral alterations in response to unexpected stimuli , uncontrollable reminders of trauma , and other negative , threatening , and trauma-related events point to PTSD as a disorder of disrupted learning from reminders of negative events; however , the specific components of anomalous learning in PTSD remain unknown . As an initial step toward addressing this issue , we adopt a computational psychiatry approach ( Montague et al . , 2012; Wang and Krystal , 2014; Maia and Frank , 2011 ) , using quantitative specification of neural and behavioral learning processes to investigate the neurocomputational substrates of PTSD . Computational model-based approaches to learning provide a mechanistic framework for understanding the detrimental impact of unexpected negative stimuli and reminders of negative events in PTSD . Error-guided models of reinforcement learning ( RL ) have robustly shown that unexpected outcomes ( i . e . , value ‘prediction errors’ ) drive learning by directly updating the value of the cues associated with those outcomes ( Rescorla and Wagner , 1972; Sutton and Barto , 1998 ) . A related family of hybrid reinforcement learning models combines prediction-error based learning with a dynamically changing attention modulation variable ( i . e . , a cue’s associability value ) that scales with the magnitude of prediction errors previously associated with a particular cue . In these models , trial-by-trial associability values associated with particular cues gate the learning of subsequent outcomes associated with these cues ( Li et al . , 2011; Pearce and Hall , 1980; Le Pelley , 2004 ) . Thus , these models contain separate parameters for error-based learning rate and associability updating that together govern how strongly current and past prediction errors , respectively , affect learning . While commonly used tasks that assess cue-salience ( Todd et al . , 2015 ) and attention to threat ( Naim et al . , 2015; Vythilingam et al . , 2007 ) test important components of aversive processing in PTSD , they are more limited for explaining how changes in attention to negative stimuli may affect subsequent behavioral choices , as reinforcement learning algorithms allow in the context of value-based learning tasks . More generally , computational model-based approaches allow fitting of models of neural function to behavioral choices and imaging data and thus facilitate the separation of mechanistic processes ( e . g . , responses to associability value , associability updating , learning rate , prediction error responses ) related to attention and learning from negative and positive events . In the hybrid RL framework , PTSD , and symptoms of hypervigilance in particular , may reflect disproportionate attentional processes that drive maladaptive , heightened responses to stimuli with a history of unexpected outcomes . The neural substrates of reinforcement learning suggest further compelling links between associability-modulated learning and PTSD , as the brain networks involved in both overlap . In particular , work in humans and rodents has identified roles for the ventral striatum , anterior cingulate , and amygdala in encoding prediction error ( Rangel et al . , 2008; Pagnoni et al . , 2002; Garrison et al . , 2013 ) and for the amygdala and insula in encoding associability values ( Li et al . , 2011; Roesch et al . , 2012 ) . In PTSD , affective stimuli consistently elicit altered neural activation in a network that prominently also includes the amygdala , insula , and prefrontal regions ( Hayes et al . , 2012; Etkin and Wager , 2007 ) . Of translational relevance , attention , learning , and the amygdala have all been behavioral and neural targets of promising new therapies for PTSD ( Badura-Brack et al . , 2015; Craske et al . , 2014; Langevin et al . , 2016 ) ; elucidating the component neurobehavioral mechanisms associated with learning in PTSD may refine these targets . In brief , extant data indicate that neural and behavioral distinctions between solely prediction error-based and associability-modulated learning contribute uniquely to neural and behavioral correlates of learning and may clarify processes underlying the heightened sensitivity to unexpected stimuli in PTSD . To assess this possibility , we implemented a probabilistic learning task during functional magnetic resonance imaging ( fMRI ) in combat-deployed military veterans with and without posttraumatic stress disorder . We posited that if attention-modulated learning plays a role in PTSD , hybrid RL models incorporating associability should predict participants’ choices better than learning models without associability . Furthermore , PTSD severity , particularly symptoms related to hyperarousal ( Lissek and van Meurs , 2015 ) , should correlate preferentially both with enhanced associability updating and with increased activity in neural structures encoding associability values ( i . e . , amygdala and insula ) . Critically , these relationships would not be expected between PTSD and solely error-based learning rate or error-related activity in neural structures encoding value and prediction error ( i . e . , ventral striatum , ventromedial prefrontal cortex ) .
Combat-deployed military veterans ( N = 74 ) completed a probabilistic learning task in the loss and gain domains while undergoing fMRI scanning ( Figure 1; full task description in Materials and methods ) . All veterans had served at least one tour in Iraq or Afghanistan since 2001 , had experienced Criterion A deployment-related trauma , and were recruited from a larger study by our group examining biomarkers of mood and anxiety disorders . To be considered in the present analyses , participants were further required to demonstrate behavioral engagement on the relevant portions of the probabilistic learning task ( N = 68 veterans; see Materials and methods for exclusion breakdowns , full inclusion/exclusion criteria , and exclusion details for sub-analyses ) . Veterans were assessed for PTSD using the Clinician Administered PTSD Scale ( CAPS [Blake et al . , 1995] ) and for other psychiatric disorders with the Structured Clinical Interview for DSM-IV ( SCID [First et al . , 1996] ) . Participants exhibited a range of PTSD symptoms , with 39 veterans meeting DSM-IV ( American Psychiatric Association , 2000 ) criteria for PTSD ( see Supplementary file 1 , Table 1A for clinical and demographic information ) ; the other 29 veterans had previous deployment-related trauma exposure as assessed by the CAPS interview but did not meet criteria for PTSD , resulting in 39 participants with PTSD and 29 participants without PTSD for primary analyses . In both loss and gain domains , veterans showed robust learning , as evidenced by increasing likelihood of choosing the ‘better’ option from chance on the first trial to near 80% correct after ~15 trials ( Figure 2a and Figure 2—figure supplement 1 ) . An adaptive design titrated the task for participants to achieve sufficient learning ( i . e . , block length was adjusted once participants achieved learning; see Materials and methods for details ) ; reflecting this , performance accuracy ( % better choice ) did not differ between participants with and without PTSD in the gain or loss domains ( gain: t41 = −1 . 29 , p>0 . 1; loss: t41 = 0 . 459 , p>0 . 1 ) . As an initial step toward evaluating the role of associability in learning in PTSD , participants’ choices were fit to a prediction error-based reinforcement learning ( RL ) model with and without a dynamic associability value ( κ ) -modulated learning rate on the prediction error ( δ ) , as in Li et al . ( 2011 ) . In this model , the associability value κ of a chosen stimulus changes on a trial-by-trial basis based on a combination of the magnitude of previous prediction errors and a static associability weight η , a parameter which varies by participant and indicates the extent to which the magnitude of recent prediction errors updates trial-by-trial associability values ( see Materials and methods for full model specifications and Figure 2b for a trial-by-trial plot of the time course and relationship between prediction error and associability values ) . We verified via simulation that associability weight ( η ) did not directly affect performance ( Figure 3—figure supplement 1a ) , allowing us to dissociate the effects of associability updating from general performance deficits . Consistent with our hypothesis , including associability in the RL model significantly improved model fit for the majority of participants and did so during loss learning only ( Figure 3a; protected exceedance probability of model with versus without associability: 0% in gain , 100% in loss ) . The role of associability in loss , but not gain , learning is consistent with prior data showing heightened orienting and attentional biases toward negative information in PTSD ( Li et al . , 2011; Boll et al . , 2013 ) ; thus , our subsequent analyses focused on learning variables in the loss domain ( see Appendix 2 , Supplementary Results for supplemental data related to gain learning and model-agnostic support for the presence of associability-modulated updating during loss learning only ) . The role of associability in loss learning was further corroborated by robust effects of associability values on behavior during the task . First , choices predicted by the associability RL model showed high correspondence with participants’ actual choices ( correlation of bins of predicted vs . observed choices: r = 0 . 997 , p<0 . 001; Figure 3—figure supplement 1b ) and did not differ between participants with and without PTSD ( t-test of subjects’ log likelihoods and PTSD diagnosis: t66 = −0 . 33 , p>0 . 1 ) . Reaction times were significantly and positively correlated with trial-by-trial associability values ( κ ) , reflecting greater decision latency as the associability value of the chosen option increased ( Figure 3b; average per-subject regression beta value of reaction time predicting associability , controlling for expected value of chosen option and trial number: . 232; t-test assessing difference from 0: t68 = 8 . 28 , p<0 . 001 ) . Next , we regressed trial-by-trial estimates of prediction error ( δ ) and associability-modulated prediction error ( κ*δ ) , respectively , computed from participants’ individually estimated parameter values , against their trial-by-trial switching behavior ( switch or no switch , a measure of responsivity to outcomes ) . Associability-modulated prediction error significantly predicted switching choices above prediction error alone ( χ21 = 323 . 0 , p<0 . 001; Figure 3c ) . In addition , associability weight and other model parameters were recoverable through simulation ( Figure 3—figure supplement 1c ) , and associability value showed a robust neural effect independent of PTSD diagnosis ( Figure 3d; Supplementary file 1 , Table 1B ) . These data together indicate that the associability RL model fit participants’ behavior and neural activity well and support the incorporation of associability in the RL model of loss learning . If associability-modulated learning plays a role in PTSD , individuals with PTSD ought to have greater associability weights ( η; see Materials and methods for model specifications ) . To test this possibility , participants’ individually estimated associability weights , reflecting the degree to which associability values are updated based on recent unsigned prediction errors during loss learning for each participant , and unmodulated learning rates ( α ) were compared between participants with and without a PTSD diagnosis ( see Appendix 1: Supplementary Methods for individual parameter estimation details ) . Associability weights were increased in participants with PTSD ( t62 = 4 . 01 , p<0 . 001 ) while unmodulated learning rate did not differ between groups ( t62 = 0 . 63 , p>0 . 1; Figure 4a ) , supporting the hypothesis of a greater emphasis on modulation of loss learning by attention in PTSD . Given the high co-occurrence of depression in PTSD ( O'Donnell et al . , 2004 ) , we also tested the specificity of increased associability weight to PTSD versus depression . Specifically , we enrolled a separate cohort of gender- , estimated IQ- , and smoking status-matched participants with a current diagnosis of major depressive disorder ( MDD; N = 20; see Materials and methods for MDD participant details ) , but not PTSD . These MDD-only participants performed the same learning task , and we also fit the behavior of these participants to the RL model with associability . Compared to MDD-only participants , the participants with PTSD had significantly higher associability weights ( t57 = 7 . 25 , p<0 . 001 ) but similar unmodulated learning rates ( t57 = 1 . 44 , p>0 . 1 ) . Therefore , despite the high comorbidity between PTSD and depression ( O'Donnell et al . , 2004 ) , increased associability-modulated learning appears specific to PTSD and not to mood-related psychopathology . To investigate the instantiation of neural substrates of associability- and prediction error- based learning in PTSD , we first regressed trial-by-trial estimates of associability value ( κ ) and prediction error ( δ ) , respectively , during loss learning against subjects’ neural responses to the outcome event ( per [Li et al . , 2011; Esber et al . , 2012; Roesch et al . , 2010a]; see Materials and methods for design matrix specifications ) . Corroborating the behavioral findings of greater associability value updating in participants with PTSD , neural encoding of associability values showed a significant relationship with PTSD at an FDR-corrected whole brain significance level in a network of regions including bilateral amygdala and insula , hypothesized areas of relevance for PTSD and associability-based learning ( Pitman et al . , 2012; Li et al . , 2011 ) ( Figure 4b; Supplementary file 1 , Table 1C; see Figure 3d for associability-related signaling across participants after accounting for PTSD and covariates ) . To further test the localization of the increased associability-related activation in PTSD to our a priori hypothesized areas of amygdala and insula , we extracted beta values from these anatomical regions of interest ( see Materials and methods for ROI definition ) . PTSD was significantly related to associability-related activation in these areas ( amygdala: t37 = 2 . 45 , p<0 . 05; insula: t37 = 3 . 87 , p<0 . 001 ) . Replacing the binary PTSD diagnosis with a dimensional measure of PTSD symptom severity ( total CAPS score ) across all veteran participants resulted in a similar pattern of effects ( Figure 5a; Supplementary file 1 , Table 1D ) . PTSD was not related to neural responses to prediction error at this whole brain level ( Figure 5—figure supplement 1a ) . Follow up analyses covaried for presence of psychotropic medication , a positive screen for mild traumatic brain injury , or smoking status , and additionally tested effects within a subgroup of veterans with and without PTSD who were free from psychotropic medication and matched on estimated IQ; none of these covariates was significantly related to neural or behavioral results involving associability , and in the matched subgroup , PTSD remained related to behavioral and neural encoding of associability value ( see Appendix 2 , Supplemental Results for details ) . The lack of whole-brain relationship between neural prediction error signals and PTSD could be due to our conservative multiple comparison correction . To assess this possibility , we further investigated the relationship between prediction error activation and PTSD within prediction error related regions of interest ( ROIs ) derived from a trauma-unexposed reference cohort , separate from the veteran cohort ( described in Materials and methods ) . Neural responses in these ROIs ( including ventral striatum and vmPFC ) also did not show significant effects of PTSD for prediction error , even in neural regions strongly related to prediction error signaling ( see Materials and methods for details; reference group prediction error activation is shown in Figure 4—figure supplement 1 ) . Independent of PTSD , participants showed significant prediction error activation in striatum ( left striatum: t42 = 3 . 56 , p<0 . 001; right striatum: t42 = 3 . 02 , p< . 005 ) , further supporting intact PE-related signal that is unaffected by PTSD . To assess which PTSD symptom clusters are more associated with increased neural signaling of associability value , we examined the symptom clusters of re-experiencing , avoidance/numbing , and hyperarousal ( American Psychiatric Association , 2000 ) . Specifically , we tested the degree to which symptom severity in each cluster was related to neural correlates of trial-by-trial associability and prediction error , respectively . The hyperarousal and avoidance/numbing symptom clusters showed the most extensive neural responses corresponding with associability ( Figure 5a; Supplementary file 1 , Tables 2E and 2F ) , with little relationship with re-experiencing symptoms ( Figure 5a; Supplementary file 1 , Table 2G ) . Finally , if neural encoding of associability is relevant for real-world behavioral disruptions in PTSD , the combination of PTSD and neural responsivity to associability value ought to be related to participants’ likelihood of adjusting behavioral choices based on past experiences . As described above , PTSD and the hyperarousal and avoidance/numbing symptom clusters showed a strong relationship with neural activation to associability , suggesting that the interaction of PTSD and neural substrates of associability should predict switching behavior over and above neural activation to associability value alone . We therefore carried out mixed effects logistic regression models predicting switching behavior as a result of the interaction of the previous trial’s outcome and associability value-related neural activation , with and without PTSD symptoms as an additional interaction term ( see Materials and methods for regression model specifications ) . The addition of PTSD diagnosis , hyperarousal symptoms , and avoidance/numbing symptoms each significantly improved model fit for bilateral amygdala after accounting for multiple comparisons ( αBonferroni = . 006; PTSD diagnosis:χ22 = 12 . 5 p< . 005; hyperarousal: χ22 = 12 . 6 , p< . 005; avoidance/numbing: χ22 = 12 . 0 , p< . 005; Figure 5b ) , while re-experiencing symptoms did not ( χ22 = 6 . 0 , p>0 . 01; similar patterns were observed for bilateral insula; Figure 5—figure supplement 1b and c ) . A secondary analysis inspecting the components of this interaction revealed overall greater tendency to switch with greater associability value-related neural activation in participants with PTSD relative to controls , with additional effects of large relative to small outcomes leading to a greater tendency to switch in controls in the amygdala and insula ROIs and in PTSD in the amygdala ROI; the effect of outcomes in PTSD in the insula ROI was reversed ( Figure 5—figure supplement 2a and b ) . Analogous regressions with prediction error related activation in striatum and vmPFC were implemented to test the specificity of effects to associability; these analyses indicated that no symptom clusters improved model fit for prediction error-related neural responses predicting behavioral choices ( Figure 5—figure supplement 1b and c ) .
Previous research has implicated altered learning and attentional processes in PTSD , but the neuromechanistic basis of these dysfunctions has been unclear . Here , we used a computational model-based approach and identified increased associability-based modulation of loss learning , neurally and behaviorally , in combat-deployed military veterans as a function of PTSD; prediction errors and unmodulated learning rate were unchanged by PTSD . The greater neural sensitivity to associability values was particularly strong with increasing hyperarousal and avoidance/numbing symptoms and interacted with symptom severity to predict participants’ behavioral choices . These results point to increased reliance on attention-based modulation of learning in PTSD that is guided by a network of brain regions including the amygdala and insula . A number of earlier studies have connected greater attention to perceived threat , disrupted cognitive processing in the presence of negative , salient stimuli , and increased orienting to unexpected events in PTSD ( Aupperle et al . , 2012; Bar-Haim et al . , 2007; Blair et al . , 2013; Morey et al . , 2009; Naim et al . , 2015 ) . However , the mechanistic underpinnings of this hypersensitivity to unexpected outcomes have been unclear . We posited that the hypersensitivity may derive from either ( i ) heightened solely error-related learning that increases learning directly from unexpected outcomes ( i . e . , prediction errors ) , or ( ii ) disproportionate attentional allocation to these prediction errors which magnifies the effects of these unexpected outcomes in future decision-making . Our approach differentiates between these possibilities , and the data support the latter hypothesis: neither static error-based learning rate nor neural prediction error responses were associated with PTSD , while increased associability updating based on loss prediction error signals increased with PTSD at both the behavioral and neural levels . These results suggest the disruptive effect of salient negative stimuli observed in PTSD is related to greater involvement of associability-related learning processes that are based on previous unexpected negative experiences with similar stimuli , rather than disrupted responses to immediate cues . The current findings further link extant neural data showing amygdala and insula disruptions in PTSD ( Hayes et al . , 2012; Brown et al . , 2014; Rauch et al . , 2006 ) with the learning processes that these regions support . In particular , animal research has strongly implicated amygdala-mediated , attention-modulated processes in learning ( Esber et al . , 2012; Holland and Gallagher , 1999 ) , and support for the amygdala’s role in these processes is emerging in human neuroimaging studies of associative learning ( Li et al . , 2011; Boll et al . , 2013 ) . In addition , intriguing evidence suggests that transient lesions of amygdala functioning through direct stimulation reduces PTSD symptoms ( Langevin et al . , 2016 ) . The insula , which here similarly demonstrates increased associability-related activation with PTSD , also plays a key role in associability-based learning ( Li et al . , 2011 ) and related functions including altering outcome value and computing costs ( Skvortsova et al . , 2014; Palminteri et al . , 2012 ) . Previously found alterations in neural connectivity and responses to affective stimuli in amygdala/insula may further derive from increases in attention- or salience-modulated learning associated with these regions that emerges as hypersensitivity to potential threat ( Etkin and Wager , 2007; Brown et al . , 2014; Rauch et al . , 2006; Seeley et al . , 2007 ) . We note that previous work on associability-based learning in non-psychiatric participants has shown a prominent role of associability in learning from other forms of aversive stimuli , such as threat of shock ( Li et al . , 2011; Boll et al . , 2013 ) , which also show disruptions in PTSD ( Lissek and van Meurs , 2015 ) . While the increased updating of associability values observed here in PTSD during monetary loss learning should apply generally to learning from other classes of negative stimuli , it will be important in future investigations to explicitly test the role of associability-based learning for PTSD in the context of threatening or trauma-related cues which have been shown to impair fear extinction in the disorder ( Milad et al . , 2009; Norrholm et al . , 2011 ) as well as to connect our findings to models of other behavioral disruptions in mood/anxiety ( e . g . , [Mkrtchian et al . , 2017] ) and to explore under what conditions the greater associability weight in PTSD is adaptive versus maladaptive ( as in [Vythilingam et al . , 2007; Zinchenko et al . , 2017] ) . Rich literatures support associability as a measure of cue-specific salience or attention allocation ( Li et al . , 2011; Pearce and Hall , 1980; Le Pelley , 2004; Roesch et al . , 2012 ) , but other stimulus properties and forms of attention may also affect learning in PTSD ( Dayan et al . , 2000; Yu and Dayan , 2005; de Berker et al . , 2016 ) . In addition , as previous work has found associability-related learning in the amygdala to be dopamine-dependent ( Esber et al . , 2012 ) , the intact error-related but disrupted associability-related signaling in PTSD found in the present study raises questions about potential intermediate neural substrates involved in this dissociation ( Nasser et al . , 2017 ) . Future work on the relationship between error- and associability-related learning signals is needed to further clarify the neural systems-level disruptions in this relationship in PTSD . The associations among neural encoding of associability value , specific PTSD symptoms , and behavioral choices support the translational relevance of the present findings for identifying and refining new neuromechanistically-informed targets of treatment ( Bowers and Ressler , 2015 ) . Here , hyperarousal and the related numbing/avoidance symptoms ( Flack et al . , 2000 ) showed the strongest relationships with neural substrates of associability; interactions of these neural substrates with these PTSD symptoms also predicted participants’ behavioral choices during the loss learning task . The clinical manifestation of hyperarousal in PTSD includes excessive sustained vigilance ( American Psychiatric Association , 2000 ) and resembles what heightened updating of associability values would predict; specifically , reminders of trauma can be conceptualized as stimuli associated with unexpected negative outcomes , with the greater associability updating seen here in PTSD causing stimuli associated with a recent history of these surprising outcomes to command greater attention and increased updating . Thus , addressing certain components of attention or cue-salience in learning-based interventions ( e . g . , prolonged exposure [Craske et al . , 2014; Schnurr et al . , 2007] ) or incorporating a learning context into neural or cognitive therapies ( Badura-Brack et al . , 2015; Langevin et al . , 2016; Khanna et al . , 2015 ) may provide targeted benefit for reducing symptoms of PTSD . The increased updating of associability values in PTSD may reflect a higher prior belief that changes in prediction error will occur , a difference in processing changes in prediction errors , or a combination , which could also serve as mechanistic treatment targets . More generally , associability and other computational model-identified dysfunctions may serve as new , precise , functional targets for novel and extant treatment approaches . In particular , disruptions in associability-related attention or cue-salience neural circuits may be improved with computational model-based behavioral retraining techniques . Likewise , neural substrates of associability may be evoked during learning tasks and targeted with real-time neuromodulation . The increased updating of associability values reported here also suggests mechanistic insight into the impaired fear extinction and retention of extinction memories that have previously been reported in PTSD ( Milad et al . , 2009; Norrholm et al . , 2011 ) . Specifically , greater updating of associability-modulated learning in PTSD may help explain several phenomena seen during extinction in PTSD . Greater associability signaling during cues associated with uncertain outcomes , such as during the early phases of fear extinction , would create a lack of habituation ( Norrholm et al . , 2011; Collins and Schiller , 2013 ) . Meanwhile , as stimuli become more predictable , greater associability values lead to reduced learning , which may weaken retention of repeatedly presented information such as during later stages of extinction ( Craske et al . , 2014; Milad et al . , 2009 ) . Increased updating of associability values in PTSD may also increase the likelihood of inferring new states after surprising outcomes , rather than supporting inhibitory learning about current states , thus reducing the ability to connect conditioned and extinguished contexts and leading to impairments in extinction retention and generalization ( Dunsmoor et al . , 2015; Morey et al . , 2015 ) . We note that while the present work is consistent with earlier studies in non-psychiatric controls identifying associability-related neural activity at the outcome event ( Li et al . , 2011; Esber et al . , 2012; Roesch et al . , 2010a , 2010b ) , neural substrates of associability have also been observed with predictive cues ( Roesch et al . , 2012 ) ; this distinction deserves further study in future work investigating and addressing the nature of learning disruptions in PTSD . Despite the high prevalence and great societal , personal , and fiscal cost of PTSD , and particularly combat-related PTSD ( Richardson et al . , 2010 ) , the mechanistic underpinnings of the disorder remain unclear . Here , a neurocomputational psychiatry approach distinguishes between competing hypotheses about dysfunctional processes in PTSD ( Montague et al . , 2012; Stephan and Mathys , 2014 ) and shows that PTSD is related to increased neural and behavioral substrates of attention-based modulation of loss learning , while solely prediction error-based learning is unchanged . More generally , these findings suggest that by integrating human functional neuroscience , computational model-based analyses , and gold-standard clinical assessments , new neuromechanistic targets of intervention for PTSD and other mental disorders may be identified and tested .
Participants were US military veterans ( N = 74 ) with combat deployments to Iraq or Afghanistan since 2001 . All veterans had served at least one tour in Iraq or Afghanistan since 2001 and were part of a larger study by our group examining biomarkers of mood and anxiety disorders . All participants provided written informed consent , and all procedures were approved by the Institutional Review Boards of Baylor College of Medicine , the Salem Veterans Affairs Medical Center , and Virginia Tech . Veterans served by the Houston , TX and Salem , VA Veterans Affairs Medical Centers were recruited through VA medical record searches , provider referrals , and community advertisements . Enrollment inclusion criteria for the larger biomarker study included: meeting criteria for current PTSD or non-PTSD , age 18 to 64 , English speaking , normal or corrected to normal vision , and verbal IQ greater than 80 . Enrollment exclusion criteria included: contraindications to MRI scanning ( e . g . , implanted ferrous metal , claustrophobia ) , loss of consciousness greater than 30 min , behaviors meeting criteria for substance abuse or dependence ( excluding nicotine dependence ) in the past 30 days , and current/past psychotic or bipolar disorders . For the veteran cohort , psychiatric diagnoses were assessed using the Structured Clinical Interview for DSM-IV ( SCID [First et al . , 1996] ) and the Clinician Administered PTSD Scale ( CAPS [Blake et al . , 1995] ) , administered by trained study staff . Using the ‘Rule of 3’ ( frequency +intensity > 3 [Blanchard et al . , 1995] ) diagnostic criterion on the CAPS . Total PTSD severity was constructed from the summed severity score for all symptoms . Severity scores for the three DSM-IV symptom clusters , measuring re-experiencing ( CAPS questions B1-B5 ) , avoidance and numbing ( questions C6-C12 ) , and hyperarousal ( questions D13-D17 ) symptoms were created to assess the severity of each symptom cluster . Participants also completed the Beck Depression Inventory-II ( BDI [Steer et al . , 1999] ) measuring depression symptoms; the Combat Exposure Scale ( CES [Lund et al . , 1984] ) , measuring combat trauma exposure , and the Wechsler Test of Adult Reading ( WTAR [Wechsler , 2001] ) , measuring approximate verbal IQ . In addition , participants completed a demographics questionnaire assessing age , gender , ethnicity , years of education , and medications taken . Two participants did not complete the CES; their scores were imputed based on other variables ( CAPS , BDI , age , and gender ) using the ‘mi’ package in R ( Su et al . , 2011 ) . To be included in the present analyses , veterans were further required to demonstrate behavioral engagement on the relevant portions of the probabilistic learning task ( i . e . , sufficient loss learning , criteria detailed in the Probabilistic learning task section ) and have successfully completed the functional MRI portion of the study . In addition , all included veteran participants were required to have experienced trauma satisfying criterion A1 of PTSD according to the DSM-IV ( American Psychiatric Association , 2000 ) , and veteran participants without PTSD were required to be free from current DSM-IV diagnoses and not taking psychotropic medication . N = 68 veterans satisfied these additional criteria and were considered for all subsequent behavioral analyses ( see fMRI data collection and preprocessing for imaging analysis inclusion criteria ) . Among these 68 veterans , thirty-nine ( N = 39 ) met DSM-IV ( American Psychiatric Association , 2000 ) criteria for PTSD ( see Supplementary file 1 , Table 2A for clinical and demographic information ) ; the other N = 29 veterans had previous deployment-related trauma exposure as assessed by the CAPS interview but did not meet criteria for PTSD . Participants excluded did not differ from those retained for all analyses on demographic measures ( age , gender , household income level , proportion nonwhite , proportion with greater than high school education ) , PTSD severity , or proportion with a PTSD diagnosis ( all ps > 0 . 05 ) . Two additional cohorts of participants were recruited from the Houston , TX and southwest Virginia areas . The first , a non-psychiatric civilian reference cohort ( N = 23 ) was not included in the full set of analyses but was used to construct independent regions of interest to test in the veteran cohort ( see fMRI procedures for more information ) . The second cohort of participants ( N = 20 ) consisted of participants with a current diagnosis of Major Depressive Disorder and no current or past PTSD , matched to the participants with PTSD on gender , smoking status , and estimated IQ . Behavior from this clinical comparison cohort was fit to the associability RL model and parameter estimates were compared to the participants with PTSD ( see Relationship between model parameters and PTSD for more information ) . The focus of the present work was on learning in PTSD; as such , given the high co-occurrence of depression in PTSD , the MDD-only comparison group was used here to test the specificity of the main behavioral effects to PTSD . Participants in the non-psychiatric reference cohort , used to construct independent neural regions of interest , had psychiatric diagnostic status assessed by the SCID ( N = 17 ) or the Mini International Neuropsychiatric Interview ( MINI [Lecrubier et al . , 1997]; N = 6 ) . Participants also completed the WTAR to estimate IQ and a demographics questionnaire . Participants included for ROI construction reported no trauma exposure that qualified for Criterion A1 for PTSD and were matched to the veteran cohort on age , gender , and estimated IQ . Participants in the MDD psychiatric reference cohort , whose behavioral learning parameters were compared to the participants with PTSD , had psychiatric diagnostic status assessed by the SCID and met criteria for current Major Depressive Disorder and did not meet current or lifetime criteria for PTSD . These participants also completed the BDI to assess current depression symptoms , the WTAR to estimate IQ , and a demographics questionnaire to assess age , gender , and smoking status . Participants were scanned on a 3T Siemens Tim Trio MR scanner . Echoplanar images were collected in 34 4 mm slices at a 30° hyperangulation from the anterior-posterior commissure ( AC-PC ) line ( TR = 2000 ms , TE = 30 ms , flip angle = 90° , matrix = 64×64 , voxel size 3 . 4 × 3 . 4×4 . 0 mm3 ) . A high-resolution ( 1 mm3 ) anatomical Magnetization Prepared Rapid Gradient Echo ( MPRAGE ) T1 image ( TR = 1200 ms , TE = 2 . 66 ms , flip angle = 12° ) was collected to aid in registration . All imaging analyses were conducted using SPM8 for fMRI ( Wellcome Trust Centre for Neuroimaging , http://www . fil . ion . ucl . ac . uk/spm/software/spm8 ) . Preprocessing consisted of: slice timing correction , realignment to the first functional scan , coregistration to the participant’s structural image , normalization to the MNI template , and smoothing to ensure Gaussianity ( 6 mm FWHM ) . Participants with motion greater than 3 mm or 0 . 5 radians in any direction were excluded ( n = 4 ) . Functional images were visually inspected for signal drop out in ventral areas , including ventromedial prefrontal cortex and amygdala , and were excluded if significant signal loss was present ( n = 3 ) . An additional two participants were excluded due to other data quality issues . Participants completed a probabilistic loss and gain learning task ( two-arm bandit , adapted from [Pessiglione et al . , 2006] ) while undergoing fMRI scanning ( Figure 1 ) . The task was presented in pseudo-randomized alternating blocks consisting of all loss learning or gain learning trials , respectively . On each trial , participants were presented with two abstract stimuli . One stimulus had a higher ( 75% ) probability of leading to a better monetary outcome and a lower probability ( 25% ) of leading to a worse monetary outcome , while the probabilities for the other stimulus were reversed ( i . e . , smaller probability of better outcome and larger probability of worse outcome ) . Participants selected one stimulus using a MRI-compatible button box ( Current Designs , Inc . ) . The participant’s choice was framed for a jittered viewing time of 2–4 s , after which the outcome ( monetary amount gained or lost ) was shown for 2 s . Participants were not shown the outcome associated with the unchosen stimulus . A fixation cross was shown between each trial for a jittered viewing time of 1–3 s . For gain trials , the better outcome ranged from +70 to +80 cents , while the worse outcome ranged from +20 to +30 cents . For loss trials , the outcomes similarly ranged from −20 to −30 and −70 to −80 cents . At the beginning of each block , high and low outcome values were randomly chosen with replacement from uniformly distributed outcome pairs {20 , 70} , {25 , 75} , or {30 , 80} and kept consistent within blocks ( the outcome displays indicated ‘You Lose [amount]’ for loss blocks , and ‘You Gain [amount]’ for gain blocks ) . Blocks containing gain and loss trials alternated . Each block consisted of novel stimuli , which required participants to re-learn the contingencies between stimuli and outcomes within each block . As our goal was to examine mechanistic processes associated with learning , including in individuals who may differ in learning processes , the task used an adaptive algorithm to ensure that participants successfully learned the contingencies and continued to learn throughout the task . In order to obtain a sufficient number of learning trials , block length was determined based on the proportion of correct choices ( i . e . , choosing the better option ) : blocks ended when performance reached at least 70% correct based on a running average of the last 10 choices , with the additional specification that the first block be at least 15 trials long . The task ended when the participant had at least 25 correct and 25 incorrect choices in each of the loss and gain conditions . The total number of trials per participant ranged from 50 to 70 for loss trials and 50–68 for gain trials . Participants completed an average of 4 . 14 gain blocks and 4 . 58 loss blocks; the number of blocks and trials completed did not differ between PTSD and veteran control participants for gain or loss ( all ps > 0 . 2 ) . The comparable number of trials and blocks completed in each group indicates that the amount of task titration was similar on average between the PTSD and veteran control participants . Before entering the scanner , participants were presented with task instructions , completed a practice round , and were given the opportunity to ask questions . They were not provided information about the full statistical structure of the task but were provided an initial $10 endowment , and informed that payment would be based on actual performance . To ensure participants were attending to the task and had behavior suitable for model fitting ( Sokol-Hessner et al . , 2009 ) , participants who switched options in either gain or loss blocks less than 10% of the time were excluded from analyses comparing gain and loss behavior ( N = 19; 8 control veterans and 11 veterans with PTSD ) . For analyses of loss behavior only , participants excluded for low switching during gain blocks only were included in analyses ( see Comparison of model fits for RL models for more information ) . To identify the reinforcement learning ( RL ) model that best explained participants’ data , models with and without associability were tested; models were evaluated separately for gain and loss trials . Following standard temporal difference RL ( Sutton and Barto , 1998 ) the expected value ( Q ) of the stimulus on the next trial ( t + 1 ) was updated with the product of the learning rate ( α; range 0 to 1 ) and prediction error ( δ ) : ( 1 ) QA ( t+1 ) =QA ( t ) +α∗ δ ( t ) Trial-by-trial prediction error δ ( t ) was computed as the difference between the actual outcome ( R ) and the expected value ( Q ) : ( 2 ) δt=R't-Q ( t ) The outcome R was modulated by reward sensitivity ( ρ ) ( Huys et al . , 2013 ) , comprising a multiplier on the value of the loss or gain value further from 0 ( i . e . , outcomes ranging ±0 . 70 to ±0 . 80 ) : ( 3 ) R'At=ρRAt where A represents the chosen option . A decay parameter for the unchosen option B ( γ; range 0+ ) ( Boorman et al . , 2009; Collins and Frank , 2016; Niv et al . , 2015; Cavanagh , 2015 ) was also included: ( 4 ) QBt+1=γQB ( t ) The probability of each choice was modeled with a softmax function incorporating inverse temperature ( β; range 0+ ) : ( 5 ) PA ( t+1 ) =eβQA ( t+1 ) eβQA ( t+1 ) +eβQB ( t+1 ) Since inverse temperature and reward sensitivity may not be uniquely identifiable in the RL model , inverse temperature was first estimated in a model without reward sensitivity . The RL model with reward sensitivity was then estimated with inverse temperature fixed at the group mean rather than estimated as a free parameter ( similar to [Pessiglione et al . , 2006] ) . For the associability RL model , learning rate was modulated on a trial-by-trial basis by an associability value for the chosen stimulus ( κ ) ( Li et al . , 2011 ) . An associability weight parameter ( η; range 0 to 1 ) controlled the extent to which the magnitude of previous prediction errors updated the trial-by-trial associability value of a particular stimulus . Associability values were initialized at one and updated for each stimulus separately . Associability values were constrained to stay above . 05 ( Li et al . , 2011; Le Pelley , 2004 ) . Therefore , similar to the constant parameter of learning rate and the trial-by-trial estimate of prediction error in classic RL models , this model added a constant parameter of associability weight and a trial-by-trial estimate of associability value of the chosen stimulus . ( 6 ) κAt+1=1-ηκAt+η|δt| ( 7 ) QAt+1=QAt+α* κAt*δt Regressors for imaging analyses used the prediction error , associability value , and probability at trial t , prior to updating associability value for the next trial . Because novel stimuli were used each block , the expected value and associability value were reset at the beginning of each block . The same parameters were used for all blocks of the same condition , but separate parameters were used per condition , resulting in one learning rate for loss and one learning rate for gain , one associability weight for gain and one for loss , and so on . Model comparison was computed using Bayesian Model Selection ( Rigoux et al . , 2014 ) . Each participant’s probability for each model was transformed into corrected AIC ( AICc ) to penalize models with greater numbers of parameters . These values were then fed into a variational approximation of BMS , resulting in a predicted probability of each model per subject and an overall protected exceedance probability of each model over all subjects . This approach allows for between-subject heterogeneity in model probabilities while enabling a group-level model comparison . Reinforcement learning model parameters were estimated separately for loss and gain trials , based on previous work showing differential influences of reward and punishment during learning in similar tasks ( Palminteri et al . , 2012; Pessiglione et al . , 2006 ) . To test the appropriateness of this approach , we first conducted a logistic regression predicting the probability of switching choices based on the previous three outcomes , including the condition ( loss or gain ) as an interaction term on the previous outcome . This interaction term was significant ( z = 2 . 124 , p<0 . 05 ) , suggesting different learning patterns during loss and gain learning . As a second test of the appropriateness of separate estimates for loss and gain , we compared a model with parameters combined across loss and gain trials to a model separating all parameters by condition . The model with all parameters separated by condition fit better ( AICc improvement in 91% of participants , protected exceedance probability of 100% ) , confirming different learning patterns in loss and gain conditions . Additionally , we tested the fit of a basic reinforcement learning model , with parameters of learning rate and inverse temperature , versus our model with parameters of learning rate , reward sensitivity , and decay ( prior to adding the additional parameter of associability weight ) and found that the more complex model fit better , with a protected exceedance probability of 100% . Reinforcement learning models with and without associability were then compared within loss and gain conditions separately . 81% of participants ( 85% of veteran controls and 78% of PTSD participants ) showed a greater probability of the model with associability in loss , while no participants showed a greater probability of the associability model during gain learning . This difference in frequencies between conditions was significantly different ( χ21 = 59 . 0 , p<0 . 001 ) . The overall protected exceedance probability of the associability model reflected this difference , with a protected exceedance probability of 100% in loss and 0% in gain ( Figure 3a ) . As a result of the improved model fit in the loss condition only , subsequent analyses focused on loss learning trials . To maximize power , participants with suitable loss behavior but a low rate of switching during gain learning ( n = 16 ) or who were excluded from imaging analyses ( n = 9 ) were included in model-based behavioral analyses , resulting in a sample size of 68 ( 39 PTSD and 29 veteran controls ) ; analyses comparing loss and gain learning used the more restricted sample of 43 participants ( 23 PTSD and 20 veteran controls ) with complete data across conditions and modalities ( i . e . , loss , gain , behavior , neuroimaging ) . Analyses of these participants with full data in all four categories are reported in Appendix 2 , Supplementary Results , Model-based loss analyses in restricted sample and are consistent with results from the larger group . To delineate the effect of associability weight on performance accuracy , we conducted a simulation of participants’ performance at different values of the associability weight . Other parameters were set at the group means and the associability weight was varied from . 05 to . 95 in steps of . 05 . Data from fifty participants with two blocks of 25 trials each were simulated for each value of the associability weight . The mean and standard error of the proportion of correct options chosen were calculated for each value of associability weight . Performance did not systematically vary with the value of the associability weight and the correlation between associability weight and performance was not significant ( r = 0 . 10 , p>0 . 1; Figure 3—figure supplement 1a ) . According to theories of associability-based learning ( Le Pelley , 2004 ) , stimuli that have been poorly predicted in the past should draw more attention during subsequent learning trials , resulting in increased associability values and longer decision times . Therefore , we tested whether reaction times scaled with the associability value of the chosen cue . Each participant’s reaction times were modeled in a regression equation with predictors of the chosen cue’s associability value , the chosen cue’s expected value , and the trial number; trials with reaction times shorter than 333 milliseconds or longer than five seconds were excluded ( 3 . 07% of trials ) . Figure 3b shows participants’ average beta values of the relationship between associability value and reaction time during loss learning . The average beta value was . 232 , and significantly greater than 0 ( one sample t-test: t68 = 8 . 28 , p<0 . 001 ) , reflecting longer decision times for stimuli with larger associability values . The average beta value for associability value predicting reaction time when including the previous trial’s prediction error as an additional predictor was similarly significant ( t68 = 7 . 21 , p<0 . 001 ) , indicating that the effect of associability value on reaction time was not due to concomitant prediction error . This effect was confirmed in a mixed effects regression using the ‘lme4’ package ( Bates et al . , 2015 ) in R including all participants’ reaction times and accounting for the nesting of responses within subjects . Model fit was compared between a model predicting reaction times from the chosen cue’s associability value , the chosen cue’s expected value , and trial number versus a model without associability value; the model with associability value was a significantly better fit ( likelihood ratio test: χ21 = 8 . 67 , p< . 005 ) . Trial number was included as a covariate in these analyses to account for general adaptation effects; however , trial number and associability were uncorrelated ( average correlation across subjects: r = −0 . 12 ) . To investigate the associability RL model’s performance in predicting participants’ behavior , each participant’s trials were binned ( using 5 bins total ) based on the probability of choosing the correct choice as predicted by the model . This binning resulted in a proportion of correct choices made when the predicted probability of choosing the correct stimulus was 0–20% , 31–40% , and so on per participant . The mean and standard error of the proportion of correct choices per bin was calculated across subjects . The model showed good concordance with actual behavior; binned predicted performance was strongly correlated with mean proportion of correct choices per bin ( r4 = 0 . 9986 , p<0 . 001; Figure 3—figure supplement 1b ) . As an additional test of the ability of the RL model’s associability value to predict behavior in the loss condition , mixed effects logistic regression models were used to assess the effects of prediction error alone and prediction error modulated by associability value on model free switching behavior using the ‘lme4’ package in R . The dependent variable was whether or not the participant switched choices from the previous trial to the current trial ( 0 = no switch , 1 = switch ) . The basic regression included terms for the previous trial’s outcome ( 1 = small loss , 0 = large loss ) and for the nesting of trials within subjects ( e . g . , switch ~ outcome 1 trial previous + [1|subject] ) . The fit ( AIC ) for this model was compared against two more complex models: ( 1 ) adding an interaction of the previous trial’s outcome with the previous trial’s prediction error ( e . g . , outcome 1 trial previous * PE 1 trial previous ) and ( 2 ) adding an interaction of the previous trial’s outcome , prediction error , and associability value ( outcome 1 trial previous * PE 1 trial previous * associability value 1 trial previous ) . Prediction error and associability value estimates were taken from the associability RL model using individual parameter estimates . The difference in AIC between the basic regression model and models adding prediction error and prediction error * associability is plotted in Figure 3c . Standard error on the improvement in AIC was calculated using a jackknife approach ( Efron and Gong , 1983 ) . The relationships between PTSD diagnosis and individual behavioral parameter estimates for associability weight and learning rate were computed in R using a linear regression with additional regressors of BDI , CES , age , and gender . The variance inflation factor for PTSD in this analysis was within acceptable limits ( VIF = 1 . 56 ) . The beta values for PTSD diagnosis ( control vs . PTSD; error bars of standard error ) predicting associability weight and learning rate , respectively , are plotted in Figure 4a . As an additional test of the specificity of these findings to PTSD versus general psychopathology , RL parameters from the behavior of an additional psychiatric reference cohort with diagnoses of MDD but not PTSD ( see Participants , above ) were compared to the participants with PTSD , covarying for age . A linear regression model was used to compare these groups accounting for age; a regression with additional covariates of gender , estimated IQ , smoking status , and depression severity showed similar results supporting the specificity of increased associability to PTSD ( PTSD diagnosis effect: t53 = 7 . 29 , p<0 . 001 ) . For first level analyses , analyses of prediction error used prediction error ( δ ) as a parametric modulator at the time of outcome . Based on previous work showing electrophysiological and BOLD response to associability at time of outcome ( Li et al . , 2011; Esber et al . , 2012; Roesch et al . , 2010a ) , associability analyses used associability value ( κ ) as a parametric modulator on the outcome event . An additional analysis combined these parametric modulators in the same first-level analysis , with prediction error entered as the first parametric modulator and associability value as the second; this approach did not meaningfully differ from entering these parametric modulators in separate first-level analyses ( see Supplementary file 1 , Table 1H for second level results for this approach ) . All analyses used the probability of the chosen option ( computed by the softmax function ) as a parametric modulator at time of cue presentation and a second parametric modulator of outcome amount at the time of outcome . All outcome and cue events were modeled as stick functions . Prediction error , associability value , and probability were computed based on each subject’s individually estimated parameters and were z-transformed prior to entering at the first level ( Lebreton and Palminteri , 2016 ) . Additional regressors of no interest included button press , block number , and six motion regressors . Data were high pass filtered with a cutoff of 128 s . Group level analyses were set to a voxel-level cluster forming threshold of p<0 . 001 and then cluster-level corrected at p<0 . 05 topological false discovery rate ( FDR ) for the whole brain ( Woo et al . , 2014 ) . To assess the impact of PTSD ( or symptom severity ) on neural substrates of prediction error and associability , PTSD diagnosis or Clinician Administered PTSD Scale ( CAPS ) symptom cluster score was entered at the group level with covariates of depression , combat exposure , age , and gender . Follow-up analyses included the presence of psychotropic medication and mTBI as covariates ( see Appendix 2 , Supplementary Results , Subgroup Analyses for details ) . To test the robustness of our results to the thresholding method used , the effect of PTSD diagnosis on associability-related neural signal at the second level was checked using a nonparametric thresholding approach . Using SnPM13 ( version 1 . 05; http://warwick . ac . us/snpm ) , 10 , 000 permutations were carried out with PTSD as the covariate of interest and depression , combat exposure , age , and gender as covariates of no interest . Results were thresholded at p<0 . 05 FWE , resulting in a corrected threshold of t > 5 . 36 . Results with this permutation analysis were similar to those reported in the main text and are displayed in Supplementary file 1 , Table 1I . To test whether the lack of evidence for a relationship between neural prediction error and PTSD severity was due to conservative whole-brain correction , we followed up with a region of interest analysis in areas involved in prediction error . To select areas most likely to be involved in prediction error , functional regions of interest ( ROIs ) were created based on activations from the probabilistic learning task in a non-veteran reference cohort ( see Participants , above ) . Group-level activations for prediction error and associability were thresholded at p<0 . 005 uncorrected with a cluster extent ≥20 voxels . Activation clusters were binarized as masks and used to create ROIs ( 21 for gain PE , 13 for loss PE ) . The first eigenvariate of the beta values in each ROI was extracted for the veteran group . Values were read into R and regressed against PTSD diagnosis along with covariates of BDI , CES , age , and gender . To verify intact prediction error signaling , ROIs in the striatum were used in one-way t-tests to determine if mean activation for the veteran cohort was significantly different from zero . To evaluate the relevance of neural substrates of associability to behavioral disruptions in PTSD , we tested whether interactions among PTSD , neural activation in areas related to associability or prediction error , and previous outcomes predicted switching behavior . The anatomically defined regions of interest were created in the following manner: amygdala from centromedial and laterobasal subregions from the Jühlich atlas ( Amunts et al . , 2005 ) ; striatum from caudate , putamen , and globus pallidus from the AAL atlas ( Tzourio-Mazoyer et al . , 2002 ) and nucleus accumbens from the IBASPM atlas in WFU PickAtlas; anterior insula from portion of insula anterior to y = 10 from the AAL atlas; and ventromedial PFC from medial orbitofrontal cortex from AAL . Each subject’s beta values for associability value-related activation ( in amygdala and insula ) and prediction error-related activation ( in striatum and vmPFC ) were extracted from the respective ROIs . The regression included terms for the interaction of the ROI beta values with PTSD diagnosis or symptom cluster severity and the previous trial’s outcome ( 1 = small loss , 0 = large loss ) as well as the outcomes of two and three trials previous and for the nesting of trials within subjects ( e . g . , switch ~ previous trial’s outcome * associability-related amygdala beta value * PTSD diagnosis + outcome 2 trials previous + outcome three trials previous + [1|subject] , plus all lower order terms ) . The effects of the three previous outcomes were included in the regression as they were all found to affect switching behavior . Models were estimated using the ‘lme4’ package in R . Likelihood ratio tests compared this model to a model with the same ROI , but without the PTSD terms ( main effect and interaction ) . Changes in model fit , rather than the significance of individual regressors , was chosen due the unreliability of p-values in mixed effects models such as the one used here ( Snijders , 2011 ) . Therefore , improvement in model fit with the addition of a term is analogous to this term being significant in the regression model . Alpha level was set at p<0 . 006 ( Bonferroni corrected for 8 tests ) . Figure 5b shows the chi squared value from these likelihood ratio tests for amygdala and striatum , with chi squared values corresponding to p<0 . 005 noted for reference . Analyses with all ROIs are shown in Figure 5—figure supplement 1b . Although the dependent and independent variables in this analysis ( switching behavior and associability-related neural activation ) are related to behavioral associability parameters , this relationship reflects the underlying theorized relationship among behavior , the hybrid RL model , and neural activation rather than representing a circular analysis . To assess subject-level variability in the likelihood ratio tests , jackknife analyses ( Efron and Gong , 1983 ) were run on each hierarchical logistic regression . The distribution of the chi-squared values are shown in Figure 5—figure supplement 1c . Supporting the main analyses ( Figure 5 ) , the interaction of associability-related neural activation in amygdala with PTSD diagnosis , hyperarousal symptoms , and to a lesser extent avoidance/numbing symptoms , all showed the majority of jackknife chi squared estimates above the corrected significance levels . Meanwhile , the interaction of associability-related neural activation in amygdala with re-experiencing , as well as prediction error-related neural activation in striatum with PTSD diagnosis and all symptom clusters , all showed the majority of estimates below significance . | Posttraumatic stress disorder , or PTSD for short , is a serious psychiatric disorder that sometimes occurs after someone has experienced a dangerous or threatening event . People with PTSD are prone to overreact to unexpected reminders of these events , and are often hypervigilant for danger . Why these symptoms occur is not yet clear , but it is thought that people with PTSD may have learning problems that lead them to overestimate the likelihood of danger . Advanced tools from computer science and mathematics have helped scientists to study how the brain learns . These tools may now provide more insight into how diseases like PTSD disrupt learning . Scientists use computer models of learning to test how humans make choices and react to their outcomes . These models build on the idea that humans make choices based on what they predict an outcome will be , and then learn when they update their expectations based on the accuracy of their predictions . Now , Brown et al . show that people with PTSD have an increased learning response to surprising events – these are defined in this study as outcomes that are inconsistent with participants’ predictions . In the experiments , 74 combat veterans who had experienced trauma in Iraq or Afghanistan underwent a type of brain scanning procedure , while they played a gambling-like game . Some participants had PTSD , others did not . Both groups learned to make choices that minimized the loss of money . However , learning in veterans with PTSD was strongly influenced by how much attention they paid to surprising outcomes . Moreover , the brain areas that help to process attention to surprise were highly active in people with PTSD . Brown et al . added a third group of participants with depression to the study to verify that the learning changes were PTSD-specific . This depression-only group did not have differences in attention to surprise . Many treatments for PTSD focus on exposing individuals to feared situations and trauma memories , so that individuals can learn that these situations are no longer dangerous . Computational modeling and neuroimaging may help scientists pinpoint the sources of learning deficits , such as increased attention to surprising outcomes . Identifying the different possible causes of learning problems may lead to new or more precise learning-based treatments for PTSD and other learning-related conditions . Understanding how learning-related brain changes develop may also help find ways to prevent and better diagnose PTSD and other psychiatric disorders . | [
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] | 2018 | Associability-modulated loss learning is increased in posttraumatic stress disorder |
Perfringolysin O ( PFO ) is a prototypical member of a large family of pore-forming proteins that undergo a significant reduction in height during the transition from the membrane-assembled prepore to the membrane-inserted pore . Here , we show that targeted application of compressive forces can catalyze this conformational change in individual PFO complexes trapped at the prepore stage , recapitulating this critical step of the spontaneous process . The free energy landscape determined from these measurements is in good agreement with that obtained from molecular dynamics simulations showing that an equivalent internal force is generated by the interaction of the exposed hydrophobic residues with the membrane . This hydrophobic force is transmitted across the entire structure to produce a compressive stress across a distant , otherwise stable domain , catalyzing its transition from an extended to compact conformation . Single molecule compression is likely to become an important tool to investigate conformational transitions in membrane proteins .
The initial demonstration that single macromolecules could be mechanically stretched with exact knowledge of the associated forces ( Smith et al . , 1992; Kellermayer et al . , 1997; Rief et al . , 1997; Tskhovrebova et al . , 1997 ) has enabled unprecedented access to measurements of the physical and chemical inter-molecular interactions ( Bippes and Muller , 2011; Bustamante et al . , 2000; Fisher et al . , 2000; Hu and Li , 2014; Zhuang and Rief , 2003 ) . Moreover , pulling on single molecules with tensile forces has enabled detailed probing of functional and structural transitions within proteins , in particular those that involve a separation of specific structural elements ( Puchner and Gaub , 2012; Zhang et al . , 2009; del Rio et al . , 2009 ) . Yet , tensile forces cannot be used to probe transitions that involve a decrease in the distance between structural elements such as the closing of a substrate access gate , for example , as the force acts in a direction opposite to the structural movement . In these instances , what is needed is the controlled application of a compressive force at the single molecule level . Perfringolysin O ( PFO ) is an intriguing pore-forming toxin that undergoes a significant reduction in the distance between two of its domains during pore formation ( Gilbert , 2005; Hotze and Tweten , 2012 ) . During the prepore-to-pore transition , the distance between the D1 and D4 domains is reduced as a result of conformational changes in the D2 domain ( Figure 1 ) : this domain is elongated in the prepore but collapses into a more compact structure in the pore , largely accounting for the remarkable 40 Å decrease in height relative to the bilayer surface first observed by atomic force microscopy ( AFM ) ( Czajkowsky et al . , 2004; Tilley et al . , 2005 ) . Also within this prepore-to-pore step , an α-helical bundle ( the Trans-Membrane Helices ( TMHs ) ) in the D3 domain converts into an amphipathic β-sheet that inserts in the bilayer and lines the aqueous membrane pore . While these changes in the D2 and D3 domains have been firmly established , the mechanism by which these changes are coordinated remains unresolved . In fact , the physical processes associated with long-distance structural communication in proteins are poorly understood in general ( Cui and Karplus , 2008; Whitley and Lee , 2009; Li et al . , 2011 ) . Herein , using single molecule compressive force spectroscopy and single molecule AFM together with all-atom molecular dynamics ( MD ) simulations , we demonstrate that intra-protein stresses are the driving force of the structural coordination between the D2 and D3 domains and , ultimately , the catalyst of the collapse of the D2 domain . 10 . 7554/eLife . 08421 . 003Figure 1 . Structure of PFO and the known structural changes during pore formation . ( A ) The water-soluble monomer consists of four domains , D1 to D4 . ( B ) AFM images showing the large height difference between prepore and pore complexes . Scale bar: 50 nm . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 003
As the D2 and TMHs directly contact each other in the PFO prepore and are well separated in the pore , it was previously suggested that the loss of these contacts during the prepore-to-pore transition might have precipitated the collapse of an inherently unstable D2 domain ( Czajkowsky et al . , 2004 ) . However , extensive equilibrium MD simulations ( >0 . 3 µs ) of a PFO monomer without the TMHs shows that the D2 domain remains in an extended conformation in the absence of the TMHs interactions , fluctuating in height by only a few Å ( 2 . 9 Å RMS height deviation ) ( Figure 2A ) . Thus , the loss of contact with the TMHs alone is insufficient to trigger the conversion of the D2 domain into a compact structure , a conclusion consistent with recent work showing that the TMHs in the thermally trapped PFO prepore are unfolded with significant conformational freedom ( Sato et al . , 2013 ) while cryo-electron microscopy ( cryo-EM ) images of a related cytotoxin , pneumolysin , showed that the D2 domain in this state is in its extended conformation ( Tilley et al . , 2005 ) . 10 . 7554/eLife . 08421 . 004Figure 2 . Steered MD simulations and energy landscape associated with the membrane-directed descent of the D3 domain . ( A ) The change in height of PFO during extended equilibrium simulations following the removal of the TMHs . ( B ) The D2 domain restructures as a result of a downward force ( here 250 pN ) applied to the D3 domain . Inset: the initial and final structures of the steered MD simulations . ( C ) Detailed view of the D2/D4 interface ( the boxed region in [B] ) . ( D ) Potential of mean force profile calculated from ABF simulations using the height of the D3 region ( specifically the red sphere ) as the reaction coordinate . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 00410 . 7554/eLife . 08421 . 005Figure 2—figure supplement 1 . Fully extended TMHs are sufficiently long to touch the membrane surface from their position in the prepore . ( A ) The TMHs were modeled as β-strands and positioned in an essentially vertical orientation , overlapping the Cα backbone atoms of residues Gln180 , Tyr226 , Thr276 , and Val322 ( red and blue spheres are the native structure and modeled β-strand atoms ) . Also shown in brown stick representation are the residues , Thr490 and Leu491 , in D4 that are believed to directly contact cholesterol ( Farrand et al . , 2010 ) , and thus indicate the position of the membrane surface . This orientation reflects the approximate path of D3 descent studied in most steered MD simulations and ABF calculations . ( B ) The TMHs would contact the membrane surface even if tilted by ~∼10° from the orientation depicted in ( A ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 00510 . 7554/eLife . 08421 . 006Figure 2—figure supplement 2 . Structural changes in each domain during the steered MD simulations described in Figure 2 . Shown is the root-mean-squared-deviation ( RMSD ) of the backbone Cα atoms of each domain from their initial conformation throughout the trajectory . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 00610 . 7554/eLife . 08421 . 007Figure 2—figure supplement 3 . Height of the D2 domain following the release of the constant force on the D3 domain at the end of the steered MD simulations described in Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 00710 . 7554/eLife . 08421 . 008Figure 2—figure supplement 4 . Steered MD simulations with different applied forces and force directions . ( A ) Simulations at different force magnitudes . The direction of the applied force is approximately that of the β-strands depicted in Figure 2—figure supplement 1A . ( B ) Shown are the trajectories for force applications of 150 pN in the 'vertical' and '10° tilt' directions depicted in Figure 2—figure supplement 1A and B , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 00810 . 7554/eLife . 08421 . 009Figure 2—figure supplement 5 . Overlap between the electron density profile from cryo-EM images of the pneumolysin pore ( Tilley et al . , 2005 ) and the PFO structure at the final stages of the steered MD simulations . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 009 Closer inspection of this prepore conformation , in fact , reveals that fully extended TMHs are of a sufficient length to contact the bilayer surface ( Figure 2—figure supplement 1 ) . This extended conformation exposes hydrophobic residues in the TMHs to water , and so the membrane contact would generate a bilayer-directed hydrophobic force on the TMHs , driving them toward the membrane interior . Such a hydrophobic force exerted on a water-exposed transmembrane region has been reported in the folding of bacteriorhodopsin ( Kessler et al . , 2006 ) and implicated in the translocon-mediated membrane integration of transmembrane helices ( Ismail et al . , 2012 ) . To examine the effect of this bilayer-directed force on the conformation of the protein , we employed steered MD simulations , applying a downward constant force specifically on the TMHs . These calculations reveal that the most significant structural effect of this force is the collapse of the D2 domain ( Figure 2B ) , with the rest of the structure remaining largely unchanged ( Figure 2—figure supplement 2 ) . That is , the tensile force on the TMHs is effectively transmitted through the protein to generate a compressive stress across the D2 domain that can catalyze its collapse . During the simulations , after modest initial changes in conformation , the protein structure remained relatively stable owing to the presence of an energy barrier that prevented further conformational changes . Finally , this barrier was overcome , and the height dropped rapidly to a value ∼40 Å lower than its initial height ( Figure 2B ) . In this final conformation , the lower portion of the D2 domain is still a β-sheet and lies almost perpendicular on the surface of the D4 domain , pivoting at Lys381 . More importantly , the D2 domain remains in this compact conformation even after the force is relieved ( for >40 ns ) ( Figure 2—figure supplement 3 ) , suggesting that this conformation is a relatively stable state . The D2/D4 interface in this conformation covers 842 Å ( Kellermayer et al . , 1997 ) similar to that observed for interfacial contacts in oligomeric proteins ( Miller et al . , 1987 ) , and the buried hydrophobic residues ( Phe75 , Tyr389 , Tyr415 ) in the interface ( Figure 2C ) likely contributed to the stability of this compact state . As these residues are highly conserved in the cholesterol-dependent cytolysins ( CDC ) family ( Supplementary file 1 ) , it is possible that this structure is a common feature of other CDC toxins . For these simulations , the applied force was 250 pN; different forces consistently produced similar results , but the time scale was significantly longer at lower forces ( Figure 2—figure supplement 4 ) . Overall this collapsed structure is consistent with the electron-density profile of the pneumolysin pore by cryo-EM ( Tilley et al . , 2005 ) ( Figure 2—figure supplement 5 ) . To further understand the energy associated with this transition , extensive adaptive biasing force ( ABF ) simulations ( Chipot and Hénin , 2005 ) yielded the energy landscape along the reaction coordinate , the height of the D3 domain . This landscape ( Figure 2D ) exhibits a broad minimum at heights associated with the prepore conformation ( from −10 to 8 Å ) and a local minimum at heights associated with the pore conformation ( centered at ∼40 Å ) , separated by an energy barrier of ∼23 kcal/mol ( ∼38 kBT ) from the prepore minimum . Thus , the extended structure of the D2 domain is indeed associated with the energetically minimized conformation of PFO . Of note , there is a sharp rise in the energy at ∼25 Å , where the energy increases by ∼17 kcal/mol over a distance of ∼6 Å . Such a barrier height would require a time frame of days to be overcome by thermal fluctuations alone ( see 'Materials and methods' ) , far longer than the observed timescales of the prepore-to-pore transition ( Hotze et al . , 2001 ) . Thus , this is the primary barrier that must be reduced by the force generated by the bilayer-TMHs interaction in order to drive PFO into its pore conformation . As the aforementioned steered MD simulations revealed that it is , ultimately , a compressive stress across the D2 domain that is responsible for its collapse , we reasoned that this process could be experimentally probed with compressive forces applied by an AFM probe on individual complexes if the tensile forces owing to the bilayer-TMHs interaction could be prevented . In this regard , a mutant protein , PFOG57C-S190C , has been identified that spontaneously assembles into prepore complexes on the membrane like the wild-type protein but does not proceed to the pore state owing to a disulfide bridge that inhibits the restructuring of the TMHs ( Figure 3A ) ( Czajkowsky et al . , 2004; Hotze et al . , 2001 ) . When this constraint is removed by adding Dithiothreitol ( DTT ) , this mutant quickly converts into a native-like pore ( Czajkowsky et al . , 2004; Hotze et al . , 2001 ) . Thus , this mutant enables investigation of just the energy barrier of the D2 collapse under compressive forces , without any complicating effects associated with the D3-bilayer interaction ( Figure 3B and Figure 3—figure supplement 1 ) . Steered MD simulations confirmed that under compressive forces mimicking those that would be applied with AFM , the D2 domain of this mutant recapitulates the same collapse as that of the D2 domain under tensile forces applied to the TMHs ( Figure 3—figure supplement 2 ) . Indeed , direct application of constant compressive forces with an AFM tip to individual PFOG57C-S190C prepore complexes reproducibly induced the characteristic height reduction in the absence of any reducing agent , leaving the general morphology of the complexes otherwise unchanged ( Figure 3C and additional images are presented in Figure 3—figure supplement 3 ) . We note that this pore-like structure does not revert back to the prepore conformation upon removing the applied force , indicating that this compact structure is relatively stable , consistent with the MD simulations . We further note that this applied force ( 110 pN/monomer ) is >10-fold smaller than the force required to break a covalent bond over this time period ( see 'Materials and methods' ) ( Grandbois et al . , 1999 ) . 10 . 7554/eLife . 08421 . 010Figure 3 . Single molecule compression provides a direct measure of the energy landscape of the D2 collapse . ( A ) PFOG57C-S190C prepore-trapped mutant . ( B ) Schematic of the experiment . ( C ) A force of 110 pN/monomer ( ∼4000 pN/complex ) applied for 1 s to a single prepore complex ( blue arrow ) catalyzed the collapse to the pore-like height . Image size: 200 x× 220 nm2 . ( D ) Force and time dependence of the AFM probe-induced D2 collapse . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 01010 . 7554/eLife . 08421 . 011Figure 3—figure supplement 1 . Scanning electron micrograph of the AFM tip used in these experiments shows a symmetric apex with a radius of ∼25 nm , consistent with manufacturer’s specifications . The compressive force applied with such AFM probes to the center of the PFO complexes ( roughly 35 nm in diameter ) is thus expected to be essentially equally distributed among all monomers of the complex . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 01110 . 7554/eLife . 08421 . 012Figure 3—figure supplement 2 . Steered MD simulations reveal the collapse of the D2 domain under the application of compressive force to PFOG57C-S190C . ( A ) The D2 domain restructures as a result of a downward force ( 175 pN in total ) applied to a section of a carbon nanotube that directly contacts the top of the D1 domain . The disulfide bridge linking G57C and S190C that remains present throughout the simulations is shown in yellow . ( B ) Height of the D2 domain following the release of the constant force on the nanotube ( removing the nanotube atoms from the simulation ) at end of the steered MD simulations described ( A ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 01210 . 7554/eLife . 08421 . 013Figure 3—figure supplement 3 . Additional AFM images showing the forced collapse of prepore-locked PFOG57C-S190C complexes . From the top-left to bottom-right panels , compressive forces ( ∼110 pN/monomer or ∼4000 pN/complex , 1s ) were applied sequentially to the complexes marked by the white , blue , green , and pink arrows . In each case , the complex collapsed to the smaller pore-like height . Image size: 300 x× 325 nm2 . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 01310 . 7554/eLife . 08421 . 014Figure 3—figure supplement 4 . The AFM compressive data is well described by a two-state , single energy barrier model . For this plot , the data shown in Figure 3D were transformed according to Equation 3 ( see Materials and methods ) . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 014 As expected for a thermally driven transition , this force-catalyzed conversion is dependent on the magnitude and the duration of the applied force ( Figure 3D ) . Assuming a simple , single barrier model commonly employed in force spectroscopic experiments ( Hu and Li , 2014; Evans , 2001; Fernandez et al . , 2010 ) , the probability to induce the pore-like state under an applied force , F , for a pulse period , t , is given by ( 1 ) Ppore = 1 -e-kft where ( 2 ) kf = ko × eFxβkBT and ko = A × e-∆GokBT and xβ is the reaction coordinate distance to the energy barrier peak from the minimum , ∆Go is the barrier height , A is the attempt frequency , kB is Boltzmann’s constant , and T is the temperature . Each 1s and 5s dataset is well described by the same single barrier model ( Figure 3—figure supplement 4 ) . Thus , we simultaneously fitted both datasets to the above equations , yielding xβ = 5 . 1 ± 0 . 8 Å and ∆Go = 16 . 3 ± 0 . 6 kcal/mol ( see 'Materials and methods' ) . This energy barrier is smaller than that observed in previous single molecule protein domain unfolding experiments ( Fernandez et al . , 2010; Dietz and Rief , 2008 ) , as might be expected since the D2 domain is not unfolded during this transition . Importantly , there is excellent agreement between the experimental values and the aforementioned ABF calculations ( 6 Å and 17 kcal/mol , respectively ) , thereby providing quantitative support for the model . Since the hydrophobic force generated by the TMHs-bilayer interaction is critical for the prepore-to-pore transition , any weakening of this interaction is expected to cause a measurable reduction in the rate of transition . Therefore , we investigated the prepore-to-pore transition in the presence of a detergent ( n-dodecyl-β-D-maltoside , DDM ) , since detergent binding to hydrophobic residues in the TMHs should reduce this hydrophobic force on the TMHs ( Figure 4A ) . As shown in Figure 4B , incubating DDM with the mutant PFOG57C-S190C prepore complexes , followed by the addition of DTT , prevents conversion to the pore-like structure . This effect was dependent on the concentration of detergent , with a maximal inhibition observed with nearly 20 µM DDM ( Figure 4B ) , a similar concentration as that observed for binding to exposed hydrophobic regions in other proteins ( Kragh-Hansen et al . , 2001 ) . 10 . 7554/eLife . 08421 . 015Figure 4 . Effect of detergent on the prepore-to-pore transition and final model . ( A ) Potential outcomes of binding of detergent to hydrophobic residues within the TMHs . ( B ) AFM images showing the inhibition of the pore-like height with increasing concentration of the detergent , DDM . Inset image size: 550 x× 550 nm2 . ( C ) Schematic model of the mechanism by which conformational changes in the D2 and TMHs are coordinated . Compressive intra-protein stresses generated by the bilayer-directed forces on the unfolded TMHs catalyze the restructuring of the D2 domain to its collapsed conformation in the pore . DOI: http://dx . doi . org/10 . 7554/eLife . 08421 . 015 In summary , the combination of single molecule measurements and MD computations has enabled direct identification of the essential role of intra-protein forces in driving the PFO complex into its final pore conformation ( Figure 4C ) . We speculate that such intra-protein forces may also be a critical factor in driving coordinated conformational changes between distant domains in many other systems , which are presently not fully characterized mechanistically ( Cui and Karplus , 2008; Changeux and Edelstein , 2005 ) . These results also demonstrate that single molecule compressive force spectroscopy can be an effective means by which previously inaccessible physical mechanisms of conformational coordination within membrane proteins in particular may now be probed and quantified .
The initial structure for the equilibrium simulations was the atomic model of PFO ( pdb: 1PFO ) ( Rossjohn et al . , 1997 ) . All systems were solvated in TIP3 water in 0 . 15 M NaCl and minimized and equilibrated using VMD/NAMD and the CHARMM 27 force field ( Humphrey et al . , 1996; Mackerell , 1998; Phillips et al . , 2005 ) . Langevin dynamics were employed to maintain a constant temperature of 310 K and a Nose-Hoover Langevin piston was used to maintain a constant pressure of 1 atm . The particle mesh Ewald algorithm was employed to treat electrostatic interactions , and the van der Waals interactions were treated with a cut-off of 12 Å . The integration step was set to 2 fs . For the simulations without the TMHs , the residues between Tyr187 to Val221 and Thr283 to Ser316 were removed and the remaining regions resealed . The height of the protein was determined from the distance between the centers-of-mass of the Cα backbone atoms for Lys127/Pro128 and Ala401/Tyr402 . The steered MD simulations were performed using the structure of PFO without the TMHs to avoid complications arising from unfolding of the TMHs and their contact with the other regions of the protein . In each simulation , the lower loop regions of domain 4 ( residues 399 to 404 , 434 to 438 , 459 to 468 , and 489 to 493 ) were held fixed to mimic their vertically immobile association with the membrane surface during pore formation . A constant force was applied to the Cα atoms of residues Met222 and Ser317 following the standard NAMD protocol . The height of the D3 domain was determined from the positions of the Cα atoms of Met222 and Ser317 . The height of the D2 domain was determined from the locations of the Cα atoms of Thr86 and Lys378 . The direction of force application for most simulations was chosen as the shortest path from the Cα atoms of Met222 and Ser317 to the expected location of the membrane surface , avoiding direct collision with the D4 domain . In particular , this direction corresponds to a ∼15° tilt from the vertical ( the long axis of PFO ) , away from the protein , as depicted in Figure 2—figure supplement 1A . Other directions were also investigated , including the ∼10° tilt direction depicted in Figure 2—figure supplement 1B . For the simulations with an applied compressive force , we studied the mutant protein , PFOG57C-S190C . The downward force was applied to the top of the protein by a section of a carbon nanotube ( generated with VMD ) , as shown in Figure 3—figure supplement 2 . The sampling space was limited to the separation of two centers of mass , one located in the D3 domain and the other near the bottom of the D4 domain , applying a restraining harmonic potential to the Cα atoms of Met222 and Ser317 to constrain the motion along the reaction coordinate such that it changed only vertically . The direction of this pathway is approximately that depicted in Figure 2—figure supplement 1A . One center of mass included the Cα atoms of Leu396 and Val484 and the other included the Cα atoms of residues 184 , 185 , 223 , 224 , 280 , 281 , 318 , and 319 . In addition , as with the steered MD simulations , the lower loop residues ( residues 399 to 404 , 434 to 438 , 459 to 468 , and 489 to 493 ) were held fixed during these calculations . Two different ABF runs were performed: first with a distance range between the aforementioned centers-of-mass of 1 . 5 Å and then piecing together these results as the initial potential of mean force profile for calculations with distance ranges of 5 Å . The latter were performed to guarantee accuracy of the calculated profile , particularly at the junction between neighboring 1 . 5 Å segments . The size of each ABF sampling bin for all calculations was 0 . 1 Å . All simulations , run within NAMD , were run long enough to observe the convergence of the system to ensure accurate sampling of the free energy profile: at the end of the second ABF run , every ABF 0 . 1 Å bin had been sampled over 100 , 000 times . The mutant PFOG57C-S190C was produced and purified as before ( Hotze et al . , 2001 ) . Egg phosphatidylcholine ( eggPC ) , cholesterol ( chol ) , and 1 , 2-bis ( 10 , 12-tricosadiynoyl ) -sn-glycero-3-phosphocholine ( diynePC ) were purchased from Avanti Polar Lipids ( Alabaster , AL , US ) . n-dodecyl-β-D-maltoside ( DDM ) was purchased from Anatrace ( Maumee , OH , US ) . All other chemicals were purchased from Sigma ( St . Louis , MO , US ) . The supported membranes containing mutant PFOG57C-S190C were formed by sequentially depositing two separately prepared lipid monolayers onto a mica substrate , followed by the injection of the protein into small Teflon wells , as previously detailed ( Czajkowsky et al . , 2004 ) . For the single molecule compressive force experiments , the composition of the first monolayer ( facing mica ) was eggPC , and the second monolayer was eggPC:chol at 50:50 mol% . For the experiments with the detergent , supported bilayers of a similar composition were dissolved too quickly following the addition of detergent to enable investigation . Instead , for these experiments , the first monolayer was composed of diynePC and the second monolayer was eggPC:chol ( 50:50 mol% ) . Following deposition of the diynePC monolayer onto mica , the sample was irradiated with UV light ( Bio-Rad ) for 10 min to induce cross-linking of the lipids . As is evident from Figure 4B , the PFOG57C-S190C complexes proceed to their pore-like height in the presence of DTT , just as in bilayers where the first monolayer is composed of eggPC ( Czajkowsky et al . , 2004 ) . For all experiments , the final concentration of the protein in the well was ∼15 µg/ml , and the buffer in the well consisted of buffer A ( 10 mM sodium phosphate , pH 7 ) . After incubating for 45 min , the sample was extensively washed and then , for the compressive force experiments , imaged in the AFM under buffer A . For the experiments with detergent , the sample was first washed in buffer B ( 10 mM HEPES , 0 . 1 M NaCl , 25 mM CaCl2 , pH 7 . 5 ) , then washed and incubated for 5 min in buffer B with DDM ( at the concentrations indicated in Figure 4B ) , then washed and incubated for 10 min in buffer B with DDM and 2 . 5 mM DTT , then washed and incubated for 10 min in buffer B with DDM and 2% glutaraldehyde , followed finally by an extensive wash in buffer B , a final incubation in 20 mM glycine , pH 6 , and a final wash and imaging in buffer B . The chemical fixation was necessary because stable imaging of the protein complexes directly in the detergent solution was not possible . The histogram data presented in Figure 4B was determined from imaging five different regions in each of three different samples at each condition . Imaging was performed in the contact mode with a Nanoscope II AFM ( Bruker - Digital Instruments , Santa Barbara , CA , US ) using oxide-sharpened ‘twin-tip’ Si3N4 cantilevers with a spring constant of 0 . 06 N/m . The spring constant was verified by measuring the thermal induced oscillation . The typical scan rate was 9 Hz , and the imaging force was minimized to 0 . 1 nN . The piezoscanner ( 14 mm , D scanner , Bruker - Digital Instruments , Santa Barbara , CA , US ) was calibrated using a variety of samples including mica and the cholera toxin B subunit . Scanning electron microscopy of the AFM probe ( shown in Figure 3—figure supplement 1 ) was obtained with a JEOL 7800F microscope ( Peabody , MA , US ) . Compressive forces have been previously applied with AFM to probe mesoscopic mechanical properties of various biomaterials ( Lee et al . , 2013; Kodama et al . , 2005 ) . In our experiments , after obtaining an image of the sample at small applied forces in scan sizes of 300 to –400 nm , the tip was positioned within the center of a given complex by zooming in with progressively smaller scan sizes ( generally 2 ) , arriving at a sample location with the complex of interest in the center of a scan size of ∼80 nm . Once one-half of the complex was imaged , we immediately zoomed in on its center , set the scan size to zero , and then applied the larger constant force for a specific duration ( 1 or 5 s ) . The force was then immediately reduced to detach the tip from any contact from the sample , the scan size was adjusted to ∼500 nm , and then the tip was re-engaged at smaller forces to obtain an image of the sample to determine whether or not the larger applied force catalyzed the height reduction in the complex . In this way , the applied force was equally distributed on each of the monomers within the complex . No complex was probed more than once . We measured the time between the final zoom-in step and the application of force to be less than 5 s . We measured the extent of lateral drift in our system to be less than 0 . 03 nm/s , which is similar to published reports ( King et al . , 2009 ) . Thus , we expect to not have drifted by more than 0 . 15 nm from the center of the complex prior to force application . As the complex is 35 nm in diameter , this drift imparts only a slight deviation to an equal force on all subunits . Although wild-type PFO complexes and other CDC toxins form a range of sizes , from incomplete arcs to complete rings ( Czajkowsky et al . , 2004; Sonnen et al . , 2014 ) , these prepore mutant PFOG57C-S190C complexes formed almost exclusively complete rings of an identical size . Only the complete rings were studied here . Based on the radial periodicity of the monomers in the complete rings ( Czajkowsky et al . , 2004 ) and high-resolution images of the rings ( data not published ) , the complete rings contain 36 subunits . Thus , in the text , we referred to the applied force either with respect to the entire complex or to each monomer ( dividing the total applied force by 36 ) . As such , the total applied force range used here ( 1800 pN to –3800 pN ) corresponds to 50 pN to –106 pN per monomer , which is at least an order of magnitude smaller than the force needed to break a covalent bond over this time frame ( 1400 pN to –2000 pN ) ( Grandbois et al . , 1999 ) . On average , more than 30 individual measurements were performed at each applied force , resulting in more than 600 individual single molecule measurements obtained from tens of samples and using tens of different AFM cantilevers . To obtain a measure of the energy barrier height ( ∆Go ) and the reaction coordinate distance ( xβ ) from the AFM data , we first sought evidence that each of the two datasets ( the 1s and 5s data ) was individually consistent with a similar single barrier model . To this end , Equation 1 was linearized with respect to force as ( 3 ) y ( F ) =ln ( ln ( 11−Ppore ) ) =ln ( k0t ) + FxβkBT and then we determined whether the fits of each of the 1 s and 5 s datasets ( excluding the data for which Ppore is equal to 1 to avoid division by zero ) to this equation yielded a similar value for the slope ( or xβ ) , using the force , F , per monomer . As shown in Figure 3—figure supplement 4 , each of the datasets was indeed described by this equation with similar values of xβ: xβ = 4 . 6 ± 1 . 9 Å for the 1 s dataset and xβ = 4 . 4 ± 1 . 8 Å for the 5s dataset . With this assurance , we then globally fit the entire 1 s and 5 s datasets to Equation 1 , yielding xβ = 5 . 1 ± 0 . 8 Å and ko = 1 . 7 × 10-5 ± 2 . 8 × 10-5/s . Using an attempt frequency of 107/s , consistent with previous force spectroscopy studies ( Yu et al . , 2012 ) , we obtain ( using Equation 2 ) : ∆Go = 16 . 3 ± 0 . 6 kcal/mol . For the barrier height obtained from the ABF calculations ( 17 kcal/mol ) , using a similar attempt frequency and Equation 2 yields ko = 6 . 9 × 10-6/s , which corresponds to a ( force-free ) lifetime of ∼1 . 7 days . | Proteins are made up of chains of amino acids that need to fold into intricate three-dimensional shapes to work correctly . But some proteins also have to change their shape drastically when they work . Mechanical forces that change the shape of a protein can therefore be used to determine how a protein folds and how it changes its structure when working . Although researchers have developed techniques to analyze the effect of force on single proteins , most studies carried out so far have investigated the effect of stretching ( or tensile forces ) to understand structural changes that naturally involve an extension within the protein . However , many proteins undergo structural changes that involve a compaction in their shape . How these changes occur remains poorly understood because , for these , methods to apply compressive forces to single proteins are required . Perfringolysin O ( PFO for short ) is a protein that is made by a bacterium that causes food poisoning in humans . PFO makes pores in the membrane that surrounds cells . This causes the cell’s contents to leak out , killing the cell . When inserting into the membrane , PFO changes from an elongated “prepore” state to a compact pore-forming state . Czajkowsky et al . now use a combination of single molecule techniques and computer simulations to investigate how PFO undergoes this compaction . Previous work had identified a mutant PFO protein that arrests at the prepore state . Applying a compressive force to the top of this prepore-trapped PFO as it sits on the membrane transmitted forces across the entire PFO protein . This ultimately produced a compressive force across a distant part of the protein that caused the protein to change from the elongated prepore state to the compact , pore-like shape . If a compressive force was not applied , the PFO protein remained in the prepore state . Czajkowsky et al . further found that this compressive force is naturally produced by distant water-repellent parts of the naturally occurring protein interacting with the cell membrane . Therefore , internal forces can transmit across proteins to drive shape changes in distant regions . In the future , the methods developed in this study could be applied to analyze other naturally occurring changes in proteins where shape compaction happens when working . | [
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"biophysics"
] | 2015 | Single molecule compression reveals intra-protein forces drive cytotoxin pore formation |
The tomato russet mite , Aculops lycopersici , is among the smallest animals on earth . It is a worldwide pest on tomato and can potently suppress the host’s natural resistance . We sequenced its genome , the first of an eriophyoid , and explored whether there are genomic features associated with the mite’s minute size and lifestyle . At only 32 . 5 Mb , the genome is the smallest yet reported for any arthropod and , reminiscent of microbial eukaryotes , exceptionally streamlined . It has few transposable elements , tiny intergenic regions , and is remarkably intron-poor , as more than 80% of coding genes are intronless . Furthermore , in accordance with ecological specialization theory , this defense-suppressing herbivore has extremely reduced environmental response gene families such as those involved in chemoreception and detoxification . Other losses associate with this species’ highly derived body plan . Our findings accelerate the understanding of evolutionary forces underpinning metazoan life at the limits of small physical and genome size .
The free-living microarthropod Aculops lycopersici ( Tryon ) belongs to the superfamily of the Eriophyoidea ( Arthropoda: Chelicerata: Acari: Acariformes ) that harbors the smallest plant-eating animals on earth ( Keifer , 1946; Navia et al . , 2010; Sabelis and Bruin , 1996 ) . Eriophyoids are known by many names including gall , blister , bud , and rust mites , depending on the type of damage they cause ( Hoy , 2004 ) . Since the 1930s , the tomato russet mite A . lycopersici has been reported as a minor pest of cultivated tomato ( Solanum lycopersicum L . ) worldwide ( Massee , 1937 ) . For unknown reasons , it has emerged in recent years as a significant pest of tomatoes in European greenhouses ( Moerkens et al . , 2018 ) . While it is extremely small – only ~50 μm wide and 175 μm in length ( Figure 1a , b ) – it can reach high population densities ( Figure 1c ) . The damage it causes to plants superficially resembles that of microbial disease ( Figure 1d ) , for which it is often misdiagnosed , and controlling it is troublesome ( Gerson and Weintraub , 2012; Van Leeuwen et al . , 2010 ) . The mite feeds on plant epidermal cells ( Royalty and Perring , 1988 ) , which are relatively low in nutrients , with needle-shaped mouth parts ( stylets ) that allow the transfer of saliva and the uptake of cell contents ( Nuzzaci and Alberti , 1996 ) . The first visible signs of a russet mite infestation are a rapid local collapse of the leaf hairs ( trichomes ) on the stem , leaflet or petiole upon which the mites are feeding ( van Houten et al . , 2013 ) . This is followed by withering and necrosis of infested leaves , which ultimately leads to a bronzed or russet color , from which the mite owes its name ( Jeppson et al . , 1975; Kawai and Haque , 2004 ) . Although it is now a global pest on tomato , it can survive on many related solanaceous plants ( nightshade family ) such as potato , tobacco , petunia , nightshade , and various peppers ( Perring and Farrar , 1986 ) , as well as on a few hosts outside the nightshade family ( Perring and Royalty , 1996; Rice and Strong , 1962 ) . The Eriophyoidea belong to the Chelicerata , a subphylum of Arthropoda which includes spiders , scorpions , ticks , and mites . The Eriophyoidea consists of three families – Phytoptidae , Eriophyidae ( or eriophyids , to which A . lycopersici belongs ) , and Diptilomiopidae , and comprises 357 herbivorous genera found on more than 1800 different plant species ( Oldfield , 1996; Zhang , 2011 ) . Eriophyoids are known to manipulate host plant resource allocation and resistance , and many species do so by inducing the formation of plant galls ( de Lillo et al . , 2018 ) , possibly by secreting molecular mimics of plant hormones in their saliva ( De Lillo and Monfreda , 2004; de Lillo and Skoracka , 2010 ) . Although A . lycopersici is not a gall-inducing species , it nevertheless manipulates the defense mechanisms of its tomato host to its benefit . Through an unknown mechanism during feeding , this mite suppresses the jasmonic acid ( JA ) signaling pathway ( Glas et al . , 2014; Schimmel et al . , 2018 ) . This blocks the ability of the tomato host plant to produce defensive metabolites and proteins against herbivorous insects and mites ( Alba et al . , 2015; Howe and Jander , 2008 ) , thereby rendering the plant defenseless . The consequences of suppressing host defenses for the herbivore’s selective environment may be variable depending on the degree of host specialization ( Blaazer et al . , 2018; Kant et al . , 2015 ) but for mite species that can feed on multiple hosts , there are indications of a trade-off between the ability to suppress defenses and the ability to cope with xenobiotics ( Kant et al . , 2008; Wybouw et al . , 2015 ) . Many species of eriophyoid mites cause little damage to their hosts ( Jeppson et al . , 1975 ) , or alternatively induce damage indirectly as vectors of pathogens ( Navia et al . , 2013 ) . In contrast , while A . lycopersici is not known to vector plant diseases , its ability to alter the chemistry and morphology of tomato severely weakens the plants , which are then overwhelmed and killed by exponentially growing A . lycopersici populations ( Figure 1c , d; Perring , 1996 ) . In addition to being a priority pest of tomato , A . lycopersici and related eriophyoids are among the most extreme examples of miniaturization in arthropods . As one of the smallest documented animal species ( Bailey and Keifer , 1943 ) , with dimensions smaller than some single-celled organisms ( Polilov , 2015 ) , it is not surprising that A . lycopersici has a derived morphology . Compared to almost all adult arachnids outside of the Eriophyoidea , which have a body plan with eight legs , A . lycopersici has only four legs ( Figure 1a , b ) . Further , reproductive structures , which are located at the terminal end in other mites , are positioned in the central ventral region ( Nuzzaci and Alberti , 1996 ) . This type of morphology has resulted in altered reproductive behavior wherein males , instead of direct insemination , deposit spermatophores ( packets of sperm ) in the environment that are subsequently picked up by females ( Al-Azzazy and Alhewairini , 2018; Oldfield and Michalska , 1996 ) . Despite these morphological and behavioral innovations , A . lycopersici retains the haplodiploid mechanism of sex determination characteristic of many other mite species ( Anderson , 1954 ) . Further , female A . lycopersici mites can lay up to four eggs per day , and the generation time is as little as 5 days under optimal conditions ( Kawai and Haque , 2004; Rice and Strong , 1962 ) . These features , which resemble those of other agriculturally important mite herbivores , result in rapid overexploitation of the host plant and have undoubtedly contributed to the importance of this species as a field and greenhouse pest of tomato . Here , we present the genome of A . lycopersici , the first for an eriophyoid mite . At only 32 . 5 Mb , it is the smallest arthropod genome reported to date ( Grbić et al . , 2011; Waldron et al . , 2017 ) . As revealed by contrasting the genomic architecture of the tomato russet mite with other sequenced arthropods , including the two-spotted spider mite Tetranychus urticae ( Grbić et al . , 2011 ) , a generalist herbivore often found in co-infestations alongside A . lycopersici ( Glas et al . , 2014 ) , we elucidate mechanisms underlying dramatic genome reduction . In particular , we observed typical features of streamlined genomes ( Arkhipova , 2018; Hessen et al . , 2010a ) , including a marked reduction in the distance between adjacent genes , and few repetitive sequences . Massive loss of introns was apparent . Moreover , reductions in specific genes and gene families , such as environmental response genes , associate with A . lycopersici’s ability to suppress host plant defenses as well as its derived morphology . The genome therefore sheds light not only on mechanisms of extreme metazoan genome reduction , but also on the interplay between gene content and the lifestyle of small herbivores that manipulate their environment .
We assembled the genome of A . lycopersici into seven scaffolds of cumulative length 32 . 53 Mb , of which 99 . 98% is represented on scaffolds 1–5 of lengths 12 . 44 , 10 . 50 , 3 . 66 , 3 . 57 and 2 . 36 Mb , respectively . The remaining two scaffolds are each <6 kb in length , in addition to a mitochondrial genome scaffold . The observed assembly length is similar to the length estimated by a k-mer analysis with genomic sequence reads ( 34 . 81 Mb ) . Separate genome completeness estimates with CEGMA ( Parra et al . , 2007 ) and BUSCO ( Simão et al . , 2015 ) located 90 . 7% and 86 . 0% of the expected core eukaryotic genes , respectively; these values are within the same range as those for T . urticae , the only other sequenced chelicerate herbivore , and for which a high-quality Sanger assembly is available ( 95 . 16% and 92 . 07% , respectively ) . As an additional assessment of completeness , we generated a de novo assembly of the A . lycopersici transcriptome using deep , paired-end Illumina RNA-seq reads derived from mixed sex and developmental stages , and aligned it to the genome sequence . We found that 98 . 2% of transcript contigs could be located on the reference sequence . Of the remaining 243 unplaced transcript sequences , only eight had similarity to known arthropod sequences; the others had homology to bacterial , fungal , or plant sequences , or lacked homology to sequences in existing databases . Annotation of the A . lycopersici genome by automated methods , coupled with extensive manual curation , revealed only 10 , 263 protein-coding genes . As assessed against other mite genomes , including T . urticae , Dermatophagoides pteronyssinus ( the European house dust mite ) ( Waldron et al . , 2017 ) , and Metaseiulus occidentalis ( a phytoseiid predatory mite ) ( Hoy et al . , 2016 ) , as well as the Drosophila melanogaster and human genomes , several features of genic organization in A . lycopersici stand out ( Table 1 ) . The fraction of the genome comprising coding sequence is highest in A . lycopersici , and the distance between genes is the lowest . Associated with the compact genic landscape of A . lycopersici ( Figure 2 and Figure 2—figure supplements 1–6 ) , the percentage of the genome consisting of transposable elements was merely 1 . 54% , which is more than fourfold less than that observed in several other mite genomes , or in the insect D . melanogaster ( Figure 2—figure supplement 1 , Supplementary file 1 — ‘Table S1’ Tab ) . Nevertheless , sequences homologous to the major classes of transposable elements , such as DNA transposons , including Helitrons , as well as both long terminal repeat ( LTR ) and non-LTR retrotransposons , were detected ( Supplementary file 1 — ‘Table S1’ Tab and ‘Table S2’ Tab ) . Across the A . lycopersici genome , extended regions of low genic composition and high TE density were not observed ( Figure 2—figure supplement 2 ) , consistent with the purported holocentric chromosome architecture ( lack of regional centromeres ) of eriophyoid mites ( Helle and Wysoki , 1996 ) . We also observed that the A . lycopersici genome has only 3057 introns in coding sequences ( CDS introns ) , which is more than an order of magnitude fewer than the 44 , 881 in the 90 Mb T . urticae genome , and the 35 , 841 in the 70 . 8 Mb D . pteronyssinus genome . Strikingly , nearly 84% of A . lycopersici protein coding genes were intronless , which is more than threefold higher than observed for the other mite species we analyzed , and more than fivefold higher than for D . melanogaster ( Table 1 ) . To further investigate the dynamics of intron evolution , we evaluated patterns of intron gain and loss in orthologous genes among A . lycopersici and 17 other animal genomes using the Malin analysis pipeline ( Csurös , 2008; Figure 2 , and Figure 2—figure supplements 3 and 4 , and Supplementary file 2 ) . At 29 , 447 conserved intron sites ( Figure 2a ) , A . lycopersici has a mere 207 introns . This is an ~11 fold reduction from that seen in the species with the next lowest counts , the European house dust mite D . pteronyssinus , at 2292 . Apart from A . lycopersici , Acari intron loss rates were broadly similar to those observed for other arthropods , except for M . occidentalis , for which high rates of both intron loss and gain were apparent , a finding previously reported ( Hoy et al . , 2016 ) . However , the rate of intron loss in A . lycopersici was higher than observed in M . occidentalis ( Figure 2b ) , and in contrast to M . occidentalis , intron gains were minimal ( Figure 2—figure supplement 4 ) . The only evidence for retention of the minor spliceosome in A . lycopersici comes from the presence of a single canonical U12 ( minor ) intron in the gene aculy03g00270 that encodes an ultra-conserved calcium channel ( splice sites AT-AC in intron one of length 12 . 5 kb ) . Splicing of this large intron is supported by RNA-seq read alignments , and the orthologous intron one of the T . urticae orthologue of this gene is one of the three U12 introns documented previously in T . urticae ( Grbić et al . , 2011 ) . Although relatively few conserved introns are present in the A . lycopersici genome , they exhibit a bias toward 5’ gene ends ( Figure 2—figure supplement 5 ) , and compared to most arthropods , the median intron length is larger ( Table 1 and Figure 2—figure supplement 6 ) . In a single copy ( orthologous ) gene set for which introns were lost in A . lycopersici , but conserved in five other closely related or high-quality mite or insect genomes ( see Materials and methods ) , the impact of intron loss on A . lycopersici-encoded protein sequences was generally minimal . In fact , in the respective protein sequences spanning 97 of 100 A . lycopersici-specific intron loss events ( 97% ) , multi-species alignments did not reveal insertions or deletions ( indels ) of amino acid residues ( e . g . Figure 2c , and Supplementary file 1 — ‘Table S3’ Tab and Supplementary file 3 ) ; for the remaining few cases ( 3% ) , the respective sites of loss events in A . lycopersici were coincident with the gain or loss of one or several amino acid residues ( e . g . Figure 2d ) . Within this gene set , similar findings were apparent for the larger number of A . lycopersici intron losses as compared to intron sites conserved between the two closest relatives ( D . pteronyssinus and T . urticae; Supplementary file 3 ) . Despite striking examples of intronless genes arising from the loss of multiple conserved introns , as for aculy03g01320 ( Figure 2c ) , some A . lycopersici genes have both lost and retained arthropod conserved introns ( i . e . aculy02g00250 , aculy03g02140 , and aculy01g28080 , Supplementary file 3 ) . As revealed by the clustering algorithm implemented in the CAFE software ( Han et al . , 2013 ) , A . lycopersici exhibits one of the highest rates of gene family contractions ( 1725 ) , and by far the lowest rate of gene family expansions ( 206 ) , among the 18 metazoans we analyzed ( Figure 3; input data for the analysis are provided in Supplementary file 4 and Supplementary file 5 ) . It also has the lowest average expansion per gene family ( Supplementary file 1 — ‘Table S4’ Tab ) . Of the 105 gene families that were identified as ‘rapidly evolving’ in A . lycopersici , only four – as represented by orthogroups ( OGs ) OG0000007 ( containing an Asteroid domain: IPR026832 ) , OG0000546 ( containing a Major Facilitator Superfamily , or MFS , domain: IPR011701 ) , OG0000583 ( containing a Troponin domain: IPR001978 ) , and OG0002260 ( hypothetical proteins ) – were identified as expanding . The remaining 101 families were all identified as contracting ( Supplementary file 1 — ‘Table S5’ Tab ) . Six of these contracting families did contain more than 10 members in A . lycopersici ( OG0000000 , containing a Zinc finger C2H2-type domain: IPR013087; OG0000003 , containing a Homeobox domain: IPR001356; OG0000005 , containing a Serine protease , trypsin domain: IPR001254; OG0000014 , containing a Cytochrome P450 domain: IPR001128; OG0000015 , containing a G-protein-coupled receptor , rhodopsin-like domain: IPR000276; G0000025 , containing a Homeobox domain: IPR001356 ) and , except for OG0000014 containing members of the P450 family , which is known to have only few orthologous relationships ( Feyereisen , 2011 ) , on average 72 . 2% of retained A . lycopersici genes had an orthologue in the majority of chelicerate species ( Supplementary file 1 — ‘Table S6’ Tab ) . Further , among the 101 rapidly contracted gene families we identified families previously implicated in mite and insect xenobiotic detoxification ( Dermauw et al . , 2013a; Dermauw et al . , 2013b; Snoeck et al . , 2018; Van Leeuwen and Dermauw , 2016 ) – carboxyl/choline esterases ( CCEs: OG0000021 and OG0001201 ) , cytochrome P450 monooxygenases ( CYPs: OG0000014 , OG0000030 and OG0000052 ) , glutathione-S-transferases ( GSTs: OG0000102 , OG0000124 ) , short-chain dehydrogenases/reductases ( SDRs: OG0000096 ) , ATP-binding cassette ( ABC ) transporters ( ABCs: OG0000051 and OG0000109 ) and MFS proteins ( OG0000029 , OG0000071 , OG0000099 , OG0000187 ) ( Supplementary file 1 — ‘Table S5’ Tab and ‘Table S7’ Tab ) . Given the role of these families in herbivory and host plant use ( Després et al . , 2007; Heckel , 2014; Van Leeuwen and Dermauw , 2016 ) , we analyzed a selection of these gene families in detail ( see the following sections ) . We also found 315 orthogroups with no members in A . lycopersici but at least one member in all other arthropod species . This is the highest number of absent orthogroups of all arthropods included in our analysis , is ~2-fold more than those lacking in D . pteronyssinus ( 171 ) , and more than threefold those absent in T . urticae ( 101 ) ( Supplementary file 1 — ‘Table S8’ Tab ) . A gene ontology ( GO ) enrichment analysis for D . melanogaster members within these conserved arthropod orthogroups without A . lycopersici members revealed that N-acetylglucosamine metabolic process ( GO:0006044 ) , transferase activity ( GO:0016740 ) and Golgi apparatus ( GO:000579 ) were the most highly significantly enriched GO terms within the Biological Process , Molecular Function and Cellular Component GO categories , respectively ( Supplementary file 1 — ‘Table S9’ Tab ) . Lastly , we found that 427 D . melanogaster essential genes ( Aromolaran et al . , 2020 ) coded for members of 390 orthogroups . Forty-eight of these essential orthogroups did not have members within the Acariformes , the mite superorder comprising A . lycopersici , D . pteronyssinus , and T . urticae , while 21 ( 5 . 4% ) orthogroups were absent in A . lycopersici but present in other acariform mites ( Supplementary file 1 — ‘Table S10’ Tab ) . Furthermore , in a number of cases , orthogroups absent in A . lycopersici harbor conserved genes with potential roles in the development of tissues or structures that are absent or modified in the russet mite relative to other chelicerates or insects ( see also Discussion , and Results section , ‘Loss of highly conserved transcription factors’ ) . For instance , orthologues of Drosophila unkempt , a known developmental regulator , and Drosophila dachs , essential for appendage growth , are both absent in A . lycopersici but present in all other arthropods ( OG0002898 and OG0006002 , respectively ) . Dachs is known to interact with four-jointed ( Buckles et al . , 2001 ) , which is also absent in A . lycopersici , even though it is present in all insect and chelicerate species included in our analysis ( OG0003305 ) . Finally , fat belongs , together with dachs and four-jointed , to the Fat/Hippo pathway and plays a key-role in tissue proliferation and development in both invertebrates and vertebrates ( Simon et al . , 2010 ) . Although dachsous , another player in this pathway , is present ( aculy04g02000 in OG0001018 ) , a fat orthologue could not be identified in A . lycopersici while this orthologue was found in other acariform mites ( OG0000383 , Supplementary file 1 — ‘Table S7’ Tab ) . We curated the A . lycopersici genome for sequences encoding established detoxification enzymes ( Després et al . , 2007; Heckel , 2014; Van Leeuwen and Dermauw , 2016 ) including GSTs , CCEs , and CYPs . In A . lycopersici , detoxification gene families are especially reduced , with a mere 4 GSTs , 8 CCEs , and only 23 CYPs ( Table 2 , Figure 4a , and Figure 4—figure supplements 1 , 2 and 3; Van Leeuwen and Dermauw , 2016 ) . In particular , the number of GSTs and CCEs is remarkably low ( see Discussion ) . This finding was corroborated by mining of the A . lycopersici transcriptome assembly ( the 4 GSTs and 8 CCEs present in the genome assembly were also present in transcriptome assembly , with no other transcript contigs with homology to GSTs or CCEs identified ) . Of note , half of the GSTs and almost all ( 7 out of 8 ) CCE genes in A . lycopersici are evolutionarily conserved across chelicerates or arthropods ( Figure 4—figure supplements 1 and 2 ) . We also examined transporters of the ABC family and MFS proteins that have been implicated in detoxification responses in arthropod species , although transporters in both of these families have diverse other roles as well ( de la Paz Celorio-Mancera et al . , 2013; Dermauw et al . , 2013a; Dermauw et al . , 2013b; Dermauw and Van Leeuwen , 2014; Govind et al . , 2010 ) . In contrast to genes encoding ‘classic’ detoxification enzymes like CYPs , CCEs , or GSTs , dramatic reductions in ABC transporter genes were not observed . For example , A . lycopersici has 9 ABCC and 16 ABCG transporters , while 22 and 2 are present in M . occidentalis and 39 and 23 are present in T . urticae , respectively ( Table 2 , Figure 4—figure supplement 4 ) . Further , in contrast to the trend for contractions of the classic detoxification gene families , we also observed two A . lycopersici expansions - comprising three orthogroups , OG0000024 , OG0000546 , and OG0006109 - of the MFS , which is involved in membrane-based transport of small molecules ( Figure 4b , Figure 4—figure supplement 5; Pao et al . , 1998; Yan , 2015 ) . To see if A . lycopersici’s specialized lifestyle has had a notable impact on chemoreception , we also exhaustively mined and annotated the A . lycopersici genome for gustatory receptors ( GRs ) , degenerin/epithelial Na+ channels ( ENaCs ) , ionotropic receptors ( IR ) and transient receptor potential ( TRP ) channels . Members of these four families have been previously documented to play important roles in sensing environmental ( chemical ) cues in other arthropod species ( Damann et al . , 2008; Hoy et al . , 2016; Ngoc et al . , 2016; Robertson et al . , 2003; Rytz et al . , 2013; Whiteman and Pierce , 2008 ) . The GR family , which contains seven transmembrane spanning regions ( Touhara and Vosshall , 2009 ) and is linked to the detection of sweet and bitter compounds ( Silbering and Benton , 2010 ) , was the most strongly reduced , with only two of these genes identified ( Figure 4c , Figure 4—figure supplement 6 ) , as opposed to the 447 intact GRs reported in T . urticae ( Ngoc et al . , 2016 ) . Further , only four ENaCs are present in the A . lycopersici genome ( Figure 4d , Figure 4—figure supplement 7 ) . Members of this family have recently been shown or suggested to be chemoreceptors for diverse compounds in insects and mites , but some family members likely have highly conserved roles in acid sensing ( Ben-Shahar , 2011; Silbering and Benton , 2010 ) , as well as in the perception of mechanical or osmotic cues ( Ben-Shahar , 2011; Zelle et al . , 2013 ) . Of the two ENaCs likely to play these conserved roles in T . urticae , one is in a well-supported clade with a single ENaC in the tomato russet mite ( aculy04g09940 ) ( Figure 4 , Figure 4—figure supplement 7 ) . The IR family , which has been linked to odorant detection ( Joseph and Carlson , 2015 ) , humidity and temperature sensing in D . melanogaster ( Enjin et al . , 2016 ) , is markedly reduced in A . lycopersici compared to most insects and M . occidentalis ( Hoy et al . , 2016 ) . However , the numbers are similar to those in T . urticae ( each has four putative IRs with strong bootstrap support ) , including homologues of the highly conserved IR25a and IR93a receptors ( Figure 4—figure supplement 8 ) . Interestingly , A . lycopersici may have as few as six ionotropic glutamate receptors ( iGluRs ) , as compared to 14 in T . urticae ( Figure 4—figure supplement 8 ) ; proteins in this family are related to IRs , but have ultra-conserved roles in synaptic transmission in animals ( Benton et al . , 2009 ) . Finally , we found both expansions and contractions of the TRP family ( Figure 4—figure supplement 9 ) . Like the other sequenced herbivorous mite , T . urticae , no orthologue of TRPA1 was located , but orthologues for TRPgamma , NopmC , and TRPML are present , with three copies of NopmC as compared to T . urticae's two . Unlike T . urticae , members of the TRPP and TRPM clades were completely absent in the russet mite , but strikingly , two putative members of the TRPV clade ( Inactive and Nanchung ) , previously thought to be lost in mites and ticks ( Peng et al . , 2015; Regier et al . , 2010 ) , appear to be present . Among two vertebrates , one nematode and the 15 arthropod species we analyzed , A . lycopersici has the lowest number ( 364 ) of transcription factor ( TF ) genes ( Supplementary file 1 — ‘Table S11’ Tab ) . Nevertheless , when accounting for the total number of genes by species , the TF fraction in A . lycopersici ( 3 . 55% ) is higher than that of T . urticae ( 2 . 98% ) , and is within the range reported for metazoan animals ( 4 . 7% ±1 . 4 , Charoensawan et al . , 2010 ) . However , a lower number of the PFAM TF domains Zinc finger ( zf-C2H2 and zf-CCHC ) , Forkhead , Homeobox , Hormone ( nuclear ) receptor , HLH , bZIP_2 and T-box were found in A . lycopersici compared to all other species included in our analysis ( Supplementary file 1 — ‘Table S11’ Tab ) . In addition , A . lycopersici orthologues of the Hairy Orange protein family ( hey , cwo and deadpan ) have lost the Hairy Orange domain ( Figure 4—figure supplement 10 ) , while an orthologue of D . melanogaster SoxNeuro could not be identified in A . lycopersici despite being present in the spider and Acari genomes examined ( Figure 4—figure supplement 11 ) . Among nuclear receptors ( NRs ) , we identified eight canonical NRs in the A . lycopersici genome ( E78 , HR3 , EcR , two RXRs , ERR , FTZ-F1 , HR96 ) that contained both a DNA-binding domain ( DBD ) and a ligand-binding domain ( LBD ) . However , no homologues of the evolutionary conserved NRs HNF4 , HR39 , HR78 , and HR83 ( Bodofsky et al . , 2017; Bonneton and Laudet , 2012 ) , nor a homologue of the T . urticae Photoreceptor-specific NR ( PNR ) , were detected in the A . lycopersici genome , even though HR78 , HNF4 , and PNR are present in D . pteronyssinus ( Supplementary file 1 — ‘Table S12 . 1’ Tab and ‘Table S12 . 2’ Tab ) . Further , for six nuclear receptors ( E75 , DSF , HR4 , HR38 , HR51 , and SVP ) that are evolutionary conserved across arthropods and normally have a canonical ( DBD+LBD ) structure ( Fahrbach et al . , 2012; Grbić et al . , 2011; Hwang et al . , 2014; Litoff et al . , 2014 ) , an LBD was not predicted for the respective A . lycopersici homologues . LBDs for all of these except HR4 were predicted for both the D . pteronyssinus and T . urticae homologues ( Supplementary file 1 — ‘Table S12 . 1’ Tab and ‘Table S12 . 2’ Tab , Figure 4—figure supplements 12–17 ) . The basic helix-loop-helix ( bHLH ) gene family is an ancient family found in fungi , plants , and animals , and members of this family are essential both for organisms to respond to environmental factors , as well as for cellular differentiation during development ( Skinner et al . , 2010 ) . The D . melanogaster achaete and scute bHLH genes play crucial roles in bristle development ( García-Bellido and de Celis , 2009 ) . Within the bHLH family group we found that T . urticae , M . occidentalis and I . scapularis have five bHLH proteins with an achaete-scute InterPro domain ( IPR015660 ) , while only three were found in both D . pteronyssinus ( g4111 . t1 , g7028 . t1 and g6164 . t1 ) and A . lycopersici ( aculy01g18470 , aculy01g18540 and aculy02g28230 ) . A number of other specific transcription factors that are highly conserved among most arthropods are also absent from the A . lycopersici genome . For A . lycopersici , we were unable to identify proboscipedia , a member of the Hox gene family . Members of this family ( labial , proboscipedia , Hox3/zen , Deformed , Sex combs reduced , fushi tarazu , Antennapedia , Ultrabithorax , abdominal-A , and Abdominal-B ) encode homeodomain transcription factors and act to determine the identity of segments along the anterior–posterior axis in arthropods ( Hughes and Kaufman , 2002 ) . Proboscipedia is present in all chelicerate genomes ( horseshoe crab , scorpions , spiders , mites and ticks ) for which Hox genes have been analyzed ( Figure 5 , Supplementary file 1 — ‘Table S13 . 1’ Tab and ‘Table S13 . 2’ Tab , Supplementary file 6; Di et al . , 2015; Hoy et al . , 2016; Kenny et al . , 2016; Schwager et al . , 2017 ) , and is believed to be ancestral to all arthropods ( Pace et al . , 2016 ) . Of particular note , proboscipedia is located in close proximity ( <35 kb ) of labial in Acariformes , but in Aculops labial was the only Hox gene that was present on scaffold 2 ( Supplementary file 1 — ‘Table S14’ Tab ) . Furthermore , A . lycopersici lacks a homologue of the T-box encoding gene org-1 ( Figure 4—figure supplement 18 ) , which in D . melanogaster plays a pivotal role in diversification of circular visceral muscle ( Schaub and Frasch , 2013 ) . Finally , we also mined the A . lycopersici genome for transcription factors and other genes involved in circadian rhythm ( so-called ‘clock’ genes ) ( Supplementary file 1 — ‘Table S15’ Tab ) . Orthologues of the helix-loop-helix TFs cycle , Clock and tango and the bZIP TF vrille were identified in the A . lycopersici genome . However , we did not identify period and timeless , known negative regulators of Clock and cycle ( Lee et al . , 1999; Peschel and Helfrich-Förster , 2011 ) . Other circadian regulators , like the circadian photoreceptor cryptochrome and the bZIP TF PAR-domain protein 1ε , were also not identified , even though these are present in T . urticae ( Hoy et al . , 2016 ) . We identified 18 putatively intact horizontal gene transfer ( HGT ) candidate genes ( Supplementary file 1 — ‘Table S16’ Tab ) , and performed subsequent phylogenetic analyses that suggested that nine were acquired from a foreign source . Seven of these genes code for UDP-glycosyltransferases ( UGTs ) , members of which have well documented roles in xenobiotic detoxification ( Snoeck et al . , 2019 ) . Phylogenetic inference with all T . urticae¸ D . pteronyssinus and A . lycopersici UGTs ( 80 , 27 , and 7 , respectively ) indicated that the seven UGTs in the tomato russet mite genome were the result of a lineage-specific expansion ( Figure 4—figure supplement 19 ) . Although we did not observe a clear phylogenetic signature of HGT ( Wybouw et al . , 2016 ) , our phylogenetic reconstruction is consistent with previous studies which indicated that , prior to the formation of the Acariformes lineage , an ancestral mite species laterally acquired a UGT gene copy from a bacterial source ( Ahn et al . , 2014; Wybouw et al . , 2018 ) . Two intact genes of bacterial origin ( aculy01g38350 and aculy04g02470 ) were also identified in the tomato russet mite genome that are predicted to code for enzymes in the microbial and plant pantothenate biosynthesis pathway ( an apparent duplicate of aculy01g38350 was also uncovered , but the coding sequence was disrupted , and it lacked expression , suggesting it is a pseudogene ) ( Figure 6 ) . PCR amplification linked both laterally acquired genes with either neighboring intron-containing genes ( aculy01g38350 ) or conserved eukaryotic genes ( aculy04g02470 is located next to aculy04g02480 , which encodes a Gtr1/RagA protein ) ; in addition , an aculy01g38350 transcript ( Illumina contig 1934 ) had a polyA tail , suggestive of eukaryotic transcription ( Figure 6—figure supplement 1 ) . Pantothenate , or vitamin B5 , is a life-essential compound , and whereas plants and bacteria are able to synthesize this compound de novo , animals rely on dietary uptake . Genes for pantothenate synthesis are present in tetranychid mites , and genomic and phylogenetic approaches have pointed to an ancient HGT event prior to speciation within the Tetranychidae family for both genes . Constrained tree tests rejected the topology where ketopantoate hydroxymethyltransferase of A . lycopersici was the sister lineage to the group of spider mite biosynthetic proteins , but not for pantoate β-alanine ligase , suggesting that A . lycopersici acquired the ketopantoate hydroxymethyltransferase gene from a different bacterial donor species ( Figure 6 , Approximately Unbiased tests , p-value cut-off of 0 . 01 ) . In T . urticae the acquisition of pantothenate biosynthetic genes is accompanied by the horizontal gene transfer of two methylenetetrahydrofolate dehydrogenases ( MTHFDs ) , enzymes of the folate pathway and connected to the pantothenate biosynthesis pathway ( Wybouw et al . , 2018 ) . Although such a HGT was not detected in A . lycopersici , an expansion of MTHFDs was detected compared to other mite species ( OG0000706 in Supplementary file 1 — ‘Table S7’ Tab ) . Small molecules or proteins produced in salivary glands are one mechanism by which arthropod herbivores can manipulate the defenses of their host plants . As A . lycopersici is able to potently suppress tomato defenses ( Glas et al . , 2014; Schimmel et al . , 2018 ) , we predicted its secretome , and found that 612 of the 10 , 263 annotated A . lycopersici proteins ( 6% ) are putatively secreted ( Supplementary file 1 — ‘Table S17’ Tab ) . Only one of the more than 600 secreted A . lycopersici proteins ( aculy02g17370 , a glycosyl hydrolase , family 13 , IPR013780 ) had a best BLASTp hit with a T . urticae protein that was previously identified in T . urticae saliva using an LC-MS/MS ( Jonckheere et al . , 2016 ) . More than half ( 351 ) of these proteins were absent in orthogroups in non-herbivorous arthropod species , and are less than 350 amino acids in length . Only 15 of these 351 proteins belonged to an orthogroup with more than one member in A . lycopersici ( OG0006384 , OG0009325 , OG0009954 and OG0010904 ) . Among these , OG0009325 contains three short A . lycopersici proteins <90 amino acids in length ( aculy01g11450 , aculy01g12600 , and aculy01g12690 ) . Of note , the gene encoding the single T . urticae representative in this group , tetur24g01070 , was previously found to be overexpressed in the T . urticae salivary gland region ( Jonckheere et al . , 2016 ) . OG0006384 , on the other hand , contains cysteine peptidases ( Peptidase C1A , papain C-terminal domain; InterPro IPR000668 ) , which are enzymes reported to have key roles in plant-pathogen/pest interactions ( Shindo and Van der Hoorn , 2008 ) , and for which two lineage-specific expansions are present in A . lycopersici ( Figure 4—figure supplement 20 ) . We also characterized components of small RNA pathways that might be of potential relevance for agricultural control methods . The A . lycopersici genome harbors highly conserved miRNA sequences , such as let-7 , miR-1 , and miR-9a ( Supplementary file 1 — ‘Table S18’ Tab ) . However , in contrast to T . urticae , a clear A . lycopersici homologue of Exportin-5 , a dsRNA-binding protein mediating nuclear transport of pre-miRNAs ( Bohnsack et al . , 2004; Kim , 2005 ) , is lacking , suggesting a deviating miRNA pathway in A . lycopersici . In line with the latter hypothesis , we could not identify an A . lycopersici homologue of Staufen , while this gene is present in T . urticae ( Grbić et al . , 2011; Supplementary file 1 — ‘Table S19’ Tab ) and other arachnids ( OrthoDb v 9 . 1 , group EOG091G07A0 and EOG090Z04UZ , respectively ) and was shown to negatively modulate miRNA activity in the nematode C . elegans ( Ren et al . , 2016 ) . The A . lycopersici genome contains , in line with T . urticae , clear homologues of Dicer-1 , Loquacious , Drosha and Pasha and an expansion of the AGO1 and PIWI/AGO3 subfamilies . Of note , we found one A . lycopersici protein ( aculy02g00240 ) that was highly homologous to the T . castaneum Dicer-1 enzyme ( bitscore of 294 ) and that contained both an RNA-binding domain ( PAZ-domain , cl00301 ) and the RNAse III domain ( cd00593 ) while two A . lycopersici proteins ( aculy02g04810 and aculy02g19970 ) showed reciprocal BLASTp hits with T . castaneum Dicer-2 and Dicer-1 , respectively , but were relatively short ( about 500 amino acids ( aa ) compared to 1726 aa for aculy02g00240 ) and only contained the RNAse III domain . However , the genes encoding these proteins are located next to a sequencing gap in the current assembly and it could be that gene-models for these Dicer-like enzymes are not complete . Similar to T . urticae , we could not identify clear homologues of R2D2 and AGO2 ( Grbić et al . , 2011; Supplementary file 1 — ‘Table S19’ Tab ) , suggesting that the siRNA pathway is either absent or non-canonical in both mite species ( Okamura et al . , 2011 ) . Further , important players in the PIWI-interacting RNA ( piRNA ) pathway ( Iwasaki et al . , 2015 ) were identified in the A . lycopersici genome ( PIWI/AGO3 , Zucchini , Armitage , Maelstrom and SoYb; Supplementary file 1 — ‘Table S19’ Tab ) , while homologues of Armitage and Zucchini could not be identified in T . urticae , which is in line with the recently suggested non-canonical piRNA pathway in T . urticae ( Supplementary file 1 — ‘Table S19’ Tab , Huang et al . , 2014; Mondal et al . , 2018b ) . Finally , RNA-dependent polymerases are known to be essential for the amplification of the RNA silencing effect ( systemic RNAi ) in C . elegans and some plants ( Tomoyasu et al . , 2008 ) . Genes encoding these enzymes are absent in insect genomes while 1 to 5 have been reported in Acari genomes ( Grbić et al . , 2011; Hoy et al . , 2016; Joga et al . , 2016; Mondal et al . , 2018a; Zong et al . , 2009 ) . Surprisingly , we could not identify RNA-dependent polymerase genes in the A . lycopersici genome ( Supplementary file 1 — ‘Table S19’ Tab ) , which might indicate that these genes have been lost since the divergence of Eriophyoidea from other acariform lineages . However , as systemic RNAi does seem to occur in some insect orders , for example , Coleoptera ( Joga et al . , 2016 ) , we cannot exclude that systemic RNAi might also occur in A . lycopersici .
At only 32 . 5 Mb , the A . lycopersici genome is the smallest sequenced arthropod genome to date . In contrast to its closest sequenced relatives , the majority of genes lack introns , few repetitive sequences are present , and many genes conserved in most animals are absent . Compared to its larger relatives , the simplification of A . lycopersici’s body plan , and that of eriophyoid mites more generally , is reminiscent of that observed in other microarthropods ( Maderspacher , 2016 ) . The compressed genome architecture of A . lycopersici is in line with genome streamlining concepts ( Hessen et al . , 2010a; Hessen et al . , 2010b ) , some of which speculate that maintaining a high growth rate in nutritionally limited environments ( in this study the plant epidermis ) may be a driver for the evolution of compact genomes . Further , the extreme reduction of several environmental response gene families aligns with predictions that follow from ecological specialization theories ( Devictor et al . , 2010; Futuyma and Moreno , 1988; Laland et al . , 2016 ) since the mite’s suppression of plant defenses may allow for such families to minimize during the course of its evolution . Finally , this first eriophyoid genome provides a resource for methods of early detection of mite infestations using molecular markers , and its reduced complement of defense genes – a common source of pesticide resistance – may also reveal novel Achilles’ heels for the control of A . lycopersici . But foremost , this genome is a milestone for accelerating our understanding of the evolutionary forces underpinning metazoan life at the limits of small physical and genome size .
A . lycopersici individuals were reared in insect cages ( BugDorm-44590DH , Bug Dorm Store , MegaView Science , Taichung , Taiwan ) in a walk-in growth chamber on tomato plants ( Solanum lycopersicum , cv . Castlemart ) that were between 3 and 6 weeks old . The climate room was set to day/night temperatures of 27°C/25°C , a 16/8 hr light/dark regime and 60% relative humidity . Harvesting of A . lycopersici mites was performed by detaching highly infested tomato leaflets and placing them in 1 . 5 mL Eppendorf tubes . Eppendorf tubes were filled with water and mites ( adults , juveniles and eggs ) were washed off by rinsing and briefly vortexing the tubes . The tubes were then centrifuged ( 13 , 000 rpm for 2 min ) , after which bulk tomato tissue was removed and water was pipetted away . Contamination from tomato tissue was limited to small amounts ( less than ~5% ) of material consisting primarily of tomato trichomes . Resulting ‘pellets’ of russet mites were frozen in liquid nitrogen and stored at −80°C until DNA was extracted . DNA was extracted using a modified version of the CTAB method ( Doyle and Doyle , 1987 ) . Sixty µg of DNA dissolved in TE buffer was sent to Eurofins MWG Operon ( Ebersberg , Germany ) for sequencing . Sequencing reads were produced with the standard Roche/454 sequencing protocol on the GS FLX system running Data Analysis Software Modules version 2 . 3 . Three different libraries were prepared and sequenced in accordance with the recommendations of Roche/454: random primed shotgun , 8 kb paired-end , and 20 kb paired-end . From the shotgun library the mean length was 503 bp , while for the 8 kb and 20 kb libraries the mean lengths were 366 bp and 359 bp , respectively . Sequencing reads were trimmed to remove adapters and low-quality bases , as well as to split each paired-end read into a forward and reverse pair; this yielded a total of 1 , 854 , 028 shotgun reads , 1 , 076 , 303 reads from the 8 kb library , and 1 , 274 , 414 reads from the 20 kb library . Contigs were assembled by the in-house pipeline of Eurofins MWG Operon ( Ebersberg , Germany ) based on Newbler 2 . 6 ( Margulies et al . , 2005 ) . Following scaffolding and filtering for plant ( tomato ) , prokaryotic , and adaptor sequences , a reference for the nuclear genome was generated that consisted of seven scaffolds ( scaffolds 1 , 2 , 3 , 4 , 5 , 11 , and 17 ) with a total length of 32 . 53 Mb ( the Newbler ‘peakDepth’ , or coverage , for the assembly was 38 ) . An additional scaffold ( scaffold 6 ) of length 13 . 5 kb consisted of the mitochondrial genome . A k-mer size estimate of the A . lycopersici genome was performed using the genomic 454 sequence reads and Jellyfish 2 . 2 . 6 ( Marçais and Kingsford , 2011 ) . Following the recommendations of T . Nishiyama ( http://koke . asrc . kanazawa-u . ac . jp/HOWTO/kmer-genomesize . html ) , genome size was estimated by running Jellyfish ( Marçais and Kingsford , 2011 ) with the following settings ‘-t 24 iC -s 20M’ for all odd k-mer values from 17 to 31 , with averaging of the results provided from the eight different estimates . Completeness of the genome was also assessed using CEGMA 2 . 5 ( Parra et al . , 2007 ) as well as BUSCO v3 ( Simão et al . , 2015 ) , as well as with an alignment of the A . lycopersici Illumina-based transcriptome assembly to the genomic scaffolds ( see below , and Results section ) . Mixed developmental stages ( adults , juveniles , and eggs ) were collected from tomato leaflets as was done for DNA preparation . Similar to DNA extraction , small amounts of tomato trichome contamination were evident , but at low levels . RNA was extracted using a Qiagen RNeasy kit ( Qiagen , Hilden , Germany ) according to the manufacturer’s instructions . Forty-five µg of RNA was provided to Eurofins MWG Operon for library preparation according to standard Roche protocols . Following poly ( A ) selection and strand-specific cDNA library preparation , the library was analyzed on a Shimadzu MultiNA microchip electrophoresis system ( Shimadzu , Kyoto , Japan ) to verify that the gel size selection was in the range of 500–800 bp . A total of 1 , 370 , 892 sequencing reads were collected using a Roche GS FLX system employing the Titanium series chemistry . After trimming of cDNA reads to remove low quality reads and adapter sequences , the remaining 1 , 370 , 005 reads were assembled using MIRA ( Chevreux et al . , 2004 ) . RNA was extracted from eight A . lycopersici pools using the Qiagen RNeasy purification kit ( Qiagen , Hilden , Germany ) with the following adaptations: Step 3: 50 µl of RNEasy lysis buffer ( RLT ) + ß -mercaptoethanol were added to the mite pool in a 1 . 5 mL tube , followed by 1–2 min of cell lysis performed by twisting and turning a 1 . 5 mL-tube-pestle . Three hundred µl of RLT + -mercaptoethanol was then used to rinse the pestle; Step 11: RNA was eluted in 30 µl RNAse-free water and stored on ice . All samples were stored at −20°C . Strand-specific paired-end RNA library preparation and sequencing were carried out by the Centro Nacional de Análisis Genómico ( Barcelona , Spain ) to yield a total of 86 . 6 million 101 bp read pairs . To construct a transcriptome assembly from the Illumina RNA-seq reads , the reads were first aligned to the A . lycopersici reference genome sequence using STAR 2 . 5 . 2b ( Dobin et al . , 2013 ) with the following settings: twopassMode Basic , sjdbOverhang 100 , and alignIntronMax 20000 . Reads that did not align to the reference were subsequently aligned against the tomato genome release SL 2 . 50 ( Tomato Genome Consortium et al . , 2012 ) to filter out contamination from the host plant with the same settings used to align to the mite genome except for alignIntronMax , which remained unspecified . The reads that did not align to the tomato genome were pooled with the reads that aligned to the A . lycopersici genome and imported into CLC Genomics Workbench 9 . 0 . 1 ( https://www . qiagenbioinformatics . com/ ) , where they were trimmed using the default parameters ( quality score limit 0 . 05 and a maximum of two ambiguous nucleotides ) before being assembled with the default settings and a minimum contig length of 200 . The resulting 13 , 428 transcript sequences were aligned back to the A . lycopersici genome assembly using BLAST 2 . 5 . 0+ ( Camacho et al . , 2009 ) to provide a measure of the genome completeness for transcribed regions . Of the 243 transcripts that did not align , 23 had no hits in any database , and 108 , 84 and 20 appeared to be from bacterial , plant and fungal sources , respectively . Only eight had homology to arthropod sequences present in the NCBI NR , NT , Other Genomic , RefSeq Genomic , RefSeq RNA , Representative Genomes , and WGS databases ( downloaded January 9 , 2017 ) . A first-pass annotation was produced using EuGene ( Schiex et al . , 2001 ) specifically trained for the studied genome using the 454 transcript read data as a guide . As a consequence of the close proximity of adjacent genes ( see Results and Table 1 ) , we observed that transcript contigs often merged adjacent genes , creating apparent chimeric genes . To circumvent this issue , only junctions spanning introns as assessed from the aligned 454 data were kept after mapping . Besides using transcript data , protein homology to the invertebrate section from RefSeq , curated proteins from SWISSprot and the proteome from T . urticae were used . Subsequently , the annotation was revised in several ways . The deep dataset of Illumina RNA-seq reads was aligned to the genome using the default settings of Bowtie 2 . 2 . 3 ( Langmead and Salzberg , 2012 ) /TopHat 2 . 0 . 12 ( Kim et al . , 2013 ) , as well as STAR 2 . 5 . 2b ( Dobin et al . , 2013 ) with the parameters described previously . Transcripts from the CLC transcriptome assembly were also located on the genome using BLAT 36 ( Kent , 2002 ) . Then , Cufflinks 2 . 2 . 1 ( Trapnell et al . , 2013 ) and TransDecoder ( Release 20140704 ) ( Haas et al . , 2013 ) were used to identify additional ORFs of over 300 bp in length that had not been detected by EuGene . Resulting gene models were then added where supported by the strand-specific RNA-seq reads and/or transcript alignments . The compact nature of the A . lycopersici genome , coupled with the finding that most genes were intronless ( Table 1 ) , made it feasible to then manually inspect all gene models against the aligned Illumina RNA-seq read data . This inspection step was performed using the Integrative Genomics Viewer ( Robinson et al . , 2011 ) , which allowed simultaneous display of gene models and RNA-seq read alignments . Manual adjustments to gene models , where required , were performed using GenomeView N29 ( Abeel et al . , 2012 ) . Additionally , members of specific gene families were expertly annotated as described in the section ‘Comparative analyses with specific gene families’ , with resulting adjustments also incorporated in the final annotation . GenomeTools 1 . 5 . 10 ( Gremme et al . , 2013 ) was used to sort , correct phase information , and validate the resulting GFF3 . Coding gene numbers and the percentages of intronless genes were calculated with the ‘stat -exonnumberdistri’ command of the GenomeTools 1 . 5 . 6 package ( Gremme et al . , 2013 ) using the respective GFF3 annotation files as input ( Table 1 ) . Where multiple isoforms were present for a gene , only the longest isoform was used for this and subsequent analyses . Regions of the respective genomes were then classified as coding , intergenic or intronic by parsing the location of coding sequences ( CDS ) from the respective GFF3 annotation files; due to the unreliability of untranslated sequence prediction or their complete absence in some annotations , these regions were not considered . In instances where CDS sequences overlapped , their coordinates were merged so that no region of the genome would be counted multiple times . Regions of the genome between the start and end of the CDS sequences of adjacent genes were classified as intergenic , while regions of the genome within genes that did not fall into CDS coordinate blocks were classified as intronic ( in instances where genes were located within the introns of other genes , the CDS sequences of the genes within the introns were classified as coding , with the remaining portion counted as intronic ) . The consensus of the repeated DNA ( ≥2 copies ) in the genome was constructed by employing RepeatScout ( v . 1 . 0 . 5 ) ( Price et al . , 2005 ) . The repeats that were ≥90% identical with a minimum overlap of 40 bp were assembled using CAP3 ( Huang and Madan , 1999 ) . Gene families were identified based on homology with cellular genes by employing tBLASTx 2 . 2 . 28+ ( Altschul et al . , 1997 ) searches against the Refseq mRNA database at NCBI and BLASTn 2 . 2 . 28+ ( Altschul et al . , 1997 ) searches against the annotated genes in the A . lycopersici genome . All candidate gene families were filtered upon manual verification . The remaining repeats were classified by REPCLASS ( Feschotte et al . , 2009 ) and RepeatMasker ( Smit et al . , 2013 ) protein searches ( http://www . repeatmasker . org/cgi-bin/RepeatProteinMaskRequest ) . The repeats that were classified based on the structure or TSD module of REPCLASS were manually verified . The criteria of requiring at least one defined end were used to classify a repeat as a TE . To identify if the elements had at least one defined end , the unclassified repeats ( ≥65 bp ) were aligned with the respective copies with extended flanking sequences using MUSCLE ( Edgar , 2004 ) . Repeats were classified and full-length copies were extracted when possible . To identify low copy non-LTR retrotransposons , the non-LTR proteins from the related mite T . urticae were used as queries in homology-based tBLASTn 2 . 2 . 25+ ( Altschul et al . , 1997 ) searches against the A . lycopersici genome . To identify the genomic coverage , the curated repeat library was used to mask the genome using RepeatMasker ( v 4 . 0 . 5 ) ( Smit et al . , 2013 ) . The final RepeatMasker output was parsed using parseRM . pl ( Kapusta et al . , 2017; Kapusta , 2017 ) to identify the contribution of TEs ( Figure 2—figure supplement 1 , Supplementary file 1 — ‘Table S1’ Tab ) . Last , a gene and TE density plot was constructed using karyoploteR version 1 . 14 . 0 ( Gel and Serra , 2017 ) and the GFF3 annotation file of the A . lycopersici genome ( Table 1—source data 1 ) and the RepeatMasker output ( Supplementary file 1 — ‘Table S2’ Tab ) , respectively . The longest protein isoforms for the following organisms were extracted for orthologue identification: A . lycopersici ( current genome ) , Anopheles gambiae AgamP4 . 7 ( Holt et al . , 2002 ) , Bombyx mori ASM15162 ( Ensembl release 37 ) ( Mita et al . , 2004 ) , Caenorhabditis elegans Wormbase release WS261 ( The C . elegans The C . elegans Sequencing Consortium , 1998 ) , Centruroides sculpuratus CEXE 0 . 5 . 3 ( Schwager et al . , 2017 ) , Danio rerio GRCz10 ( Ensembl release 89 ) ( Howe et al . , 2013 ) , Daphnia pulex PA42 3 . 0 ( Ye et al . , 2017 ) , Dermatophagoides pteronyssinus ( ASM190122v2 ) ( Waldron et al . , 2017 ) , Drosophila melanogaster Flybase release 6 . 16 ( Adams et al . , 2000; Gramates et al . , 2017 ) , Homo sapiens GRCh38 . p10 ( Ensembl release 89 ) ( Lander et al . , 2001; Venter et al . , 2001 ) , Ixodes scapularis ( IscaW1 . 5 ) ( Gulia-Nuss et al . , 2016 ) , Limulus polyphemus 2 . 1 . 2 ( Simpson et al . , 2017 ) , Metaseiulus occidentalis 1 . 0 ( GNOMON release ) ( Hoy et al . , 2016 ) , Parasteatoda tepidariorum 1 . 0 ( Schwager et al . , 2017 ) , Pediculus humanus PhumU2 ( Ensembl release 36 ) ( Kirkness et al . , 2010 ) , Strigamia maritima Smar1 ( Ensembl release 36 ) ( Chipman et al . , 2014 ) , T . urticae ( ORCAE August 11 , 2016 release ) ( Grbić et al . , 2011 ) , and Tribolium castaneum Tcas5 . 2 ( Ensembl release 36 ) ( Richards et al . , 2008 ) . The identification of orthologous protein sequences was performed with OrthoFinder 1 . 1 . 8 ( Emms and Kelly , 2015 ) using BLAST 2 . 6 . 0+ . We found 147 single-copy orthologues across all species that we then aligned using MAFFT 7 . 305b ( Katoh and Standley , 2013 ) with ‘genafpair’ and ‘maxiterate 1000’; a concatenation of the alignments for the 147 orthologues was then generated prior to trimming with trimAl 1 . 4 . rev15 ( Capella-Gutiérrez et al . , 2009 ) using the ‘strictplus’ option . The trimmed sequences were used for a phylogenetic reconstruction with RAxML 8 . 2 . 12 ( Stamatakis , 2014 ) using the LG+G+F model as identified for phylogenetic reconstruction by ProtTest 3 . 4 . 2 ( Darriba et al . , 2011 ) according to the Akaike Information Criterion , and a total of 1000 rapid bootstrap replicates ( ‘-f a -x 12345’ option ) . Although the ‘estimate proportion of invariable sites ( +I ) ’ was also recommended by ProtTest , the developer of RAxML v8 , on page 59 of the RAxML v8 . 2 . X manual , cautions against using this option , and this and all subsequent optimal models for reconstructions with RAxML were adjusted to adhere to this developer recommendation . Orthologous protein clusters were selected for intron analysis on the basis of the following criteria: the cluster had to have at least one orthologue from A . lycopersici , orthologous protein sequences from at least 14 other species had to be present , and no species could have more than three orthologous proteins in the cluster; when multiple orthologues for a single species were present , only the longest one was retained . The sequences in these clusters were aligned using MAFFT 7 . 305b ( Katoh and Standley , 2013 ) with the settings previously described . GNU Parallel ( Tang , 2011 ) was used to align multiple clusters at once . Custom Python scripts using BioPython 1 . 70 ( Chapman and Chang , 2000 ) and the BCBio GFF parser ( Chapman , 2016 ) were used to parse and append intron position information to the FASTA sequence identifier line as required by Malin ( Csurös , 2008 ) . The 2371 clusters that met the requisite criteria , along with the tree built from the 147 single-copy orthologues , were used in the Malin analysis ( Csurös , 2008 ) . Intron positions for gain/loss analysis were selected from those that were considered unambiguous in A . lycopersici and at least 11 other species , with five amino acids present on either side of the intron position ( a Malin criteria to reduce the possibility of incorrect inference resulting from misalignments ) . To investigate the consequence of intron losses in A . lycopersici on predicted protein sequences , which can shed light on underlying mechanisms of loss ( see Discussion ) , a subset of orthogroups was selected for which sequences for each of A . lycopersici , D . pteronyssinus , T . urticae , M . occidentalis , B . mori and D . melanogaster were present as single copies ( 1216 in total ) ; apart from A . lycopersici , the five other species were selected because of their close phylogenetic position to A . lycopersici ( Figure 3 ) , and/or because they have high-quality genomes and annotations . The protein sequences for the six species for each of these orthogroups were aligned using MAFFT 7 . 407 with the settings previously described , and a table of intron sites for these orthogroups was generated in Malin using the following settings: Minimum nongap positions: 0 ( On both sides ) ; Minimum unambiguous characters at a site: 1; There must be at least one unambiguous character in the following clades: All unselected . From this table , intron positions that were present and had the same phase in all arthropods except A . lycopersici ( indicating a high degree of conservation ) , and for which Malin identified a missing intron in a region of unambiguous alignment for A . lycopersici sequences , were manually examined across all protein sequence alignments to assess if intron loss events in the respective genes introduced gains or losses of residues in the encoded products . The classification results for these sites ( 100 in total among 80 orthogroups ) , are included in Supplementary file 1 — ‘Table S3’ Tab; the sequence alignments and annotations of intron positions for each orthogroup are given in Supplementary file 3 . The OrthoFinder analysis ( see section ‘Analysis of intronic features’ ) generated 86 , 686 orthologous groups ( OGs ) in total , of which 13 , 817 contained more than one protein ( Supplementary file 1 — ‘Table S7’ Tab ) . InterProScan 5 . 25–64 . 0 ( Quevillon et al . , 2005 ) was run to assign domains to each of the proteins in all 18 species , and the domain information was subsequently assigned to the OrthoFinder OGs using the KinFin software ( Laetsch and Blaxter , 2017 ) and an associated Python script ( functional_annotation_of_clusters . py with the options: ‘–p 0 . 3 and –x 0 . 3’ ) . Two different strategies were used to identify contracted and/or expanded gene families in A . lycopersici . First , we used the CAFE software to detect contracted/expanded orthologous groups ( orthogroups , OGs ) among 18 metazoan species , while the second strategy was focused on OG expansions within the acariform mites , A . lycopersici , D . pteronyssinus and T . urticae using an arbitrary rule . OrthoFinder 1 . 1 . 8 ( Emms and Kelly , 2015 ) with BLAST 2 . 6 . 0+ was used to identify OGs among the proteomes of 18 metazoan species ( see ‘Analysis of intronic features’ for proteome versions that were used as input for OrthoFinder ) . To maximize the probability of achieving convergence in the maximum likelihood analysis performed in CAFE , OGs were processed to remove OGs present in only a few species and were subsequently divided into OGs having <100 gene copies in any species ( ‘small’ OGs ) and orthogroups having one or more species with ≥100 gene copies ( ‘large’ OGs ) ; see ‘Known Limitations’ section in CAFE 4 . 0 Manual of March 14 , 2017 and section 2 . 2 . 4 of the CAFE 4 . 0 tutorial online at https://iu . app . box . com/v/cafetutorial-pdf , and also Casola and Koralewski , 2018 . We retained 6 , 496 OGs that occurred in no less than 10 out of 18 species consisting of 6 , 467 ‘small’ OGs and 29 ‘large’ OGs . Together with an ultrametric species tree the ‘small’ OG dataset was used as input in CAFE to estimate the birth/death parameter λ ( the probability that a gene will be gained or lost ) and to identify rapidly evolving OGs ( p-value threshold of 0 . 05 ) . The estimated λ ( 0 . 00055594301461 ) was then used to identify rapidly evolving OGs in a CAFE analysis with ‘large’ OGs and using the same p-value threshold and ultrametric species tree as in the CAFE analysis with ‘small’ OGs . The ultrametric species tree used as input in both CAFE analyses was obtained by using the species tree generated for the Malin intron analysis , subsequently rooting this tree using vertebrates as outgroup , and converting this rooted tree into an ultrametric tree using the convert_to_ultrametric ( ) command in the Tree package of the ETE toolkit ( ete 3 . 0 . 0b35 ) ( Huerta-Cepas et al . , 2016 ) . Next , branch lengths of the ultrametric tree were scaled to time units using the software treePL ( Smith and O'Meara , 2012 ) with the following options: 'smooth = 0 . 01 , numsites = 41107 ( number of sites in the alignment used for the Malin analysis ) , thorough , opt = 4 , moredetailad , optad = 2 , optcvad = 2 , moredetailcvad’ and using seven calibration timepoints: the divergence time between Eriophyoidea and Sarcoptiformes ( 352–410 MYA ) , Sarcoptiformes and Trombidiformes ( 410–421 MYA ) and Mesostigmata and Ixodida ( 283–418 MYA ) as derived from Xue et al . , 2017 , and the divergence time between D . melanogaster and A . gambiae ( 211–335 MYA ) , Scorpiones and Araneae ( 379–410 MYA ) , Mandibulata and Chelicerata ( 560–642 MYA ) and H . sapiens and D . rerio ( 425–446 MYA ) , as obtained from TimeTree ( Kumar et al . , 2017 ) on February 20 , 2019 . The options used in treePL were determined following the ‘Quick run’ guidelines of the treePL wiki ( Smith , 2012 ) . The output of the two CAFE analyses ( ‘small’ and ‘large’ OGs ) was summarized using a Python script ( cafetutorial_report_analysis . py using the ‘-l’ option and with a p-value cutoff set to 0 . 05 ) available at the CAFE tutorial website ( https://iu . app . box . com/v/cafetutorial-files/folder/22161236877 , accessed February 20 , 2019 ) . The tree with OG expansions and contractions was visualized in MEGA 6 . 0 ( Tamura et al . , 2013 ) and edited with Corel Draw software ( Corel Draw , Inc ) ; the list of rapidly evolving ( expanding or contracting ) OGs can be found in Supplementary file 1 — ‘Table S5’ Tab . Rapidly contracting A . lycopersici gene families with more than ten members were analyzed for the percentage of A . lycopersici members showing orthology with the majority of chelicerate species ( Supplementary file 1 — ‘Table S6’ Tab ) . Orthology was determined based on the Orthofinder generated output in the ‘Orthologues_Aculops_lycopersici’ folder . One of the six rapidly contracted A . lycopersici families belonged to the CYP family and was excluded from the analysis , as only few orthology relationships has been observed within this family ( Feyereisen , 2011 ) . Apart from gene families that we identified as expanded in the high-level CAFE analysis , we looked as well for more subtle expansions across acariform mites . Across all orthogroups identified by Orthofinder , we identified eleven orthogroups with ( 1 ) A . lycopersici having more than five members and ( 2 ) A . lycopersici having twofold more members than the average number in T . urticae and D . pteronyssinus ( OG0000024 , OG0000271 , OG0000546 , OG0000706 , OG0004829 , OG0006109 , OG0006384 , OG0007553 , OG0007554 , OG0008410 , OG0008412 ) . For two orthogroups ( OG0007554 , OG00084112 ) , no InterPro domain could be assigned , while OG0000271 , OG0000706 , OG0004829 , OG0006384 , OG0007553 , and OG0008410 contained proteins with a DnaJ domain ( IPR011701 ) , Formate-tetrahydrofolate ligase domain ( IPR000559 ) , Acyltransferase 3 domain ( IPR002656 ) , a Peptidase C1A domain ( IPR000668 ) , Chromo domain ( IPR023780 ) and a Lipase/vitellogenin domain ( IPR013818 ) , respectively . The proteins of the three remaining orthogroups ( OG0000024 , OG0000546 , and OG0006109 ) belonged to the Major facilitator superfamily ( MFS , IPR011701 or IPR024989 ) . For D . melanogaster proteins belonging to orthogroups with ( 1 ) members in all included arthropods except A . lycopersici and ( 2 ) a maximum of two D . melanogaster members ( 343 D . melanogaster proteins in total , Supplementary file 1 — ‘Table S8’ Tab ) , we performed an Over-Representation analysis ( ORA ) using the WEB-based GEne SeT AnaLysis Toolkit ( Liao et al . , 2019 ) . An ORA was performed for each GO category ( Biological Process , Molecular Function and Cellular Component ) using default settings ( and ‘genome protein coding’ as reference set ) and a Benjamini-Hochberg multiple testing correction ( false discovery rate , FDR , of 0 . 05 ) . In addition , we also identified those orthogroups that contain purported D . melanogaster essential genes , using the list of 427 essential genes provided in the respective study’s first supplementary data table ( Aromolaran et al . , 2020 ) . We specifically analyzed genes and gene families associated with herbivory in other animals , as well as those associated with physiological or developmental process related to A . lycopersici’s life history or derived morphology ( GSTs , CCEs , CYPs , ABC transporters , MFS proteins , proteases , chemosensory receptors , and transcription factors , including Hox genes ) . We also characterized genes involved in processes including circadian rhythm , small RNA pathways , and potential regulation of plant defense responses ( secreted proteins ) . We performed a genomic HGT screen as previously described in Wybouw et al . , 2018 . Briefly , the A . lycopersici proteome was aligned with metazoan and non-metazoan proteome databases and the bitscores of the best BLASTp hits were recorded . For each protein query , the h-index metric was calculated by subtracting the best metazoan bitscore from the best non-metazoan bitscore . An A . lycopersici gene was designated as a horizontally transferred gene candidate when it exhibited a best non-metazoan bitscore ≥75 and an h-index ≥30 . In our screen , we also performed a tBLASTn-search against the tomato russet mite scaffolds using all identified horizontally transferred T . urticae genes as queries . Maximum-likelihood phylogenies were subsequently constructed for all A . lycopersici horizontally transferred gene candidates , except for a putative UGT pseudogene that was located on scaffold 5 between coordinates 140 , 638 and 140 , 871 . All complete A . lycopersici UGT genes were sent to the UGT Nomenclature Committee to obtain unique UGT gene names ( https://prime . vetmed . wsu . edu/resources/udp-glucuronsyltransferase-homepage ) . For the final phylogenetic reconstruction of the pantothenate biosynthetic genes , homologues of aculy01g38350 ( ketopantoate hydroxymethyltransferase , panB ) and aculy04g02470 ( pantoate β-alanine ligase , panC ) were identified by BLASTn and tBLASTn searches ( E−10 cut-off ) against the nonredundant nucleotide and protein NCBI databases , respectively , and were grouped based on their position in the tree of life ( fungi , animals , bacteria , plants , and other ) . Proteins were selected per group based on manual inspection of the alignments and were combined with homologues as identified by Wybouw et al . , 2018 . In addition , we also added a panC homologue of the mealybug Ferrisia virgata to the final set of proteins ( Husnik and McCutcheon , 2016 ) . For the phylogenetic analysis of UGTs , we added UGT protein sequences from the annotated genome assembly of the house dust mite D . pteronyssinus ( Waldron et al . , 2017 ) to our UGT phylogenetic reconstruction . Applying an E-value of E−10 as the cut-off for the alignments , 27 D . pteronyssinus sequences were identified by reciprocal BLASTp-searches between the D . pteronyssinus proteome and the 87 T . urticae and A . lycopersici UGT sequences . Protein sequences were aligned using the online version of MAFFT v7 . 380 ( Katoh et al . , 2019 ) ( available at https://mafft . cbrc . jp/alignment/software/ ) with 1000 iterations and the options ‘E-INS-i’ and ‘reorder’ ( see Supplementary file 7 ) . Protein models were selected based on the Akaike Information Criterion using ProtTest 3 . 4 ( Darriba et al . , 2011 ) ( panB: LG+G , panC: LG+G , and UGT: LG+G+F ) . Maximum likelihood analyses were performed using RAxML v8 HPC2-XSEDE ( Stamatakis , 2014 ) on the CIPRES Science Gateway ( Miller et al . , 2010 ) with 1000 rapid bootstrap replicates ( ‘-f a -x 12345’ option ) . An additional maximum likelihood tree reconstruction with ultrafast bootstrapping ( 1000 replicates ) was performed for the pantothenate biosynthetic proteins using IQ-TREE version 1 . 6 . 12 ( Hoang et al . , 2018; Nguyen et al . , 2015 ) . ModelFinder identified LG+I+G4 as the best protein model based on the Bayesian Information Criterion ( Kalyaanamoorthy et al . , 2017 ) . Constrained tree tests for alternative topologies whereby A . lycopersici is the sister lineage to the spider mite pantothenate biosynthetic proteins were performed using the approximately unbiased test of IQ-TREE version 1 . 6 . 12 ( 10 , 000 RELL replicates ) ( Shimodaira , 2002 ) . The random number seed was set at 12345 . Last , the physical location of the aculy01g38350 and aculy04g02470 genes in the A . lycopersici genome was examined by PCR amplification . A . lycopersici mites were collected by soaking infested tomato leaves overnight in 40 mL of 70% ethanol . Mites in ethanol were centrifuged at 2000 rpm for 1 min , ethanol was removed , and pelleted mites were ground using liquid nitrogen . One mL of CTAB buffer with 2% beta-mercaptoethanol and 1% proteinase K was added to the ground mites , followed by incubation in a warm water bath at 56°C . Next , samples were washed with 1 ml of choloroform:isoamyl alcohol ( 21:1 ) and DNA was precipitated with isopropanol on ice for 1 hr . Primer sequences that successfully amplified genomic regions are listed in Supplementary file 1 — ‘Table S20’ Tab . PCRs were performed using the recommended protocol for Phusion High Fidelity polymerase ( Thermo Scientific , The Netherlands ) and 1 μL of extracted DNA ( 50 ng/microL ) and 0 . 2 μM of each primer . PCR conditions for fragment 1 and 3 were 98°C for 30 min , followed by 35 cycles of denaturation at 98°C for 10 s , annealing at 55°C for 30 s , and extension at 72°C for 1 min ( fragment 1 ) or 45 s ( fragment 3 ) followed by a final extension step at 72°C for 5 min . PCR conditions for fragment two were as follows: 98°C for 30 s , 5 cycles of 98°C for 10 s , 65°C for 10 s , 72°C for 60 s , five cycles of 98°C for 10 s , 60°C for 10 s , 72°C for 60 s , and 20 cycles of 98°C for 10 s , 60°C for 10 s , 72°C for 60 s , followed by a final extension step at 72°C for 3 min . Resulting amplicons were Sanger sequenced by Eurofins ( Leiden , The Netherlands ) using PCR ( with ‘PCR’ suffix ) and sequencing ( with ‘seq’ suffix ) primers as indicated in Supplementary file 1 — ‘Table S20’ Tab . | Arthropods are a group of invertebrates that include insects – such as flies or beetles – arachnids – like spiders or scorpions – and crustaceans – including shrimp and woodlice . One of the tiniest species of arthropods , measuring less than 0 . 2 millimeters , is the tomato russet mite Aculops lycopersici . This arachnid is among the smallest animals on Earth , even smaller than some single-celled organisms , and only has four legs , unlike other arachnids . It is a major pest on tomato plants , which are toxic to many other animals , and it feeds on the top cell layer of the stems and leaves . Tomato growers need a way to identify and treat tomato russet mite infestations , but this tiny species remains something of a mystery . One way to tackle this pest may be to take a closer look at its genome , as this could reveal what genes the mite uses to detoxify its diet . Examining the mite’s genome could also reveal information about how evolution handles creatures becoming smaller . An area of particular interest is the overall size of its genome . Not all of the DNA in a genome is part of genes that code for proteins; there are also sections of so-called ‘non-coding’ DNA . These sequences play important roles in controlling how and when cells use their genes . In the human genome , for example , just 1% of the DNA codes for protein . In fact , most human protein-coding genes are interrupted by sequences of non-coding DNA , called introns . Here , Greenhalgh , Dermauw et al . sequence the entire tomato russet mite genome and reveal that not only is the mite's body size miniature: these tiny animals have the smallest arthropod genome reported to date , almost a hundred times smaller than the human genome . Part of this genetic miniaturization seems to be down to massive loss of non-coding DNA . Around 40% of the mite genome codes for protein , and 80% of its protein coding genes contain no introns . The rest of the miniaturization involves loss of genes themselves . The mites have lost some of the genes that determine body structure , which could explain why they have fewer legs than other arachnids . Additionally , they only carry a small set of genes involved in sensing chemicals and clearing toxins , which could explain why they are mostly found on tomato plants . Greenhalgh , Dermauw et al . ’s findings shed light on what may happen to the genome at the extremes of size evolution . Sequencing the genomes of other mites could reveal when in evolutionary history this genetic miniaturization occurred . Furthermore , a better understanding of the tomato russet mite genome could lead to the development of methods to detect the infestation of plants earlier and be highly beneficial for tomato agriculture . | [
"Abstract",
"Introduction",
"Results",
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"Materials",
"and",
"methods"
] | [
"evolutionary",
"biology",
"genetics",
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] | 2020 | Genome streamlining in a minute herbivore that manipulates its host plant |
Rhythmic activity in the theta range is thought to promote neuronal communication between brain regions . In this study , we performed chronic telemetric recordings in socially behaving rats to monitor electrophysiological activity in limbic brain regions linked to social behavior . Social encounters were associated with increased rhythmicity in the high theta range ( 7–10 Hz ) that was proportional to the stimulus degree of novelty . This modulation of theta rhythmicity , which was specific for social stimuli , appeared to reflect a brain-state of social arousal . In contrast , the same network responded to a fearful stimulus by enhancement of rhythmicity in the low theta range ( 3–7 Hz ) . Moreover , theta rhythmicity showed different pattern of coherence between the distinct brain regions in response to social and fearful stimuli . We suggest that the two types of stimuli induce distinct arousal states that elicit different patterns of theta rhythmicity , which cause the same brain areas to communicate in different modes .
Oscillatory brain activity , mostly categorized to the theta ( 3–12 Hz ) , beta ( 12–30 Hz ) , and gamma ( 30–80 Hz ) bands , is thought to coordinate neural activity in vast neuronal assemblies dispersed over different brain regions ( Buzsáki and Draguhn , 2004 ) . This type of coordination may underlie high level cognitive functions , such as speech and social communication ( Uhlhaas and Singer , 2006; Uhlhaas et al . , 2009 ) that are impaired in autism spectrum disorders ( ASD ) ( Geschwind , 2009 ) . Increasing evidence suggest that individuals with ASD show deficits in long-range neuronal communication associated with low-frequency rhythms , such as the theta rhythm ( Geschwind and Levitt , 2007; Rippon et al . , 2007; Wass , 2011 ) . Nonetheless , a clear connection between rhythmic brain activity and social behavior has not yet been established . Mammalian social organization depends on the ability to recognize and remember individual conspecifics ( Wiley , 2013 ) . This social recognition memory ( SRM ) can be assessed in rodents using their innate tendency to investigate novel conspecifics more persistently than familiar ones ( Gheusi et al . , 1994 ) . In the SRM habituation–dishabituation test , social memory is assessed by the gradual reduction in the amount of time the animal spends investigating a particular social stimulus during consecutive encounters ( Ferguson et al . , 2002 ) . This short-term memory was shown to be mediated mainly by chemical cues ( semiochemicals ) perceived via the main and accessory olfactory systems ( Dulac and Torello , 2003 ) . Upon binding of semiochemicals to the receptors expressed by the sensory neurons of the main olfactory epithelium and the vomeronasal organ , sensory information is conveyed to the main ( MOB ) and accessory ( AOB ) olfactory bulbs , respectively ( Dulac and Wagner , 2006 ) . Both bulbs then project , directly and indirectly , to the medial amygdala ( MeA ) ( Pro-Sistiaga et al . , 2007; Kang et al . , 2011 ) that is thought to transfer the information to the hippocampus through the lateral septum ( LS ) ( Bielsky and Young , 2004 ) . The MOB projects also to several cortical areas comprising the primary olfactory cortex , of which the piriform cortex ( Pir ) is best characterized ( Wilson and Sullivan , 2011 ) ( Figure 1 ) . 10 . 7554/eLife . 03614 . 003Figure 1 . A simplistic scheme of sensory information flow in the network of brain regions thought to underlie social recognition memory . Social olfactory cues are detected by sensory neurons in the main olfactory epithelium ( MOE ) and vomeronasal organ ( VNO ) . These neurons project to the main ( MOB ) and accessory ( AOB ) olfactory bulbs , which transmit information , either directly or indirectly ( via the cortical nucleus of the amygdala—CoA ) to the medial amygdala ( MeA ) . The MOB also innervates the piriform cortex ( Pir ) . The MeA projects to the lateral septum ( LS ) , which innervates the hippocampus ( Hip ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 003 Here we hypothesized that social behavior is associated with an elevation of rhythmic activity in the network of brain areas that process social stimuli . To examine this hypothesis , we recorded electrophysiological activity from the brains of freely behaving adult male rats performing the SRM paradigm ( Video 1 ) . A telemetric system was used to record from wire electrodes chronically implanted in the five aforementioned brain regions: MOB , AOB , MeA , LS , and Pir ( Dulac and Wagner , 2006 ) . We found that social encounters were associated with enhancement of brain rhythmic activity , specifically at 7–10 Hz range , in all brain regions . This enhancement that was proportional to the degree of novelty of the social stimulus appeared to reflect an internal brain-state associated with social arousal . In contrast , a fear-conditioned tone , which is associated with fear arousal , induced rhythmicity in the low theta range ( 3–7 Hz ) in the same network of brain regions . Moreover , social and fearful stimuli elicited different patterns of change in coherence between the distinct brain regions . We hypothesize that these two types of stimuli induce distinct arousal states in the animal , which are reflected by the different kinds of theta rhythmicity . We further suggest that the distinct types of theta rhythmicity support different modes of communication between the various brain areas . These in turn may modify cognitive processes such as memory acquisition and recall depending on the value and saliency of the stimulus by enhancing synchronous neuronal activity between remote neuronal assemblies . 10 . 7554/eLife . 03614 . 004Video 1 . Social encounter between two adult male rats in the experimental arena . The recorded subject carries a black transmitter equipped with a flashing led light on its head . Frame number is shown in the right low corner . The graph below the video shows the LFP recorded in the AOB ( blue ) , MOB ( red ) , and MeA ( green ) . The bottom graph shows raster plots of spikes detected from the recorded multi-unit activity signal . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 004
Electrophysiological recordings were carried out in the brains of freely behaving adult male rats performing the SRM habituation–dishabituation paradigm ( Figure 2A ) . We first analyzed the dynamics of the local field potential ( LFP ) in the course of the behavioral paradigm . A highly rhythmic LFP was recorded in all brain areas during social encounters ( Figure 2B ) . Power spectral density ( PSD ) analysis of the LFP showed a prominent peak at ∼8 Hz , typical for the high theta band ( Buzsáki and Draguhn , 2004 ) , in all areas ( Figure 2C ) . The value of this peak , termed theta power ( TP ) , was very low in the absence of a social stimulus ( Base , Figure 2D–E ) but increased profoundly during the first encounter ( Enc . 1 ) . It then gradually decreased during further encounters with the same stimulus ( Enc . 2–4 ) , but increased again when another novel stimulus was introduced ( Enc . 5 ) . These changes in theta power during SRM testing closely followed the changes in investigation time ( IT ) ( Figure 2F ) , with both parameters appearing to correlate with the degree of stimulus novelty . 10 . 7554/eLife . 03614 . 005Figure 2 . Theta rhythmicity in the rat brain is enhanced during social encounters , in correlation with the novelty of the social stimulus . ( A ) A scheme of the habituation–dishabituation SRM paradigm . ( B ) Examples of LFP traces recorded in the MOB , LS , and MeA during a social encounter . ( C ) Power spectral density ( PSD ) analyses of a 5-min LFP recording from all five brain areas during a social encounter . Gray bar represents the 7–9 Hz band . ( D ) Superimposed PSD analyses of LFP recordings from the MeA of one animal during the various stages of the SRM test . ( E ) As in D , zooming on the 4–10 Hz range . ( F ) The ∼8 Hz PSD peak ( TP ) and social investigation time ( IT ) for the same experiment as in D , plotted as a function of the encounter number . Encounter 0 represents no stimulus ( Base ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 005 We next analyzed the effect of social and non-social stimuli on the dynamics of investigation time and theta power in all recorded brain areas . As exemplified in Figure 3A ( lower panel ) , exposure of an animal to either type of stimulus caused similar dynamics of the investigation time . However , there was a vast difference with regards to the theta power response to the social and non-social stimuli: whereas significant theta power modulation that was similar across all brain regions was observed with social stimuli , whether awake or anesthetized , object and odor stimuli did not cause such an effect ( Figure 3A , upper panels ) . 10 . 7554/eLife . 03614 . 006Figure 3 . Theta rhythmicity is modulated by the novelty of social , but not other tested stimuli . ( A ) TP for all brain areas ( upper ) as well as IT ( lower ) during the SRM test of one animal , using awake and anesthetized social stimuli as well as object and smell stimuli , all except smell tested with the same animal . ( B ) Mean TP for the various brain regions averaged ( ±SEM ) and plotted as a function of the test stage , for social ( blue , n = 8 ) and object ( red , n = 6 ) stimuli . A significant difference was found between the various encounters in all brain regions for social stimuli ( p < 0 . 005 , one-way repeated measures ANOVA , Figure 3—source data 1A ) , while no difference was found for object recognition ( p > 0 . 05 , Figure 3—source data 1B ) . Post hoc paired t-test showed significant differences between Enc . 1 and Enc . 4 as well as between Enc . 4 and Enc . 5 ( dashed lines ) in all brain regions for social stimuli ( *pcorr < 0 . 05 , Figure 3—source data 2 ) . ( C ) As in B , for the IT of the social and object paradigms . Unlike the TP , both paradigms showed similarly significant modulation of the IT ( Figure 3—source data 1–2 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 00610 . 7554/eLife . 03614 . 007Figure 3—source data 1 . Theta power ( TP ) modulation between encounters . One-way ANOVA ( repeated measures ) test was used to determine whether there is a significant difference between the mean ΔTP of all five encounters during either social ( 1a ) or object ( 1b ) recognition . The assumption of normality was assessed by Lilliefors and Shapiro–Wilk tests . Sphericity was assessed by Mauchly's test . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 00710 . 7554/eLife . 03614 . 008Figure 3—source data 2 . Statistical assessment of habituation and dishabituation . Paired t-tests were used for the social ( 2a ) and object ( 2b ) recognition paradigms , to examine if the differences between Enc . 1 and Enc . 4 ( habituation ) , as well as between Enc . 4 and Enc . 5 ( dishabituation ) are statistically significant . Tests were one-sided and corrected for multiple comparisons using Bonferroni's correction . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 008 Combined analyses of the modulation of both theta power and investigation time in animals exposed to social and object stimuli are presented in Figure 3B . Social stimuli caused a marked increase of mean theta power during Enc . 1 compared to Base , with the MOB and AOB showing the largest changes ( 6 . 2 dB/Hz ) and other areas showing more moderate ones ( 4 . 0–5 . 1 dB/Hz ) . In all regions tested , the theta power decreased gradually during the habituation phase ( Enc . 1–4 ) but returned the values obtained in Enc . 1 after dishabituation ( Enc . 5 ) ( p < 0 . 005 one-way repeated measures ANOVA , *pcorr < 0 . 05 post-hoc paired t-test , Figure 3—source data 1–2 ) . In contrast , object stimuli elicited a much weaker initial change from Base to Enc . 1 ( 1 . 1–2 . 7 dB/Hz ) in all brain regions . Furthermore , the theta power showed modulation during the object paradigm similarly to the social paradigm only in the MOB , and even this change was not statistically significant ( p > 0 . 05 , Figure 3—source data 1 ) . In a sharp contrast to the theta power , comparison of the investigation time of the social and object paradigms showed a highly similar course and magnitude of habituation and dishabituation that were statistically significant in both cases ( Figure 3C , Figure 3—source data 1–2 ) . Taken together , these results show that in almost all recorded brain areas , theta power is modulated by the degree of novelty of social but not object stimuli . The lack of theta power modulation despite the clear investigation time modulation induced by object stimuli rejects the possibility that the theta rhythmicity is caused by the investigative behavior . We therefore reasoned that rather , theta power modulation may reflect processes that are either directly driven by the sensory input ( Bottom-Up processes ) or induced by an internal state of the brain that is modulated by the saliency of the social stimulus ( Top-Down processes ) . In order to distinguish between these two possibilities , we continued our recordings for 5 min after the stimulus was removed from the arena ( Post 1–5 ) . As depicted in Figure 4A , the theta rhythmicity did not cease with the removal of the social stimulus following Enc . 1 , but remained at a high level during most of the Post 1 period ( for spectrograms of the full experiment , see Figure 4—figure supplements 1–5 ) . Plotting the mean instantaneous theta power as a function of time , revealed that this was true for all encounters with a social stimulus . In contrast , encounters with object stimuli were followed by a sharp drop in the theta power to a low level almost immediately following stimulus removal ( Figure 4B , for all other brain areas see Figure 4—figure supplements 6–7 ) . This significant reduction in mean theta power between the Enc . and Post periods of the object paradigm was characteristic of all brain areas ( Figure 4C , *p < 0 . 05 paired t-test , Figure 4—source data 1 ) . In contrast , high theta power levels were found in both these periods in the social paradigm ( p > 0 . 05 ) . Moreover , all encounters with social stimuli showed a steep but gradual increase in theta power during the first 15 s in which the stimulus was being transferred into the arena ( Figure 4A , D , gray bars ) . This rise in theta power probably reflects the subject's anticipation for a social meeting , as there was no similar increase with object stimuli ( Figure 4D ) . Altogether , these data suggest that the changes in theta power during the SRM test reflect a graded internal brain state of arousal that is proportional to the novelty of the social stimulus and slowly fades away after its removal . 10 . 7554/eLife . 03614 . 009Figure 4 . Modulation of the theta rhythmicity by social stimulus novelty reflects an internal state in the brain . ( A ) Color-coded spectrograms of the LFP recorded in the MOB ( upper ) , LS ( middle ) , and MeA ( lower ) for 5 min before ( Base ) , during ( Enc . 1 ) , and after ( Post 1 ) the first encounter of the SRM test . All spectrograms are averages of five animals ( 4 animals for LS ) . Gray bar marks the 15 s needed for stimulus transfer to the arena . ( B ) Upper—instantaneous ΔTP ( change from mean Base ) in the LS averaged over four rats ( ±SEM ) during the Enc . and Post periods of all trials ( 1-5 ) , for social ( left , n = 5 ) and object ( right , n = 4 ) paradigms . The 15-min breaks between last Post and next Enc . periods are labeled with gray bars . Lower—mean ( ±SEM ) values for the corresponding periods shown above . ( C ) Comparison of mean ΔTP averaged over all trials ( 1-5 ) for each brain area , between the Enc . and Post periods of the social and object paradigms ( *p < 0 . 05 , paired t-test , Figure 4—source data 1 ) . ( D ) Left—the instantaneous ΔTP shown in B , expanded to show the initial 50 s of all encounters . Gray area represents the 15 s needed for stimulus transfer to the experimental arena . Right—The same for object stimuli . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 00910 . 7554/eLife . 03614 . 010Figure 4—source data 1 . Comparison of ΔTP between Enc . and Post periods . Paired t-tests were used to compare between the mean ΔTP across Enc . vs the mean ΔTP across Post periods . The assumption of normality was assessed by Lilliefors and Shapiro–Wilk tests . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 01010 . 7554/eLife . 03614 . 011Figure 4—figure supplement 1 . Mean LFP spectrograms across the SRM paradigm for the AOB . Color-coded spectrograms ( 0–20 Hz ) of the LFP recorded in the AOB during the SRM test . Gray bar marks the 15 s needed for stimulus delivery to the arena . Mean of five animals . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 01110 . 7554/eLife . 03614 . 012Figure 4—figure supplement 2 . Mean LFP spectrograms across the SRM paradigm for the MOB . Color-coded spectrograms ( 0–20 Hz ) of the LFP recorded in the MOB during the SRM test . Gray bar marks the 15 s needed for stimulus delivery to the arena . Mean of five animals . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 01210 . 7554/eLife . 03614 . 013Figure 4—figure supplement 3 . Mean LFP spectrograms across the SRM paradigm for the MEA . Color-coded spectrograms ( 0–20 Hz ) of the LFP recorded in the MEA during the SRM test . Gray bar marks the 15 s needed for stimulus delivery to the arena . Mean of five animals . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 01310 . 7554/eLife . 03614 . 014Figure 4—figure supplement 4 . Mean LFP spectrograms across the SRM paradigm for the LS . Color-coded spectrograms ( 0–20 Hz ) of the LFP recorded in the LS during the SRM test . Gray bar marks the 15 s needed for stimulus delivery to the arena . Mean of four animals . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 01410 . 7554/eLife . 03614 . 015Figure 4—figure supplement 5 . Mean LFP spectrograms across the SRM paradigm for the Pir . Color-coded spectrograms ( 0–20 Hz ) of the LFP recorded in the Pir during the SRM test . Gray bar marks the 15 s needed for stimulus delivery to the arena . Mean of five animals . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 01510 . 7554/eLife . 03614 . 016Figure 4—figure supplement 6 . Comparison of mean instantaneous TP between social and object stimuli , for the AOB and MOB . Upper panels—instantaneous ΔTP ( change from mean Base ) in each brain area averaged over all animals ( mean ± SEM ) during the Enc . and Post periods of all trials ( 1–5 ) , for social ( left , n = 5 rats ) and object ( right , n = 4 rats ) paradigms . The 15-min breaks between last Post and next Enc . periods are labeled with gray bars . Lower panels—mean ( ±SEM ) values for the corresponding periods shown above . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 01610 . 7554/eLife . 03614 . 017Figure 4—figure supplement 7 . Comparison of mean instantaneous TP between social and object stimuli , for the MeA and Pir . Upper panels—instantaneous ΔTP ( change from mean Base ) in each brain area averaged over all animals ( mean±SEM ) during the Enc . and Post periods of all trials ( 1–5 ) , for social ( left , n = 5 rats ) and object ( right , n = 4 rats ) paradigms . The 15 min breaks between last Post and next Enc . periods are labeled with gray bars . Lower panels—mean ( ±SEM ) values for the corresponding periods shown above . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 017 The theta rhythmicity recorded in the network may reflect a single rhythm originating from one source . In that case , the various brain regions are expected to display high correlation and similar dynamics of coherence in their rhythmicity . Alternatively , if it represents a combination of multiple independent rhythms arising from several sources , we expect low correlation and differential dynamics of coherence between various brain regions . To discriminate between these possibilities , we first examined the cross-correlation of the LFP , filtered in the theta range , between the MeA and the other brain areas . Despite the fact that both areas are directly connected to the MeA , the strongest correlation appeared with the LS and the weakest with the MOB ( Figure 5A–D ) . Moreover , whereas the correlation between the MeA and LS was significantly higher during Enc . 1 ( blue ) compared to Base ( red ) , the MOB showed consistently low correlation with the MeA during both periods . The presence of a social stimulus thus appears to differentially affect the correlation of theta rhythmicity between distinct brain areas . 10 . 7554/eLife . 03614 . 018Figure 5 . Differential and dynamic correlation of theta rhythmicity between specific brain regions . ( A ) Upper—superimposed LFP traces ( filtered 5–11 Hz ) from the MeA ( black ) and LS ( colored ) of one animal during Base ( left , red ) and Enc . 1 ( right , blue ) . Lower—cross-correlations between both regions for each of the 300 s recorded during the same periods , with peaks labeled by colored dots . ( B ) Same as A for the MeA and MOB . ( C ) Middle—distribution of the cross-correlation peaks for the data in A . Borders—histograms of the cross-correlation peaks in the correlation ( right ) and lag ( bottom ) axes . Mean ± SD are marked to the left ( correlation ) or above ( lag ) the histograms . ( D ) Same as C for the data in B . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 018 We next analyzed the coherence of the LFP signal among all brain areas during the Base , Enc . 1 , and Post 1 periods of the SRM paradigm . As depicted in Figure 6A , the coherence between the MeA and the LS showed several prominent peaks , especially in the theta and gamma bands . Yet , while no change was recorded in the gamma band , the theta coherence showed a significant increase between the Base and Enc . 1 . Furthermore , similarly to theta rhythmicity itself ( Figure 4 ) , the high coherence at theta range persisted during the Post 1 period despite the lack of a social stimulus ( Figure 6A , C ) . In contrast , the coherence in theta band between the MeA and MOB remained low throughout all periods ( Figure 6B , C ) . Analyses across all regions revealed a hierarchy in the theta coherence between the MeA and all other areas , ranging from a low level with the MOB and AOB , medium coherence with the Pir and high coherence with the LS ( Figure 6D ) . This notion of functional hierarchy between brain regions is strengthened by the fact that despite their largest physical distance , the highest level of theta coherence was found between the MeAs in the two hemispheres ( Figure 6—figure supplements 1 , 3 ) . Furthermore , the theta coherence between the MeA and the higher brain centers ( Pir , LS ) significantly increased during Enc . 1 and Post 1 ( *pcorr < 0 . 05 , paired t-test , Figure 6—source data 1 ) , while no change was recorded between the MeA and both areas of the olfactory bulb ( MOB , AOB , pcorr > 0 . 05 ) . This suggests the existence of at least two independent theta rhythms , one that governs the olfactory bulb and another that dominates higher brain structures . This conclusion is further supported by the findings that the MOB shows opposite relationships with all other brain areas; high coherence with the AOB and low coherence with the higher areas ( Figure 6E , Figure 6—figure supplements 2 , 3 ) . Moreover , a significant enhancement in theta coherence with the AOB was observed during Enc . 1 and Post1 ( *pcorr < 0 . 05 , paired t-test , Figure 6—source data 1 ) , while all other regions showed no change ( pcorr > 0 . 05 , paired t-test ) . Interestingly , similar enhancement of theta coherence between the AOB and MOB was recorded with object stimuli , while these stimuli did not cause any enhancement of the coherence between the MeA and LS or Pir ( Figure 6F , G , Figure 6—source data 1 ) . Together , these data support multiple sources of theta rhythmicity in the network . 10 . 7554/eLife . 03614 . 019Figure 6 . Theta coherence between specific brain regions increases during social encounter . ( A ) Mean ( n = 10 animals ) coherence ( 0–100 Hz ) of the LFP signals recorded in the MeA and LS during Base , Enc . 1 , and Post 1 periods . ( B ) Same animals , coherence analysis between the MeA and MOB . ( C ) Spectrograms ( 0–20 Hz ) of the coherence analyses shown in A ( between MeA and LS , upper panel ) and B ( between MeA and MOB , lower panel ) . ( D ) Mean coherence at 8 Hz between the MeA and all other areas ( MOB , AOB n = 11; LS , Pir n = 10 ) during the Base , Enc . 1 , and Post 1 periods of social encounter ( *pcorr < 0 . 05 , paired t-test , Figure 6—source data 1A ) . ( E ) Same as D , for coherence of the MOB with all other areas ( *pcorr < 0 . 05 , paired t-test , Figure 6—source data 1B ) . ( F ) Same as D , for object stimuli ( Figure 6—source data 1C ) . ( G ) Same as E , for object stimuli ( *pcorr < 0 . 05 , paired t-test , Figure 6—source data 1D ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 01910 . 7554/eLife . 03614 . 020Figure 6—source data 1 . Assessment of change in theta Coherence from Base to either Enc . 1 or Post 1 . The change from Base to Enc . 1 ( upper ) and from Base to Post 1 ( lower ) , in theta coherence during social recognition between the MeA and all other areas ( 1a ) and between the MOB and all areas ( 1b ) , as well as during object recognition between the MeA and all other areas ( 1c ) , and between the MOB and all areas ( 1d ) , was statistically validated using paired t-tests , corrected for multiple comparisons ( Bonferroni correction ) . The assumption of normality was assessed by Lilliefors and Shapiro–Wilk tests . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 02010 . 7554/eLife . 03614 . 021Figure 6—figure supplement 1 . Mean spectrograms of coherence between the MeA and all other areas during trial 1 of the SRM paradigm . Color-coded spectrograms ( 0–20 Hz ) of the mean LFP coherence ( MOB , AOB—n = 11; LS , Pir—n = 10 , cMeA—contralateral MeA—n = 3 ) between the MOB and all other brain areas , during the first trial of SRM test , each depicting continuous 15 min divided to the Base , Enc . 1 , and Post 1 periods . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 02110 . 7554/eLife . 03614 . 022Figure 6—figure supplement 2 . Mean spectrograms of coherence between the MOB and all other areas during trial 1 of the SRM paradigm . Color-coded spectrograms ( 0–20 Hz ) of the mean LFP coherence ( MeA , AOB—n = 11; LS , Pir—n = 10 , cMeA—contralateral MeA—n = 3 ) between the MOB and all other brain areas , during the first trial of SRM test , each depicting continuous 15 min divided to the Base , Enc . 1 , and Post 1 periods . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 02210 . 7554/eLife . 03614 . 023Figure 6—figure supplement 3 . Mean theta coherence during trial 1 of the SRM paradigm . ( A ) Mean coherence at 8 Hz between the MeA and all other areas ( MOB , AOB n = 11; LS , Pir n = 10 , cMeA—contralateral MeA—n = 3 ) during the Base , Enc . 1 , and Post 1 periods of the SRM paradigm . ( B ) Same as A , for coherence of the MOB with all other areas . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 023 Theta rhythmicity was previously found to be elicited in several brain regions during states of arousal , mainly in response to fearful stimuli ( Knyazev , 2007 ) . This phenomenon was best studied in the context of fear learning in a network of brain regions comprising the basolateral complex of the amygdala ( lateral and basolateral amygdala ) , hippocampus , and medial prefrontal cortex ( Pape and Pare , 2010 ) . In this network , a recall of a fearful memory , induced by a fear-conditioned stimulus , elicits robust theta rhythmicity that shows high coherence between these brain regions ( Paré and Collins , 2000; Pare et al . , 2002; Seidenbecher et al . , 2003; Pape et al . , 2005; Popa et al . , 2010 ) . Here , we examined whether the brain state-induced theta rhythmicity during the SRM paradigm is similar to the fear-induced rhythmicity . To address this question , we compared the theta rhythmicity induced by a social encounter to that of a fear stimulus within the social network that we investigated . To that end , a new cohort of six animals was implanted with wire electrodes as before , with an additional electrode in the nucleus accumbens ( NAcc ) , which was recently shown to be involved in social motivation ( Dölen et al . , 2013; Gunaydin et al . , 2014 ) . These animals were fear-conditioned by coupling a 40-s long tone to an electrical foot shock for five consecutive times separated by 180-s intervals ( Figure 7—figure supplement 1A ) . A day later the electrical activity was recorded in two consecutive sessions , each following a 30 min of habituation to the arena . The first session was recorded during a recall of fear memory ( FC experiment ) , and the second during a 5-min long encounter with a novel social stimulus ( SR experiment ) . During the FC experiments ( Figure 7—figure supplement 1B ) , introduction of the fear-conditioned tone caused animals to begin moving intensively , followed by immobility ( freezing ) towards the end of the tone , in anticipation of the foot shock . The freezing response was especially significant at the end of the first tone ( Figure 7—figure supplement 1C ) . Thus , the fear-conditioned tone caused a robust arousal state that was associated with intense movement of the conditioned animals . We then compared the theta rhythmicity between the FC and SR experiments . A PSD analysis of the LFP signals recorded in the LS during 5 min prior to stimulus introduction ( Base ) yielded a similar profile in both cases ( Figure 7A , red ) . However , the PSD was very different between the two types of stimuli during the first 15 s following stimulus introduction ( Stimulus ) ( Figure 7A , blue ) . Whereas the fear stimulus caused a marked peak at the low theta range ( 3–7 Hz ) , the social stimulus resulted in a peak at the high theta range ( 7–10 Hz ) . This change is clearly observed when subtracting the Base PSD from the Stimulus profile ( Figure 7B ) . These differences appeared in all recorded brain regions ( Figure 7C ) and Statistical analysis showed a highly significant interaction between the type of experiment ( FC or SR ) and theta band ( Figure 7D ) ( **p < 0 . 01 , two-way repeated measures ANOVA , Figure 7—source data 1 ) . Thus , we conclude that fearful and social stimuli cause changes in very different ranges of theta rhythmicity in the same limbic network of brain regions . We suggest that these different types of theta rhythmicity reflect distinct arousal states; the low theta reflects aversive arousal that is associated with fear while the high theta reflects appetitive arousal associated with a social encounter . 10 . 7554/eLife . 03614 . 024Figure 7 . Distinct types of theta rhythmicity are induced by social and fearful stimuli . ( A ) PSD analyses ( 0–20 Hz ) of LFP signal recorded in the LS of one animal , 5 min prior to stimulus introduction ( Base , red ) and 15 s following it ( Stimulus , blue ) during fear memory recall ( left , FC ) and social encounter ( right , SR ) . ( B ) The change between Stimulus and Base PSD analyses ( Stimulus minus Base ) shown in A , for FC and SR , superimposed . ( C ) Mean change in PSD profile for all brain areas of the same six animals during the FC ( continuous lines ) and SR ( dashed lines ) experiments . ( D ) Mean ( ±SEM ) values of the peak change in PSD in the low ( 4–8 Hz , red and blue ) and high ( 8–12 Hz , pink and light blue ) theta ranges for the FC ( red and pink ) and SR ( blue and light blue ) experiments ( **p < 0 . 01 , experiment X theta range interaction , two-way repeated measures ANOVA , Figure 7—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 02410 . 7554/eLife . 03614 . 025Figure 7—source data 1 . Comparison of change in theta power in low and high theta bands between social and fearful stimuli . Comparison of the change in theta power between social recognition ( SR ) and fear conditioning ( FC ) at high and low theta ranges , statistically validated using two-way repeated measures ANOVA ( p—experiment X theta range interaction ) . The assumption of normality was assessed by Lilliefors and Shapiro–Wilk tests . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 02510 . 7554/eLife . 03614 . 026Figure 7—figure supplement 1 . Arousal-driven locomotion during recall of fear memory . ( A ) A schematic drawing of the fear conditioning session , comprising five events of 40-sec tone ( gray bar ) followed by brief electrical foot shock ( red bar ) . ( B ) Locomotion activity of one animal during the recall of fear memory , 1 day after fear conditioning , plotted as a function of the experimental stage . Gray bars represent the 40-sec long tone . Tone start is marked on the X-axis by T1…5 and tone end by S1…5 . ( C ) Mean locomotion ( n = 6 animals ) during fear recall around the first tone , as a function of time . Tone started 15 sec from the beginning of the experiment and is marked by a gray bar . Note the intense locomotion of the animals during most of the tone , as opposed to their freezing at the end of it , when expecting the electrical foot shock . Black dashed line represents the 15-sec period during which theta activity was calculated . Error bars represent SEM . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 026 We next examined how the coherence between the various brain regions changes in response to the two types of arousing stimuli . Figure 8A depicts the coherence between the MeA and LS during Base and Stimulus periods of FC and SR experiments , respectively . The change in coherence of the two stimuli is presented in Figure 8B and reveals a positive peak at the high theta range for the social encounter , and at the low theta range for the fear memory recall . A quantitative analysis of all coherence changes within the network in both ranges showed that this tendency generally holds for all pairs of brain regions ( Figure 8C ) . Accordingly , most pairs showed a statistically significant interaction between the type of experiment ( FC or SR ) and theta band ( high or low ) ( *p < 0 . 05 , **p < 0 . 01 , two-way repeated measures ANOVA , Figure 8—source data 1 ) . Nevertheless , the magnitude of changes was different between distinct pairs . For example , the changes in the coherence between the LS and NAcc were much smaller than those recorded between the Pir and NAcc and did not show any statistical significance . Moreover , the increases of coherence between the AOB-MOB and MOB-Pir pairs were much bigger in SR compared to the FC experiment . We conclude that the distinct arousal states are characterized by distinct patterns of coherence changes within that same network of brain regions ( Figure 9 ) . 10 . 7554/eLife . 03614 . 027Figure 8 . Distinct changes in theta coherence between various brain regions are induced by social and fearful stimuli . ( A ) Coherence analyses ( 0–20 Hz ) of LFP signal recorded in the LS and MeA of one animal , 5 min prior to stimulus introduction ( Base , red ) and 15 s following it ( Stimulus , blue ) during fear memory recall ( left , FC ) and social encounter ( right , SR ) . ( B ) The change between Stimulus and Base coherence analyses ( Stimulus minus Base ) shown in A , for FC and SR , superimposed . ( C ) Mean ( ±SEM ) values of the peak change in coherence between all possible couples of brain areas in the low ( 4–8 Hz , red and blue ) and high ( 8–12 Hz , pink and light blue ) theta ranges for the FC ( red and pink ) and SR ( blue and light blue ) experiments ( *p < 0 . 05 , **p < 0 . 01 , experiment X theta range interaction , two-way repeated measures ANOVA , Figure 8—source data 1 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 02710 . 7554/eLife . 03614 . 028Figure 8—source data 1 . Comparison of change in coherence in low and high theta bands between social and fearful stimuli . Comparison of the change in coherence between social recognition ( SR ) and fear conditioning ( FC ) at high and low theta ranges ( right ) , statistically validated using two-way repeated measures ANOVA ( p—experiment X theta range interaction ) . The assumption of normality was assessed by Lilliefors and Shapiro–Wilk tests . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 02810 . 7554/eLife . 03614 . 029Figure 9 . Different patterns of coherence change characterize the distinct arousal states . Graphical color-coded presentation of the mean changes in coherence for the FC and SR experiments . DOI: http://dx . doi . org/10 . 7554/eLife . 03614 . 029
This study demonstrates that an encounter with a social stimulus causes increased LFP rhythmicity in the high theta range ( 7–10 Hz ) , in a network of limbic brain areas associated with social memory . Strikingly , the change in theta rhythmicity is directly proportional to the novelty of the social partner , and may thus be considered a neuronal correlate of short-term social memory ( Liebe et al . , 2012 ) . Since the modulation of theta rhythmicity is observed even when anesthetized stimuli are used , we infer that it does not depend on the behavior of the social stimulus . Despite the similarity in investigative behavior , such modulation of theta rhythmicity is not observed with object stimuli , suggesting that it is social specific . Since the augmented theta rhythmicity and the associated increase in theta coherence persist beyond the removal of the social stimulus itself , we conclude that these parameters do not mirror sensory inputs but rather reflect a state of arousal that slowly fades away . This is in agreement with the fact that the increase in theta power occurs prior to the actual introduction of the social stimulus in the arena , suggesting increased arousal due to the anticipated social encounter . Finally , since the change in theta rhythmicity during the SRM test correlates with the novelty of the social stimulus , we posit that it reflects a graded level of arousal , which is proportional to the stimulus saliency . One of the questions that arise from the study is whether the social encounter-induced state of arousal is elicited by the ‘social’ quality of the stimulus or whether it simply results from the complexity of the stimulus . Notably , the social stimulus is much more complex than the single object or odor stimuli that we used as controls . It emits a complex mixture of odors and semiochemicals , and in addition to the main and accessory olfactory systems , it also stimulates the visual , auditory , and somatosensory systems . It is not likely that the full complexity of the social stimulus may be mimicked by the use of any artificial mixture of odors , hence the possibility that the arousal state results from the complexity of the stimulus cannot be excluded . On the other hand , at least as regards to fear-associated arousal , it is well documented ( Takahashi et al . , 2008 ) that a very simple cue is sufficient to evoke a state of arousal , such that is observed by the freezing of rodents in response to the pure odorant 2 , 3 , 5-Trimethyl-3-thiazoline ( TMT ) , a component of fox odor ( Fendt et al . , 2003 ) , or to a pure tone in a fear conditioning paradigm ( Rogan et al . , 1997 ) . This suggests that the factor that determines the state of arousal is not the complexity of the stimulus but rather the information it embodies with regards to the natural environment of the animal . Many studies , both in animals and humans , have linked brain theta rhythmicity to the processing of emotional cues ( Sainsbury and Montoya , 1984; Sainsbury et al . , 1987a; Sainsbury et al . , 1987b; Aftanas et al . , 2001; Balconi and Pozzoli , 2009; Knyazev et al . , 2009; Maratos et al . , 2009; Luo et al . , 2013 ) . In animals theta rhythmicity was mostly studied in the hippocampus ( Buzsáki , 2002 ) , where it was classified into two types , Type 1 and Type 2 . The atropine-insensitive Type 1 theta rhythmicity shows higher frequency ( 8–12 Hz ) and is thought to be associated mainly with voluntary movement . In contrast , atropine-sensitive Type 2 rhythmicity is characterized by lower frequency ( 4–8 Hz ) and is thought to be linked to arousal during states of immobility ( Bland , 1986; Sainsbury , 1998 ) . Notably , Type 2 rhythmicity was mostly studied using states of fear and aversive stimuli and was shown to be induced by neutral stimuli if conditioned by fear or introduced in the presence of predators ( Sainsbury and Montoya , 1984; Sainsbury et al . , 1987a; Sainsbury et al . , 1987b ) . The relationship of the two types of hippocampal theta rhythmicity and similar rhythms recorded from other brain regions , such as in our case , should be cautiously examined for several reasons . First , recent studies showed that in the hippocampus itself there are differences in the profile of theta rhythmicity between the earlier studied dorsal hippocampus and the more recently studied ventral hippocampus ( Adhikari et al . , 2010 ) , the latter of which shows theta rhythmicity with stronger association to the one recoded in the mPFC ( Jacinto et al . , 2013 ) , and may be dissociated from the dorsal hippocampus under certain conditions such as decision making ( Schmidt et al . , 2013 ) . Second , even for the dorsal hippocampus the dichotomy between the two types of theta rhythmicity is far from being perfect with Type 2 rhythmicity reported to reach 12 Hz at some states and Type 1 rhythmicity reported to disappear during certain movements ( Sainsbury , 1998 ) . Interestingly , researchers reported that in cats the correlation between movement and Type 1 rhythmicity was good at the beginning of the experiments , when a lot of exploratory and object manipulation behavior was observed , but deteriorated towards the end of the experiments , when the animals were still moving but were uninterested in the task ( Sainsbury , 1998 ) . This might suggest that in the hippocampus too , high frequency Type 1 theta may be associated with sensory information processing during ‘positive’ arousal states associated with motivational voluntary movements , such as exploration , while low frequency Type 2 theta may be linked to ‘negative’ arousal states , such as those caused by fear , which is usually associated with freezing . Regardless of the nature of hippocampal theta oscillations , theta rhythmicity associated with emotional states was reported in several other brain areas ( Bland et al . , 1993; Bland and Oddie , 2001; Pignatelli et al . , 2012 ) . Of particular interest is the finding that theta rhythmicity in a limbic network that includes the hippocampus , medial prefrontal cortex and the basolateral complex of the amygdala ( lateral and basolateral amygdala ) is associated with fear memories . Importantly , the consolidation and recall of long-term fear memory was found to be associated with elevated coherence of the theta rhythmicity in this network ( Paré and Collins , 2000; Seidenbecher et al . , 2003; Pape et al . , 2005; Popa et al . , 2010; Lesting et al . , 2013 ) , while its extinction was associated with a decline in coherence , in a brain-region dependent manner ( Narayanan et al . , 2007 ) . Moreover , interfering with theta coherence through local electrical micro-stimulation affected fear-memory recall and extinction depending on theta phase ( Lesting et al . , 2013 ) . Thus , coordinated arousal-induced theta rhythmicity within this network seems to be involved in consolidation and recall of aversive memories ( Popa et al . , 2010; Lesting et al . , 2013 ) . Here , we demonstrated for the first time that similar phenomena occur in a distinct network of limbic areas that are linked to social memory , in the course of social encounters . Importantly , a comparison of the theta activity between social and fearful stimuli revealed that although both cause a state of arousal , the patterns of theta rhythmicity and coherence within the same network are completely different . First , in agreement with previous studies ( Paré and Collins , 2000; Seidenbecher et al . , 2003; Pape et al . , 2005; Popa et al . , 2010; Lesting et al . , 2013 ) , the recall of fear memory causes rhythmicity in the low theta range , while a social encounter elicits rhythmic activity in the high theta range . This suggests the existence of two types of arousal: fear-associated arousal and social-related arousal . Second , each of these conditions caused a distinct pattern of coherence changes between the same regions of the network . Given these results , we hypothesize that the distinct types of theta rhythmicity promote different communication protocols ( Kepecs et al . , 2006 ) for the coordination of neural activity in the network , which depends on the emotional state of the animal . Our results are in agreement with the hypothesis that theta rhythmicity facilitates cognitive processes such as memory formation that are associated with emotionally salient stimuli ( Pelletier and Paré , 2004 ) . The source and distribution of theta rhythms in the mammalian brain are not fully understood ( Pignatelli et al . , 2012 ) . This issue was extensively studied in the hippocampus ( Buzsáki , 2002 ) , which was shown to have the capacity to self-generate theta rhythmicity ( Goutagny et al . , 2009 ) . Yet , as described above theta rhythmicity also exists in various cortical and limbic areas , where it shows dynamic coherence with the hippocampal theta rhythm . One area shown to display robust theta rhythmicity is the olfactory bulb , where it follows the rhythm of respiration ( ‘sniff cycle’ ) ( Rojas-Libano et al . , 2014 ) . Sniffing , similarly to whisking , is a sensory sampling activity , the rate of which dynamically changes throughout the theta band and is strongly influenced by internal arousal and motivational state of the animal ( Clarke and Trowill , 1971; Chang , 1992 ) . Specifically , high-frequency sniffing ( 8–12 Hz ) develops in anticipation of reward delivery ( Freeman et al . , 1983; Monod et al . , 1989; Kepecs et al . , 2007; Wesson et al . , 2008 ) . The olfactory bulb theta rhythm and sniffing are not usually coherent with the hippocampal rhythm . However , in some odor-based learning tasks these rhythms do become transiently coherent ( Macrides et al . , 1982; Kay , 2005; Martin et al . , 2007 ) , a process that was suggested to be mediated by cholinergic neurons in the medial septum ( Tsanov et al . , 2014 ) . Interestingly , whisking was shown to get occasionally phase locked with the sniff cycle ( Cao et al . , 2012; Ranade et al . , 2013 ) or with the hippocampal theta rhythm ( Komisaruk , 1970 ) during exploratory behavior . Thus , various generators of theta rhythmicity in the brain , such as those reflected by sniffing , whisking , or the hippocampal theta rhythm may become dynamically coupled by the brain neuromodulatory systems . While we did not monitor sniffing in our experiments , several recent studies reported changes in sniffing during both social interactions ( Assini et al . , 2013; Wesson , 2013 ) and fear conditioning ( Shionoya et al . , 2013 ) . These studies showed that the sniff cycle adopt high-range theta rhythmicity during social interactions and low-range rhythmicity during fear conditioning . These differences are probably reflected by the distinct rates of theta rhythmicity that we record in the MOB and AOB during these conditions . This may explain our observation of high coherence between MOB-AOB and the low coherence each of them display with all other regions . Moreover , while the coherence between the MOB-AOB is increased during exploration of both social and object stimuli , the coherence between the LS–MeA increases only during social interactions . Thus , the theta rhythmicity displayed by the AOB and MOB probably emerges from a distinct generator , most likely the sniff cycle , that is separate from the one causing rhythmicity in higher brain areas . Furthermore , the significant differences in correlation and coherence dynamics between the various limbic areas suggest the involvement of distinct generators as well . For example , neither paradigm showed significant coherence changes between the LS-NAcc , as opposed to a significant increase in coherence between the LS-MeA or LS-Pir during social interactions . It should be noted that these differences cannot not be accounted for by local diffusion of LFP signals , since the LS is much closer to the NAcc than to the MeA or Pir . Direct synaptic connections cannot explain these differences either as the MeA shows very low coherence with the AOB , despite the strong bidirectional connections between them , but rather displays the highest coherence with the contralateral MeA , despite the lack of direct synaptic pathway ( Canteras et al . , 1995 ) . Therefore , the differential coherence changes between distinct pairs of brain regions during the various conditions are most likely mediated by either a common input to these regions or via brain-region specific neuromudulatory systems . However , the arousal-driven modulation of theta rhythmicity which seems to be common to all brain regions is probably mediated by a general , brain-wide neuromodulatory mechanism such as neurohormonal activity ( Lee and Dan , 2012; Marder , 2012 ) . An ever growing body of evidence implies rhythmic brain activity in various cognitive processes , particularly in memory acquisition and recall ( Fell and Axmacher , 2011; Buzsáki and Watson , 2012; Cannon et al . , 2014 ) . Specifically , slow frequency rhythms such as the theta rhythm , are hypothesized to mediate communication between brain regions and to promote the temporal binding of neural assemblies in these areas into coherent networks subserving specific cognitive processes ( Buzsáki and Draguhn , 2004; Jutras and Buffalo , 2010; Benchenane et al . , 2011; Fell and Axmacher , 2011 ) . During the last decade , several prominent theories implied a disordered or weak communication among brain regions as a major deficit underlying ASD etiology and symptoms ( Brock et al . , 2002; Uhlhaas and Singer , 2006; Geschwind and Levitt , 2007; Kana et al . , 2011; Wass , 2011 ) . Indeed , multiple recent studies found reduction in the power and coherence of slow brain rhythms , such as the alpha and theta rhythms , in ASD individuals ( Murias et al . , 2007; Coben et al . , 2008; Isler et al . , 2010; Barttfeld et al . , 2013; Doesburg et al . , 2013; Machado et al . , 2013; Kikuchi et al . , 2015 ) . In agreement with these findings , our results suggest that arousal-driven theta rhythmicity may help bind correlated neuronal assemblies in distinct brain areas participating in cognitive and emotional processes underlying social behavior . A disruption of the correlated neuronal activity associated with the theta rhythmicity is likely to impair these processes ( Uhlhaas and Singer , 2006; Geschwind and Levitt , 2007; Buzsáki and Watson , 2012 ) resulting in atypical social behaviors .
Sprague-Dawley ( SD ) male rats ( 5–6 weeks of age , 250–300 gr ) served as subjects while SD or Wistar Hola/Hannover male rats ( 5–6 weeks of age , 250–300 gr ) served as stimuli . All rats were purchased from Harlan Laboratories ( Jerusalem , Israel ) and housed in groups ( 2–5 per cage ) in the SPF rat facility of the University of Haifa under veterinary supervision , food and water available ad libidum , lights on between 7:00 and 19:00 . Experiments were performed in a strict accordance with the guidelines of the University of Haifa and approved by its Animal Care and Use Committee . We used home-made electrodes for implantation . Stimulating electrodes were prepared by twisting together two stainless steel wires ( A-M Systems , Sequim , WA , USA ) with bare diameter of 0 . 005” ( Coated-0 . 008” ) . Recording electrodes were prepared from Tungsten wire ( A-M Systems ) with bare diameter of 0 . 008” ( Coated-0 . 011” ) soldered to stainless steel wire . For reference/ground wire , we used stainless steel wires attached to a small screw . The rats were anesthetized with subcutaneously injected Ketamine ( 10% 0 . 09 cc/100 gr ) and Medetomidine ( 0 . 1% 0 . 055 cc/100 gr ) . Anesthesia level was monitored by testing toe pinch reflexes and held constant throughout surgery with consecutive injections . The body temperature of the rat was kept constant at approximately 37°C , using a closed-loop temperature controller connected to a rectal temperature probe and a heating-pad placed under the rat ( FHC , Bowdoin , MA , USA ) . Anesthetized rats were fixed in a stereotaxic apparatus ( Stoelting , Wood Dale , IL , USA ) , with the head flat , the skin was gently removed , and holes were drilled in the skull for implantation of electrodes and for reference/ground screw connection . Stimulating electrodes were placed in the left AOB ( A/P = +3 . 0 mm , L/M = +1 . 0 mm , D/V = −4 . 0 mm at 50° ) and MOB ( A/P = +7 . 08 mm , L/M = +1 . 0 mm , D/V = −5 . 5 mm ) . Recording electrodes were placed in antero-ventral area of the MeA ( A/P = −2 . 4 mm , L/M = +3 . 18 mm , D/V = −8 . 5 mm ) , LS ( A/P = −0 . 24 mm , L/M = +0 . 4 mm , D/V = −4 . 4 mm ) , and Pir ( A/P = +3 . 2 mm , L/M = +3 . 5 mm , D/V = −5 . 5 mm ) , as well as in the NAcc ( A/P = +1 . 2 mm , L/M = +1 . 4 mm , D/V = −5 . 8 mm ) in later experiments . Each electrode location was verified by its typical field potential signal , evoked in the MeA and LS by AOB stimulation ( Gur et al . , 2014 ) and in the Pir by MOB stimulation ( Cohen et al . , 2013 ) . Following verification implanted electrodes ( one at a time ) were fixed by dental cement ( Stoelting ) . When all electrodes were in place , the free ends of the stainless steel wires ( including one wire for each stimulation electrode ) were wired up to a connector which was then connected to the skull by dental cement , followed by skin is suturing . To avoid a need of soldering , procedure that could damage brain tissue due to excessive heat , we used gold pins inserted to the connector holes under pressure which destroyed the wires isolation to create a contact between the wires and the pins . After surgery , Amoxicillin ( 15% , 0 . 07 cc/100 gr ) was injected daily ( for 3 days ) to prevent contamination . Rats allowed recovery for at least 7 days before experiments . All experiments were video-recorded from above the arena ( see Video 1 ) by a CCD camera ( Prosilica GC1290 GigE , Allied Vision Technology , Taschenweg , Germany ) . Electrophysiological recordings where made using an 8-channel wireless recording system ( W8 , Multi Channel Systems , Reutlingen , Germany ) . Recoded signals ( sampled at 1 kHz , low-pass filtered at 0–300Hz ) were synchronized with the video recordings by start signal sent through a digital to USB converter ( NI USB-6008 , National Instruments , Austin , TX , USA ) controlled by a self-written LabVIEW program ( National Instruments ) . The experimental arena comprised a three-layer box ( inner dimensions: width—26 cm , length—28 cm , height—40 cm ) with door on its front side . The inner layer was made of material ( cloth ) stretched on cuboid metal carcass to soften mechanical bumps of the recording system . The outer layer was made of adhesive black tape to prevent light entrance . A stainless steel net serves as a faraday cage in between these layers and the Multi-Channel wireless receiver was placed between it and the inner layer . During the experiment , the arena was illuminated by dim red light . We used a double floor made of two plastic slices that can be separately removed . Overall , we recorded from 22 animals , of them , 11 were tested with the social paradigm , 6 with the object paradigm ( 1 animal was tested with both ) and 6 animals were tested with both fear conditioning and social encounter . Social recognition memory using anesthetized stimuli was performed in two animals and smell recognition was tested in three animals . The sample size is not always the same for all brain regions since in some of the recorded animals we lost the signals from specific electrodes due to various causes . At the beginning of each experiment , the tested rat was taken out of its home cage and the wireless transmitter was fastened to the connector on its head by a male-to-male Interconnect header ( Mill-Max Mfg . Oyster Bay , NY , USA ) with 18 pins . Following 0 . 5–1 hr of habituation in the experimental arena , the rat was subjected to social , object , smell recognition test ( Figure 2A ) , or fear conditioning test ( Figure 7—figure supplement 1 ) . Each encounter initiated by pressing ‘start’ button on LabVIEW virtual instrument that sends synchronizing start signal to the camera and the wireless system . Then , during a period of 15 s , the stimulus was removed from its cage and delivered into the experimental arena . At the end of each encounter following stimulus removal , the upper floor slice is taken out and thoroughly cleaned with 70% ethanol and water to remove any odors left by the stimulus . It was then put back below the other slice 5 min after stimulus removal . Rat stimuli were individually placed in clean covered plastic box and held in the experiment room throughout the experiment . The two stimulus animals used for each paradigm were always from different rat strains . Anesthetized animal stimuli were subcutaneously injected Ketamine ( 10% 0 . 09 cc/100 gr ) and Medetomidine ( 0 . 1% 0 . 055 cc/100 gr ) 10 min prior to experiment . As object stimuli , we used clean metal office stapler and hole-puncher . For smell recognition , we used small metal-net balls filled with cloth soaked with artificial food smells of citrus and vanilla . The metal-net ball was attached to the cage floor by hot melt adhesive . It should be noted that obviously , both object and smell stimuli are much poorer sources of chemosignals that social stimuli . Fear conditioning took place in a Plexiglas rodent conditioning chamber with a metal grid floor dimly illuminated by a single house light and enclosed within a sound attenuating chamber ( Coulbourn Instruments , Lehigh Valley , PA , USA ) . Rats were habituated to the chamber for 1 hr before fear conditioning . During fear conditioning rats were presented with five pairings of a tone ( CS; 40 s , 5 kHz , 75 dB ) that co-terminated with a foot-shock ( US; 0 . 5 s , 1 . 3 mA ) . The inter-trial interval was 180 s . The fear recall experiments were conducted a day later in the experimental arena described above , using the same procedure without the electrical foot shocks . After completion of the experiments , the rats were anesthetized and killed with an overdose of Isoflurane ( Abbott Laboratories , Chicago , IL , USA ) . The brains are removed and placed in PFA ( 4% in PBS ) over night , followed by sectioning to 200-µm slices using vibrating slicer ( Vibroslice , Campden Instruments , Lafayette , IN , USA ) . The locations of the implanted electrode tips were identified using binocular and compared to the Pexinos-Watson rat brain atlas ( Paxinos and Watson , 2007 ) . All analyses were done using self-written MATLAB programs ( MathWorks , Natick , MA , USA ) . In all cases , when LFP signals were filtered we used band-pass filter between 5 and 11 Hz ( high theta band ) using MATLAB ‘fir1’ function . Statistical analyses were performed using MATLAB , except for repeated measures ANOVA analyses that were conducted using SPSS ( IBM ) statistical software . Each brain region was separately analyzed . Parametric t-test and ANOVA tests were used if data were found to be normally distributed ( Lilliefors and Shapiro–Wilk tests ) . Bonferroni's corrections were performed for multiple comparisons using t-test . One-sided t-tests were used when a change in specific direction was expected before the experiment . | For the brain to function correctly , the activities of multiple regions must be coordinated . This coordination is thought to be carried out by waves of electrical activity in the brain . One of the most prominent signals within these waves is called the theta rhythm . The theta rhythm is thought to help coordinate neural activity between the regions of the brain that are involved in learning and memory . However , theta rhythms also appear when subjects encounter emotional stimuli , which suggests that they might have a role in social cognition . Consistent with this idea , theta rhythms are reduced in individuals with autism spectrum disorders , but the exact nature of the relationship between theta rhythms and social behavior has remained unclear . Tendler and Wagner have now addressed this question directly by implanting electrodes into five brain regions that are active when rats engage in social interactions . Exposing a rat to a social stimulus , such as an unfamiliar visitor rat , caused the intensity of theta rhythms to increase in this network . This change was temporary , with the theta rhythms gradually returning to normal as the novelty of the visitor wore off . An increase in the intensity of theta rhythms also occurred in the same network when the rats encountered a fearful stimulus , such as a tone that had previously signaled the delivery of a mild electric shock . Notably , however , the fearful stimulus led to an increase in low frequency theta rhythms , whereas the social stimulus led to an increase in high frequency theta rhythms . These results suggest that social and fearful stimuli give rise to two different forms of alertness or arousal , which are reflected by the two types of theta rhythms in this network within the brain . Tendler and Wagner also suggest that the distinct frequencies of theta rhythms might be used to support different forms of communication between various regions of the brain , depending on the emotional value of the stimuli ( for example , are they social or fearful stimuli ? ) encountered by the animal . This means that emotional states might be dictating cognitive processes such as learning and memory . | [
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] | 2015 | Different types of theta rhythmicity are induced by social and fearful stimuli in a network associated with social memory |
The Glucocorticoid Receptor ( GR ) alters transcriptional activity in response to hormones by interacting with chromatin at GR binding sites ( GBSs ) throughout the genome . Our work in human breast cancer cells identifies three classes of GBSs with distinct epigenetic characteristics and reveals that BRG1 interacts with GBSs prior to hormone exposure . The GBSs pre-occupied by BRG1 are more accessible and transcriptionally active than other GBSs . BRG1 is required for a proper and robust transcriptional hormone response and knockdown of BRG1 blocks recruitment of the pioneer factors FOXA1 and GATA3 to GBSs . Finally , GR interaction with FOXA1 and GATA3 binding sites was restricted to sites pre-bound by BRG1 . These findings demonstrate that BRG1 establishes specialized chromatin environments that define multiple classes of GBS . This in turn predicts that GR and other transcriptional activators function via multiple distinct chromatin-based mechanisms to modulate the transcriptional response .
The Glucocorticoid Receptor ( GR , encoded by the NR3C1 gene ) is a type I nuclear receptor that elicits the transcriptional response to glucocorticoid steroid hormones . This transcriptional response is essential for human health and development . Glucocorticoids such the synthetic hormone Dexamethasone ( Dex ) are utilized to activate GR signaling to treat human auto-immune and inflammatory diseases and to promote fetal lung development . Understanding the mechanisms through which the transcriptional response to glucocorticoids is generated is critical for human health and for the further development of disease treatments . A detailed mechanism for GR transcriptional activity has been established through the examination of GR at model genes such as the Mouse Mammary Tumor Virus ( MMTV ) . Upon hormone binding , GR enters the nucleus and binds to regions in the chromatin known as GR binding sites ( GBSs ) . At MMTV , GR binding triggers the recruitment of other factors including the SWI/SNF chromatin remodeling complex ( Cordingley et al . , 1987; Fryer and Archer , 1998 ) . Recruitment of the SWI/SNF complex induces the reorganization of nucleosomes around GR binding sites , which in turn facilitates binding of other transcription factors and potentiates transcriptional activation ( Archer et al . , 1994; Wallberg et al . , 2000 ) . The mammalian SWI/SNF chromatin remodeling complex is comprised of one of two catalytic ATPases , BRG1 and BRM , and 10 or more BRM/BRG1-asssociated factor ( BAF ) subunits . The so-called BAF complex is critical throughout embryonic development and is among the most commonly mutated protein complexes in human cancers ( Shain and Pollack , 2013; Kadoch et al . , 2013; Wu et al . , 2017 ) . BRG1 and the BAF subunits also play critical roles in mediating the transcriptional response to glucocorticoid signaling . GR interacts directly with BAF57 , BAF60A , and BAF250 , and requires the catalytic ATPase activity of BRG1 to promote transcriptional activation of MMTV ( Nie et al . , 2000; Inoue et al . , 2002; Hsiao et al . , 2003 ) . Chromatin remodeling by the BAF complex was required for the subsequent recruitment of RNA Polymerase II and other transcription factors to the MMTV promoter ( Johnson et al . , 2008 ) . Transcriptional activation of MMTV was also promoted by the recruitment of a complex containing Ku70/86 , Topoisomerase IIβ , and Poly ( ADP-ribose ) polymerase one by BRG1 ( Trotter et al . , 2015 ) . Thus , chromatin remodeling through the BAF complex is a critical component of GR signaling . Beyond the requirement for chromatin remodeling by the BAF complex , the underlying chromatin landscape appears to play a crucial role in patterning the hormone response . GR preferentially binds to regions in the chromatin that are pre-accessible as measured by DNase hypersensitivity or formaldehyde-assisted isolation of regulatory elements ( John et al . , 2011; Burd et al . , 2012 ) . These findings indicated that GR chromatin interactions were predetermined by other chromatin interacting factors . Pioneer factors , transcription factors that can bind to and open regions of closed chromatin , have been implicated in the pre-patterning of GR binding . The pioneer factors C/EBPβ and AP1 pre-occupied a large proportion of GR binding sites in mouse liver and mammary cells , and were required to maintain chromatin accessibility at GR binding sites ( Biddie et al . , 2011; Grøntved et al . , 2013 ) . Similarly , FOXA1 pre-bound a large number of Estrogen Receptor ( ER , another nuclear hormone receptor closely related to GR ) binding sites and was required for ER binding and transcriptional activity . Conversely , recent work has demonstrated that hormone signaling through both ER and GR promoted the redistribution of FOXA1 chromatin interactions ( Swinstead et al . , 2016 ) . These findings helped to demonstrate that current models of GR activity fail to fully account for the complexities of GR signaling at a genomic scale , and that more sophisticated and diverse models are required to describe the mechanisms through which GR initiates a transcriptional response . In this study , we examined mechanisms of GR transcriptional regulation through genome-scale analyses of hormone-induced changes in transcriptional activity and the binding patterns of GR , BRG1 , and pioneer factors . We identify distinct classes of GR binding site based upon the binding profile of BRG1 before and after hormone treatment . BRG1 binding to GR sites prior to hormone marked GR binding sites that were pre-accessible and enriched for marks of transcriptionally active chromatin . BRG1 was required for a robust GR transcriptional response , as disruption of BRG1 expression dramatically altered the profile of hormone-induced differentially expressed genes . GR binding sites that were pre-bound by BRG1 were also enriched for motifs of pioneer factors such as FOXA1 and GATA3 , and BRG1 binding at pioneer factor binding sites in untreated cells was predictive of GR binding upon hormone treatment . Furthermore , BRG1 expression was required for Dex-induced recruitment of additional FOXA1 and GATA3 to GR binding sites . Taken together , our data suggest that GR elicits the transcriptional response to hormone via multiple distinct mechanisms that are reliant on the pre-patterning of specialized chromatin environments through the actions of the BAF complex and additional factors .
Current models of GR function commonly depict the hormone-dependent recruitment of the BRG1-containing SWI/SNF chromatin remodeling complex to GBSs ( Cordingley et al . , 1987; Fryer and Archer , 1998; Archer et al . , 1994; Wallberg et al . , 2000 ) . This recruitment of BRG1 facilitates the opening of chromatin around the GBS to enhance the ability of GR to elicit transcriptional effects . However , recent work demonstrates that GBSs exhibit chromatin accessibility prior to hormone treatment , suggesting that some mechanism pre-patterns the chromatin environment around GBSs ( John et al . , 2011; Burd et al . , 2012 ) . To investigate a potential role for BRG1 in this phenomenon , we preformed chromatin immunoprecipitation with high-throughput sequencing ( ChIP-seq ) in the A1-2 model cell line ( Figure 1 ) ( Archer et al . , 1994 ) . We obtained data of high depth ( >60 million reads per GR or BRG1 ChIP-seq ) and called peaks using a 0 . 001 false discovery rate cutoff to ensure high confidence in identifying GR and BRG1 binding sites . One hour of hormone exposure was sufficient to induce a massive DNA binding response by GR , with 29934 GR binding sites/peaks identified specifically in Dex-treated cells ( Figure 1A ) . The number of peaks called from our dataset falls within the range of peak numbers called by GR ChIP-seq experiments in other cell lines ( Swinstead et al . , 2016; Starick et al . , 2015 ) . Dex treatment also had a robust effect on the chromatin localization of BRG1 . While 50 , 000 + BRG1 peaks were identified in each condition , only 33582 peaks were shared while 17699 were specific to EtOH-treated cells and 20658 were specific to Dex-treated cells ( Figure 1C , Figure 1—figure supplement 1A ) . This rearrangement of BRG1 chromatin localization is consistent with BRG1 being recruited to GBSs upon hormone treatment . To verify this , we examined the overlap between BRG1 and GR peaks and found that 58% of GR peaks are overlapped by a BRG1 peak ( Figure 1B , C ) . Surprisingly , there was significant overlap in both EtOH and Dex-treated cells ( Figure 1B ) , with 12034 GR peaks bound by BRG1 in both conditions , and 5565 bound by BRG1 in a Dex-specific manner ( Figure 1C ) . 12223 GR peaks were not bound by BRG1 , and a total of 54340 BRG1 peaks ( including 15093 Dex-specific and 17699 EtOH-specific ) did not overlap GR ( Figure 1C , Figure 1—figure supplement 1B ) . These findings indicate that a large subset of the subsequent GR peaks are bound by BRG1 prior to hormone treatment consistent with the concept that BRG1 could be involved in pre-patterning GBSs . To further dissect the relationship between GR and BRG1 , we defined three classes of GR peak: Class I peaks as GR peaks lacking any overlap with a BRG1 peak , Class II peaks as GR peaks overlapped by BRG1 peaks in both EtOH- and Dex-treated conditions , and Class III peaks as GR peaks that overlapped only Dex-specific BRG1 peaks ( Figure 1C ) . Collectively , Class I GR peaks were narrower and showed less overall GR enrichment than Class II or Class III peaks ( Figure 1D , F ) . BRG1 was not enriched at Class I GR peaks , was constitutively enriched at Class II GR peaks , and was induced by Dex-treatment at Class III peaks ( Figure 1E , G ) . The peak classes were easily identifiable at gene level coverage ( Figure 1H–J ) and GR and BRG1 enrichment patterns were independently validated by ChIP-QPCR ( Figure 1K ) . Thus , we utilized these three GR peak classes in our subsequent analyses to examine how differential patterns of BRG1 interaction could define the GR-mediated hormone response . Given the differences in BRG1 distribution we next sought to determine whether the GR peak classes also exhibited distinct chromatin environments . Short , sub-nucleosome length ATAC-seq reads were used as a measure of chromatin accessibility , and were strongly enriched at Class II peaks independent of Dex treatment . Thus , Class II GR peaks are accessible prior to hormone treatment ( Figure 2A ) . Conversely , minimal accessibility was detected at Class I and III GR peaks in EtOH-treated cells , indicating that prior to hormone treatment , these GR peaks were largely inaccessible ( Figure 2A ) . Upon 1 hr Dex exposure , low but distinct levels of accessibility were induced , predominantly at class III peaks where BRG1 binding was also induced ( Figure 2A ) . Thus , chromatin accessibility at GR peak classes was directly correlated with the pattern of BRG1 occupancy . We next examined histone modifications at the GR peak classes for differentially enriched active or repressive marks . Histone 3 Lysine 27 acetylation ( K27ac ) , a histone modification associated with transcriptionally active chromatin at TSSs and active enhancers , was only detected at Class II GR peaks ( Figure 2B ) . Histone 3 Lysine four monomethylation ( K4me1 ) , a marker of both inactive/poised and active enhancers , was enriched at all three peaks classes , but had a unique pattern at Class II peaks ( Figure 2C ) . While K4me1 enrichment was centered on the GR peak at Class I and III peaks , Class II peaks had broader K4me1 enrichment with an apparent trough of enrichment directly over the GR peak ( Figure 2C ) . None of the peak Classes displayed strong enrichment of Histone 3 Lysine 27 trimethylation ( K27me3 ) , a repressive chromatin modification ( Figure 2D ) . Taken together with the ATAC-seq data , the patterns of histone modifications at GR peaks were associated with enhancer-like chromatin marks . The strong ATAC accessibility signal and K27ac enrichment at Class II peaks suggested that they might represent GR binding to active enhancers . On the other hand , the relatively low levels of ATAC accessibility and the patterns of K4me1 and K27ac at Class I and III peaks suggested that they represented GR binding to inactive or poised enhancers . Furthermore , Class II GR peaks represent a distinct set of GR peaks that are associated with BRG1 as well as marks of accessible and transcriptionally active chromatin . To characterize transcriptional events associated with GR chromatin interaction , we investigated whether any GR peaks were proximal to functionally engaged transcriptional start sites ( TSSs ) . We previously used Start-seq to identify actively transcribed TSSs in A1-2 cells ( Lavender et al . , 2016 ) . We divided these TSSs into those correlated with active , annotated gene TSSs and those that were greater than 2 kb from any gene TSS and represent putative active enhancer TSSs . GR ChIP-seq signal after 1 hr of Dex treatment was modestly enriched over active gene TSSs ( Figure 2—figure supplement 2 ) , however this was dwarfed by the level of GR enrichment at active enhancer TSSs identified in either EtOH- and Dex-treated cells ( Figure 2E–G ) . The average GR enrichment over active gene TSSs was not markedly increased when the analysis was restricted to genes that were differentially expressed ( DEGs ) following 1 , 4 , 8 , or 18 hr of Dex treatment ( Figure 2—figure supplement 2 ) Thus , GR was much more frequently associated with enhancer transcription than gene transcription . Unlike GR , BRG1 ChIP-seq signal was broadly detected and similarly enriched at all active TSSs ( Figure 2E ) . Over active gene TSSs and EtOH-detected active enhancers , the average levels BRG1 enrichment appeared unaffected by Dex ( Figure 2E–F ) . This was consistent with the predominantly Class II-specific enrichment of ATAC-seq and H3K27ac ChIP-seq signal and indicated that BRG1 was constitutively associated with most active TSSs in A1-2 cells . Furthermore , ATAC-seq accessibility and K27ac were also strongly enriched at hormone-independent BRG1 peaks that did not overlap GR peaks ( Figure 2—figure supplement 1 ) . Taken together , these data indicated that BRG1 was largely associated with open and active chromatin independently of GR , and that BRG1 peaks that were not affected by hormone treatment most strongly exhibited these characteristics . However , a Dex-induced increase in BRG1 enrichment was observed at active enhancer TSSs detected in Dex-treated cells ( Figure 2G ) , and more modestly at the TSSs of Dex-induced DEGs ( Figure 2—figure supplement 3 ) . Thus , TSSs with altered transcriptional activity upon Dex exposure also exhibited hormone-induced BRG1 enrichment . K27ac and K4me1 ChIP-seq suggested that GR peak classes were differentially associated with active and inactive/poised enhancers ( Figure 2B–C ) . To further dissect this relationship , we looked to see how many peaks in each class were in close proximity to active enhancer and gene TSSs . When considering active TSSs called in untreated cells , 38% of Class II peaks were within 1 kb of a TSS , compared to 2% of Class I peaks and 7% of Class III peaks ( Figure 2H ) . However , when considering active TSSs called in cells treated with Dex for 1 hr , a significant portion of each peak class was within 1 kb of a TSS ( 26% Class I , 45% Class II , and 38% Class III ) ( Figure 2I ) . Despite the induction of transcriptional activity near a significant subset of Class I and III peaks , K27ac was not observably induced at these peaks ( Figure 2B ) whereas the pattern of K4me1 was unaffected ( Figure 2C ) . Taken together with the patterns of K27ac and K4me1 , these data indicated that Class II peaks represented GR interactions with accessible chromatin and transcriptionally active enhancers . Furthermore , they indicated that Class I and III peaks represented GR interactions with inactive or poised enhancers that could be activated upon GR binding , but exhibited limited accessibility and were devoid of the K27ac mark . To further investigate the role of BRG1 in regulating the transcriptional hormone response , we performed RNA-seq in cells which harbor an inducible shRNA targeting BRG1 ( A1A3 cells , previously described in [Burd et al . , 2012] ) . Treatment with Doxycycline for 72 hr resulted in an 80–85% reduction in BRG1 protein levels as well as partial reduction in the nuclear levels of GR protein ( Figure 3—figure supplement 1A ) . RNA-seq performed at 1 hr Dex treatment in A1-2 cells yielded approximately 200 DEGs ( Lavender et al . , 2016 ) . In order to capture a more robust transcriptional hormone response for analysis , we used an 8 hr Dex treatment in A1A3 cells . In normal conditions , 1244 DEGs ( Fold Change > 1 . 5 , p-value<0 . 01 , false discovery rate <0 . 05 ) were identified following 8 hr of Dex treatment ( Figure 3A ) . 743 of these DEGs failed to meet the same fold-change and significance cutoffs in Dex treated BRG1-KD cells , indicating BRG1 was required the transcriptional response to Dex . Intriguingly , BRG1-KD cells had 114 Dex-regulated DEGs that were not called DEGs in control Dex-treated cells , indicating that BRG1 also suppressed the hormone responsiveness of a small number of genes ( Figure 3A ) . Visualizing the changes in gene expression by heatmap revealed that while the hormone response was largely muted or suppressed following BRG1 knockdown , a significant number of genes showed equal or greater hormone responses following BRG1 knockdown ( Figure 3B ) . Indeed , the absolute fold change of both ‘common’ and ‘lost’ DEGs was reduced in BRG1-KD cells , and increased in ‘gained’ DEGs ( Figure 3C ) . Together , these data indicated that BRG1 was required for both a robust transcription hormone response and to suppress ectopic hormone responsiveness . Decreased levels of GR protein in BRG1-KD cells suggested that part of the BRG1 effect might instead result from insufficient nuclear GR . While the increased hormone-responsiveness of the 114 ‘gained’ DEGs in BRG1-KD cells served as evidence that this was not the case , we sought to directly test whether decreased GR was the dominant factor in the loss of dex-induction of ‘lost’ DEGs . Reduction of GR protein levels by 50–70% by siRNA resulted in a modest loss of dex-induced transcription at candidate ‘lost’ genes ( Figure 3—figure supplement 1B–C ) . However , BRG1-KD resulted in a much stronger loss of dex-induced transcription ( Figure 3—figure supplement 1C ) . As such , these data strongly suggest that the changes in the Dex response in A1A3 cells are predominantly driven by the silencing of BRG1 , and not by the more modest decrease in nuclear GR levels . We sought to correlate gene expression changes in A1A3 cells with the presence and proximity of GR peaks . DEG TSSs tended to be closer to GR peaks , and the percentage of DEGs with GR peaks within 50 kb was more than double that of non-DEG obsTSSs ( 77 . 2 to 29 . 8% ) . Thus , while GR binding is distal from gene TSSs , genes that are regulated by GR tend to have a higher degree of local GR binding sites . When considering the closest GR peak to each DEG , the different types of DEGs had different proportions of GR peaks classes , with Class II peaks enriched among the closest peaks to ‘common’ and ‘gained’ DEGs ( Figure 3D ) . Comparing the distance from the closest GR peak to each DEG TSS , Class II peaks were also closer than Class I or Class III peaks ( Figure 3E ) . Overall , ‘common’ DEGs had closer GR peaks than ‘gained’ or ‘lost’ DEGs , with the median distance from TSSs to closest GR peaks of 6889 , 20094 , and 22104 bp , respectively ( Figure 3F ) . DEGs that were ‘gained’ or ‘lost’ also had fewer GR peaks within 50 kb of their TSSs ( Figure 3G ) . Taken together , these data indicate that BRG1 presence was more critical for hormone responsiveness at genes where GR binding was the most distal . GR frequently binds to degenerate GR recognition sequences or GR response elements ( GREs ) ( Starick et al . , 2015 ) and can also interact with other regions of the genome through cooperation with or tethering by other transcription factors such as AP-1 , NFκB , and STAT proteins ( Biddie et al . , 2011; Rao et al . , 2011; Langlais et al . , 2012 ) . To determine whether our GR peaks classes segregated distinct sequence specificities , we first searched under GR peaks for perfect GREs . Using the total set of GR peaks , perfect GREs were found under 28 . 3% of the peaks and GREs with single mismatches were found under an additional 51% of peaks ( Figure 4A ) . Perfect GREs were more common in Class I and Class III peaks ( 33 and 35 . 3% , respectively ) than Class II peaks ( 19 . 4% ) ( Figure 4A ) . Motif analysis revealed similar patterns , with GREs , Androgen Receptor motifs , and Progesterone Receptor motifs being strongly enriched under all three peak classes , but with the lowest enrichment levels under Class II peaks ( Figure 4B ) . Conversely , Class II peaks were most strongly enriched for other transcription factors . Motif analysis revealed that Class II peaks exhibited the highest average level of motif enrichment ( Figure 4C ) . This was largely driven by FOX and GATA motifs , which were more strongly enriched under Class II peaks than Class I or Class III peaks ( Figure 4D ) . To validate these predictive analyses , we pulled data from 25 ENCODE transcription factor ChIP-seq experiments in Mcf7 cells ( Figure 4—source data 1 ) and generated a meta-profile of transcription factor ChIP enrichment over the three GR peak classes ( Figure 4E ) . Class II GR peaks displayed the strongest levels of enrichment , while Class I and Class III peaks showed only low to moderate levels of enrichment ( Figure 4E ) . Motif analyses yielded several other interesting motif families to consider in the context of BRG1 and the GR peak classes . SP/KLF and POU motifs were specifically enriched under Class II and Class III peaks ( Figure 4—figure supplement 1 ) , which suggested that GR binding in cooperation with these transcription factor families may also involve BRG1 . On the other hand , STAT , NFATC , and OLIG motifs were most strongly enriched under Class I GR peaks ( Figure 4—figure supplement 1 ) , indicative of transcription factor interactions that may occur in the absence of BRG1 . Taken together , these analyses revealed that the three GR peak classes had distinguishable DNA sequence content and that the BRG1-GR interaction could be moderated by other transcription factors . Recent work has suggested that the hormone response is coordinated by functional interactions between nuclear hormone receptors and pioneer factors such as FOXA1 and GATA3 ( Biddie et al . , 2011; Grøntved et al . , 2013; Carroll et al . , 2005; Holmqvist et al . , 2005; Laganiere et al . , 2005; Hurtado et al . , 2011 ) . As we observed differential enrichment of FOXA1 and GATA3 motifs under the GR peak classes , we performed ChIP-seq to examine the interaction of these factors at each peak class . Both FOXA1 and GATA3 showed strong levels of enrichment at Class II GR peaks in both untreated and 1 hr Dex treated cells ( Figure 5A–B ) . Class I and Class III peaks had similarly low levels of FOXA1 and GATA3 in untreated cells ( Figure 5A–B ) . However , at Class III peaks , there was a marked increase in the detected levels of FOXA1 and GATA3 binding upon 1 hr of Dex treatment ( Figure 5A–B ) comparable to the Dex-induced enrichment of BRG1 at these peaks ( Figure 1E ) . Thus , at GR peaks , the pattern of pioneer factor binding correlated with that of BRG1 binding . To determine whether BRG1 was required for pioneer factor binding at GR peaks , we performed GR and FOXA1 ChIP-seq in A1A3 cells . The levels of FOXA1 and GATA3 protein were similar between control and BRG1-KD cells ( Figure 3—figure supplement 1 ) , indicating that BRG1 was not required for their expression . In control cells treated with Dex for 1 hr , a Dex-induced increase in FOXA1 and GATA3 enrichment was observed at all three GR peak classes , with the most substantial increase occurring at Class III peaks ( Figure 5C–D , left columns ) ) . In vehicle treated BRG1-KD cells , the loss of BRG1 appeared to have little effect on the enrichment of FOXA1 and GATA3 at GR peaks ( Figure 5C–D , red lines ) . In contrast , the DEX-induced increase in FOXA1 and GATA3 enrichment at GR peaks was almost completely blocked in BRG1-KD cells ( Figure 5C–D , blue lines ) . Taken together , these experiments demonstrate that BRG1 was not required for pioneer factor interaction at GR peaks . However , BRG1 was required for hormone-induced changes in pioneer factor enrichment at GR peaks . We next sought to take a pioneer factor-centric approach to determine whether the presence of BRG1 affected the interaction of GR with pioneer factors . In vehicle-treated cells , BRG1 peaks intersected 16 . 4% of FOXA1 peaks ( 1594 peaks , Figure 6A , D ) and 14 . 5% of GATA3 peaks ( 1145 peaks , Figure 6B , E ) . These peaks were predominantly unique , with only 249 of the FOXA1 +BRG1 peaks intersecting a GATA3 +BRG1 peak , similar to the overall proportion of overlap between FOXA1 and GATA3 peaks ( Figure 6C ) . For both FOXA1 and GATA3 , perfect GRE motifs were present at similar proportions between peaks with or without BRG1 , with approximately 31% of FOXA1 peaks and 28% of GATA3 peaks having a perfect GRE motif within 500 bp of the center of the peak ( Figure 6—figure supplement 1 ) . Despite this , GR binding at FOXA1 and GATA3 peaks was almost entirely restricted to peaks that intersected BRG1 peaks ( Figure 6D–E ) . As BRG1 was present at these sites in both untreated and Dex-treated cells ( Figure 6D–E ) , these are Class II GR peaks . ATAC-seq nucleosome-free reads and K27ac ChIP-seq signal were also predominantly restricted to pioneer factor peaks that intersected BRG1 ( Figure 6D–E ) . Thus , the presence of BRG1 at pioneer factor peaks in untreated cells was predictive of subsequent GR binding upon Dex treatment . These findings suggest that BRG1 is involved in pre-patterning a subset of pioneer factor binding sites to facilitate GR binding upon hormone treatment and that pioneer factor binding alone is not predictive of GR binding . Class II GR peaks represent GR binding to regions of chromatin that are pre-patterned by BRG1 and pioneer factors .
The requirement for BRG1-mediated chromatin remodeling in potentiating the transcriptional response to glucocorticoid signaling at model genes ( e . g . MMTV ) was established over two decades ago . Our examination of the genomic glucocorticoid response demonstrates a previously undescribed role of BRG1 in patterning the underlying chromatin architecture . Our data reveals that BRG1 interacts with approximately 40% of GR binding sites prior to hormone treatment , and an additional 20% of GR binding sites upon hormone treatment . BRG1 is also broadly associated with transcriptional activity at active gene and enhancer TSSs . The patterns of BRG1 binding at GR biding sites prior to and upon hormone signaling allowed us to define three classes of GR binding site ( Figure 7 ) . These classes exhibited distinct patterns of underlying chromatin accessibility , transcriptional activity , histone modification , and transcription factor motif enrichment and binding . These findings are corroborated by the observation that GR bound enhancers exist in three distinct chromatin states in mouse mammary adenocarcinoma cells ( Johnson et al . , 2018 ) . Class I and Class III peaks gain chromatin accessibility upon Dex exposure and are associated with Dex-specific enhancer TSSs , suggesting that they represent regulatory elements that are activated only upon hormone treatment . Class II GR binding sites are strikingly enriched for chromatin that is active and accessible prior to hormone signaling and appear to represent GR binding to active enhancers . Our examination of GR binding sites yielded several interesting observations regarding the nature of GR binding and transcriptional activity . The majority of GR binding sites are not associated with transcriptional activity , indicating that GR binding to chromatin is not sufficient to activate transcription . This is especially evident at Class I peaks , which are devoid of active chromatin markers and display minimal ATAC accessibility . Active and accessible chromatin was only detected at Class II GR peaks where BRG1 localization was constitutive/hormone-independent . This active chromatin environment was pre-existing , and was largely unchanged by Dex exposure and GR binding . However , half of Class II peaks do not have active TSSs within 1 kb , and not all Class II peaks have appreciable K27ac enrichment . Furthermore , BRG1 binding was also not sufficient to generate a fully activated chromatin environment at GR binding sites , as Dex-induced recruitment of BRG1 at Class III peaks occurs without a concomitant induction of K27ac . The implications for these phenomena are wide-ranging . Enrichment of H3K4me1 at Class I and III GR peaks suggests that these binding sites are poised for transcriptional activation , and yet , Dex-induced binding of GR and BRG1 do not yield conversion of these sites to a more active chromatin profile such as that observed at Class II peaks . Thus , it appears that a large subset of GR chromatin interactions are transcriptionally unproductive and uneventful in terms of the effect on the chromatin environment . Despite this , Start-seq reveals that transcriptional activity is gained at ~25% of Class I peaks and ~30% of Class III peaks upon Dex exposure . This suggests that the induction of transcriptional activity at GR binding sites occurs independently of the induction of common active chromatin characteristics . Intriguingly , ~60% of Dex-regulated DEGs are lost following BRG1 knockdown , and Class II and III GR binding sites represent ~60% of GR binding sites . The suppression of the GR transcriptional response following BRG1 knockdown suggested that BRG1 interaction with GR binding sites is required for GR-mediated transcriptional regulation of many genes . Surprisingly , over 100 genes gained hormone-responsiveness following BRG1 knockdown , indicating that at some genes , BRG1 prevents GR from eliciting transcriptional activity . Thus , BRG1 plays a critical role in patterning the GR transcriptional response . As the number of GR peaks is substantially greater than the number of GR-regulated genes , and most genes have multiple GR peaks of different classes within the surrounding several hundred kilobases , it is difficult to clearly associate individual GR peaks or peak classes with specific genes . The distal nature of GR binding events and the enhancer-like characteristics of the chromatin under GR binding sites indicate that the GR signaling largely regulates transcription through modulation of enhancer activity . On the other hand , BRG1 was enriched at most active gene and enhancer TSSs , implicating a widespread role for BRG1 in facilitating transcriptional activity . A reasonable hypothesis for GR signaling would be that BRG1 binding at gene TSSs and GR binding sites/enhancers promotes chromatin looping . Long-range chromatin interactions have been implicated in GR transcriptional activity ( Vockley et al . , 2016; Hakim et al . , 2009 ) . It has also been suggested that clusters of GBSs interact with each other over long ranges to synergistically regulate transcription of target genes Holmqvist et al . , 2005; Vockley et al . , 2016 ) . Identifying such long-range interactions between GR and BRG1 would provide more insight into whether the different classes of GR binding sites are differentially utilized in regulating transcription . Such long range interactions could potentially provide functional rationale for the existence of ‘unproductive’ GR binding sites , which could be associated with transcriptionally active GR binding sites to cooperatively regulate transcriptional output . Alternatively , GR and BRG1 could regulate gene expression through decompaction of broad regions of chromatin surrounding gene TSSs and enhancers , such as has been reported at the Fkbp5 and Ms4xxx loci in macrophages ( Jubb et al . , 2017 ) . In either case , removing individual or combinations of GR peaks around candidate DEGs will allow for interrogation of how multiple GR binding events are coordinated to elicit a transcriptional response . The eventual identification of which GR binding sites are critical for transcriptional regulation in a given cell type will have wide-ranging implications for the pharmacological targeting of GR signaling for disease treatment . We observed that BRG1 binding at Class II binding sites is predictive of potential GR interactions with pioneer factor binding sites . This finding is intriguing when considered along with the recent finding that FOXA1 binding is reorganized upon activation of ER or GR ( Swinstead et al . , 2016 ) . Like FOXA1 , BRG1 binding is significantly reorganized upon hormone treatment and the majority of EtOH-specific and Dex-specific BRG1 peaks are not associated with GR binding sites [Figure 1 , Figure 1—figure supplement 1] . Thus , the rearrangement of BRG1 and FOXA1 binding does not appear to be dependent on direct interaction with GR . While these rearrangements occur within an hour of hormone treatment , it is possible that the reorganization of BRG1 and FOXA1 binding occurs secondarily to the initial GR binding events , which occur rapidly within the first several minutes of hormone treatment ( unpublished data ) . Alternatively , it is also possible that a subset of interactions between GR , BRG1 and FOXA1 on chromatin are not detectable by standard ChIP methods . Single molecule analysis of GR , BRG1 , and FOXA1 indicated that the majority of chromatin interactions occur with residence times of approximately 1 s , and a minority of events occur with longer residence times of 5 to 10 s ( Swinstead et al . , 2016; Paakinaho et al . , 2017 ) . Thus , chromatin binding by these factors in individual cells is highly dynamic . While ChIP-seq experiments represent the overall binding profile of these factors in large populations of cells , they still represent snapshots of the chromatin interaction profiles of these factors and could fail to detect the full expanse of rapid and dynamic binding events . As such , it remains unclear whether there is any set hierarchy of binding or timing/order of events of FOXA1 , BRG1 , and GR chromatin interactions . As suggested by the distinct patterns of interactions at GR peak classes , it is likely that several series of binding events occur at GR binding sites prior to and upon hormone signaling . Elucidating these distinct mechanisms will help to unravel the basic mechanisms of nuclear receptor signaling and the role of pioneer factors and chromatin remodeling complexes in facilitating chromatin interactions and modulating transcriptional output .
T47D derived A1-2 ( Archer et al . , 1994 ) and A1-A3 ( Burd et al . , 2012 ) cells were cultured as previously described ( Burd et al . , 2012 ) . Both cell lines were authenticated by STR profiling and tested negative for mycoplasma . Dexamethasone treatments were performed using 100 nM Dexamethasone or ethanol vehicle for 1 or 8 hr for ChIP-seq and RNA-seq experiments , respectively . To knockdown BRG1 expression in A1-A3 cells , cells were treated for 72 hr with Doxycycline . ChIP experiments were largely performed as previously described ( Takaku et al . , 2016 ) . Cells were fixed with 1% formaldehyde at 37C for 10 min for all targets except BRG1 , for which cells were fixed for 20 min . After quenching with glycine , cell pellets were washed Hypotonic buffer ( 10 mM HEPES-NaOH pH 7 . 9 , 10 mM KCl , 1 . 5 mM MgCl2 , 340 mM sucrose , 10% glycerol , 0 . 1% Triton X-100 , and HALT protease inhibitors ( ThermoFisher ) ) and resuspended in Shearing buffer ( 10 mM Tris-HCl pH 8 . 0 , 1 mM EDTA , 0 . 5 mM EGTA , 0 . 5 mM PMSF , 5 mM Sodium Butyrate , 0 . 1% SDS , and HALT protease inhibitors ( ThermoFisher ) and chromatin was fragmented by sonication with the Covaris S220 . Chromatin was diluted two-fold in 2xIP buffer ( 20 mM Tris-HCl pH 8 . 0 , 300 mM NaCl , 2 mM EDTA , 20% Glycerol , 1% Triton X-100 , 0 . 5 mM PMSF , 5 mM Sodium Butyrate , and HALT protease inhibitors ( ThermoFisher ) ) and immunoprecipitation was performed with antibodies specific to BRG1 ( lab-made , [Trotter et al . , 2015] ) , GR ( Santa Cruz M-20 ) , FOXA1 ( Abcam ab23738 ) , GATA3 ( Cell Signaling D13C9 ) , and H3K27ac ( Abcam ab4729 ) and ratios of 1 ug antibody per 100 ug chromatin . Immune complexes were captured using protein A and G dynabeads , washed once each with low salt ( 20 mM Tris-HCl pH 8 . 0 , 150 mM NaCl , 2 mM EDTA , 1% Triton X-100 , 0 . 1% SDS ) , high salt ( same as low salt buffer , except 500 mM NaCl ) , and LiCl buffer ( Tris-HCl pH 8 . 0 , 250 mM LiCl , 2 mM EDTA , 1 % NP-40 , 1% ( wt/vol ) sodium deoxycholate ) , and twice with TE . Eluted DNA was RNaseA and Proteinase K treated and purified using Qiagen PCR purification columns . ChIP-seq libraries were generated using the Illumina Nextara-XT library generation kit , and sequenced on the Illumina MiSeq and NextSeq platforms . For all ChIP-seq experiments , biological duplicates or triplicates were performed , and all presented ChIP-seq data are representative single experimental replicates . Examples of reproducibility of multiple replicates are presented in Figure 1—figure supplement 2 . Adapter sequences were trimmed from ChIP-seq reads using Cutadapt ( Martin , 2011 ) and low quality reads were removed from analysis using Sickle ( Joshi NA et al . , 2011 ) . Alignment was performed with Bowtie2 ( Langmead and Salzberg , 2012 ) . Aligned reads were sorted and processed with Samtools ( Li et al . , 2009 ) and de-duplicated using Picard Tools ( http://broadinstitute . github . io/picard ) . Peaks were called using MACS2 ( Zhang et al . , 2008 ) and Homer ( Heinz et al . , 2010 ) using a false discovery rate cutoff of 0 . 001 , and regions of high depth or with high signal in untreated or input samples were used to filter out false positive peak calls . Peak overlaps and distance analyses were performed using Bedtools ( Quinlan and Hall , 2010 ) . Coverage files and heatmaps were generated using Deeptools ( Ramírez et al . , 2016 ) . Motif analyses were performed using AME ( McLeay and Bailey , 2010 ) . RNA was isolated from treated A1-2 and A1A3 cells using Qiagen RNeasy kits with on-column DNase treatment . ThermoFisher SuperScript III or BioRad iScript were used to synthesize DNA and qPCR was run with BioRad ssoAdvanced Universal SYBR Green Supermix . For RNA-seq , RNA quality was validated with RNA 6000 RNA Pico Kit on the Agilent Bioanalyzer 2100 . RNA-seq libraries were generated at the National Intramural Sequencing Center using Ribo-Zero Gold and sequenced on an Illumina HiSeq 2500 . Adapter sequences were trimmed from RNA-seq reads using Cutadapt ( Martin , 2011 ) and low quality reads were removed from analysis using Sickle ( Joshi NA et al . , 2011 ) . Alignment was performed using STAR ( Dobin et al . , 2013 ) to generate coverage tracks and using Salmon ( Patro et al . , 2017 ) and to obtain gene counts for differential expression analysis using limma-voom ( Law et al . , 2014 ) with cutoffs of Fold Change > 1 . 5 , p-value<0 . 01 , and False Discovery Rate < 0 . 05 . All ChIP-seq and RNA-seq data generated for this publication have been deposited in NCBI's Gene Expression Omnibus ( Edgar et al . , 2002 ) and are accessible through GEO Series accession number GSE112491 ( https://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE112491 ) . | Steroid hormones play a number of roles in the body , including controlling the immune system and the body’s response to stress . Artificially produced steroid hormones may also be used as part of treatments for cancer . The hormones affect the behavior of cells by binding to and activating hormone receptor proteins . The receptors can then interact with the cell’s DNA to change the activity of nearby genes . Gaining access to particular sites on a strand of DNA is not always easy . Cells pack DNA into a structure called chromatin . In some regions the DNA is so tightly wrapped in the chromatin that the receptors cannot access it . The structure of the chromatin therefore affects how a cell responds to steroid hormones . Inaccessible regions of chromatin can be ‘opened up’ by two groups of proteins , known as remodeling proteins and pioneer factors . Hormone receptors can work with these proteins to access particular DNA regions , but exactly how all these proteins work together was not fully understood . Hoffman et al . have now used DNA and RNA sequencing technologies to examine the roles of a hormone receptor called the glucocorticoid receptor , a remodeling protein called BRG1 , and various pioneer factors in human breast cancer cells . This revealed three ways in which the glucocorticoid receptors worked with the other proteins when binding to chromatin . These could be distinguished by the pattern of BRG1 molecules bound to the DNA . Further investigation showed that BRG1 controls how the glucocorticoid receptor affects the activity of genes . In addition , BRG1 influences how the receptor interacts with pioneer factors when it is bound to DNA . Future research into how these proteins work together could ultimately help us to improve how we use steroid hormones to treat diseases . | [
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Most cortical neurons fire regularly when excited by a constant stimulus . In contrast , irregular-spiking ( IS ) interneurons are remarkable for the intrinsic variability of their spike timing , which can synchronize amongst IS cells via specific gap junctions . Here , we have studied the biophysical mechanisms of this irregular spiking in mice , and how IS cells fire in the context of synchronous network oscillations . Using patch-clamp recordings , artificial dynamic conductance injection , pharmacological analysis and computational modeling , we show that spike time irregularity is generated by a nonlinear dynamical interaction of voltage-dependent sodium and fast-inactivating potassium channels just below spike threshold , amplifying channel noise . This active irregularity may help IS cells synchronize with each other at gamma range frequencies , while resisting synchronization to lower input frequencies .
From the Hodgkin and Huxley model onwards , we have a good understanding of the dynamical basis of regular or periodic firing , and of various kinds of burst firing ( FitzHugh , 1961; Hindmarsh and Rose , 1984; Hodgkin and Huxley , 1952 ) . In contrast , the nature of intrinsically irregular firing has resisted elucidation , and appears to be a more complex phenomenon . Irregularity of firing in neurons can arise because of fluctuating patterns of synaptic input due to spontaneous activity ( Destexhe et al . , 2001 ) , or from stochastic fluctuations in the release of transmitter ( Ribrault et al . , 2011 ) . In some regions of the brain , though , certain types of neuron show strikingly high irregularity of firing even when isolated in vitro ( Cauli et al . , 1997; Grace and Bunney , 1984; Ascoli et al . , 2008 ) . The cellular mechanisms of such intrinsic irregularity are unknown , though the stochastic gating of the ion channels involved in spike generation seems likely to play a part . Effective chaos in the nonlinear dynamics of the voltage-dependent ion channels involved in spike generation could also contribute to irregular patterns of membrane potential ( Durstewitz and Gabriel , 2007; Fan and Chay , 1994 ) . In the cerebral cortex , the function of intrinsically irregular firing is of particular interest . Within the neural circuitry of the neocortex are various types of inhibitory interneuron , several of which have been implicated in the generation of distinct synchronous oscillations at various frequencies from slow ( <1 Hz ) to very fast ( >100 Hz ) , such as the theta ( 4–10 Hz ) , beta ( 10–30 Hz ) and gamma ( 30–80 Hz ) oscillations ( Buzsáki , 2006 ) . For example , the fast-spiking ( FS , parvalbumin-expressing , basket morphology ) cell network has a crucial role in the emergence of the gamma rhythm ( Cardin et al . , 2009; Hasenstaub et al . , 2005 ) . Recent evidence suggests the possibility of a similar specific role for the low-threshold-spiking ( LTS , somatostatin-positive , Martinotti ) cell network in lower frequency theta or beta rhythms ( Fanselow et al . , 2008; Vierling-Claassen et al . , 2010 ) . One type of interneuron , however , is distinguished by its intrinsically irregular repetitive firing , showing a broad , apparently random dispersion of its interspike intervals , as opposed to bursting , even when pharmacologically disconnected from any synaptic input . These irregular-spiking ( IS ) neurons ( Cauli et al . , 1997 ) seem to have both a distinctive mechanism of spike timing control , and possibly a unique role during synchronous network oscillations . To enable specific targeting of IS cells , we used a mouse line with green fluorescent protein ( GFP ) linked to the promoter for Gad2 ( GAD65; López-Bendito et al . , 2004 ) , in which fluorescently labeled neurons in somatosensory cortex predominantly have an IS phenotype ( Galarreta et al . , 2004 ) . These cells express CCK , VIP and 5HT3a receptors ( Sugino et al . , 2006 ) . They are concentrated in layer 2 ( López-Bendito et al . , 2004 ) , and derive primarily from the caudal ganglionic eminence during development ( López-Bendito et al . , 2004; Lee et al . , 2010 ) . They connect specifically to each other by gap junctions and mutually inhibitory synaptic connections , which together enable precisely-synchronized irregular firing ( Galarreta et al . , 2004 ) . Their wide axonal arborizations through many layers of the cortex and inhibition of pyramidal cells ( Galarreta et al . , 2004 , 2008 ) suggest that they could exert a powerful influence on the network . Another distinctive property of these cells is their expression of CB1 cannabinoid receptors , which can suppress their inhibitory output to pyramidal cells , following depolarization of the postsynaptic cell ( Galarreta et al . , 2008 ) . Although they make up a large proportion of inhibitory interneurons in superficial layers , they have received much less attention than other classes of interneuron , such as FS and LTS cells . In this study , we ask: what mechanisms underlie the striking irregularity of firing , and what are the functional consequences of this in an oscillating cortical network ? Using a combination of patch-clamp recording in slices of somatosensory cortex , time series analysis and computational modeling , we show that IS neurons generate robust , intrinsically irregular firing by nonlinear interactions of voltage-dependent currents and channel noise . The degree of irregularity is tuned by the level of a fast-inactivating potassium conductance , and voltage-dependent sodium and potassium channel openings contribute a high level of voltage noise at threshold . The effect of these mechanisms is that these cells reject synchronization to a low frequency ( 10 Hz ) , while synchronizing effectively to higher , gamma frequencies , a property which could give them a prominent role in gating local cortical gamma oscillations .
In the cortex of Gad2-GFP mice , fluorescent cell bodies are concentrated in layer 2 , with dendrites concentrated in layers 1 and 2/3 and axons which ramify through the cortical layers ( Figure 1a ) . The morphology of fluorescent neurons was varied , with bitufted , bipolar and multipolar cells observed , as described by Galarreta et al . ( 2004 ) . Cells had input resistances of 331 ± 164 MΩ and passive time constants of 15 . 4 ± 7 . 7 ms ( mean ± SD , n = 82 ) . In response to a step current stimulus in a whole-cell current-clamp recording , 77% ( 82/106 ) of the cells showed a characteristic pattern of action potentials ( APs ) at irregular intervals , with fairly deep and slow afterhyperpolarizations , often following an initial adaptation phase ( Figure 1bi ) . Irregular spiking interneurons displayed larger somata ( ≈15 µm diameter ) and more prominent projections than did the remaining 23% of GFP+ neurons , which had a regular-spiking response ( excluded from analysis , except when stated ) , as described by Galarreta et al . ( 2004 , 2008 ) . 10 . 7554/eLife . 16475 . 003Figure 1 . Irregular-spiking in a population of cortical inhibitory interneurons . ( a ) Distribution of Gad2-GFP mouse neurons in the somatosensory cortex ( top ) . Below is the detailed morphology of a typical irregular-spiking interneuron which was filled with neurobiotin . White arrow indicates the axon initial segment . Irregular-spiking Gad2-GFP interneurons were consistently found in superficial layers and displayed noticeably bigger somata . Stacked confocal images of cells in a 300 µm thick slice; scale bars , 150 μm and 50 μm . ( b ) , ( i ) Irregular spiking in response to a constant 120 pA current step . Resting potential was −68 mV . After an initial fast spike doublet , firing settles into an irregular pattern of spikes , separated by noisy fluctuations of membrane potential . ( ii ) Raster plot of spike times in 30 successive responses to the same current step , separated by 10 s intervals . Spike train corresponding to ( bi ) is indicated by an arrow at left . ( c ) Close-up view of the interspike membrane potential fluctuations in three consecutive trials from the ensemble shown in ( bii ) . Spikes have been truncated . ( d ) , ( i ) Higher frequency firing in another cell , excited by a 220 pA constant current stimulus . ( ii ) The distribution of 2730 interspike intervals ( ISIs ) in one cell , fitted with a gamma distribution: f ( t ) =1Γ ( n ) τ ( t−trτ ) n−1exp ( tr−tτ ) , t>tr where n is 2 . 29 , τ is 20 . 7 ms , and refractory period tr is 35 . 05 ms . CV ( ISI ) = 0 . 38 , mean firing frequency is 13 . 6 Hz . ( e ) , Gad2-GFP cortical interneurons display the same irregular-spiking pattern in primary culture ( 12–16 DIV; n = 10 ) . Irregularity increased with development , and was observable even at higher firing frequencies ( 15–20 Hz ) as in cortical slices . ( f ) Patch-clamp recording of a GFP+ neuron in culture . Scale bar 50 μm . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 003 The irregular trajectory of action potential intervals in IS neurons varied from trial to trial ( Figure 1bii ) , and the membrane potential showed quite large , variable fluctuations between spikes ( Figure 1c ) . Over long periods of continuous stimulation , the distribution of interspike intervals was skewed and unimodal , and could be reasonably well-fitted by a gamma function ( Figure 1d ) . Irregularity was quantified as the coefficient of variation of interspike intervals or CV ( ISI ) , the ratio of the standard deviation of intervals to their mean ( see Materials and methods ) , which is equal to 1 for a Poisson point process , and 0 for a perfectly periodic process . CV ( ISI ) was reduced at higher stimulus levels and firing frequencies , and was quite variable from cell to cell , but ranged from 0 . 1 ( fairly regular ) to 0 . 6 at a firing frequency of ≈10 Hz ( CV10 Hz = 0 . 28 ± 0 . 15 , n = 45 ) . The irregularity persisted in the presence of blockers of ionotropic glutamate and GABAA receptors and is therefore presumably generated intrinsically , rather than by noisy synaptic input . The intrinsic nature of the IS was confirmed in primary cultures of dissociated Gad2 neurons , which displayed a similar spiking pattern , despite simpler morphology and reduced connectivity ( Figure 1e , f ) . To characterize the dynamics of irregular spiking , we first examined return maps of interspike intervals – scatter plots of each interval against its predecessor – which displayed no discernible fine structure ( Figure 2a , b ) . We therefore looked at the predictability of higher-order sequences of intervals using recurrence plots ( Eckmann et al . , 1987; Marwan et al . , 2007 ) . First , sequences of interspike intervals were embedded – that is , translated into all sub-sequences of length m , the embedding dimension – each of which defines a point in m-dimensional embedding space , and can be thought of as a piece of 'recent history' . For example , Figure 2c ( top ) illustrates two similar embedding points of dimension m = 3 occurring within two different interval sequences . Similarity of dynamical state is measured by proximity in this space ( Figure 2c , bottom ) , and this can be generalized to any m . A cross-recurrence plot of two sequences of intervals , A and B , for example two successive spiking responses to an identical step current stimulus , is a matrix in which element ( x , y ) has a value representing the distance between the xth embedding point of A and the yth embedding point of B ( Eckmann et al . , 1987; Marwan et al . , 2007 ) ( see Materials and methods for further details ) . Figure 2e shows an example in which Euclidean distance in embedding space is represented by color , so that close recurrences show up as colored dots on a gray background . Diagonal lines of slope one , many examples of which can be seen in Figure 2e , indicate periods when the trajectory of one time series evolves similarly to the trajectory of the other . Examples of four different recurrent ISI sequence 'motifs' , identified from the recurrence plot in Figure 2e , are shown in Figure 2f . Recurrence can be quantified as follows . Applying a threshold to the cross-recurrence plot , so that element ( x , y ) = 1 if the distance between Ax and By is less than a threshold neighborhood size ε ( see Figure 2d ) , or zero otherwise , gives a binary cross-recurrence plot , in which the density of 1’s is defined as the degree of recurrence , and the fraction of these which lie within diagonals of length 2 or greater is defined as the degree of determinism . Randomly shuffling the time series before embedding destroys significant recurrence and determinism ( allowing statistical testing of their significance ( see Figure 2—figure supplement 1 , and Materials and methods ) . We calculated cross-recurrence plots between successive pairs of trials ( 5–30 s in duration ) , omitting the first 450 ms of firing in each trial to exclude initial adaptation , for 10 neurons which showed long periods of stationary responses ( Figure 2d ) , at average firing frequencies between 4 and 17 Hz . Using a standard sequence size or embedding dimension m = 4 , and a neighborhood size ( ε ) of one standard deviation of the ISIs , we found that in five of ten cells , both recurrence and determinism were significant ( p<0 . 05 , z-test ) , while only recurrence was significant in a further two cells , and in the three remaining cells , neither recurrence nor determinism were significant . See Figure 2—source data 2 for details . Note that , unlike the related technique of nonlinear prediction ( Kantz and Schreiber , 1997; Sprott , 2003 ) , the detection of significant recurrence and dynamical determinism by recurrence plot quantification is less confounded by nonstationarity , and relatively insensitive to the exact choice of m and ε . Thus , irregular sequences of spikes generated during a constant stimulus in about half of IS neurons show recurrent , correlated sequences of four or more successive intervals . It seems likely that the concerted action of voltage-dependent ion channel populations would be involved in producing such determinism . We found similar recurrence and determinism in a conductance-based biophysical model of these cells , described below , when applying the same analysis procedure to its spike trains ( Figure 6 , Figure 6—figure supplement 1 ) . We noted that those cells that failed to show significant recurrence and determinism had particularly strong voltage noise in their interspike intervals ( not shown ) . 10 . 7554/eLife . 16475 . 004Figure 2 . Predictability and nonlinearity of interspike interval sequences . ( a ) Examples of two contrasting ISI return maps extracted from a regular-spiking cell ( blue , mean frequency 9 . 67 Hz , CVISI = 0 . 075 ) and an irregular-spiking cell ( red , mean frequency 9 . 65 Hz , CVISI = 0 . 207 ) . ( b ) Segments of corresponding spike trains . ( c ) Principle of recurrence analysis . The dynamical state of the process is represented by vectors of consecutive ISIs , or embedding points . In this example , point Ax in a 3-dimensional embedding of an interspike interval sequence A ( whose coordinates are ISIsx-2 , x-1 and x ) is similar to point By in interspike interval series B ( top ) , because their distance is less than a threshold ε ( bottom ) . ( d ) Selection of stationary sequences of stimulus trials for recurrence analysis . The mean ISI in each trial lasting 8 s , repeated at 25 s intervals , is plotted with its standard deviation ( filled circles and error bars ) , and the standard error of the mean ( filled squares ) . Sections of the time series were accepted as sufficiently stationary if the average trial-to-trial change in mean ISI was less than half the average standard error of the mean ( e . g . region shown in dashed gray rectangle ) . ( e ) Example cross-recurrence plot between two consecutive stimulus trials , A and B , embedding dimension m = 4 , ε = one standard deviation of the ISIs . Position ( x , y ) is colored according to the Euclidean distance between the length-4 ISI sequences at position x in A and y in B . Thus blue points reflect recurrence of very similar patterns . ( f ) Four examples of repeated patterns or 'motifs' of ISIs in sequence B corresponding to the patterns at positions ( i ) – ( iv ) in sequence A , as indicated in ( e ) . See Figure 2—figure supplement 1 and Figure 2—source data 2 for recurrence plot quantification . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 00410 . 7554/eLife . 16475 . 005Figure 2—source data 1 . Numerical values for Figure 2d . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 00510 . 7554/eLife . 16475 . 006Figure 2—source data 2 . Table showing details of recurrence plot analysis in ten cells . Nonstationarity is the ratio of the average change in mean ISI in consecutive trials , divided by the standard error of the mean ISI . Time series with nonstationarity > 0 . 5 were rejected . In five of ten cells , both recurrence and determinism were significant ( p< 0 . 05 ) , only recurrence was significant in a further two cells , while in the three remaining cells , neither recurrence nor determinism were significant . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 00610 . 7554/eLife . 16475 . 007Figure 2—figure supplement 1 . Significance testing of recurrence and determinism of interspike interval sequences . ( a ) Example cross-recurrence plot for the response to one 30 s current step trial against that of the subsequent trial . Threshold ( ε ) = σISI , embedding dimension m = 4 . Each point colored black denotes where sequences of 4 ISIs in each of the two trials were closer than ε to each other . ( b ) random shuffling of both sets of ISIs results in a loss of recurrence ( proportion of black points in the matrix ) and determinism ( fraction of black points within diagonals of length 2 or greater . ( c ) Distribution of the recurrence values ( each of which is the mean over 7 successive pairwise comparisons of consecutive 10 s trials during stationary firing ) for 1000 shuffled surrogates ( each ISI sequence in the CRP is randomly permuted ) , compared to the actual corresponding measured recurrence level ( indicated by vertical dotted gray line ) over the same set of trials . ( d ) the same for the determinism ( fraction of recurrent points lying within diagonals of length ≥ 2 ) . A z-test ( Matlab ztest , right-tailed ) confirms that both recurrence ( p<7 . 52e-9 ) and determinism ( p<0 . 023 ) are significant in this case . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 007 Next , we investigated the biophysical mechanisms which underlie the irregular firing . Clearly , one potentially relevant phenomenon is the noisy fluctuation in the membrane potential which switches on above −50 mV ( Figure 3a ) . We found that these fluctuations depended on voltage-gated sodium channels , since they were eliminated by applying tetrodotoxin ( TTX; Figure 3b , n = 6 cells ) . To further investigate the unitary properties of voltage-gated sodium channels , we carried out cell-attached recordings in somatic patches . Characteristic ≈20 pS inward openings were observed , concentrated soon after the beginning of the depolarization ( Figure 3c ) , with an extrapolated reversal potential of about +120 mV positive to the resting potential , as expected for single voltage-gated sodium channels ( Sigworth and Neher , 1980 ) . We also observed frequent late openings of the same channel amplitude , up to 100 ms following +40 mV depolarizations from rest , ( Figure 3c and d , in 4 out of 5 patches containing transient Na channels ) . Whole-cell recordings further confirmed the presence of a non-inactivating , TTX sensitive inward current , evoked in response to a slowly depolarizing ramp ( Figure 3e , prominent in 11/13 cells ) , when K+ and Ca2+ currents were reduced with TEA ( 2 mM ) , 4-AP ( 2 mM ) and Cd2+ ( 200 μM ) . Similar 'persistent' sodium current ( NaP ) and channel openings have been described in many neurons and excitable cells ( Kiss , 2008 ) . Thus , stochastic , voltage-dependent gating of sodium channels could be involved in generating irregularity of firing . Sodium-channel-driven subthreshold noise has been observed in other cell types ( White et al . , 1998 ) , but without appearing to produce the high level of firing irregularity observed in IS cells at ≈10 Hz firing frequencies ( Alonso and Klink , 1993 ) . The deterministic recurrence of the interspike intervals suggests that another active mechanism might also be involved . 10 . 7554/eLife . 16475 . 008Figure 3 . Voltage-gated sodium channel activation is required for noisy subthreshold voltage fluctuations . ( a ) The amplitude of subthreshold fluctuations ( see example waveform in inset ) rises sharply above a threshold membrane potential ( ≈ −50 mV ) . Measurements for 23 cells indicated by different symbols . The curve shows a fit to a model of combined NaP ( 1950 channels ) and gKt ( 180 channels ) single channel noise , see Materials and methods for details ) . Inset: three example traces for one cell during step current stimulation of 60 , 90 and 100 pA , showing the onset of membrane potential noise . ( b ) Fluctuations are blocked by applying tetrodotoxin ( TTX , 100 nM ) . Membrane potential traces in another IS cell with and without perfusion of TTX , in response to the same current step , which is subthreshold in the steady-state after an initial doublet ( top ) . Corresponding amplitude histogram of the membrane potential ( bottom ) . ( c ) Membrane current in a cell-attached patch in response to repeated depolarizing steps , from RP-20 mV to RP+40 mV , as indicated . RP = resting membrane potential . Sodium channel openings are both transient , within 10 ms of the depolarization , and persistent , occurring late in the depolarization . ( d ) Transient and persistent openings at higher time resolution . ( e ) Whole-cell recordings confirming the presence of a TTX-sensitive , non-inactivating inward current at the firing threshold potential range ( −55 mV ) . A slowly depolarising ramp ( 20 mV/s , from −80 mV to −10 mV , −70 mV holding potential ) was applied in the presence of TEA ( 2 mM ) , 4-AP ( 2 mM ) and Cd+2 ( 200 μM ) in order to eliminate K+ and Ca+2 currents , with TTX ( 500 nM ) added during the trial shown in red . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 008 Both voltage-gated and calcium-activated potassium channels contribute to spike repolarization and spike after hyperpolarizations in cortical neurons . However , neither blockers of calcium-activated potassium channels ( iberiotoxin and apamin ) nor intracellular perfusion of a fast calcium buffer ( BAPTA ) diminished irregularity of firing ( see Figure 4—figure supplement 1 ) , and we concluded that intracellular calcium signaling is not centrally involved in the dynamics of intrinsic irregularity . We therefore next examined the voltage-dependent potassium currents , which are of key importance in determining action potential generation and shape ( Bean , 2007 ) . In particular , we focussed on those whose voltage-dependence of gating might allow dynamical interaction with the sodium channels . Whole-cell voltage-clamp of the outward currents in response to families of step depolarizations revealed an early transient outward or A-type potassium current ( Figure 4a ) , which could be isolated by applying a pre-pulse protocol ( Amarillo et al . , 2008 , Maffie et al . , 2013 ) in the presence of 5 mM TEA to remove slower K+ currents ( Figure 4b , n = 9 ) . Fits of the voltage-dependence of the peak conductance and of the steady-state inactivation of this transient potassium conductance ( gKt ) showed that activation and inactivation curves overlapped around the threshold ( Figure 4c , n = 18 cells for inactivation , n = 36 cells for activation ) , peaking within 1–2 ms , and inactivating over about 20–30 ms ( Figure 4d , top ) . Additionally , this fast inactivating outward current recovered from inactivation with a time constant of around 40 ms at −70 mV ( Figure 4d , bottom ) . These properties are not consistent with Kv1 channels ( the current was insensitive to 1 μM α-dendrotoxin , n = 4 , not shown ) , including Kv1 . 4 ( recovery from inactivation in the range of milliseconds rather than seconds , see Wickenden et al . , 1999 ) , nor with channels from the Kv3 family ( transient currents were TEA insensitive , see Figure 4—figure supplement 2 ) . The gating properties closely resemble those of Kv4-family voltage-dependent potassium channels in pyramidal neurons ( Birnbaum et al . , 2004 ) , and this was further supported by its sensitivity to 4-AP ( Figure 4e top , n = 6 ) and the specific Kv4 . 2/4 . 3 blocker phrixotoxin ( PhTX; Figure 4e bottom , n = 7 ) , which produced a partial , reversible block of 55% at a concentration of 5 µM . We fitted conventional Hodgkin-Huxley type models to the voltage-step responses of this current ( see Materials and methods ) , and estimated a peak transient conductance at 0 mV ( gmax0 , see Materials and methods ) of 22 . 37 ± 14 . 41 nS ( n = 8 cells , mean ± SD ) . This current would be expected to delay the rise of membrane potential just before spike initiation . Although the membrane potential leading into spikes was generally highly fluctuating , averaging the waveform of hundreds of action potential , aligned with the fastest point of the upstroke , consistently showed the presence of a clear dip or inflection in the rising phase , about 10 ms before the start of the fast upstroke ( Figure 4f ) , which we attribute to this current . There was also a high density of single channel currents in some cell-attached patches ( n = 7 ) with similar activation and inactivation properties ( Figure 4g and h ) , implying some clustering in the membrane , as previously described for Kv4 channels ( Alonso and Widmer , 1997; Jinno et al . , 2005 ) . The fast and small-amplitude single channel openings in these recordings were not well-resolved , but appeared to comprise step transitions corresponding to a single channel chord conductance of about 10–12 pS ( assuming EK ≈−90 mV ) . The single channel conductance of Kv4 channels is not extensively-characterized , but reports vary from ≈5 pS to ≈20 pS in low potassium external solutions , and it is sensitive both to external potassium concentration and to association with accessory proteins such as KChIPs ( Holmquist et al . , 2002; Cooper and Shrier , 1989 ) . Thus , overall , the transient potassium conductance recorded at the soma strongly resembles reported descriptions of Kv4-mediated conductance . 10 . 7554/eLife . 16475 . 009Figure 4 . IS neurons express a fast transient outward current with similar kinetics to Kv4 . ( a ) Whole-cell currents in response to a family of voltage steps from −80 to 0 mV in 5 mV steps . ( b ) A-type current separated from other outward current components . The remaining step-evoked current following a pre-pulse ( −30 mV , 200 ms ) capable of inactivating A-type current was subtracted from total current . Voltage steps from −50 to +40 in 10 mV steps . Recordings were carried out in the presence of 5 mM TEA in order to block slowly activating K+ currents . ( c ) Voltage-dependence of steady-state activation and inactivation . ( d ) Activation ( red ) and inactivation ( blue ) time constants of dissected A-type current ( top ) and recovery of inactivation time constant ( bottom ) . ( e ) The fast inactivating outward current found in these cells was sensitive to the A-type current blocker 4-AP ( 7 mM ) and the Kv4-specific blocker phrixotoxin ( PhTX , 5 µM ) . Top panels: total currents in control , drug application , and washout , as indicated . Lower panel: PhTX block of transient outward current fraction , separated as in ( b ) . ( f ) Average of 537 aligned APs following ISIs lasting longer than 100 ms shows a small prespike dip or inflection , attributed to the activation of the transient K+ current . ( g ) , ( i ) Isolated transient outward current with a single exponential fitted to the decay phase ( τ = 13 . 05 ms ) . ( ii ) Example current from a cluster of transient K+ channels in a cell-attached patch ( step from RP-30 mV to RP+50 mV at the time indicated by arrow , outward current plotted upwards ) , fitted with the same exponential time constant as in ( i ) . ( h ) Dependence of patch current on the potential of a 500 ms prepulse before a step from RP-30 to RP+50 mV , showing that it inactivates over the range RP+10 mV to RP+40 mV . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 00910 . 7554/eLife . 16475 . 010Figure 4—source data 1 . Numerical values for Figure 4c , d and e . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 01010 . 7554/eLife . 16475 . 011Figure 4—figure supplement 1 . Irregularity is not diminished by buffering intracellular calcium . An IS cell recorded with a patch pipette containing normal intracellular solution and stimulated with a steady current stimulus of 150 pA ( left ) is then repatched with a pipette containing intracellular solution , to which 10 mM BAPTA , a fast calcium and high-affinity buffer has been added ( right ) , and stimulated with the same current level . Control CV ( ISI ) = 0 . 22 ( 125 ISIs ) , BAPTA CV ( ISI ) = 0 . 44 ( 129 ISIs , excluding initial 700 ms transient responses ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 01110 . 7554/eLife . 16475 . 012Figure 4—figure supplement 2 . Fast inactivating outward current is insensitive to TEA ( 2 mM ) . Voltage steps to −10 mV from −80 mV holding potential ( n = 6 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 01210 . 7554/eLife . 16475 . 013Figure 4—figure supplement 3 . Gad2-GFP cortical interneurons from primary cultures display the two conductances required for spiking irregularity . ( a ) Cells expressed a large , fast-inactivating outward current ( n = 4 ) . Steps from −75 mV to 0 mV , held at −80 mV ( b ) In some cases ( n = 3 ) , after measuring spike irregularity , cells were repatched with a Cs based solution and locally perfused with Cd , TEA and 4AP , to block K+ and Ca2+ currents . Slow ramp depolarization ( from −80 to −10 , 20 mV/s ) revealed persistent sodium current activating approximately at −55 mV , as in the slice preparation . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 013 To test whether and how this inactivating K+ current is involved in generating irregular firing , we injected a synthetic dynamic conductance ( Robinson and Kawai , 1993; Sharp et al . , 1993 ) with the kinetics and voltage-dependence measured from voltage clamp , which should have the same electrical effect as the native conductance at the soma . Artificial conductance injection of gKt was sufficient to modulate the spiking irregularity of intrinsically irregular Gad2 interneurons ( Figure 5a–c , Figure 5—figure supplement 2 ) . When negative gKt was injected , i . e . subtracting from the dynamics of the native conductance in these cells ( as shown in Figure 5d for voltage clamp currents ) , we saw a striking regularization of firing in the range of frequency examined , as well as a reduction in the afterhyperpolarization ( AHP ) amplitude . On the other hand , injecting positive gKt induced an increase in the irregularity of firing , accompanied by a more prominent subthreshold membrane potential fluctuation between spikes ( Figure 5c ) . The effect of gKt on CV ( ISI ) was consistent especially at lower firing frequencies ( e . g . 10 Hz , Figure 5e , n = 42 cells ) , and it was even more evident when the total gKt injected was normalized to the capacitance of each cell , which is proportional to its plasma membrane area ( Figure 5f ) . Pharmacological block of gKt by 4-AP or phrixotoxin gave a similar result to negative conductance injection , reducing irregularity of firing ( See Figure 5—figure supplement 3 ) . 10 . 7554/eLife . 16475 . 014Figure 5 . Injection of synthetic gKt modulates spiking irregularity . ( a ) Positive and negative gKt injection in the same cell at the same frequency range ( 8–10 Hz ) . While −8 . 7 nS injection ( gmax0 , See Materials and methods ) caused a reduction in the AHP amplitude and regularized the firing pattern , injecting +8 . 7 nS created more evident noisy plateaus before some APs , resulting in more irregular firing . Red bottom trace shows the current passed during the positive gKt conductance injection ( outward , hyperpolarizing current plotted downwards ) . ( b ) Effect of gKt on spiking irregularity in another cell , showing its consistency over different firing frequencies . ( c ) Close-up of the membrane potential trajectories from ( a ) , +gKt ( red ) superimposed on control ( black ) , showing extended and increased noisy subthreshold fluctuations produced by the gKt conductance . ( d ) Potassium currents during a family of step depolarizations from −80 mV to −60 , −50 , … +10 mV . Subtraction of 3 . 92 nS of the fast-inactivating Kv current by dynamic-clamp largely cancels the transient component , leaving a residual , non-inactivating delayed rectifier current . ( e ) Relative changes in CV ( ISI ) at 10 Hz firing frequency induced by addition or subtraction of gKt conductance . Data from 42 cells: points are individual measurements , with some cells measured at two or more different conductance levels . Wilcoxon non parametric test , p<9 . 8 × 10–16 for positive gKt , and p<1 . 6 × 10–8 for negative gKt . ( f ) Relationship between relative change in CV ( ISI ) at 10 Hz firing frequency and injected gKt conductance . The linear regression fit is superimposed . Pearson’s correlation r = 0 . 59 , p<2 . 66 × 10–12 . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 01410 . 7554/eLife . 16475 . 015Figure 5—source data 1 . Numerical values for Figure 5e and f . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 01510 . 7554/eLife . 16475 . 016Figure 5—figure supplement 1 . Injecting a shunting conductance at the soma , causing a large reduction in input resistance , modifies the action potential amplitude and shape , and divides down membrane potential fluctuations , but does not regularize firing . ( a ) Top: example of control spiking during a 45 pA current step . A linear conductance of 2 nS , reversing at −70 mV was applied during the lower trace ( red ) , and stimulus current increased to 115 pA to produce an equal firing frequency . CV in the control , calculated over 97 ISIs in repeated trials and excluding initial 400 ms of responses , was 0 . 46 . CV with the shunting conductance , was similar , at 0 . 385 ( 195 ISIs ) . ( b ) Overlaid averaged action potentials , with ( red ) and without ( black ) the shunting conductance . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 01610 . 7554/eLife . 16475 . 017Figure 5—figure supplement 2 . Example spike patterns for three different cells with ( a ) negative gKt conductance injection and three different cells with ( b ) positive gKt injection , showing decreased and increased irregularity respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 01710 . 7554/eLife . 16475 . 018Figure 5—figure supplement 3 . Effect of pharmacological block of A-type current in IS cells is consistent with the effect of the negative gKt injection . ( a ) When 4-AP was locally perfused at 200 or 50 µM , the CVISI decreased by 44% ( n = 5 ) , while PhTX 5 µM caused a mean 23% reduction ( b , n = 3 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 018 Having shown experimentally that the transient potassium current plays a key role in controlling irregular firing in IS neurons , we sought to understand how it might do so , by studying a computational model of these cells . We constructed a conductance-based biophysical model , in which the key gKt and NaP conductances could be modeled either as stochastic or deterministic elements . A two-compartment model was used , comprising a somatic compartment which contained voltage-dependent conductances , linked to a passive dendritic compartment . The dendritic compartment was included in order to capture , in a simplified way , the extended spatial aspect of the cell morphology . Similar to a widely-used model of fast-spiking inhibitory interneurons ( Erisir et al . , 1999; Gouwens et al . , 2010 ) , the soma included Kv1 and Kv3 voltage-dependent potassium conductances and a sodium conductance . To this , however , was added a gKt conductance based on the voltage-clamp findings above , and a persistent sodium conductance ( NaP ) . NaP and gKt were modeled either deterministically or stochastically with a dynamic noise variance ( see Materials and methods for details ) . In the deterministic model , interspike intervals were of two types: long , almost stationary pauses , and periods of subthreshold oscillation , of unstable and variable amplitude , at a frequency of about 28 Hz ( Figure 6a ) . In a three-dimensional subspace of the ( 8-dimensional ) phase space of the model , displaying the activation variable of gKt as x , the membrane potential as y , and the sodium inactivation variable as z , some of the dynamical structure underlying this behaviour can be seen ( Figure 6b , Video 1 ) . The subthreshold oscillations correspond to variable numbers of circuits around an unstable-amplitude cycle in one region of phase space , before the system escapes into the upstroke of a spike . Long pauses correspond to a transition to another critically slow region of phase space where h , m , and V remain at an almost fixed point , while Kv1 activation ( n ) slowly subsides , eventually leading to an escape from this region , either directly into a spike , or into a period of subthreshold oscillations . Thus , this set of conductances gives two dynamical mechanisms for generating irregular interspike intervals: variable numbers of circuits of unstable amplitude subthreshold oscillations , and long pauses in a slow region of phase space . The activation of gKt is seen to vary considerably for different spikes ( note the spread in values of mKt in the afterhyperpolarization in Figure 6b ) . 10 . 7554/eLife . 16475 . 019Figure 6 . Irregular firing in a simple biophysically-based model . ( a ) Two-compartment model with Nav , Kv1 , Kv3 and gKt-type conductances shows complex spike timing , as a result of unstable subthreshold oscillations and trapping in a nearly-fixed state . g¯kt = 7 nS , stimulus current , 100 pA . For other parameters , see Materials and methods . ( b ) Unstable subthreshold oscillations and a fixed-point 'ghost' seen in the phase trajectory of the model with zero noise in the ( mKt , hNa , V ) subspace ( 101 pA , 7 nS g¯kt ) . ( c ) Adding noisy non-inactivating ( persistent ) sodium channel conductance ( equivalent to 500 channels ) and noisy gKt ( equivalent to 7 nS or 700 channels ) masks subthreshold oscillations , but preserves high spike irregularity . Stimulus current 90 pA . ( d ) gKt channel noise is strongly amplified by voltage-dependent sodium conductance . Subthreshold membrane potential noise for a stimulus current of 72 pA , with 7 nS g¯kt and 10 nS g¯NaP , either stochastic or deterministic , and for the case in which all sodium current is blocked ( 'TTX' ) , and stimulus current of 90 pA , to polarize the membrane to the same range of membrane potential as without sodium current . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 01910 . 7554/eLife . 16475 . 020Figure 6—figure supplement 1 . Statistics and significant recurrence and determinism of time series generated by the computational model . ( a ) Example ISI distribution for the stochastic model , with 700 gKt channels , 500 NaP channels , and stimulus current of 83 pA . Note similarity to experimental ISI distribution ( Figure 1dii ) . ( b ) Example cross-recurrence plot between two step depolarizations of the biophysical model with a small level of stochastic channel conductance ( 40 pS NaP , 30 pS gKt ) . ( c ) Level of recurrence , indicated by the dashed line , was significantly higher ( p<1 . 3 × 10–13 , z-test ) than the distribution of recurrence when one time series in each comparison was randomly shuffled ( histogram ) . ( d ) Level of determinism was similarly higher than that of randomly-shuffled surrogates ( p<9 . 3 × 10–5 ) . As the level of stochasticity is increased , the significance of both recurrence and determinism diminishes . Stimulus consisted of 96 pA steps lasting 20 s , total number of ISIs was 10 , 099 , average firing frequency = 10 . 1 Hz , CV ( ISI ) = 0 . 22 . See Materials and methods for details of analysis . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 02010 . 7554/eLife . 16475 . 021Video 1 . Movie showing dynamics in phase space of the deterministic model . Corresponds to the trajectory shown in Figure 6b . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 021 Changing gKt and NaP conductances to a stochastic form somewhat obscures the difference between the pauses and subthreshold oscillations , causing more irregular and variable fluctuations in the subthreshold oscillations ( Figure 6c ) . The subthreshold noise amplitude is highly dependent on the stochastic gKt , since it is greatly reduced if gKt is deterministic ( Figure 6d ) . It is only slightly reduced if NaP is deterministic , but greatly reduced if all voltage-gated sodium conductance is removed ( Figure 6d , 'TTX' - right hand side ) . This implies that subthreshold fluctuations are dominated by gKt-driven stochastic fluctuations which are strongly amplified by the voltage-gated sodium conductance – both are required . The greater importance of gKt noise over NaP noise is largely due to its much longer correlation time ( 10 ms versus 1 ms ) , which means that gKt noise is much less filtered by the membrane time constant . Thus , these strong subthreshold membrane potential fluctuations appear to be actively-amplified channel noise , somewhat like noise-driven subthreshold oscillations , as described in entorhinal stellate neurons ( Dorval and White , 2005 ) . This result suggests that , although the fit of the subthreshold membrane potential noise variance by the 'voltage-clamped' channel noise of NaP and gKt channels ( Figure 3a , see Materials and methods ) appears to describe the onset of this noise reasonably well , the numbers of channels are probably overestimated , as the powerful active amplification of fluctuations is not taken into account . The action of gKt in promoting irregular firing across the range of frequencies is visualized in Figure 7 , in which the firing frequency is plotted as a function of both stimulus current and the amount of gKt conductance included in the model , with the surface colored to indicate the CV ( ISI ) . As the amount of gKt in the membrane is increased to 12 nS , a region of structurally-stable variability is created for firing frequencies up to 20–30 Hz , above which frequency the CV ( ISI ) subsides , as seen in recordings , in both deterministic ( Figure 7a ) and stochastic ( Figure 7b ) forms of the model . In the deterministic form of the model , CV ( ISI ) reaches values of ≈1 , much higher than experimentally observed ( red region of surface in Figure 7a ) . However , the addition of noise dilutes the irregularity of this high-CV region to ≈0 . 3 ( Figure 7b ) , as seen experimentally . The stochastic model shows a much more linear firing frequency-current ( f-I ) characteristic , as observed in actual recordings – i . e . the dynamic noise linearizes the input-output relation of these neurons . The distribution of ISIs produced by the stochastic model also resembles experimental distributions ( Figure 6—figure supplement 1 , panel a ) . 10 . 7554/eLife . 16475 . 022Figure 7 . gKt enhances irregularity in deterministic and stochastic biophysical models . ( a ) Surface showing the dependence of firing frequency on the total gKt and stimulus current level , colored according to the CV ( ISI ) of firing . Regions of low CV ( ISI ) correspond to periodic firing , while regions of high variability arise through the pausing and unstable subthreshold oscillation mechanisms . ( b ) Analogous plot for the stochastic model containing voltage-dependent noise fractions due to 1000 persistent sodium channels , and different numbers of 10 pS gKt channels equivalent to the conductance indicated . Inset example voltage traces ( 1 s of firing ) : ( a ) bottom: 101 pA , g¯Kt 7 nS; top left: 106 . 7 pA , 10 nS g¯Kt; top right: 89 pA , g¯Kt 0 . 5 nS . ( b ) bottom: 90 pA , 500 gKt channels ( = 5 nS ) ; top: 95 pA , 500 gKt channels . ( c , d ) Irregularity in simulated gap-junction-coupled ensemble of IS cells ( 700 gKt channels ( = 7 nS ) , 500 NaP channels ( = 5 nS ) ) . ( c ) Cross-correlation of spike trains in one pair of neurons within a symmetrically-connected network of five IS neurons ( inset ) , each excited by a constant stimulus of 90 pA . Exact synchrony appears as coupling is strengthened , as indicated by the single sharp peak centered on 0 ms . See Materials and methods , Spike Analysis , for details of calculation of cross-correlation . ( d ) Firing frequency , CV ( ISI ) and synchrony – the fraction of spikes in one cell which occur within ± 10 ms of spikes in the other cell – as a function of the gap-junctional conductance . CV ( ISI ) is undiminished even for highly synchronous firing , with strong gap-junctional conductance . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 022 Irregular spiking could exert a far greater impact in the cortical network if it were synchronized amongst IS neurons , which are connected with each other in a specific gap-junction-coupled network ( Galarreta et al . , 2004 ) . However , the mechanism of irregularity proposed here depends on the intrinsic dynamics and noise sources within individual cells . It seems possible that the impact of fluctuations generated within individual cells could be diluted when cells are connected in an electrical network . Therefore , we simulated small networks of symmetrically-coupled stochastic IS neurons . In a 5-cell network , firing became highly synchronous as gap junction conductance was increased , as seen in the sharp central peak of cross-correlation ( Figure 7c ) . However , CV ( ISI ) was maintained at the same level as for uncoupled cells , even with strong coupling and complete synchrony ( Figure 7d ) . This perhaps non-intuitive result implies that in effect , nonlinearly-amplified fluctuations are cooperative amongst cells and are well-coupled by the current flow through gap junctions . The intrinsic irregularity of firing of IS neurons , which is distinctive amongst the cell types of the cortical network , raises the question of what these neurons do , particularly in the context of the regular firing which underlies organized oscillations in many frequency bands ( Buszáki and Draguhn , 2004 ) . This particular type of IS neuron directly inhibits pyramidal neurons , and it has been suggested that it might promote asynchronous firing and thereby resist synchronous oscillations ( Galarreta et al . , 2008 ) . In order to test how these cells integrate periodic inputs , we examined their ability to synchronize their spikes to rhythmic oscillation in a naturalistic stimulus consisting of several conductance components: a stationary , noisy AMPA receptor-type excitatory conductance and an oscillating ( 10 Hz or 40 Hz ) GABAA receptor-type shunting inhibitory conductance , combined with simultaneously adding or subtracting gKt using dynamic-clamp ( Figure 8a ) . Figure 8b shows an example of an IS cell subjected to an elevation of gKt ( +3 . 57 nS gmax0 ) . This depresses the synchrony of spikes to the gGABA rhythm ( Figure 8bii , iii ) across the range of oscillation amplitudes tested . Conversely , subtraction of gKt from another cell ( Figure 8c ) enhanced synchrony over a wide range of oscillation amplitudes . These striking effects of gKt on synchrony to 10 Hz inputs are not observed , however , for 40 Hz input ( Figure 8d , summary statistics for the whole set of cells at both frequencies are shown in Figure 8e ) . Thus modulation of irregularity of IS neurons by the level of gKt ( see Figures 5 and 6 ) appears to determine their ability to synchronize to oscillatory inhibition , and the dynamics of gKt are such that it can counteract 10 Hz but not 40 Hz rhythms . Rejection of synchronization results from the intrinsically irregular dynamics at lower frequencies , around 10 Hz , while resonance with a noise-obscured subthreshold oscillation ( whose frequency in the deterministic model is 28 Hz ) could contribute to the stronger synchronization at higher frequencies . Thus the native gKt of IS cells allows them to resist synchrony to lower network frequencies such as 10 Hz , while complying readily with higher , gamma frequency rhythms . This could have the effect of destabilizing lower frequency network oscillations while helping to stabilize higher-frequency rhythms , and help to determine the times of onset and offset of organized gamma-frequency firing in the network . 10 . 7554/eLife . 16475 . 023Figure 8 . Synchronization to oscillating inhibition is controlled by gKt . ( a ) Naturalistic stimulus protocol . The cell was stimulated with a constant step of AMPA conductance ( gAMPA , reversing at 0 mV ) with added conductance Ornstein-Uhlenbeck noise ( standard deviation 2% of the step amplitude , τ = 5 ms ) , combined with a sinusoidal GABAA conductance ( gGABA , reversing at −60 mV ) and introduction of positive , zero or negative gKt . gAMPA was adjusted so that the cell fired close to the frequency of the gGABA inhibitory oscillation . ( b ) Effect of adding gKt on a slightly irregular-firing cell at 10 Hz gGABA . ( i ) Step current response ( black ) , response to oscillatory conductance stimulus with ( green ) or without ( blue ) addition of 3 . 57 nS gKt . ( ii ) Spike entrainment synchrony ( see Materials and methods , Spike Analysis ) to the 10 Hz gGABA oscillation as a function of the oscillation amplitude . Synchrony rises progressively with oscillation amplitude in control ( blue ) , and is depressed by the addition of 3 . 57 nS gKt ( green ) . ( iii ) Spike phase histogram for pooled responses to lower amplitude gGABA oscillations ( up to 1 nS ) , showing a reduction in the sharpness of synchrony . ( c ) Example of subtracting gKt in another irregular-firing cell at 10 Hz gGABA . ( i ) example responses . ( ii ) Subtraction of gKt ( red ) increased synchrony to gGABA oscillation over a wide range of amplitudes , when compared to control ( blue ) . ( iii ) spike phase histograms for pooled responses up to 1 nS gGABA oscillations . Subtraction of gKt enhances the phase preference . ( d ) Lack of effect of gKt on synchronization to 40 Hz ( gamma ) oscillation . ( e ) Summary of effects of gKt perturbation on synchrony in different cells . Each symbol denotes an experiment on an individual cell , showing the ratio of synchrony , evaluated at ≈1/3 of the maximum gGABA amplitude applied in each case , during gKt injection , normalized to its control value with no injection . 10 Hz: gKt addition ( n = 10 , green ) and subtraction , ( n = 10 , red ) ; 40 Hz: gKt addition ( n = 6 , green ) or subtraction ( n = 7 , red ) . At 10 Hz , but not 40 Hz , gKt perturbation has a significant effect . Wilcoxon nonparametric rank sum test , p=6 . 5 × 10–5 for both positive and negative gKt , and p=0 . 36 and 0 . 69 at 40 Hz for positive and negative gKt respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 02310 . 7554/eLife . 16475 . 024Figure 8—source data 1 . Numerical values for Figure 8b , c , d and e . DOI: http://dx . doi . org/10 . 7554/eLife . 16475 . 024
Here we have used a combination of experiment and modeling to show that the voltage-dependent gating and stochastic activation of fast-inactivating potassium and sodium channels play major roles in generating the intrinsic irregularity of cortical irregularly-spiking ( IS ) inhibitory interneurons . We also showed that at frequencies matching firing frequencies where this irregularity is high ( up to 20 Hz ) , these cells strongly reject synchronization to the naturalistic oscillating input . This finding is especially relevant considering that irregular-spiking VIP interneurons fire at 10–15 Hz in vivo ( whisking and non-whisking activities , Lee et al . , 2013 ) . IS cells have been hard to define functionally , because of the profusion of types of inhibitory interneuron in the cortex , and because irregular-spiking behavior may also arise from fluctuations in synaptic input or membrane integrity during recordings . The development of a genetically-modified mouse in which intrinsically IS cells are labeled with GFP has allowed targeted study of a relatively homogeneous population of IS neurons ( Galarreta et al . , 2004 , 2008; López-Bendito et al . , 2004 ) . Inducible in vivo genetic fate mapping ( Miyoshi and Fishell , 2011; Miyoshi et al . , 2010 ) has been used to show that these IS interneurons originate from the caudal ganglionic eminence relatively late in development ( E16 ) , express 5HT3a receptors , VIP and calretinin , and form about 10% of CGE-derived interneurons , which dominate the more superficial layers of cortex and comprise about 30% of all cortical interneurons . Within upper layer 2 , the lamina in which they are concentrated , IS cells may make up a large proportion , perhaps 50% ( López-Bendito et al . , 2004 ) , of interneurons . Though we know quite a lot about their functional synaptic connectivity , and its regulation by CB1 receptors ( Galarreta et al . , 2004 , 2008 ) , the origin of the irregular spiking behavior itself has remained unknown . Predictability of spike trains of irregular-spiking cortical neurons has been examined in a previous study ( Englitz et al . , 2008 ) , which concluded that the variability is not a consequence of low-dimensional , effective deterministic processes . However , that study did not examine the genetically-defined population of neurons studied here . In contrast , we found that there was both significant recurrence and determinism , i . e . predictability , in sequences of spike intervals , in about half of the cells examined ( Figure 2 ) . We propose that this predictability is linked to the dynamics of a prominent low-threshold fast-inactivating voltage-gated potassium conductance interacting with voltage-dependent sodium conductance , including a persistent fraction , which enhances the activation of sodium channels around AP threshold ( Figures 3 and 4 ) . Evidence for this was the sensitivity of membrane fluctuations to TTX , the strong modulation of irregularity by injecting artificial inactivating K+ conductance , and the ability to reproduce this phenomenon in a biophysical model ( Figures 6 and 7 ) . We suggest that the fast-inactivating K+ current that we found in these cells is likely to be mediated by Kv4 potassium channel subunits , for several reasons . Not only does its fast recovery from inactivation ( ≈40 ms ) exclude the other main candidate , Kv1 . 4 , but the current was partially blocked by PhTX , and had a weak voltage dependence for activation and inactivation kinetics , which are known properties of Kv4-mediated currents . IS cells in this same GFP mouse model have been shown to express high levels of Kv4 . 2 ( kcnd2 ) mRNA , higher than in a population of pyramidal cells and seven times higher than in a population of fast-spiking interneurons ( Sugino et al . , 2006 ) , but only very low levels of Kv1 . 4 ( kcna4 ) transcripts . The kinetics and voltage dependence of activation and inactivation also match well those described for Kv4 . 1/4 . 2 in pyramidal neurons ( Birnbaum et al . , 2004 ) . However , further work will be needed to prove definitively the identity of these channels . Interestingly , we found that GFP+ cells in dissociated primary cortical cultures also showed robust intrinsic irregular firing ( Figure 1e ) , and expressed transient K+ and persistent Na+ currents ( Figure 4—figure supplement 3 ) . This suggests that normal morphology and circuit formation in development are not required for the irregularity . It is possible that the relatively high mature input resistance of IS neurons ( 331 MΩ ) , compared to other types of interneuron could directly lead to greater variability , since single-channel voltage noise should be bigger . However , we found that when we injected an intense ( 2 nS ) static shunting conductance , reversing at −70 mV , near to the resting potential – effectively greatly reducing input resistance , the action potential is reduced in amplitude , and afterhyperpolarizations and interspike membrane potential fluctuations are strongly diminished , but irregularity is not reduced ( Figure 5—figure supplement 1 ) . Likewise , adding the artificial K+ conductance , which also decreases input resistance , caused increased , not decreased firing variability . This indicates that irregularity is produced by a more complex dynamical mechanism , driven partially by stochastic channel opening , but also dependent on the nonlinearity of the transient K+conductance . The model that we have implemented suggests two deterministic active mechanisms for high spike time variability: long 'pause' states , in which the dynamical state is presumably trapped near the 'ghost' of a fixed point , and unstable subthreshold oscillations . Both these mechanisms exist over a fairly wide range of values of gKt conductance density and stimulus level ( Figure 7 ) , but depend on the presence of gKt . The high irregularity and variability of the purely deterministic form of the model is suggestive of deterministic chaos , although a rigorous proof of chaos in the model would require for example proof of a positive Lyapunov exponent , and is beyond the scope of this study . At the same time , however , it is clear that significant dynamical noise must be involved to some extent in the irregularity , as a result of the single-channel characteristics of the main voltage-dependent channels involved . ISI distributions in IS neurons appear to be shaped by the single-channel current fluctuations around AP threshold ( Figures 3 and 4 ) , both of sodium channels including persistent ones , and of gKt channels . Adding noise in the model , which mimics the single-channel activity of these channels around the AP threshold , realistically obscures the regularity of the subthreshold oscillation , and leads to ISI distributions very similar to those observed experimentally ( Figure 6—figure supplement 1 , panel a ) . While still clearly preserving the gKt-induced region of high-CV firing ( Figure 7b ) , the addition of noise changes the unnaturally high CV ( ISI ) of the deterministic model ( ≈1 ) to a value compatible with the experiments ( ≈0 . 3 ) . Thus , we believe that the interaction of both elements , the nonlinear deterministic Hodgkin-Huxley equations and the single-channel dynamical noise is needed for an adequate description of irregular spiking . Both the deterministic and stochastic components of the model have measurable biophysical parameters . Other , related dynamical models of irregular firing in neurons have been proposed . For example , a bifurcation analysis ( Golomb et al . , 2007 ) of an FS cell model , incorporating different levels of slowly-inactivating potassium current ( Id , probably corresponding to Kv1 channels ) showed that higher levels of this current can produce 'stuttering' behavior associated with subthreshold oscillations , as also seen experimentally in FS neurons ( Tateno et al . , 2004 ) . Dispersion of interspike intervals produced by variations in amplitude of subthreshold oscillations of a much higher frequency ( 100–150 Hz ) characterized in spinal motoneurons has been termed 'mixed-mode' oscillation ( Manuel et al . , 2009 , Iglesias et al . , 2011 ) . Noise-induced switching between a fixed point and a spiking limit cycle has been shown to produce high irregularity in the Hodgkin-Huxley model ( Rowat , 2007 ) . Recently , Stiefel et al . suggested that fast-activating K+ currents could promote this kind of switching behavior in IS neurons , leading to high irregularity ( Stiefel et al . , 2013 ) . Although the mechanism that we propose here is both more specific and more complex , the basic necessity for an interaction between noise and strong nonlinearity assisted by fast K+ channels is consistent with these studies . Interestingly , A-type potassium and NaP currents have also been implicated in the generation of theta-frequency ( 5–10 Hz ) membrane potential oscillations in hippocampal interneurons ( Morin et al . , 2010 , Skinner , 2012 ) , possibly through the dynamical mechanism of 'critical slowing' , in which the amplitude of noise-driven fluctuations grows near a bifurcation . This may also be relevant in IS neuron membrane potential fluctuations , and further studies of their sensitivity to noise near AP threshold would be merited . The active irregularity produced by coordinated activation of populations of voltage-dependent channels and their activation-dependent single-channel noise , which we propose , may have at least two important advantages . First , it is an energetically-favorable way to generate high spike interval irregularity in individual cells , while minimizing unnecessary membrane potential fluctuation , because fluctuations switch on sharply just below AP threshold , and their active amplification makes them highly effective at controlling spike timing . Second , IS cells are linked to each other in a specific gap-junction-coupled network , and also inhibit each other through GABAA receptor-mediated synapses ( Galarreta et al . , 2004 , 2008 ) . This would be expected to enhance the local synchrony of irregular firing ( Gouwens et al . , 2010 ) , potentially greatly increasing its impact on network activity . The partly active , deterministic nature of irregularity and the subthreshold dynamics would help to coordinate the sources of irregularity in different cells , via current flow through gap junctions . Examining the synchronization of ensembles of stochastically-modeled IS neurons connected through gap junctions ( Figure 7c , d ) , we found that even with high gap-junctional coupling and resultant complete synchrony of firing , irregularity is maintained just as high as in isolated neurons . This non-trivial result implies that the network of IS neurons can indeed fire with both high irregularity and precise synchrony . Overall , these results suggest that coordinated irregular firing is important for the cortex . Synchronous oscillation , although it is a population activity that is relatively easily detected and studied , and which may provide a timing mechanism for processing , is also low-dimensional and limited in its capacity to represent information . Synchronous irregular firing may help to create diverse network firing patterns , useful in representation of information , and to find solutions to optimization problems in pattern recognition . It may also enhance STDP-based learning ( Christodoulou and Cleanthous , 2011 ) , and could be important in decision-making and generation of spontaneous choices . The coupled network of IS neurons could also control initiation and termination of periods of synchronous regular oscillations , consistent with the rejection of synchronization to low-frequency rhythms that we observed ( Figure 8 ) , which was enhanced by the addition of artificial gKt . In conclusion , we have provided evidence that , in addition to the direct effect of stochastic channel noise , IS neurons have a specific nonlinear deterministic mechanism that drives spike time irregularity . The mechanism depends on a nonlinear interaction of Kv4 potassium and sodium channels around AP threshold . This novel mechanism appears to allow this group of neurons to have a coordinated , and hence powerful , impact on concerted activity in the cortex .
A genetically-modified mouse line was used , in which GFP was linked to the promoter for Gad2 ( López-Bendito et al . , 2004 ) . At ages between P30 and P60 , animals were sacrificed in accordance with the UK Home Office regulations under the Animals ( Scientific Procedures ) Act of 1986 , and 300 μm sagittal slices of the neocortex were cut with a tissue slicer ( Leica VT1200S , Leica UK , Cambridge ) , using standard techniques described elsewhere ( Morita et al . , 2008; Kim and Robinson , 2011 ) . Slices were observed using an upright microscope ( Olympus BX51WI , XLUMPlanFI 20X/0 . 95W objective ) with infrared illumination and an oblique condenser , combined with epifluorescence to visualize GFP-expressing neurons . Primary cultures of dissociated cortical Gad2-GFP neurons were obtained by methods similar to those described by Schroeter et al . ( 2015 ) . Extrahippocampal cortex was isolated at E17 or P0 , and cultured for 12–17 days in vitro . All protocols followed UK Home Office regulations for the care and use of animals . During recording , slices were superfused with a solution containing ( mM ) : 125 NaCl , 25 NaHCO3 , 2 . 5 KCl , 1 . 25 NaH2PO4 , 2 CaCl2 , 1 MgCl2 , 0 . 01 glycine , 25 D-glucose , maintained at a pH of 7 . 4 by bubbling with 95% O2 , 5% CO2 gas mixture . In most experiments , 10 µM CNQX , 10 µM APV and 10 µM gabazine were added to silence background synaptic activity in the slice . For whole-cell recordings , the following pipette filling solution was used ( mM ) : 105 K gluconate , 30 KCl , 10 4- ( 2-hydroxyethyl ) -1-piperazineethanesulfonic acid ( HEPES ) , 4 ATP-Mg , 0 . 3 GTP-Na , 10 creatine phosphate-Na ( adjusted to pH 7 . 3 with KOH , −10 mV liquid junction potential ( LJP ) ) . In recordings with elevated calcium buffering ( see text ) , the concentration of K gluconate was reduced to 90 mM , and 10 mM 1 , 2-bis ( o-aminophenoxy ) ethane-N , N , N' , N'-tetraacetic acid ( BAPTA ) -Na was added . In the case of whole-cell recordings of persistent sodium currents , the following intracellular solution was used ( mM ) : 90 Cs methanesulfonate , 30 CsCl , 10 BAPTA , 10 HEPES ( adjusted to pH 7 . 3 with HCl , −12 mV LJP ) . For cell-attached recordings , the following pipette solution was used ( mM ) : 150 NaCl , 2 . 5 KCl , 12 . 5 tetraethylammonium ( TEA ) chloride , 2 CaCl2 , 1 MgCl2 , 10 HEPES . Potassium currents were measured with 300 nM TTX added to the bath solution . Blockers were dissolved in a HEPES-buffered artificial cerebrospinal fluid and puff-applied through a glass pipette of around 50 µm in tip diameter . Salts were obtained from Sigma-Aldrich ( Dorset , UK ) , and channel and receptor blockers from Tocris Bioscience ( Bristol , UK ) , with the exception of phrixotoxin , which was acquired from Abcam ( Cambridge , UK ) . Recordings were carried out at 30–33 ºC . Whole-cell recording in current-clamp and voltage-clamp modes , and cell-attached single-channel recording were carried out using a Multiclamp 700B amplifier ( Molecular Devices , Sunnyvale , CA , USA ) , and Matlab ( Mathworks , Natick , MA , USA ) scripts calling NI-DAQmx library functions ( National Instruments , Austin , TX , USA ) to acquire and generate analog waveforms , using a National Instruments X-series DAQ interface . For current-clamp and voltage-clamp , the built-in series resistance compensation and capacitance cancellation circuitry of the Multiclamp were used . Pipettes ( 5–10 MΩ before sealing ) were pulled from borosilicate glass capillaries ( GC150F-7 . 5 , Harvard Apparatus , Kent , UK ) , and , for single-channel recordings , coated with Sylgard ( Dow Corning Europe , Belgium ) , and fire-polished . Signals were filtered at 6 kHz ( −3 dB , 4-pole Bessel ) and sampled at 20 kHz with 16-bit resolution . For conductance injection / dynamic-clamp ( Destexhe and Bal , 2009 ) experiments , a hard real-time SM2 system ( Cambridge Conductance , Cambridge , UK ) was used , with low-latency AD and DA converters , and a digital signal processor ( TMS C6713 ) , running at a sample / update rate of > 50 kHz ( <20 µs ) ( Robinson , 2008 ) . Soma size ( see Results ) was used to select putative IS cells in experiments where spiking pattern was not assessed , e . g . cell-attached recordings . All measurements are given as mean ± standard error of the mean ( SEM ) , unless otherwise stated . To test for differences between two conditions , the two-sided Wilcoxon rank sum test ( Matlab Statistics Toolbox function ranksum ) , equivalent to the Mann-Whitney U test , was used . n , the number of samples , and p , the probability of observing the two distributions under the null hypothesis that they have equal medians , are given in all cases , and p<0 . 05 is taken as the significance level . Spike times were determined as the times of positive-going threshold crossings of the membrane potential at a threshold set at 10 mV below the peak of action potentials . Variability of the phase of spikes during sinusoidal stimulation ( Figure 8 ) was characterized by a phase order parameter ( Pikovsky et al . , 2001 ) or synchrony of entrainment S=⟨cos2ϕ⟩+⟨sin2ϕ⟩ ( where ϕ is the phase ) , which varied between 0 ( phases distributed uniformly between 0 and 2π ) and 1 ( phases all identical ) . Spike times within the first 250 ms of each response were omitted , to exclude initial adaptation from the analysis . The cross-correlation function of firing between gap-junction-coupled model neurons was calculated by binning times of occurrence of spikes in one cell relative to those in another cell ( Figure 7c ) . The instantaneous firing rate was obtained by dividing the number of spikes in each time bin ( 0 . 5 ms ) by the total simulation period and by the time bin . Synchrony of spikes between coupled model neurons ( Figure 7d ) was characterized as the fraction of spikes in one cell which occur within ± 10 ms of a spike in the other cell ( obtained by integrating the cross-correlation function between −10 and +10 ms ) . Recurrence plot ( RP ) analysis ( Eckmann et al . , 1987 ) was carried out using the Cross Recurrence Plot ( CRP ) Matlab toolbox ( Marwan et al . , 2007 ) . Briefly , let the time series of sequential interspike intervals be indexed as { x1−m+1 , x1−m+2 , … , x1 , x2 , … , xN } . For each trial , the initial transient in the first 450 ms was excluded , slow within-trial nonstationarity ( <10% change in local average ISI ) was removed by subtracting the least-square fit of the sequence to a second-order polynomial , and ISIs were normalized to zero mean , unit standard deviation . The state of the system at each interval i can be represented by a vector of length m of the immediately preceding intervals: x→i=[ xi−m+1 , xi−m+2 , … , xi ] . The time series is said to be embedded with dimension m . The elements in the RP matrix are determined as followsRi , j={1 : x→i≈x→j0 :x→i≈x→j , i , j=1 , 2 , … , N , where N is the number of sequential states , and x→i≈x→j means equality within a distance ( or error ) of ε . Points of value 1 are plotted as black dots , 0 as white . Recurrence ( for a given ε ) is defined as the fraction of points in the RP which are 1 , while determinism is the proportion of recurrent points which lie within diagonal lines of slope one and length greater than one . To measure distance between embedding points , we used a Euclidean norm ( Marwan et al . , 2007 ) , and ε was set at one standard deviation of the ISIs . The significance of both recurrence and determinism was measured by calculating the distribution of surrogate values obtained by randomly permuting the two time series in each cross-recurrence comparison , one thousand times , and a z-test to estimate the probability p of obtaining the result by chance , with p<0 . 05 deemed significant . A Hodgkin-Huxley-type model with one activation variable ( m ) and one inactivation variable ( h ) was fitted to step responses in voltage-clamp , such that IKt=gKt ( V−EK ) . For dynamic conductance injection , three different parameter sets obtained from the experiments were used , differing slightly in the activation and inactivation kinetics , as follows . The onset of TTX-dependent voltage noise with depolarization ( Figure 3a ) was fitted by assuming that it was due only to non-inactivating ( persistent ) voltage-dependent sodium channels and inactivating gKt channels , in a single passive cell compartment ( i . e . without considering the effect of changes in the membrane potential on channel gating , including active amplification by the large Na conductance in the cell ) , whose passive conductance is G , and membrane time constant is τcell . Let τm=1/ ( αm+βm ) and m∞=αm/ ( αm+βm ) . Then by calculating the Lorentzian components of the single-channel noise expected from the Hodgkin-Huxley model , filtering with the membrane time constant and integrating over all frequencies ( Schneidman et al . , 1998 ) , we obtain the following distribution of membrane potential variance , for NaP channels:σV2=N2i2m∞3G2[ 3m∞2 ( 1−m∞ ) τmτm+τcell+3m∞ ( 1−m∞ ) 2τm/2τm/2+τcell+ ( 1−m∞ ) 3τm/3τm/3+τcell ] N is the number of channels and i , the single channel current is given by γ ( V−ENa ) , where γ is the single-channel conductance . For gKt channels , i=γ ( V−EK ) andσV2=N2i2mKt , ∞rKt , ∞G2[ hKt , ∞ ( 1−mKt , ∞ ) τmKtτmKt+τRC+mKt , ∞ ( 1−hKt , ∞ ) τhKtτh+τRC+ ( 1−mKt , ∞ ) ( 1−hKt , ∞ ) τ1τ1+τRC ] where τ1=τmKtτhKt/ ( τmKt+τhKt ) . The total membrane noise variance was taken as the sum of these two components . A reduced conductance-based model of IS neurons was implemented in Java ( called from Matlab , see Source code 1 in irregmodelcode . zip ) , based on a standard model of fast-spiking inhibitory interneurons ( Erisir et al . , 1999; Gouwens et al . , 2010 ) , to which was added a second compartment modeling dendritic membrane , a gKt potassium conductance whose kinetics was obtained from fits to the voltage clamp data shown in Figure 4 , and noise sources representing the effects of persistent sodium and gKt channel openings . A somatic compartment , of capacitance C = 8 . 04 pF and passive leak conductance gL = 4 . 1 nS , was connected with an intracellular resistance Ri of 2 GΩ to a passive compartment representing the remote dendritic membrane , which had a capacitance CD of 80 pF and a leak conductance gD of 0 . 5 nS . Transient sodium ( Na ) and persistent sodium ( NaP ) , Kv1 ( K1 ) , Kv3 and gKt type potassium and static leak ( L ) conductances were inserted at the soma . The system of differential equations describing the model was as follows . The somatic voltage V was determined by a Langevin equation containing noise terms X for NaP and gKt channel fluctuations:CdVdt= ( g¯Nam3h+g¯NaPm3 ) ( ENa−V ) + ( g¯K1n4+ g¯K3p2+ g¯KtmKthKt ) ( EK−V ) +gL ( EL−V ) +XNaP+XKt+Istim The voltage of the passive dendritic compartment was determined by:CDdVDdt= ( V−VD ) Ri+gD ( EL−VD ) The kinetics of the gating variables of voltage-dependent channels were determined as follows ( units of mV for voltage , ms−1 for rates ) : dxdt=αx ( V ) ( 1−x ) −βx ( V ) x , for x∈{m , h , n , p} , where αm ( V ) = ( 3020−40V ) / ( exp ( ( −75 . 5+V ) /−13 . 5 ) −1 ) , βm ( V ) =1 . 2262/exp ( V/42 . 248 ) , αh ( V ) =0 . 0035/exp ( V/24 . 186 ) , βh ( V ) =− ( 0 . 8712+0 . 017V ) / ( exp ( ( 51 . 25+V ) /−5 . 2 ) −1 ) , αn ( V ) =− ( 0 . 616+0 . 014V ) / ( exp ( − ( 44+V ) /2 . 3 ) −1 ) , βn ( V ) =0 . 0043/exp ( ( 44+V ) /34 ) , αp ( V ) = ( 95−V ) / ( exp ( − ( V−95 ) /11 . 8 ) −1 ) , βp ( V ) =0 . 025/ ( V/22 . 222 ) , and dmKtdt=mKt , ∞−mKtτmKt and dhKtdt=hKt , ∞−hKtτhKt , with mKt , ∞ ( V ) =11+exp ( ( −30−V ) /10 ) , τmKt ( V ) =0 . 346exp ( −V/18 . 272 ) +2 . 09 hKt , ∞ ( V ) =11+exp ( 0 . 0878 ( V+55 . 1 ) ) , τhKt ( V ) =2 . 1exp ( −V/21 . 2 ) +4 . 627 g¯Na=900 nS , g¯K1=1 . 8 nS , g¯Na=1800 nS , gL=4 . 1 nS , EL=−70 mV , EK=−90 mV , ENa=60 mV . Values of g¯Na , g¯K1 and g¯K3 are unchanged from those used for fast-spiking interneurons in Erisir et al . ( 1999 ) and Gouwens et al . ( 2010 ) , while gL was adjusted to give a resting input resistance similar to those measured in IS neurons . g¯Kt was set to 7 nS or varied as described in the text ( deterministic case ) , or 700 × 10 pS channels , or varied as described ( stochastic case ) . g¯NaP was set to 10 nS ( deterministic ) or 500 × 20 pS channels ( stochastic case , see below ) . The single persistent sodium channel current was given by i=γ ( ENa−V ) where i is the single sodium channel current and γ is the single channel conductance , set to 20 pS . Macroscopic persistent Na current was given by INaP=I¯NaP+X , where the deterministic mean current term was given by I¯NaP=Nim3 in which N is the number of persistent sodium channels ( 0 for the deterministic model , see text ) , and the noise term X was updated at each time step by the exact update formula for an Ornstein-Uhlenbeck process ( Gillespie , 1996 ) :Xt+Δt=Xtexp ( −Δt/τo ) +ξσNaP2 ( 1−exp ( −2Δt/τo ) ) where Δt is the time step of integration , τo is the mean opening burst time of persistent Na channel openings , set at 1 ms , and ξ is a normally-distributed ( mean 0 , variance 1 ) random number . The variance of X changed dynamically , according to the mean level of persistent sodium current , as:σNaP2=iI¯NaP−I¯NaP2/N . gKt noise was modeled similarly , a single channel conductance of 10 pS , and a mean opening burst time of 10 ms , estimated from the recordings . A fourth-order Runge-Kutta method ( Press et al . , 2002 ) was used to integrate deterministic variables , with a time step of 5 or 1 µs . The value of the noise term was updated in parallel , as described above , and interpolated linearly at the midpoint of full Runge-Kutta steps . This gave identical results to an Euler-Maruyama method ( Kloeden and Platen , 1992 ) , but with improved stability and efficiency . | Neurons send information to other neurons in the brain by generating fast electrical pulses called action potentials ( or spikes ) . When stimulated by input signals of a constant size , neurons generally respond with regular patterns of spiking leading to rhythmical brain activity . However , neurons known as irregular spiking interneurons are unique: the relationship between the input they receive and whether or not they produce a spike appears to be random . The molecular mechanism behind this phenomenon is not clear . Mendonça et al . set out to investigate whether irregular spiking is truly random , or whether there is some degree of predictability . The experiments used genetically modified mice in which irregular spiking interneurons were specifically labeled with a fluorescent protein , which made them easier to find to record their electrical activity . Sophisticated statistical analyses showed that these neurons are not firing at random . Instead , there is a pattern to the timings of the spikes they produce . It was previously known that electrical spikes in neurons are generated by sodium ions and potassium ions moving across the membrane that surrounds each cell . Proteins called ion channels provide routes for these ions to pass through the membrane . Mendonça et al . show that compared to other neurons , irregular spiking interneurons have larger numbers of a specific type of potassium ion channel . Mimicking the effect of increasing the number of these potassium ion channels in the interneurons made the firing pattern of these neurons more irregular , while decreasing the number of these channels made the firing patterns more regular and predictable . A computer model of an irregular spiking interneuron showed that the activity of these potassium ion channels and a type of sodium ion channel plays a key role in producing irregular electrical spiking . Further analysis showed that irregular spiking interneurons can synchronize their activity with fast , but not slow , rhythms in brain activity . The findings of Mendonça et al . suggest that irregular spiking interneurons can disrupt slow regular electrical activity in the brain . Rhythms in brain activity vary depending on whether we are awake or asleep , and are altered in diseases such as epilepsy and schizophrenia . Now that we have a better understanding of how irregular spiking interneurons work , it should be possible to find out how they coordinate their activity with each other , and what effect they have on animal behavior . | [
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] | 2016 | Stochastic and deterministic dynamics of intrinsically irregular firing in cortical inhibitory interneurons |
Dopamine ( DA ) neurons of the ventral tegmental area ( VTA ) integrate cholinergic inputs to regulate key functions such as motivation and goal-directed behaviors . Yet the temporal dynamic range and mechanism of action of acetylcholine ( ACh ) on the modulation of VTA circuits and reward-related behaviors are not known . Here , we used a chemical-genetic approach for rapid and precise optical manipulation of nicotinic neurotransmission in VTA neurons in living mice . We provide direct evidence that the ACh tone fine-tunes the firing properties of VTA DA neurons through β2-containing ( β2* ) nicotinic ACh receptors ( nAChRs ) . Furthermore , locally photo-antagonizing these receptors in the VTA was sufficient to reversibly switch nicotine reinforcement on and off . By enabling control of nicotinic transmission in targeted brain circuits , this technology will help unravel the various physiological functions of nAChRs and may assist in the design of novel therapies relevant to neuropsychiatric disorders .
Cholinergic neurotransmission provides a widespread and diffuse signal in the brain ( Picciotto et al . , 2012; Sarter et al . , 2009 ) . ACh alters neurotransmitter release from presynaptic terminals and affects neuronal integration and network activity , by acting through two classes of membrane receptors: metabotropic muscarinic receptors and ionotropic nicotinic ACh receptors ( nAChRs ) . nAChRs consist of hetero- and homo-pentameric arrangements of α and β subunits ( 9 and 3 genes , respectively ) , yielding a high combinatorial diversity of channel composition , localization and function ( Zoli et al . , 2015 ) . Nicotinic neuromodulation controls learning , memory and attention , and has been associated with the development of numerous neurological and psychiatric disorders , including epilepsy , schizophrenia , anxiety and nicotine addiction ( Taly et al . , 2009 ) . Understanding how nAChRs mediate such diverse functions requires tools for controlling nicotinic neurotransmission in defined brain circuits . ACh is a modulator of the VTA , a midbrain DAergic nucleus key in the processing of reward-related stimuli and in addiction ( Di Chiara and Imperato , 1988; Pignatelli and Bonci , 2015; Volkow and Morales , 2015 ) . The pedunculopontine and laterodorsal tegmental nuclei ( PPN and LDT ) are the two major cholinergic inputs to the VTA ( Beier et al . , 2015 ) . Optogenetic activation of PPN and LDT neurons modulates the firing patterns of VTA DA cells and reward-associated behaviors ( Lammel et al . , 2012; Dautan et al . , 2016; Xiao et al . , 2016 ) , implicating ACh in these processes . Yet , whether ACh directly affects neuronal excitability at the post-synaptic level , or whether it potentiates the release of other neurotransmitters through pre-synaptic nicotinic and muscarinic receptors is not known . Brain nAChRs are expressed in high densities in the VTA , and in strategic places such as somatic and dendritic sites on GABAergic , glutamatergic and DAergic VTA cells , as well as on pre-synaptic terminals from extra-VTA afferents and from intra-VTA GABAergic interneurons ( Changeux , 2010; Zoli et al . , 2015 ) . They are also present on DAergic terminals in the Nucleus Accumbens ( NAc ) and the prefrontal cortex ( Grady et al . , 2007; Changeux , 2010 ) . Genetic and pharmacological manipulations have implicated VTA nAChRs in tuning the activity of DA neurons and in mediating the addictive properties of nicotine ( Mameli-Engvall et al . , 2006; Maskos et al . , 2005; Morel et al . , 2014; Naudé et al . , 2016; Picciotto et al . , 1998; Tapper et al . , 2004; Tolu et al . , 2013 ) . However , understanding the mechanism by which ACh and nicotine participate in these activities requires to comprehend the spatio-temporal dynamics of nAChRs activation . Genetic manipulations can eliminate specific nAChRs , but they cannot provide kinetic information about the time course of nAChR signals that could be crucial for actuating VTA circuits and goal-oriented behaviors . Moreover , gene knock-out can have unintended consequences , which include compensatory changes in expression of other receptors or channels , homeostatic adaptations and developmental impairments ( King et al . , 2003 ) . Pharmacological agents allow activation or inhibition of nAChRs , but they diffuse slowly in vivo , they have limited subtype specificity and they cannot be targeted to genetically-defined neuronal cell types . To fill this gap between molecular and circuit knowledge , we have developed the optogenetic pharmacology for rapid and reversible photocontrol of genetically-targeted mammalian neurotransmitter receptors ( Kramer et al . , 2013 ) . We previously demonstrated light-controllable nAChRs ( LinAChRs ) in Xenopus occcytes , a heterologous expression system ( Tochitsky et al . , 2012 ) . Here , we deployed strategies for acutely and reversibly controlling nicotinic transmission in the VTA in the mammalian brain , in vivo . β2* receptors account for the great majority of VTA nAChRs and are crucial for the pathophysiology of nicotine addiction ( Maskos et al . , 2005; Faure et al . , 2014 ) . We demonstrate acute interruption of nicotinic signaling in the VTA and reveal that endogenous pontine ACh strongly impacts on the firing patterns of VTA DA neurons . Moreover , we reversibly prevented the induction of nicotine preference in behaving mice by locally photo-antagonizing the effect of nicotine on VTA β2* nAChRs . This approach to optically antagonize neurotransmitter receptors in vivo will help sense the different temporal dynamics of ACh concentrations , and unravel the contribution of specific nAChR isoforms to nicotinic neuromodulation of neural circuits and associated behaviors , including drug abuse .
The vast majority of nAChRs in the mouse VTA contains the β2 subunit ( Zoli et al . , 2015; Faure et al . , 2014 ) . Therefore , we engineered this subunit to enable installation of light sensitivity . We transposed the rat β2E61C mutation , used previously in nAChRs expressed in Xenopus oocytes ( Tochitsky et al . , 2012 ) , to the mouse β2 subunit to generate a photosensitizable receptor that traffics and functions normally in the mouse brain . The single cysteine-substitution , which is used for the anchoring of the photoswitchable tethered ligand Maleimide-Azobenzene-Homocholine ( MAHoCh ) , faces the agonist binding sites ( Figure 1A ) . MAHoCh has a photo-isomerizable azobenzene group , flanked on one side with a thiol-reactive maleimide moiety for conjugation to the cysteine , and on the other with a homocholine ligand for competitive antagonism of nAChRs ( Figure 1B ) . In darkness , the azobenzene group adopts the thermally stable , extended trans configuration . Illumination with near-UV ( e . g . 380 nm ) light isomerizes the azobenzene core to the twisted , cis configuration . The cis isomer reverses to trans either slowly in darkness or rapidly in green light ( e . g . 500 nm ) . Receptor activation in response to ACh agonist remained unaltered in darkness after conjugation of MAHoCh to β2E61C . However , agonist activation is blocked in 380 nm light , when cis MAHoCh occupies the agonist binding pocket ( Figure 1C ) . Photo-control is bi-directional , and antagonism is relieved under 500 nm light when MAHoCh is in its trans form . To verify whether nAChR currents could be photo-controlled , the β2E61C mutant was co-expressed with the WT α4 subunit in Neuro-2a cells ( Figure 1D ) . Cells were treated with MAHoCh and any remaining untethered photoswitch was washed away prior to electrophysiological recordings . As expected , currents evoked by both carbamylcholine ( CCh ) and nicotine were strongly inhibited under 380 nm light , when tethered cis MAHoCh competes with the agonist ( Figure 1E ) . Currents rapidly ( <500 ms ) and fully returned to their initial amplitude upon 525 nm light illumination . Repeated light flashes reduced and increased current amplitude without decrement , consistent with photochemical studies showing that azobenzenes are very resistant to photobleaching ( Szymański et al . , 2013 ) . Spectroscopic measurements show that cis MAHoCh reverts to trans in darkness , but very slowly , with a half-life of 74 min in solution ( Tochitsky et al . , 2012 ) . Consistent with this , we found that nAChR responses remained suppressed in darkness for at least ten minutes after a single flash of 380 nm light , but quickly recovered upon illumination with 525 nm light ( Figure 1F ) . Hence , LinAChR could be rapidly toggled between its functional and antagonized forms upon brief illumination with the proper wavelength of light , but could also remain suppressed several minutes in darkness , eliminating the need for constant illumination . We then tested whether nAChR currents could be photo-controlled in VTA DA neurons using β2LinAChR . To this aim , we virally targeted the cysteine-mutant β2 subunit together with eGFP under the control of the ubiquitous pGK promoter to the VTA of WT mice ( Figure 2A ) . As expected , transgene expression was found at the injection site throughout the VTA both in TH+ and TH- neurons ( Figure 2B , Figure 2—figure supplement 1A ) . In contrast , expression was absent in the PPN and LDT ( Figure 2—figure supplement 1B ) , in agreement with the lack of retrograde transport for lentiviruses ( Mazarakis et al . , 2001 ) . Four to six weeks after viral infection , transduced coronal slices were treated with MAHoCh , and nicotine-induced currents were recorded from GFP-positive DA neurons . VTA DA neurons were identified based on their anatomical localization and electrophysiological properties , ( i . e . pacemaker activity and typical action potential waveform ) , which are robust indicators of the DAergic signature ( Figure 2—figure supplement 2A–B ) . Currents evoked by a local puff of nicotine were strongly inhibited under 380 nm light , and fully restored under 525 nm light ( Figure 2C ) . Photo-inhibition was robust at both low and high concentrations of nicotine , and was absent in non-transduced slices treated with MAHoCh ( Figure 2D ) . The degree of photo-inhibition was smaller than that observed in heterologous expression system , suggesting that only a subset of β2* receptors incorporated the cysteine-mutated β2 . Importantly , over-expression of β2E61C did not significantly affect the amplitude of nicotine-induced currents ( Figure 2E ) , indicating that the total number of functional nAChRs at the cell surface was unchanged . Moreover , MAHoCh alone had no detectable off-target effect on other endogenous ion channels or on resting or active membrane properties of the cell ( Figure 2—figure supplement 2C , D ) , indicating that the effect of light was specific for β2E61C* nAChRs . Overall , these experiments show that β2E61C associates with endogenous nAChR subunits in DA neurons , to produce receptors with normal neurophysiological roles , while allowing specific photo-control of nicotinic signaling . VTA DA neurons show two distinct patterns of electrical activity: tonic , regular-spiking in the low frequency range and transient sequences of high-frequency firing , referred to as bursts ( Paladini and Roeper , 2014 ) . Bursting activity , which is a crucial signal for behavioral conditioning ( Tsai et al . , 2009 ) , is under the control of excitatory afferents from the PPN and LDT ( Lodge and Grace , 2006; Paladini and Roeper , 2014; Floresco et al . , 2003 ) . We asked whether endogenous pontine ACh modulates the firing patterns of VTA DA neurons through β2*nAChRs . Testing this hypothesis required to deploy strategies for acutely manipulating nicotinic transmission in vivo , since DA neurons discharge only in pacemaker-like tonic activity in brain slices , due to cholinergic and glutamatergic afferents being severed ( Grace and Onn , 1989 ) . To this aim , we used a microdrive multielectrode manipulator ( System mini matrix with five channels , Figure 3A ) directly mounted onto the head of an anaesthetized mouse . This system allowed us to stereotaxically deliver the photoswitch and record the spontaneous activity of putative DA ( pDA ) neurons , while delivering alternating flashes of 390 and 520 nm light in the VTA ( Figure 3A , B ) . β2E61C was virally transduced in the VTA of WT mice and recordings were performed three to four weeks after infection . MAHoCh was infused in the VTA at least an hour before starting the electrophysiological recordings , to allow the excess of untethered photoswitch to be cleared . We first found that the spontaneous activity of pDA neurons from WT and transduced animals were not significantly different in darkness ( Figure 3—figure supplement 1A ) , indicating that viral expression of β2E61C did not affect the native physiology of the cells . We then checked whether alternatively switching light between 390 and 520 nm ( 20 cycles ) affected the spontaneous firing of pDA neurons , by calculating the absolute percent of photoswitching ( defined as the absolute value of ( ( Freq520 – Freq390 ) /Freq390 ) ) . Importantly , we found that switching wavelength impacted the spontaneous firing rate of MAHoCh-treated pDA neurons of transduced animals , but not of control WT animals ( Figure 3C , D ) , further evidencing that the effect of light is specific to the anchoring of MAHoCh to the β2 cysteine mutant . For transduced animals , only a fraction of pDA neurons responded to light . To separately evaluate responding from non-responding neurons , we set a threshold ( 15% absolute photoswitching ) to exclude 95% of the control neurons ( Figure 3D ) . Based on this threshold , about a third ( 33/93 ) of the pDA neurons of transduced animals responded to light , compared to 1/28 for control animals . Non-responding neurons probably were either not transduced , or received too little endogenous cholinergic drive . We then compared the activity of each responding pDA neuron under both wavelengths of light and observed that some neurons responded with increased firing and some with decreased firing . A majority of the neurons ( Type 1 , 24/33 ) showed decreased activity under 390 nm ( Figure 3E ) , and a transient increase upon switching back to 520 nm , consistent with a direct nAChRs antagonism on VTA DA neurons by cis MAHoCh and relief from antagonism when MAHoCh is switched to its trans state . The increase in firing upon relief from antagonism suggests that ambient ACh is sufficient to drive nAChRs in an activated state . In addition , bursting activity was significantly reduced in 390 nm light in Type 1 neurons , when β2*nAChRs were antagonized ( Figure 3F ) . Hence , these receptors play a causal role in determining the firing patterns of VTA DA neurons . A smaller fraction of pDA neurons ( Type 2 , 9/33 ) showed the opposite profile , i . e . increased activity under 390 nm light compared to 520 nm ( Figure 3G ) . This observation suggests that extracellular ACh acts on β2*nAChRs to exert an inhibitory drive on a sub-population of VTA DA neurons , possibly through an indirect network mechanism or through β2LinAChRs expressed on GABAergic interneurons . In Type 2 pDA neurons , we observed no effect of light on AP bursts ( Figure 3—figure supplement 1B ) . Altogether , these results indicate that spontaneously-released ACh acts through post-synaptic β2*nAChRs ( i . e . receptors expressed on intra-VTA neurons , see Figure 2B ) to bi-directionally modulate the tonic firing and increase the bursting activity of VTA DA neurons . This excitatory/inhibitory nicotinic drive is consistent with the duality of the responses observed upon optogenetic activation of pontine cholinergic axons ( Dautan et al . , 2016 ) , yet it directly implicates nicotinic- and not muscarinic- ACh receptors . It is also consistent with the concurrent excitations and inhibitions observed in DA neurons upon nicotine systemic injections ( Eddine et al . , 2015 ) . In WT mice , VTA DA neurons respond to nicotine with a rapid increase in firing frequency and in bursting activity , and these responses are totally absent in β2-/- mice ( Maskos et al . , 2005 ) . Several pre- and post-synaptic mechanisms have been proposed to explain the effects of nicotine on DA cell firing ( Juarez and Han , 2016; Faure et al . , 2014 ) . We tested whether blocking VTA β2LinAChRs resulted in a decrease response to nicotine in DA cells . To this aim , VTA DA neurons transduced with β2E61C were recorded in vivo using the juxta-cellular technique , which enables long , stable recordings and multiple drug injections ( Figure 4A , B ) . Neurons that were successfully filled with neurobiotin ( 3 out of 7 ) were subsequently immuno-histologically identified as DAergic ( Figure 4—figure supplement 1A ) . We found that the nicotine-induced variation in firing rate was much smaller under 390 nm light , when receptors were antagonized , and illumination with 520 nm light fully restored the initial response ( Figure 4C , D and Figure 4—figure supplement 1B ) . Three of seven neurons tested showed spontaneous bursting , and all of these responded to nicotine by a variation in spikes within bursts ( SWB ) that appeared reduced under 390 nm light . Importantly , the response recorded from transduced animals was similar to that observed in WT animals ( Figure 4—figure supplement 1C , D ) , further supporting the idea that the basic neurophysiological properties of DA neurons are unaffected by the viral transduction . Altogether , these experiments show that the effect of nicotine can be reversibly blocked with high spatial , temporal and pharmacological precision in defined brain structures , here the VTA . The VTA is crucial for the motivational properties of many drugs of abuse , including nicotine ( Di Chiara and Imperato , 1988; Volkow and Morales , 2015 ) . In rodents , nicotine increases the activity of VTA DA neurons ( Mameli-Engvall et al . , 2006; Maskos et al . , 2005 ) and boosts DA release in the NAc ( Di Chiara and Imperato , 1988 ) , signaling its reinforcing , rewarding effect . We tested whether optically blocking β2*nAChRs of the VTA was sufficient to prevent nicotine from producing its reinforcing properties . To this aim , we chronically implanted above the transduced VTA a guide cannula for local delivery of the chemical photoswitch and light ( Figure 5A ) and subjected mice to a conditioned-place preference ( CPP ) protocol ( Figure 5B ) . Proper transduction and placement of the cannula guide were confirmed immunohistochemically ( Figure 5—figure supplement 1A ) . Consistent with previous reports ( Walters et al . , 2006 ) , WT animals showed a significant place preference for nicotine while β2-/- mice did not ( Figure 5C and Figure 5—figure supplement 1B ) . To determine whether nicotine preference could be reversibly photo-controlled in individual animals , CPP tests were conducted with two groups of β2E61C-transduced animals . Pairings were performed first with nicotine and 390 nm light for group 1 , and with nicotine and 520 nm light for group 2 . Two months after the first CPP test , nicotine pairing was performed with the alternative light condition , i . e . 520 nm light for group 1 and 390 nm light for group 2 . For both groups , animals showed preference to nicotine under 520 but not under 390 nm light ( Figure 5D , E ) . These results cannot be attributed to changes in general activity behavior , since locomotion was not affected by viral transduction or light ( Figure 5—figure supplement 1C ) . Altogether , these experiments show that nicotine-CPP can be reversibly switched on and off in the same animal , by manipulating β2*nAChRs selectively located in the VTA .
In this study , we used an optogenetic pharmacology strategy ( Kramer et al . , 2013 ) and demonstrated pharmacologically-specific , rapid local and reversible manipulation of brain nAChRs in behaving mice . Classical opsin-based optogenetics aims at turning specific neurons on or off for decoding neural circuits ( Kim et al . , 2017 ) . Our strategy expands the optogenetic toolbox beyond excitation and inhibition by providing acute interruption of neurotransmission at the post-synaptic level , and provides mechanistic understanding of how specific transmitters and receptors contribute to modulation of circuits and behaviors . Our method for photosensitizing receptors relies on the covalent attachment of a chemical photoswitch on a cysteine-modified receptor mutant . The photochemical properties of the azobenzene photoswitch make this strategy ideally suited for reversibly controlling neurotransmitter receptors with high efficacy and at speeds that rival synaptic transmission ( Lemoine et al . , 2013; Levitz et al . , 2013; Lin et al . , 2015; Szobota et al . , 2007 ) . Comparatively , strategies for photosensitizing proteins based on the fusion of light-sensitive modules ( Rost et al . , 2017 ) or chromophore-assisted light-inactivation ( Lin et al . , 2013; Takemoto et al . , 2017 ) are too slow or irreversible , respectively . Due to the constrains of bioconjugation , in vivo use of photoswitch-tethered receptors in mice has been restricted to the eye ( Gaub et al . , 2014 ) and to superficial layers of the cerebral cortex ( Levitz et al . , 2016; Lin et al . , 2015 ) . Here , we demonstrate rapid on and off control of neuronal nAChRs in deep brain structures and in freely behaving animals . Our data show that photoswitch delivery resulted in an absolute subtype-specificity control of β2*nAChRs , with no apparent off-target effect . Labeling was rapid ( minutes ) and , due to its covalent nature , persisted for many hours ( we have detected strong photosensitization in vivo up to 9 hr after treatment ) . Importantly , due to the thermal stability of MAHoCh , receptor function was unperturbed in darkness , while brief flashes of light were sufficient to bistably toggle LinAChR between its resting and antagonized states . The cysteine-modified subunit was transduced in the VTA of WT mice . This resulted in a local replacement of the native β2 subunit with the cysteine-mutated version , while leaving nicotinic signaling in other brain regions ( notably cholinergic pontine afferents ) unaffected . Even though the WT β2 subunit remained in transduced cells , photoswitch treatment resulted in robust photo-sensitization of cysteine-mutated β2*nAChRs , indicating incorporation into heteromeric receptors . The pool of receptors remained apparently unchanged , most likely because endogenous nAChR subunits ( e . g . α4 ) limit the total number of heteropentamers at the cell surface . Replacing the WT subunit by its cysteine counterpart in a knock-in animal would guarantee complete gene replacement and untouched expression profile . Yet , viral transduction affords the advantage of allowing the engineered receptor to be targeted for expression in specific types of neurons and in defined neuronal circuits . We used this feature to optically control nAChRs at the level of VTA neurons ( both DAergic and non-DAergic cells , see Figure 2B ) , while leaving pre-synaptic receptors from various afferents unaffected , which would be impossible with a transgenic animal . Collectively , our results show that β2E61C competes with native subunits to form functional receptors that , once labeled with MAHoCh , retain their natural functions in darkness , and are made photo-controllable . Cholinergic neurons from PPN and LDT project extensively to the VTA and substantia nigra ( Beier et al . , 2015 ) and are thought to form connections with downstream DAergic and GABAergic neurons through non-synaptic volume transmission . Optogenetic activation of cholinergic pontine axons induces post-synaptic currents in VTA DA neurons that have both nicotinic and glutamatergic signatures ( Xiao et al . , 2016 ) , suggesting that extracellular ACh potentiates glutamate release by activating nAChRs located on axon terminals . Contrasting with this view , we show here that activation of post-synaptic ( i . e . from intra-site ) β2 nAChRs by endogenous ACh is sufficient to fine tune both the tonic and burst firing modes of VTA DA neurons . Furthermore , our results add temporal and causal considerations to previous genetic studies ( Mameli-Engvall et al . , 2006; Tolu et al . , 2013 ) by establishing a direct relationship between the activity of β2 nAChRs and the firing patterns of VTA DA neurons . The rebound activity that occurred within 500 ms after de-antagonizing LinAChRs indeed suggests that , even though cholinergic inputs to the VTA are considered sparse , the extracellular levels of ACh are sufficient to activate a large population of receptors and greatly modify the electrical activity of DA neurons . Moreover , we identified a sub-population of VTA DA neurons that is inhibited when β2 nAChRs are de-antagonized , which suggests multiple functional mechanisms by which the cholinergic brainstem neurons may influence the activity of midbrain DA neurons . These results are coherent with the growing body of evidence that show that VTA DA neurons are heterogeneous in their physiological properties ( Morales and Margolis , 2017; Yang et al . , 2018 ) and in their responses to drugs ( Juarez and Han , 2016 ) , including nicotine ( Eddine et al . , 2015 ) . The rewarding properties of nicotine , and especially reinforcement during the acquisition phase of addiction , implicate an elevation of DA in the NAc ( Di Chiara and Imperato , 1988 ) . Nicotine administration directly depolarizes and activates VTA DA neurons and , consequently , increases extracellular striatal DA ( Maskos et al . , 2005; Tolu et al . , 2013 ) . Nicotine can also increase DA neuron firing by acting on GABAergic and glutamatergic afferent terminals , from local interneurons and projection fibers ( Mansvelder et al . , 2002 ) . Finally , nicotine also modulates DA release by desensitizing nAChRs expressed in the striatum at the level of DA terminals ( Rice and Cragg , 2004 ) . These different studies suggest alternative circuit mechanisms to explain the outcome of nicotine action on VTA circuitry , for reviews see ( Juarez and Han , 2016; Faure et al . , 2014 ) . We took advantage of the anatomical and cellular resolution of our approach , and locally blocked the effect of nicotine on VTA DA and non-DA neurons , while leaving pre-synaptic receptors of afferents from other brain areas and of striatal DA terminals unaffected . Our results show that β2*nAChRs of VTA neurons are a key player of both the response to nicotine at the cellular level , and the rewarding properties of this addictive substance at the behavioral level . Importantly , blocking the excitatory phasic input produced by nicotine was sufficient to completely prevent reinforcement learning . This is consistent with our results concerning the ability of β2 nAChRs to tune burst firing in DA neurons , and with the fact that activation of LDT-to-VTA cholinergic neurons causes positive reinforcement ( Dautan et al . , 2016; Xiao et al . , 2016 ) . All together , these results strongly suggest that these receptors have a central role in reward processing . There is a considerable interest to target-specific nAChRs and specific circuits to treat psychiatric disorders such as addiction , depression or schizophrenia . Yet , we do not know which native receptor subtype mediates specific physiological or pathological function , hampering development of clinically effective drugs , notably for preventing or treating addiction . Optogenetic pharmacology offers the unique opportunity to locally and reversibly ‘knock-out’ the function of a specific receptor isoform in vivo , and to directly evaluate within the same animal the consequences at the cellular , circuit and behavioral levels . Our approach should be applicable to other photo-activatable and -inhibitable nAChR subtypes and other neuronal circuits , and may provide a platform for examining new translational strategies for treating neuropsychiatric disorders .
65 Wild-type male C57BL/6J mice were obtained from Janvier Laboratories ( France ) and 6 knockout SOPF-HO-ACNB2 ( β2-/- ) male mice were obtained from Charles Rivers Laboratories ( France ) . β2-/- mice were generated as described previously ( Picciotto et al . , 1995 ) . Even though WT and β2-/- mice are not littermates the mutant line was generated more than 20 years ago , and has been back-crossed more than 20 generations with the WT C57BL/6J line and is more than 99 . 99% C57BL/6J . All experiments were performed on mice between 8 and 16 weeks of age . All experiments were performed in accordance with the recommendations for animal experiments issued by the European Commission directives 219/1990 , 220/1990 and 2010/63 , and approved by Sorbonne Université . MAHoCh was synthesized as described previously ( Tochitsky et al . , 2012 ) and was stored as concentrated stock solutions ( 100 mM ) in water-free DMSO at −80°C . For cell labeling , aqueous solutions of MAHoCh were prepared extemporaneously . Light intensities were measured with a power meter ( 1916 R , Newport ) equipped with a UV-silicon wand detector ( 818-ST2-DB Newport ) . The cDNAs for the WT mouse β2 and α4 nAChR subunits were from previously-designed pIRES ( CMV promoter ) or pLenti ( pGK promoter ) vectors ( Maskos et al . , 2005 ) . All the constructs are bi-cistronic , with an IRES-eGFP sequence designed to express eGFP and the nAChR subunit using the same promoter . The pLenti construct also contains the long terminal repeats , WPRE and virus elements for packaging into lentiviral vectors . The single cysteine mutation E61C was inserted into pIRES-CMV-β2-IRES-eGFP and pLenti-pGK-β2-IRES-eGFP by site-directed mutagenesis using the Quickchange II XL kit ( Agilent ) . Mutations were verified by DNA sequencing . Lentiviruses were prepared as described previously ( Maskos et al . , 2005 ) with a titer of 150 ng of p24 protein in 2 μl . We used Neuro2A cells ( Sigma Aldrich #89121404-1VL ) , a mouse neuroblastoma cell line classically used for nAChRs expression ( Xiao et al . , 2011 ) . Cells were certified by Sigma-Aldrich . Mycoplasma contamination status were negative . Briefly , Neuro2A cells were cultured in Dulbecco’s Modified Eagle’s Medium ( DMEM ) , supplemented with 10% Foetal Bovine Serum ( FBS ) , 1% non-essential amino-acids , 100 units/ml penicillin , 100 mg/ml streptomycin and 2 mM glutamax in a 5% CO2 incubator at 37°C . Cells were transfected overnight with a 1:1 ratio of α4 and β2E61C subunits ( pLenti-pGK-α4-IRES-eGFP and pLenti-pGK-β2E61C-IRES-eGFP ) , using calcium-phosphate transfection method ( Lemoine et al . , 2016 ) . Cells were used 2–3 days after transfection for electrophysiology . Prior to recordings , cells were labeled with MAHoCh ( 20 μM in external solution ) for 20 min . WT mice ( 6–8 weeks ) were anaesthetized with 1% isoflurane gas and placed in a stereotaxic frame ( David Kopf ) . A small craniotomy was made above the location of the VTA . A lentivirus containing the construct pGK-β2E61C-IRES-eGFP was injected in the VTA ( 1 μl at the rate of 0 . 1 μl/min ) with a 10 μl syringe ( Hamilton ) coupled with a polyethylene tubing to a 36 G cannula ( Phymep ) , with the following coordinates [AP: −3 . 1 mm; ML:±0 . 4 mm; DV: −4 . 7 mm from bregma] . Mice were then housed during at least 4 weeks before electrophysiology or behavior experiments . 4–8 weeks after viral infection , mice were deeply anesthetized with an i . p . injection of a mixture of ketamine ( 150 mg/kg , Imalgene 1000 , Merial ) and xylazine ( 60 mg/kg , Rompun 2% , Bayer ) . Coronal midbrain sections ( 250 µm ) were sliced using a Compresstome ( VF-200; Precisionary Instruments ) after intra-cardiac perfusion of cold ( 0–4°C ) sucrose-based artificial cerebrospinal fluid ( SB-aCSF ) containing ( in mM ) : 125 NaCl , 2 . 5 KCl , 1 . 25 NaH2PO4 , 5 . 9 MgCl2 , 26 NaHCO3 , 25 Sucrose , 2 . 5 Glucose , 1 Kynurenate . After 10 min at 35°C for recovery , slices were transferred into oxygenated ( 95% CO2/5% O2 ) aCSF containing ( in mM ) : 125 NaCl , 2 . 5 KCl , 1 . 25 NaH2PO4 , 2 CaCl2 , 1 MgCl2 , 26 NaHCO3 , 15 Sucrose , 10 Glucose at room temperature for the rest of the day . Slices were labeled individually with MAHoCh ( 70 μM ) in oxygenated aCSF ( 1 ml ) for 20 min , and transferred to a recording chamber continuously perfused at 2 ml/min with oxygenated aCSF . Patch pipettes ( 5–8 MΩ ) were pulled from thin wall borosilicate glass ( G150TF-3 , Warner Instruments ) using a micropipette puller ( P-87 , Sutter Instruments ) and filled with a K-Gluconate based intra-pipette solution containing ( in mM ) : 116 KGlu , 20 HEPES , 0 . 5 EGTA , 6 KCl , 2 NaCl , 4 ATP , 0 . 3 GTP and 2 mg/mL biocytin ( pH adjusted to 7 . 2 ) . Cells were visualized using an upright microscope with a Dodt contrast lens and illuminated with a white light source ( Scientifica ) . A 460 nm LED ( pE-2 , Cooled ) was used for visualizing eGFP positive cells ( using a bandpass filter cube , AHF ) . Optical stimulation was applied through the microscope with two LEDs ( 380 and 525 nm , pE-2 , CoolLED ) , with a light output of 6 . 5 and 15 mW , corresponding to 5 and 11 . 7 mW/mm2 at the focal plane , respectively . Whole-cell recordings were performed using a patch-clamp amplifier ( Axoclamp 200B , Molecular Devices ) connected to a Digidata ( 1550 LowNoise acquisition system , Molecular Devices ) . Currents were recorded in voltage-clamp mode at −60 mV . Signals were low pass filtered ( Bessel , 2 kHz ) and collected at 10 kHz using the data acquisition software pClamp 10 . 5 ( Molecular Devices ) . Electrophysiological recordings were extracted using Clampfit ( Molecular Devices ) and analyzed with R . To record nicotinic currents from GFP-positive Neuro2A cells , we used the following external solution ( containing in mM ) : 140 NaCl , 2 . 8 KCl , 2 CaCl2 , 2 MgCl2 , 10 HEPES , 12 glucose ( pH 7 . 3 with NaOH ) . We used a computer-controlled , fast-perfusion stepper system ( SF-77B , Harvard Apparatus ) to apply nicotine-tartrate ( 100 μM , Sigma-Aldrich ) or carbamylcholine chloride ( CCh , 1 mM , Sigma-Aldrich ) , with an interval of 2 min , under different light conditions . To record nicotinic currents from VTA DA neurons , local puffs ( 500 ms ) of nicotine tartrate ( 30–500 μM in aCSF ) were applied every minute , while alternating wavelengths , using a glass pipette ( 2–3 μm diameter ) positioned 20 to 30 μm away from the soma and connected to a picospritzer ( World Precision Instruments , adjusted to ~2 psi ) . DA neurons were characterized in current clamp mode as described in ( Lammel et al . , 2008 ) , see Figure 2—figure supplement 2A . In some instances , at the end of the recording , the pipette was retracted carefully to allow labeling of the neuron with biocytin ( Marx et al . , 2012 ) . 4–8 weeks after viral infection , mice were deeply anaesthetized with chloral hydrate ( 8% , 400 mg/kg i . p . ) , supplemented as required to maintain optimal anesthesia throughout the experimental day . The scalp was opened and a hole was drilled in the skull above the location of the VTA . The saphenous vein was catheterized for intravenous administration of nicotine . Prior to recordings ( at least 1 hr ) , 500 nl of a 400 μM solution of MAHoCh in aCSF were injected within the VTA at a rate of 50 nl/min . Extracellular recording electrodes were made from 1 . 5 mm O . D . /1 . 17 mm I . D . borosilicate glass ( Harvard Apparatus ) using a vertical electrode puller ( Narishige ) . Under a microscope , the tip was broken to obtain a diameter of 1–2 µm . The electrodes were filled with a 0 . 5% Na-Acetate solution containing 1 . 5% of neurobiotin tracer yielding impedances of 20–50 MΩ . Electrophysiological signals were amplified with a headstage ( 1x , Axon Instruments ) coupled to a high-impedance amplifier ( Axoclamp-2A , Axon Instruments ) and audio monitored ( A . M . Systems Inc . ) . The signal was digitized ( Micro-2 , Cambridge Electronic Design ) , sampled at 12 . 5 kHz and recorded using Spike2 software ( CED ) . DA neurons were sampled in the VTA with the following coordinates: [AP: −3 to −4 mm; ML:+0 . 3 to+0 . 6 mm; DV: −4 to −4 . 8 mm , from Bregma] . Spontaneously active pDA neurons were identified on the basis of previously established electrophysiological criteria: 1 ) regular firing rate; 2 ) firing frequency between 1 and 10 Hz; 3 ) half AP >1 . 1 ms . After a baseline recording of at least 5 min , a saline solution ( 0 . 9% sodium chloride ) was injected into the saphenous vein , and after another 10 min , injections of nicotine- tartrate ( 30 μg/kg ) were administered via the same route in a final volume of 10 μl and under different light conditions ( Dark – 390 nm – 520 nm ) . Successive injections ( up to 6 ) were performed after the neuron returned to its baseline , or when the firing activity returned stable for at least 3 min . Light was applied through an optical fiber ( 500 µm core , NA = 0 . 5 , Prizmatix ) inserted within the glass pipette electrode and coupled through a combiner to 390/520 nm ultra-high-power LEDs ( Prizmatix ) , yielding an output intensity of 4–8 mW at the tip of the fiber for each wavelength . Light was TTL-controlled and applied 10 s before nicotine injection , for 30 s total . When possible , neurons were electroporated and neurobiotin was expulsed from the electrode using positive current pulses as already described ( Pinault , 1996; Eddine et al . , 2015 ) . Spikes Within Bursts ( SWB ) were identified as a sequence of spikes with the following features: ( 1 ) short intervals , ( 2 ) progressively decreasing spike amplitude , and ( 3 ) a progressively increasing inter-spike interval ( ISI ) . When considering extracellular recordings , most studies use two criteria to automatically detect bursts: ( 1 ) their onset are defined by two consecutive spikes with an interval inferior to 80 ms , whenever ( 2 ) they are closed with an interval greater than 160 ms ( Grace and Bunney , 1984 ) . Firing rate and %SWB were measured on successive windows of 60 s , with a 45 s overlapping period . Responses to nicotine are presented as the mean percentage of firing frequency or %SWB variation from the baseline ±SEM . For photoswitching , maximum of firing variation induced by nicotine occurring 200 s after the injection in purple and green was normalized to the maximum of firing variation in darkness . Spikes were extracted with Spike2 ( CED ) and analyzed with R ( https://www . r-project . org ) . 4–8 weeks after viral infection , mice were deeply anaesthetized with chloral hydrate ( 8% , 400 mg/kg i . p . ) , supplemented as required to maintain optimal anesthesia throughout the experiment . The scalp was opened and a hole was drilled in the skull above the location of the VTA . We used a MiniMatrix ( Figure 3A , Thomas Recording ) allowing us to lower within the VTA: up to 3 tetrodes ( Tip shape A , Thomas Recording , Z = 1–2 MΩ ) , a stainless-steel cannula ( OD 120 μm , Thomas Recording ) for photoswitch injection and a tip-shaped quartz optical fiber ( 100 μm core , NA = 0 . 22 , Thomas Recording ) for photostimulation . The fiber was coupled to a 390/520 nm LED combiner ( Prizmatix ) with an output intensity of 200–500 μW at the tip of the fiber for both wavelengths . These five elements could be moved independently with micrometer precision . 500 nl of MAHoCh ( 400 μM in aCSF ) were infused ( rate: 1 nl/s ) within the VTA , and tetrodes were subsequently lowered in the same zone to record neurons . Spontaneously active pDA neurons were recorded at least 30 min after MAHoCh infusion and were identified on the basis of the electrophysiological criteria used for juxtacellular recordings . The optical fiber was then lowered 100–200 μm above the tetrodes . Baseline activity was recorded for 200 s in darkness , prior to applying 5 s light flashes of alternative wavelengths ( 390 nm / 520 nm ) . Electrophysiological signals were acquired with a 20 channels pre-amplifier included in the Mini Matrix ( Thomas Recording ) connected to an amplifier ( Digital Lynx SX 32 channels , Neuralynx ) digitized and recorded using Cheetah software ( Neuralynx ) . Spikes were detected using a custom-written Matlab routine and sorted using a classical principal component analysis associated with a cluster cutting method ( SpikeSort3D Software , Neuralynx ) . Neurons were considered as responding when their change in firing rate ( % Photoswitching ) at the transition from violet to green light exceeded a threshold of 15% , defined as the maximal % photoswitching observed in controls . This threshold was used for all recorded neurons in every condition . To extract the spikes contained within bursting episodes ( SWB ) we used the same criteria described in the juxtacellular recordings section . They are represented as the frequency of SWB because of the short analysis window ( 5 s ) . All the data were analyzed with R ( https://www . r-project . org ) and Matlab ( MathWorks ) . Following stereotaxic viral infection in the VTA ( as described above ) , mice were implanted with a chronic opto-fluid guide cannula ( Doric Lenses Inc , Canada , see Figure 5A ) using the same coordinates . This guide ( length = 3 mm from skull surface , ID/OD = 320/430 μm ) has interchangeable threaded connectors and is used either with a fluid injection needle ( protruding to 4 . 8 mm from skull surface ) for delivering MAHoCh , or with an optic fiber injector ( 240 μm core , NA = 0 . 63 , protruding to 4 . 8 mm from skull surface ) coupled to a ceramic ferrule ( 1 . 25 mm ) for light delivery . In-between experiments , a plug is used to close the guide cannula and thus seal the implant . The implant is attached to the skull with a dental cement ( SuperBond , Sun Medical ) . The Conditioned Place Preference ( CPP ) box ( Imetronic , France ) consists of a Y-maze with one closed arm , and two other arms with manually operated doors . Two rectangular chambers ( 11 × 25 cm ) with different cues ( texture and color ) , are separated by a center triangular compartment ( side of 11 cm ) . One pairing compartment has grey textured floor and walls and the other one has smooth black and white striped walls and floor . The first day ( pretest ) of the experiment , mice ( n = 6–8 animals/group ) explored the environment for 900 s ( 15 min ) and the time spent in each compartment was recorded . Pretest data were used to segregate the animals with equal bias so each group has an initial preference score almost null , indicating no preference on average . On day 2 , 3 and 4 , animals received an i . p . injection of nicotine tartrate ( 0 . 5 mg/kg , in PBS ) or an equivalent injection of saline ( PBS ) , and immediately confined to one of the pairing chamber for 1200 s ( 20 min ) . The CPP test was performed using a single nicotine concentration ( 0 . 5 mg/kg ) which is known to induce preference in mice ( Walters et al . , 2006 ) . Groups were balanced so the animals do not always get nicotine in the same chamber . On the evening of the same day , mice received an injection of the alternate solution ( nicotine or saline ) and were placed in the opposite pairing chamber . The saline control animals received a saline injection in both pairing compartments . On day 5 ( test ) , animals were allowed to explore the whole open-field for 900 s ( 15 min ) , and the time spent in each chamber was recorded . The preference score ( ps ) is expressed in seconds and is calculated by subtracting pretest from test data . Trajectories and time spent on each side are calculated based upon animal detection . Place preference and locomotor activity were recorded using a video camera , connected to a video-track system , out of sight of the experimenter . A home-made software ( Labview 2014 , National Instruments ) tracked the animal , recorded its trajectory ( 20 frames per s ) for 15 min and sent TTL pulses to the LED controller when appropriate ( pairing sessions ) . For optogenetic pharmacology experiments , MAHoCh ( 400 μM in aCSF , 500 nl in 5 min ) was injected early in the morning of pairing days ( 2 , 3 and 4 ) under light gas anesthesia ( Isoflurane 1% ) . 520/390 nm light was applied during pairing sessions ( day 2 , 3 and 4 ) , on both sides , through a patch cord ( 500 μm core , NA = 0 . 5 , Prizmatix , Israel ) connected to the implanted ferrule with a sleeve and to the 390/520 nm combined UHP-LEDs ( Prizmatix ) . Light was applied with the following pattern: 2 s pulses à 0 . 1 Hz with a measured output intensity of 10 mW at the tip of the patch cord . Light was not applied during pre-test and test . Behavioral data were collected and analyzed using home-made LabVIEW ( National Instruments ) and Matlab ( MathWorks ) routines . After patch-clamp experiments , individual slices ( 250 μm ) were transferred in 4% paraformaldehyde ( PFA ) for 12–24 hr and then to PBS , and kept at 4°C . At the end of in vivo experiments , transduced mice received , under deep anesthesia ( Ketamine/Xylazine ) , an intra-cardiac perfusion of ( 1 ) PBS ( 50 ml ) and ( 2 ) paraformaldehyde ( 4% PFA , 50 ml ) and brains were rapidly removed and let in 4% PFA for 48–72 hr of fixation at 4°C . Serial 60 μm sections of the ROI were cut with a vibratome . Immunohistochemistry was performed as follows: Floating VTA brain sections were incubated 1 hr at 4°C in a solution of phosphate-buffered saline ( PBS ) containing 3% Bovine Serum Albumin ( BSA , Sigma; A4503 ) and 0 . 2% Triton X-100 and then incubated overnight at 4°C with a mouse anti-Tyrosine Hydroxylase antibody ( TH , Sigma , T1299 ) at 1:200 dilution and a rabbit anti-GFP antibody ( Molecular Probes , A-6455 ) at 1:500 dilution in PBS containing 1 . 5% BSA and 0 . 2% Triton X-100 . The following day , sections were rinsed with PBS and then incubated 3 hr at 22–25°C with Cy3-conjugated anti-mouse and Cy2-conjugated anti-rabbit secondary antibodies ( Jackson ImmunoResearch , 715-165-150 and 711-225-152 ) at 1:200 and 1:1000 dilution respectively in a solution of 1 . 5% BSA and 0 . 2% Triton X-100 in PBS . In the case of biocytin/neurobiotin labelling , TH identification of the neuron was performed using AMCA-conjugated Streptavidin ( Jackson ImmunoResearch ) at 1:200 dilution . Floating pons sections were incubated 1 hr at 4°C in a solution of phosphate-buffered saline containing 0 . 2% Gelatine from cold-water fish skin ( Sigma; G7041 ) and 0 . 25% Triton X-100 ( PBS-GT ) and then incubated overnight at 4°C a goat anti-Choline Acetyl-Transferase antibody ( ChAT , Merck-Millipore , AB144 ) at 1:200 dilution and a chicken anti-GFP antibody ( Aves Lab , GFP-1020 ) at 1:500 dilution in PBS-GT . The following day , sections were rinsed with PBS and then incubated 3 hr at 22–25°C with a donkey anti-goat Alexa 555-conjugated ( Invitrogen , A21432 ) and donkey anti-chicken Alexa 488-conjugated ( Jackson ImmunoResearch , 703-545-155 ) at 1:200 and 1:1000 dilution respectively in a solution of PBS-GT . After three rinses in PBS ( 5 min ) , wet slices were mounted using Prolong Gold Antifade Reagent ( Invitrogen , P36930 ) . Microscopy was carried out either with a confocal microscope ( Leica ) or an epifluorescence microscope ( Leica ) , and images captured using a camera and analyzed with ImageJ software . No statistical methods were used to predetermine sample sizes . Data are plotted as mean ±SEM . Total number ( n ) of observations in each group and statistics used are indicated in figure and/or figure legend . Unless otherwise stated , comparisons between means were performed using parametric tests ( two-sample t-test ) when parameters followed a normal distribution ( Shapiro test p>0 . 05 ) , and non-parametric tests ( here , Wilcoxon or Mann-Whitney ( U-test ) ) when this was not the case . Homogeneity of variances was tested preliminarily and the t-tests were Welch-corrected if needed . Multiple comparisons were Holm-Bonferroni corrected . Comparison between the cumulative distributions of in vivo multi-unit recordings between controls and LinAChRs ( Figure 3D ) was performed using a Kolmogorov-Smirnov test . p>0 . 05 was considered to be not statistically significant . | Acetylcholine is one of the most abundant chemicals in the brain , with key roles in learning , memory and attention . Neurons throughout the brain use acetylcholine to exchange messages . Acetylcholine binds to two different classes of receptors on neurons: nicotinic and muscarinic . As the name suggests , nicotinic receptors also respond to nicotine , the main addictive substance in tobacco , while muscarinic receptors respond to muscarine , present in certain poisonous mushrooms . Nicotinic and muscarinic receptors each consist of many different subtypes . But standard pharmacology techniques cannot discriminate between the effects of acetylcholine binding to these different subtypes . Likewise , they cannot distinguish between acetylcholine binding to the same receptor subtype on different neurons . Durand-de Cuttoli , Mondoloni et al . have now developed a new nanotechnology that uses light to target specific acetylcholine receptor subtypes in freely moving mice . The technology was tested in a brain region called the VTA , which is part of the brain’s reward system . Experiments showed that when acetylcholine binds to a specific subtype of nicotinic receptors on VTA neurons – called β2-containing receptors – it makes the neurons release the brain's reward signal , dopamine . Switching these receptors on and off changed how the mice responded to nicotine . With the receptors switched on , mice preferred locations associated with nicotine . Switching the receptors off removed this preference . Nicotine may thus be addictive in part because it triggers VTA neurons to release dopamine via its actions on β2-containing nicotinic receptors . This new technology will help reveal the mechanisms of action of acetylcholine and nicotine . Blocking the effects of nicotine at a specific time and place in the mouse brain may uncover the receptors and brain regions that drive nicotine consumption . Smoking remains a major cause of preventable death worldwide . This new approach could help us develop strategies to prevent or treat addiction . | [
"Abstract",
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"neuroscience"
] | 2018 | Manipulating midbrain dopamine neurons and reward-related behaviors with light-controllable nicotinic acetylcholine receptors |
The soluble isoform of leptin receptor ( sOb-R ) , secreted by the liver , regulates leptin bioavailability and bioactivity . Its reduced levels in diet-induced obesity ( DIO ) contribute to hyperleptinemia and leptin resistance , effects that are regulated by the endocannabinoid ( eCB ) /CB1R system . Here we show that pharmacological activation/blockade and genetic overexpression/deletion of hepatic CB1R modulates sOb-R levels and hepatic leptin resistance . Interestingly , peripheral CB1R blockade failed to reverse DIO-induced reduction of sOb-R levels , increased fat mass and dyslipidemia , and hepatic steatosis in mice lacking C/EBP homologous protein ( CHOP ) , whereas direct activation of CB1R in wild-type hepatocytes reduced sOb-R levels in a CHOP-dependent manner . Moreover , CHOP stimulation increased sOb-R expression and release via a direct regulation of its promoter , while CHOP deletion reduced leptin sensitivity . Our findings highlight a novel molecular aspect by which the hepatic eCB/CB1R system is involved in the development of hepatic leptin resistance and in the regulation of sOb-R levels via CHOP .
Leptin , predominantly produced by and secreted from white adipocytes , conveys information regarding the status of energy storage and availability to the brain to maintain energy homeostasis . It binds the leptin receptor in hypothalamic neurons to reduce food intake and increase energy expenditure in coordination with other adipokines and gastric peptides ( Allison and Myers , 2014; Pan and Myers , 2018 ) . Molecularly , leptin stimulates the secretion of α-melanocortin stimulating hormone ( α-MSH ) from proopiomelanocortin ( POMC ) neurons at the arcuate nucleus ( ARC ) and inhibits the secretion of the orexigenic peptides neuropeptide-Y ( NPY ) and Agouti-related protein ( AgRP ) ( Flak and Myers , 2016 ) . Genetic leptin deficiency or lack of functional leptin receptor results in morbid obese and insulin resistance phenotypes in animals ( Lepob/ob or Leprdb/db mice , respectively ) ( Tartaglia et al . , 1995; Zhang et al . , 1994 ) . In humans , congenital leptin deficiencies are rare , leading to hyperphagia and early-onset obesity , which can be reversed with a leptin replacement therapy ( Mantzoros , 1999 ) . However , most cases of obesity are characterized by hyperleptinemia , indicating that obesity is a leptin-resistant state , where leptin signaling is impaired . Whereas many of the actions of leptin are attributed to its effects in the brain , it also has a broad range of physiological effects in the periphery such as angiogenesis , bone formation , lipid and carbohydrate metabolism , nutrient absorption , and insulin homeostasis ( Sáinz et al . , 2015 ) . In fact , the lack of a response to leptin due to the development of resistance to the hormone may directly affect the central and peripheral actions of leptin , leading to a dysregulated energy balance . For instance , the liver , a central organ in the regulation of whole-body energy homeostasis , constitutes an important target for leptin as it regulates hepatic gluconeogenesis and insulin sensitivity as well as lipid metabolism ( Frühbeck and Nutr , 2002 ) . Therefore , defects in leptin action , which occur in a state of hepatic leptin resistance , impair hepatic function and lead to hyperglycemia , hyperinsulinemia , and dyslipidemia ( Frühbeck and Nutr , 2002 ) . Various mechanisms have been linked to the development of diet-induced obesity ( DIO ) -related central and peripheral leptin resistance , including limited CNS access of leptin due to saturated transport machinery , uncoupling of leptin from its receptor ( due to rare genetic mutations or intracellular modulators ) , leptin-induced downregulation of its hypothalamic receptor , and several circulating factors such as the soluble isoform of leptin receptor ( sOb-R ) ( reviewed in Engin , 2017; Martin et al . , 2008 ) . Both in humans and mice , the leptin receptor gene ( LEPR and Lepr , respectively ) encodes four membrane-anchored isoforms , which differ in the length of their cytoplasmic tail . The long isoform , Ob-Rb , is considered to convey the most robust cellular response to leptin , while the shorter isoforms ( Ob-Ra , Ob-Rc , and Ob-Rd ) carry a weaker signal . In addition , sOb-R , which lacks the trans-membrane and intracellular domains , also exists . In humans , sOb-R is exclusively generated via proteolytic shedding of membrane-anchored isoforms ( Maamra et al . , 2001 ) , whereas in mice , it is produced by both transcription of a designated isoform ( Ob-Re ) and ectodomain shedding of Ob-Rb and Ob-Ra ( Ge et al . , 2002; Li et al . , 1998 ) . sOb-R , mainly produced by hepatocytes , is the main leptin-binding protein in human plasma , regulating leptin’s bioavailability and bioactivity ( Lammert et al . , 2001; Yang et al . , 2004 ) . In fact , studies have shown that the circulating levels of sOb-R are inversely correlated with body weight and free leptin levels ( Ogier et al . , 2002 ) . In addition , sOb-R levels are increased following weight loss ( Laimer et al . , 2002; Reinehr et al . , 2005 ) , and its overexpression in mice increases leptin sensitivity ( Huang et al . , 2001; Lou et al . , 2010 ) , supporting the key role of sOb-R in the development as well as the reversal of leptin resistance . The endocannabinoid ( eCB ) system , a major regulator of energy homeostasis ( Cristino et al . , 2014; Fride et al . , 2005; Pagotto et al . , 2006; Ruiz de Azua and Lutz , 2019; Silvestri and Di Marzo , 2013 ) , evokes various cellular/metabolic pathways via the activation of two G-protein-coupled receptors , cannabinoid type-1 ( CB1R ) and type-2 ( CB2R ) receptors , by the main eCBs , N-arachidonoylethanolamine ( AEA ) and 2-arachidonoylglycerol ( 2-AG ) . The eCB/CB1R system is highly overactive during obesity ( Engeli , 2008; Engeli et al . , 2005; Matias et al . , 2006; Monteleone et al . , 2005 ) , and both central and peripheral stimulations of this system have been suggested to contribute to the development of the metabolic syndrome , including leptin resistance ( Engeli et al . , 2005; Matias et al . , 2008; Pagotto et al . , 2005 ) . Studies have shown that leptin's ability to regulate food intake and peripheral lipid metabolism depends upon hypothalamic CB1Rs ( Buettner et al . , 2008; Cardinal et al . , 2014; Di Marzo et al . , 2001; Jo et al . , 2005; Malcher-Lopes et al . , 2006 ) . Recent evidence demonstrates that peripheral CB1R signaling has the ability to modulate leptin activity too . By using peripherally restricted CB1R blockers , we have recently demonstrated that DIO-related hyperleptinemia is completely reversed by increasing leptin's renal clearance and decreasing its secretion from adipocytes ( Tam et al . , 2012; Tam et al . , 2010 ) . Additionally , we have shown that the reversal of hypothalamic leptin resistance in obese mice treated with the peripherally restricted CB1R blocker , JD5037 , is mediated via re-sensitizing the animals to endogenous leptin and re-activating POMC neurons ( Tam et al . , 2017 ) . Several lines of evidence suggest that hypothalamic neurons , including POMC , undergo endoplasmic reticulum ( ER ) stress during DIO , which may contribute to the development of leptin resistance ( Ozcan et al . , 2009; Ramírez and Claret , 2015 ) . We have previously reported that pharmacological inhibition of peripheral CB1Rs ( by AM6545 ) reverses the high-fat diet ( HFD ) -induced hepatic elevation in the ER stress marker phospho-eIF2α ( Tam et al . , 2010 ) . Since ER stress strongly affects protein translation and secretion ( reviewed in Ron and Walter , 2007 ) , we hypothesized that the eCB/CB1R system plays a direct role in the regulation of sOb-R levels and hepatic leptin signaling involves the ER stress signaling pathway .
To evaluate the direct contribution of CB1R to the regulation of sOb-R levels , we first utilized a pharmacological inhibition paradigm of CB1R in DIO mice by using the peripherally restricted CB1R inverse agonist JD5037 . Similar to previous findings ( Mazor et al . , 2018 ) , a significant reduction in serum levels of sOb-R was documented in obese mice , an effect that was ameliorated by JD5037 treatment ( Figure 1A ) . Since sOb-R is mainly produced by the liver ( Lammert et al . , 2001 ) , we also analyzed the content of sOb-R in liver homogenates from these animals and found a similar trend as in serum ( Figure 1B ) . Measurements of the Lepr-s ( Ob-Re ) isoform revealed that JD5037 treatment also affected its transcription and protein levels ( Figure 1C–E ) . Moreover , the protein expression of two additional isoforms of LEPR ( Ob-Rb and Ob-Ra ) in liver homogenates was also decreased in DIO mice and normalized following JD5037 treatment ( Figure 1D–E ) . To further establish the contribution of hepatic CB1R to the HFD-induced decrease in sOb-R levels , we utilized the liver-specific CB1R null ( LCB1 cKO ) mice , a genetic deletion model of mice that lacks CB1R specifically in hepatocytes ( mouse model generation is described in Osei-Hyiaman et al . , 2008 ) . When fed with a HFD , these mice gain similar weight to their wild-type ( WT ) littermate controls [ ( Osei-Hyiaman et al . , 2008 ) and Figure 1F]; however , they are less prone to develop liver steatosis , dyslipidemia , and leptin resistance ( Osei-Hyiaman et al . , 2008 ) , making hepatic CB1R a central regulator of obesity-related liver complications . We were therefore not surprised to find that the liver specific deletion of CB1R was sufficient to maintain normal circulating levels of sOb-R in obese LCB1 cKO mice ( Figure 1A ) . Similarly , the hepatic gene and protein expression of sOb-R and the other LEPR isoforms were not affected by the HFD feeding ( Figure 1B , C and G , H ) , suggesting that hepatic CB1R most likely regulates sOb-R levels . To test the functional relevance of our findings to hepatic leptin signaling , we measured the phosphorylation levels of STAT3 , the gold-standard measure of leptin signaling ( reviewed in Allison and Myers , 2014 ) , in mouse livers following exogenous leptin administration in vivo . Whereas both lean WT and LCB1 cKO mice showed elevated pSTAT3/STAT3 ratio in response to leptin ( Figure 1I , J ) , only obese LCB1 cKO mice remained leptin sensitive ( Figure 1K , L ) . These results are in line with findings from Osei-Hyiaman and colleagues ( Osei-Hyiaman et al . , 2008 ) , demonstrating that LCB1 cKO mice are resistant to obesity-induced hyperleptinemia . Additional support for the regulation of sOb-R by hepatic CB1R derived from another transgenic mouse model ( hTgCB1 cKO ) , in which CB1R is expressed only in hepatocytes ( mouse model generation is described in Liu et al . , 2012; Tam et al . , 2010 ) . These mice , despite being resistance to DIO like global CB1R KO mice [ ( Liu et al . , 2012; Tam et al . , 2010 ) and Figure 1M] , demonstrate increased circulating leptin levels when fed a HFD ( Liu et al . , 2012 ) . In accordance with that , the circulating and hepatic sOb-R levels in these mice were decreased by 50% following 14 weeks consumption of a HFD ( Figure 1N–Q ) . Moreover , the hepatic pSTAT3/STAT3 ratio did not respond to exogenous leptin , suggesting reduced hepatic leptin sensitivity ( Figure 1R , S ) . Hence , overexpression of CB1R in the liver alone compromises hepatic leptin sensitivity and recapitulates the HFD-induced downregulation of sOb-R observed in WT mice . Next , we assessed whether a direct activation of CB1R in hepatocytes induces a reduction in sOb-R levels . To test this , we treated cultured hepatocytes with the synthetic CB1R agonist noladin ether ( NE ) for 24 hr . We analyzed both culture media and cell lysates and found that , similar to obesity , direct activation of CB1R also decreased sOb-R levels in the culture media . This was also the case with intracellular levels of other LEPR isoforms measured . This CB1R-mediated reduction in ObR levels was completely reversed by blocking CB1R using JD5037 ( Figure 2 ) . DIO-induced ER stress in the development of leptin resistance has been previously suggested ( Ozcan et al . , 2009; Ramírez and Claret , 2015 ) . Similar to our previous findings ( Tam et al . , 2010 ) , treatment of HFD-fed mice with JD5037 normalized p-eIF2α levels ( Figure 3—figure supplement 1A , B ) , suggesting relieved ER stress following CB1R blockade . In agreement with these findings , a comparable ratio of hepatic phospho-to-total eIF2α ratio was documented in lean and obese LCB1 cKO mice ( Figure 3—figure supplement 1C , D ) . Measuring the expression levels of the ER stress marker C/EBP homologous protein ( CHOP ) revealed surprising findings , since both the hepatic mRNA ( Ddit3 ) and protein levels of CHOP were downregulated in obese WT mice , despite the suggested ER stress . Its expression levels were reversed above control levels by JD5037 , and remained comparable between lean and obese LCB1 cKO mice ( Figure 3A–D ) . In fact , CHOP levels were positively correlated with the levels of sOb-R in both our experimental paradigms , leading us to hypothesize that CHOP may directly be involved in the regulation of sOb-R . To test our hypothesis , we compared the metabolic efficacy of JD5037 in obese CHOP KO mice and their littermate controls . Whereas JD5037 was almost equieffective in reducing body weight and fat mass in both obese mouse strains ( Figure 3E–G ) , it improved plasma cholesterol levels as well as hepatic steatosis in WT mice only ( Figure 3H–J ) . The reduced ability of peripherally restricted CB1R blockade to improve dyslipidemia and hepatic steatosis in CHOP KO mice led us to measure the hepatic eCB 'tone' in these mice . Strikingly , we found that the basal levels of AEA and 2-AG were markedly higher in CHOP KO mice than in the WT control group . Moreover , the increased eCB levels in CHOP KO mice remained unchanged following a consumption of HFD as well as JD5037 treatment ( Figure 3—figure supplement 2A , B ) . This could be partially explained by the differences documented in the mRNA expression patterns of fatty acid amide hydrolase ( Faah ) , monoacylglycerol lipase ( Mgll ) , N-acyl phosphatidylethanolamine phospholipase D ( Napepld ) , and diacylglycerol lipase alpha ( Dagla ) , the degrading and synthesizing enzymes of both eCBs , respectively ( Figure 3—figure supplement 2C–F ) . Overall , these data indicate that CHOP may play a pivotal role in modulating hepatic eCB 'tone' , and that it is required for the beneficial effects of CB1R blockade on dyslipidemia and hepatic steatosis . Measuring the effect of CHOP deficiency on sOb-R levels revealed comparable circulating levels of sOb-R in lean and obese mice in the two mouse strains . However , JD5037 failed to restore sOb-R levels in CHOP KO mice ( Figure 4A ) . The assessment of Lepr-s mRNA expression and sOb-R protein content in the livers of both strains documented reduced baseline levels in CHOP KO mice , compared to WT , which still remained low following HFD consumption and/or JD5037 treatment ( Figure 4B , C ) . A similar trend was observed in the protein level of two more LEPR isoforms ( Compare Figure 1D , E to Figure 4D , E ) . The HFD-induced hyperleptinemia was vastly reduced by JD5037 treatment in WT mice , whereas it was only partially ameliorated by JD5037 in CHOP KO animals ( Figure 4F ) . Interestingly , the hepatic pSTAT3/STAT3 ratio in lean CHOP KO mice was comparable before and after stimulation with exogenous leptin ( Figure 4G , H ) . Taken together , our data suggest that the regulation of sOb-R levels is CHOP-dependent . In addition , regulation of the soluble isoform by CHOP can consequently affect circulating leptin levels and hepatic leptin sensitivity , possibly , in a CB1R-dependent manner . To further investigate this concept , we directly activated CB1R ( with NE ) in immortalized hepatocytes originated from WT or CHOP KO mice . Similar to a HFD consumption in mice ( Figure 3A ) , a direct activation of CB1R downregulated CHOP mRNA expression ( Figure 5A ) . We validated this by measuring the expression levels of Ppp1r15a , a downstream target of CHOP ( Hu et al . , 2018 ) , and found that its expression was also reduced in NE-treated WT hepatocytes , and remained unchanged in CHOP KO cells ( Figure 5B ) , suggesting that CB1R activation in fact leads to reduced CHOP expression and activity . Whereas NE was able to reduce sOb-R levels in WT hepatocytes , it had the opposite effect in CHOP KO hepatocytes , suggesting that CB1R may regulate sOb-R levels in other mechanisms independently of CHOP ( Figure 5C–E ) . The consistent correlation between CHOP and sOb-R levels implies that CHOP is a positive regulator of Ob-Re . To validate this further , we analyzed Ob-Re levels in WT and CHOP KO hepatocytes treated with tunicamycin ( TM ) , a potent inducer of ER stress . Treatment with TM for 6 hr led to an expected and robust expression of CHOP mRNA and protein in WT cells ( Figure 6A , B ) . Importantly , this was accompanied with elevated mRNA expression levels of Lepr-s as well as secreted levels of sOb-R into the culture media in WT , but not CHOP KO hepatocytes ( Figure 6C–E ) . Increased levels of sOb-R in culture media were also documented when we exogenously overexpressed myc-tagged CHOP in WT hepatocytes ( Figure 6F , G ) , supporting a direct role for CHOP in Lepr gene regulation . By using a luciferase reporter assay , in which the −650 to +850 ( relative to transcription start site ) region of the LEPR promoter was cloned into firefly luciferase expressing vector , we found that CHOP expression and luciferase activity in transfected cells was induced using TM ( Figure 6H ) , while CB1R activation using NE ( which downregulates CHOP expression as seen in Figure 5A ) had an opposite effect in WT , but not in CHOP KO cells . These data support the involvement of CHOP in CB1R-dependent regulation of sOb-R . To further explore the possibility that CHOP can directly bind the Lepr promoter and control its expression , we performed several chromatin immunoprecipitation ( ChIP ) experiments . In silico analysis of the Lepr promoter region revealed a putative binding site , corresponding to five of six nucleotides that compose a core sequence for CHOP binding ( GRCm38:CM000997 . 2 . Chromosome 4: 101 , 717 , 929–101 , 717 , 934 ) ( Ubeda et al . , 1996 ) . As seen in the CHOP precipitates ( Figure 6I ) , there was a twofold increase in the recovery of the qPCR product amplified with a primer set flanking the putative CHOP binding site , in cells that were treated with TM . A similar enrichment was seen in Ppp1r15a ( GADD34 ) , a well-known target of CHOP . This increase was limited to WT hepatocytes , validating the specificity of CHOP IP . Taken together , our data suggests that CHOP is able to occupy the Lepr promoter and directly regulate sOb-R levels in response to HFD consumption and/or CB1R activation . The molecular signaling pathway ( s ) by which eCBs/CB1R regulates CHOP levels calls for further investigation . Nevertheless , one putative mechanism may involve Trib3 , a multifunctional protein upregulated during ER stress by the PERK-ATF4-CHOP pathway , which mediates cell death . Trib3 represses its own expression by inhibiting the transcription of both ATF4 and CHOP ( Jousse et al . , 2007; Mathur et al . , 2014; Ohoka et al . , 2005 ) . In addition , many studies describe Trib3 as a key factor in mediating the anti-tumor effect of cannabinoids ( reviewed in Velasco et al . , 2016 ) . Our in vivo data indicate that HFD induces the mRNA and protein expression levels of hepatic Trib3 , and that treatment with JD5037 restores these levels . This effect is limited to WT mice , whereas in CHOP KO mice , Trib3 levels did not change in response to HFD nor JD5037 treatment . Similarly , a direct activation of CB1R using NE upregulated Trib3 expression in WT , but not in CHOP KO hepatocytes ( Figure 6—figure supplement 1A–E ) , suggesting that Trib3 is indeed induced via CB1R signaling , and negatively regulates CHOP levels . Further support for this hypothesis comes from our data in Figure 6—figure supplement 2 , where we show that ATF4 protein levels are reduced in WT mice following the consumption of HFD , and are normalized by JD5037 , whereas they remain unchanged in lean and obese LCB1 cKO mice ( Figure 6—figure supplement 2B–D ) . Moreover , the ATF4 levels were reduced in hTgCB1 cKO mice fed with a HFD , as compared to lean STD-fed mice ( Figure 6—figure supplement 2E–G ) . Overall , the CB1R-related changes in ATF4 expression were found to be well correlated with CHOP as well as with the sOb-R levels , placing ATF4 downstream of CB1R and upstream of CHOP in this cascade .
Since only free leptin crosses the blood–brain barrier ( BBB ) and induces leptin signaling , the sOb-R , which sequesters free leptin in the serum and is considered as the main binding protein for leptin in the circulation , practically regulates leptin’s bioavailability and activity and can potentially affect leptin sensitivity/resistance . This is also true for peripheral tissues , where sOb-R/leptin complexes cannot bind to and activate membrane anchored leptin receptors . Many human and animal studies have demonstrated that sOb-R levels are inversely correlated with plasma levels of leptin , BMI , and adiposity ( Chan et al . , 2002; Lahlou et al . , 2000; Laimer et al . , 2002; Ogier et al . , 2002; Reinehr et al . , 2005 ) , suggesting that low levels of the soluble isoform contribute to obesity-related hyperleptinemia and subsequently , leptin resistance . In contrast to pathological conditions with a positive energy balance ( i . e . obesity ) , human clinical situations associated with energy deficiency ( i . e . starvation and/or anorexia nervosa ) are characterized by upregulated circulating levels of sOb-R ( Monteleone et al . , 2002; Reinehr et al . , 2005; Stein et al . , 2006; Zepf et al . , 2012 ) . Moreover , individuals carrying a mutated allele of LEPR , which leads to enhanced shedding of the leptin binding domain , have normoleptinemia and they are not obese ( Lahlou et al . , 2002 ) . For these reasons , the sOb-R most likely plays a key role in the formation of central and peripheral leptin resistance conditions . Yet , only limited knowledge exists about the molecular mechanisms that regulate sOb-R production and secretion . Using multiple cultured cell types , Gan and colleagues have shown that TNFα may induce cell surface expression of Ob-Rb as well as sOb-R levels ( Gan et al . , 2012 ) . In addition , an in vitro study has demonstrated that increasing the concentration of recombinant sOb-R diminishes STAT3 phosphorylation in response to leptin stimulation , but pre-incubation of leptin with recombinant sOb-R forms ligand-receptor complexes do not affect leptin-mediated STAT3 phosphorylation ( Yang et al . , 2004 ) . In vivo , it has been described that leptin stimulation as well as food deprivation specifically induce the expression of sOb-R in mouse liver ( Cohen et al . , 2005 ) . It has been also demonstrated that in contrast to mice , the human sOb-R , exclusively generated through proteolytic cleavage of the extracellular domain of membrane-anchored isoforms ( Maamra et al . , 2001 ) , is shed into the circulation by two well-known proteolytic enzymes , ADAM10 and ADAM 17 , belong to the 'ADAM's family' ( reviewed in Schaab and Kratzsch , 2015 ) . As we could not detect significant up/down regulation in the expression levels of these proteins in our experimental paradigms ( Figure 2—figure supplement 1 ) , and the fact that we detected all changes in both mRNA and protein levels , we reasoned that the observed alterations in the circulating levels of sOb-R result from altered hepatic expression and secretion of the Lepr gene rather than decreased shedding . In fact , the regulation of ADAM10 and ADAM17 is complex and involves transcription , dynamic trafficking , cellular localization , and activity ( Edwards et al . , 2008; Reiss and Bhakdi , 2017 ) . Whereas the current study is focused on the transcriptional regulation of sOb-R by CB1R , other Ob-R isoforms , also expressed in humans , display ( at the gene and protein levels ) a trend similar to Ob-Re following either CB1R activation or blockade . These isoforms ( Ob-Ra , Ob-Rb ) serve as substrates for ectodomain shedding; therefore , their transcriptional regulation may indirectly influence the sOb-R levels and be relevant to human physiology . In addition , numerous stimuli , such as activation of protein kinase C , an increase in intracellular calcium , lipotoxicity , and apoptosis , may contribute to the proteolytic cleavage of the extracellular leptin receptor domain ( Maamra et al . , 2001; Schaab et al . , 2012 ) . However , to the best of our knowledge , a molecular mechanism responsible for the decreased expression/shedding of sOb-R in obesity was never reported . Here we describe , for the first time , the involvement of the eCB/CB1R system in regulating sOb-R levels and consequently leptin's activity . The importance of the eCB/CB1R system in regulating normal energy homeostasis as well as mediating obesity-related comorbidities is well acknowledged ( review in Simon and Cota , 2017 ) . In fact , its pivotal interaction with leptin has been first described in 2001 , demonstrating that leptin reduces the content of hypothalamic eCBs ( Di Marzo et al . , 2001 ) and attenuates eCB-mediated ‘retrograde' neuronal CB1R signaling ( Jo et al . , 2005; Malcher-Lopes et al . , 2006 ) . On the other hand , activating CB1R by eCBs may , in turn , regulate leptin levels and signaling , as suggested previously in women with anorexia nervosa , whose AEA levels are elevated ( Monteleone et al . , 2005 ) , whereas their leptin levels are reduced . In fact , we have previously shown that CB1R activation in adipocytes and pre-junctional sympathetic fibers innervating the adipose tissue stimulates leptin biosynthesis and release , and its activation in the proximal tubules of the kidney inhibits leptin degradation and renal clearance ( Tam et al . , 2012 ) , thus possibly contributing to leptin resistance . In agreement with these findings , peripheral CB1R blockade has been shown to ameliorate obesity-related hyperleptinemia , and subsequently restores leptin sensitivity in obese mice ( Tam et al . , 2012 ) . By using both pharmacological and genetic approaches that target hepatic CB1R , our findings here suggest another novel mechanism by which the eCB system may regulate hepatic leptin resistance . Specifically , peripheral blockade and hepatic deletion/overexpression of CB1R modulate the expression levels of the sOb-R isoform in hepatocytes and its subsequent release into the circulation , reversing the CB1R-mediated decrease in sOb-R levels and hepatic leptin resistance during obesity . One should point out that although liver-specific CB1R KO mice retained higher levels of circulating sOb-R when fed a HFD , they were equally susceptible to DIO as their WT controls . Similarly , hepatic-specific CB1R transgenic mice in the CB1R KO background remained resistant to DIO while displayed significantly lower circulating sOb-R levels , as compared to their littermates . These data suggest that while liver CB1R expression is a major contributor to circulating sOb-R levels , their roles in regulating systemic/central energy balance will need to be further validated . Nevertheless , the contribution of hepatic CB1R to the regulation of hepatic leptin resistance was clearly demonstrated here by showing that DIO leads to a loss of leptin sensitivity in WT animals , but not in liver-specific CB1R null obese mice . In line with these findings , overexpression of CB1R in hepatocytes of lean mice inhibited leptin-induced STAT3 phosphorylation . These results , linking CB1R with hepatic leptin signaling , may significantly advance our understanding of CB1R’s role in modulating hepatic gluconeogenesis and insulin sensitivity as well as lipid metabolism . Indeed , genetic deletion of CB1R in hepatocytes partially protects mice from developing DIO-related hepatic steatosis , hyperglycemia , dyslipidemia , and insulin resistance ( Osei-Hyiaman et al . , 2008 ) , whereas its overexpression in hepatocytes contributes to insulin resistance via inhibition of insulin signaling and clearance ( Liu et al . , 2012 ) . In this sense , given that leptin regulates lipid , glucose , and insulin homeostasis in the liver , and that these metabolic functions are impaired in rodent models of increased eCB/CB1R ‘tone’ , a role of CB1R-induced hepatic leptin resistance in regulating these processes can be postulated . In accordance with our findings , Palomba and colleagues reported that CB1R activation interferes with leptin's activity in hypothalamic ARC neurons ( Palomba et al . , 2015 ) . On the other hand , opposite findings were reported by Bosier and colleagues , demonstrating that pharmacological or genetic deletion of CB1R in astrocytes downregulates Ob-Rb expression and leptin-mediated functional responses , whereas JZL195 ( a dual MAGL and FAAH inhibitor ) upregulates these features ( Bosier et al . , 2013 ) . These differences can be explained by the distinct roles hepatocytes , astrocytes , and neurons play in peripheral and central metabolic regulations , and by cell-specific roles for CB1R in this regulation . As our findings demonstrate a similar effect of hepatic CB1R activation/overexpression or blockade/deletion on the different isoforms of LEPR , it seems equally possible that hepatic CB1R may affect DIO-related hepatic leptin resistance by not only modulating sOb-R levels , which controls leptin’s activity , but also by modulating the expression of Ob-Rb in hepatocytes . Further investigations would allow us to differentiate between these two pathways . Obesity is often characterized by an ER stress and consequently an adaptive unfolded protein response ( UPR ) , operated by three parallel sensors: activating transcription factor 6 ( ATF6 ) , inositol requiring enzyme 1α ( IRE1α ) , and protein kinase R-like ER kinase ( PERK ) ( Walter and Ron , 2011 ) . The activation of the latter induces the phosphorylation of eIF2α , which , in turn , inhibits transcription and protein synthesis ( Ron , 2002 ) . In case of an extreme ER stress conditions , CHOP is activated by the PERK signaling pathway and executes ER stress-mediated apoptosis ( Hu et al . , 2018; Zinszner et al . , 1998 ) . In fact , ER stress has been shown to contribute to the development of hypothalamic leptin resistance , by impairing the transport of leptin across the BBB and suppressing STAT3 phosphorylation ( El-Haschimi et al . , 2000; Hosoi et al . , 2008; Ozcan et al . , 2009; Zhang et al . , 2008 ) . In addition , under physiological conditions , excess nutrients increases the demand for protein synthesis by the liver , leading to ER stress and UPR activation , which resolves the stress within hours ( Oyadomari et al . , 2008 ) . Nevertheless , chronic ER stress in the liver was demonstrated in both obese mice and humans ( Ozcan et al . , 2004; Puri et al . , 2008 ) . Here we demonstrate that obese mice had elevated levels of phosphorylated eIF2α , indicating increased ER stress , an effect that was reversed by peripheral CB1R blockade and was absent in LCB1 cKO . These findings are in agreement with our previous reports , where we reported that a neutral CB1R antagonist ( AM6545 ) has the ability to reduce the HFD-induced upregulation in hepatic eIF2α ( Tam et al . , 2010 ) , and that hepatic activation of CB1R induces ER stress and contributes to insulin resistance ( Liu et al . , 2012 ) . Unexpectedly , we found that the hepatic gene and protein levels of CHOP and its upstream regulator ATF4 were significantly decreased in obese mice , and were upregulated by peripheral CB1R blockade . This observation , although counterintuitive , is conceptually in agreement with several previous reports that describe a modulated UPR signaling with altered sensitivity or output that might implicate conditions of persistence/repeated stress ( Chambers et al . , 2012; Gomez and Rutkowski , 2016; Preissler et al . , 2015; Yang et al . , 2015 ) . Apart from its role in ER stress-mediated apoptosis , CHOP has been implicated in regulating other processes such as inflammation ( Endo et al . , 2006; Nakayama et al . , 2010 ) , insulin resistance ( Maris et al . , 2012; Song et al . , 2008 ) , and adiposity . Specifically in the liver , Chikka and colleagues suggest that CHOP is a suppressor of key regulators of lipid metabolism like Cebpa , Ppara , and Srebf1 ( Chikka et al . , 2013 ) , and demonstrate that CHOP-deficient mice tend to develop hepatic steatosis in response to bortezomib-induced ER stress . This is in agreement with an earlier report describing higher body weight and adiposity in female CHOP KO mice compared to WT controls ( Ariyama et al . , 2007 ) . In contrast , we show that male CHOP KO mice and their WT littermate controls gain comparable amount of weight and have similar body composition following exposure to an HFD for 14 weeks . Nevertheless , we did see a trend toward increased liver triglycerides . An interesting observation was that the eCB ‘tone’ of lean and obese CHOP KO mice was comparable . To the best of our knowledge , a direct regulation of eCB synthesis or degradation by CHOP has never been described . Thus , our data imply a possible link between the two . In fact , it is possible that the higher basal eCB levels seen in CHOP KO mice in comparison with their littermate controls are the consequence of leptin’s reduced ability to inhibit AEA and 2-AG production . This may be due to the reduced level of sOb-R found in the liver of CHOP KO animals . This hypothesis , although not tested here and which needs further experimental corroboration , is in accordance with the findings of others who demonstrated such a mechanism in the hypothalamus and in adipocytes treated with leptin ( Di Marzo et al . , 2001; Matias et al . , 2006 ) . In addition , JD5037 failed to reverse many of the metabolic abnormalities , such as HDL and LDL content as well as liver triglycerides in DIO CHOP KO mice . It also had much smaller effect on total body fat mass then in WT DIO mice , suggesting an obligatory role of CHOP in mediating the metabolic improvements induced by CB1R blockade . Whereas basal circulating levels of sOb-R were comparable between WT and CHOP KO mice , its levels were markedly lower in the liver of CHOP KO mice as well as cultured hepatocytes . Moreover , sOb-R remained low in these mice even on HFD , supporting a role for CHOP in regulating the synthesis of sOb-R in the liver . The failure of JD5037 to elevate sOb-R levels in obese CHOP KO mice places CHOP downstream of CB1R in this molecular cascade . As mentioned earlier , the molecular signaling pathway ( s ) by which eCBs or CB1R regulates CHOP levels is outside the scope of this work . However , two possible mechanisms might be relevant . The first putative mechanism may involve Trib3 , which is known to be induced in a broad range of cells and in response to multiple forms of cellular stress such as ER stress , excess of free fatty acids , oxidative stress , hypoxia , hyperglycemia , and toxins ( reviewed in Ord and Ord , 2017 ) . Interestingly , it has been shown that Δ9-THC as well as synthetic cannabinoid agonists upregulate Trib3 expression ( Blázquez et al . , 2006; Carracedo et al . , 2006; Salazar et al . , 2013; Vara et al . , 2011 ) to engage apoptosis in variable cancer models . Moreover , Cinar et al . demonstrated that hepatic CB1R induces ER stress in hepatocytes by increasing de novo synthesis of ceramides ( Cinar et al . , 2014 ) , which are also involved in Trib3 upregulation following ER stress ( Carracedo et al . , 2006 ) . In line with our observation that Trib3 levels are negatively correlated with ATF4 , CHOP , and sOb-R and are elevated following direct or indirect activation of CB1R , we suggest that Trib3 is a molecular linker between CB1R and the ATF4/CHOP complex . In fact , Trib3 directly interacts with and inhibits ATF4 and CHOP , forming a negative feedback on the regulation of their activity ( Ohoka et al . , 2005 ) . This Trib3-induced negative modulation of ATF4 and CHOP has been suggested to contribute to the fine-tuning of ATF4- and CHOP-dependent transcription in stressed cells , such as hepatocytes exposed to fatty acid flux . With the extensive body of evidence demonstrating a wide range of molecules that are known to regulate Trib3 expression , our current findings highlight CB1R as a novel possible modulator that regulates Trib3 transcription , thus suggesting that CB1R activation may disrupt the ER stress signaling pathway involving eIF2α , ATF4 , and CHOP . However , the molecular events that link CB1R and Trib3 require further assessment , and direct regulation of ATF4 by CB1R cannot be excluded . Second possible mechanism has to do with CHOP being a cAMP responsive protein , so its expression is induced via a cAMP response element ( CRE ) ( Conkright et al . , 2003; Pomerance et al . , 2003; Ramji and Foka , 2002; Wilson and Roesler , 2002 ) . CB1R , a G-protein coupled receptor ( GPCR ) , which upon activation recruits Gi protein , can inhibit the activity of adenylyl cyclase and reduce the levels of cAMP ( Turu and Hunyady , 2010 ) . It is therefore plausible that a decline in cAMP following CB1R activation inhibits CHOP transcription . This hypothesis is more appealing if one considers the pivotal role of cAMP in regulating liver metabolism ( Wahlang et al . , 2019 ) , and takes into account the fact that reduced levels of cAMP were documented in HFD-fed mice ( Zingg et al . , 2017 ) . Yet , further studies will need to explore the specific molecular pathways linking together hepatic eCB/CB1R system and CHOP . In conclusion , we report a new role for the hepatic eCB/CB1R in the development of hepatic leptin resistance , by reducing the expression and/or subsequent release of sOb-R ( Figure 7 ) . We show that peripherally restricted CB1R antagonism has the ability to restore sOb-R levels , contributing to the reversal of obesity-induced hyperleptinemia . We also suggest that upon CB1R blockade in hepatocytes , ATF4 as well as CHOP levels are upregulated via reduced Trib3 expression . CHOP , in turn , directly binds the LEPR promoter and promotes the expression of sOb-R .
All animal studies were approved by the Institutional Animal Care and Use Committee of the Hebrew University of Jerusalem ( AAALAC accreditation #1285; Ethic approval numbers MD-14–14008 and MD-19–15951 ) . Animal studies are reported in compliance with the ARRIVE guidelines ( NC3Rs Reporting Guidelines Working Group et al . , 2010 ) , and are based on the rule of the replacement , refinement , or reduction . All the animals used in this study were housed under specific pathogen‐free ( SPF ) conditions , up to five per cage , in standard plastic cages with natural soft sawdust as bedding . The animals were maintained under controlled temperature of 22–24°C , humidity at 55 ± 5% , and alternating 12 hr light/dark cycles ( lights were on between 7:00 and 19:00 hr ) , and provided with food and water ad libitum . C57Bl/6 ( Envigo , Israel ) , LCB1 cKO , and hTgCB1 cKO ( kindly provided by Dr . George Kunos , NIH ) or B6 . 129S ( Cg ) -Ddit3tm2 . 1Dron/J ( CHOP KO , The Jackson Laboratory #005530 ) , and their WT littermate controls were used for in vivo experiments . All mice were male and 8–10 weeks old at the beginning of each experiment . To generate DIO ( body weight >42 g ) , mice were fed with a standard diet ( STD; 14% Kcal fat , 24% Kcal protein , 62% Kcal carbohydrates; NIH-31 rodent diet ) or a HFD ( 60% Kcal fat , 20% Kcal protein , and 20% Kcal carbohydrates; Research Diet , D12492 ) for 14 weeks . Then , obese mice were randomly divided into the experimental groups . Treatment with JD5037 ( 3 mg/kg , ip ) or vehicle ( 1% Tween80 , 4% DMSO , 95% Saline ) was conducted for 7 days , and 12 hr following the last dose , the mice were euthanized by a cervical dislocation under anesthesia , and blood and livers were harvested for further analyses . For leptin sensitivity test , mice were fasted for 24 hr before an ip administration of recombinant mouse leptin ( 3 mg/kg ) . One hour following leptin administration , mice were euthanized and livers were harvested and processed for phosphorylated STAT3 detection using western blot . WT or CHOP KO immortalized hepatocytes ( described in Uzi et al . , 2013 ) , confirmed to be mycoplasma-negative , were maintained in DMEM ( 01-100-1A; Biological Industries , Israel ) supplemented with 5% FCS , 100 mM glutamine , 100 mM Na-Pyruvate , and Pen/Strep . Cells were cultured at 37°C in a humidified atmosphere of 5% CO2/95% air . To test the effect of CB1R activation , cells were seeded in 6-well plates ( 25 × 104 cells/well ) for 24 hr . Then , growth medium was replaced with a serum-free medium ( SFM ) for an additional 12 hr . At the morning of the experiment the medium was replaced with fresh SFM containing either vehicle ( EtOH ) , 2 . 5 µM NE ( Cayman Chemicals , Ann Arbor , Michigan ) or a combination of 100 nM JD5037 ( Haoyuan Chemexpress Co . , Ltd ) and 2 . 5 µM NE . After 24 hr , cells were harvested for further analyses as described below . Soluble leptin receptor was quantified by an ELISA kit , capable to differentiate the soluble isoform from other isoforms , according to manufacturer’s instructions ( E03S0226; Shanghai Bluegene Biotech , China ) . Briefly , for serum measurements , we diluted serum in saline ( 1:2 ) and 100 µL from the diluted samples were analyzed . For hepatic measurements , 50–100 mg tissue samples were homogenized in 300 µL of 1× PBS and centrifuged for 5 min in 5000 rpm; 100 µL of cleared lysates were analyzed . Data were normalized to sample protein content , determined using the Pierce BCA Protein Assay Kit ( Thermo Scientific , IL ) . To measure sOb-R protein content in cell culture media , we used trichloroacetic acid ( TCA ) precipitation protocol; 350 µL of 100% TCA were added to 1 . 6 mL culture media , vortexed , and incubated for 30 min on ice . Samples were then centrifuged to pellet proteins ( 14 , 000 rpm , 10 min , 4°C ) . Pellets were washed in 100% acetone , resuspended in 0 . 1 M NaOH and protein loading dye , and analyzed by western blot . Ponceau staining of the blots was used as loading control for quantification . Validation of LEPR antibody . The specificity of the anti-LEPR antibody was validated in a control experiment ( Figure 1—figure supplement 1 ) , where mouse LEPR was overexpressed in kidney cell line by using a viral infection . The viral vector encoded Ad-GFP-mLEPR ( ADV-263380 , VECTOR BIOSYSTEMS Inc ) was used in a multiplicity of infection of 50 , and cells were harvested for western blot analysis 24 hr post infection . For total mRNA isolation , tissue samples or hepatocytes were washed in 1× PBS and harvested using Bio-Tri RNA lysis buffer ( Bio-Lab , Israel ) . Extracted RNA was treated with DNase I ( Thermo Scientific , IL ) , and reverse transcribed using the Iscript cDNA kit ( Bio-Rad Laboratories , CA ) . Quantitative PCR reactions for Lepr-s , Ddit3 , or Ppp1r15a were performed using iTaq Universal SYBR Green Supermix ( Bio-Rad Laboratories , CA ) , and the CFX connect ST system ( Bio-Rad Laboratories , CA ) . Relative quantity ( RQ ) values of all tested genes were normalized to Ubc . Primers are listed in Supplementary file 1 . Tissue samples or hepatocytes were washed in cold 1× PBS , and harvested in a RIPA buffer ( 25 mM Tris-HCl pH 7 . 6 , 150 mM NaCl , 1% NP-40 , 1% sodium deoxycholate , 0 . 1% SDS ) , vortexed and incubated for 30 min at 4°C , and then centrifuged for 10 min at 14 , 000 rpm . Protein concentrations were determined using the Pierce BCA Protein Assay Kit ( Thermo Scientific , IL ) . Cleared lysates were supplemented with protein sample buffer , resolved by SDS-PAGE ( 4–15% acrylamide , 150 V ) and transferred to PVDF membranes using the Trans-Blot Turbo Transfer System ( Bio-Rad Laboratories , CA ) . Membranes were incubated for 1 hr in 5% milk ( in TBS-T ) to block unspecific binding , washed briefly , and incubated overnight at 4°C with the following primary antibodies: LEPR ( NB-120–5593 , Novus ) , phosphorylated STAT3 ( 9145 , Cell Signaling ) , STAT3 ( 9139 , Cell Signaling ) , phosphorylated eIF2α ( 9721 , Cell Signaling ) , eIF2α ( 9722 , Cell Signaling ) , ATF4 ( 11815 , Cell Signaling ) , CHOP ( 2895S , Cell Signaling ) , Trib3 ( ab137526 , Abcam ) , or β-Actin ( ab49900 , Abcam ) . Anti-rabbit ( ab97085 , Abcam ) or mouse ( ab98799 , Abcam ) horseradish peroxidase ( HRP ) -conjugated secondary antibodies were used for 1 hr at room temperature , followed by chemiluminescence detection using Clarity Western ECL Blotting Substrate ( Bio-Rad Laboratories , CA ) . Densitometry was quantified using ImageLab software . Protein RQ was calculated as the ratio between LEPR to total protein signal ( ponceau ) in culture media supernatants and to β-actin in cell and tissue lysates . Total body fat and lean masses were determined by EchoMRI-100H ( Echo Medical Systems LLC , Houston , TX , USA ) . HDL and LDL measurements were done using the Cobas C-111 chemistry analyzer ( Roche , Switzerland ) . Tissue lipids were extracted as described in Folch et al . , 1957 , and quantified using Triglyceride Assay Kit ( ab65336; Abcam ) . Data were normalized to tissue weight . eCBs were extracted , purified , and quantified in liver homogenates , as described previously ( Drori et al . , 2019; Udi et al . , 2017 ) . LC-MS/MS was analyzed on an AB Sciex ( Framingham , MA , USA ) Triple Quad 5500 mass spectrometer coupled with a Shimadzu ( Kyoto , Japan ) UHPLC System . eCBs were detected in a positive ion mode using electron spray ionization ( ESI ) and the multiple reaction monitoring ( MRM ) mode of acquisition . The levels of each compound were analyzed by monitoring multiple reactions . The molecular ion and fragment for each compound were measured as follows: m/z 348 . 3→62 . 1 ( quantifier ) and 91 . 1 ( qualifier ) for AEA , m/z 379 . 3→287 . 3 ( quantifier ) and 91 . 1 ( qualifier ) for 2-AG . The levels of AEA and 2-AG in samples were measured against standard curves and normalized to tissue weight . WT hepatocytes were transfected with mCHOP-WT-9E10-pCDNA1 vector ( Addgene plasmid #21913 ) using Lipofectamin 3000 . Cells were harvested 24 hr post-transfection and CHOP expression was validated by western blot analysis . Mus musculus Ob-R promoter sequence ( GRCm38:CM000997 . 2 . Chromosome 4: 101 , 716 , 750–101 , 718 , 250 forward strand ) was cloned into pGL3-basic vector ( E1751 , Promega ) . Reporter vector and Renilla luciferase vector were then co-transfected into WT or CHOP KO hepatocytes using Lipophectamin 3000 . Twenty-four hours post transfection , cells were treated with either vehicle , 2 . 5 µg/mL tunicamycin ( 11089-65-9; Holland Moran , Israel ) or 2 . 5 µM NE for indicated period . At the end of the experiment , luciferase activity was measured using Dual-Glo Luciferase Assay System ( E2920 , Promega ) . Data are presented as the ratio between Firefly and Renilla luciferase activity . WT or CHOP KO hepatocytes were seeded in 100 mm plate and left to adhere ( 5 × 106 cells per plate; three plates for each sample ) . The next day , cells were treated with either DMSO or 2 . 5 µg/mL tunicamycin for 6 hr to induce CHOP expression . At the end of incubation , cells were washed in 1× PBS , fixed with 1% formaldehyde for 10 min , then quenched with 125 mM glycine and harvested from plates . Following centrifugation , cells were resuspended in a lysis buffer and sonicated for 12 cycles of 30 s pulse followed by 30 s rest in 70% amplitude . Sheared DNA was diluted in a ChIP dilution buffer and pre-cleared with magnetic ProteinG-sepharose beads for 4 hr at 4°C . Ten percent of the lysate was removed and saved as ‘Input’ . The rest of the lysate was divided and each part was incubated overnight at 4°C with 2 . 5 µg of either anti-H3 ( ab1791 , Abcam ) , anti-CHOP ( 2895S , Cell Signaling ) , or IgG isotype control . Antibody-chromatin complexes were precipitated with magnetic protein G-Sepharose beads , washed with low salt , high salt , lithium chloride , and Tris-EDTA buffers . DNA was then eluted from beads , digested with proteinase K , and purified; 1 . 5 µL of clean DNA was used in a qPCR reaction using specific primers for Gapdh or Lepr promoter region . For a positive control , Ppp1r15a primers were used . For each sample , we calculated the ratio between the RQ ( expressed as % of input ) of ObR promoter qPCR product in αCHOP IP relative to αH3 IP . This ratio was normalized to the ratio of Gapdh qPCR product to control for nonspecific binding . Data are expressed as the fold-change of this ratio in tunicamycin-treated compared to vehicle-treated cells . ChIP primers are listed in Supplementary file 1 . The data and statistical analysis comply with the recommendations on experimental design and analysis as reported previously ( Curtis et al . , 2018 ) . Randomization was used to assign samples to the experimental groups and treatment conditions for all in vivo studies . Data collection and acquisition of all in vivo and in vitro experimental paradigms were performed in a blinded manner . Data are presented as mean ± SEM . Unpaired two-tailed Student’s t-test was used to determine variations between two groups . Results in multiple groups were compared by ANOVA followed by a Bonferroni post hoc analysis using GraphPadPrism v6 for Windows . Post hoc tests were conducted only if F was significant , and there was no variance inhomogeneity . Significance was set at p<0 . 05 . | When the human body has stored enough energy from food , it releases a hormone called leptin that travels to the brain and stops feelings of hunger . This hormone moves through the bloodstream and can affect other organs , such as the liver , which also help control our body’s energy levels . Most people with obesity have very high levels of leptin in their blood , but are resistant to its effects and will therefore continue to feel hungry despite having stored enough energy . One of the proteins that controls the levels of leptin is a receptor called sOb-R , which is released by the liver and binds to leptin as it travels in the blood . Individuals with high levels of this receptor often have less free leptin in their bloodstream and a lower body weight . Another protein that helps the body to regulate its energy levels is the cannabinoid-1 receptor , or CB1R for short . In people with obesity , this receptor is overactive and has been shown to contribute to leptin resistance , which is when the brain becomes less receptive to leptin . Previous work in mice showed that blocking CB1R reduced the levels of leptin and allowed mice to react to this hormone normally again , but it remained unclear whether CB1R affects how other organs , such as the liver , respond to leptin . To answer this question , Drori et al . blocked the CB1R receptor in the liver of mice eating a high-fat diet , either by using a drug or by deleting the gene that codes for this protein . This caused mice to have higher levels of sOb-R circulating in their bloodstream . Further experiments showed that this change in sOb-R was caused by the levels of a protein called CHOP increasing in the liver when CB1R was blocked . Drori et al . found that inhibiting CB1R caused these obese mice to lose weight and have healthier , less fatty livers as a result of their livers no longer being resistant to the effects of leptin . Scientists , doctors and pharmaceutical companies are trying to develop new strategies to combat obesity . The results from these experiments suggest that blocking CB1R in the liver could allow this organ to react to leptin appropriately again . Drugs blocking CB1R , including the one used in this study , will be tested in clinical trials and could provide a new approach for treating obesity . | [
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"biology"
] | 2020 | CB1R regulates soluble leptin receptor levels via CHOP, contributing to hepatic leptin resistance |
Myosin V and VI are antagonistic motors that cohabit membrane vesicles in cells . A systematic study of their collective function , however , is lacking and forms the focus of this study . We functionally reconstitute a two-dimensional actin-myosin interface using myosin V and VI precisely patterned on DNA nanostructures , in combination with a model keratocyte actin meshwork . While scaffolds display solely unidirectional movement , their directional flux is modulated by both actin architecture and the structural properties of the myosin lever arm . This directional flux can be finely-tuned by the relative number of myosin V and VI motors on each scaffold . Pairing computation with experimental observations suggests that the ratio of motor stall forces is a key determinant of the observed competitive outcomes . Overall , our study demonstrates an elegant mechanism for sorting of membrane cargo using equally matched antagonistic motors , simply by modulating the relative number of engagement sites for each motor type .
Membrane sorting in the secretory and endocytic pathways occurs in the midst of the actin cytoskeleton , and involves a range of unconventional myosins that link membrane components to the actin network ( Hartman et al . , 2011 ) . However , traditional reconstituted systems to study membrane traffic do not incorporate the effects of actin-myosin interactions ( Lee et al . , 2004; Zanetti et al . , 2012 ) . Additionally , while unconventional myosins are necessary for timely membrane traffic , their functional role is not apparent in live cell studies ( Hasson et al . , 1997; Sahlender et al . , 2005; Hartman et al . , 2011 ) . The bulk of our knowledge of unconventional myosin function instead stems from single molecule biophysical and structural studies , which demonstrate distinct functional regimes for actin-myosin interactions including bi-directional motion , unidirectional transport , and mechano-sensitive anchoring ( Trybus , 2008; Spudich and Sivaramakrishnan , 2010 ) . There remains , however , a considerable gap between the insights gained from single-motor studies and a mechanistic understanding of cargo transport in living cells . Furthermore , membrane trafficking often involves multiple disparate motor types , and their collective function cannot be trivially extrapolated from single molecule studies . In this study , we focus on myosin V and VI , two opposing unconventional myosins that co-reside on membrane vesicles in neuronal growth cones ( Suter et al . , 2000 ) . Myosin V has been implicated in secretory traffic , whereas myosin VI facilitates timely endocytosis ( Suter et al . , 2000; Kneussel and Wagner , 2013 ) . Individual myosin V and VI molecules within a transport ensemble may coordinate , cooperate , or mechanically impede one another to influence collective movement ( Rogers et al . , 2009; Sivaramakrishnan and Spudich , 2009; Lu et al . , 2012 ) . Hence , studies with mixed motor ensembles are essential to define the function of myosins in membrane trafficking . All myosins share a conserved catalytic domain that converts the chemical energy of ATP hydrolysis into a unidirectional mechanical stroke of the motor lever arm . In the case of myosin V and VI , they are considered evenly matched antagonistic motors ( Trybus , 2008; Spudich and Sivaramakrishnan , 2010 ) . Both motors are thought to bind membrane cargo as dimers; myosin V through a coiled-coil motif following its lever arm that natively homodimerizes it , and myosin VI presumably through dimeric adaptor proteins that link it to cargo ( Mehta et al . , 1999; Buss and Kendrick-Jones , 2008 ) . Homodimers of either myosin move processively on actin filaments with similar step sizes ( V—36 nm; VI—30 nm ) , stepping kinetics ( V –12 s−1; VI –9 s−1 ) , and stall forces ( V ∼3 pN; VI ∼2 pN ) albeit in opposing directions ( Mehta et al . , 1999; Rief et al . , 2000; Rock et al . , 2001; Nishikawa et al . , 2002; Yildiz et al . , 2003; Altman et al . , 2004; Uemura et al . , 2004 ) . All myosin levers , with the exception of myosin VI , swing towards the barbed ( plus ) end of the actin filament . In the case of myosin VI , a unique insert reverses the direction of its lever stroke towards the pointed ( minus ) end of the actin filament ( Liao et al . , 2009; Spudich and Sivaramakrishnan , 2010 ) . With the plus-ends of actin networks oriented toward the cell periphery , plus-end directed myosin V thus contributes to exocytosis , whereas minus-end directed myosin VI is critical to endocytosis ( Hartman et al . , 2011 ) . Finally , despite their many similarities , myosin V and VI have structurally distinct lever arms . The myosin V lever consists of six light chain binding IQ-motifs wrapped with calmodulin light chains ( Trybus , 2008 ) . The myosin VI lever is composed of two calmodulin-binding IQ-motifs followed by a pliable proximal tail domain , and a semi-rigid single α-helical domain ( Spudich and Sivaramakrishnan , 2010 ) . Translating the detailed structural understanding of individual myosin V and VI into cellular function , specifically when they cohabit the same scaffold , remains an outstanding challenge . Ali et al . ( 2011 ) reported that tethering a single myosin V and a single VI homodimer on a quantum dot leads to unidirectional motion on single actin filaments , with myosin V dominating the competition ( 79% of processive runs towards the plus-end of actin filaments ) . We recently extended this finding to DNA nanostructures containing two myosin V and two myosin VI molecules interacting with a keratocyte-derived actin network ( Hariadi et al . , 2014 ) . While we did observe solely unidirectional movement , in contrast to Ali et al . ( 2011 ) , myosin V and VI were evenly matched in our system ( 52% of processive runs towards the keratocyte cell periphery ) . Our previous study focused on trajectory shapes and did not address this observed discrepancy in the outcome of the competition . Further , the generality of these observations for different ratios of myosin V and VI and the mechanisms that control directionality remain unexplored and form the focus of this study . Here , we use DNA nanotechnology to precisely scaffold defined collections of myosin V and VI and pair it with both single actin filaments and a model cellular actin network derived from the extensive lamellipodium of fish epidermal keratocytes ( Hariadi et al . , 2014 ) . Consistent with previous reports ( Ali et al . , 2011; Hariadi et al . , 2014 ) , we observe solely unidirectional movement regardless of actin architecture or relative myosin number . However , for matched scaffolds we find that the directional flux is dependent on both actin architecture and the structural properties of the myosin lever arm . This directional flux is finely-tuned by the relative number of myosin V and VI motors on each scaffold . By pairing computation and experiment , we identify a single mechanical parameter that defines regimes in any motor ensemble wherein this mechanism is likely to be observed . Overall , our study demonstrates an elegant mechanism for sorting of membrane cargo simply by modulating the relative number of engagement sites for each motor type . For matched , but opposing motors such as myosin V and VI , this mechanism is necessary and sufficient to precisely control sorting of tethered scaffolds .
To investigate the role of actin organization in trafficking , DNA nanostructures containing a defined number of antagonistic myosins ( V and VI; Figure 1A; Figure 1—figure supplements 1 , 2; Supplementary file 1 ) were examined on two distinct actin architectures , namely one-dimensional actin filaments ( Figure 1B , D , F ) and dense two-dimensional actin networks ( Svitkina and Borisy , 1998; Schaus et al . , 2007 ) ( Figure 1C , E , G ) . Precise positioning of myosin V and VI on the origami scaffold was achieved using myosins labeled with single-stranded DNA oligonucleotides complementary to attachment sequences projecting from the scaffold strand ( 1–6 per scaffold; Figure 3—figure supplement 1 ) . DNA nanostructures with varying numbers of myosin are denoted as xV:yVI , where ‘x’ is the number of myosin V dimers and ‘y’ is the number of myosin VI dimers per scaffold . For the 2D actin networks , we used detergent-extracted keratocytes ( Hariadi et al . , 2014 ) ( Figure 1C ) , which have a sufficiently large surface area ( ∼10 μm × ∼30 μm ) allowing for simultaneous tracking of multiple myosin-labeled scaffolds . Experiments involving 1D actin filaments provide a confined set of actin-myosin interactions , with each myosin having either a forward ( red rectangle ) or a backward ( gray rectangle ) binding site available ( Figure 1F ) . The 2D actin networks , on the other hand , provide a more complex energy landscape for the myosins to navigate , as there are multiple binding sites for both forward ( red arc ) and backward ( gray arc ) steps ( Figure 1G ) . 10 . 7554/eLife . 05472 . 003Figure 1 . Reconstitution of myosin-driven cargo sorting on 1D and 2D actin tracks . ( A ) Illustration of a programmable DNA scaffold ( Rothemund , 2006 ) with six attachment sites at the vertices of a hexagon ( dashed-line , 35-nm side ) , yielding 122 unique myosin V and VI combinations . Myosin V and VI were engineered with SNAP tags ( Hariadi et al . , 2014 ) for covalent attachment of unique DNA oligonucleotides . These DNA oligonucleotides hybridize with complementary sequences extending from the scaffold . ( B–C ) Representative snapshot of scaffold-motor complexes ( green ) on actin filaments ( B ) and a keratocyte actin network ( C ) . Actin was stabilized and labeled with Alexa488-phalloidin ( red ) . ( D–E ) Schematics depicting the interaction of scaffolds ( yellow ) with 1 myosin V ( red ) and 1 myosin VI ( blue ) on an actin filament ( D ) and on the surface of the keratocyte actin network ( E ) . The motors and actin tracks are drawn approximately to scale . The keratocyte actin network is depicted by actin filaments oriented at ±35° , which corresponds to the characteristic Arp2/3 branch angle ( Maly and Borisy , 2001 ) . Mesh size of the keratocyte actin network ( ∼30 nm ) ( Svitkina et al . , 1995 ) is comparable to the step size of myosin V ( ∼35 nm ) and VI ( ∼30 nm ) ( Rock et al . , 2001; Yildiz et al . , 2003 ) . ( F–G ) Hand-over-hand model of dimeric myosin stepping on 1D ( F ) and 2D ( G ) actin tracks . The competition between antagonistic myosins gives rise to inter-motor tension depicted as a simple harmonic spring ( orange ) . For inter-motor tension below the stall force , the trailing head ( light red ) moves 36 nm forward ( red arrow ) to a new position within the forward-step target zone ( shaded red areas ) , while the leading head ( gray ) remains stationary . High inter-motor tension induces a backward step ( black arrow ) of the leading head to a target site within the back-step target zone ( shaded gray areas ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05472 . 00310 . 7554/eLife . 05472 . 004Figure 1—figure supplement 1 . Flat rectangular DNA origami scaffold . The main core of the nanostructure is a flat rectangular DNA origami scaffold with 10 . 44 bp/turn , consisting of 24 DNA helices ( Woo and Rothermund , 2011 ) . The scaffold strand is depicted as a continuous black line . The six attachment sites for myosin-DNA complexes are shown as black hexagons . The scaffold is decorated with 23 Cy3 fluorophores ( shown as red light bulbs ) to facilitate a high signal-to-noise ratio imaging . DOI: http://dx . doi . org/10 . 7554/eLife . 05472 . 00410 . 7554/eLife . 05472 . 005Figure 1—figure supplement 2 . Sequence diagram for a flat rectangular DNA origami scaffold . The scaffold strand is displayed in dark blue . The truncated edge staples strands are shown in orange . The staple strand sequences are listed in Supplementary file 1 . DOI: http://dx . doi . org/10 . 7554/eLife . 05472 . 005 Two previous reports suggest that equal numbers of myosin V and VI anchored to the same scaffold display solely unidirectional movement ( Ali et al . , 2011; Hariadi et al . , 2014 ) . However , they disagree in the observed outcome of the competition . Myosin V dominates the competition ( 79% ) when it is tethered to myosin VI through a quantum dot ( 2 total ) and the two compete on a single actin filament ( Ali et al . , 2011 ) . In contrast , myosin V and VI are evenly matched ( myosin V wins 52% ) when two of each motor ( 4 total ) are tethered to a DNA nanostructure and they compete on a two-dimensional cellular actin network . This discrepancy between the observations could stem from either the scaffold type ( quantum dot vs DNA nanostructure ) , the total motor number ( 2 vs 4 ) , or the actin architecture ( single filament vs keratocyte-derived actin network ) . We first tested the influence of scaffold type by assessing the competition between a single myosin V dimer and a single myosin VI dimer on 1D actin filaments ( Figure 2 ) . In positive controls , ØV:2VI scaffolds ( Figure 2A ) move toward the minus-end of the actin filaments , whereas 2V:ØVI scaffolds ( Figure 2C ) travel toward the plus-end . Consistent with previous reports ( Ali et al . , 2011; Hariadi et al . , 2014 ) , scaffolds with both myosin V and myosin VI ( 1V:1VI ) commit to a single direction on actin filaments ( >99%; Figure 2B ) with no directional reversal detected . The movement of 1V:1VI scaffolds on single actin filaments is dominated by plus-end directed movement ( Φout = 68 ± 1%; Figure 2E ) , which is qualitatively consistent with previous observations using quantum dot scaffolds ( 79% plus-end directed [Ali et al . , 2011] ) . Hence , scaffold type ( quantum dot vs DNA nanostructure ) is not the key determinant of competitive outcome . We next examined the influence of actin architecture . In contrast to single actin filaments , both plus and minus-end directed movement is equally represented ( Φout = 52 ± 1%; Figure 2E ) for 1V:1VI scaffolds moving along 2D keratocyte actin networks . Hence , the discrepancy between previous reports using quantum dots ( Ali et al . , 2011 ) and DNA nanostructures ( Hariadi et al . , 2014 ) stems primarily from the actin architecture . 10 . 7554/eLife . 05472 . 006Figure 2 . Unidirectional movement and sorting of scaffolds with myosin V and VI along single actin filaments . ( A–C ) Kymographs showing the movement of indicated motor ensembles along actin filaments . Scaffolds with myosin V and VI display unidirectional movements toward plus-or minus-ends of the actin filament . The gray hexagon represents the organization of attachment sites on the scaffold , the red and blue arrows denote myosin V and VI , respectively . ( D ) Speed of plus-end ( blue ) and minus-end ( red ) directed movement of indicated scaffolds on actin filaments . Error bars are S . E . M . ( E ) Relative frequency of plus-end ( n+ ) and minus-end ( n− ) directed movement for 1V:1VI scaffolds on actin filaments and keratocyte actin networks . Outward flux ( Φout ) is defined as the fraction of plus-end directed trajectories . Error bars are S . E . M . and were generated by bootstrapping ( N ≥ 202 trajectories; ≥3 experiments ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05472 . 006 In order to assess the role of relative motor number on competitive outcome , we next tested scaffolds with varying ratios of myosin V and myosin VI motors ( xV:yVI; Figure 3—figure supplement 1 ) on 2D actin networks ( Figure 3A ) . In every combination , the origami scaffold commits to a single direction , either towards the cell periphery or the cell center ( Figure 3B ) . The relative number of scaffolds that move to the cell center and cell periphery ( Φout or Φin ) , however , varies linearly with the fraction of myosin V or myosin VI ( Figure 3B–D ) . Thus , while the scaffolds have a dedicated direction of movement on both 1D and 2D actin landscapes , the underlying competition ( tug-of-war ) systematically influences the directional flux . 10 . 7554/eLife . 05472 . 007Figure 3 . Scaffolds show unidirectional movement along actin networks with directional flux controlled by relative motor number . ( A ) Schematic of scaffold-myosin movement on the surface of the keratocyte actin network . Inward ( Φin ) and outward directional flux ( Φout ) are calculated as the fraction of myosin scaffolds moving towards the cell center and periphery , respectively . ( B ) Sample trajectories of scaffolds on keratocyte actin networks with movement towards the cell center in blue and the cell periphery in red . ( C ) Summary plot depicting influence of relative motor number on directionality ( red and blue ) and speed ( radius ) . The plot is aligned such that the y-axis represents total motor number ( myosin V + myosin VI ) , whereas the x-axis represents the difference between the two myosin types ( myosin V − myosin VI ) . Red or blue dashed lines correspond to scaffolds with equal numbers of myosin V or VI motors , respectively . ( D ) Outward flux ( Φout ) varies linearly with the difference between the number of myosin V and VI ( green line , R² = 0 . 80 ) . Positive and negative values indicate net movement towards cell periphery and cell center , respectively . Error bars are S . E . M . and were generated by bootstrapping ( N = 58–1897 trajectories; 3–4 keratocytes ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05472 . 00710 . 7554/eLife . 05472 . 008Figure 3—figure supplement 1 . Scaffolds precisely patterned with myosin V and/or VI . Configurations of myosin V and/or VI scaffolds ( xV:yVI ) used in this study . The table is aligned such that Y-axis represents total motor number ( x + y ) , whereas X-axis represents the difference between the two myosin types ( x − y ) . Red or blue dashed lines denotes scaffolds with equal numbers of myosin V or VI motors , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 05472 . 008 The speed of nanostructures ( 1V:1VI = ( + ) 162 ± 7 nm/s; ( − ) 66 ± 5 nm/s ) along actin filaments is significantly slower than nanostructures containing only two myosin V ( 2V:ØVI = ( + ) 273 ± 8 nm/s ) or two myosin VI ( ØV:2VI = ( − ) 130 ± 7 nm/s ) ( Figure 2D ) . Likewise , the speed of nanostructure movement on the keratocyte network decreases as the difference in the number of the two motor types approaches zero ( Figure 3C ) . These reductions in speed with antagonistic motors are in agreement with the previously published experiments involving quantum dots conjugated to one myosin V and one myosin VI ( Ali et al . , 2011 ) . Based on the reduction in speed for antagonistic ensembles , as compared to groups of one myosin type , we hypothesized that all of the motors can continuously interact with the actin tracks and collectively engage in competition . To test this hypothesis , scaffolds were formed with three myosin V and three myosin VI ( 3V:3VI ) , where one of the motor types was attached by photo-cleavable linkers ( Figure 4A–B ) . Regardless of which motor type is cleaved , removal of one myosin type from the competition increases the speed and results in a single direction of movement ( Figure 4C–F ) . The directional switch and increase in speed after photo-cleavage indicate that all motors , regardless of type , are able to access the actin tracks and engage in continuous competition . Together , these observations suggest that the collective movement is due to a continuous interaction of both motor types , and not due to detachment of losing motors from the actin track ( or scaffold ) , when overpowered by the winning motor . Lastly , the underlying continuous interaction is also consistent with our previous observation that myosin V changes the trajectory shape of ensembles of myosin V and VI on 2D actin tracks ( Hariadi et al . , 2014 ) . 10 . 7554/eLife . 05472 . 009Figure 4 . Disengagement of one motor species resolves the competition . ( A–B ) Schematics of mixed-motor scaffolds ( gray hexagons ) with three myosin V ( A; red arrows ) or three myosin VI ( B; blue arrows ) attached by photo-cleavable linkers . UV-induced photo-cleavage ( purple lightning bolt ) converts mixed-motor scaffolds to scaffolds with only myosin VI ( A ) or only myosin V ( B ) . ( C–F ) Representative scaffold trajectories for photo-cleavable experiments on keratocyte networks and their corresponding mean speed . Black squares mark the start of the trajectories , and purple circles indicate the start of photo-cleavage . Individual data points in each trajectory , before and after photo-cleavage , are indicated as open or closed circles , respectively . Disengagement of myosin V ( C and D ) or myosin VI ( E and F ) results in movement toward cell-center or cell-periphery , respectively . In all cases , photo-cleavage leads to significant increase in speed ( p < 0 . 01 ) . Error bars are S . E . M . ( N ≥ 19 trajectories; ≥ 5 keratocytes ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05472 . 009 To gain insight into the structural mechanisms of the observed directional flux , a minimal stochastic simulation was used to model the contributions of inter-motor tension and intra-motor strain to the competition between opposing motors ( Figure 5; ‘Materials and methods’ ) . In the model , two opposing motors are coupled mechanically through a linear spring of strength ks ( Figure 5A ) . Since the motor proteins are the most flexible components of the scaffold–motor complex , ks is dominated by the flexibility of the myosin motors . Each motor consists of two catalytic heads that are connected by a lever arm with flexural rigidity kF . Each motor also has a comparable , albeit mismatched , stall force ( 1 ≤ Fhigh/Flow ≤ 2 ) , where Fhigh and Flow are the stall forces of the stronger ( myosin V ) ( Mehta et al . , 1999; Uemura et al . , 2004 ) and weaker ( myosin VI ) ( Rock et al . , 2001; Nishikawa et al . , 2002; Altman et al . , 2004 ) motors , respectively . Our model assumes that a motor can only perform a forward step if the resulting inter-motor tension ( T ) is less than its stall force ( Figure 5A ) . A successful step increases the inter-motor tension by ∆T = ks•s , where s is the motor step size . Initially T is set to zero and both motors take forward steps stochastically in opposite directions , increasing T with each step . This sequence of movement proceeds until a forward step increases T beyond the stall force of the stepping motor , which undergoes a conformational change that leads to its preferential back-stepping ( Gebhardt et al . , 2006 ) thereby relieving inter-motor tension ( Ali et al . , 2011 ) . 10 . 7554/eLife . 05472 . 010Figure 5 . Stall force ratio , actin architecture , and myosin rigidity together tune directional flux . ( A ) Minimal model of coupled myosin V ( red ) and myosin VI ( blue ) movement on an actin filament ( green ) . The net compliance in the coupled system is modeled as a simple harmonic spring with stiffness ks . Each motor takes either a forward or backward step , based on whether the inter-motor tension after the step ( T ) is below or above the stall force ( Flow − myosin VI; Fhigh − myosin V ) . ( B ) Outward flux of the mixed-motor ensemble ( Φout ) on single actin filaments as a function of the normalized inter-motor tension per step ( ∆T/Flow ) and stall force ratio ( rs = Fhigh/Flow ) . Based on previously reported stall forces for myosin V ( Mehta et al . , 1999; Uemura et al . , 2004 ) and VI ( Rock et al . , 2001; Nishikawa et al . , 2002; Altman et al . , 2004 ) , rs = 1 . 5 and is indicated by the gray shaded region ( left ) . The corresponding experimentally measured Φout ( ⊗; Figure 2 ) and rs = 1 . 5 yield a ∆T/Flow = 0 . 55 ± 0 . 01 . ( C ) Schematic forward step of a myosin with flexible ( left ) or rigid ( right ) lever arm on a digitized keratocyte actin network ( green ) . The motor domains of the stepping motor ( light blue shoes ) , non-stepping motor ( gray shoes ) , lever arms , inter-motor linkage ( pre-step—black spring; post-step—orange spring ) , and digitized actin network are drawn approximately to scale . The forward step results in an increase in both the inter-motor tension ( ∆T ∝ ks ) and the intra-motor torsion ( τ ∝ kF ) . A flexible forward stepping motor ( kF/ks << 1 ) minimizes inter-motor tension ( ∆Tlow ) . A rigid forward stepping motor ( kF/ks >> 1 ) minimizes intra-motor torsion ( ∆Thigh ) . ( D ) Simulated ∆T as a function of kF/ks . Varying lever arm rigidity ( kF/ks ) is sufficient to modulate ∆T . ( E ) Outward flux of the mixed-motor ensemble ( Φout ) on the keratocyte actin network as a function of the relative tension per step of the two motors ( ∆Thigh/∆Tlow ) . Gray shaded region ( left ) indicates the parameter space for ∆T/Flow = 0 . 55 ± 0 . 01 ( see B ) . The corresponding experimentally measured Φout ( ⊕; Figures 2 , 3 ) yields a ∆Thigh/∆Tlow = 1 . 20 ± 0 . 05 . This enhanced ∆T for rigid motors evens out the competition on a branched 2D network compared to single filament tracks . DOI: http://dx . doi . org/10 . 7554/eLife . 05472 . 01010 . 7554/eLife . 05472 . 011Figure 5—figure supplement 1 . Description of stochastic simulation . Stochastic simulations for myosin-scaffold movement along an actin network were performed in Mathematica based on the following rules ( Hariadi et al . , 2014 ) . ( A ) TEM image of a keratocyte actin network ( Hariadi et al . , 2014 ) . ( B ) To investigate the influence of network structure to the stepping dynamics ( Figure 5—figure supplement 2 ) , the TEM image in ( A ) was first scaled by a factor of 0 . 5–1 . 25 . The image was then skeletonized to derive the position of actin filaments ( green lines ) as described in Sivaramakrishnan and Spudich ( 2009 ) . Every pixel is a possible binding site for a myosin motor head . ( C ) Next , we calculated the orientation of each actin filament relative to the polarity field vector for each pixel in the digitalized image . A 7 × 7 box was centered over each pixel , and based on the skeletonized filament in the search box each pixel was fit to a linear function . The local filament direction was then calculated by taking the inverse tangent of this fit . Pixels that fit poorly ( R2 < 0 . 25 ) were excluded ( 14% of the detected pixels in [B] ) from the simulation . The energy for each binding site was calculated from these filament directions ( see L ) . For our model , the myosin pair consisted of either two identical myosin dimers with lever arm stiffness kF . Each myosin dimer has two motor domains ( gray sandals ) , and each myosin pair is linked through their centers of mass by a linear spring ks . Finally , in each myosin a leading ( ① or ② ) and a trailing head is indicated . ( D ) Motor 1's trailing head is placed randomly on an actin filament . ( E–H ) The position of the leading head ( E ) and the second myosin ( F–H ) are randomly assigned with only two restrictions . First the inter-motor distance between myosin heads must be 36 ± 7 . 2 nm ( gray arc; [E and H] ) . Second distance between the centers of mass of a motor pair must be 65 ± 15 nm ( red ring; [F and H] ) . ( I ) The position of all motor heads , the centers of mass for each myosin dimer , and the center of the two centers of mass are tracked during each simulation step . ( J ) Myosin V and VI dimers step stochastically on actin filaments with exponentially distributed dwell times . In our simulations , an exponential distribution of mean dwell times based on the cycle rates of myosin V and VI ( De La Cruz et al . , 1999 , De La Cruz et al . , 2001 ) was used to derive the dwell times for each motor step . In this example , t1> t2 and myosin 2 moves first . ( K ) For a motor to step , the trailing head of motor ( motor 2 ) pivots about the lead head and its binding site is determined by the following criteria: ( a ) The binding site must be 36 ± 7 . 2 nm pixel from the leading head ( gray arc ) . ( b ) The new center of mass for stepping motor ( motor 2 ) must be within 65 ± 15 nm ( red ring ) from the center of mass of the non-stepping motor ( myosin 1 ) . ( c ) The stepping myosin must proceed in a forward direction determined by the actin network polarity . ( L ) For each pixel meeting these requirements ( i ) , the energy Gi and Boltzmann probability Pi are calculated . ( M ) A binding site for each new leading head is then stochastically choosen based on the calculated Boltzmann probabilities calculated in ( L ) . ( N ) The change in inter-motor tension is then calculated ( ∆T = ∆Tpost − ∆Tpre ) . The simulation was repeated for ≥400 times . The tension change ∆T was quantified and presented in Figure 5D , Figure 5—figure supplements 2 , 3 . DOI: http://dx . doi . org/10 . 7554/eLife . 05472 . 01110 . 7554/eLife . 05472 . 012Figure 5—figure supplement 2 . Actin network pore size alters tension generated . ( A–D ) The interlaced actin network used in the stochastic simulation ( Figure 5D–E and Figure 5—figure supplement 1 ) . The network is generated by scaling a skeletonized TEM image of the keratocyte actin network by a factor of 0 . 5 ( A—blue ) , 0 . 75 ( B—orange ) , 1 ( C—green ) , and 1 . 25 ( D—red ) ( Figure 5—figure supplement 1 ) . Given the mean pore size of the meshwork in the unscaled image ( panel C; ∼30 nm ( Svitkina and Borisy , 1998 ) ) , the mean mesh size of the generated networks is estimated to be 15 nm ( A ) , 23 nm ( B ) , 30 nm ( C ) , and 38 nm ( D ) . ( E ) For realistic flexibility of motor kF / ks < 10 , tension generated per step ( ∆T ) of myosin with kF / ks = 0 . 01–100 is influenced by the network structure . In these simulations , the inter-motor stiffness was set to 0 . 05 pN/nm . DOI: http://dx . doi . org/10 . 7554/eLife . 05472 . 01210 . 7554/eLife . 05472 . 013Figure 5—figure supplement 3 . Inter-motor stiffness influences inter-motor tension . Change in the inter-motor distance ( ∆xpost − ∆xpre = ∆T/ks ) for the simulated steps of two myosin motors ( Figure 5—figure supplement 1 ) with flexural rigidity kF , connected by an inter-motor spring of varying stiffness ( ks = 0 . 005 ( blue ) , 0 . 015 ( orange ) , 0 . 05 ( green ) , and 0 . 15 ( red ) pN/nm ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05472 . 013 Stochastic simulations that follow this model lead to solely unidirectional movement , with the relative number of plus ( n+ ) and minus ( n− ) end directed scaffolds dependent on the normalized inter-motor tension per step ( ∆T/Flow ) and stall force ratio ( rs = Fhigh/Flow ) ( Figure 5B ) . For equally matched motors ( rs = 1 ) , there is an equal probability of trajectories moving in either direction ( Φout = 50% ) . For 1 < rs < 2 , the model shows that Φout can be tuned from 50% to 100% depending on the value of ∆T/Flow ( Figure 5B ) . For ∆T/Flow < 0 . 5 , the inter-motor tension exceeds the stall force of the weaker motor , with the stronger motor winning most of the competitions ( Φout > 80% ) . However , for 0 . 5 < ∆T/Flow < 1 and stochastic stepping , there is a finite and increasing probability of inter-motor tension exceeding the stall force of the stronger motor ( ‘Materials and methods’ ) , resulting in the weaker motor winning the competition ( 60% < Φout < 80% ) . This regime captures the experimentally measured Φout of 68% ( ⊗; Figure 5B ) , given the previously reported stall forces of myosin V ( Mehta et al . , 1999; Uemura et al . , 2004 ) and VI ( Rock et al . , 2001; Nishikawa et al . , 2002; Altman et al . , 2004 ) . In our model for movement on a 1D actin filament track , the ∆T for myosins with equal step sizes is the same regardless of which motor steps forward ( ∆T = ks•s ) . Parallel simulations on digitized keratocyte actin networks , however , incorporate an additional parameter , namely flexural rigidity of the myosin lever arm kF ( Hariadi et al . , 2014 ) ( Figure 5C and Figure 5—figure supplement 1 ) , to account for the misalignment of the lever arm relative to the local actin filament where the myosin head is bound ( Figure 5 and Figure 5—figure supplement 1L ) . Simulations on these networks show that the mean inter-motor tension per step ( ∆T ) can be significantly influenced by relative torsional stiffness ( kF/ks ) , regardless of network mesh size or inter-motor stiffness ( Figure 5C–D and Figure 5—figure supplements 1–3 ) . On a 2D network , the higher the kF/ks of a motor , the greater the ∆T when it steps forward . Thus one can model movement along a 2D network with a similar simulation on a 1D track by redefining ∆T such that different ∆T values are accrued in each step based on which motor steps forward ( ∆Thigh and ∆Tlow for rigid and flexible motors respectively ) . For such simulations on 2D networks , linking a rigid motor ( kF/ks >> 1; ∆Thigh ) to a more flexible one ( kF/ks << 1; ∆Tlow ) is sufficient to systematically bias the competition in favor of the flexible motor ( ↓Φout with ↑∆Thigh/∆Tlow; Figure 5E ) . The experimentally measured Φout on 2D networks is significantly lower than on single actin filaments ( ⊗ vs ⊕; Figure 5E ) . Based on this measurement , the simulations estimate a ∆Thigh/∆Tlow = 1 . 20 ± 0 . 05 ( ⊕; Figure 5E ) that is consistent with a higher flexural rigidity for myosin V ( Hariadi et al . , 2014 ) ( Figure 5D ) . Therefore , our simulations reveal that the greater flexural rigidity of myosin V compared to myosin VI is sufficient to equalize the competition on 2D networks . As described above , the model shows that the outward flux of scaffolds composed of opposing motors on actin is influenced by the interplay between inter-motor tension and intra-motor strain ( Figure 5 ) . To test this model , we engineered a myosin V/VI chimera containing the myosin V motor domain with the flexible myosin VI lever arm and a myosin VI/V chimera consisting of the myosin VI motor domain with a rigid myosin V lever arm . These chimeras allow us to assess the competition involving opposing motors with similar lever arm rigidity , specifically 1V/VI:1VI ( Figure 6A ( i ) ; flexible vs flexible ) and 1VI/V:1V ( Figure 6A ( ii ) ; rigid vs rigid ) . In both cases , the flexural rigidities and the changes in tension per step ( ∆T ) of the opposing motors were estimated to be similar . The outward flux for ensembles involving either chimera ( Figure 6A; i or ii ) on 2D actin networks are significantly higher than scaffolds with 1V:1VI ( Figure 6B ) . The higher outward flux indicates that balancing the tension per step ( ∆T ) between the antagonistic motors is sufficient the restore the dominance of the stronger motor ( myosin V ) on 2D actin networks . 10 . 7554/eLife . 05472 . 014Figure 6 . Switching lever arms restores myosin V dominance . ( A ) Scaffold and motor schematics used in the lever arm competition experiments . Lever arm rigidity was balanced by engineering the myosin V motor domain with the flexible lever arm of myosin VI ( i; flexible vs flexible competition ) , or the myosin VI motor domain chimera with the rigid lever arm of myosin V ( ii; rigid vs rigid competition ) . Arrowheads and arrowtails depict the myosin heads and lever arms , respectively ( red—myosin V; blue—myosin VI ) . ( B ) Outward flux ( Φout ) of indicated motor ensembles . Error bars are S . E . M . and were generated by bootstrapping ( N ≥ 126 trajectories; ≥ 3 keratocytes ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05472 . 014
Myosin V and VI are antagonistic motors that cohabit membrane vesicles in neuronal growth cones ( Suter et al . , 2000 ) . Myosin V is implicated in secretory traffic , whereas myosin VI is important for timely endocytosis ( Suter et al . , 2000; Sahlender et al . , 2005; Kneussel and Wagner , 2013 ) . While previous studies have examined the competition between myosin V and VI ( Ali et al . , 2011; Hariadi et al . , 2014 ) , a systematic measurement of their collective behavior is unexplored and formed the focus of this study . We report that while antagonistic motor ensembles display solely unidirectional movement , as previously reported ( Ali et al . , 2011; Hariadi et al . , 2014 ) , their directional sorting can be linearly tuned by the relative number of the two motor types . Further , this directional sorting can be modulated independently by the myosin lever arm and the actin architecture . These observations support a simple generalizable model , wherein competitive outcome is dependent on the ratio of the stall forces of the antagonistic motor types . Taken together , our findings provide an elegant mechanism for regulating vesicle sorting mediated by unconventional myosins , without the need to either segregate motor subtypes to distinct cargo ( Hartman et al . , 2011 ) or engage regulatory proteins that preferentially modulate the accessibility or activity of one of the motor types ( Fu and Holzbaur , 2014 ) . One model for the observed unidirectional transport is that only a single myosin on the DNA scaffold interacts with the actin network at any given time . Under these conditions , the probability of movement towards the cell center or periphery is proportional to the relative number of myosin VI or V , respectively . However , this mechanism for the observed sorting is refuted by four distinct observations . First , as previously reported ( Hariadi et al . , 2014 ) , scaffold run length linearly increases with motor number ( Supplementary file 2 ) , suggesting that multiple motors on the same scaffold are capable of interacting with the actin network . Second , scaffold speed changes substantially with varying number and type of myosin motors ( Figure 3 ) . For scaffolds with a given number of myosins , those with a single subtype move faster than those with both myosin V and VI . The reduction in speed with both myosin subtypes is consistent with the trailing myosin interacting with the actin network , despite the unidirectional movement of the scaffold . This interpretation is consistent with the coordinated back-stepping of the trailing motor observed by Ali et al . ( 2007 ) . Third , for scaffolds with both subtypes the speed decreases as the numbers of myosin V and VI are more evenly matched . This systematic reduction in speed alone argues for progressive engagement of additional antagonistic motors with the actin network . Fourth , experiments with photo-cleavable myosin-scaffold linkages further show that removal of the antagonistic myosin leads to either an increase in speed ( release trailing motor ) or a reversal in direction ( release leading motor ) ( Figure 4 ) . Altogether , given these observations we propose an alternate model that emphasizes inter-motor interactions ( Figure 5 ) . Our model identifies the ratio of the stall forces ( rs = Fhigh/Flow ) of the antagonistic motors as the key parameter that determines the outcome of scaffold sorting on 1D or 2D actin filament tracks . For ensembles with mismatched motors ( rs > 2 ) , the model predicts unidirectional movement led solely by the stronger motor . However , for motors of similar strength ( 1 < rs < 2 ) , the model predicts that either motor may lead the unidirectional motion , with the directional flux of scaffolds dependent on rs . These predictions are consistent with the unidirectional movement observed here , and in a previous report based on experiments using a single myosin V and a single myosin VI attached to a quantum dot ( Ali et al . , 2011 ) . Further , the model is in agreement with a recent report of unidirectional trajectories for DNA scaffolds linked to both kinesins and dyneins on 1D microtubules ( Derr et al . , 2012; Roberts et al . , 2014 ) . However , it differs from the bi-directional movement of isolated endosomes that are driven by a combination of native kinesin and dynein motors ( Soppina et al . , 2009 ) . We speculate , though , that bi-directional movement stems from the influence of additional regulatory elements on native endosomes ( Kunwar et al . , 2011 ) . Lastly , our model explains the differences in sorting observed in actin filaments vs 2D networks , and the role of the myosin lever arm in regulating sorting . In essence , motors with greater intra-motor torsional strain ( rigid lever ) experience a larger inter-motor tension per step and hence lose their competitive edge on 2D networks . The lever arm of myosin is primarily regarded as a mechanical amplifier in its chemo–mechanical cycle ( Spink et al . , 2008 ) . Our study , however , suggests a broader regulatory role for the lever arm in membrane trafficking . We find that the structural properties of the myosin lever arm control the directional flux of scaffolds on our model cellular actin network , thus having implications on sorting of vesicular cargo . Beyond this observation , structural elements in myosins have been shown to influence motility on actin networks . For instance , an extension of the myosin X lever arm is necessary for its preferential processive movement on parallel actin bundles , but not on single actin filaments ( Brawley and Rock , 2009; Nagy and Rock , 2010 ) . Myosin VI , on the other hand , has a unique three-helix bundle in its lever arm , which can unfold to alter the motor's structural properties ( Mukherjea et al . , 2014 ) . Furthermore , for groups of myosin V and VI , the flexibility of the lever arm controls trajectory shapes on 2D actin networks ( Hariadi et al . , 2014 ) . In addition to myosin structure , actin architecture also influences myosin function . For example , single myosin V and VI have different stepping dynamics at actin filament intersections ( Ali et al . , 2007 ) then on actin bundles ( Ali et al . , 2013 ) . An in situ motility assay using detergent-extracted cells also reported that individual myosin V , VI , and X dimers show preferential motility on different actin architectures ( Brawley and Rock , 2009 ) . Together , these studies suggest a subtler regulation of cellular processes that emerges from unique structural features in myosins that modulate either individual or collective actin-myosin interactions .
1× Assay Buffer ( AB Buffer ) : 25 mM imidazole ( pH 7 . 5 ) , 4 mM MgCl2 , 1 mM EGTA , 25 mM KCl , 1 mM DTT; 1× AB . BSA: AB buffer + 1 mg/ml BSA; 1× AB . BSA . CAM: AB . BSA buffer + 9 µM calmodulin . Benzyl-guanine NHS ester ( BG-GLA-NHS; NEB , Ipswich , MA ) was covalently linked to the C6-amine modified oligonucleotides ( BG-oligo 1 and BG-oligo 5; Supplementary file 1 ) . Briefly , 0 . 17 mM C6-amine-oligo-Cy3 was incubated with 11 . 6 mM BG-GLA-NHS in 0 . 1 M NaBO3 for 2–4 hr at 37°C with shaking . BG-labeled oligo was purified twice through Illustra G-50 micro columns ( GE Healthcare , Pittsburgh , PA ) equilibrated in 2 mM Tris , pH 8 . 5 . BG-oligo concentration was determined from absorbance at 260 nm . DNA nanostructures were prepared based on the detailed description in our previous work ( Hariadi et al . , 2014 ) . The sequences for the scaffold and all oligonucleotides are listed in Supplementary file 1 . Each origami scaffold is labeled with 23 Cy3 molecules ( Figure 1—figure supplements 1 , 2; Figure 3—figure supplement 1; Supplementary file 1 ) for high signal-to-noise imaging and contains a biotinylated-strand to facilitate removal of unbound myosins . Single-stranded M13mp18 DNA ( NEB ) were mixed with fourfold excess of short stable strands ( IDT , Coralville , IA ) , followed by 2 hr annealing as previously described ( Rothemund , 2006 ) . Intact scaffolds were separated from excess staple strands using Amicon Ultra 100K cutoff spin columns ( EMD Millipore ) . Purified scaffolds were mixed with excess labeled myosin , a mixture of 42-nt oligos with randomized sequences ( blocking oligos ) , and 1–5 µM calmodulin in 1× AB . BSA . After 10 min of incubation at room temperature , excess streptavidin-coated magnetic beads ( NEB ) were added and incubated at room temperature with shaking for 10 min . The beads were washed with AB . BSA . CAM . Finally , the beads were incubated in AB . BSA . CAM containing an imaging solution of 2 mM ATP , 1 mM phosphocreatine , 0 . 1 mg/ml creatine-phosphokinase , 45 µg/ml catalase , 25 µg/ml glucose oxidase , 1–2% glucose , and excess elution strand for strand displacement of origami from streptavidin magnetic beads . The centers of mass of the two motors are connected by a simple linear spring of stiffness ks and initial inter-motor tension T = 0 . The stall force for myosin V and VI are defined as Fhigh and Flow , respectively . The dwell time is assumed to be exponentially distributed , and a series of discrete dwell times are derived from previously measured mean dwell times for myosin V ( 170 ms ) and VI ( 215 ms ) ( Hariadi et al . , 2014 ) . The motor with the shorter dwell time steps first . A myosin step increases T . a . For movement on an actin filament , a myosin step increases the tension by N ( ∆T , 0 . 1 ∆T ) regardless of motor type , where N ( μ , σ ) represents a normal distribution with mean = μ and standard deviation = σ . b . For movement on keratocytes , a myosin V step increases the tension by N ( ∆Thigh , 0 . 1 ∆Thigh ) whereas a myosin VI step increases the tension by N ( ∆Tlow , 0 . 1 ∆Tlow ) . After each motor step , the resulting T is used to modify the dwell time of each motor as follows:a . A linear force-speed relationship is assumed for both motors . b . T is used to calculate the mean speed ( ν ) and mean dwell time ( ∝ ν−1 ) for each motor . The load-dependent speed is given by v = v0 ( 1 − T/Fstall ) , where νo is the zero-strained speed , Fstall is the stall force of the stepping motor ( Fhigh or Flow ) and T is the inter-motor tension ( 0 ≤ T ≤ Fstall ) . c . The discrete dwell time distribution of each motor is modified in proportion to the estimated mean dwell time after each step . The new discrete dwell time distribution is used to identify the next stepping motor , with a repeat of steps 5-7 . If T is larger than the stall force of the stepping motor ( Fhigh or Flow ) , then this motor undergoes a conformational change that leads to preferential back-stepping ( Gebhardt et al . , 2006 ) . This motor is designated as the ‘losing’ motor . The scaffold is now primed for unidirectional movement lead by the ‘winning motor’ . Steps 1–8 are simulated over ≥1000 times for each value of ∆T , rs ( Fhigh/Flow ) , and for simulations on keratocytes a given value of ( ∆Thigh/∆Tlow ) . For each simulation:a . If the stronger motor ( Fhigh ) is the winning motor n+ = n+ + 1 . b . If the weaker motor ( Flow ) is the winning motor n− = n− + 1 . For each condition outward flux is calculated as Φout = n+/ ( n+ + n− ) . Φout measurements are plotted over a range of normalized ∆T ( ∆T/Flow ) , rs , and ( ∆Thigh/∆Tlow ) . Estimate inter-molecular tension per step ( ∆T ) for movement on keratocytes as a function of kF/ks ( Figure 5C–D and Figure 5—figure supplements 1–3 ) —Stochastic simulations of movement of motor ensembles with lever arm flexural rigidity kF and inter-motor stiffness ks on the digitized actin network were performed in Mathematica ( Figure 5—figure supplement 1 ) . For movement on keratoctes , ∆T after each step is given by ks • ( ∆xpost − ∆xpre ) ≤ ks • s , where ∆xpost and ∆xpre are the inter-motor extensions before and after the step and s is the myosin step size on the actin filament . Note that movement on keratocytes gives rise to lower ∆T than the collective movement on single actin filament ( ∆T = ks • s ) . Mean and standard deviation in ∆T for each kF/ks were computed from 400 simulated steps ( Figure 5D ) . | Proteins and other molecules can be moved around a cell within bubble-like compartments called vesicles . These vesicles can travel along filaments made of a protein called actin , which forms a network that criss-crosses the cell . A family of motor proteins called myosin bind to the vesicles and are responsible for pulling them along the actin filaments . For example , myosin V pulls vesicles towards the ‘plus-end’ of the filament or the outer edges of the cell , while myosin VI pulls them in the opposite direction towards the ‘minus-end’ or the interior of the cell . Both proteins are often found on the same vesicle , and it is not clear in which direction such a vesicle will move . Hariadi et al . have shed new light on this question by sticking different combinations of myosin V and myosin VI proteins to a tiny nanostructure made of DNA and using a microscope to watch it move on actin . When a nanostructure with one myosin V and one myosin VI protein was placed on a single actin filament , it moved towards the plus-end of the filament . However , when it was placed on a two-dimensional network of actin filaments , the nanostructure was equally likely to move in either direction . Therefore , the architecture of the actin filaments influences the outcome of the competition between the two motor proteins . When both types of myosin protein were present , the nanostructure was pulled along the filament more slowly than when only one type was present . This suggests that myosin V and myosin VI are involved in a ‘tug of war’ on the actin filament . Next , Hariadi et al . altered the numbers of myosin V and myosin VI proteins on the nanostructure . The direction in which the nanostructure moved depended on the ratio of motor proteins present: when there were more myosin V proteins than myosin VI proteins , the nanostructure moved towards the plus-end , and vice versa . Hariadi et al . 's findings suggest that cells direct the movement of vesicles around a cell by altering the relative number of myosin V and myosin VI proteins bound to each vesicle . | [
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] | 2015 | Tuning myosin-driven sorting on cellular actin networks |
Tactile information available to the rat vibrissal system begins as external forces that cause whisker deformations , which in turn excite mechanoreceptors in the follicle . Despite the fundamental mechanical origin of tactile information , primary sensory neurons in the trigeminal ganglion ( Vg ) have often been described as encoding the kinematics ( geometry ) of object contact . Here we aimed to determine the extent to which Vg neurons encode the kinematics vs . mechanics of contact . We used models of whisker bending to quantify mechanical signals ( forces and moments ) at the whisker base while simultaneously monitoring whisker kinematics and recording single Vg units in both anesthetized rats and awake , body restrained rats . We employed a novel manual stimulation technique to deflect whiskers in a way that decouples kinematics from mechanics , and used Generalized Linear Models ( GLMs ) to show that Vg neurons more directly encode mechanical signals when the whisker is deflected in this decoupled stimulus space .
Rats , like many rodents , rely heavily on tactile information from their vibrissae ( whiskers ) to explore their world . Tactile signals are generated both during active whisker movement – when the rat brushes and taps its whiskers against objects – and during passive contact . Deformations of the vibrissae are transduced by mechanoreceptors in the follicle ( Ebara et al . , 2002 ) , and the resulting electrical signals are integrated by primary sensory neurons in the trigeminal ganglion ( Vg ) . From the Vg , signals are relayed to the brainstem trigeminal nuclei , thalamus , and primary somatosensory cortex . Neurons in the Vg are thus the 'gatekeepers' of tactile information for the vibrissal trigeminal system ( Jones et al . , 2004a; Leiser and Moxon , 2006 , 2007 ) . Several studies have demonstrated that rodents can use their vibrissae to localize objects with high precision ( Kleinfeld and Deschênes , 2011; Knutsen and Ahissar , 2009; Knutsen et al . , 2006; Krupa et al . , 2001; Mehta et al . , 2007; O'Connor et al . , 2010; Pammer et al . , 2013 ) . Accordingly , previous work has focused on quantifying the response of Vg neurons in terms of kinematic ( geometric ) variables of contact , including radial distance to an object , angular position , and angular velocity ( Gibson and Welker , 1983a , 1983b; Jones et al . , 2004a , 2004b; Leiser and Moxon , 2007; Lichtenstein et al . , 1990; Lottem and Azouz , 2009 , 2011; Lottem et al . , 2015; Shoykhet et al . , 2000 , 2003; Szwed et al . , 2003 , 2006 ) . An alternative possibility is that Vg neurons relay a high fidelity encoding of whisker mechanics – forces and moments at the base of the whisker – to be processed at later stages of the trigeminal pathway . If Vg neurons were to encode kinematic variables , a transformation from mechanical variables at the base of the whisker into kinematic variables would have to occur within the follicle ( Whiteley et al . , 2015 ) and/or through the primary afferent integration of mechanoreceptor responses . Here , we directly address the question of whether Vg neurons represent mechanical or kinematic variables . It is challenging to disentangle these alternatives because the kinematics and mechanics of contact are tightly coupled under most standard experimental protocols; this coupling is especially strong during small angle deflections and when deflections occur near the whisker base . To date , this intrinsic coupling and the absence of mechanical modeling have prevented a quantitative evaluation of the extent to which Vg neurons respond to kinematic vs . mechanical inputs . In the present study , we developed a novel manual stimulation technique that allowed us to impose large angle deflections far from the whisker base , and thereby to systematically explore large regions of the tactile input space in which mechanics and kinematics decouple . We recorded from single Vg neurons in both anesthetized and awake animals , extracted the kinematics of contact from high-speed video , and computed the mechanics of contact using a quasi-static model of whisker bending . We then used Generalized Linear Models ( GLMs ) to quantify Vg responses in terms of both sets of variables and investigate which description more accurately predicts Vg firing rate . We found that only when the input space is large and kinematics are decoupled from mechanics does mechanical information better predict firing activity for a majority of Vg neurons .
We recorded high-speed video ( 300 fps ) during manual deflection of 18 single whiskers in anesthetized rats while simultaneously recording neural responses from 22 Vg neurons . Example neural data are shown in Figure 1A–C . Whiskers were deflected with a hand-held graphite probe in two directions ( rostro-caudal and caudo-rostral ) , with amplitudes up to several mm . Stimulation was delivered at variable radial distances that ranged up to ~90% of the whisker length , and at two speeds: 'fast' and 'slow . ' Note that manual stimulation caused radial distance of contact , velocity , and deflection amplitude to vary across deflections . The two dimensional ( 2D ) whisker shape was tracked in each video frame to quantify the kinematic and mechanical variables of contact . 10 . 7554/eLife . 13969 . 003Figure 1 . Example Vg recordings from both anesthetized and awake rats . Data from five neurons in the anesthetized animal ( A–C ) and two neurons in the awake animal ( D–E ) . Left: Heatmaps of isolated spike waveforms over all recordings of each neuron . Two waveforms in A and B indicate simultaneously recorded neurons . Scale bars are 20 μV , 200 μs; width of waveforms is 1 . 5 ms . Right: Segments of bandpass filtered ( 300–6 , 000 Hz ) raw neural traces during periods of passive deflection in the anesthetized animal ( A–C ) or active contact in the awake animal ( D–E ) . Gray shading indicates periods of contact . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 003 Kinematic variables are illustrated in Figure 2A and consist of the radial distance of contact ( r ) , the angular displacement ( θdeflection ) , and the velocity of deflection ( V , the temporal derivative of θdeflection , not shown ) . Kinematic variables were extracted directly from the shape of the whisker , as detailed in Materials and methods . During non-contact times , all kinematic variables are undefined . 10 . 7554/eLife . 13969 . 004Figure 2 . Mechanical and kinematic variables associated with contact . ( A ) Schematic of the kinematic variables of contact . The shape and position of the whisker when at rest is in gray . The variable r indicates the straight-line distance from the basepoint to the contact point . During passive deflections , the relevant angle is θdeflection , the angle between the line segment that connects the basepoint to the current point of contact and the line segment that connects the basepoint to the initial contact point . The velocity V , not shown , is the temporal derivative of θdeflection . ( B ) Schematic of the mechanical variables of contact: bending moment ( M ) , and the transverse ( Fy ) and axial ( Fx ) components of the applied force ( Fapplied ) . All variables are computed at the whisker base . ( C ) Examples of mechanical and kinematic variables during six manually delivered passive deflections in the anesthetized rat . Shading denotes contact episodes . The stimulations are similar but not identical to each other; this imparts a naturalistic variability to the tactile inputs . Units for Fx and Fy are μN; M is in μN-m; r is in mm; θ is in degrees; and V is in degrees/s . ( D ) In the awake rat , θdeflection is no longer well defined , and the relevant angle is θpush , the angle swept out by the tangent to the whisker at its base as the whisker deflects against an object . The velocity V is the temporal derivative of θpush . The figure illustrates that θhead , the angle between the tangent to the whisker at its base and the midsagittal plane , is not a valid kinematic variable to explain neural responses because it varies independently of contact . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 004 The mechanical variables of contact were computed numerically based on the full tracked whisker shape using a quasi-static , frictionless model of elastic beam bending ( see Materials and methods; [Birdwell et al . , 2007; Quist and Hartmann , 2012; Solomon and Hartmann , 2008 , 2010] ) . As illustrated in Figure 2B , in 2D the three mechanical signals at the base of the whisker are bending moment ( M ) , transverse force ( Fy ) , and axial force ( Fx ) . Because the mechanical model is quasi-static , all mechanical signals are exactly zero during periods of non-contact . Examples of both mechanical and kinematic variables are shown in Figure 2C , which shows the signals evoked during six passive deflections of the whisker at two different radial distances . Shaded regions indicate contact episodes . Notice that each deflection varies slightly from every other deflection , reflecting the naturalistic variability of manual stimulation . In a separate group of animals we recorded high-speed video ( 1000 fps ) while rats explored a vertical pole ( seven whiskers , nine neurons ) . Examples of neural data recorded in the awake animal are shown in Figure 1D–E . Whisker shape was tracked , and the kinematic and mechanical variables of contact were calculated . Video 1 compares examples of manually delivered deflections and active whisking behavior . 10 . 7554/eLife . 13969 . 005Video 1 . Comparison of active whisking with passive , manual deflection . Two seconds of high speed video ( A ) as an awake , body restrained rat whisks against a peg , and ( B ) as the whisker is passively deflected using manual stimulation in the anesthetized animal . Videos are slowed by factors of ~16 and ~15 , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 005 The variables that describe active whisking are the same as those for passive contact , except that the calculation of the angular position of contact must change . In the awake animal , the contact point does not move with respect to the whisker basepoint , so θdeflection is not well defined . Instead the relevant angle is θpush ( Figure 2D , bottom left ) , the angle swept out by the tangent of the whisker base from the time of contact onset to the current time ( Bagdasarian et al . , 2013; Kaneko et al . , 1998; Mehta et al . , 2007; Quist and Hartmann , 2012; Solomon and Hartmann , 2006 , 2011 ) . Given that the present work aims to compare the relative ability of mechanical and kinematic variables to describe Vg responses , which are strongly affected by contact , it is not appropriate to use the angle of the whisker with respect to the midsagittal plane ( θhead ) as a kinematic variable . The angle θhead contains no information about contact; note in Figure 2D that θhead varies significantly throughout the trial , while θpush varies only during contact . If the variable θhead were used as an input , it would unfairly favor a mechanical explanation for Vg firing because it would add a variable with no contact information to the kinematic hypothesis . We have not included whisking phase ( i . e . the relative value of θhead within each whisking cycle ) as a potential explanatory variable for the response of Vg neurons . Although this variable is represented in Vg responses during non-contact whisking ( Wallach et al . , 2016 ) and is of clear importance in central trigeminal structures ( Curtis and Kleinfeld , 2009; Fee et al . , 1997 ) , the present study is limited to an analysis of contact whisking , during which kinematic and mechanical coding can be directly compared . To determine the extent to which Vg neurons encode the mechanics or kinematics of contact , it is essential to observe contact conditions under which these two sets of input variables are decoupled . Figure 3 compares kinematic and mechanical variables computed for two whiskers during active exploration ( Figure 3A–B ) to those observed during passive , manual deflection ( Figure 3C–D ) . 10 . 7554/eLife . 13969 . 006Figure 3 . Manual stimulation reliably decouples mechanical and kinematic variables . Mechanical and kinematic variables of contact are shown across trials of active whisking ( rows A and B , whiskers C1 and Gamma respectively ) and passive manual stimulation ( rows C and D , whiskers B1 and D1 respectively ) . Awake trials were 3 . 02 s ( A ) and 12 . 9 s ( B ) in duration; passive trials were 64 . 67 s ( C ) and 114 . 53 s ( D ) in duration . Each point represents the observed mechanical and kinematic inputs for a 1 ms time bin . The x-axis depicts the angular coordinate of contact in degrees , the y-axis either the axial force ( Fx , units of μN ) or moment ( M , units of μN-m ) . Color represents the radial distance of contact in mm . During manual deflection , a larger input space is sampled . The actual range spanned by the mechanical variables depends on whisker identity . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 006 Mechanical and kinematic variables are often tightly coupled during awake behavior ( Figure 3A ) . Although some degree of decoupling is possible in the actively whisking animal ( Figure 3B ) , the explored regions in input space depend on the animal’s behavior . It is challenging to reliably sample a large , decoupled input space with the awake animal . In contrast , manual stimulation offers a simple and reliable method to explore a large , decoupled region of the input space ( Figure 3C–D ) . Manual stimulation can involve large angle deflections ( up to 60° ) at large radial distances ( up to 45 mm ) more consistently than in the actively behaving animal . Exploring these large regions decouples the kinematic and mechanical inputs , allowing us to address the question of whether Vg neurons encode mechanics or kinematics . Videos 2–5 show rotating views of three dimensional versions of the plots in Figure 3 , now including the radial distance of contact r as a third axis . 10 . 7554/eLife . 13969 . 007Video 2 . 3D visualization of mechanical and kinematic relationships for one neuron recorded in the awake animal . Rotating view of inputs to the neuron shown in Figure 3A , active exploration . Radial distance is represented along the third axis . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 00710 . 7554/eLife . 13969 . 008Video 3 . 3D visualization of mechanical and kinematic relationships for a second neuron recorded in the awake animal . Rotating view of inputs to the neuron shown in Figure 3B , active exploration . Radial distance is represented along the third axis . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 00810 . 7554/eLife . 13969 . 009Video 4 . 3D visualization of mechanical and kinematic relationships for one neuron recorded in the anesthetized animal . Rotating view of inputs to the neuron shown in Figure 3C , manual deflection . Radial distance is represented along the third axis . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 00910 . 7554/eLife . 13969 . 010Video 5 . 3D visualization of mechanical and kinematic relationships for a second neuron recorded in the anesthetized animal . Rotating view of inputs to the neuron shown in Figure 3D , manual deflection . Radial distance is represented along the third axis . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 010 It is possible that the rigidity with which the whisker base is held during contact differs between the awake and anesthetized animal . In the awake animal , capillaries at the level of the cavernous sinus could increase hydrostatic pressure and thereby the rigidity of the whisker-follicle junction ( Rice , 1993 ) . In addition , the activation of muscles surrounding the whiskers could increase the rigidity of the follicle with respect to the mystacial pad . Either or both of these changes near the whisker base could alter the whisker’s deformation in response to an applied force . Given that the follicle-whisker junction has been shown to be rigid in the anesthetized animal ( Bagdasarian et al . , 2013 ) , blood-based hydrostatic changes are unlikely to be responsible for differences in rigidity between awake and anesthetized states . Changes in muscle activation , however , are a potentially significant effect that remains to be fully investigated . In the anesthetized animal , we observed large translations and rotations of the follicle in the skin when a force is applied to the stiff , proximal portion of the whisker ( Video 6 ) . Translations and rotations were not observed during contacts at the more flexible , distal portion of the whisker; this rigidity is similarly observed in the awake animal , where mystacial muscles prevent movement of the follicle during contact . 10 . 7554/eLife . 13969 . 011Video 6 . Comparison of distal and proximal contact in the anesthetized rat . High speed video of distal and proximal contacts ( 3 s per clip , slowed by a factor of ~5 ) highlights the movement of the follicle relative to the skin when contact is made close to the whisker base . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 011 We therefore restricted our analyses in the anesthetized animal to distal contacts ( ≳40% of the whisker length ) , where the apparent rigidity of the whisker-follicle-skin interface is significantly greater than the rigidity of the whisker at contact and the follicle does not move appreciably during contact . We employed generalized linear models ( GLMs ) to determine the relative importance of kinematic and mechanical variables in predicting neural firing . GLMs include linear combinations of the history of various input variables , as well as the non-linear characteristic of biological neurons , to predict the firing rate of a neuron given previously observed stimulus inputs and the resultant spiking patterns ( Pillow et al . , 2008 ) . The GLM approach lends itself to the analysis of both active and passive deflections . 'Full model' GLMs were constructed using the three mechanical and the three kinematic variables ( Fy , Fx , M , r , θ , V ) as input variables ( predictors ) for the observed spike train at 1 ms resolution . We invoke a formulation of the GLM in which the predictors are convolved with a set of nonlinear basis functions ( 'raised cosine bumps' ) that cover a desired temporal window into the past over which to consider the stimulus history ( Pillow et al . , 2008 ) . Here , we choose the five dimensional basis shown in Figure 4A . Each predictor thus gives rise to five 'convolved predictors' , each with the temporal structure of the corresponding basis function . The basis functions extended 75 ms into the past , to match the temporal extent of the cross-correlations between the observed spikes and the various predictors while not being longer than the shortest inter-stimulus interval . 10 . 7554/eLife . 13969 . 012Figure 4 . Optimal linear filters indicate that moment is the most important predictor of Vg neural firing . ( A ) The non-linear basis of 'raised cosine bumps' . ( B ) Average absolute value of the GLM fit coefficients ( β ) across all neurons . βl refers to the coefficient of lth cosine basis function , with β1 being the most recent and precise , and β5 being the most delayed and diffuse . Shading corresponds to the basis function plotted in ( A ) . Two neurons have been omitted from this aggregate analysis because their outlying coefficients β ( order 1013 ) distorted the averages reported here . ( C ) The linear combination of the basis functions bl plotted in ( A ) with the coefficients βlj obtained from the GLM fit allows us to obtain predictor specific filters αj , shown here as a function of time ( truncated at 20 ms for visualization ) for an example neuron . These filters quickly decay to zero , indicating that the majority of the information important to the cell is contained in the preceding few milliseconds . For the cell shown here , moment , transverse force , and angular displacement are important input signals , with moment being the most important . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 01210 . 7554/eLife . 13969 . 013Figure 4—figure supplement 1 . Examples of spike-triggered averages of the six input variables for the cell shown in Figure 4C . Shaded regions are standard error of the mean , too small to be observed for most traces . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 013 This procedure gives us a total of 30 'convolved predictors' ( 6 predictors * 5 basis functions ) that are the inputs to the model . The GLM then fits optimal coefficients ( βlj , 1≤l≤5 , 1≤j≤6 ) for each of the 30 convolved predictors , where l is the index of the basis function and j is the index of the predictor . The model includes one additional coefficient β0 for a constant term . These 31 coefficients are used to construct a linear combination of the 30 convolved predictors; this linear combination is the argument to a sigmoidal nonlinearity that outputs the instantaneous probability of firing at every 1 ms time bin . Before convolving with the basis set , the predictors are whitened to have zero mean and unit standard deviation . This allows us to compare β coefficients for different predictors that would otherwise be on different scales . Figure 4B shows the mean absolute value of the β coefficients across all neurons . Each set in this figure refers to a particular basis function; the coefficients labeled as β1 actually comprise all six coefficients β1j , 1≤j≤6 , where the index j labels the predictors ( Fy , Fx , M , r , θ , V ) . The six coefficients labeled as β1 represent the weight of the most temporally recent and precise time period as specified by the basis function b1; this period covers 0 to 4 ms into the past with a peak time at 0 ms . The most recent time period is clearly the most important in predicting spikes for all six predictors . Subsequent sets of coefficients represent the importance of more distant past times , as specified by the corresponding basis functions shown in Figure 4A . The very small values of the coefficients β5 associated with the basis function b5 indicate that there is no need to look much further than 25 ms into the past . Among all predictors , moment M has the largest coefficient β for the first four basis functions; this indicates that on average , moment is the most important predictor of firing activity . As detailed in Materials and methods , it is useful to obtain predictor specific filters αj , 1≤j≤6 , as a linear combination of the basis functions bl , 1≤l≤5 , with the coefficients βlj , 1≤l≤5 , 1≤j≤6 obtained from the GLM fit . These predictor-specific filters , shown in Figure 4C for an example neuron , illustrate the impact of each predictor on the neuron’s firing . Note that the filters shown in Figure 4C decay to zero after about 15 ms , and that for this neuron , a change in moment from negative to positive , a negative θ , and a negative Fy are the inputs that drive the cell to fire . An alternative characterization of inputs relevant to Vg firing follows from calculating spike-triggered averages ( STA ) for each of the input variables . The STAs for the neuron depicted in Figure 4C are shown in Figure 4—figure supplement 1 . Bending moment is not only the most important input to the example neuron in Figure 4C , but also emerges as the most important input across all neurons in Figure 4B . However , all input variables contribute to the GLM fits . Different neurons might respond strongly to different combinations of input variables . To quantify whether kinematic or mechanical variables provide better predictions of firing activity , we constructed separate GLMs that had access to only the kinematic variables or only the mechanical variables . We refer to these models as 'subset models' . We calculated the coefficient of determination ( R2 ) between the predicted spiking probability given by these subset models and the predicted spiking probability of the full model . Note that this metric is not a measure of how well the models predict the neuron’s firing , but rather of how much of the information captured by the full model can be accounted for by either of the two subset models . Examples of the relationship between the subset model predictions and the full model predictions are shown in Figure 5A . For neuron 24 , the predictions of the mechanical subset model correspond well to those of the full model ( R2 = 0 . 88 ) , while the predictions of the kinematic subset model do not ( R2 = 0 . 08 ) . This result indicates that the information present in the mechanical variables accounts for most of the information that the full model uses to predict spike rates . The opposite is true for neuron 8: the information present in the kinematic variables better accounts for the information that the full model uses to predict spike rates . 10 . 7554/eLife . 13969 . 014Figure 5 . Comparison between full and subset models . ( A ) The firing rate prediction of each subset model is plotted against the prediction of the full model . The predictions are probability of a spike in each 1 ms time bin . For neuron 24 in the first row , the mechanical model is well correlated with the full model and the kinematic model is not; the opposite is true for neuron 8 in the second row . ( B ) The R2 between the firing rate predicted by the full model and the firing rate predicted by each subset model ( mechanical on the x-axis; kinematic on the y-axis ) . Each data point represents one neuron . The triangles represent neurons recorded during active contact; the circles represent neurons recorded during manual deflections . Red markers correspond to models that predict the cell’s spike rate better than the median accuracy ( R>0 . 30 ) . Gray markers indicate poor prediction accuracy ( R≤0 . 30 ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 01410 . 7554/eLife . 13969 . 015Figure 5—source data 1 . Summary data used to create Figure 5B are reported . Coefficients of determination ( R2 ) calculated between the subset model predictions and the full model predictions for each recorded neuron are listed in columns B and C . Column D indicates whether the neuron was recorded in the awake or anesthetized animal . The accuracy of the full model for each neuron ( Pearson Correlation Coefficient , R-value ) is listed in column E . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 015 The quality of the subset models is quantified over all neurons in Figure 5B , which plots the R2 values between the predictions of the mechanical subset model and those of the full model against the R2 values between the predictions of the kinematic subset model and those of the full model . An inverse relationship is apparent , indicating that if the predictions of one subset model account well for the predictions of the full model , the predictions of the other subset model do not . So far , our analysis has not addressed the quality of the full model predictions . To quantify the accuracy of the full model , we computed the Pearson Correlation Coefficient ( R ) between the GLM predicted rate and the observed spike rate , obtained by smoothing the spike train with a Gaussian kernel ( σ=15 ms; see Materials and methods ) . In Figure 5B , data points are shaded red if their R is above the median R value ( 0 . 3 ) , and grey if their R is equal to or below the median R value . A majority of the red markers ( 10/15 ) fall below the diagonal , suggesting that when the full model relies on the information provided by the mechanical subset of input variables , the model performs better . We next asked how well the full model and the subset models could predict the spike rate of each neuron . The distribution of R values for the full model is shown in Figure 6A . The median R value across all neurons is 0 . 30 . There was no significant difference between active contact and passive deflections ( Wilcoxon rank-sum test p=0 . 18 ) . 10 . 7554/eLife . 13969 . 016Figure 6 . Mechanical models outperform kinematic models for manual deflections . Pearson correlation coefficients ( R ) between GLM predictions and observed spike rate smoothed at 15 ms are compared between the full model and the subset models . ( A ) Histogram of Pearson correlations between the spike rates predicted by the full model and the observed spike rates , for all neurons . ( B ) Percent error between the R value for the full model and for each of the subset models is plotted for each neuron . Active contact responses are plotted as magenta triangles , manual deflections as cyan circles . Values close to zero indicate that the subset model performed almost as well as the full model; values less than zero indicate that the subset model performed better than the full model . Histograms indicate the distributions of the percent differences of each subset model for active contacts ( magenta ) , manual deflections ( cyan ) , and the whole population ( gray ) . For the data shown here ( see text ) , results for the subset model trained on mechanical data are significantly closer to zero for manual deflections but not for active contacts . ( C ) The R values for the two subset models are plotted against each other . Points that lie below the diagonal indicate that the mechanical model better predicted the spike rate than the kinematic model . Color and marker scheme same as in ( B ) . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 01610 . 7554/eLife . 13969 . 017Figure 6—source data 1 . Summary data used to create Figure 6 are reported . The accuracies ( Pearson Correlation Coefficient , R-value ) of the full models , the mechanical models , and the kinematic models for each cell are listed in columns B , C , and D respectively . Column E indicates whether the neuron was recorded in the awake or anesthetized animal . DOI: http://dx . doi . org/10 . 7554/eLife . 13969 . 017 We then asked how the accuracy of the subset models compares to that of the full model for both active contacts and manual deflections . In Figure 6B we plot the distribution of the percent error between the full model and each of the subset models . Percent errors near zero indicate that the subset model performed as well as the full model; values below zero indicate that the subset model performed better than the full model . The data shown in Figure 6B omit two points for which the full model performs worse than both subset models . These points also exhibited the worst full model performance , with R values smaller than 0 . 05 . All subsequent analyses omit these two points . For the manual stimulation data , the median percent error for the mechanical subset models tend to lie closer to zero than the median percent error for the kinematic models ( Wilcoxon signed rank test p<0 . 05 ) ; in contrast , there is no such trend for the active contact data ( Wilcoxon signed rank test p=0 . 43 ) . Finally , in Figure 6C , we compared the accuracy of the mechanical subset model versus that of the kinematic subset model for both active contacts and passive , manual deflections . We found that 75% ( 15/20 ) of neurons recorded with passive stimulation lie below the diagonal ( linear model slope 95% CI = [0 . 20 0 . 96] , paired t-test p<0 . 05 ) , while those neurons recorded with active touch lie closer to the diagonal ( 6/9 above , 3/9 below; linear model slope 95% CI = [-0 . 42 , 1 . 73] , paired t-test p=0 . 37 ) . These results indicate that although the mechanical model better predicts firing during manual stimulation , there is no evident preference for kinematic or mechanical models during active touch . The input space characterization in Figure 3 explains why it is not possible to distinguish between subset models during active contact: in this scenario , the input space is relatively small and the kinematics and mechanics tend to be more tightly coupled than under manual , passive stimulation . If the inputs to the two subset models are highly coupled – as they are in the active case – then these models receive similar input information and neither can expected to predict Vg activity better than the other .
Historically , responses of neurons in the vibrissotrigeminal system have been described in terms of whisker kinematics ( Gibson and Welker , 1983b; Jones et al . , 2004a , 2004b; Leiser and Moxon , 2007; Lottem et al . , 2015; Moore et al . , 2015; Shoykhet et al . , 2000; Simons , 1978; Szwed et al . , 2003 , 2006; Zucker and Welker , 1969 ) . More recently , however , studies have suggested that mechanics offer an alternative explanation for firing properties of neurons at multiple levels of the trigeminal pathway ( Campagner et al . , 2016; Chen et al . , 2015; Hires et al . , 2015; Xu et al . , 2012 ) . The possibility that Vg neurons encode the mechanics of touch is not inconsistent with the body of literature describing kinematic encoding , because mechanical and kinematic variables are often inherently coupled . It is common to stimulate whiskers through small angles close to the base , so that almost no bending of the whisker occurs ( Gibson and Welker , 1983b; Jones et al . , 2004a , 2004b; Lichtenstein et al . , 1990; Zucker and Welker , 1969 ) . Under these stimulation conditions there is no room for mechanics and kinematics to decouple , making it impossible to distinguish between these two coding possibilities . Campagner et al . ( 2016 ) elegantly demonstrate this coupling during passive stimulation with a piezoelectric ( piezo ) bender . They show that during piezo stimulation , curvature change and angle are tightly correlated; GLMs based on either of these variables therefore produce indistinguishable predictions . They further show that in the awake animal , in contrast , curvature change and angle are decorrelated; they attribute this decorrelation to the awake condition . The novel manual stimulation paradigm of the present work demonstrates that kinematics and mechanics are not necessarily coupled during passive stimulation , nor necessarily decoupled during active contact ( Figure 3 ) . Decoupling is essential to distinguish between the two possible coding schemes in the trigeminal ganglion . The novel manual whisker stimulation protocol employed in the present work allows us to reliably explore larger regions of input space in which the strong coupling between mechanics and kinematics breaks down ( Figure 3 ) . By working in this decoupled regime , the present study demonstrates that Vg neurons more closely represent mechanical rather than kinematic variables during contact . The optimal filters produced by the GLM more heavily weight the mechanics of contact; on average , bending moment is the most important predictor in models that have access to both mechanical and kinematic inputs ( Figure 4B ) . Furthermore , in cases where mechanics ( rather than kinematics ) account for most of the predictive ability of the full model , the full model better predicts the spiking behavior of the neuron . The predictive accuracy of models with access to only mechanical inputs is frequently as good as that of models with access to all inputs; this is less frequently the case for models with access to only kinematic inputs ( Figure 6B ) . Finally , models with access to only mechanical inputs perform better than those with access to only kinematic inputs ( Figure 6C ) . Importantly , the improved predictive accuracy attributed to mechanical variables is seen only for experiments in which the kinematics and mechanics are decoupled and thus carry distinct information . In our experiments , body-restrained awake animals only infrequently exhibited the type of whisking behavior that would be required to sample a large input space and decouple kinematics and mechanics . Accordingly , models of Vg responses in the awake animal based on mechanical variables rarely outperformed those based on kinematic variables , mirroring the null result observed by Campagner et al . ( 2016 ) during passive stimulation when mechanical and kinematic information were coupled . It is worth emphasizing that our conclusions , as well as those of Campagner et al . ( 2016 ) , regarding the comparative ability of kinematic and mechanical variables to predict the firing of Vg neurons , are based on a simple model of neural encoding: that Vg neurons respond to a linear combination of relevant features of the stimulus , followed by a global static nonlinearity that accounts for the Poisson statistics of the spike generation process . This is the conceptual framework that underlies the choice of GLM models , whose ability to predict the firing of Vg neurons in response to passive stimulation was first established by Bale et al . ( 2013 ) . In asking which set of variables , kinematic vs mechanical , are better predictors of Vg activity when used as inputs to a GLM model , we ask which set of variables is more informative within the hypothesis of linear-non-linear ( LNL ) encoding . At first glance , some results of the present work may appear to contradict those of Campagner et al . ( 2016 ) . Our results show that mechanical models perform better than kinematic models in anesthetized experiments but show little distinction in the awake animal . In direct contrast , Campagner et al . ( 2016 ) find similar performance of mechanical and kinematic models in the anesthetized animal but that mechanical models perform better than kinematic models in the awake preparation . The fundamental reason for the apparent discrepancy is that in the awake animal Campagner et al . ( 2016 ) use a kinematic variable ( θhead ) that varies independently of object contact , but a mechanical variable ( change in curvature ) that varies only with contact . Given that the response of Vg neurons is strongly correlated with contact ( Leiser and Moxon , 2007; Zucker and Welker , 1969 ) , the mechanical variable will necessarily have a higher predictive value , especially at 100 ms time scales that match the duration of a whisk . The reason θhead is independent of contact is that this angle is measured with respect to the midline of the animal’s head . In contrast , change in curvature at the base ( a proxy for bending moment ) is measured independently of the whisker’s position relative to the head . The angle θhead and curvature change will be decoupled in the awake experiments because contact with an object can occur at different positions relative to the head . For example , a whisker can exhibit very similar curvature changes regardless of whether it makes contact with a peg at θhead = 70° or at θhead = 110° . In Campagner et al . ( 2016 ) Figure 4G it is clear that if one were to account for the value of θhead at the initial contact with the pole , curvature change would be strongly correlated with an angle that would not be θhead but θpush – the angle used in the present work and in other studies of mechanical coding of object location ( Bagdasarian et al . , 2013; Birdwell et al . , 2007; Kaneko et al . , 1998; Pammer et al . , 2013; Solomon and Hartmann , 2011 ) . Campagner et al . ( 2016 ) briefly address this point . Their results from the awake animal show smaller differences in performance between models based on kinematic or mechanical inputs when θpush is used as the kinematic variable , consistent with the present findings . In the anesthetized experiments of Campagner et al . ( 2016 ) , θhead and curvature are always strongly correlated because the whisker is trimmed ( to 5 mm ) , angles of deflection are relatively small ( 10° ) , and the deflection is always applied at the same value of θhead . Had these experiments used large amplitude deflections and/or deflections further from the whisker base , θhead would presumably have decoupled from curvature changes . More subtly , the quantification of mechanical inputs differs between the present work and that of Campagner et al . ( 2016 ) . Forces and moments at the whisker base cannot be measured directly because any sensor placed at the whisker base would interfere with the whisker’s mechanics . Campagner et al . , ( 2016 ) use curvature change at the base as a proxy for bending moment , an approximation based on linear elastic beam theory ( Beer et al . , 2015 ) . In contrast , we use a validated quasi-static model of whisker bending to compute the forces and moments at the base during contact ( Birdwell et al . , 2007; Huet and Hartmann , 2016; Huet et al . , 2015; Solomon and Hartmann , 2008 , 2010 ) . This model accounts for the full shape of the whisker and offers the advantage of computing the axial and transverse forces in addition to bending moment . Our work and that of Campagner et al . ( 2016 ) agree that Vg neurons encode mechanical variables more robustly than kinematic variables; we suggest that the consistency of this result across studies helps interpret recent data demonstrating phase coding in Vg neurons during free air whisking ( Wallach et al . , 2016 ) . The work of Campagner et al . ( 2016 ) shows that during non-contact whisking , a GLM with access to angular acceleration can account for much of the Vg firing . With the assumption that Vg neurons are mechanically sensitive , our analyses suggest that the phase encoding described by Wallach et al . ( 2016 ) and the angular acceleration tuning described by Campagner et al . ( 2016 ) both result from inertial forces on the follicle that occur during periods of high angular acceleration ( Boubenec et al . , 2012; Quist et al . , 2014 ) . Many Vg neurons are known to respond during both non-contact and contact whisking ( Leiser and Moxon , 2007; Szwed et al . , 2003 ) . Here we propose that the encoding of mechanical signals provides a unified explanation for both phase tuning during non-contact whisking and responses during contact . Ultimately , a dynamic model that describes inertial forces during non-contact whisking will be required to verify this hypothesis . It remains unknown how downstream neurons might distinguish Vg spikes that encode phase and hypothetically represent inertial forces from Vg spikes that represent contact forces . In this light , the results of all four recent studies ( Campagner et al . , 2016; Quist et al . , 2014; Wallach et al . , 2016 , the present study ) provide strong support to the view that Vg neural responses more generally represent the mechanical deformations that occur at the level of the follicle , and that apparent correlations between Vg firing and kinematics are a result of inherent correlations between kinematics and mechanics . This line of evidence suggests that previous results describing the encoding of kinematic variables in the Vg correspond to scenarios characterized by strong correlations between kinematic and mechanical variables . It remains possible that central brain regions take advantage of this inherent correlation to extract behaviorally relevant information about object location or features; there is support from both simulation ( Solomon and Hartmann , 2011 ) and behavioral ( Bagdasarian et al . , 2013; Pammer et al . , 2013 ) studies indicating that rodents could use a combination of Fx and M to determine the 2D location of a contact point . Our models were unable to reach very high prediction accuracies ( median R value = 0 . 30 , max = 0 . 65 ) ; this performance is not as good as might be expected in view of previous evidence that Vg neuron responses are highly precise and repeatable given identical stimuli ( Bale et al . , 2015; Jones et al . , 2004a , 2004b ) . We offer four explanations for these seemingly low correlation values . First , we note that in the present study , R value is only computed during contact , in order to avoid inflation of this statistic due to periods of non-contact when spiking is absent ( anesthetized ) or sparse ( awake ) . When correlation coefficients were computed to include both periods of contact and non-contact in the awake animal , median R-values increased from 0 . 27 to 0 . 47 for kinematic models and from 0 . 26 to 0 . 38 for mechanical models . Including periods of non-contact in model evaluation will tend to inflate model performance; any variable that captures transitions between contact and non-contact will easily predict the associated changes in Vg firing rate . Second , the present work , as well as the majority of reports of Vg neuron firing activity in both awake and anesthetized experiments , is based entirely on a 2D analysis , even though there is ample evidence that the whisker moves in 3D ( Hobbs et al . , 2015 , 2016a; Huet and Hartmann , 2014 , 2016; Huet et al . , 2015; Knutsen et al . , 2008; Yang and Hartmann , 2016 ) and that Vg neurons are directionally tuned in three dimensions ( Jones et al . , 2004a; Lichtenstein et al . , 1990; Minnery and Simons , 2003 ) . Third , the quasi-static models used to compute forces and moments at the base of the whisker omit the effects of friction and whisker dynamics , including collisions and vibrations ( Boubenec et al . , 2012; Jadhav et al . , 2009; Quist et al . , 2014; Ritt et al . , 2008; Wolfe et al . , 2008; Yan et al . , 2013 ) . To predict spikes at high temporal resolution would require the use of a dynamic model and the ability to track the whisker at spatiotemporal resolutions beyond the capability of the videographic approaches used here . Lastly , our models are based on linear combinations of stimuli that vary over wide ranges . The only nonlinearity in the model , a static nonlinearity applied to the linear combination as a whole , accounts for the Poisson nature of spiking statistics . This type of simplified Linear-Nonlinear ( LNL ) model offers strong mathematical advantages; in the case of a GLM , a guarantee that the fitting function that determines the coefficients of the model is convex and has a unique solution easily reachable by gradient methods . However , these models do not allow for linear combinations or nonlinearities that could be specific to some regions in the space of inputs . As our experimental methods sample wider regions of input space , it seems reasonable to expect that a single linearized assumption over the full space followed by a single , global nonlinear transformation will prove to be too simplistic . The relatively low quality of prediction achieved here thus might signal the limitations of this type of GLM . Another limitation of our approach is a time resolution of 15 ms , considerably less than the ms or even sub-ms resolution exhibited by Vg neurons ( Bale et al . , 2015; Jones et al . , 2004a ) . Temporal resolution was similarly limited in the study of Campagner et al . ( 2016 ) , who employed a 100 ms window in contrast to our 15 ms Gaussian kernel . This limit is due in part to experimental constraints in the temporal resolution of the kinematic and mechanical variables chosen as explanatory variables for Vg activity and used as GLM inputs , as addressed in both Results and Materials and methods . As discussed above , the quasi-static models used to compute forces and moments at the base of the whisker further limit the achievable time resolution . In addition , both our work and that of Campagner et al . ( 2016 ) use a similar single-trial modeling approach . Trial averaging would have allowed us to predict spike timing with higher accuracy ( Bale et al . , 2013 ) , but would have required precise duplication of motor command across trials . The variability of whisking behavior in awake animals prevents this duplication . As for the deflection experiments in anesthetized animals , precise duplication could only be achieved by sampling within a narrow region of stimulus space , an approach deliberately avoided here in order to achieve kinematic and mechanical decoupling . Our work thus offers predictive accuracies as high as can be achieved within these experimental and modeling limitations . The results point towards the conclusion that mechanics more accurately predict primary sensory neuron firing than kinematics , within the hypothesis of linear-non-linear ( LNL ) encoding , and when the two sets of variables are decoupled . A more stringent test of this hypothesis would require a full 3D characterization of both kinematic and mechanical signals at higher spatiotemporal resolution , a full dynamic model of the whisker for computing forces and moments at its base , and possibly an increased level of modeling sophistication beyond GLMs . Ultimately , access to a large , decoupled input space is likely to be critical in understanding the coding properties of Vg neurons during natural behavior . Body or head restrained animals tend to generate relatively stereotyped , small angle whisking motions ( Deutsch et al . , 2012 ) that sample the input space within the coupled regime ( Figure 3 ) . However , tactile information acquired through whisking during exploratory behavior is varied and complex ( Arkley et al . , 2014; Carvell and Simons , 1990; Grant et al . , 2009; Hobbs et al . , 2016a , 2016b; Mitchinson et al . , 2007; Saraf-Sinik et al . , 2015; Schroeder and Ritt , 2016; Sellien et al . , 2005; Towal and Hartmann , 2008; Voigts et al . , 2015 ) . Neurons of the Vg must be able to encode the signals associated with the full range of potential stimuli , including large angle deflections and very distal contacts . By adopting a mechanical characterization of tactile information , we can quantify the large input space available during tactile sensation in a manner that incorporates the true shape and deformability of the whisker .
Animals were anesthetized with a ketamine-xylazine hydrochloride combination delivered intraperitoneally ( 60 mg/kg ketamine , 3 . 0 mg/kg xylazine , and 0 . 6 mg/kg acepromazine maleate ) . Four or five stainless steel screws were placed in the skull over neocortical areas and covered in dental acrylic . For anesthetized recordings this structure was affixed to the surgical bed; for chronic ( awake ) recordings , it formed the base of the electrode implant . A small ( ~1 mm diameter ) craniotomy was then performed in order to allow access to the trigeminal ganglion ( Vg ) , at location ~2 mm caudal relative to bregma and ~2 mm lateral to the midline . A single tungsten electrode ( FHC , Bowdoin , ME; typical impedance 2–5 MΩ ) was lowered to a depth of ~10 mm until multi-unit responses to whisker deflections could be heard . The electrode was then lowered more slowly until isolated single neuron responses to tactile stimulation of a single vibrissa were obtained . For chronic recordings , the electrode was then fixed in place using dental acrylic . In some animals , electrodes were bilaterally implanted in the Vg . Recordings from awake , chronically implanted animals were started no sooner than four days after surgery and continued for up to three weeks . All chronic implantation surgeries were performed in a sterile field . Five animals were used to test the responses of Vg neurons to passive , manual deflection . After performing the craniotomy described above , single tungsten electrodes ( FHC ~1 MΩ ) were lowered to a depth of ~10 mm until a neuron that responded to the deflection of a single whisker was isolated . We recorded video from a top-down view at 300 fps with an exposure time of 1 ms ( Teledyne Dalsa Genie HM640; Waterloo , Canada ) . Neural signals were amplified on an A-M Systems ( Sequim , WA ) four channel amplifier ( 1000x gain ) with analog bandpass filtering between 10 Hz and 10 kHz before digital sampling at 40 kHz using Datawave SciWorks ( Loveland , CO ) . After acquisition , traces were digitally bandpass filtered between 300 Hz and 6000 Hz before spike sorting . Spikes were identified and sorted offline , and spike times were rounded to the nearest ms for comparison with video data . Examples of raw data are shown in Figure 1A–C . In order to robustly track the whisker in the high-speed video , the surrounding fur was removed with depilatory cream ( Nair; Church and Dwight , Ewing , NJ ) and surrounding whiskers were either trimmed or held back against the fur . Care was taken not to deform the whisker or the mystacial pad during recordings . Whiskers were deflected manually by pressing a 0 . 3 mm graphite rod against the whisker ( Video 1B ) . Between 20 and 40 deflections were applied at variable radial distances ( up to 90% of the whisker length ) , at two velocities and two directions ( rostral to caudal , and caudal to rostral ) for a total of 80–160 deflections per whisker . Analyses were restricted to distal contacts ( >40% of the whisker length ) , where the follicle does not move appreciably during contact . Whiskers were also held in a deflected position for periods of about 3 s to test adaptation characteristics . All deflections were on the order of several mm . Seven animals were gentled for 8–10 days prior to surgery . During gentling , rats were acclimated to restraint in a V-shaped fabric bag that prevented body movement but permitted head and neck movements . Starting four days after surgery , on each day of testing we gently restrained the rat and again confirmed that each neuron responded to tactile stimulation of one and only one whisker . All other whiskers on that side of the rat’s face were trimmed to the level of the fur . Rats were then placed in the fabric bag , and high-speed video ( Photron FastCam , San Diego , CA; either 1024PCI or 512PCI ) was used to record the top-down view of the rat’s head as it whisked against a rigid vertical peg ( 3 mm diameter ) . Video was taken at 1000 fps , with a shutter speed of 1/3000 s to reduce motion blur . Signals from Vg neurons were recorded with a Triangle Biosystems ( Durham , NC ) 8-channel preamplifier ( 2x gain ) and a custom-built amplifier ( 500x gain ) . Signals were analog band-pass filtered between 0 . 33 Hz and 10 kHz before sampling at 40 kHz using Datawave SciWorks . Traces were then digitally bandpass filtered between 300 Hz and 8000 Hz before spike sorting . Spikes were identified and sorted offline , and spike times were rounded to the nearest ms for comparison with video data . Examples of raw data are shown in Figure 1D–E . For both anesthetized and awake experiments , whisker shape was extracted from each video frame using the software "Whisk" ( Clack et al . , 2012 ) . The kinematic and mechanical variables of contact were computed from the whisker shape; see Figure 2 of Results . The kinematic variables of contact are: radial distance ( r ) , angle of contact ( θpush or θdeflection ) , and angular velocity ( V ) . The variable r is the linear distance between the basepoint and the contact point . The variable θdeflection is valid for manual deflection; as illustrated in Figure 2A , it is the angle between two line segments: one that connects the initial point of contact to the whisker basepoint and one that connects the current contact point to the whisker basepoint ( Gibson and Welker , 1983a , 1983b; Lichtenstein et al . , 1990; Lottem and Azouz , 2009 , 2011; Shoykhet et al . , 2000 , 2003 ) . The variable θpush is valid for active whisking; as illustrated in Figure 2D , it represents the angle swept out by the tangent to the whisker at its base from the time of contact onset to the current time ( Bagdasarian et al . , 2013; Quist and Hartmann , 2012; Solomon and Hartmann , 2011 ) . The velocity ( V ) is the temporal derivative of either θdeflection or θpush . The mechanical variables of contact are the axial force ( Fx ) , the force parallel to the whisker axis near its base , positive pointing out of the follicle; the transverse force ( Fy ) , the force perpendicular to the whisker axis , directed in the rostral direction; and the bending moment ( M ) , the moment about the vertical z-axis that passes through the whisker base . Mechanical variables were computed using a quasi-static model of whisker bending ( Birdwell et al . , 2007; Quist and Hartmann , 2012; Solomon and Hartmann , 2008 , 2010 ) . All mechanical and kinematic data were median filtered to eliminate point outliers . Variables computed from video acquired at 300 fps were linearly interpolated to 1000 Hz for comparison with spike times on the 1 ms scale . Velocity was calculated using a central difference approximation of the angular component of contact and low pass filtered at 85 Hz . The spike train was smoothed with a Gaussian kernel with standard deviation σ to find the rate r ( t ) : ( 1 ) r ( t ) = 12πσ2 ∑j=1Ne− ( t−tj ) 22σ2 , where N is the total number of spikes , σ is the standard deviation of the kernel , and tj is the time of spike j . The standard deviation σ of the Gaussian kernel was varied between 1 ms and 500 ms to observe the effect of temporal smoothing on the quality of predictions . An optimal kernel width of σ=15 ms was chosen for all subsequent analyses . This was the smallest value of σ , below which we observed a sharp decrease in the quality of predictions . Each GLM is of the form: ( 2 ) p ( t ) =f ( ∑j=1K∑t′=0ταj ( t′ ) xj ( t−t′ ) ) . Here p ( t ) is the probability that the neuron emits a spike in the 1 ms time interval centered at time t , f is a logistic nonlinearity , and j sums over all the predictor variables . Each one of these variables xj , 1≤j≤K , contributes to the argument of the logistic nonlinearity through its current value and its values in the preceding τ time bins , weighted by the filter parameters αj ( t′ ) , 0≤t′≤τ . Full models used ( r , θ , V , Fx , Fy , M ) as predictor variables ( K=6 ) , while subset models had access to either kinematic variables ( r , θ , V ) or mechanical variables ( Fx , Fy , M ) , so that K=3 . Since the neural response is quantified as a spike either present or absent in each 1 ms time bin , the statistics process being modeled is Bernoulli and the nonlinearity is sigmoidal ( McCullagh and Nelder , 1989 ) : ( 3 ) f ( u ) = 11+e−u . The GLM finds the filters {αj ( t′ ) } , 0≤t′≤τ , 1≤j≤K that maximize the likelihood of the observed spiking activity . To enforce continuity of the filters as a function of time and reduce the number of coefficients needed to specify the model , it is convenient to introduce a basis of 'raised cosine bumps' bl ( t ) , 1≤l≤L ( Pillow et al . , 2008 ) . Here we used the L=5 basis shown in Figure 4A . The functions peak at 0 ms ( l=1 ) , 1 ms ( l=2 ) , 3 ms ( l=3 ) , 8 ms ( l=4 ) , and 17 ms ( l=5 ) ; the basis covers 75 ms into the past . The expansion of each filter in terms of this basis , namely ( 4 ) αj ( t′ ) =∑l=1Lbl ( t′ ) βlj , results in an interesting reformulation of the GLM: ( 5 ) p ( t ) =f ( ∑j=1K∑l=1Lβlj x~lj ( t ) ) , where the input variables to the model x~lj ( t ) are now 'convolved predictors , ' the filtered versions of the original input variables , namely: ( 6 ) x~lj ( t ) =∑t′=0τbl ( t′ ) xj ( t−t′ ) . In this formulation , the problem of fitting the parameters of the GLM is reduced from that of finding the filters αj ( t′ ) , 1≤j≤K , 0≤t′≤τ , to that of fitting a smaller number of parameters: the coefficients βlj , 1≤l≤L , 1≤j≤K . To evaluate each GLM we implemented ten-fold cross-validation , using 90% of each neuron’s dataset to fit the coefficients βlj . The fitted GLM was used to predict the spike rate on the remaining 10% of the data . This procedure was repeated ten times , so that the entire neural response was eventually predicted from a model whose coefficients were fit on independent data . This method prevents overfitting and allows the model to be evaluated based on how well it generalizes to new data . The quality of each GLM was quantified through the correlation coefficient between the predicted rate p ( t ) of Equation 5 and the rate r ( t ) obtained from Equation 1 . Data for non-contact periods were omitted in calculations of correlation coefficients . Given that Vg neurons do not fire during non-contact , a precise prediction of no activity during these periods would have unduly inflated model performance . Predictions were tracked only during contact periods . The percent difference between the subset models and the full model was calculated as 100* ( Rfull− Rsubset ) Rfull , where R is the Pearson Correlation Coefficient between the observed spike rate r ( t ) and the predicted spike rate p ( t ) obtained with either the full model or one of the two subset models . | Animals must gather sensory information from the world around them and act on that information . Specialized sensory cells convert physical information from the environment into electrical signals that the brain can interpret . In the case of hearing , this physical information consists of changes in air pressure , and for vision , it is patterns of light bouncing off of objects . Rodents rely heavily on touch information from their whiskers to explore their world . When a whisker touches an object , it deforms and bends . The first neurons to respond to whisker touch – so called primary sensory neurons – represent contact between the whisker and the object in the form of electrical signals , but exactly how they do this is unclear . One possibility is that primary sensory neurons encode the movement of the whisker itself . Whenever a whisker touches an object , the whisker is deflected in a particular direction by a particular amount and at a particular speed . These movement-related features are referred to as the “kinematic” properties of whisker-object contact . Alternatively , these whisker sensory neurons might be more concerned with the forces at the base of the whisker caused by object contact . These forces are the “mechanical” properties of whisker-object contact . Bush , Schroeder et al . set out to determine whether the electrical response of these whisker sensory neurons mainly encode kinematic or mechanical information . However , these two types of information are often closely related to each other: put simply , small whisker movements tend to accompany small forces and vice versa . Bush , Schroeder et al . therefore devised a method to deliver touch stimuli to the whiskers in a way that separates kinematic from mechanical information . Mathematical models were then developed to compare how well the neurons represent each type of information . The models showed that whisker sensory neurons generally encode mechanical signals more directly than kinematic ones . This information adds to our understanding of how animals learn about the world through their senses . However , the analysis of Bush , Schroeder et al . relies on the long-standing simplification that whisker motion is two-dimensional , whereas in reality whiskers move in three dimensions . Therefore , a future challenge is to examine how sensory neurons represent information about touch , such as the location or shape of an object , during three-dimensional whisker-object contact . | [
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] | 2016 | Decoupling kinematics and mechanics reveals coding properties of trigeminal ganglion neurons in the rat vibrissal system |
The rapid evolution of a trait in a clade of organisms can be explained by the sustained action of natural selection or by a high mutational variance , that is the propensity to change under spontaneous mutation . The causes for a high mutational variance are still elusive . In some cases , fast evolution depends on the high mutation rate of one or few loci with short tandem repeats . Here , we report on the fastest evolving cell fate among vulva precursor cells in Caenorhabditis nematodes , that of P3 . p . We identify and validate causal mutations underlying P3 . p's high mutational variance . We find that these positions do not present any characteristics of a high mutation rate , are scattered across the genome and the corresponding genes belong to distinct biological pathways . Our data indicate that a broad mutational target size is the cause of the high mutational variance and of the corresponding fast phenotypic evolutionary rate .
In a given phylogenetic clade of organisms , some phenotypic traits evolve faster than others or faster than in other groups . When they in addition appear to evolve directionally , this is called an evolutionary trend ( Gould , 1988; McShea , 1994; McShea , 2000 ) . Classical examples are the reduction in digit number of horses , the increase in brain size in hominids or the change in fractal complexity of suture lines in the fossil record of ammonites ( McNamara , 2006 ) . A possible explanation for fast evolutionary change of a trait is the sustained action of natural selection on the trait , acting in either a directional or a diversifying manner . A second explanation arises from the fact that the available phenotypic variation onto which natural selection acts is not uniform along all axes of phenotypic space ( developmental constraints or the ‘arrival of the fittest’ ) ( Gould , 1977; Cheverud , 1984; Alberch and Gale , 1985; Maynard Smith et al . , 1985; Arthur , 2004; Dichtel-Danjoy and Félix , 2004; Denver et al . , 2005; Rifkin et al . , 2005; Landry et al . , 2007; Stoltzfus and Yampolsky , 2009; Wagner , 2014; Hether and Hohenlohe , 2014; McGuigan and Aw , 2017; Hine et al . , 2018 ) . Indeed , upon random mutation , some axes of phenotype space are more readily explored than others . In other terms , the mutational variance may not be equal along different axes of phenotype space and this may sufficiently affect the rate of evolution at the phenotypic level . Natural selection may act in an orthogonal manner to the mutational variance in phenotype space ( that is , may select on a trait with low mutational variance ) ; and along the axis of high mutational variance , it may act in the same direction or in the opposite direction . Phenotypic evolution then results from the combination of the mutational variance and natural selection . The present study addresses the causes of high mutational variance along some directions of phenotypic space . Two non-mutually exclusive explanations may underlie such phenomenon , the first at the molecular level , the second at the level of genotype-phenotype mapping: ( 1 ) some DNA sequences , such as short tandem repeats , are more prone to spontaneous mutation; ( 2 ) a higher mutational variance could be due to a higher mutational target size affecting this phenotype . These two factors may act jointly . In the first case , mutational hotspots affecting the phenotype disproportionately increase mutational variance for this trait . Specifically , short repeat regions in a gene may favour DNA replication slippage and recombination , leading to gain or loss of repeats ( Heale and Petes , 1995; Gemayel et al . , 2010 ) , or result in fragile DNA conformation susceptible to double-strand breaks ( Xie et al . , 2019 ) . Such highly mutable repeats may lie in a coding region ( Verstrepen et al . , 2005 ) or within regulatory sequences of a gene ( Vinces et al . , 2009; Chan et al . , 2010 ) . Their variation has been shown to affect various phenotypes in different organisms ( Levdansky et al . , 2007; Undurraga et al . , 2012; Gemayel et al . , 2017; Dai and Holland , 2019 ) and in humans to lead to diseases such as Huntington and fragile X syndromes ( Budworth and McMurray , 2013 ) . Consequently , the high mutability of some DNA regions may accelerate the evolution of specific traits . Examples are the fast-evolving dog head shape ( Fondon and Garner , 2004 ) or the recurrent pelvic fin reduction in sticklebacks ( Chan et al . , 2010; Xie et al . , 2019 ) . In the second case , the higher mutational variance of a phenotype may be due to a larger mutational target size rather than a high mutation rate at a given locus: the mutational variance increases with the number of genes ( and size of gene regions ) whose mutation alters the phenotype . This may be the case for a phenotype that is sensitive to small quantitative alterations , for example in biochemical pathways . The construction of such a trait may indeed be affected by mutations at many loci , many of which may only affect the trait at low penetrance . In another case , bacterial tolerance to antibiotics , mutations to tolerance are frequent because mutations affecting bacterial growth or lag time result in tolerance ( Girgis et al . , 2012; Girgis et al . , 2009; Fridman et al . , 2014; Brauner et al . , 2016; Khare and Tavazoie , 2020 ) . Some traits are indeed known to be highly polygenic in natural populations . Some authors even proposed an ‘omnigenic’ model , where phenotypic variation may result from variation at many genes outside the core pathways known to regulate the phenotype ( Boyle et al . , 2017 ) . This model fits quantitative genetic data of human diseases ( Liu et al . , 2019 ) . However , the number of loci segregating in natural populations also depends on factors such as population structure and selection . To address the origin of a high mutational variance , a more direct approach is needed and more data need to be collected to evaluate how much and in which context each of the above scenarios - highly mutable loci versus a broad mutational target - contributes to a fast rate of phenotypic evolution . We use the nematode vulva to explore this question . This developmental system relies on six precursor cells , with several useful features: ( 1 ) the developmental fate of the six homologous cells can be followed in a wide range of species; ( 2 ) the mutational variance of the different precursor cells can be compared on the same scale; ( 3 ) much knowledge has been accumulated on the specification of vulval precursor cell ( VPC ) fates through laser cell ablation studies and developmental genetics . The six vulva precursors are born aligned along the ventral epidermis of the young larvae and are numbered P3 . p to P8 . p from anterior to posterior ( Figure 1a ) . The six cells initially share an identical fate of ventral epidermal blast cells . Under the influence of several signalling pathways , each precursor cell differentiates with a specific terminal fate , creating reproducible patterns of cell fates shared by taxonomic groups of varying size ( Figure 1 ) . As showed earlier , the developmental fate of one of these six cells , P3 . p , by far evolves faster than that of the other Pn . p cells , both within and among species in the Caenorhabditis genus ( Delattre and Félix , 2001; Kiontke et al . , 2007; Braendle et al . , 2010; Pénigault and Félix , 2011a ) . While P5 . p , P6 . p and P7 . p divide several times to form the vulva under the influence of EGF and Notch signaling , P4 . p and P8 . p most often divide once and their daughters fuse with the large epidermal syncytium hyp7 at the end of the third larval stage ( L3 ) . Their fate does not evolve in most of the Caenorhabditis genus . In contrast , P3 . p may either fuse to the hyp7 syncytium already at the end of the L2 stage ( with no further cell division possible ) or divide once in the L3 stage ( Sternberg , 2005; Félix , 2012 ) . For simplicity , we will refer to this trait as a binary choice between absence or presence of division , which we quantify as a frequency of division in an isogenic population . Isogenicity of the population is obtained easily in the two nematode species we use here , C . elegans and C . briggsae , because they reproduce through selfing ( with the possibility of controlled outcrossing with males for genetic analysis ) . We previously showed using mutation accumulation ( MA ) lines that the particularly fast rate of phenotypic evolution of P3 . p fate in the Caenorhabditis genus is very likely explained by its high mutational variance ( Braendle et al . , 2010 ) . MA experiments are ideal to test whether some traits vary more than others upon spontaneous mutation and to address the origin of variation in mutational variance . Since the effect of selection is reduced to a minimal fertility requirement at each random generational bottleneck , the mutational variance as measured in MA experiments can be compared to evolution with natural selection in the wild ( the intraspecific standing genetic variance and the interspecific divergence ) to infer the role of natural selection . In this manner , we previously showed that P3 . p division frequency likely evolved driven by its high mutational variance and under minimal selection ( Braendle et al . , 2010 ) . Indeed , when either C . elegans or C . briggsae wild isolates are subjected to spontaneous mutation accumulation , P3 . p cell fate had the highest phenotypic variance compared with the other five cells . P4 . p showed the second highest mutational variance and standing genetic variance , yet an order of magnitude lower than P3 . p ( Figure 1 and Figure 1—figure supplement 1; Braendle et al . , 2010 ) . Thus , in this system as for wing shape in drosophilids ( Houle and Fierst , 2013; Houle et al . , 2017 ) or mitotic spindle traits in Caenorhabditids ( Farhadifar et al . , 2015; Farhadifar et al . , 2016 ) , the mutational variance matches the evolutionary pattern , with the added advantage here of comparing homologous cells . Here , we use MA lines to test whether P3 . p fate evolvability is caused by a high mutation rate at few loci or by a broad mutational target affecting P3 . p fate . To this end , we selected five MA lines showing P3 . p fate divergence with the ancestral line . We combine whole-genome sequencing , genetic linkage analysis of the phenotype in recombinant lines and candidate testing through mutant and CRISPR genome editing to identify causal mutations and the corresponding loci . In each line , we found a single causal mutation . The five causal mutations are in five different genomic regions , are not associated to highly mutable sequences and are different in nature ( two SNPs , one small deletion and two large deletions ) . Functionally , only one of them affected an expected gene involved in the Wnt pathway , a ‘core’ signaling pathway known to regulate Pn . p fusion to the epidermis in the L2 stage ( Pénigault and Félix , 2011b ) . Two other loci encode general regulators of transcription and translation , while the two final loci lack functional annotation . We conclude that the fast evolutionary rate of change in P3 . p cell fate may be explained by a broad mutational target for this trait .
Estimating accurate frequencies for a binary trait requires a high number of individuals . We selected fifteen MA lines derived from two C . briggsae ( HK104 and PB800 ) and two C . elegans ( PB306 and N2 ) wild ancestors that had accumulated mutations for 250 generations ( Figure 2a and Figure 2—figure supplement 1 ) with a putatively deviant P3 . p division frequency from a previous study ( Braendle et al . , 2010 ) . We phenotyped the selected lines again with their corresponding ancestral line with a large number of animals and in replicate experiments ( see Figure 2 , Figure 2—figure supplement 1 , Supplementary file 1 and Materials and methods ) . This led us to reduce the selection to six MA lines ( two C . briggsae and four C . elegans lines ) that displayed large differences in P3 . p division frequency compared to their corresponding ancestral line , ranging from 19% to 53% ( Figure 2b ) . These were MAL 211 and 296 derived from HK104 ( C . briggsae ) , MAL 418 , 450 and 488 derived from PB306 ( C . elegans ) , and MAL516 from N2 ( C . elegans ) . We aimed to identify the spontaneous mutations that had appeared during the 250 generations of mutation accumulation with two main goals: ( 1 ) provide a reliable list of molecular markers for genetic linkage analysis; ( 2 ) find candidates for the causal mutation . Genomic DNA of the selected MA lines and their respective ancestor was sequenced at an average sequencing depth of 20x ( Supplementary file 2 ) . We used a combination of tools to cover a diversity of possible mutations ( SNPs , short indels and structural variants ) . Prioritizing the first goal , we endeavoured to minimize false positive calls in two ways ( see Materials and methods and Figure 3—figure supplement 1 ) . First , we filtered out variants that were not unique to a MA line in a cohort derived from the same ancestor , so as to eliminate possible background variants that may have been missed in the ancestor . Such variants were particularly abundant in MA lines derived from the HK104 and PB306 ancestral backgrounds , which differ greatly from the reference genome of each species ( AF16 for C . briggsae and N2 for C . elegans , respectively ) . Second , we excluded error-prone repeats from the short-variant analysis . These two filters excluded potential loci that could explain P3 . p fate variation; in spite of this , the genetic linkage analysis should identify the chromosomal interval where the causal variant lies . A more sensitive variant analysis in this candidate interval would then be possible if the causal variant was not found in the first stringent analysis ( which turned out not to be required ) . With this strategy , we listed 595 de novo mutations in the six MA lines , spread along the genome ( Figure 3—figure supplement 2 and Supplementary file 3 ) . These mutations were mostly short ( i . e shorter than the 100 bp read length ) indels ( 341 ) , SNPs ( 250 ) , and four large deletions ( Figure 3—figure supplement 3 ) . We reliably used the SNPs from these calls directly as genetic markers: indeed , all but one over 60 SNP tested were validated by direct re-sequencing ( Figure 3—figure supplement 3 , see Materials and methods ) . Five of the six MA lines were further processed to genetically map the causal mutations affecting P3 . p division frequency . The genetic mapping method relies on the same logic for all five MA lines ( with some differences in the crossing schemes and selection strategies , see Materials and methods and Figure 3—figure supplements 4–8 ) generating several backcrossed lines , phenotyping and sorting them as ‘ancestor-like’ or ‘MA-line-like’ according to their phenotype ( Figure 3; blue and red bars and dots , respectively ) then genotyping them for a set of relevant de novo mutations identified above . Backcrossed lines were selfed for several generations to render them mostly homozygous . In all cases , the phenotype segregated as a single locus . A candidate genetic interval was defined as the minimum interval that bears the MA line genotype in all phenotypically MA-line-like backcrossed lines and the ancestral genotype in all phenotypically ancestor-like backcrossed lines . Serial backcrosses ( once to four times ) allowed to reduce the genetic interval , which still ranged from 4 to 15 Mbp ( Figure 3 and Supplementary file 4 ) . Importantly , we identified intervals on four different chromosomes ( I , III , IV and X ) and two distinct regions on chromosome III . The genetic intervals were thus distinct in each line , excluding that recurrent mutations at a common locus could control the evolution of P3 . p in the MA lines . The genetic intervals only contained few mutations ( from 1 to 10 ) . Predictions of functional impacts pointed to an obvious candidate lesion for each line . Four candidate lesions affected the coding region of a gene and the fifth was a large deletion spanning 10 genes ( Figure 3 and Figure 4a ) : two non-synonymous nucleotide substitutions in MAL 296 and 450 , and deletions of 16 , 1344 and 54 , 355 base pairs in MAL 488 , 516 and 418 , respectively . The four single-gene mutations were validated by directly editing the genome of the ancestral line with CRISPR/Cas9-mediated homologous recombination technology to reproduce the mutation observed in the MA line ( Supplementary file 5 , see Materials and methods ) . In the case of the two non-synonymous nucleotide substitutions , we also introduced synonymous mutations in the guide RNA to avoid Cas9 re-cutting ( Supplementary file 5 ) and hence used controls with the synonymous mutations but without the candidate non-synonymous substitution ( Figure 4b and c ) . In the case of the 16 and 1344 base pairs ( bp ) deletions ( Figure 4d and e ) , we provided a repair template that fully matched the sequence of the MA line in this region . In the case of the 54 , 355 bp deletion in MA line 418 , we separately induced frameshifting indels via CRISPR/Cas9 in the coding region of seven genes within the interval and found that the deletion of one of them , Y75B8A . 8 , reproduced the P3 . p phenotype of the MA line ( Figure 4f and S11 ) . This is in concordance with the analysis of different mutant lines for genes at this locus ( Figure 4—figure supplement 1c ) . In all five cases , genome editing of the ancestor reproduced the change in P3 . p division frequency observed in the MA line ( Figure 4 ) . These results were confirmed by phenotyping two independent CRISPR lines ( Figure 4 ) and independent alleles of the same gene ( Figure 4—figure supplement 1c ) . The induced mutations also reproduced pleiotropic alterations of vulva traits or other phenotypes that were co-segregating with P3 . p behavior during the backcrosses ( Supplementary file 6 ) – while some other phenotypes were eliminated by backcrossing . These results demonstrated that the five candidate mutations identified by genetic linkage analysis were necessary and sufficient to explain the evolution of P3 . p division frequency . The molecular nature of the five mutations was diverse ( Figures 4a and 5a ) : two non-synonymous single-nucleotide substitutions , a small 16 bp deletion and two larger deletions of 1 , 344 bp and 54 , 355 bp . The substitutions are a T-to-G transversion and a T-to-C transition , which are not the most frequent substitution types in Caenorhabditis spontaneous mutation accumulation lines ( Denver et al . , 2012 ) . Considering the three-bp motif ( with the mutant base at the 3' end ) ( Saxena et al . , 2019 ) , the corresponding motifs ( ATT and AGT , respectively ) were not reported to be those with the highest spontaneous mutation rates either . Small deletions have lower mutation rates than single-nucleotide substitutions ( Saxena et al . , 2019; Konrad et al . , 2019 ) . As for the large deletions , they appear less frequent that large insertions/gene duplications ( Konrad et al . , 2018 ) . Thus , these five mutations do not point to particularly frequent types of mutation . Next , we analysed the surrounding sequences of the causal mutations and their local and global genomic contexts and found no common element among the five mutations: they lie in regions with different GC contents ( from 16% to 50% in a 50 bp window centered on the causal mutation ) , in regions either rich or poor in repeats , in chromosome arms or centres ( Figure 5b and Figure 5—figure supplement 1a–i ) . Repeats are associated to higher mutation rates ( Heale and Petes , 1995; McDonald et al . , 2011 ) . In sequence data of other C . elegans spontaneous MA lines ( Saxena et al . , 2019 ) , we indeed found an overrepresentation of mutations in repeated sequences: 42% of mutations ( n = 3469 ) were found in repeated sequences that represent 20% of the genome ( X2-test: p-value<2 . 2×10−16; however , note that false-calling rates are expected to be higher in repeats ) . Of the causal mutations , the two substitutions and the 16 bp deletion do not lie in repeats . The 3' breakpoint of the large 54 , 355 bp deletion lies within a repeat ( Figure 5b ) , but is far away from the causative gene Y75B8A . 8 that lies at the 5' end of this 54 kb deletion ( Figure 4—figure supplement 1 ) . The other large deletion , however , lies in an AT-rich region ( two introns of the gcn-1 gene ) that may be classified as 'tandem and inverted repeats' and the two breakpoints correspond to a 20 bp direct repeat with two mismatches ( Figure 5—figure supplement 1j ) . We therefore directly inquired whether this deletion ( and the other mutations ) occurred recurrently at a detectable frequency by analyzing sequence data of other MA lines ( Saxena et al . , 2019: 75 other MA lines , 3469 nuclear mutations ) . We did not find any other mutation at the corresponding positions and the closest mutations were at least 4 kb away ( Supplementary file 7 ) . This result excludes an extremely high mutation rate at the position of the five causal mutations . However , the size of the MA line dataset limits our ability to detect quantitative differences in mutation rates that could be significant at evolutionary time scales . We thus used two further datasets with abundant variation: the Million Mutation Project ( MMP , Thompson et al . , 2013 ) and the Caenorhabditis elegans Natural Diversity Resource ( CeNDR , Cook et al . , 2017 ) . The MMP dataset provides enough power , but is derived from lines after chemical and/or ultraviolet mutagenesis aiming at producing deletions ( 2007 strains with about 400 mutations each , Thompson et al . , 2013 ) . None of the five nucleotide positions ( breakpoints for deletions ) were mutated in this dataset ( Supplementary file 7 ) . One deletion was found in gcn-1 but breakpoints do not match the identified direct repeats . The caveat of using the MMP dataset is that the pattern of artificially induced mutations may differ from that of spontaneous mutation . Second , we explored the C . elegans natural diversity ( almost 3 million genomic variations from 766 wild strains; Cook et al . , 2017 ) , and none of the positions ( the breakpoints for deletions ) were mutated either ( Supplementary file 7 ) . The caveat of using this dataset is that selection has acted on the polymorphism pattern; note however that the gcn-1 repeats lie in intronic regions where mutations may have less functional impact ( Figure 5—figure supplement 1j ) . We thus conclude that the five identified mutations are not in mutational hotspots . We next wondered whether the underlying genes ( rather than the precise positions ) - the first level of sequence to phenotype mapping - could display higher mutation rates . The mutation rate of a gene depends on its length and the mutation rate of its sub-sequences . Among the five genes , gcn-1 and to a lesser extent Y75B8A . 8 stand out as large genes ( measured from 5'UTR to 3'UTR , including introns ) : they are the 10th and 841st longest genes among the 21 , 803 C . elegans protein-coding genes , respectively ( Figure 6—figure supplement 1a ) . Their total repeat content is longer , mainly in introns for gcn-1 and in both introns and exons for Y75B8A . 8 ( Figure 6—figure supplement 1b ) . In the 75 C . elegans MA lines we analyzed , none of the five genes showed a second hit in their exons , even though some other genes were recurrently mutated , including in exons ( Figure 6a ) . In the MMP and CeNDR , genes accumulate mutations as predicted by their length ( Figure 6b , c ) , thus gcn-1 is often hit . gcn-1 retains natural variations at a higher rate than the average of genes , due to introns , where variations are less likely to impact protein function ( Figure 6c ) . From these data , we concluded that the five causative genes do not present particularly high mutation rates given their length . If only polymorphisms annotated with a predicted high or moderate impact on protein function are taken into account , most genomes of wild isolates at CeNDR do not bear such variants for sfrp-1 and cdk-8 ( 99% and 97% respectively , n = 330 ) , likely due to purifying selection ( Figure 6—figure supplement 1c ) . Non-synonymous polymorphisms are more frequent for the three other causative genes ( Figure 6—figure supplement 1c ) . This suggests that variations in the protein sequence corresponding to these three genes do not generate strongly counter-selected phenotypes in nature . Further experiments are required to quantify how much this natural polymorphism contributes to the high standing genetic variance measured for P3 . p ( Figure 1—figure supplement 1 ) . We then aimed to understand how these different loci affect P3 . p cell fate by analysing the nature of the underlying genes . One of the five genes , sfrp-1 , was an obvious candidate regulating the Wnt pathway; the other four were not . SFRP-1 ( Secreted Frizzled Receptor Protein-1 , mutated in C . briggsae MA line 296 ) is a highly conserved secreted Frizzled protein that inhibits Wnt signaling by sequestering Wnts . In C . elegans , the sfrp-1 gene is expressed in the anterior part of the nematode and the protein counter-acts the effect of posteriorly secreted Wnts ( Harterink et al . , 2011; Figure 4—figure supplement 2b ) . Since P3 . p is highly sensitive to the posterior Wnt gradient ( Pénigault and Félix , 2011b ) , loss of sfrp-1 should increase the frequency of P3 . p division . Indeed , we observed an increase in P3 . p division frequency for C . briggsae MA line 296 and the corresponding sfrp-1 genome edits compared to the HK104 ancestor ( Figure 4b ) . Using an available null mutant line in C . elegans , we showed that the effect of sfrp-1 on P3 . p division is conserved in both species , and opposite to the effect of a decrease in canonical Wnt signaling through a null bar-1 mutation ( Figure 4—figure supplement 2a ) . The mutation in MA line 296 is a missense in the cystein-rich Frizzled domain that binds the Wnt ligand , changing a conserved asparagine into a histidine ( Figure 4a ) . The cdk-8 gene ( cyclin-dependent kinase-8 , mutated in C . elegans MA line 450 ) codes for a subunit of the Mediator complex . This conserved eukaryotic multiprotein complex interacts with chromatin , transcription factors and the RNA Polymerase II machinery and regulates the transcription of many genes ( Grants et al . , 2015; Angeles-Albores and Sternberg , 2018 ) . Its specificity of action on transcription is controlled by distinct dissociable subunits , such as the CDK-8 module . In C . elegans , the CDK-8 module acts in a highly pleiotropic fashion yet a P3 . p division frequency phenotype was not previously reported . In the ventral epidermis , the CDK-8 module was shown to act at many other steps , contributing in the L1 stage to the fusion to hyp7 of anterior and posterior Pn . p cells ( such as P2 . p and P9 . p ) ( Yoda et al . , 2005 ) , to the block of division of all VPCs in the L2 stage ( Clayton et al . , 2008 ) and to the level of induction of 2° and 1° VPC fates via cell-autonomous repression of EGF and Notch signalling in the L3 stage; these activities being mostly revealed in a sensitized genetic background ( Moghal et al . , 2003; Grants et al . , 2016; Underwood et al . , 2017 ) . We found that mutation in three other genes encoding components of the CDK-8 module also increased P3 . p division frequency in an otherwise wild-type genetic background ( Figure 4—figure supplement 2c , d ) : cic-1 , dpy-22/mdt-12 and let-19/mdt-13 . The valine-to-alanine substitution in the protein kinase domain found in MA line 450 likely causes a strong reduction-of-function , since the phenotypes such as dumpy animals or P3 . p division frequency were indistinguishable from those in animals bearing the null deletion allele cdk-8 ( tm1238 ) ( Grants et al . , 2016; Figure 4—figure supplement 2c and Supplementary file 7 ) . To test whether CDK-8 acts independently of the Wnt signalling pathway to modulate P3 . p division frequency , we performed epistatic analysis by combining null mutants of cdk-8 and bar-1 . The double mutants showed an intermediate level of P3 . p division frequency ( Figure 4—figure supplement 2c ) , thus cdk-8 was not epistatic to bar-1 suggesting that CDK-8 functions independently of the Wnt signalling pathway . In sum , CDK-8 is part of a large complex that is a general regulator of transcription; its mutation , although not lethal , is likely to affect many processes that are sensitive to the level of transcription of one or several of the many downstream genes . The gcn-1 gene ( homolog of yeast General Control Non-derepressible ) is a large protein of 2651 amino-acids ( aa ) including several Armadillo repeats , conserved throughout eukaryotes . The GCN-1 protein is involved in translational control . GCN-1 promotes the phosphorylation of the eukaryotic initiation factor eIF2α ( Nukazuka et al . , 2008 ) , which is thought to globally repress translation while activating expression of a few specific genes in many eukaryotes . This pathway is known to be active under various environmental stresses and to regulate global metabolic homeostasis ( Rousakis et al . , 2013; Figure 4—figure supplement 2f ) . Local repression of this pathway by semaphorin signalling is required for C . elegans male ray morphogenesis ( Nukazuka et al . , 2008 ) . The gcn-1 mutation in the MA line 516 deletes the entire 21st exon and flanking intronic regions removing a part of the translation elongation factor three protein domain that is required for the efficient phosphorylation of eukaryotic initiation factor 2 ( Hirose and Horvitz , 2014 ) . From comparison with another partial deletion allele , gcn-1 ( nc40 ) , the MA line mutation is likely a reduction-of-function allele ( Figure 4—figure supplement 2e ) . GCN-1 had not been involved so far in the regulation of P3 . p division . Little is known about the two last genes . Y75B8A . 8 , entirely deleted in MA line 418 , codes for a 715-aa protein lacking any known functional domain and homology outside nematodes . The protein bears features of intrinsically disordered proteins , including polyglutamine stretches in the N-terminal half ( https://wormbase . org/species/c_elegans/protein/CE34135#065-−10 ) . The homologous protein in the parasitic nematode Haemonchus contortus is found in excretory and secretory products and is able to bind the interleukin IL2 of its mammalian host ( Wang et al . , 2019 ) . In C . elegans , the 3’UTR of Y75B8A . 8 regulates RNA editing of the ADSR-2 mRNA ( Wheeler et al . , 2015; Washburn and Hundley , 2016 ) . This gene was not known to affect Pn . p cell development . Finally , R09F10 . 3 is a 468-aa protein with a weak similarity to the Mediator subunit MED27 at its C-terminus and no detectable similarity of the N-terminal part ( https://wormbase . org/species/c_elegans/protein/CE33810#065-−10 ) . The short deletion in MA line 488 induces a frameshift and an early stop codon truncating more than 40% of the protein length . This gene was not known to affect Pn . p cell development .
In this study , we report the first identification of mutations underlying a trait’s high mutational variance in mutation accumulation lines . Using the highly tractable development of Caenorhabditis nematodes at the cellular scale , their powerful genetics and the recent advances in genome editing , we could precisely characterize mutational events that drove the fast evolution of a trait in a controlled evolutionary experiment . Our random sampling of mutations driving the evolution in P3 . p division frequency in MA lines hit five different genes with no signature of high mutation rates , which could be connected to at least three different functional modules: Wnt signalling , transcriptional control by the Mediator complex and translational control through GCN-1 . A the level of the genes , one of them ( gcn-1 ) is particularly long so it is likely to be the target of mutations , whereas three of them are quite short . Using this quantitative genetics approach , we were able to find new regulators of P3 . p developmental fate that are available for further developmental studies . This is a small sample of possible mutations and already demonstrates that the cellular process of P3 . p division is sensitive to variation in a larger number of genes and pathways . We conclude that the higher mutational variance of P3 . p cell division is not specifically due to the higher mutability of particular DNA sequences and cannot be predicted from the genome sequence . Instead , it is a consequence of a broad mutational target impacting this cell fate specification , thus to the developmental context controlling the decision of P3 . p to either fuse with hyp7 in the L2 stage or to further divide in the L3 stage . This result on the role of genotype-phenotype mapping in the evolutionary rate has broad implications in evolutionary biology of any organism ( unicellular , multicellular , viruses ) . In addition , mutational effects on the phenotype are of obvious consequences in genetic disease and in the phenotypic progression of cancerous tumors . An obvious further question is whether the mutations found in MA lines are representative of those responsible for P3 . p evolution in natural populations . At least three out of five identified mutations affected important fitness-related traits such as body morphology or fertility , as well as other vulva traits ( albeit at much lower frequency than changes observed for P3 . p , Supplementary file 6 ) . The fast evolutionary rate of change in P3 . p cell fate could then be driven: ( 1 ) by the subset of mutations with little pleiotropy in the corresponding genetic background ( different from that tested here ) or with pleiotropic effects that can be soon compensated for , or ( 2 ) by pleiotropic mutations that can be selected positively for their effect in other tissues ( Duveau and Félix , 2012 ) . Among ‘target’ genes , the most polymorphic in natural populations could be a reservoir of natural mutations affecting P3 . p ( Figure 6—figure supplement 1c ) . We also note that we selected large-effect mutations on purpose to ease the genetic mapping . It is possible that small-effect mutations would appear less pleiotropic . In any case , the diversity of functional pathways identified in this study offers opportunities to generate such non-pleiotropic small-effect mutations . A prediction from our present work is that mapping genetic determinants of P3 . p division frequency in natural isolates should identify many different small-effect loci , possibly involving more functional pathways . Such an experiment remains however practically difficult to carry out , given the binary nature of the trait that imposes to score the phenotype of numerous isogenic animals to estimate reliable frequencies , the current low-throughput phenotyping and the highly multigenic nature of the trait . From a developmental perspective , the reason why P3 . p cell fate has such a broad mutational target likely lies in the sensitivity of this cell fate decision to small quantitative alterations in many biochemical pathway activities or in this cell's position . Indeed , we previously showed that P3 . p division frequency is sensitive to halving the dose of either of the two Wnt ligands that are secreted from the posterior end of the animal ( Pénigault and Félix , 2011b ) . P3 . p is located at the fading end of the posterior-to-anterior Wnt gradient and may therefore often receive a Wnt dose that is below the threshold required for its division , while P4 . p and the most posterior cells are more robustly induced . In addition to core Wnt pathway genes , mutations acting on other biochemical pathways and in other cells ( e . g . neurons; see Modzelewska et al . , 2013 ) could affect P3 . p fate if they resulted in small variations in Wnt gradient levels , cellular position within the gradient , or interpretation of the gradient . In addition , the sustained expression of the Hox gene lin-39 is required to prevent Pn . p cell fusion in the L2 stage ( Eisenmann and Kim , 2000 ) , independently of Wnt signalling ( Pénigault and Félix , 2011b ) . Hox gene regulation may be a further mutational target underlying the high mutational variance of P3 . p fate . In summary , P3 . p is located at a very sensitive position that results in its developmental fate being highly sensitive to stochastic , environmental and genetic variation ( Braendle and Félix , 2008 ) . The broad mutational target that we find here is consistent with this developmental sensitivity . Variability of cell fates among the six vulva precursors evolved significantly among rhabditids . In another genus of the same family , Oscheius , P3 . p cell fate is not highly variable ( it does not divide ) , whereas P4 . p and P8 . p cell fates vary extensively both within and among species ( Delattre and Félix , 2001 ) . It would be interesting to test whether these different evolutionary rates correspond to an evolution in the respective mutational variances explained by broader mutational targets . The assembly and annotation of the Oscheius tipulae genome makes now possible to identify functional pathways involved in development of this species ( Besnard et al . , 2017; Vargas-Velazquez et al . , 2019 ) . This would offer a way to study how the evolution of developmental mechanisms correlates with the evolution of mutational variance and ultimately results in the evolution of evolutionary rates .
All strains used in this study are listed in Supplementary file 8 with their genotype and origin . MA lines derived from four ancestors and the ancestor stocks were originally obtained from Dr . Charles Baer ( C . elegans N2 and PB306 and C . briggsae HK104 and PB800 ) ( Baer et al . , 2005 ) . We used MA lines perpetuated by single-hermaphrodite transfer for 250 generations . All lines were cryo-preserved using standard methods ( Stiernagle , 2006 ) and freshly thawed prior to experiments . Unless otherwise stated , all experiments were carried out with strains cultured at 20°C on NGM ( Nematode Growth Agar ) plates seeded with Escherichia coli OP50 , following standard procedures ( Brenner , 1974; Stiernagle , 2006 ) . Fresh cultures of ancestor and MA lines were regularly thawed from cryopreserved stocks to avoid further drift . All strains were cleaned by hypochlorite treatment ( Stiernagle , 2006 ) before initiating experiments . To synchronize nematodes , three to five L4-stage hermaphrodite larvae were transferred to a fresh culture plate at 20°C . When most of their offspring reached the L4 stage ( typically after three days , and up to five days for slow-growing strains ) , vulval cell fates were scored on larvae in the early to mid L4 larval stages , when Pn . p descendants display arrangements typical of each fate . Nematodes were anaesthetized with 1 mM sodium azide and mounted onto an agar pad for Nomarski microscopy observation ( Wood , 1988 ) . A fusion of P3 . p at the L2 stage leaves a single nucleus in the large ventral syncytium ( 'S' or 4° fate ) , indicating that P3 . p cell exited the vulva differentiation process ( Figure 1A ) . The absence of L2 fusion allows P3 . p to undergo a round of cell division in the L3 stage , revealed by the presence of two nuclei in the syncytium ( 'SS' or 3° fate ) , because its daughter cells also fuse with the syncytium during L3 stage . More rarely , unfused P3 . p cells can be partially or fully induced to other vulva fates ( 2° or 1° fates ) . The division frequency of P3 . p for a line ( a binary trait ) was estimated on samples of at least 50 nematodes per biological replicate . The number of animals scored per line was a compromise with the number of lines assayed and the number of biological replicates on different days . We use biological replicates in the sense that the measure was performed on different generations of animals of the same line , assayed on different days . Since P3 . p cell fate has been shown to be sensitive to environmental variation ( Braendle and Félix , 2008 ) , experiments were generally performed by batch including several strains and a common control , for example the ancestral line or the parental line in the case of backcrosses ( see below ) . Masking of the strain identifier was not used . All scores of P3 . p division frequency used in this study are provided as Supplementary file 1 . Whole genomes of six MA lines of interest and their corresponding ancestral strain were re-sequenced . Each strain was freshly thawed and bleached from cryopreserved stocks . The strain was amplified on four 90 mm diameter plates of NEA medium ( NGM enriched with agarose [Richaud et al . , 2018] ) seeded with E . coli OP50 , until the onset of starvation . Nematodes were collected , washed in M9 ( Stiernagle , 2006 ) to remove E . coli , and centrifuged . A pellet of 200–400 µl of animals was resuspended in 400 µl Cell Lysis Solution ( Qiagen Gentra Puregen Cell kit ) with 5 µl proteinase K ( 20 mg/ml ) and lysed overnight at 56°C under shaking in Cell Lysis Solution ( Qiagen Gentra Puregen Cell kit ) with proteinase K ( 20 mg/ml ) . Lysates were incubated for 1 hr at 37°C after adding 10 µl of RNAse A ( 20 mg/ml ) and proteins were precipitated with 200 µl of Protein Precipitation Solution ( Qiagen Gentra Puregen Cell kit ) . After centrifugation , DNA was precipitated from the supernatant with 600 µl of isopropanol , washed twice with ethanol 70% , dried for 1 hr and finally resuspended in 100 µl TE buffer . This procedure typically yielded concentrations of ~500 ng/µL ( range: 200 ng to 1 µg per µl ) of high-quality genomic DNA . Short insert libraries ( mean insert size around 500 bp ) were prepared by BGI ( http://www . genomics . cn/en/index ) and paired-end sequenced on Illumina Hiseq2000 with 100 bp reads to obtain 2 . 2 Gb ( aiming at ~20 x mean coverage ) of clean data per samples after manufacturer’s data filtering ( removing adapter sequences , contamination and low-quality reads ) . Raw sequencing data generated for this study are accessible via the ENA website ( https://www . ebi . ac . uk/ena ) with accession numbers listed in Supplementary file 2 . To efficiently genotype de novo mutations in MA lines and all backcrossed lines , we optimized a procedure of variant discovery with high specificity , avoiding time-consuming assays of false positive calls ( Figure 3—figure supplement 1 ) . After routine quality checks with FastQC ( Andrews , 2017 ) , clean reads were mapped using bwa with 'mem' algorithm and '-aM' options ( Li and Durbin , 2009 ) to the relevant reference assembly corresponding to WormBase releases WS243 and WS238 for C . elegans and C . briggsae , respectively ( http://www . wormbase . org/ ) . Resulting bam files were further processed with samtools ( Li et al . , 2009 ) to remove unmapped reads or secondary alignments and to keep only mapped reads in a proper pair . The analysis was further performed using the GATK tool suite ( McKenna et al . , 2010 ) ( v3 . 6 or later ) with default parameters ( unless otherwise stated ) , and by adapting the authors' recommendations of best practices ( DePristo et al . , 2011; Van der Auwera et al . , 2013 ) . Read mappings were pre-processed by tagging duplicate reads with Picard ( http://broadinstitute . github . io/picard ) , by re-aligning reads around indels ( GATK tool suite ) and by one round of Base Quality Score Recalibration ( GATK tool suite ) with the HaplotypeCaller tool , resulting in analysis-ready bam files for each sequenced sample . To call short variants ( SNPs and indels generally less than 100 bp ) , these bam files were separately pre-called for variants using the tool HaplotypeCaller in a gVCF mode ( option '-ERC GVCF' ) . Finally , a joint genotyping ( with GATK's tool GenotypeGVCFs ) was performed using as inputs all the gVCF records of a cohort consisting of the ancestor strain and its derived MA lines . This yielded one unique vcf file per cohort containing the genotypes of all strains of that cohort at each site where at least one strain bears a variation ( compared to the reference genome used ) . We then applied conservative criteria to specifically identify de novo mutations that appeared and fixed during the course of the 250 generations of mutation accumulation . Since all strains are expected to be nearly fully homozygous by constant inbreeding , all heterozygous positions were filtered out . We also removed positions not supported by a coverage superior or equal to 3 . Most of the remaining variations are background variations present in the ancestor strain compared to the reference genome of each species , that of strain N2 for C . elegans and of strain AF16 for C . briggsae . Within a cohort , especially with many MA lines , the variations shared by all strains are very likely ancestral alleles inherited from the ancestor . This high similarity of variation within a cohort was used to increase the specificity of the calls for the PB306 and HK104-derived cohorts ( the N2-derived cohort had few background mutations ) . In both cohorts , background variations were used to train a Variant Quality Score Recalibration ( VQSR ) . In practice , shared variant sites within a cohort were split into background SNPs and indels to perform parallel recalibrations ( tool VariantRecalibrator in mode SNP and INDEL , respectively ) . These training sets of variants were considered to be representative of true sites and were then used to train the model with a prior likelihood of Q12 ( 93 . 69% ) , corresponding to options 'training = true , truth = true , prior = 12 . 0' . In the case of HK104 , we added another training dataset , consisting of validated SNP markers previously used to genotype recombinant progeny between the HK104 and the reference strain AF16 ( Koboldt et al . , 2010; Ross et al . , 2011 ) and the 13 new polymorphisms ( SNPs or small indels ) that were directly validated by pyrosequencing ( see below ) . This additional set is small ( 948 variants , see the list in vcf format in Supplementary file 9 ) but has a high degree of confidence: we fixed the prior likelihood to Q15 ( 96 . 84% ) ( other parameters of VariantRecalibrator: training = true , truth = true , prior = 12 . 0 ) . Then , each type of variant was recalibrated ( tool ApplyRecalibration ) so that 99% of the training dataset should be contained in this quality tranche ( option '--ts_filter_level 99 . 0' ) . Finally , for each MA line , sites containing an allele passing the VQSR threshold but different from the ancestral line were selected and classified based on the number of other MA lines within the cohort that shared the same genotype . Since spontaneous mutations are rare events and each MA line is an independent replicate of the mutation accumulation experiment , only the variants unique to one MA line were considered as trustful candidates for de novo mutations . Identical mutations found in several MA lines of the same cohort could be either false positive ( i . e a background variation present in the ancestor strain that was missed ) or a potential mutational hotspot ( Denver et al . , 2012 ) . However , the small size of our cohort does not allow to answer this point . For MA line 516 , we simply selected all variants that differed from the re-sequenced N2 ancestor without performing VQSR . Since repetitive sequences are prone to sequencing or mapping errors , we used versions of reference genomes with masked repetitions , as identified by RepeatMasker software ( http://www . repeatmasker . org/ ) run with default parameters ( masked versions are directly available on WormBase , masking 21 . 9% and 14 . 6% of bases in C . elegans and C . briggsae genomes , respectively ) . However , we observed variations specifically called when using such genome versions , suggesting masking artefacts . To eliminate these , the entire variant discovery pipeline was also applied on the non-masked version of the reference genome and only variations called in both analyses were kept . The above procedure only retrieves SNPs and short indels ( the longest indel of our final list is 87 bp long , absolute mean indel size is about 18 bp ) . To detect larger structural variations ( SV ) like long indels ( >100 bp ) , copy number variations ( CNV ) , repeats ( inverted or tandem ) or translocations , we used a second approach based on two different complementary callers ( Lin et al . , 2015 ) : the read-pair algorithm Breakdancer ( Chen et al . , 2009 ) and the split-read algorithm Pindel ( Ye et al . , 2009; Figure 3—figure supplement 1 ) . Here again , the whole procedure was optimized to achieve high specificity and reduce false-positive calls . A non-masked version of the genome was used with both programs to generate bam files . In the BreakDancer pipeline , bam files were also filtered to keep only properly mapped reads ( see above ) and submitted to breakdancer-max command with default options . For each cohort , bam files of the ancestral line and derived MA line ( s ) were processed in parallel and results were converted to vcf format . For each MAL , variants found in the ancestor line were substracted with leniant criteria to account for the low precision of breakpoint positions achieved by structural variant ( SV ) callers: two SVs were considered identical if they were of the same type within a 100 bp window ( corresponding to read size ) and with a difference in size lower than 50% . Then , the following heuristic hard filters were applied ( determined on the distribution of the corresponding parameters ) : QUAL > 90; 50 bp <= SVLEN <= 1 000 000 bp and 25 <= DP <= 150 or 2 <= DP <= 150 for C . elegans and C . briggsae , respectively . We observed that many false positive calls were generated close to repeated regions where many reads map wrongly . Hence , all variations called in a two kbp region ( four times the insert size ) where the mean coverage was superior to 100 ( five times the mean coverage ) were filtered out . Finally , as for short variations , all MALs of a cohort were compared to keep only unique variations per MAL ( using aforementioned leniant criteria for SV comparison ) . In parallel , unfiltered bams were processed with Pindel ( with parameter --max_range_index 6 ) and for each MAL , variants found in the ancestral line and other MALs of the cohort were filtered out . Finally , the lists of variants generated by Breakdancer and Pindel were intersected to keep only SV called by both procedures . This yielded few candidate SV ( 16 for the 6 MALs ) , all deletions , which were directly inspected with an alignment viewer in both MAL and PL . Only four large deletions passed this ultimate filter . All variants found by the two procedures ( short and long variants ) are listed in Supplementary file 3 . About 11% of the candidate calls from our short-variant-discovery pipeline were directly tested by pyrosequencing . Variations were not randomly chosen , but selected to be used as helpful genotyping markers during the genetic mapping of the causative locus affecting P3 . p division frequency . However , this selection was constrained by the low number of variations per MA line ( typically eight per chromosome in C . elegans and 34 in C . briggsae ) . Prior to any evidence , two to three variations were selected on each chromosome ( ideally one variation in the middle of each chromosomal arm , one in the centromeric region if variations in the arms were excessively shifted to the tips ) . After the mapping gave the first genetic evidence , additional candidate variations were tested to restrict the mapping interval in the relevant chromosome . SNPs were preferred over indels . Regions containing long stretches of a single nucleotide were avoided , both because the initial call is less likely and because the interpretation of pyrosequencing results is harder in such contexts . Pyrosequencing assays were performed as previously described on a PyroMark Q96 ID instrument ( Besnard et al . , 2017 ) , using universal biotinylated primers ( Duveau and Félix , 2012 ) . Genotyping assays included the reference genome , the ancestral line and the tested MA line , ie: for the N2 cohort , N2 ( reference and ancestor ) and MA516; for PB306's cohort , N2 ( reference ) , PB306 ( ancestor ) , and either MA line 418 , 450 or 488; for HK104's cohort , AF16 ( reference ) , HK104 ( ancestor ) and MA296 . Candidate SV calls were assayed by PCR with oligonucleotides flanking the predicted deletions . PCR products were controlled on electrophoresis and Sanger-sequenced . Genotyping primers are listed in Supplementary file 10 . From the initial MA line panel , only MA line 211 was not back-crossed due to time constraints . For all back-crosses , males of the ancestral line were placed with ( preferably old , sperm-depleted ) hermaphrodites of the mutation accumulation line to back-cross ( P0 ) . F1 cross-progeny were isolated on fresh plates and allowed to lay eggs . They were transferred every day to new plates to ease the separation of parents and offspring and synchronization of the F2 offspring . Occasionally , F1 hermaphrodites were eventually lysed and genotyped by pyrosequencing to ensure they were true cross progeny . Several F2 animals were isolated for each cross and gave rise to an independent line of one back-cross increment compared to the initial P0 . Serial back-crosses are noted as 1X , 2X , etc . Different strategies and crossing schemes were applied for the different MA lines ( Figure 3—figure supplements 4–8 ) . The first strategy consisting in crossing without selection was applied for the first back-cross of MAL296 and the second back-cross of MAL516 . In this case , several random F2 hermaphrodites were isolated , without scoring the vulva or selecting for any other phenotype . A second strategy consisted in selecting F2 based on a phenotype . MAL296 2X and 3X lines were generated by selecting F2 hermaphrodites showing a divided ( 'SS' ) fate for P3 . p . MAL516 1X lines were generated by selecting for Egl ( egg-laying ) or Pvl ( protruding vulva ) phenotypes , which were apparent in MAL516 . Back-crossed lines of the PB306 cohort ( MAL 418 , 450 and 488 ) were generated by selecting a Mendelian recessive ( dumpy , small , slow-growth or low-brood-size ) phenotype versus wild-type F2 hermaphrodites , in equal amounts . Indeed , all three parent MA lines present a mixture of these phenotypes: this strategy was designed to test a linkage between these obvious morphological phenotypes and P3 . p cell fate . Since the linkage was confirmed at each back-cross level , these selection criteria were kept over serial back-crosses ( up to 4X for MAL 418 and 450 ) . For these two lines , the morphological phenotype was used to accelerate the crossing scheme: wild-type F1 hermaphrodites ( necessarily cross-progeny given the recessive transmission of morphological defects ) were directly crossed with PB306 males , resulting in new F1 progeny that were isolated on fresh plates . Due to Mendelian segregation , only half of these new F1 carried a mutant allele and segregates mutant F2 progeny . Only these F1 plates were retained to select both mutant and WT F2 . Resulting lines have two increment back-cross levels compared to the initial P0 ( for instance , 4X starting from a 2X-line ) . In all strategies , F2s were singled on fresh plates and perpetuated in parallel by single-hermaphrodite transfer for four to five generations to maximize homozygosity at all loci , and finally amplified for cryo-preservation . For each MA line , the set of validated de novo mutations constituted genetic markers spanning all chromosomes . Independent back-crossed lines were scored for P3 . p behaviour and then genotyped for some of these markers in order to identify first a linked chromosome , and then a shorter interval depending on the availability of markers ( See Figure 3 and Supplementary file 4 ) . All lines were not systematically genotyped for all markers , except for the candidate mutation . CRISPR/Cas9-mediated homologous recombination ( HR ) was used to mimic the candidate mutation of MA lines 296 , 450 , 488 and 516 . HR was performed using single-stranded DNA oligonucleotide repair templates ( ssDORT ) with 35 bp 5’ and 3’ homology arms , following a combination of previously described methods ( Paix et al . , 2017b; Paix et al . , 2017a; Dokshin et al . , 2018 ) . Briefly , the trans-activating CRISPR RNAs ( tracrRNAs; ordered from IDT ) were individually annealed with CRISPR RNA guides ( crRNAs ) by incubation at 95°C for 5 min and cooling to room temperature ( ~23–25°C ) for another 5 min to generate single-guide RNAs ( sgRNA ) . Then , recombinant Streptococcus pyogenes Cas9 nuclease V3 ( IDT ) was incubated with sgRNAs for 10 min at 37°C to form ribonucleoprotein complexes . Next , ssDORTs , plasmids and nuclease-free water were added to the mix and centrifuged at 10 , 000 rpm for 2 min before loading into the needle . The mixes were micro-injected into gonads of 1 day old adult hermaphrodites ( P0 ) of the ancestral lines . F1 progeny was singled from plates displaying the highest number of dumpy ( Dpy ) or roller ( Rol ) phenotypes . Two days later , single F1s were PCR screened for HR replacements using primers flanking the target region ( outside the ssDORT sequence ) and one HR-specific primer . Non-Rol or non-Dpy progeny ( F2 or F3 ) of positive F1 animals were singly propagated to generate homozygous progeny and further genotyped by PCR . Genomic replacements were confirmed by Sanger sequencing . crRNAs were designed in http://crispr . mit . edu/ ( Zhang lab ) for C . elegans editings and http://crispor . tefor . net/ for C . briggsae , and ordered from IDT . To generate the large deletion of 1382 bp in the exon 21 of gcn-1 ( as found in MAL516 ) , we used two crRNAs ( crRNA . gcn-1 . E21 . prox . g1 and crRNA . gcn-1 . E21 . dist . g1 ) to generate double strand breaks ( DSB ) flanking the deletion breakpoints and a ssDORT ( gcn-1 . E21 . rt ) to generate the large deletion by HR repair . We used the following injection mix: 0 . 25 μg/μl Cas9 protein ( IDT ) , 57 μM tracrRNA , 22 . 5 μM of crRNA . gcn-1 . E21 . prox . g1 and 22 . 5 μM of crRNA . gcn-1 . E21 . dist . g1 , 110 ng/μl gcn-1 . E21 . rt4 repair template , 40 ng/μl of the plasmid pRF4::rol-6 ( su1006 ) as an injection marker , and 50 ng/μl of empty pBluescript plasmid . The mix was injected into gonads of 1 day old adult N2 hermaphrodites ( Baer 'ancestral N2' stock ) . The missense mutation in codon 40 from a valine ( CTT ) into an alanine ( GCT ) of cdk-8 ( as found in MAL450 ) was generated using a crRNA guide ( crRNA . cdk-8 . E2 . g1 ) that induces a DSB located 11 bp from the target region and a ssDORT ( cdk-8 . E2 . rt1 ) with the missense mutation and nine silent mutations to prevent Cas9 re-cutting and minimise template switching . To control for the silent mutations , we generated control lines with another ssDORT ( cdk-8 . E2 . rt2 ) that only has the nine silent mutations . We used the following injection mix: 0 . 3 μg/μl Cas9 protein ( IDT ) , 40 μM tracrRNA and 30 μM of crRNA . cdk-8 . E2 . g1 , 10 μM tracrRNA and 7 . 5 μM of crRNA . dpy-10 ( IDT ) as a co-CRISPR marker , 110 ng/μl cdk-8 . E2 . rt1 repair template ( or cdk-8 . E2 . rt2 ) , 50 ng/μl of empty pBluescript plasmid , and 0 . 5 μM dpy-10 repair template . The mix was injected into gonads of 1-day-old adult ancestral PB306 hermaphrodites . To generate the 16 bp deletion in the exon 4 of the R09F10 . 3 locus ( as found in MAL488 ) , we used a crRNA guide ( crRNA . R09F10 . 3 . E4 . g1 ) to generate a DSB in the target region and a ssDORT ( R09F10 . 3_E4 . rt1 ) to generate the small deletion by HR repair using the following injection mix: 0 . 3 μg/μl Cas9 protein ( IDT ) , 40 μM tracrRNA and 30 μM of crRNA . R09F10 . 3 . E4 . g1 , 10 μM tracrRNA and 7 . 5 μM of crRNA . dpy-10 ( IDT ) as a co-CRISPR marker , 110 ng/μl R09F10 . 3_E4 . rt1 repair template , 50 ng/μl of empty pBluescript plasmid , and 0 . 5 μM dpy-10 repair template . The mix was injected into gonads of 1 day old adult ancestral PB306 hermaphrodites . The missense mutation in codon 59 from an asparagine ( AAT ) to a histidine ( CAT ) of sfrp-1 in C . briggsae ( as found in MAL296 ) was edited using a crRNA guide ( crRNA . sfrp-1 . E2 . g1 ) that induces a DSB 10 bp from the target region and a ssDORT ( sfrp-1 . E2 . rt1 ) with the missense mutation and eight silent mutations . To control for the eight silent mutations , we generated control lines with another ssDORT ( sfrp-1 . E2 . rt2 ) that only has the silent mutations . We used the following injection mix: 1 μg/μl Cas9 protein ( IDT ) , 30 mM KCl and 4 mM HEPES pH7 . 5 , 40 μM tracrRNA and 30 μM of crRNA . sfrp-1 . E2 . g1 , 10 μM tracrRNA and 7 . 5 μM of crRNA . dpy-1 as a co-CRISPR marker , 110 ng/μl sfrp-1 . E2 . rt1 repair template ( or sfrp-1 . E2 . rt2 ) , and 50 ng/μl of empty pBluescript plasmid . The mix was injected into gonads of 1-day-old adult ancestral HK104 hermaphrodites . To validate the 54 , 355 bp deletion on the chromosome IIIR of MA line 418 and identify the causal gene ( s ) , we generated frameshifting indels in the seven protein-coding genes within the deleted region , using CRISPR/Cas9 editing without repair template ( non-homologous end-joining ) as described in Friedland et al . , 2013; Arribere et al . , 2014 . Guide RNAs were designed with the CRISPOR online program ( Haeussler et al . , 2016 ) . To generate the pU6-target-sgRNA plasmid , we replaced the dpy-10 target site with the desired target gene site in the pJA58 plasmid ( Arribere et al . , 2014 ) , using the Q5 Site-Directed Mutagenesis Kit ( New England BioLabs ) and the online tool NEBasechanger to design the mutagenesis primers . For genome editing , young adult PB306 hermaphrodites were injected with the following injection mix: 100 ng/μl of the pU6-target-sgRNA plasmid , 50 ng/μl of Peft-3::Cas9-SV40NLS::tbb-2 3’UTR plasmid ( Friedland et al . , 2013 ) , 60 ng/μl pJA58 plasmid as co-CRISPR marker and 10 ng/μl of the pPD118 . 33 plasmid ( Pmyo2::GFP ) as co-injection marker . We then singled the F1 progeny from plates with a high number of animals displaying the Dpy phenotype and GFP expression . F1s were screened by PCR for indels with flanking primers . Non-Dpy progeny of positive F1s were rendered homozygous and mutations were characterized by Sanger sequencing . All oligonucleotides used for CRISPR/Cas9-mediating genome editing ( guides , repair templates and genotyping primers ) are listed in Supplementary file 11 . The sequences in ancestor line , MA line and edited lines are provided in Supplementary file 5 . The GC content of DNA sequences was computed using bedtools ( Quinlan , 2014 ) . Extraction of sequences with different annotations was performed using the R package 'GenomicFeatures' ( R Development Core Team , 2015; Lawrence et al . , 2013 ) . Repeats were retrieved from ‘masked’ genome fasta files available from Wormbase ( WS243 and WS234 for C . elegans; WS238 for C . briggsae ) . To compare mutation rates in other C . elegans datasets , the mutation found in C . briggsae sfrp-1 was transposed to the homologous base pair in C . elegans . Additional filters were applied to the published list of mutations found in the dataset of Saxena et al . , 2019 , in order to remove most likely false positive calls: overlapping SNPs or indels at the same locus in the same line ( initial calling procedure was performed separately ) , SNPs at 2 bp or less from an indel in the same line , identical mutations shared by related lines ( likely background mutations ) , groups of identical mutations over large chromosomal regions found in multiple lines ( possible cross-contamination during sequencing ) . Since this previous study did not look for large structural variants , we systematically looked with the Tablet alignment viewer ( Milne et al . , 2013 ) , using bam files kindly provided by the authors , for large deletions falling in the exons of the five causal genes in all the MA lines of the dataset and tested all dubious instances by direct PCR and Sanger sequencing ( see Supplementary file 7b ) for the list of tested MA lines and re-sequenced genomic regions ( corresponding PCR oligonucleotide sequences are listed in Supplementary file 10b ) . We did not detect any structural variants in the exons of the five causal genes . Functional annotations of natural polymorphisms were predicted using snpEff . How snpEff classifies the putative effect of genomic variants into high or moderate is available online ( https://www . elegansvariation . org/help/Variant-Prediction/ ) . Computing and plotting different genomic features ( Figures 5 and 6 ) was performed with R using custom scripts and the ggplot2 package . Differences between P3 . p division frequencies were evaluated using pair-wise Fisher exact tests with false-discovery rate ( fdr ) level of 0 . 05 to correct for multiple testing ( R , fmsb package ) . The resulting pair-wise matrix of adjusted p-values was used to generate post-hoc labeling of each strain . Other statistics were computed using R , stats package ( R Development Core Team , 2015 ) ( specifically confidence intervals with prop . test , Pearson's correlation test with cor . test and X2-test with chisq . test ) . All raw sequencing data supporting the conclusions of this article have been submitted to ENA . Study and sample accession numbers corresponding to the sequencing data of the ancestor and MA lines used in this study are listed in Supplementary file 2 . Custom scripts were used to pipeline the different tools during the variant analysis ( bash/python scripts ) or to perform statistical analysis and to plot results ( R scripts ) . The data set containing all mutations in the mutagenized strains of the Million Mutation Project was downloaded online ( http://genome . sfu . ca/mmp/mmp_mut_strains_data_Mar14 . txt ) . Hard-filtered Variant data of the latest release of the Caenorhabditis Natural Diversity Resource ( release ID: 20180527 ) was downloaded online ( https://www . elegansvariation . org/data/release/latest ) . | Heritable characteristics or traits of a group of organisms , for example the large brain size of primates or the hooves of a horse , are determined by genes , the environment , and by the interactions between them . Traits can change over time and generations when enough mutations in these genes have spread in a species to result in visible differences . However , some traits , such as the large brain of primates , evolve faster than others , but why this is the case has been unclear . It could be that a few specific genes important for that trait in question mutate at a high rate , or , that many genes affect the trait , creating a lot of variation for natural selection to choose from . Here , Besnard , Picao-Osorio et al . studied the roundworm Caenorhabditis elegans to better understand the causes underlying the different rates of trait evolution . These worms have a short life cycle and evolve quickly over many generations , making them an ideal candidate for studying mutation rates in different traits . Previous studies have shown that one of C . elegans’ six cells of the reproductive system evolves faster than the others . To investigate this further , Besnard , Picao-Osorio et al . analysed the genetic mutations driving change in this cell in 250 worm generations . The results showed that five mutations in five different genes – all responsible for different processes in the cells – were behind the supercharged evolution of this particular cell . This suggests that fast evolution results from natural selection acting upon a collection of genes , rather than one gene , and that many genes and pathways shape this trait . In conclusion , these results demonstrate that how traits are coded at the molecular level , in one gene or many , can influence the rate at which they evolve . | [
"Abstract",
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"Results",
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"evolutionary",
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] | 2020 | A broad mutational target explains a fast rate of phenotypic evolution |
In vestibular cerebellum , primary afferents carry signals from single vestibular end organs , whereas secondary afferents from vestibular nucleus carry integrated signals . Selective targeting of distinct mossy fibers determines how the cerebellum processes vestibular signals . We focused on vestibular projections to ON and OFF classes of unipolar brush cells ( UBCs ) , which transform single mossy fiber signals into long-lasting excitation or inhibition respectively , and impact the activity of ensembles of granule cells . To determine whether these contacts are indeed selective , connectivity was traced back from UBC to specific ganglion cell , hair cell and vestibular organ subtypes in mice . We show that a specialized subset of primary afferents contacts ON UBCs , but not OFF UBCs , while secondary afferents contact both subtypes . Striking anatomical differences were observed between primary and secondary afferents , their synapses , and the UBCs they contact . Thus , each class of UBC functions to transform specific signals through distinct anatomical pathways .
In the cerebellum , mossy fibers convey multimodal signals from diverse regions of the central nervous system to the granule cell layer . ‘Expansion recoding’ theories of cerebellar processing require these mossy fiber inputs to diverge to hundreds of granule cells , and their signals are integrated first by granule cell dendrites and then by the massive dendritic trees of Purkinje cells ( Albus , 1971 ) . However , mossy fiber inputs to vestibular cerebellum differ from those of other cerebellar lobes in receiving mossy fibers directly from peripheral ganglion neurons ( the primary vestibular afferents ) , as well as from brainstem nuclei ( secondary afferents ) . Each of the primary afferents carries signals from a single vestibular organ , with each organ coding head position or velocity in a given plane ( Fernández et al . , 1988 ) . Maintaining separate mossy fiber signals from specific end organ sources in ‘labeled lines’ could allow segregated ensembles of granule cells to faithfully encode head movements along specific planes . Alternatively or additionally , specific sources might undergo selective amplification by local circuitry to enhance their representation to downstream neurons . However , such mechanisms are not consistent with the general view of cerebellar function that diverse mossy fibers are simply integrated by individual granule cells , and differ primarily by short term plasticity at the granule cell synapse ( Chabrol et al . , 2015; Huang et al . , 2013 ) , necessitating an examination of mossy fiber innervation of the vestibular cerebellum . We investigated this problem by tracing vestibular cerebellar mossy fibers back to their primary and secondary sources , and forward to target neurons in the cerebellum , focusing on unipolar brush cells ( UBCs ) , because they form a processing layer prior to the well-studied granule cells . UBCs are excitatory interneurons within the granule cell layer that receive a single mossy fiber ending on their brush-like dendrite ( Harris et al . , 1993; Mugnaini and Floris , 1994; Mugnaini et al . , 2011 ) . Instead of integrating multiple inputs as granule cells do , this large synaptic contact dramatically transforms activity of one mossy fiber before projecting to hundreds of granule cells and other UBCs . In vestibular processing areas of rodent cerebellum , UBCs are present in exceptionally high density and could coordinate ensembles of granule cells to respond to single directions of movement ( Floris et al . , 1994 ) . This problem is deepened by the diversity of UBCs . Two subtypes of UBC have been described: ON UBCs respond to mossy fiber input with a prolonged depolarization and enhancement of firing while OFF UBCs are inhibited ( Borges-Merjane and Trussell , 2015 ) . Both responses last for hundreds of milliseconds , an outcome of selective receptor expression in the two subtypes ( Borges-Merjane and Trussell , 2015 ) combined with the great size the mossy fiber-UBC synaptic contact ( Mugnaini et al . , 1994 ) . Given this potent circuit element , it is critical to determine which vestibular organs map directly to cerebellum and which UBC subtypes they contact to understand vestibular representation . For example , if both subtypes receive common sensory input , then the ON/OFF distinction in UBCs would allow mossy signals to diverge , setting up distinct processing pathways within the granule cell layer , such that the OFF pathway could be a negative image of the vestibular motion . On the other hand , if each subtype receives mossy fiber input that conveys a distinct vestibular modality , then ON and OFF UBCs would mediate modality-specific transformations of extrinsic inputs . Here we show that in cerebellar lobe X of mouse , the primary representation is from a subset of angular acceleration coding neurons , and these signals reach and are amplified by ON UBCs , but not OFF UBCs . OFF UBCs by contrast only process secondary afferent signals that may contain signals integrated over multiple directions of movement , hemispheres and/or modalities . We also show that primary and secondary inputs exhibit dramatic differences in their axonal and synaptic morphology , as well as in the morphology of the UBCs they contact , which may further refine coding in the granule cell layer .
Vestibular hair cells detect head acceleration , velocity , and gravity , and convey these signals to vestibular ganglion ( VG ) neurons; within each vestibular end organ , there are two subtypes of hair cell and at least three subtypes of VG cell ( Eatock and Songer , 2011 ) . VG axons project to vestibular nuclei in the brainstem and directly to the ipsilateral vestibular cerebellum ( Dow , 1936 ) . First , we set out to determine which VG cells project centrally to cerebellum and , peripherally , which end organ and hair cell subtype those same neurons contact . In order to express transgenes in primary afferents that may project to UBCs in the vestibular cerebellum , we determined that the Glt25d2 mouse line that has Cre recombinase ( Cre ) targeted to the Colgalt2 locus ( B6-Tg ( Colgalt2-cre ) NF107Gsat ) ( Gerfen et al . , 2013 ) expresses Cre in primary afferents projecting to the vestibular nuclei and vestibular lobes of the cerebellum , by crossing it with a tdTomato reporter line ( Ai9 ) ( Figure 1A–B ) . In cerebellar lobes IX and X , these afferents appeared as mossy fibers , and were most likely primary ( first-order ) from the VG , and not those from brainstem vestibular nuclei or nucleus prepositus hypoglossi that also project to cerebellum , because no somata lying in these areas expressed Cre ( Figure 1C ) . Primary afferents did not project to flocculus or paraflocculus . VG neurons have specialized dendrites that receive input from vestibular hair cells in the five vestibular end organs: the three semicircular canals and the two otolith organs , the utricle and sacculus . There are 3 types of peripheral afferent neuron based on their dendritic morphology: ‘pure-calyx’ , which form calyx endings on Type I hair cells , bouton , which makes bouton endings on Type II hair cells , and dimorphic , which have both calyx and bouton terminals ( Fernández et al . , 1988 ) . The central regions of each end-organ are populated with ‘pure-calyx’ type dendritic endings of VG neurons expressing calretinin ( Desmadryl and Dechesne , 1992; Leonard and Kevetter , 2002 ) . Note that pure-calyx endings also receive input from Type II hair cells that contact the outer surface of the calyx . tdTomato-positive VG neurons in the Glt25d2::tdTomato mouse varied in soma size , location and calretinin expression ( Figure 1D–E ) , indicating Cre expression in diverse types of VG neurons . Indeed , some peripheral afferents that expressed tdTomato had pure-calyx endings ( based on co-labeling with calretinin ) and others had dimorphic endings ( Figure 1F–G ) . It was not possible to determine whether pure bouton endings expressed tdTomato because pure bouton endings could not be differentiated from boutons extending from the dimorphic fibers . To determine which signals are carried to cerebellum via Cre+ primary afferents in the Glt25d2 mouse line , we used retrogradely-infecting adeno-associated viruses ( retro-AAVs ) that express GFP . Unlike typical AAVs , retro-AAVs infect axons and thus allow the source of projections to the injected site to be determined ( Tervo et al . , 2016 ) . Injections of Cre-dependent retro-AAV ( AAV2-retro-CAG-Flex-GFP ) were made into lobe X to label projecting VG neurons and their peripheral afferents in the five vestibular end organs ( Figure 2A ) . GFP-expressing afferents were apparent at the injection site in lobe X ( Figure 2B ) . The VGs ipsilateral to the injected side of lobe X were immunolabeled for calretinin and imaged as whole mounts ( Figure 2C ) . In a total of 670 retrolabeled VG somata , none expressed calretinin ( n = 5 ganglia in separate experiments ) , suggesting that Cre-positive ( Cre+ ) cells with pure-calyx endings do not project to lobe X . Note that we could not be confident we imaged every VG neuron because dissection of the complete VG complex was not always possible . In two Glt25d2 mice , histological analysis of the injection site revealed numerous GFP-labeled primary afferents in lobe X and very few in lobe IX . In these experiments , all five end organs and VG ipsilateral to the injection site were successfully processed . The afferents in the end organs were almost exclusively dimorphic , having calyx endings surrounding hair cells with extending processes ending in boutons ( Figure 2D–H ) . No pure-calyx endings and only one bouton-only ending was observed ( Figure 2C ) . No retrolabeled afferents were co-labeled with calretinin ( 0 of 380 calyces , two mice , 10 end organs ) , consistent with the counts of labeled VG somata . Most of the retrolabeled afferents surrounded hair cells in the semicircular canals and the sacculus ( anterior canal , 55 , 64; horizontal canal , 22 , 35; posterior vertical canal , 55 , 50; utricle , 5 , 6; sacculus; 53 , 35; numbers are retrolabeled calyces in each experiment where injections were restricted to lobe X ) . As expected , only a few afferents in the end organs contralateral to the injection were retrolabeled , likely due to virus that diffused across the cerebellar midline ( Korte and Mugnaini , 1979 ) . Another injection labeled numerous afferents projecting to the ventral leaflet of lobe IX in addition to lobe X . As expected , there were more afferents labeled in all end organs ( anterior canal , 109; horizontal canal , 49; posterior canal , 203; utricle , 109; sacculus , 259 ) ( Figure 2—figure supplement 1 ) . Despite many more afferents labeled , none expressed calretinin and were therefore also exclusively not pure-calyx endings ( 0 of 729 calyces , one mouse , five end organs ) . The result that many more afferents were labeled in the otolith organs is consistent with more otolith afferents targeting lobe IX than lobe X ( Maklad and Fritzsch , 2003 ) . In sum , in the Glt25d2 mouse line the source of Cre+ primary vestibular afferents that project to lobe X are mostly dimorphic afferents in the ipsilateral semicircular canals and extrastriolar regions of the sacculus , and are therefore likely to predominantly convey information about angular acceleration of the head . To investigate all the primary vestibular projections to cerebellum , injections were made using a non-Cre-dependent retrograde virus ( AAV2-retro-CAG-GFP ) targeting lobe X . In all cases both lobes IX and X were infected ( as well as cerebellar nuclei ) ( Figure 2—figure supplement 2 ) . VG ipsilateral to the site of injection had many retrogradely labeled somata , including 2 . 7% that were calretinin-positive ( 34/1486 , n = 3 VG in separate experiments ) . The majority of calretinin positive cells were not retrolabeled ( 91% , 343/377 ) . Examples of central/striolar pure-calyx afferents that were retrolabeled were found in all five end organs , although they were rare , numbering only a few per end organ ( Figure 2—figure supplement 2E ) . This provides evidence that some pure-calyx afferents may project to cerebellum , but we cannot determine whether they project to lobe X , lobe IX or cerebellar nuclei , as all regions were infected . In comparison to the Cre-dependent virus , this viral injection labeled many more afferents in all the end organs , but especially in the otolith organs ( Figure 2—figure supplement 2G–H ) . In this experiment many peripheral afferents in the lateral utricle were labeled , consistent with the report that hair cell polarity relates to afferent projection pattern , with afferents innervating lateral utricle projecting to cerebellum and medial utricle projecting to vestibular nuclei ( Maklad et al . , 2010 ) . The majority of the afferents appeared to be dimorphic and were too dense/numerous to count accurately . These sources of primary afferent projections to mouse cerebellum were similar to those reported in gerbils ( Purcell and Perachio , 2001 ) . Having established that most of the primary afferents to lobe X are dimorphic VG fibers from the semicircular canals , we asked whether these fibers contact UBCs . The Glt25d2 mouse line was crossed with a channelrhodopsin ( ChR2 ) reporter line ( Ai32 ) , which caused expression of ChR2 and EYFP in primary vestibular afferents . This cross allowed specific activation of primary afferents with light in acute brain slice physiology experiments . Whole-cell patch-clamp recordings were made near ChR2-EYFP-expressing mossy fiber endings in sagittal slices of cerebellum containing lobe X , specifically targeting recordings to candidate UBC somata identified by size ( ~10 µm diameter; Figure 3A ) . ChR2 activation of primary afferents with blue light flashes caused bursts of action potentials in postsynaptic UBCs ( Figure 3B ) . Activation of primary afferents led to time-locked , depressing EPSCs , followed by a slow inward current that began at the end of the stimulation train; both responses were mediated by AMPA receptors , and are diagnostic of ON UBCs ( Figure 3C ) ( Borges-Merjane and Trussell , 2015; Lu et al . , 2017; Zampini et al . , 2016 ) . The chances of finding a UBC that happened to be contacted by a nearby labeled fiber was low . However , of 107 UBCs recorded in brain slices from 22 mice , all 13 UBCs that responded to optogenetic activation of primary afferents were ON UBCs . The response to ChR2 stimulation of primary afferents was remarkably similar to responses evoked by electrical stimulation of white matter ( Figure 3D ) . Thus , primary afferents preferentially target ON UBCs and we found no evidence for primary projections to OFF UBCs . Recorded cells were filled with biocytin for post hoc imaging during whole-cell recording ( Figure 3E–F ) . Biocytin fills confirmed the UBC morphology of the recorded cells and allowed visualization of the contacts between presynaptic EYFP-labeled mossy fiber axon and biocytin filled postsynaptic brush in six experiments ( Figure 3—figure supplement 1 ) . This approach provided views of the complex morphology of these synaptic interfaces . 3D renderings were made in order to estimate the surface area of the brush and the area of the brush that contacted the mossy fiber ( Figure 3G–H , Figure 3—figure supplement 2 ) . Although this is not a direct measure of the transmitter release regions , it quantifies the area of apparent contact where transmission occurs . The area of the brush that contacted the mossy fiber was 99 . 45 ± 40 . 95 µm2 ( mean ± SD ) . The area of the UBC brush itself was 446 . 68 ± 86 . 35 µm2 ( mean ± SD ) , and thus nearly a quarter of the dendrite was available for synaptic contact . We tested whether the morphology of these connections correlated with the synaptic responses of the UBCs . The postsynaptic fast EPSC was positively correlated with UBC brush area , but not the contact area between the mossy fiber and brush ( Figure 3I ) . The slow EPSC amplitudes that occur at the offset of stimulation and decay times did not correlate with the contact area between the mossy fiber and UBC or the brush area ( Figure 3J ) . This lack of correlation may suggest that the postsynaptic AMPARs that mediate this slow current are at some distance from the sites of contact with the mossy fiber , or that glutamate removal by diffusion or transport shape this current ( Lu et al . , 2017 ) . mGluR1 is expressed by ON UBCs and not by OFF UBCs , while calretinin is expressed by OFF UBCs and not by ON UBCs ( Borges-Merjane and Trussell , 2015 ) . Calretinin expression thus marks pure-calyx afferents of the vestibular end organs , as well as cerebellar OFF UBCs . Immunohistochemical labeling of these two markers of UBC subtype in cerebellar sections of Glt25d2::tdTomato mice expressing tdTomato in primary afferents revealed numerous projections to mGluR1-expressing UBCs , but not to calretinin-expressing UBCs ( Figure 3K–M ) , confirming the physiological analysis . To quantify the proportion of UBCs that receive input from these primary vestibular afferents a systematic random sampling approach was taken that ensured all of the granule cell layer of lobe X had an equal probability of being sampled ( see Materials and methods ) . Overall 145 mGluR1-expressing UBCs were counted , 29 of which received primary afferent input ( 20% ) . In the same brain sections 96 calretinin+ UBCs were counted , none of which received primary afferent input . Thus , a direct VG projection to lobe X targets mGluR1-expressing ON UBCs but not calretinin-expressing OFF UBCs . Although the expression of Cre appeared random in the VG ( see above ) , it is possible that the Glt25d2 line may express Cre specifically in a subpopulation of VG neurons that target mGluR1-expressing UBCs , rather than a representative population . To label VG neurons that do not express Cre in the Glt25d2 line , we injected GFP or tdTomato-expressing viruses with different serotypes ( AAV9 , AAV2-retro , AAV-PHP . S ) in the posterior semicircular canal ( Figure 4 ) . This viral approach infected populations of VG neurons of various sizes presumed to be a different population than those that express Cre in the Glt25d2 mouse line and was therefore a complimentary approach to label diverse VG neuron types ( Figure 4B–E ) . Of the VG neurons infected , ~8 . 4% expressed calretinin and were therefore the ‘pure-calyx’ type ( Out of 636 virus labeled neurons in 4 VG , 54 expressed calretinin ) . Vestibular primary afferents were labeled in lobe X and their apparent mossy fiber swellings were imaged along with immunohistochemically localized mGluR1 and calretinin-expressing UBCs ( Figure 4F–G ) . Of the 240 mossy fiber terminals imaged , 79 mGluR1-expressing UBCs had brushes interdigitated with the terminals ( n = 4 mice ) . No calretinin-expressing UBCs were seen making contact to primary vestibular afferents . The morphology of the primary afferents was striking . They seldom branched as they projected along the white matter in the sagittal plane . The morphology of these primary afferents is compared directly with secondary afferents below . Taken together , these data provide multiple lines of evidence showing that primary vestibular afferents project exclusively to mGluR1-expressing ON UBCs and not to calretinin-expressing OFF UBCs . Since primary vestibular afferents excite ON UBCs , we asked whether they might also trigger inhibitory control of the same UBCs . Indeed , some UBCs that received direct primary afferents had ChR2-evoked fast disynaptic inhibitory post synaptic currents ( IPSCs ) in addition to monosynaptic EPSCs . In many cases , activation of primary afferents evoked IPSCs alone in UBCs and granule cells , without a typical ON or OFF synaptic response ( Figure 3—figure supplement 3 ) . The onset of these IPSCs occurred at a delay consistent with disynaptic inhibition in all cases ( 5 . 74 ± 1 . 42 ms ( mean ± SD ) , n = 23 ) . In most of these UBCs some component of the IPSC was blocked by GABAAR antagonist SR-95531 and the remaining current was blocked by glycine receptor antagonist strychnine ( Figure 3—figure supplement 3B ) . Presumably cerebellar Golgi cells , which co-release GABA and glycine , are the source of this disynaptic inhibition ( Rousseau et al . , 2012 ) . Thus , the same population of primary vestibular afferents both excite ON UBCs and activate a pathway that leads to their inhibition . The primary afferents that contacted ON UBCs also contacted granule cells , but generated clearly different physiological responses . Optogenetic activation of Cre+ afferents resulted in fast EPSCs in granule cells , but never exhibited a slow AMPAR-mediated EPSC at the offset of stimulation ( Figure 3—figure supplement 4A ) . In contrast , the peak and decay time of the first EPSC in the response train was similar to postsynaptic responses of ON UBCs ( Figure 3—figure supplement 4B , Figure 3—figure supplement 5 ) . The contact area between mossy fiber and granule cell claw was only about 15% of those measured between primary afferent and UBC brush ( Figure 3—figure supplement 4C–K ) . These results are consistent with the hypothesis that the slow EPSC of UBCs results from pre and postsynaptic structure , and is not simply a feature of mossy fiber transmitter release per se . Granule cells also received inhibition at a latency consistent with disynaptic input from Golgi cells ( 5 . 52 ± 0 . 40 ms ( mean ± SD ) , n = 10 , Figure 3—figure supplement 3C ) . These data indicate that Cre+ primary afferents contact granule cells and Golgi cells , but specifically target the ON subtype of UBCs . A major target of the vestibular primary afferents is the medial vestibular nucleus of the brainstem ( MV ) . The principal neurons of MV project secondary vestibular afferents to lobe X , and therefore represent a second potential source of mossy fiber input to UBCs . To target ChR2 to this secondary vestibular pathway , viral injections were made into MV of mGluR2-GFP mice , which express GFP in UBCs ( Borges-Merjane and Trussell , 2015 ) . The virus ( AAV1-CAG-ChR2 ( H134R ) -mCherry ) expressed the same variant of ChR2 as that expressed in the Ai32 ( ChR2-EYFP ) mouse line , fused to mCherry ( red fluorescent protein ) . Three weeks after infection , acute brain slices were prepared and whole-cell patch-clamp recordings of UBCs were made near mCherry-labeled secondary afferents ( Figure 5A ) . The location of the injection site was histologically confirmed in all experiments . Primary afferent axons local to the injection site were only rarely infected , as identical injections into Glt25d2::ChR2-EYFP mice showed few co-labeled neurons ( see below ) . Out of 108 UBCs recorded in brain slices from 14 mice , nine postsynaptic UBCs were of the OFF subtype: ChR2 activation of secondary afferents caused initial fast EPSCs that then depressed and led to a slow IPSC that caused a pause in spiking ( Figure 5B ) . The IPSC was blocked by the mGluR2 antagonist LY341495 in all cases tested ( Figure 5C ) . In four additional cases , secondary vestibular afferents projected to ON UBCs , based on the presence of a slow inward current response ( Figure 5D ) . Seven of the OFF UBCs were filled with biocytin and 3D rendered in order to estimate the brush area and contact area between the mossy fiber and the brush ( Figure 5E–H , Figure 5—figure supplement 1A ) . As was seen with ON UBCs that received primary input , the fast EPSC amplitude correlated with the UBC brush area of these OFF UBCs ( Figure 5I ) . No correlations between the slow IPSC and mossy fiber-UBC contact area or UBC brush area were found ( Figure 5J ) . The EPSCs of secondary afferent-receiving ON UBCs were larger than those of secondary afferent-receiving OFF UBCs ( Figure 5K ) . EPSCs of ON UBCs that received secondary afferents were similar to ON UBCs that received primary afferents ( secondary: 45 . 17 ± 8 . 26 pA vs primary: 46 . 35 ± 14 . 09 pA , ( mean ± sem ) , t-test , p=0 . 954 , n = 11 ) . All four ON UBCs were recovered for histological analysis ( Figure 5—figure supplement 1B ) . One of the ON UBCs had two brushes , which is a rare morphology ( Braak and Braak , 1993; Mugnaini and Floris , 1994 ) . To corroborate these physiological results we took an anatomical approach using the same ChR2-mCherry expression in MV and utilized mGluR1 and calretinin expression to identify ON and OFF UBCs ( Figure 5L ) . Of 231 mGluR1+ UBCs counted , 44 ( 19% ) received labeled mossy fiber input . Of 114 calretinin+ UBCs counted , 19 ( 17% ) received labeled mossy fiber input . Thus , although their populations differ in number , a similar proportion of mGluR1+ and calretinin+ UBCs are innervated by secondary afferents . Primary and secondary afferents in the cerebellum appeared to have different morphologies ( Figure 6 ) , suggesting that mossy fiber structure may differ depending upon their source . To compare the primary and secondary afferents in the same sections , a mCherry expressing virus ( AAV1-CAG-ChR2 ( H134R ) -mCherry ) was injected into the right MV of Glt25d2::ChR2-EYFP reporter mice . ChR2 , being a transmembrane protein , targeted the fused mCherry or EYFP proteins to the membranes of primary and secondary afferents . Labeled secondary afferents were more numerous than primary afferents ( Figure 6A–B ) , although their number is somewhat artificial given the incomplete labeling of both VG and MV neurons . In addition to lobe X , primary and secondary afferents projected to ventral leaflet of lobe IX , where UBCs are also present in high density relative to other lobes ( Harris et al . , 1993 ) . Primary afferents only projected into IXc , whereas secondary afferents also projected into the more caudal lobe IXb ( Figure 6C–D ) . The terminals of primary fibers were often ‘rosette-like’ , similar to those of secondary afferents , but in many cases the elaborate protrusions from the main fiber ran along a longer length of the axon than the more spherically shaped secondary afferents ( Figure 6E–F ) . The thickness of the primary and secondary afferent axons between terminals was clearly different ( Figure 6E–F ) . Measurement of axon diameter between rosettes ( mean of several measurements along axon >5 µm from mossy terminal swellings ) indicated that primary afferents were significantly thicker than secondary afferents ( Figure 6G ) . This was also the case for afferents that contacted UBCs that were recovered along with biocytin cell fills in physiology experiments , providing further evidence that primary afferents were not infected by viral injections into MV ( Figure 6H ) . In addition , primary afferents labeled by viral injection into the posterior semicircular canal were similar in thickness and morphology to the primary afferents in the Glt25d2::ChR2-EYFP line , and larger than the secondary afferents ( Glt25d2-labeled primary afferents: 1 . 20 ± 0 . 34 µm , n = 48; AAV-labeled primary afferents: 1 . 13 ± 0 . 34 µm , n = 58; AAV-labeled secondary afferents: 0 . 80 ± 0 . 28 µm , n = 100 ( mean ± SD ) Figure 6G ) . Thus the differences between primary and secondary afferents are not due to pathology caused by life-long expression of ChR2-EYFP in the Glt25d2::ChR2-EYFP line . Surprisingly , the differences in morphology based on source of input also extended to the postsynaptic cells . The UBCs that received secondary afferent input had larger dendritic brushes than UBCs that received primary input . In some cases , the primary-receiving UBC had a brush that wrapped around the primary afferent itself , rather than around an apparent swelling or rosette ( Figure 7A , Figure 3—figure supplement 1 ) , indicating that release sites can be located at these regions of the axon . Secondary-receiving UBCs were more likely to contact a spherically shaped rosette ( Figure 7B , Figure 5—figure supplement 1 ) . Measurements of the contact area between afferent and UBC , indicated that the area between primary afferents and UBCs was smaller than the contact area between secondary afferents and UBCs , likely due to the more complex rosettes made by secondary afferents ( Figure 7C ) . Even the dendritic brush ( including non-synaptic membrane ) of secondary-receiving UBCs was larger in area and volume than the brushes of primary-receiving UBCs ( Figure 7D ) . These differences were not due to OFF UBCs being larger than ON UBCs , because the secondary-afferent receiving ON UBCs were similar in size to the secondary-receiving OFF UBCs . The contact area between the brush and mossy fiber relative to the entire surface area of the brush was similar between primary and secondary-receiving UBCs ( t-test , p=0 . 180 , n = 17 ) . This suggests that the postsynaptic brush develops in such a way to match the anatomy of the earlier maturing mossy fiber ( Ashwell and Zhang , 1998; Sekerková et al . , 2004 ) . Finally , UBCs targeted by primary and secondary afferents even differed in soma size , regardless of ON/OFF subtype ( Figure 7E ) . Thus , the global morphology of UBCs is tuned to the source of mossy fiber . Besides the ON/OFF distinction described previously , some UBCs respond to electrical stimulation of white matter with a peculiar slow-rising EPSC ( Figure 8A–B ) ( Zampini et al . , 2016 ) . These AMPAR-mediated EPSCs are distinct from typical synaptic responses due to their slow activation during the stimulus and slow decay upon cessation of stimulation and their lack of fast EPSCs; notably , they lacked the slow inward current that appears only after transmission ceases , characteristic of the ON UBC . Previously these build-up responses were considered to arise from variation in apposition of receptors and release sites at mossy fiber terminals ( Zampini et al . , 2016 ) . Here we asked if they represent a different form of input with unique origin . Build-up EPSCs were always blocked by AMPAR antagonists which in some cases revealed a small mGluR2-mediated IPSC ( Figure 8B ) . In other cases , primary afferent stimulation evoked a small IPSC mediated by mGluR2 , which , when blocked revealed the build-up EPSC ( Figure 8D ) . Electrical stimulation of white matter activates all axons nearby , including primary , secondary and intrinsic mossy fibers from UBCs . Therefore , the source and mechanism underlying these build-up EPSCs are not easily studied using conventional approaches . In the present experiments that utilized Glt25d2::ChR2-EYFP mice to stimulate primary afferents selectively , access to pre- and postsynaptic morphology allowed us to investigate the basis of these build-up responses in detail . ChR2-evoked build-up EPSCs were small ( Figure 8A ) , with a 6 . 06 ± 2 . 05 pA ( mean ± SD ) peak in response to ten stimuli at 50 Hz , and had a decay time constant of 445 . 38 ± 302 . 43 ms ( n = 5 UBCs ) . EPSCs in response to single light flashes could be resolved and were smaller than responses to trains . In all four cases in which build-up EPSCs were evoked by primary afferent optogenetic activation and the UBC was recovered , there was no mossy fiber contact to the UBC brush ( Figure 8C–D ) . Rather , in all of these cases , primary afferents contacted UBC somata . Additionally , in all cases of recovered UBCs that did not have apparent build-up EPSCs , none had a ChR2-expressing mossy fiber contacting the soma ( n = 5 primary-receiving UBCs ) ( cf . Figure 3 , Figure 3—figure supplement 1 ) . Thus , the build-up response represents activity of an unconventional UBC input , but generated by a primary afferent . If the build-up response is due to somatic synapses , then a UBC receiving both contact to the brush and to the soma would be predicted to have a typical ON UBC response ( due to the brush contact ) with an additional build-up EPSC ( due to the soma contact ) . Indeed , in a primary-receiving ON UBC that had contact with the same mossy fiber on both the brush and the soma , the ChR2-evoked response was a combination of the typical fast EPSC plus a build-up EPSC ( Figure 8E ) . Both currents were mediated by AMPARs , as they were blocked by GYKI . Shank1 , a postsynaptic density protein , confirmed that postsynaptic receptors may be present at the somatic membrane in regions that appear to contact the primary afferent ( Figure 8E ) . In some UBCs the build-up EPSC could be evoked by either ChR2 stimulation or electrical stimulation ( Figure 8F ) . At higher electrical stimulation intensity a generic ON UBC response appeared , but it could not be evoked by ChR2 stimulation . This may have been due to low ChR2 expression in the mossy fiber . In 3 UBCs with the build-up response to ChR2 stimulation , further electrical stimulation evoked an ON response . In no case did electrical stimulation evoke an OFF UBC response , and thus it may be that axosomatic synapses are only made onto ON UBCs .
Purkinje cells are considered the site of multimodal integration in the cerebellum , due to their enormous number of granule cell inputs . Recent studies have highlighted the integrative aspects of cells in the granule cell layer as well . Granule cells have multiple dendrites , allowing them to receive signals from multiple modalities ( Chabrol et al . , 2015; Huang et al . , 2013; Knogler et al . , 2017; Sawtell , 2010 ) . By contrast , UBCs receive only a single mossy fiber input to their dendritic brush and therefore do not integrate multiple modalities , instead maintaining the activities of ensembles of postsynaptic granule cells segregated in a ‘labeled line’ . Such an arrangement may be of particular advantage in cerebellar vestibular processing vs other cerebellar modalities . The typical pattern of integration by granule cells could disrupt vestibular processing by mixing inputs from the five vestibular end organs ( per ear ) that sense head movements in different directions . Instead , UBCs could act as an input layer prior to the granule cells to allow divergence to parallel ensembles of postsynaptic granule cells that each faithfully represent head movement along the axes of different end organs . Convergence must occur at some point to integrate signals from the canals and otoliths , which is necessary to estimate orientation relative to gravity , and this may happen at the granule cell and/or Purkinje cell level . Alternatively , or in addition , convergence occurring in the MV could be processed specifically by UBCs that receive secondary input . Further experiments are necessary to explore whether primary and secondary pathways target distinct populations of granule cells , either through UBCs or directly , and whether the primary and secondary-receiving granule cells vary in physiological response or morphology , as do UBCs . MV neurons receive multiple primary afferent inputs , feedback inhibition from Purkinje cells , and can be inhibited by stimulation of the contralateral vestibular organs ( Shimazu and Precht , 1966; Uchino et al . , 1986 ) . Thus , secondary vestibular mossy fibers may carry signals integrated from multiple end organs and both hemispheres to both ON and OFF UBCs . This is a strikingly different pattern of connectivity than the ON UBCs that receive primary afferent input from a cluster of hair cells in a single end organ and a single VG subtype . OFF UBCs do not appear to receive input from primary afferents at all , and may therefore only process signals that have been integrated by MV . This circuitry indicates that OFF UBCs process bilateral vestibular signals to pause input to ensembles of granule cells , perhaps in a push-pull circuit that could contribute to reflexive eye movements . The rate of input to secondary afferent-receiving UBCs may be preserved by MV neurons , which are known to respond to synaptic input with high-fidelity EPSCs whose amplitudes are rate-invariant ( McElvain et al . , 2015 ) . In addition , granule cells respond faithfully to mossy fiber input in vivo , responding to a burst of mossy fiber input with a burst of action potentials that is similar in duration ( Arenz et al . , 2008; Chadderton et al . , 2004 ) . It is at UBCs where profound signal processing occurs through highly rate-dependent EPSC amplitudes and long duration responses that may even be inverted by OFF UBCs ( Kennedy et al . , 2014 ) . This extended response could be particularly important in the cerebellum . A longer duration burst of action potentials conveyed to the parallel fiber-Purkinje cell synapse will be more likely to drive the Purkinje cell because of facilitation at this synapse and would extend the window of integration within which climbing fiber activity can influence circuit learning . At the level of the vestibular granule cell , it is likely that a single neuron integrates both primary and secondary inputs ( Chabrol et al . , 2015 ) but also intrinsic mossy fiber input from UBCs . ( Chabrol et al . , 2015 ) emphasized that mossy fibers from different sources may exhibit different forms of short-term plasticity , and these characteristic time-dependent responses impact the integrative function of the granule cell . Given the radical transformation of mossy fiber input by UBCs , which results in prolonged or delayed firing , or cessation in activity in vivo ( Kennedy et al . , 2014 ) , granule cells that receive some dendritic input from a UBC’s intrinsic mossy fiber likely will be dominated by that input while the UBC is active . However , paired recordings between presynaptic UBCs and postsynaptic granule cells will be necessary to test this hypothesis . Additionally , whether a single granule cell integrates input from multiple UBCs carrying primary and secondary signals will be an important next step to understanding the integration that occurs in vestibular cerebellum . Brodal and Drablos ( 1963 ) suggested that vestibular lobes of rat cerebellum contain a population of mossy fibers that differ from those of other lobes . The fact that they reported these fibers in flocculus , despite a dearth of primary afferents ( Newlands and Perachio , 2003; Osanai et al . , 1999 ) , implies that these fibers may have been intrinsic mossy fibers of UBCs , as suggested by Rossi et al . ( 1995 ) . Differences in morphology between mossy fibers based on their source have been reported in non-vestibular lobes of the cerebellum . Mossy fibers projecting from the deep cerebellar nuclei are larger , are more likely to have filipodia projecting from the rosette , and have more boutons , than those projecting from basal pontine nuclei ( Gao et al . , 2016; Gilmer and Person , 2017 ) . While mossy fibers originating from different regions that project to lobe X vary in presynaptic plasticity ( Chabrol et al . , 2015 ) , we find that these axons also exhibit characteristic morphological features that may support differences in electrical activity level . Primary afferents were quite thick , projected along the white matter of lobe X , and only rarely branched . It is perhaps not surprising that the primary vestibular afferents in the cerebellum are large given that their diameter in the vestibular nerve is among the thickest in the brain ( mean , ~3 µm ) ( Gacek and Rasmussen , 1961 ) ; notably , these also have elevated tonic firing rates of >100 Hz ( Jones et al . , 2008 ) . The relatively thin diameter of the secondary afferents suggests lower firing rates ( Perge et al . , 2012 ) . Indeed , vestibular nucleus neurons that respond to vestibular stimulation in vivo have spontaneous firing rates between 0 and 30 Hz in cats and ~65 Hz in squirrel monkeys ( Cullen and McCrea , 1993; Shimazu and Precht , 1965 ) . In mice , the best approximation of spontaneous firing of secondary mossy fibers may be the EPSCs recorded in granule cells in the flocculus that could be modulated by vestibular stimulation . These EPSCs occurred at ~13 Hz under anesthesia ( Arenz et al . , 2008 ) , much lower than vestibular nerve fibers . Some UBCs that were postsynaptic to primary afferents had their dendritic brush wrapping around smooth parts of the axon , providing anatomical evidence that synapses may exist along the length of the axon in addition to the terminal swellings . This is corroborated by the finding that smooth parts of the primary afferent contacting a UBC soma could evoke EPSCs . Such differences in en passant mossy terminal morphology might affect efficiency of propagating action potentials . In build-up responses , contacts are made directly to the UBC soma , clearly out of reach of the dendrite . Previous descriptions of build-up responses in UBCs speculated that such responses might arise from misalignment of a mossy fiber active zone relative to an AMPAR cluster ( Zampini et al . , 2016 ) . Instead , the observation that build-up EPSCs occur specifically when mossy fibers appear to contact UBC somas demonstrates a novel basis for these synaptic currents . This conclusion depended upon recovering many filled cells after optogenetically stimulating labeled mossy fibers . Apparently , UBC somata express some AMPA receptors sufficient to respond to somatic inputs . Indeed , outside-out patch-clamp recording has previously shown that AMPARs do function in somatic membranes of UBCs ( Kinney et al . , 1997 ) . Previous analysis of electron micrographs highlighted mossy fiber terminals that contacted Golgi cell somata , forming large convoluted ‘en marron’ synapses ( Chan-Palay and Palay , 1971 ) , which are distinct from the club-like endings contacting UBCs . Mossy fibers touching granule cell somata have also been observed , although these same mossy fibers only made definitive synaptic contacts with nearby granule cell dendrites ( Palay and Chan-Palay , 1974 ) . The fortuitous observation of somatic contacts by mossy fibers that were associated with distinct postsynaptic responses suggests that somatic inputs could represent a previously unappreciated form of transmission in the granule region of the cerebellum .
C57BL/6J-TgN ( grm2-IL2RA/GFP ) 1kyo ( referred to as mGluR2-GFP ) of both sexes were used to identify UBCs ( Borges-Merjane and Trussell , 2015; Nunzi et al . , 2002; Watanabe et al . , 1998 ) . Male C57BL/6J-Tg ( Colgalt2-cre ) NF107Gsat/Mmucd ( referred to as Glt25d2 ) mice were used to express either tdTomato or ChR2-EYFP in primary vestibular afferents by crossing with Ai9 ( RCL-tdT ) ( Jackson Labs 007909 ) ( Madisen et al . , 2010 ) or Ai32 ( RCL-ChR2 ( H134R ) /EYFP ) ( Jackson Labs 024109 ) ( Madisen et al . , 2012 ) mouse lines , respectively . The TCGO mouse line was used for its sparse granule cell labeling ( C57BL/6J . Cg-Et ( tTA/mCitrine ) TCGOSbn ) ( Huang et al . , 2013; Shima et al . , 2016 ) . Wild type C57BL/6J mice were used for semicircular canal injections and for breeding . Mouse lines were maintained in the animal facility managed by the Department of Comparative Medicine and all procedures were approved by the Oregon Health and Science University’s Institutional Animal Care and Use Committee and met the recommendations of the Society for Neuroscience . Because mossy fiber and UBC synapse formation is mature in animals older than postnatal day 21 ( P21 ) ( Morin and Wood , 2001 ) , we used pups older than this age ( P21-P39 ) for experiments . Mice were overdosed with isoflurane and perfused through the heart with 0 . 01M phosphate buffered saline , 7 . 4 pH ( PBS ) followed by 4% paraformaldehyde in PBS . Brains were extracted from the skull and incubated in the same solution overnight at 4°C . Brains were transferred to 30% sucrose in PBS for >2 days . 50 μm thick sections were made on a cryostat ( HM 550 , Microm ) at −22°C and saved as floating sections in PBS . When labeling mGluR1 and calretinin , brains were transferred to PBS instead of 30% sucrose and sectioned on a vibratome . To recover cells that were filled with Biocytin during whole-cell recording , acute brain slices were fixed overnight in 4% paraformaldehyde in PBS , followed by storage in PBS . Both floating 50 μm sections and 300 μm thick acute slices were treated with the following procedures . Sections were rinsed 3 × 10 min in PBS , blocked and permeabilized in 2% BSA , 2% fish gelatin , 0 . 2% Triton X-100 in PBS for >2 hr at room temperature . Sections were incubated in primary antibodies for 2–3 days at 4°C on an orbital shaker . Sections were rinsed 3 × 10 min in PBS , followed by secondary antibodies and streptavidin for 2–3 days at 4°C on an orbital shaker . See Key Resource table for a full list of antibodies used . Sections were rinsed in PBS and in some cases incubated in 4% paraformaldehyde in PBS for 1 hr . Sections were mounted on microscope slides and coverslipped with CFM-3 ( CitiFluor ) . Mice were perfused with saline with 10 U/ml heparin warmed to 37°C , followed by 35 ml 4% PFA in 0 . 1M phosphate buffer 4°C . End organs were carefully dissected out in PBS and permeabilized and blocked in 2% Triton X-100 , 5% normal donkey serum in PBS 1 hr RT shaking . Primary antibodies were incubated for 1–3 days at 4°C shaking , then rinsed in PBS and incubated in secondary antibodies as above . End organs were coverslipped using a 0 . 12 mm spacer and CFM-3 mountant . Images were acquired on a Zeiss Elyra PS . 1 with AiryScan system that reconstructs super-resolution images from a series of images acquired under spatially structured illumination ( Gustafsson , 2000 ) . Images were processed in Zen Black or transferred to Imaris ( Bitplane ) , a multidimentional analysis program based on fluorescence intensity data . Surfaces were created on the channels that contained the UBC and mossy fiber fluorescence to isolate the structure and extract the area and volume statistics and the 3D reconstructions . A surface calculation that is part of the Imaris software was used to create a one voxel thick contact layer between the UBC and mossy fiber surfaces and the contact area was calculated . To test the ability to measure surface areas and volumes accurately , fluorescent microspheres ( Spherotech , FP4060-2 ) were imaged following the same procedures used for biocytin filled cells ( Figure 5—figure supplement 1 ) . To count UBCs innervated by primary or secondary mossy fibers , sagittal sections from Glt25d2::tdTomato mice or wild type mice that received MV injections ( identical to those made for physiology experiments ) were labeled with anti-DsRed , anti-mGluR1a and anti-calretinin as above . Hemispheres contralateral and ipsilateral to the injection were separated by a cut down the midline . Every third 50 µm thick section was histologically labeled . Sections were sampled using a pseudorandom , systematic sampling scheme throughout the mediolateral extent of lobe X . Two fields per section were counted at a random location within the granule cell layer of lobe X . This sampling scheme ensured that every part of the granule cell layer of lobe X had an equal probability of being sampled . Counting was done on a Zeiss LSM 780 confocal microscope using a 63 × 1 . 4 NA oil immersion objective . A 50 µm x 50 µm unbiased counting frame was used in which UBC somata touching two of the edges were omitted and somata touching the other two edges were included . Calretinin+ or mGluR1+ UBCs were first identified through the depth of the slice . Then whether tdTomato+ mossy fibers innervated the brush of each UBC was noted . UBCs were only counted when both the brush and the soma were apparent , in order to avoid counting calretinin+ or mGluR1+ mossy fibers that often look similar to UBC brushes . Counts of both UBC types in the same fields ensured the ratios of UBC types would not be affected by their known differential distribution in dorsal vs ventral leaflets of lobe X ( Nunzi et al . , 2002 ) . In the experiment that used semicircular canal injections to label VG neurons , sections were prepared as above , with anti-GFP , anti-mGluR1a and anti-calretinin . The labeled afferents were more sparse than in Glt25d2 , so every labeled terminal swelling was imaged along with mGluR1 and calretinin . Contacts between these virally-labeled primary afferents and mGluR1 or calretinin-expressing UBC brushes were counted in sections throughout lobe X , ipsilateral to the injected inner ear . To measure primary afferent diameter , sections of lobe X from Glt25d2::ChR2-EYFP amplified with an anti-GFP antibody were imaged . To measure secondary afferent diameter , sections of lobe X from mice injected with AAV1-CAG-ChR2 ( H134R ) -mCherry and amplified with an anti-DsRed or anti-mCherry antibody . Images were captured on a confocal microscope using 63 × 1 . 4 NA oil immersion objective . Axon diameters > 10 µm from mossy fiber terminal rosettes were measured using ImageJ . 4–8 spans across the axon were measured at ~5 µm intervals and the average was taken as the diameter . Post hoc imaging of axons that projected to biocytin filled UBCs from acute slice experiments were measured in the same way . One secondary afferent that projected to a recorded ON UBC was omitted because its 2 . 2 µm diameter was >5 SD above the mean diameter ( 0 . 8 µm ) of secondary afferents and larger than any measured primary afferent . This afferent may be from a cell type present in low number in the MV , but more work is needed to identify the origin of such fibers . To count peripheral vestibular afferents , whole mounted end organs were imaged using a Zeiss LSM 880 with fast Airyscan super-resolution and 25 × 0 . 8 NA oil immersion objective . Images were counted using ImageJ and the Cell Counter plugin . Dimorphic calyces counted when they had ( 1 ) a 3-dimentional calyx shape ( 2 ) at least one bouton process and ( 3 ) a labeled axon extending from its base . Calretinin staining clearly labeled pure-calyx afferents that are also distinguishable from their wider opening at the top . These counts are likely underestimates for the total number of retrolabeled afferents , due to tissue damage and inadequate fluorescence . More bouton-only endings may be present because they may be interpreted as being boutons extending from neighboring dimorphs . Afferent fibers were counted 10–50 µm distal to the base of the hair cells . Mice were anesthetized with isoflurane and decapitated . The brain was rapidly extracted into ice-cold high-sucrose artificial cerebral spinal fluid solution ( ACSF ) containing ( in mM ) : 87 NaCl , 75 sucrose , 25 NaHCO3 , 25 glucose , 2 . 5 KCl , 1 . 25 NaH2PO4 , 0 . 5 CaCl2 , 7 MgCl2 , bubbled with 5% CO2/95% O2 . Parasagittal cerebellum sections containing lobe X were cut at 300 µm with a vibratome ( VT1200S , Leica ) in ice-cold high-sucrose ACSF . Immediately after cutting , slices were incubated in 35°C recording ACSF for 30–40 min , followed by storage at room temperature . Recording ACSF contained ( in mM ) : 130 NaCl , 2 . 1 KCl , 1 . 2 KH2PO4 , 3 Na-HEPES , 10 glucose , 20 NaHCO3 , 2 Na-pyruvate , 2 CaCl2 , 1 MgSO4 , 0 . 4 Na-ascorbate , bubbled with 5% CO2/95% O2 ( 300–305 mOsm ) . Slices were transferred to submerged recording chamber and perfused with the ACSF heated to 33–35°C at 3 ml/min ( TC-324B , Warner Instruments ) . Slices were viewed using an infrared Dodt contrast mask and a 60X water-immersion objective ( LUMPlanFL , Olympus ) and camera ( IR-1000 , Dage-MTI ) on a fixed stage microscope ( Axioskop 2 FS Plus , Zeiss ) . In slices from mGluR2-GFP mice UBCs were identified by their GFP fluorescence . In slices from Glt25d2 mice UBCs were identified by their soma diameter ~10 μm in the granular cell layer in lobe X . All cells recorded were filled with 1 μM Alexa Fluor 594 hydrazide sodium salt ( A10438 , Molecular Probes ) in order to confirm UBC or granule cell morphology . Pipettes were pulled from thin-walled borosilicate glass capillaries ( 1 . 2 mm OD , WPI ) to a tip resistance of 5–8 MΩ . The internal pipette solution contained ( in mM ) : 113 K-gluconate , 9 HEPES , 4 . 5 MgCl2 , 0 . 1 EGTA , 14 Tris-phosphocreatine , 4 Na2-ATP , 0 . 3 Tris-GFP , with osmolality adjusted to ~290 mOsm with sucrose and pH adjusted to pH 7 . 3 with KOH . In some experiments 0 . 1–0 . 5% biocytin ( B1592 , Molecular Probes ) was added to the pipette solution . Reported voltages are corrected for a −10 mV liquid junction potential . Whole-cell recordings were amplified ( 10X ) , low-pass filtered ( 10 kHz Bessel , Multiclamp 700B , Molecular Devices ) and digitized using pClamp software ( 20–50 kHz , Digidata 1550 , Molecular Devices ) . Further digital filtering was performed offline , in most cases a 1 kHz low-pass Bessel 8-pole filter was applied . Series resistance was compensated with correction 20–40% and prediction 60–70% , bandwidth 2 kHz . Cells were voltage-clamped at −70 mV . Mossy fibers were stimulated extracellularly by applying voltage pulses ( 1–50 V , 100–250 µs ) using a stimulus generator ( Master 8 , A . M . P . I . ) via a concentric bipolar electrode ( CBBPC75 , FHC ) . ChR2 was activated using full-field blue LED light flashes ( Lambda TLED+ , Sutter ) through a GFP filter set . In some cases , a low concentration ( 50 µM ) of the K+ channel blocker 4-aminopyridine ( 4-AP ) was used to increase the reliability of ChR2-evoked transmitter release , presumably by lowering spike threshold . These cases are indicated in figure legends . Bath application of 4-AP increased the peak EPSC , increased synaptic depression and slowed the decay of the EPSC , but did not change the ON or OFF UBC response type ( Figure 3—figure supplement 5 ) . Additionally , OFF UBCs were recorded in these slices with electrical stimulation of the white matter and in the presence of 4-AP , indicating that OFF UBCs were present in these transgenic animals and that 4-AP did not block the inwardly rectifying K+ channels that mediate the OFF response ( Figure 3—figure supplement 5 ) . Viral injections were made into the medial vestibular nucleus in P21-25 mGluR2-GFP mice using a stereotax ( David Kopf ) single axis manipulator ( MO-10 , Narishige ) and pipette vice ( Ronal ) under isoflurane anesthesia . Glass capillaries ( WireTrol II , Drummond Scientific ) were pulled on a horizontal puller ( P-97 , Sutter ) , beveled at a ~45 degree angle with a 20–30 μm inside diameter using a diamond lapping disc ( 0 . 5 µm grit , 3M DLF4XN_5661X ) The scalp was cut and a small hole was drilled in the skull . The pipette was lowered into the brain at ~10 µm / s . Five-min periods before and after injection were allowed . 20–50 nl of virus was injected using stereotaxic coordinates 6 . 1 mm caudal , 0 . 8 mm lateral to bregma and 3 . 75 mm ventral to the surface of the brain . AAV1-CAG-ChR2 ( H134R ) -mCherry ( 2 . 92E12 GC/ml ) virus from the University of Pennsylvania vector core was injected into MV to label and express ChR2 in secondary mossy fibers . AAV2-retro-CAG-Flex-GFP ( 9 . 86E12 GC/ml ) or AAV2-retro-CAG-GFP ( 1 . 0E13 GC/ml ) ( Janelia Farm ) was injected into lobe X of adult Glt25d2 mice ( >12 weeks ) using stereotaxic coordinates 7 . 2 mm caudal , 0 . 5 mm lateral to bregma and 3 . 0 mm ventral to the surface of the brain . 200–400 nl of virus was used . Experiments were done 2–3 weeks after virus injection . Semicircular canal injection was done following Suzuki et al . ( 2017 ) using AAV2-retro-CAG-GFP ( 1 . 0E13 GC/ml , Janelia Farm ) , AAV2-retro-CAG-tdTomato ( 7 . 0E12 GC/ml , Addgene ) or AAV-PHP . S-CAG-tdTomato ( 1 . 7E13 , Addgene ) ( Chan et al . , 2017 ) , AAV9-CAG-ChR2 ( H134R ) -mCherry ( 2 . 96E12 GC/ml , University of Pennsylvania ) under isoflurane anesthesia . Briefly , a small hole was bored into the posterior semicircular canal using a 27 ga needle . After a 5-min period to allow the fluid leakage to slow , an injection pipette fused to PE10 tubing followed by polyimide tubing ( 0 . 0039’ ID , 0 . 0049’ OD ) was inserted into the hole and secured in place with muscle and tissue adhesive ( Vetbond ) . 2 µl volume of the virus was injected at 100 nl/min . After 5 min , the tube was removed , the hole was plugged with muscle and sealed with tissue adhesive . Mice were perfused two weeks later . | While out jogging , you have no trouble keeping your eyes fixed on objects in the distance even though your head and eyes are moving with every step . Humans owe this stability of the visual world partly to a region of the brain called the vestibular cerebellum . From its position underneath the rest of the brain , the vestibular cerebellum detects head motion and then triggers compensatory movements to stabilize the head , body and eyes . The vestibular cerebellum receives sensory input from the body via direct and indirect routes . The direct input comes from five structures within the inner ear , each of which detects movement of the head in one particular direction . The indirect input travels to the cerebellum via the brainstem , which connects the brain with the spinal cord . The indirect input contains information on head movements in multiple directions combined with input from other senses such as vision . By studying the mouse brain , Balmer and Trussell have now mapped the direct and indirect circuits that carry sensory information to the vestibular cerebellum . Both types of input activate cells within the vestibular cerebellum called unipolar brush cells ( UBCs ) . There are two types of UBCs: ON and OFF . Direct sensory input from the inner ear activates only ON UBCs . These cells respond to the arrival of sensory input by increasing their activity . Indirect input from the brainstem activates both ON UBCs and OFF UBCs . The latter respond to the input by decreasing their activity . The vestibular cerebellum thus processes direct and indirect inputs via segregated pathways containing different types of UBCs . The next step in understanding how the cerebellum maintains a stable visual world is to identify the circuitry beyond the UBCs . Understanding these circuits will ultimately provide insights into balance disorders , such as vertigo . | [
"Abstract",
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"neuroscience"
] | 2019 | Selective targeting of unipolar brush cell subtypes by cerebellar mossy fibers |
The congression of chromosomes to the spindle equator involves the directed motility of bi-orientated sister kinetochores . Sister kinetochores bind bundles of dynamic microtubules and are physically connected through centromeric chromatin . A crucial question is to understand how sister kinetochores are coordinated to generate motility and directional switches . Here , we combine super-resolution tracking of kinetochores with automated switching-point detection to analyse sister switching dynamics over thousands of events . We discover that switching is initiated by both the leading ( microtubules depolymerising ) or trailing ( microtubules polymerising ) kinetochore . Surprisingly , trail-driven switching generates an overstretch of the chromatin that relaxes over the following half-period . This rules out the involvement of a tension sensor , the central premise of the long-standing tension-model . Instead , our data support a model in which clocks set the intrinsic-switching time of the two kinetochore-attached microtubule fibres , with the centromeric spring tension operating as a feedback to slow or accelerate the clocks .
The accurate segregation of chromosomes during anaphase requires that all sister kinetochores bi-orientate , an attachment state in which sisters form stable attachments to the plus-ends of microtubules that originate at opposite spindle poles . Bi-orientation begins immediately after nuclear envelope breakdown during prometaphase when scattered chromosomes engage the nascent mitotic spindle , and concludes with the formation of the metaphase plate – a state where all sister kinetochores are bi-orientated and aligned on the equator of a bipolar spindle ( McIntosh et al . , 2012 ) . To achieve this bi-orientation , sister kinetochores must be able to undergo directed movements to the equator ( this is termed congression ) and then maintain their position prior to anaphase onset – a feature of this latter phase is oscillations of the chromosomes along the spindle axis . Directed motility is possible because one sister adopts a poleward ( P ) moving state ( the lead sister ) while the other is in an away-from-the-pole ( AP ) moving state ( the trailing sister ) . These two movement states reflect the balance of microtubule polymerisation/depolymerisation within the kinetochore-fibre ( K-fibre ) , which is typically 20–25 microtubules in human cells ( Compton , 2000; Rieder , 2005; Wendell et al . , 1993 ) . While such K-fibres are rarely coherent , there is a small polymerisation bias between sister kinetochores ( Armond et al . , 2015; VandenBeldt et al . , 2006 ) and they can be thought of as being in either a net polymerising or depolymerising state . The adaptive switching between these AP and P states then defines the directionality of chromosome motion and can give rise to the quasi-periodic oscillations that are observed in the majority of vertebrate cells ( Skibbens et al . , 1993 ) . An outstanding question is to understand the mechanisms by which the two sister kinetochores are able to communicate in order to coordinate their P/AP states and thereby generate chromosome movements . Initial investigations into the control of chromosome movement utilised time-lapse imaging in newt lung cells using video-enhanced differential interference contrast microscopy ( Skibbens et al . , 1993 ) . Kinetochores were shown to undergo periods of relatively constant velocity separated by abrupt changes in direction – a behaviour termed ‘directional instability’ . Subsequent experiments demonstrated that weakening the centromeric chromatin which links the sisters ( with a laser ) uncoupled the normally coordinated motility of sister kinetochores ( Skibbens et al . , 1995 ) . These experiments led to a model ( also see Rieder and Salmon , 1994 ) in which tension in the centromeric chromatin triggers a lead sister switch ( P-to-AP ) at a certain threshold , the loss of tension then triggering a directional switch in the second sister ( AP-to-P ) . More recent kinetochore-tracking experiments in PtK1 cells are consistent with this model and show that switching initiates at maximum inter-kinetochore stretch ( Wan et al . , 2012 ) , schematic shown in Figure 1 . The polar ejection force , which increases with proximity to the pole , pushes the chromosomes towards the metaphase plate . When chromosomes stray far from the equator , this anti-poleward force increases the load on the leading ( P ) kinetochore and promotes switching – an idea supported by experiments in newt and human cells ( Ke et al . , 2009; Stumpff et al . , 2012 ) . The standard tension model thus predicts a fixed sequence of sister kinetochore-switching events during a directional reversal – lead switch first , followed by trail . However , the timeframe for these events is short ( several seconds ) , requiring a sampling rate that avoids temporal aliasing . Existing kinetochore-tracking assays have a frame rate in the 7 . 5–15 s range ( Dumont et al . , 2012; Jaqaman et al . , 2010; Wan et al . , 2012 ) , meaning that detailed analysis of the switching mechanism has not been possible to date . 10 . 7554/eLife . 09500 . 003Figure 1 . Standard model for kinetochore directional switching . Schematic outlining the prevailing model for how sister kinetochores coordinate directional switches . As the leading , poleward-moving , kinetochore ( P; black ) moves to the right , the centromeric chromatin ( blue ) – which functions as a compliant linkage between sisters – becomes progressively more stretched ( steps i , ii ) . Stretching occurs because the trailing , away-from-the-pole , kinetochore ( AP; red ) is moving more slowly than the lead . Once sisters are at maximum stretch , the tension in the chromatin is thought to trigger the lead sister kinetochore to switch into an AP state . This results in a rapid loss of tension as both sisters are then in an AP state and moving towards each other ( step iii ) . This relaxation is thought to trigger switching of the initially trailing sister into a P-moving state ( step iv ) . Adapted from Wan et al . , 2012 . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 003
We developed a high-resolution kinetochore-tracking procedure and used a switching point detection algorithm to examine the fine detail of paired sister kinetochore trajectory data . Our previous kinetochore-tracking assay ( Jaqaman et al . , 2010 ) with a 7 . 5-s sampling time lacks the time resolution to resolve the relative sister switching order . To improve resolution , we used spinning disk confocal microscopy to capture 3D image stacks every 2 s over 150 time steps in HeLa-K cells expressing a marker for the kinetochores ( either eGFP-CENP-A or eGFP-CENP-A eGFP-Centrin1; Figure 2B; Video 1 , Video 2 ) . Phototoxicity was minimal , as >90% of cells successfully underwent anaphase . Sister kinetochores were tracked as previously described ( Jaqaman et al . , 2010 ) , except that we implemented 3D Gaussian mixture model fitting for determining sub-pixel spot locations ( Thomann et al . , 2002 see Figure 2A , C; Videos 3–5 ) – important here because our faster time sampling results in a smaller inter-frame spot displacement requiring higher localisation accuracy . This sub-pixel ( super-resolution ) tracking gives high theoretical positional accuracy ( x , y = ± 2 . 8 nm; z = ± 5 . 7 nm; see ‘Materials and methods’ ) and reveals kinetochore dynamics in exquisite detail ( Figure 2D ) . Consistent with previous work ( Jaqaman et al . , 2010; Vladimirou et al . , 2013 ) , sister kinetochores had a mean inter-kinetochore distance of ~910 nm and underwent quasi-periodic oscillations normal to the metaphase plate with a half-period of 35 s ( Figure 2—figure supplement 1A , B ) . Finally , we constructed a Bayesian switching point inference algorithm that estimates from an observed sister pair trajectory the switching times for each sister ( most probable frame ) and the directional switching events by assignment of a direction of movement to each sister ( see ‘Materials and methods’ ) . Here , we focus on coherent runs ( periods when the sisters are moving in the same direction ) and the switching events that end runs . We tested this algorithm on simulated data where the true switch time is known giving accuracies of 94% ( see ‘Materials and methods’; Figure 3A and Figure 3—figure supplement 1 ) . This switching point algorithm determined whether the leading or trailing sister switches first in a directional reversal of the sister pair and by how many frames . 10 . 7554/eLife . 09500 . 004Figure 2 . High-throughput tracking of kinetochores with sub-pixel spatial resolution . ( A ) Imaging and analysis flow chart summarising the steps of image sequence acquisition , image processing and statistical trajectory analysis . MMF- mixture model fitting , MCMC- Markov chain Monte Carlo algorithm . ( B ) 3D view of kinetochores ( eGFP-CENP-A – spot location marked by white spheres ) relative to the metaphase plate ( yellow spheres ) . ( C ) 2D images ( single Z plane ) of a sister kinetochore pair over six frames . Green tails represent kinetochore trajectories over the previous six frames ( 12 s ) . Yellow line indicates the metaphase plate intersection with the image plane . Sub-pixel kinetochore positions marked with white crosses . Scale bar = 1 µm . ( D ) Full sister pair trajectory showing the ( normal ) distance from the metaphase plate of the two sisters ( black and red ) , and the sister axis twist angle relative to the metaphase plate normal ( blue ) . Frames indicated in green correspond to the image slices shown in ( C ) . ( E ) 2D images ( single Z plane ) of the sister kinetochore pair in ( C/D ) at the minimum and maximum twist . Sister axis shown in blue , metaphase plate and its normal in yellow . Scale bar = 1 µm . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 00410 . 7554/eLife . 09500 . 005Figure 2—figure supplement 1 . Sister distance autocorrelation and distribution . ( A ) Autocorrelation of sister centre displacements ( ∆x ) over four frames ( 8 s ) normal to the metaphase plate . Purple arrow indicates the half-period of oscillations . ( B ) Histogram of sister–sister separations . Data from 55 cells , 1529 trajectories , eGFP-CENP-A cell line . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 00510 . 7554/eLife . 09500 . 006Video 1 . Length: 5 s; Real Time: 300 s; Frame Rate: 30 fps . HeLa-K eGFP–CENP-A , eGFP– Centrin1 . Z-projection through 12 μm . ( Deconvolved ) Movie of a metaphase cell ( captured at 2 s per frame ) . Movie rendered used MATLAB and ImageJ . Please also refer to Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 00610 . 7554/eLife . 09500 . 007Video 2 . Length: 16 s; Real Time: 300 s; Frame Rate: 30 fps . eGFP–CENP-A , eGFP–Centrin1 . ( Deconvolved ) Movie of a metaphase cell rendered in 3D . Movie rendered used MATLAB and IMARIS . Please also refer to Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 00710 . 7554/eLife . 09500 . 008Video 3 . Length: 16 s; Real Time: 300 s; Frame Rate: 30 fps . eGFP–CENP-A , eGFP–Centrin1 . ( Deconvolved ) Movie of a metaphase cell rendered in 3D overlaid with spot locations ( silver spheres ) as determined by the kinetochore-tracking assay . Movie rendered used MATLAB and IMARIS . Please also refer to Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 00810 . 7554/eLife . 09500 . 009Video 4 . Length: 16 s; Real Time: 300 s; Frame Rate: 30 fps . eGFP–CENP-A , eGFP–Centrin1 . ( Deconvolved ) Movie of a metaphase cell rendered in 3D overlaid with spot locations ( green spheres ) and frame-to-frame displacements ( green tracks ) as determined by the kinetochore-tracking assay . Movie rendered used MATLAB and IMARIS . Please also refer to Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 00910 . 7554/eLife . 09500 . 010Video 5 . Length: 7 . 02 s; Real Time: 300 s; Frame Rate: 30 fps . eGFP–CENP-A , eGFP–Centrin1 . ( Deconvolved ) Movie of a metaphase cell rendered in 3D overlaid with: aligned kinetochore locations ( silver spheres ) ; metaphase plate fit ( small yellow spheres ) ; spindle poles ( blue spheres ) ; spindle pole frame-to-frame displacements ( blue tracks ) ; spindle axis and spindle mid-plane ( small blue spheres ) as determined by the kinetochore-tracking assay . Movie rendered used MATLAB and IMARIS . Please also refer to Figure 2 . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 01010 . 7554/eLife . 09500 . 011Figure 3 . Kinetochore switching dynamics . ( A ) Event detection on a synthetic data set with idealised oscillation of two sisters ( for parameters , see ‘Materials and methods’ ) . True switchings are shown as orange squares with true coherent state shown at the top of the figure . Green shows depolymerising/polymerising sister state ( -/+ ) , and red shows polymerising/depolymerising sister state ( +/- ) , sister 1/sister 2 , respectively . Detected switching events shown as vertical dashed lines: coherent run initiation/end shown in cyan/purple , respectively . The sister that initiates a directional switch is indicated: lead ( L ) initiated directional switch ( LIDS ) , trail ( T ) initiated directional switch ( TIDS ) and joint ( J ) directional switch ( JDS ) . Horizontal grey line indicates position of spindle equator . Enlargements of LIDS and TIDS events . ( B ) Trajectory positions within the metaphase plate viewed along the spindle axis ( y , z ) . Colour indicates the number of events detected in the trajectory that end a coherent run , n=1529 trajectories from 55 cells . ( C , D ) Example trajectories from live cells showing detection of switching , colours as in ( A ) except inferred coherent state is shown at the top and joint switching events shown as vertical solid black lines . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 01110 . 7554/eLife . 09500 . 012Figure 3—figure supplement 1 . Autoregressive model simulation produces qualitatively realistic oscillations . A simulation of the autoregressive statistical model underlying the switch point detection algorithm with corresponding event detection using the switch point algorithm . True switchings are shown as orange squares with coherent state shown at the top of the figure ( green sisters state -/+ , red +/- ) . Detected switching events are shown as vertical dashed lines: coherent run initiation/end shown in purple and cyan , respectively . The sister detected to initiate the directional switch is indicated: lead ( L ) initiated directional switch ( LIDS ) , trail ( T ) initiated directional switching ( TIDS ) and joint ( J ) directional switching ( JDS ) . All directional switching events are correct assignments . For simulation parameters , see ‘Materials and methods’ . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 012 Next , we identified switch events in the trajectories of 1529 sister pairs across 55 eGFP-CENP-A cells and calculated the frequency that lead or trailing sisters switch first ( n=9022 events ending a coherent run ) . We note that these trajectories were distributed throughout the metaphase plate ( Figure 3B ) . We defined a directional switching event as a lead initiated directional switch ( LIDS ) if the lead sister switched at least one frame before the trailing sister and a trail initiated directional switch ( TIDS ) similarly . The remaining events correspond to sisters switching within the same frame and are denoted joint directional switches ( JDS , e . g . see Figure 3C , D ) . These criteria illustrate that there is a strong lead bias with the fraction of LIDS and TIDS being 54 . 3% and 34 . 8% , respectively , the remaining fraction ( 10 . 9% ) of events being joint . These data demonstrate that trailing sister switching is suppressed in human cells , leaving a lead-to-trail bias of 1 . 56:1 . Current directional switching models propose that switching of the lead kinetochore is initiated when the inter-sister distance ( centromere spring ) reaches a maximum stretch ( tension ) ( Rieder and Salmon , 1994; Skibbens et al . , 1995; Skibbens et al . , 1993; Wan et al . , 2012 ) . Moreover , no mechanism has been proposed to explain trail first switching; trailing sister initiated switching has also been reported for PtK1 cells at a 15% frequency ( Dumont et al . , 2012 ) . However , as we show here , the existence of trail first switching has ramifications for both the sister-sister coupling and the possible switching control mechanisms . By aligning profiles of the inter-sister distance – which reflects tension in the centromeric chromatin , 40 s before and 40 s after the first sister switching event ( Figure 4A ) , we demonstrate clearly that LIDS and TIDS both have strong pre-event and post-event inter-sister distance signatures ( Figure 4A; compare red [TIDS] and black [LIDS] traces ) . These stereotypical dynamic-tension signatures at LIDS/TIDS events ( discussed below ) demonstrate that our assignment of a LIDS or TIDS ( Figure 3 ) are robust and meaningful . 10 . 7554/eLife . 09500 . 013Figure 4 . Directional switching event signature in the inter-sister distance . ( A ) Time profiles across the first switching event of a directional switch according to type ( lead initiated directional switch [LIDS] , trail initiated directional switch [TIDS] ) . Events from trajectories where the lead ( LIDS , n=4900; red ) or trail ( TIDS , n=3143; black ) kinetochore switched first were aligned at their modal switching time ( time origin , vertical dashed purple line ) . Solid lines indicate mean values over time , dashed lines ± 1 SEM , smaller than the line thickness where not visible . ( B ) Time profiles across the first sister switch according to type ( LIDS , TIDS ) separated by prior event ( prior LIDS , subsequent LIDS , TIDS n=1536 [dark blue] , 936 [pale blue] respectively , prior TIDS and subsequent LIDS , TIDS n=986 [grey] , 628 [orange ) , respectively] ) . In ( A , B ) , the percentage stretch relative to the relaxed spring length ( determined under nocodazole treatment ) is shown on the right axis . Vertical dashed grey lines show 4 s before and 4 s after switch event . ( C ) Inter-sister distance over a standardised average coherent run after a LIDS event categorising runs that exhibit a subsequent LIDS ( dark blue ) or TIDS ( light blue ) . ( D ) Inter-sister distance after a TIDS categorising runs that exhibit a subsequent LIDS ( grey ) or TIDS ( orange ) . In ( C , D ) , run length is limited to be 6–20 frames inclusive ( 12–40 s ) and rescaled to a standard length of 1 ( proportion of run ) . Sample sizes as ( B ) . Dashed lines indicate ± 1 SEM . Vertical green and purple dashed lines indicate the start and end of the coherent run . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 01310 . 7554/eLife . 09500 . 014Figure 4—figure supplement 1 . Variability in switching times across coherent runs . Distributions for the length of coherent runs ending in a lead initiated directional switch ( LIDS ) ( black ) , trail initiated directional switch ( TIDS ) ( red ) event ( n=3396 and 2139 , respectively ) . Mann–Whitney test for identical medians , p=0 . 025 . Distribution medians shown as dashed lines ( LIDS 27 . 2 s , TIDS 26 . 6 s ) . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 01410 . 7554/eLife . 09500 . 015Figure 4—figure supplement 2 . Variability in inter-sister distance across switching events . ( A–C ) Distribution of the inter-sister distance at specified times: ( A ) 4 s prior to event , ( B ) at switching event and ( C ) 4 s after the switching event ( times are marked in Figure 4A ) . Events separated into lead initiated directional switch ( LIDS ) ( black , n=4900 ) and trail initiated directional switch ( TIDS ) ( red , n=3143 ) . Separation of medians ( shown as dashed lines ) is 36 , 2 and–107 nm , respectively ( LIDS – TIDS inter-sister distance ) , corresponding to 23 , 1 . 5 and 70% of the distribution SD . ( D–F ) . Distribution of the inter-sister distance at specified times: ( D ) 4 s prior to event , ( E ) at event and ( F ) 4 s after event conditioned on previous event type ( times marked in Figure 4B ) . Note median time order changes as time series in Figure 4B . Coherent runs are restricted to 12–40 s , sample sizes as ( B ) . Kruskal–Wallis test on homogeneity is p=2 . 9 × 10–19 , 0 . 0057 , 1 . 4 × 10–113 for 4 s prior to event , at event and 4 s after event , respectively . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 015 For a LIDS , the inter-sister distance increases corresponding to an average increase of spring stretch from 14% to 20% over the 20 s prior to the first sister switch , stabilises 4 s prior to that switch event and then decreases rapidly during the 6 s following the first sister switch to a minimum average stretch of 5% ( Figure 4A; black trace ) . Here , we used a baseline rest length of 788 nm determined from nocodazole-treated cells ( see ‘Materials and methods’ ) . The LIDS event leaves the two K-fibres in a polymerising state , which decreases the inter-sister distance; this is in line with the standard model where the inter-sister distance will then undergo extension over the following coherent run ( Figure 1 ) . The relaxation of the spring immediately prior to the first sister switch appears to be the result of the lead sister slowing down ( data not shown ) . This would be in line with experiments in Ptk1 cells in which the velocity slows at maximal inter-sister stretch ( Wan et al . , 2012 ) . Finally , the switching of the second ( trailing ) sister correlates with the reduction in spring extension to near zero ( Figure 4A ) , possibly suggesting that switching is the result of a loss of tension in accord with the standard tension model ( Rieder et al . , 1994 ) . However , the profile for a TIDS is distinct: the inter-sister distance is much lower prior to the first sister switch ( Figure 4A; red trace ) . The maximum stretch ( 25% ) is only reached 4 s after the switch has occurred . This indicates that the TIDS itself is necessary to build up tension in the centromeric chromatin while the maximum inter-sister stretch is higher than that seen during LIDS ( average 200 nm spring extension ) . The LIDS and TIDS signatures are also present if we condition on the previous event type ( Figure 4B ) , showing that although the previous event type does affect the inter-sister distance prior to the next event , the qualitative form is similar close to the switching event . The high overstretch of the centromeric chromatin during a TIDS raises a fundamental challenge to the standard tension model of kinetochore oscillations which states that the spring tension rises during a run of the sister pair ( to the left or right ) triggering a leading sister switch and relaxes during a directional switching event which triggers switching of the second sister ( Figure 1 ) . As illustrated above , this describes a repeated LIDS choreography . However , following a TIDS event , the centromeric spring tension escalates during the directional switch and starts to relax 4 s after the directional switch ( Figure 4A ) , suggesting that the tension remains high over the following run . To dissect this further , we examined the inter-sister distance over averaged coherent runs , ( n=6339 ) by aligning the start and the end of the intervening run ( rescaling time of each run to a standard length of 1 ) ( Figure 4C , D ) . For runs with a preceding switch that was either a LIDS or a TIDS , the inter-sister distance rose ( Figure 4C ) or relaxed ( Figure 4D ) over the following run , respectively . At the end of the run , the spring profile had the LIDS or TIDS signature depending on the next event type ( Figure 4C , D ) . Crucially , for runs starting after a TIDS the subsequent directional switch still has a LIDS bias ( 1 . 53 compared to a bias of 1 . 64 for a preceding LIDS ) , while the ( median ) time of the coherent run is identical to that following a LIDS ( 27 . 1 ± 0 . 21 s for TIDS , 26 . 7 ± 0 . 29 s for LIDS , p=5 . 5% ) . However , TIDS take a shorter amount of time to complete ( second sister switches ) than LIDS , median times 4 . 05 ± 0 . 15 s versus 4 . 31 ± 0 . 13 s , respectively ( p=1 . 3 x 10–4 ) . In essence , kinetochore oscillations are robust to sister switching order and the dynamics of the kinetochores is nearly identical after a TIDS and LIDS , except that the inter-sister distance decreases instead of increases , respectively ( Figure 4C , D ) . Our observations suggest that the classic choreography ( Figure 1 ) represents half of the dynamic with the inter-sister distance relaxing under a LIDS then increasing over the subsequent coherent run – depolymerising K-fibres pulling kinetochores with greater force than polymerising fibres push . However , under a TIDS the inter-sister distance is overstretched and relaxes during the subsequent coherent run – the centromeric spring force increasing the velocity of the trailing sister so that it exceeds that of the lead sister . This TIDS choreography implies that directional switching cannot be triggered by a threshold on the spring tension as overstretching and subsequent relaxation of the spring tension prior to a directional switch are a natural part of metaphase kinetochore oscillations . Kinetochores move in 3D with the kinetochore sister axis being compliant to twist away from the metaphase plate normal ( Figure 2D , E ) . The twist and inter-sister distance are in fact inversely correlated ( r=–0 . 13 , significant at p<10–200 ) , with twist showing inverted profiles over switching events relative to that for the inter-sister distance ( Figure 5 ) . Specifically , the twist of the sisters falls prior to a LIDS and increases after the switch event , while a TIDS demonstrates the opposite ( Figure 5A ) . The average twist increase seen at a LIDS relaxes over the following coherent run ( Figure 5C ) , similar to the relaxation in the spring extension seen after a TIDS . This can be explained mechanistically since a high inter-sister stretch , indicative of a high inter-sister tension , aligns the sisters , reducing the twist , while a low stretch , with low tension , allows the twist angle to increase under thermal and mechanical fluctuations . Thus , a negative correlation between twist and inter-sister distance is predicted if there is mechanical compliance in the attachment of the kinetochore to the K-fibres and the centromeric spring . 10 . 7554/eLife . 09500 . 016Figure 5 . Directional switching event signatures in the sister kinetochore twist ( angle of the sister axis to the metaphase plate normal ) . ( A ) Time profiles across the first switching event of a directional switch according to type ( lead initiated directional switch [LIDS] , trail initiated directional switch [TIDS] ) . Events from trajectories where the lead ( LIDS , n=4900; red ) or trail ( TIDS , n=3143; black ) kinetochore switched first were aligned at their modal switching time ( time origin , vertical dotted purple line ) . Solid lines indicate mean values over time , dashed lines ± 1 SEM , smaller than the line thickness where not visible . ( B ) Time profiles across the first sister switch according to type ( LIDS , TIDS ) separated by prior event ( prior LIDS , subsequent LIDS , TIDS , n=1536 and 936 , respectively , prior TIDS and subsequent LIDS , TIDS n=986 and 628 , respectively ) . ( C ) Twist after a LIDS event categorising runs that exhibit a subsequent LIDS ( dark blue ) or TIDS ( light blue ) . ( D ) Twist after a TIDS categorising runs that exhibit a subsequent LIDS ( grey ) or TIDS ( orange ) . In ( C , D ) , run length is limited to be 6–20 frames inclusive ( 12–40 s ) and rescaled to a standard length of 1 ( proportion of run ) . Sample sizes as ( B ) . Dashed lines indicate ± 1 SEM . Vertical green and purple dashed lines indicate the start and end of the run . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 016 Examination of the inter-sister distance profiles between consecutive events reveals a key pattern; on average , a TIDS is associated with a lower inter-sister distance than a LIDS 4 s prior to the event regardless of a preceding LIDS or TIDS ( Figure 4B ) , a LIDS event having a mean separation above 920 nm ( dark blue and grey traces ) , a TIDS event being below 920 nm ( orange and light blue traces ) . Coupled with the fact that the run is invariant to the nature of the preceding switching event , particularly with regard to timing ( the coherent run time to the next directional switch is identical , p=5 . 5% ) , this suggests that switching of the lead and trail sisters are governed by clocks . This could be a mechanical timing mechanism associated with the attached K-fibre , and thus force sensitive . In this way , the trailing sister is kept in a polymerising state by the pulling force from the stretched centromeric chromatin ( see model in Figure 6 ) . This may reflect the ability of the kinetochore to inhibit catastrophe when under tension – as shown by biophysical experiments using purified budding yeast kinetochores ( Akiyoshi et al . , 2010 ) . If that tension falls too low ( or fails to build up ) , a TIDS occurs ( as microtubules in the K-fibre undergo catastrophe; Figure 6 ) . The same stabilisation mechanism can be invoked to explain switching resolution – during a LIDS the second sister ( previously trailing ) , switches under the loss of the tension in the centromeric chromatin . When tension remains high enough to stabilise the trailing sister , then the lead sister switches because of its clock . The escalating force during a TIDS ( after the trailing sister switches ) could accelerate the lead sister clock triggering switching of the second sister . Again , this reflects the increase in the rescue rate of in vitro microtubules attached to kinetochores as shown in Akiyoshi et al . , 2010 , while it emphasises that the two sisters are not symmetrical and the time since their last switch event determines which sister switches . 10 . 7554/eLife . 09500 . 017Figure 6 . Tension-clock model for sister kinetochore directional switching . ( A ) Schematic outline of regulatory mechanisms that control sister kinetochore directional switching . Our data are consistent with the presence of a clock on both the leading ( black ) and trailing ( red ) sister kinetochores that sets the time at which a directional switch will occur . We propose that the molecular mechanism for the clock is the rescue/catastrophe frequency of the microtubules ( green ) within the K-fibre . We note that the kinetics of microtubule dynamic instability in vitro cannot alone explain the timing of events ( oscillation period ) ; hence , regulation of the dynamics from the kinetochore/K-fibre structure is key . The clock on the trailing sister kinetochore is force-sensitive , such that tension from stretching the centromeric chromatin results in a slow down , thereby reducing the probability that the K-fibre will drive a trail initiated directional switch ( TIDS ) . ( B ) This mechanism can explain the observed relationship between tension dynamics ( in centromeric chromatin ) and sequences of lead and/or trail initiated directional switches . Following a LIDS ( upper half ) , the spring extension is low and then stretches over the following run . If tension increases quickly enough , then the trailing K-fibre is stabilised ( the clock slows down ) and the lead kinetochore switches as the clock runs down . If tension does not build up sufficiently , then the trailing K-fibre will not be stabilised and it will therefore switch because of its clock . Following a TIDS , ( lower half ) , the spring extension is high . The run thus starts with high tension which stabilises the trailing K-fibre . Spring tension relaxes over the run , and if it drops sufficiently , trailing K-fibre stabilisation is lost and the trailing sister switches ( TIDS ) . If tension remains sufficiently high , then the clock on the leading kinetochore initiates the switching event ( LIDS ) . Thus , lead sister initiated switches can occur with falling tension ruling out the standard model ( see Figure 1 ) . ( C ) This model can also account for why the second sister switches: following a LIDS , both sisters are in an AP state , reducing the spring tension to a near-zero tension state . This rapidly destabilises the previously trailing sister leading to a switch ( AP to P ) . We suspect that the new lead sister does not switch in this situation because its clock has been re-set ( i . e . the K-fibre is new ) . When a TIDS occurs , we propose that the very high tension generated during the directional switch accelerates the clock on the sister attached to the older K-fibre ( previously the lead sister ) causing the second sister to switch . DOI: http://dx . doi . org/10 . 7554/eLife . 09500 . 017 Although we have detected clear signatures in the switching choreographies ( Figure 4 ) , these reflect regulatory and mechanical processes of a highly stochastic system . This stochasticity is evident on many scales . First , kinetochores are known to display a range of stochasticity in their trajectories , from near deterministic oscillations to the near random ( Jaqaman et al . , 2010; Magidson et al . , 2011 ) . Second , the switching times are stochastic; the duration of a coherent run has a large variability with a coefficient of variation ( standard deviation [SD]/mean ) of 0 . 45 , similar for both runs terminated by an LIDS ( mean time and SD 29 . 6 ± 12 . 7 s ) or a TIDS ( 29 . 2 ± 13 . 6 s , Figure 4—figure supplement 1 ) . Thus , although we have invoked a clock mechanism as a switching time regulator , it is inherently stochastic . This stochasticity could stem from both the number and depolymerisation/polymerisation state of individual microtubules that make up the K-fibre . The fraction of microtubules that are in a polymerising state within a K-fibre is highly variable among kinetochores ( Armond et al . , 2015 ) , indicating that growing and shrinking K-fibres are unlikely to be composed of fully coherent microtubules . Third , the signatures in Figure 4 are a mean behaviour , while variability in the inter-sister stretch throughout the dynamics is in fact large . This can be seen at the population level of trajectories , where the inter-sister distance distributions for LIDS and TIDS only show marginal separation before the switching event ( –4 s ) , are hardly separated at the event , while separation increases after the event ( +4 s ) ( Figure 4—figure supplement 2A–C ) . The inter-sister distance distribution over a switching event is in fact far from bimodal; a mixture of two Gaussian distributions requires the respective means to be separated by at least 2 SDs to be bimodal , the largest we observe is 70% at 4 s post event ( Figure 4—figure supplement 2A–C ) . The separation of these distributions does not improve even on further categorising by the prior event ( i . e . prior LIDS or TIDS; Figure 4—figure supplement 2D–F ) . Therefore , we have to conclude that the signatures shown in Figure 4 are not a universal behaviour but only detectable on averaging; that is , the actual switching process is highly stochastic . It may be that analysis of the most deterministic trajectories will reduce this stochasticity in the switching dynamics and signatures . However , directional switching may be a composite process that integrates over multiple signals , that is , tension may not be the only determinant; the stochasticity in our signatures would then be due to measuring only one of these determinants . It remains unknown to what extent the stochasticity in switching time and switching type explains the observed diversity in kinetochore trajectory dynamics , or whether other sources of variability exist . This paper demonstrates how dynamic and mechanistic insight can be extracted from high-resolution tracking data . Although the leading sister typically initiates directional switching , the reverse switching order is also frequently observed ( Figure 3D ) , with directional switching biases , timings and subsequent oscillations remaining robust to such events . By classifying switching order events , we were able to demonstrate clear stereotypical behaviour associated with these events , with both prior ( potentially causal ) and post-event signatures in the inter-sister distance dynamics . This confirms that event classification is physical and not due to noisy fluctuations in switching times coming from localisation measurement noise . Our data support a new model of kinetochore oscillations comprising mechanical clocks on both the lead and trailing sister , which likely reflect the time- and force-dependent rescue and catastrophe of the K-fibre microtubules . Our analysis suggests that the degree of stretch of the inter-sister centromeric chromatin is a major determinant in orchestrating this switching; first , a mechanism based on stabilisation of the trailing sister polymerisation state through the centromere tension , effectively slowing the trailing sister clock , and second , a clock on the lead sister that is accelerated under high tension ( Figure 6 ) . Our data indicate that the standard tension model of sister kinetochore switching ( Figure 1 ) is only able to explain part of the dynamics , while it is incompatible with the overstretch and subsequent relaxation of the inter-sister distance following a TIDS . Thus , kinetochores utilise multiple sensory and fail-safe mechanisms that ensure high-fidelity chromosome organisation within the spindle , despite high levels of stochasticity .
HeLa-K cells stably expressing eGFP-CENP-A ( Jaqaman et al . , 2010 ) or eGFP-CENP-A/eGFP-Centrin 1 were grown in Dulbecco’s modified Eagle’s medium ( Fisher , UK ) containing 10% foetal calf serum ( Fisher ) , 100 µg ml-1 penicillin and 100 µg ml-1 streptomycin maintained in 5% CO2 at 37°C in a humidified incubator . eGFP-CENP-A cells were maintained in 0 . 1 µg ml-1 puromycin ( Fisher ) . eGFP-CENP-A/eGFP-Centrin 1 cells were maintained in 0 . 1 µg ml-1 puromycin and 500 µg ml-1 Geneticin ( Fisher ) . To measure the inter-sister distance rest length , cells were treated with 2 µg ml-1 nocodazole ( Fisher ) for between 16 and 24 hr to depolymerise microtubules . We determined both the mean rest length and the population standard deviation . Cells were seeded in gridded 35-mm glass bottom dishes ( MatTek , Boston , MA ) and the media changed to Leibovitz L-15 supplemented with 10% foetal calf serum prior to imaging . Cells were imaged using a 100× 1 . 4 NA oil objective on a confocal spinning-disk microscope ( VOX Ultraview; PerkinElmer , Waltham , MA ) with a Hamamatsu ORCA-R2 camera , controlled by Volocity 6 . 0 ( PerkinElmer ) running on a Windows 7 64-bit ( Microsoft , Redmond , WA ) PC ( IBM , Armonk , NY ) . Mitotic cells were first identified using bright-field illumination to minimise phototoxicity . Image stacks ( 25 z-sections , 0 . 5 µm apart ) were collected every 2 s for 5 min ( 150 time points per video ) . Camera pixels were binned 2 × 2 , giving an effective pixel size of 138 nm in the lateral direction with a 16-bit per pixel imaging depth . Exposure conditions were set 50 ms per z-slice using a 488-nm laser set to 15% power . Image pre-processing was performed on an OSX 10 . 6 Power-Mac ( Apple ) . Time series were exported from their native Volocity format to . OME . TIFFs ( The Open Microscopy Environment ) using Volocity 6 . 0 and were then deconvolved with Huygens 4 . 1 ( SVI ) using a point spread function ( PSF ) measured from micro-bead images ( using the Huygens 4 . 1 PSF distiller ) . Deconvolved images were exported from Huygens to a . r3d format ( Applied Precision , Issaquah , WA ) and then read into MATLAB ( R2012b , MathWorks , Natick , MA ) using the loci-tools java library ( The Open Microscopy Environment ) . Images were then stored in a native MATLAB format . Sister kinetochores were detected , aligned , tracked and paired as in the original tracking assay ( Jaqaman et al . , 2010 ) , except that we implemented 3D Gaussian mixture model fitting for determining sub-pixel spot locations ( Thomann et al . , 2002 ) . In essence , fluorescence from a kinetochore is modelled as a Gaussian , the fluorescence image then being modelled as a mixture of Gaussians of variable height ( intensity ) . The protocol was tested on both eGFP-CENP-A/eGFP-centrin1 and eGFP-CENP-A cell lines , and gave significantly improved position accuracy compared to the previous centroid-based spot fitting ( Jaqaman et al . , 2010 ) , important here because of the faster time sampling which results in a smaller inter-frame spot displacement . The theoretical accuracy of localisation of a spot’s centre ( x , y = ± 2 . 8 nm; z = ± 5 . 7 nm ) was calculated using the total number of photons in the spot , the average background intensity nearby , the full-width half-maximum of the intensity profile in a given coordinate , and the voxel size ( 138 × 138 × 500 nm ) ( Thompson et al . , 2002 ) . Tracking parameters were identical to Jaqaman et al . ( 2010 ) , except that the upper limit of the search radius for aligned kinetochores was changed to 0 . 8 . Gap filling as Jaqaman et al . ( 2010 ) was implemented within the Gaussian MMF tracking . We also filter out cells entering anaphase , removed paired tracks with less than 112 consecutive time points ( 75% complete ) and a small number of tracking errors; this generated a large database of 3D paired trajectory data . Sister kinetochore movements were calculated relative to a plane fitted through the distribution of sister kinetochore positions . MATLAB software ( KiT ) is deposited on GitHub and also available on request to ADM . We developed a computational algorithm that fits a linear autoregressive statistical model to kinetochore frame-to-frame displacements that incorporates switching of the driving ( constant ) term . Specifically displacements are given by ∆X1=c0+c ( σ1 ) -aX1+bX2+N ( 0 , s2 ) , ∆X2=-c0-c ( σ2 ) -aX2+bX1+N ( 0 , s2 ) , where X1 , X2 are the positions of the sister kinetochores relative to the metaphase plate , the K-MTs lying to the right of X1 , left of X2 for sisters 1 and 2 , respectively; thus , typically X1> X2 . The first term ( c0+c ( σk ) ) , k=1 , 2 are the driving terms with a component that switches between two possible values , positive , c+ corresponding to polymerisation of the K-fibre , or negative , c- corresponding to depolymerisation . The direction sequence σk ( + , - valued for polymerisation , depolymerisation respectively , with k=1 , 2 identifying the sister ) determines which value is used . Driving terms are of opposite sign between sisters 1 and 2 because the K-fibres lie in opposite directions . If ( de ) polymerisation is turned off , the sisters relax towards the metaphase plate with an inter-sister separation of 2c0/ ( b+a ) which must be positive , thereby constraining the sign of these parameters . The third/fourth terms are relaxation terms allowing kinetochore positions to adjust to the driving term , that is , the relaxation of the inter-sister distance and distance from the metaphase plate . Finally , Gaussian noise is added to model trajectory stochasticity; this will comprise measurement noise , thermal noise and non-thermal ATP-dependent fluctuations ( Weber et al . , 2012 ) . In this model , sisters switch independently ( states encoded in σk ) from polymerisation ( + ) to depolymerisation ( - ) states ( states of their associated K-fibres ) , and vice versa; the waiting time is exponentially distributed , that is , there is no memory , location or history dependence assumed . The average waiting time to a switch event is dependent on the direction of the other sister; let p be the matrix of switching rates between the 4 sister states ++ , +- , -+ , -- parametrised by a switching rate out of coherence , p ( +-→ ++ or - - ) = p ( -+→ ++ or - - ) and out of incoherence , p ( ++ → +- or -+ ) = p ( - -→ +- or -+ ) , the sister who switches being chosen at random . There is , therefore , no switching bias intrinsic in the algorithm; biases in the experimental data can thus be detected . This model can produce stochastic saw-tooth oscillations under certain parameter regimes ( b , c0>0 and a>b are necessary ) ( Figure 3—figure supplement 1 ) , qualitatively similar to those observed for sister kinetochores . Crucially if the rate of switching out of incoherence is higher than switching out of coherence ( coherence of sister movement [same direction] is thus restored quickly ) , the model produces pseudo-periodic saw-tooth oscillations qualitatively similar to those observed . This model is thus appropriate for detecting switching times as it has the correct type of behaviour . A Markov chain Monte Carlo ( MCMC ) algorithm was used to compute the posterior distribution of the parameters and the unknown ( hidden ) sister states ( σk ) from each trajectory , that is , sample from the posterior probability density π ( a , b , c0 , c+/- , s2 , p σtk| Xtk ) . The MCMC algorithm is based on standard Gibbs and Metropolis–Hastings proposals , and recovered the true values on simulated data ( not shown ) . We used a prior on the relaxed inter-sister separation of 2c0/ ( b+a ) inferred from a nocodazole experiment ( fully depolymerised microtubules ) while all other priors are uninformative . Convergence was assessed using multiple runs; a proportion of runs failed to converge despite extending the run time ( 19% ) . These trajectories were excluded from the analysis and by visual assessment were typically highly stochastic , suggesting the oscillatory signal was weak and thus lack of convergence was not unlikely . This left a database of 1529 processed paired sister tracks in the eGFP-CENP-A cell line . Each trajectory had sufficient information to fit all the parameters using uninformative priors . Switch points were determined by identifying coherent runs ( classified as a sequence of points where the inferred direction was unchanged for at least five frames ) . Switch points into and out of a coherent run were matched to determine directional switching events ( both sisters switch direction across a directional switch , resulting in a directional switch of the sister pair ) . The algorithm was tested for correct determination of switching times on simulated data . Five hundred trajectories were simulated with parameters that gave qualitatively realistic oscillations ( c+=30 nm , c-=100 nm , b=0 . 04 , a=0 . 056 , c0=667 nm , s2=1/1000 , switching probability per frame 39% [when incoherent] , 6 . 3% [coherent] ) , a typical trajectory is shown in Figure 3—figure supplement 1 . The simulation in Figure 3A used these same parameters except when incoherent the sister who switched last cannot switch , that is , switches out of coherence always result in a directional switch of the two sisters . Directional switching points were determined with the MCMC algorithm as above and correct LIDS/TIDS calls identified . Accuracy was determined on these 500 trajectory simulations , giving an accuracy of 94% . The inferred lead bias was 1:1 , consistent with the original simulation parameters . | In human cells , DNA is arranged into structures called chromosomes . Before a cell divides it copies its entire set of chromosomes to make paired chomosomes known as sister chromatids . Then , the sister chromatids are separated to ensure that each new daughter cell contains a full set of chromosomes . A structure called the spindle is responsible for separating the sister chromatids . It is made of long filaments called microtubules that grow out from 'poles' at opposite sides of a cell . The two sister chromatids in each pair attach to microtubules that originate from opposite ends of the cell . This attachment is achieved by a protein machine called the kinetochore , which can move along microtubules as they grow or shrink . Prior to the separation of the chromatids , the paired sister chromatids are moved into positions so that they are approximately an equal distance from the two poles . For the majority of the time , one sister kinetochore moves towards the pole it is attached to ( called the lead sister ) , while the other sister moves away from the pole it is attached to ( the trailing sister ) . Then , the kinetochores swap roles and move in the opposite direction . In most cells , the pairs of sister kinetochores repeatedly switch between moving backwards and forwards giving rise to oscillations in the positions of the sister chromatids . It is not clear how the two sister kinetochores are able to communicate with each other so that they can co-ordinate their backwards and forwards movement . It is currently thought that the lead kinetochore changes direction first because of an increase in the distance between the two sisters , thereby increasing the tension between the sisters ( like a spring being stretched ) . Burroughs , Harry and McAinsh revisit this idea by looking at living human cells and tracking the movement of the kinetochores in great detail . Using computational techniques to analyse kinetochore movements in living cells , the experiments reveal that the trailing sister kinetochore can sometimes change direction before the lead sister . When this happens the sisters start to move apart until the previously leading sister switches direction , so that the sisters then movingve together in the same direction . The distance between the sisters remains high until the next time the sisters change direction , which means that 'tension' cannot solely be responsible for the communication between sister kinetochores . Burroughs , Harry and McAinsh’s findings suggest instead that sister kinetochores contain a 'clock' that decides when they will change direction . The tension between the two sister chromatids is still important , and acts to change the time of the clocks . The next challenge is to understand how these clocks work and which parts of the kinetochore are involved . | [
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] | 2015 | Super-resolution kinetochore tracking reveals the mechanisms of human sister kinetochore directional switching |
Metabolic homeostasis requires coordination between circadian clocks in different tissues . Also , systemic signals appear to be required for some transcriptional rhythms in the mammalian liver and the Drosophila fat body . Here we show that free-running oscillations of the fat body clock require clock function in the PDF-positive cells of the fly brain . Interestingly , rhythmic expression of the cytochrome P450 transcripts , sex-specific enzyme 1 ( sxe1 ) and Cyp6a21 , which cycle in the fat body independently of the local clock , depends upon clocks in neurons expressing neuropeptide F ( NPF ) . NPF signaling itself is required to drive cycling of sxe1 and Cyp6a21 in the fat body , and its mammalian ortholog , Npy , functions similarly to regulate cycling of cytochrome P450 genes in the mouse liver . These data highlight the importance of neuronal clocks for peripheral rhythms , particularly in a specific detoxification pathway , and identify a novel and conserved role for NPF/Npy in circadian rhythms .
Circadian clocks constitute an endogenous timekeeping system that synchronizes behavior and physiology to changes in the physical environment , such as day and night , imposed by the 24 hr rotation of the earth ( Zheng and Sehgal , 2012 ) . A coherent circadian system is composed of a cooperative network of tissue-specific circadian clocks , which temporally coordinate and compartmentalize biochemical processes in the organism ( Wijnen and Young , 2006 ) . Clock disruption is associated with numerous deleterious health consequences including cancer , cardiovascular disease , and metabolic disorders ( Marcheva et al . , 2010; Marcheva et al . , 2013; Turek et al . , 2005 ) . In the fruit fly , Drosophila melanogaster , the neuronal clock network is comprised of roughly 150 circadian neurons , which are grouped based on their anatomical location and function in the brain ( Allada and Chung , 2010 ) . The lateral neurons include the small and large ventral lateral neurons ( LNvs ) , the dorsal lateral neurons ( LNds ) and the lateral posterior neurons ( LPNs ) . The dorsal neurons are divided into three subgroups , dorsal neurons ( DN ) 1 , 2 , and 3 . The small LNvs ( sLNvs ) have traditionally been referred to as the central clock because they are necessary and sufficient for rest:activity rhythms under constant conditions ( Grima et al . , 2004; Stoleru et al . , 2004 ) , but recent studies also indicate an important role for the LNds ( Guo et al . , 2014 ) . The LNvs express the neuropeptide pigment dispersing factor ( PDF ) , which is important for rest:activity rhythms ( Renn et al . , 1999; Stoleru et al . , 2005; Lin et al . , 2004; Yoshii et al . , 2009 ) and for the function of circadian clocks in some peripheral tissues ( Myers et al . , 2003; Krupp et al . , 2013 ) . The LNds constitute a heterogeneous group of neurons differentiated by the expression of peptides and receptors ( Lee et al . , 2006; Johard et al . , 2009; Yao and Shafer , 2014 ) . Thus far , these peptides , which include Neuropeptide F ( NPF ) , have only been implicated in behavioral rhythms ( He et al . , 2013a; Hermann et al . , 2012; Hermann-Luibl et al . , 2014 ) . Most physiological processes require clocks in peripheral tissues , either exclusively or in addition to brain clocks . For instance , a peripheral clock located in the fat body , a tissue analogous to mammalian liver and adipose tissue ( Arrese and Soulages , 2010 ) , regulates feeding behavior ( Xu et al . , 2008; Seay and Thummel , 2011 ) and nutrient storage ( Xu et al . , 2008 ) and drives the rhythmic expression of genes involved in metabolism , detoxification , innate immunity , and reproduction ( Xu et al . , 2011 ) . Molecular clocks in the brain and fat body have different effects on metabolism , suggesting that clocks in these two tissues complement each other to maintain metabolic homeostasis ( Xu et al . , 2008 ) . Such homeostasis requires interaction between organismal clocks , but how this occurs , for example whether neuronal clocks regulate fat body clocks , as they do for some other tissue-specific clocks , is not known . In addition , the fat body clock does not regulate all circadian fat body transcripts . 40% of rhythmically expressed fat body transcripts are unperturbed by the absence of a functional fat body clock ( Xu et al . , 2011 ) , suggesting these genes are controlled by rhythmic external factors , which could include light , food , and/or signals from clocks in other tissues ( Wijnen et al . , 2006 ) . Likewise in the mammalian liver , where circadian gene regulation has been well-studied , cyclic expression of many genes persists when the liver clock is ablated ( Kornmann et al . , 2007a ) . Brain specific rescue of clock function in ClockΔ19 animals partially restored liver gene expression rhythms ( ~40% ) , albeit with compromised amplitude ( Hughes et al . , 2012 ) . The specific signals that mediate this rescue , however , were not identified , although systemic signals that regulate peripheral clocks have been identified ( Cailotto et al . , 2009; Kornmann et al . , 2007a; Reddy et al . , 2007; Oishi et al . , 2005 ) . The relative simplicity of fly neuroanatomy and physiology , the vast array of genetic tools , and the conservation of molecular mechanisms with mammals make the fly an ideal organism to dissect complex interactions between physiological systems . In this study , we found that neural clocks regulate circadian gene expression in the fly fat body , a peripheral metabolic tissue . We demonstrate that cycling of the core clock gene , period ( per ) , requires PDF-expressing cells in constant darkness . Interestingly , however , clocks in the NPF-expressing subset of LNds , as well as NPF itself , are important for driving rhythmic expression of specific cytochrome P450 genes that cycle independently of the fat body clock . Lastly , we show that Npy , the mouse homolog of NPF , regulates transcriptional circadian output in the mouse liver . Microarray analyses reveal that Npy contributes to the rhythmic expression of hundreds of transcripts in the liver , including a subset of cytochrome P450 genes . In summary , we identified a conserved role for NPF/Npy neuropeptides in the circadian system in coupling neuronal clocks to transcriptional output in peripheral tissues in flies and mice .
While some peripheral clocks in Drosophila are completely autonomous , e . g . malphigian tubules ( Hege et al . , 1997 ) , others rely upon cell-extrinsic factors , in particular the clock in the brain . For example , PDF-positive LNvs are required for rhythmic expression of clock components in the prothoracic gland , a peripheral tissue that gates rhythmic eclosion ( Myers et al . , 2003 ) . In addition , PDF released by neurons in the abdominal ganglion is necessary to set the phase of the clock in oenocytes ( Krupp et al . , 2013 ) , which regulate sex pheromone production and mating behavior ( Krupp et al . , 2008 ) . We investigated whether clocks in PDF-positive LNvs were necessary for clock function in the abdominal fat body . The molecular clock in Drosophila consists of an autoregulatory loop in which the transcription factors , CLOCK ( CLK ) and CYCLE ( CYC ) , activate expression of the genes period ( per ) and timeless ( tim ) and PER and TIM proteins feedback to inhibit the activity of CLK-CYC ( Zheng and Sehgal , 2012 ) . To disrupt the molecular clock exclusively in PDF-positive cells , we used the GAL4/UAS system to express a dominant-negative version of the CLK transcription factor , CLKΔ . CLKΔ lacks regions of its DNA-binding domain , preventing it from binding DNA and activating transcription of genes , including components of the molecular clock . However , CLKΔ can still heterodimerize with its partner , CYC , through its protein interaction domain ( Tanoue et al . , 2004 ) . Behavioral assays of Pdf-GAL4/UAS-CLKΔ flies showed that a majority of the flies had arrhythmic locomotor activity in constant darkness ( DD ) ( Figure 1A and Table 1 ) , confirming that CLKΔ expression in the LNvs disrupts circadian rhythms . 10 . 7554/eLife . 13552 . 003Figure 1 . Oscillations of per in the fat body require an intact central clock in the absence of external cues . ( A ) Representative double-plotted activity records of individual control UAS-CLKΔ/CyO ( left ) and Pdf-GAL4/UAS-CLKΔ ( right ) flies over the course of 5 days in constant darkness . ( B ) Schematic of experimental design . Male flies , aged 7–10 days , were entrained for several days in 12 hr light: 12 hr dark cycles ( LD ) . Male flies were dissected to obtain abdominal fat bodies ( dotted red box ) either on the last day in LD or on the second day of constant darkness ( DD2 ) . Graphs depict mRNA levels , normalized to α−tubulin ( atub ) , over the course of the day in the presence of light ( LD; Zeitgeber Time , ZT ) or in constant darkness ( DD2; Circadian Time , CT ) . Ablating the central clock ( Pdf-GAL4/UAS-CLKΔ ) ( red line ) does not affect per rhythms in LD ( C ) but abolishes per rhythms in DD2 compared to controls ( blue line ) ( D ) . Each experiment was repeated independently three times . The average value for each timepoint is plotted with error bars denoting the standard error of the mean ( SEM ) . Significant rhythmicity was determined using JTK_cycle . Asterisk ( * ) adjacent to genotype label indicates JTK_cycle p value <0 . 05 . See Table 3 for JTK cycle values . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 00310 . 7554/eLife . 13552 . 004Figure 1—source data 1 . Data for behavioral analysis and for qPCR analysis of per in Pdf-GAL4/UAS-CLKΔ flies . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 00410 . 7554/eLife . 13552 . 005Table 1 . Free-running rest:activity rhythms . Clock ablation in Pdf neurons ( Pdf-GAL4/UAS-CLKΔ ) or in LNd neurons ( Dvpdf-GAL4/UAS-CLKΔ; pdf-GAL80/+ ) disrupts free-running behavioral rhythms in flies . Flies with clock ablation in Npf neurons ( Npf-GAL4/UAS-CLKΔ ) and npfr mutants have normal free-running rhythms . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 00510 . 7554/eLife . 13552 . 006Table 1—source data 1 . Data for circadian analysis of fly behavior in constant darkness . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 006Genotypen% RhythmicPeriod ( hr ) FFTPdf-GAL4/UAS-CLKΔ393623 . 510 . 04UAS- CLKΔ/CyO489023 . 710 . 06Npf-GAL4/UAS-CLKΔ629824 . 010 . 06UAS-CLKΔ/+5810023 . 700 . 05npfr399523 . 660 . 05npf/+4610023 . 440 . 11Dvpdf-GAL4/UAS-CLKΔ; pdf-GAL80/+636825 . 510 . 06UAS-CLKΔ/+6310023 . 910 . 08 To assess functionality of the molecular clock in fat body tissue , we measured transcript levels of the core clock gene per in abdominal fat bodies over the course of the day ( Figure 1B ) . We found that circadian expression of per in the fat body was not altered in flies with a disrupted central clock ( Pdf-GAL4/UAS-CLKΔ ) under a 12 hr light: 12 hr dark ( LD ) cycle ( Figure 1C ) . Unlike mammals , peripheral clocks in Drosophila can detect light , which acts as the dominant entrainment signal ( Plautz et al . , 1997; Oishi et al . , 2004 ) . Therefore , under LD conditions , light may directly synchronize oscillations in per transcript levels in fat body cells , masking the effects of ablating the central clock . Consequently , we evaluated per rhythms in the absence of light . Since rhythmic gene expression dampens under constant conditions and is undetectable in the fat body by the sixth day of DD ( Xu et al . , 2011 ) , we tested rhythmic expression of per on the second day in DD ( DD2 ) . per levels were rhythmic in the fat body of control flies on DD2 . In contrast , flies expressing CLKΔ in the LNvs showed an apparent lack of per rhythms in the fat body ( Figure 1D; see Discussion ) . This suggests that the clock in PDF-positive LNvs influences the peripheral fat body clock in the absence of external environmental cues . The fat body clock regulates roughly 60% of circadian genes in the fat body; the mechanisms that drive daily cycling of the other 40% of circadian genes in this tissue are unknown ( Xu et al . , 2011 ) . Several potential mechanisms could explain rhythmic gene expression in the absence of the local tissue-specific clock , for example , light , nutrients , or clocks located in other tissues . As noted above , many tissues in Drosophila have photoreceptors . Therefore , in addition to entraining clocks to the external environment , LD cycles can drive rhythmic transcription via clock-independent pathways ( Wijnen et al . , 2006 ) . LD cycles can even drive a rhythm of feeding ( Xu et al . , 2008 ) , which could lead to cyclic expression of metabolic genes . Nutrients are known to be strong entrainment signals in peripheral tissues; in fact , rhythmic or restricted feeding , even in the absence of a clock , can drive cyclic expression of several fat body genes ( Xu et al . , 2011 ) . Another possibility is that rhythmic expression of specific fat body transcripts requires a clock in another tissue . To differentiate between light , nutrient , and clock control , we measured daily expression of genes that cycle independently of the fat body clock in Clkjrk mutants . Clkjrk mutants lack functional clocks in all tissues due to a premature stop codon that eliminates the CLK activation domain ( Allada et al . , 1998 ) . Although Clkjrk mutants cannot sustain feeding rhythms under constant conditions , LD cycles can drive feeding rhythms in Clkjrk flies albeit with a delayed phase relative to wild type flies ( Xu et al . , 2008 ) . We predicted that transcripts driven by light , or even nutrient intake driven by light , would oscillate in Clkjrk mutants in LD with the same or altered phase , while clock-dependent transcripts would not oscillate at all . The genes we tested were selected based on the robustness of their rhythms in the absence of the fat body clock ( Xu et al . , 2011 ) . We found that none of these genes displayed circadian rhythms in Clkjrk mutants , suggesting that although these genes do not require an intact fat body clock , they do require an intact clock in some other tissue ( Figure 2 ) . In addition to the loss of rhythmic expression in Clkjrk mutants , there were also differences in baseline expression levels . Rhythmic gene expression of sex-specific enzyme 2 ( sxe2 ) , a lipase and CG17562 , an oxidoreductase was eliminated in Clkjrk mutants to produce an intermediate level of gene expression throughout the day ( Figure 2A–B ) . In contrast , rhythmic expression as well as overall levels of sex-specific enzyme 1 ( sxe1 ) , a cytochrome P450 , and CG14934 , a purported glucosidase involved in glycogen breakdown , were greatly reduced in Clkjrk mutants ( Figure 2C–D ) . 10 . 7554/eLife . 13552 . 007Figure 2 . Rhythmic expression of genes that cycle independently of the fat body clock requires clocks in other tissues . Daily oscillations of several fat body clock-independent genes were tested in male mutants lacking functional clocks in all tissues , Clkjrk mutants , in LD . Rhythmicity of sxe2 ( A ) , CG17562 ( B ) , sxe1 ( C ) , and CG14934 ( D ) is abolished in Clkjrk mutants but is intact in Iso31 wild type controls . All genes were normalized to α−tubulin ( atub ) levels . Each experiment was repeated independently three times . The average value for each timepoint is plotted with error bars denoting SEM . JTK_cycle p value <0 . 05 is indicated by an asterisk ( * ) at the time of peak expression . See Table 3 for JTK_cycle p values . ZT- Zeitgeber Time . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 00710 . 7554/eLife . 13552 . 008Figure 2—source data 1 . Data for qPCR analysis of fat body clock-independent genes in Clkjrk mutants . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 008 Having established that circadian expression of genes cycling independently of the fat body clock requires an intact molecular clock elsewhere in the organism , we sought to identity the specific clock population involved . We chose to focus on the regulation of sxe1 because it has the most robust cycling profile of all the rhythmic fat body clock-independent genes . sxe1 was named on the basis of its regulation by the sex determination pathway in fly heads and is enriched in the non-neuronal fat body tissue of males ( Fujii and Amrein , 2002 ) . Early microarray studies looking for cycling transcripts in Drosophila heads also indicated that sxe1 is regulated by the circadian system ( Claridge-Chang et al . , 2001; McDonald and Rosbash , 2001; Ceriani et al . , 2002 ) . However , the nature and function of the circadian control of sxe1 are unclear . sxe1 is implicated in xenobiotic detoxification and male courtship behavior ( Fujii et al . , 2008 ) and may confer cyclic regulation to either or both of these processes . Rhythms of sxe1 expression are abolished in Clkjrk mutants in LD , and so we evaluated sxe1 regulation by other clocks in the presence of light cycles rather than under constant darkness ( Figure 2C ) . Our initial discovery that PDF neurons regulate the fat body clock in constant darkness led us to hypothesize that these neurons may also regulate fat body clock-independent genes . Abolishing the clock in PDF cells by expressing CLKΔ under Pdf-GAL4 , slightly decreased sxe1 transcript levels in the abdominal fat body , but did not abolish rhythmic expression ( Figure 3A ) . This suggests that although the PDF neurons regulate the fat body clock , these neurons are not the primary drivers of rhythmic sxe1 expression . 10 . 7554/eLife . 13552 . 009Figure 3 . NPF-expressing clock neurons regulate rhythmic expression of fat body genes , sxe1 and Cyp6a21 . ( A , B ) Ablating the molecular clock by expressing CLKΔ or CYCΔ in either the LNvs ( Pdf-GAL4 ) ( A ) or DN1s ( 911-GAL4 ) ( B ) does not eliminate rhythmic sxe1 expression in the fat body . ( C , D ) Expressing CLKΔ ( C ) or CYCΔ ( D ) using Npf-GAL4 abolishes rhythmic sxe1 expression in the fat body . ( E ) Expressing CLKΔ in a subset of LNds ( Dvpdf-GAL4;Pdf-GAL80 ) also does not eliminate cycling but reduces sxe1 expression in the fat body . ( F ) Npf-GAL4>UAS-CLKΔ abolishes rhythmic Cyp6a21 expression in the fat body . ( G ) per expression is rhythmic in flies expressing UAS-CLKΔ under Npf-GAL4 . ( H , I ) CLKΔ expression in NPF cells is restricted to adulthood using Tub-GAL80ts . ( H ) sxe1 expression is not affected with adult-specific clock ablation in NPF cells . ( I ) Rhythmic Cyp6a21 expression is affected in the fat body when Npf-GAL4>UAS-CLKΔ expression is induced in adult at 30°C . Each experiment was repeated independently at least twice . The average value for each timepoint is plotted with error bars denoting SEM . JTK_cycle p value <0 . 05 is indicated by an asterisk ( * ) next to the genotype label . See Table 3 for JTK_cycle p values . ZT- Zeitgeber Time . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 00910 . 7554/eLife . 13552 . 010Figure 3—source data 1 . Data for qPCR analysis of fat body clock-independent genes and clock genes in flies with ablated clock neurons . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 010 DN1 and LNd clusters have been implicated in the regulation of circadian behavior ( Zhang et al . , 2010; Zhang et al . , 2010; Stoleru et al . , 2004; Grima et al . , 2004 ) . In fact , DN1s were recently shown to be part of an output circuit regulating rest:activity rhythms ( Cavanaugh et al . , 2014 ) , and clocks in the DN1s are known to mediate other circadian behaviors , such as aspects of the male sex drive rhythm ( Fujii and Amrein 2010 ) . However , aside from behavioral rhythms , little is known about the functional significance of the DN1 and LNd clusters in regulating circadian outputs . We investigated whether rhythmic sxe1 expression requires clocks in the DN1 cluster by using the 911-GAL4 driver to target the DN1s ( Cavanaugh et al . , 2014 ) . Since expressing CLKΔ in the DN1s was lethal , we expressed dominant negative CYCLE , CYCΔ , in the DN1s and found the manipulation did not alter sxe1 rhythms or expression levels in the fat body ( Figure 3B ) . The six LNds express NPF ( neuropeptide F ) , sNPF ( short neuropeptide F ) , and ITP ( ion transport peptide ) in different cells , with some overlap ( Muraro et al . , 2013 ) . In adult males , NPF is expressed in 3 out of 6 LNds , as well as a subset of the LNvs and some non-clock neurons in the brain ( Lee et al . , 2006; Hermann et al . , 2012 ) . NPF is also expressed in endocrine cells in the midgut , although the role of NPF in these cells is not known ( Brown et al . , 1999 ) . We first used Npf-GAL4 to target the LNds . Interestingly , we found that expressing CLKΔ under Npf-GAL4 severely disrupted expression of sxe1 ( Figure 3C ) . This effect was not specific to the CLKΔ transgene , because sxe1 expression was also abolished using CYCΔ to disrupt clocks in NPF cells ( Figure 3D ) . Since it was possible that expression of CLKΔ or CYCΔ in non-clock NPF cells was disrupting sxe1 expression , we sought other ways to ablate the clock in LNd neurons . A subset of the LNd cluster can also be targeted with the Dvpdf-GAL4 driver in combination with pdf-GAL80 ( Guo et al . , 2014 ) . Expressing CLKΔ under Dvpdf-GAL4;pdf-GAL80 reduced sxe1 levels throughout most of the day , particularly at ZT16 , the time of peak sxe1 expression ( Figure 3E ) . The manipulation did not completely abolish rhythmic expression of sxe1 , presumably because the Dvpdf-GAL4 driver does not target all the NPF clock neurons . We also assessed the circadian expression profile of another fat body clock-independent cytochrome P450 gene , Cyp6a21 . Fat body expression of Cyp6a21 robustly cycles in wild type flies but rhythmic expression was dampened in Npf-GAL4/UAS-CLKΔ flies , with a relatively small reduction in its overall expression level ( Figure 3F ) . This suggests that clocks in NPF-positive neurons have a broader role in regulating the expression of cytochrome P450 genes in the fat body . Furthermore , ablating clocks in NPF-positive cells did not alter rhythmic expression of per , indicating that while rhythmic transcriptional output was impaired , the fat body clock remained intact ( Figure 3G ) . Together , these data suggest that a subset of LNds expressing NPF drive rhythmic expression of specific fat body genes . Next we tested whether overexpressing CLKΔ in NPF-positive neurons in adulthood is sufficient to alter circadian gene expression in the fat body . To limit the expression of CLKΔ to adulthood , we used flies with a tubulin-GAL80ts transgene ( tub-GAL80ts ) in addition to Npf-GAL4 and CLKΔ transgenes . Tub-GAL80ts ubiquitously expresses a temperature-sensitive GAL80 protein , which represses GAL4 activity at the permissive temperature of 18°C ( McGuire et al . , 2003 ) . All Npf-GAL4/UAS-CLKΔ; tub-GAL80ts/+ flies were raised at 18°C and upon reaching adulthood , control flies were kept at 18°C , while experimental flies were shifted to the restrictive temperature ( 30°C ) to induce CLKΔ expression . We found that after shifting flies to 30°C , expression of sxe1 remained rhythmic and similar to 18°C controls , suggesting adult-specific clock ablation in NPF-positive neurons is either incomplete or insufficient to affect sxe1 rhythms ( Figure 3H ) . However , this manipulation had a different effect on cyclic expression of Cyp6a21 . Robust cycling of Cyp6a21 cycling was maintained in control flies kept at the permissive temperature , although the phase was shifted , perhaps due to the different temperature ( 18°C ) required for this assay . Importantly though , rhythmic expression of Cyp6a21 was dampened by adult-specific clock ablation in LNd neurons ( Figure 3I ) . Together these data indicate that clocks in NPF-expressing neurons have differential effects on the expression of cycling fat body genes . After identifying NPF-positive clock neurons as relevant for rhythmic gene expression in the fat body , we reasoned NPF itself might act as a circadian signal . Indeed , NPF was reported to cycle in a subset of NPF-positive neurons , including LNds and LNvs ( He et al . , 2013a ) . NPF regulates a variety of behavioral processes in Drosophila including feeding ( Wu et al . , 2003; 2005; Lingo et al . , 2007; Itskov and Ribeiro , 2013 ) , courtship ( Kim et al . , 2013 ) , aggression ( Dierick and Greenspan , 2007 ) , and sleep ( He et al . , 2013b ) . Therefore , we asked if molecular clocks in NPF-positive neurons mediate free-running behavioral rhythms . We found that flies expressing CLKΔ with Npf-GAL4 as well as flies carrying a null mutation in nfpr , the gene encoding the receptor for NPF , display normal rhythms of rest:activity ( Table 1 ) . In contrast , Dvpdf-GAL4;pdf-GAL80 driving UAS-CLKΔ increased the number of arrhythmic flies and slightly lengthened the period of rhythmic flies , further indicating that Dvpdf-GAL4;pdf-GAL80 and Npf-GAL4 do not represent the exact same population of LNds ( Table 1 ) . These data suggest that NPF plays at best a minor role in regulating rhythmic locomotor behavior . However , NPF might play a role in other aspects of circadian rhythms , such as circadian control of energy homeostasis . To determine whether NPF drives rhythmic sxe1 expression in the fat body , we began by knocking down npf in all NPF-positive cells with RNA interference ( RNAi ) , as npf mutants are not available . Driving UAS-npf RNAi under Npf-GAL4 resulted in dampened but still rhythmic sxe1 expression ( Figure 4A ) . Although this manipulation vastly reduced npf levels in fly heads ( Figure 4B ) , it is possible that very small amounts of NPF can drive some level of cycling; alternatively , knockdown efficiency may have been limited in the NPF-positive clock cells . Thus we tested the null mutant of the sole NPF receptor in Drosophila , npfr ( Garczynski et al . , 2002 ) . Our results show sxe1 levels do not cycle and are dramatically reduced in npfr mutants , which phenocopies the daily sxe1 expression profile of flies expressing either CLKΔ or CYCΔ under Npf-GAL4 ( Figure 4C ) . Rhythmic expression of Cyp6a21 was also lost in the fat body of npfr mutants ( Figure 4D ) . We speculate that expressing CLKΔ under Npf-GAL4 alters the circadian production or release of NPF . Indeed , mRNA analysis of Npf-GAL4/UAS-CLKΔ heads showed that npf levels were reduced compared to controls ( Figure 4E ) while cyclic per expression , which arises from clock function in many different cells , was unaffected ( Figure 4F ) . This result is consistent with reports of loss of NPF expression in LNds of Clkjrk brains ( Lee et al . , 2006 ) . Taken together , these data suggest circadian clocks in NPF-positive cells regulate NPF expression to subsequently drive sxe1 and Cyp6a21 rhythms in the fat body . 10 . 7554/eLife . 13552 . 011Figure 4 . NPF is a critical circadian signal for sxe1 and Cyp6a21 rhythms in the fat body . ( A ) Knockdown of npf in all NPF-positive cells does not eliminate rhythmicity but reduces expression of sxe1 in the fat body at all times . ( B ) Analysis of npf knockdown efficiency in heads of Npf-GAL4/UAS-npf RNAi; DCR2 ( UAS-Dicer2 ) flies showed a significant reduction in npf levels by Student’s t-test ( **p<0 . 001 ) . ( C , D ) sxe1 and Cyp6a21 expression in the fat body are reduced and do not cycle in homozygous npfr mutants compared to heterozygous controls . ( E ) npf levels in the heads of Npf-GAL4/UAS-CLKΔ are reduced compared to controls ( UAS-CLKΔ/+ ) . ( F ) Total per levels are not altered in the heads of Npf-GAL4/UAS-CLKΔ compared to controls . Each experiment was repeated independently three times except for ( B ) which n=6 for each genotype . The average value ± SEM for each timepoint is plotted . JTK_cycle p value <0 . 05 is indicated by an asterisk ( * ) next to the genotype label . See Table 3 for JTK_cycle p values . ZT- Zeitgeber Time . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 01110 . 7554/eLife . 13552 . 012Figure 4—source data 1 . Data for qPCR analysis of fat body clock-independent genes and clock genes in flies with perturbed NPF signaling . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 012 In mammals , liver-specific circadian clocks play an important role in liver physiology via contributions to glucose homeostasis and xenobiotic clearance ( Gachon et al . , 2006; Lamia et al . , 2008 ) . Liver clock ablation in mice resembles fat body clock ablation in flies; in particular , ablating liver clocks eliminates rhythmic expression of most , but not all , circadian liver transcripts ( Kornmann et al . , 2007a ) . Furthermore , rescuing clock function specifically in the brains of ClockΔ19 mutant mice restores rhythmic expression of roughly 40% of circadian liver transcripts ( Hughes et al . , 2012 ) . These data suggest that some circadian transcripts in the liver are driven by systemic signals , perhaps emanating from the master pacemaker in the suprachiasmatic nuclei ( SCN ) of the hypothalamus ( Mohawk et al . , 2012 ) . Since we identified NPF in the regulation of circadian gene expression in the fly fat body , we reasoned that the mammalian homolog , Npy , might regulate circadian gene expression in the liver . Thus , we isolated RNA from the livers of male Npy knockout ( Npy KO ) mutant mice and wild type controls over the course of an entire day . Although there is no direct mammalian homologue of sxe1 , we noticed that a similar P450 enzyme involved in xenobiotic detoxification , Cyp2b10 , also continues to cycle in animals lacking functional liver clocks ( Kornmann et al . , 2007a ) . We measured Cyp2b10 levels in Npy KO and wild type mice and found that Cyp2b10 transcript levels did not display a circadian rhythm in Npy KOs ( Figure 5A ) . However , circadian expression of the core clock gene Rev-erb alpha was unaffected in the livers of Npy KOs confirming that the liver clock is still intact ( Figure 5B ) . We wondered whether other enzymes involved in xenobiotic detoxification are also regulated by Npy . Aminolevulinic acid synthase 1 ( Alas1 ) , is required for P450 synthesis ( Furuyama et al . , 2007 ) and was also reported to cycle in the absence of the liver clock ( Kornmann et al . , 2007a ) . Unlike Cyp2b10 , circadian expression of Alas1 was unaffected in Npy KOs , suggesting that NPY does not regulate global rhythmic detoxification in the liver ( Figure 5C ) . 10 . 7554/eLife . 13552 . 013Figure 5 . Npy regulates circadian expression of cytochrome P450 genes in the murine liver . ( A–C ) Quantitative PCR analysis in murine livers . Daily oscillations of Cyp2b10 expression ( A ) are abolished in Npy KOs compared to their background controls ( wild type ) , while oscillations of the circadian gene , Reverb alpha ( B ) , are unaffected . ( C ) Levels of another liver clock-independent gene , Alas1 , are similar in wild type and Npy KO , suggesting Npy does not regulate its rhythmicity . For qPCR data , n=3–4 mice for each genotype and time point . Transcript levels were normalized to the housekeeping gene 36B4 . ( D–F ) Microarray analysis was used to detect transcript expression in livers of Npy KO and their background controls collected over the course of 24 hr in LD . ( D ) The heatmap includes transcripts that oscillate in wild type but not in Npy KO liver . Data represent the average transcript abundance from n=2 samples for each genotype and timepoint . Here , the MetaCycle p-value cutoff of p<0 . 01 was used to identify cyclic transcripts; p>0 . 8 was considered not cyclic . ( E ) The median expression values of the wild type-only cyclic transcripts are not different between Npy KO and wild type . ( F ) Daily expression values of cytochrome P450 genes from microarrays . Cytochrome P450 genes Cyp2b10 , Cyp2r1 , Cyp17a1 , and Cyp2c70 are cylic in wild type liver but are not cyclic in Npy KO liver . Cyp3a13 and Cyp7a1 cycle robustly in both wild type and Npy KO . Graphs show average ± SEM . ZT- Zeitgeber Time . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 01310 . 7554/eLife . 13552 . 014Figure 5—source data 1 . Data for qPCR and microarray analysis in wild type and Npy knockout mouse liver . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 01410 . 7554/eLife . 13552 . 015Figure 5—figure supplement 1 . MetaCycle analysis of cycling liver transcripts in wild type and NPY KO . ( A ) Microarray analysis detects 2460 and 1330 cycling transcripts ( MetaCycle p<0 . 05 ) in WT and Npy KO liver datasets respectively . 880 cycling transcripts are common between the two datasets . ( B , C ) Scatter plot and box plot graph the phase in Npy KO relative to WT for the 880 genes with cycling expression patterns in both datasets . In general , the phase in Npy KO is delayed compared to that in WT . ( D ) Density plot graphs the distribution of baseline expression levels , bEXP ( see Materials and methods ) , for the 880 cycling transcripts in WT and KO datasets . X-axis graphs the log base 10 of bEXP value . ( E ) Density plot graphs the distribution of relative amplitudes , rAMP ( see Materials and methods ) , for the 880 cycling transcripts in WT and KO datasets . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 015 To determine the extent to which loss of Npy impacts gene expression in the liver , we performed genome-wide expression analysis on wild type control and Npy KO livers collected at 4 hr intervals over a day in LD . Using the newly developed MetaCycle package ( see Materials and methods ) and a stringent P-value cutoff of p<0 . 01 to detect cyclic transcripts , we found that 289 transcripts were cyclic in controls but not in Npy KO , indicating that the oscillation of these transcripts is under the regulation of Npy signaling ( Figure 5D and Supplementary file 1 ) . Furthermore , the loss of transcript cycling was generally not accompanied by differences in expression levels; in other words , the median transcript abundance in wild type animals correlated with that in Npy KO ( Figure 5E ) . Based on our Drosophila data and also the fact that Npy regulates Cyp2b10 expression , we speculated that Npy might have a broader role in regulating cytochrome P450 gene expression . We examined the microarray data for cyclic P450 transcripts and found several of these genes were not cyclic in Npy KOs . Notably , the microarray data confirmed our qPCR data for Cyp2b10 and indicated that Cyp2r1 , Cyp17a1 , and Cyp2c70 transcripts also cycle in wild type but not in Npy KO liver . In contrast , Cyp3a13 and Cyp7a1 transcripts cycle robustly in both genotypes . Lastly , we compared our Npy KO data to the previously reported set of liver transcripts whose expression oscillates independently of the liver clock ( Kornmann et al . , 2007a; 2007b ) . Among that set , we discovered nine additional liver clock-independent transcripts—Rbl2 , Ddx46 , Cirbp , Sqle , Ldb1 , Actg1 , Hmgcs1 , Heca , and Ctgf—that require Npy for robust rhythmic expression ( Table 2 ) . As only a subset of liver-clock independent transcripts requires Npy for oscillations , other mechanisms likely contribute to rhythmic expression of these genes ( further discussed below ) . Although many genes , including clock genes , continued to cycle in Npy knockout livers , the overall phases and amplitudes of expression for cycling transcripts in Npy KO slightly differed from those in wild type ( Figure 5—figure supplement 1 ) . Overall we found that diverse liver circadian transcripts , including cytochrome P450 genes , are influenced by Npy signaling . This report is the first to describe a role for Npy in the circadian regulation of peripheral gene expression in mammals . 10 . 7554/eLife . 13552 . 016Table 2 . MetaCycle statistics for cycling of liver clock-independent genes in wild type and Npy knockout mice . Our Npy KO data compared to the previously reported set of liver transcripts whose expression oscillates independently of the liver clock ( Kornmann et al . , 2007b ) . Ten liver clock-independent transcripts require Npy for robust rhythmic expression . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 01610 . 7554/eLife . 13552 . 017Table 2—source data 1 . Microarray data for MetaCycle analysis . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 017Liver clock-independent genes with disrupted cycling of expression in Npy KOAffymetrix transcript IDGeneWT MetaCycle P-valueWT PhaseWT Median ExpWT Relative AmpKO MetaCycle P-valueKO PhaseKO Median ExpKO Relative Amp17503756Rbl20 . 00038 . 94367 . 00 . 2170 . 07539 . 20338 . 70 . 13217287733Ddx460 . 00077 . 77264 . 20 . 2290 . 175611 . 46262 . 80 . 15817235227Cirbp0 . 00376 . 2858 . 10 . 1880 . 09247 . 8354 . 30 . 05617311807Sqle0 . 003921 . 00193 . 40 . 7900 . 76859 . 43229 . 90 . 31017365314Ldb10 . 006710 . 38234 . 10 . 1640 . 150312 . 05216 . 20 . 17117475360Cyp2b100 . 010217 . 75155 . 90 . 8690 . 07820 . 0085 . 70 . 48617331429Actg10 . 015318 . 56155 . 70 . 4700 . 35701 . 52124 . 50 . 51217290173Hmgcs10 . 02300 . 50751 . 50 . 6870 . 16254 . 83695 . 20 . 08317239493Heca0 . 02659 . 30255 . 60 . 1320 . 192110 . 93258 . 60 . 08617232235Ctgf0 . 033113 . 1837 . 90 . 3640 . 998411 . 5242 . 80 . 152Liver clock-independent gene with altered phase of expression in Npy KOAffymetrix transcript IDGeneWT MetaCycle P-valueWT PhaseWT Median ExpWT Relative AmpKO MetaCycle P-valueKO PhaseKO Median ExpKO Relative Amp17268729Fbxl200 . 00017 . 6672 . 80 . 3380 . 02873 . 3574 . 60 . 024Exp , expression level; Amp , amplitude .
In this report we dissect the role of neural clocks in the regulation of circadian gene expression in a peripheral tissue . We find that clocks in PDF-positive neurons influence cycling of the per clock gene in the Drosophila fat body in the absence of external cues . More importantly , we identify the non-cell autonomous mechanism that underlies cycling of specific fat body transcripts in Drosophila and specific liver transcripts in mice . We show that clocks in Drosophila NPF-positive neurons drive daily expression of sxe1 and Cyp6a21 , fat body genes not controlled by the fat body clock . Likewise , mammalian Npy drives rhythmic expression of specific liver transcripts , indicating a conserved role of NPF/Npy in the control of peripheral circadian rhythms . Prior to this report , it was proposed that clocks in the brain and fat body interact , but the extent of the interaction and the mechanisms driving it were not identified ( Xu et al . , 2008 ) . Our data suggest that in light:dark cycles , the central clock is not required for cycling of the fat body clock , although we cannot exclude an effect on the phase of cycling . However , in constant conditions , the clock in PDF cells influences the fat body clock , as it does the prothoracic gland clock . Future work will determine whether PDF release into the hemolymph influences the fat body clock ( Talsma et al . , 2012; Krupp et al . , 2013 ) . Why the central clock regulates only some peripheral clocks in the fly is unclear . Unlike other peripheral clocks , the fat body clock modulates behavioral rhythms , specifically the phase of feeding rhythms , in addition to its own physiology ( Xu et al . , 2008; 2011; Seay and Thummel , 2011 ) . Thus , synchrony between clocks in the brain and fat body is likely essential for metabolic homeostasis . The circadian system controls behavior and physiology in large part through its regulation of circadian gene expression ( Zheng and Sehgal , 2012 ) . Tissue-specific gene expression patterns are thought to be generated primarily by local clocks; however , few studies have comprehensively evaluated rhythmic expression driven by local clocks versus external factors . A previous comparison of gene expression profiles of flies containing or lacking an intact fat body clock found that the fat body clock only regulates ~60% of all circadian fat body genes ( Xu et al . , 2011 ) . Here we report that at least some of the other 40% of circadian fat body genes are regulated by clocks located in other tissues . We found that disrupting clocks in NPF-positive cells abolished rhythmic expression of two cytochrome P450 genes , sxe1 and Cyp6a21 . Since we specifically disrupted the molecular clock by expressing CLKΔ or CYCΔ , only NPF-positive cells containing circadian clock components should have been targeted ( LNds ) . We cannot formally exclude the possibility that expression of CLKΔ or CYCΔ in non-clock cells or even in the gut ( Brown et al . , 1999 ) contributes to this phenotype; however , the effect of targeting CLKΔ to specific LNds with the Dvpdf driver suggests that these cells contribute to the peripheral rhythm phenotype . In addition , even though NPF expression has been reported in both the LNds and LNvs ( Hermann et al . , 2012 ) , it is unlikely the LNvs regulate sxe1 rhythms , because disrupting clocks in PDF-positive LNvs does not abolish sxe1 oscillations . LNds can be synchronized by inputs from LNvs ( Guo et al . , 2014 ) , but cell-autonomous entrainment mechanisms in the LNds may limit the influence of LNvs in light:dark cycles , which may explain why ablating clocks in LNvs has a small effect on sxe1 expression . Thus , we suggest that the clocks in LNds are required for cycling of sxe1 and Cyp6a21 expression in the fat body . NPF neuropeptide reportedly modulates rest:activity rhythms in Drosophila ( Hermann et al . , 2012; He et al . , 2013a ) . We did not detect a role for clocks in NPF cells , nor for the single known NPF receptor , in the regulation of rest:activity , but it is possible that other mechanisms are utilized . However , we show that NPF regulates the expression of circadian genes in the fat body . Consistent with the assertion that NPF is the relevant output for fat body rhythms from NPF-positive cells , we also found that flies lacking functional clocks in these cells display significantly reduced npf levels ( Figure 4D ) . Interestingly , Lee et al . previously showed that npf mRNA is absent in the LNds of adult male Clkjrk mutant brains ( Lee et al . , 2006 ) . This further supports our hypothesis that NPF is regulated by the circadian clock in LNds , and its release from these neurons is necessary for mRNA rhythms of specific fat body genes . However , the effect of NPF on the fat body is likely not direct . Some insect species release NPF into the hemolymph to reach other tissues , but this does not appear to be the case in Drosophila ( Nässel and Wegener , 2011 ) . The NPF receptor may function in clock neurons in the dorsal fly brain ( i . e . DN1s ) , neurons in the suboesophageal ganglion , or neurons innervating the mushroom body ( Krashes et al . , 2009; He et al . , 2013a ) . Alternatively , NPF could signal through recently identified neurons downstream of the clock network , which are part of the circadian output circuit driving rest:activity rhythms ( Cavanaugh et al . , 2014 ) . Although much is known about the neuronal clock network , very little is known about the neurons and signals downstream of the clock network , which make up the output pathways leading to rhythms in behavior and physiology . Our discovery that NPF-positive clock neurons drive rhythmic gene expression in the fat body provides a unique opportunity to investigate the pathway ( s ) that convey circadian information from the brain to peripheral tissues . We report a striking parallel in the mammalian system , where the NPF ortholog , Npy , drives cyclic expression of specific liver genes , notably several in the cytochrome P450 pathway . Npy is not required for free-running rest:activity rhythms in mice , but it promotes phase shifts in these rhythms in response to non-photic stimuli ( Yannielli and Harrington , 2004; Maywood et al . , 2002; Besing et al . , 2012 ) . Behavioral effects of Npy are likely mediated by its brain expression , but as Npy is also expressed in the periphery , it is possible that a peripheral source contributes to cycling in the liver . Regardless , it is clear that Npy has a profound effect on circadian gene expression in the liver . Since NPF promotes feeding in Drosophila larvae ( Wu et al . , 2003; Wu et al . , 2005; Lingo et al . , 2007 ) and Npy does so in mice , it is possible NPF/Npy drive cycling in the fat body/liver through the regulation of feeding . Feeding is known to be a potent stimulus for metabolic clocks , with circadian gene expression in peripheral tissues driven by restricted feeding cycles in both flies and mammals ( Xu et al . , 2011; Gill et al . , 2015; Vollmers et al . , 2009 ) . However , under conditions of ad lib food , feeding rhythms in flies are of low amplitude and likely insufficient to drive robust cycling . Consistent with this , while cyclic expression of Cyp6a21 can be driven by a restricted feeding paradigm , as can the clock in the fat body , cycling is more robust when this paradigm is conducted in wild type versus clockless animals ( Xu et al . , 2011 ) , indicating that its regulation is not through feeding alone . Finally , time restricted feeding experiments of mice do not support the idea that restricted feeding drives cycling of Cyp2b10 in clockless mice , even though it is sufficient to maintain rhythms of many other liver genes ( Vollmers et al . , 2009 ) . Thus , while feeding cannot be discounted as an important factor , which may contribute to the cycling of the genes reported here , these genes are unique in their dependence on Npy . Only a limited subset of liver transcripts previously shown to be independent of the liver clock require Npy for cyclic expression ( Kornmann et al . , 2007a ) . Similarly , several fly genes , for example sxe2 and CG17562 , continue to oscillate when CLKΔ is expressed under Npf-GAL4 ( data not shown ) . These results suggest there are additional mechanisms regulating circadian rhythms in the fat body/liver . Why would more than one mechanism exist to couple rhythmic gene expression in a specific peripheral tissue to other clocks ? One possibility is that different mechanisms regulate distinct phases of circadian gene expression . Alternatively , different mechanisms may couple gene expression to different cell populations , processes , or behaviors . The functional importance of the interaction between NPF/Npy and fat body/liver genes in the circadian system is unclear . Cytochrome P450 genes , such as Cyp6a21 , sxe1 and Cyp2b10 , are associated with detoxification ( King-Jones et al . , 2006; Fujii et al . , 2008 ) , which is likely rhythmic , although not yet reported . Overexpression of NPFR in larvae increases foraging behavior as well as consumption of noxious or bitter compounds ( Wu et al . , 2005 ) . Indeed , NPF/Npy signaling is generally associated with an increase in feeding ( Wu et al . , 2003; 2005; Lingo et al . , 2007; Beck , 2006 ) , which can lead to ingestion of toxic substances . Thus , coordination of feeding with expression of detoxification enzymes , such as sxe1 , Cyp6a21 and Cyp2b10 , through NPF/Npy may have evolved to promote survival . Large delays between consumption of noxious substances and their removal could affect an animal’s health; thus , the need for coordination between clocks in processing such substances . Conservation of cytochrome P450 regulation from flies to mammals supports the idea that neural control of detoxification in the periphery promotes organismal fitness ( Figure 6 ) . 10 . 7554/eLife . 13552 . 018Figure 6 . NPF/Npy regulate rhythmically expressed P450 enzymes in the periphery of flies and mammals . A model of brain clock regulation of peripheral cycling . Brain clocks regulate clocks in peripheral tissues . In Drosophila , clocks in PDF-positive neurons ( LNvs ) regulate the clock in the fat body . Similarly , in mammals , clocks in the suprachiasmatic nuclei ( SCN ) have been shown to regulate peripheral clocks such as the liver clock via autonomic innervation , glucocorticoids , body temperature , and feeding . In both the fat body and liver , not all circadian transcripts depend on the local-tissue clock . Clocks in NPF-positive LNds and NPF itself regulate circadian expression of cytochrome P450 enzymes in the fly fat body . The LNvs can influence other brain clocks ( such as the LNds ) , but are not required for rhythms of fat body transcripts in LD as LNds may entrain directly to light . In mammals , Npy was previously known to be a non-photic signal involved in entraining the SCN . However , the SCN could also influence Npy production or release , which in turn drives rhythmic expression of cytochrome P450 enzymes in the liver . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 018 In this study we exclusively evaluated males , because the initial studies reporting rhythmic gene expression in the presence and absence of the fat body or liver clock in flies or mammals respectively , were based on males ( Xu et al . , 2011; Kornmann et al . , 2007a ) . Interestingly , NPF/Npy and sxe1/Cyp2b10 expression is sexually dimorphic in Drosophila ( Lee et al . , 2006; Fujii et al . , 2008 ) and mammals ( Lu et al . , 2013; Karl et al . , 2008; Urban et al . , 1993 ) , suggesting there may be some gender specificity to this entire pathway . The functional significance of sex-specific regulation is unclear , but indicates that other mechanisms could contribute to such coordination in females . This work has implications for chronopharmacology , which is based on the circadian timing of drug metabolism , transport , tolerance , and efficacy . Rhythmic expression of genes involved in drug breakdown and absorption in the liver influences drug efficacy and toxicity ( Dallmann et al . , 2014 ) , and loss of such rhythms can have long-term effects on health and lifespan ( Gachon et al . , 2006 ) . Therefore , expression of these genes may be tightly coordinated to optimize drug metabolism , and speaks to the importance of controlling the timing of drugs that have toxic side effects . The role for Npy reported here suggests it could be a potential target for improving drug efficacy and toxicity . Ultimately , understanding circadian rhythms at a systems level , including interactions between tissues and other physiological systems , will be useful from biological and clinical perspectives .
Flies were grown on standard cornmeal-molasses medium and maintained at 25°C . The following strains were used: Iso31 ( isogenic w1118 stock; ( Ryder et al . , 2004 ) ) , Pdf-GAL4 ( Renn et al . , 1999 ) , 911-GAL4 ( InSITE Library; ( del Valle Rodríguez et al . , 2012 ) ) , Dvpdf-GAL4; pdf-GAL80 ( Guo et al . , 2014 ) , Clkjrk ( Allada et al . , 1998 ) , and UAS-npf RNAi ( Vienna Drosophila Resource Center #108772 ) . The following flies were obtained from Bloomington Drosophila Stock Center: Npf-GAL4 ( #25681 ) , UAS-CLKΔ ( #36318 ) , UAS-CYCΔ ( #36317 ) , tub-GAL80ts ( #7018 ) , and npfr mutant ( #10747 ) . The previously described Drosophila Activity Monitoring Systems ( Trikinetics , Waltham , MA ) were used to monitor rest:activity rhythms under constant conditions . Roughly 1 week old male flies were entrained for at least 3 days to 12 hr light: 12 hr dark cycles ( LD ) and then transferred to constant darkness for at least 7 days . Data were analyzed using ClockLab software ( Actimetrics ) and rhythmicity of individual male flies was determined for days 2–7 of DD as described previously ( Williams et al . , 2001 ) . Male flies ( roughly 4–7 days old ) were entrained to a 12:12 LD cycle at 25°C for at least 3 days before they were harvested . The abdominal fat body was obtained by separating the fly abdomen from the rest of the body and then removing all internal organs , leaving the fat body attached to the cuticle to be collected on dry ice for RNA extraction . For tub-Gal80ts experiments , flies were raised at 18°C . Control flies were kept at 18°C , while the experimental flies were shifted to 30°C , the restrictive temperature for Gal80ts , for at least 4 days before collection . Npy knockout mice were obtained from The Jackson Laboratory ( 004545 ) along with their background strain for controls ( 002448 ) . Genotyping primers are listed on the Jackson website . 8–12 weeks old male mice were entrained to 12:12 LD cycles and fed a standard ad lib diet . Livers from Npy knockouts and their background controls were collected every 4 hr starting at lights on ( ZT0 ) and immediately frozen in liquid nitrogen . 3–4 male mouse livers were collected at every timepoint for each genotype . All procedures were approved by the University of Pennsylvania Institutional Animal Care and Use Committee . For each time point , fat bodies from 12 male flies were collected for RNA preparation . Total RNA was extracted using Trizol reagent ( Life Technologies , Grand Island , NY ) and purified using RNeasy Mini Kit ( Qiagen Inc . , Valencia , CA ) according to manufacturer’s protocol . All RNA samples were treated with RNase-free DNase ( Qiagen Inc . ) . RNA was reverse transcribed to generate cDNA using a High Capacity cDNA Reverse Transcription kit ( Life Technologies , Grand Island , NY ) . Quantitative RT-PCR was performed on a 7900HT Fast-Real-Time PCR ( Applied Biosystems ) using SYBR Green ( Life Technologies ) . The following primer sequences were used for qPCR: αtubulin ( Forward 5’ CGTCTGGACCACAAGTTCGA 3’ and reverse 5’ CCTCCATACCCTCACCAACGT 3’ ) , per ( Forward 5’ CGTCAATCCATGGTCCCG 3’ and reverse 5’ CCTGAAAGACGCGATGGTG 3’ ) , Cyp4d21/sxe1 ( Forward 5’ CTCCTTTGGTTTATCGCCGTT 3’ and reverse 5’ TTATCAGCGGCTTGTAGGTGC ) , sxe2 ( Forward 5’ TGCGGTACGATCTTTATACGCC 3’ and reverse 5’ CTAACTGGCCATTTCGGATTGA 3’ ) , CG14934 ( Forward 5’ GGAAATCACGACAATCCTCGA 3’ and reverse 5’ CCCAACTCCTCGCCATTATAAG 3’ ) , Cyp6a21 ( Forward 5’ GTTGTATCGGAAACCCTTCGATT 3’ and reverse 5’ AACCTCATAGTCCTCCAGGCATT 3’ ) , and CG117562 ( Forward 5’ACCACAGAGGTGAAACGCATCT 3’ and reverse 5’CAGCAGCAGTTCAAATACCGC 3’ ) . Transcript levels were normalized to those of αtubulin to control for the total RNA content in each sample . Kits and procedures to isolate RNA and generate cDNA from mouse livers are the same as described above for fly fat bodies . The following primer sequences were used for qPCR: Cyp2b10 ( Forward 5’ GACTTTGGGATGGGAAAGAG 3’ and reverse 5’ CCAAACACAATGGAGCAGAT 3’ ) , 36B4 ( Forward 5’ TCCAGGCTTTGGGCATCA 3’ and reverse 5’ CTTTATCAGCTGCACATCACTCAGA 3’ ) , Rev-erb alpha ( Forward 5’ GTCTCTCCGTTGGCATGTCT 3’ and reverse 5’ CCAAGTTCATGGCGCTCT 3’ ) and Alas1 ( PrimerBank ID 23956102a1 ) ( Spandidos et al . , 2008; 2010; Wang and Seed , 2003 ) . Transcript levels were normalized to the housekeeping gene , 36B4 . Significant circadian rhythmicity of transcript levels was determined using the JTK_Cycle algorithm ( Hughes et al . , 2010 ) . P values of less than 0 . 05 were considered significant . We also used two-way ANOVA for repeated measures and a Tukey’s post hoc test for differences across time ( GraphPad Prism ) . P-values are reported in Table 3 . 10 . 7554/eLife . 13552 . 019Table 3 . JTK_Cycle Statistics and Two-factor ANOVA Results . All qPCR data were tested for circadian rhythmicity with JTK_cycle test and two-way ANOVA for repeated measures and a Tukey’s post hoc test . P-values from these tests are summarized . DOI: http://dx . doi . org/10 . 7554/eLife . 13552 . 019FigureGenotypeGeneTissueJTK_cycle P-valueTime P-valueGenotype P-valueTime X Genotype P-value1C[ZT] UAS-CLK∆/+perFat Body ( FB ) 0 . 0194< 0 . 00010 . 78260 . 46791C[ZT] Pdf-GAL4/UAS-CLK∆perFB0 . 00941D[CT] UAS-CLK∆/+perFB0 . 00410 . 03130 . 66500 . 02811D[CT] Pdf-GAL4/UAS-CLK∆perFB12AIso31sxe2FB0 . 00140 . 01310 . 18520 . 08432AClkjrksxe2FB12BIso31CG17562FB0 . 00140 . 02230 . 64090 . 17462BClkjrkCG17562FB0 . 69452CIso31sxe1FB0 . 00200 . 0019<0 . 00010 . 00202CClkjrksxe1FB12DIso31CG14934FB0 . 08200 . 1146<0 . 00010 . 02662DClkjrkCG14934FB0 . 54293AUAS-CLK∆/+sxe1FB0 . 00070 . 00030 . 00490 . 70613APdf-GAL4/UAS-CLK∆sxe1FB9 . 00E-073BUAS-CYC∆/+sxe1FB0 . 03630 . 00060 . 79950 . 95923B911-GAL4/UAS-CYC∆sxe1FB0 . 00443CUAS-CLK∆/+sxe1FB0 . 00140 . 0060<0 . 00010 . 07493CNpf-GAL4/UAS-CLK∆sxe1FB0 . 19693DUAS-CYC∆/+sxe1FB0 . 0029<0 . 0001<0 . 0001<0 . 00013DNpf-GAL4/UAS-CYC∆sxe1FB13EUAS-CLK∆/+sxe1FB0 . 01960 . 00380 . 00170 . 53263EDvpdf-GAL4/UAS-CLK∆;Pdf-GAL80/+sxe1FB0 . 00013FUAS-CLK∆/+Cyp6a21FB0 . 0009<0 . 0001<0 . 00010 . 05743FNpf-GAL4/UAS-CLK∆Cyp6a21FB0 . 02593GUAS-CLK∆/+perFB0 . 05680 . 00570 . 87670 . 99023GNpf-GAL4/UAS-CLK∆perFB0 . 04414AUAS-Npf RNAi/+sxe1FB0 . 00200 . 0005<0 . 00010 . 14214ANpf-GAL4/UAS-Npf RNAisxe1FB0 . 04414BNot analyzed with JTKnpfHead-4Cnpfr/+sxe1FB0 . 01280 . 0008<0 . 00010 . 00384Cnpfrsxe1FB0 . 19694Dnpfr/+Cyp6a21FB0 . 09160 . 0319<0 . 00010 . 41824DnpfrCyp6a21FB0 . 11504EUAS-CLK∆/+npfHead10 . 7588<0 . 00010 . 8464ENpf-GAL4/UAS-CLK∆npfHead14FUAS-CLK∆/+perHead0 . 0001<0 . 00010 . 14100 . 04204FNpf-GAL4/UAS-CLK∆perHead1 . 19E-055AWild typeCyp2b10Liver0 . 00100 . 02090 . 00340 . 16405ANpy Knockout ( KO ) Cyp2b10Liver0 . 05105BWild typeReverbαLiver7 . 46E-12<0 . 00010 . 49790 . 06615BNpy KOReverbαLiver7 . 17E-095CWild typeAlas1Liver9 . 81E-060 . 00070 . 84760 . 82025CNpy KOAlas1Liver0 . 0018 Liver samples from Npy KO and wild type mice were collected every 4h over 24h ( n = 2 per genotype and timepoint ) . RNA was purified as described above . Expression profiling was done at the Penn Molecular Profiling Facility using Mouse Gene 2 . 0 ST Arrays ( Affymetrix , Santa Clara , CA , which also provided the annotation files ) . For extracting expression values of transcripts , raw CEL files were analyzed with the RMA algorithm ( Irizarry , 2003 ) implemented in the affy package in Bioconductor in R ( R 2 . 14 . 2 ) ( Gautier et al . , 2004 ) . The newly developed MetaCycle ( version 1 . 0 . 0; https://github . com/gangwug/MetaCycleV100 . git ) was used to detect circadian transcripts from time-series expression data in the wild type ( WT ) and Npy knockout ( KO ) groups , respectively . Key parameters in MetaCycle were the periodicity detection algorithms , JTK_CYCLE ( Hughes et al . , 2010 ) and Lomb-Scargle ( Glynn et al . , 2006 ) , the period length ( set at exactly 24 hr ) , and the p-value integration method ( Fisher’s method , Fisher 1956 ) . Using MetaCycle , we calculated two new features of circadian transcripts , baseline expression level ( bEXP ) and relative amplitude ( rAMP ) . The former one is defined as the average expression level of a cycling transcript within one period length , and the latter one is a normalized amplitude value with bEXP . Based on analysis results from MetaCycle , expressed transcripts ( bEXP larger than 101 . 6 ) with a p-value <0 . 01 in WT and >0 . 8 in the KO group were considered WT-specific rhythmic transcripts and shown in the heatmap . To generate the heatmap , expression values from replicate libraries in each group were averaged , median normalized by transcript , sorted by phase , and plotted as a heatmap using pheatmap in R . | Many processes in the body follow rhythms that repeat over 24 hours and are synchronized to the cycle of day and night . Our sleep pattern is a well-known example , but others include daily fluctuations in body temperature and the production of several hormones . Internal clocks located in the brain and other organs drive these rhythms by altering the activity of certain genes depending on the time of day . Animals have specific organs that contain enzymes needed to break down toxic molecules in the body , and the levels of several of these enzymes rise and fall over each 24-hour period . In mammals , these enzymes are found in the liver , but in insects they are found in an organ called the fat body . Here , Erion , King et al . set out to determine the extent to which the internal clock in the brain influences the daily rhythms of these enzymes . The experiments show that a hormone released by the nervous system is required for the levels of the detoxifying enzymes to change in 24-hour cycles . This hormone – termed Neuropeptide F in fruit flies and Neuropeptide Y in mice – is also known to stimulate both mice and fruit flies to eat . Since toxic molecules often enter the body during feeding , Erion , King et al . speculate that it may be beneficial to link the detoxification process to feeding by using the same mechanism to control both processes . The next step following on from this work would be to find out exactly how neuropeptide F drives the 24-hour rhythms in the fat body and other organs . | [
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The cutaneous wound-healing program is a product of a complex interplay among diverse cell types within the skin . One fundamental process that is mediated by these reciprocal interactions is the mobilization of local stem cell pools to promote tissue regeneration and repair . Using the ablation of epidermal caspase-8 as a model of wound healing in Mus musculus , we analyzed the signaling components responsible for epithelial stem cell proliferation . We found that IL-1α and IL-7 secreted from keratinocytes work in tandem to expand the activated population of resident epidermal γδT-cells . A downstream effect of activated γδT-cells is the preferential proliferation of hair follicle stem cells . By contrast , IL-1α-dependent stimulation of dermal fibroblasts optimally stimulates epidermal stem cell proliferation . These findings provide new mechanistic insights into the regulation and function of epidermal cell–immune cell interactions and into how components that are classically associated with inflammation can differentially influence distinct stem cell niches within a tissue .
One of the main functions of the skin is to provide the body with a barrier against external assaults while preventing excessive loss of moisture . As a result , the skin is constantly regenerating itself , but once this barrier has been breached through injury , a wound-healing response is rapidly mobilized to restore this barrier . The wound-healing response is a complex process that relies on the careful orchestration of signals coming from various cell types . Following injury , three sequential but overlapping phases are initiated , commencing with inflammation , followed by proliferation of stem cells and concluding with tissue remodeling . Despite their temporal differences , there is significant interdependence among these phases that enables the restoration of tissue function ( Gurtner et al . , 2008 ) . One such interdependency is the interaction between the inflammation and proliferation phases , which can be mediated by members of the interleukin-1 ( IL-1 ) family of cytokines ( Dinarello , 2009 ) . However , the mechanism ( s ) by which IL-1 proteins mediate proliferation of different cell types within the repairing organ remain to be elucidated . Epidermal keratinocytes are a rich source of IL-1α , which is released upon tissue damage ( Lee et al . , 1997 ) . Unlike IL-1β , which must be proteolytically processed into its active form , IL-1α is active as a zymogen and , upon secretion from epidermal keratinocytes , can play key roles in the early inflammatory phase of the wound-healing response ( Bianchi , 2007 ) . In addition , IL-1α plays an important role in mediating the reciprocal crosstalk between cells within the epidermis and dermis that stimulates the secretion of stem cell proliferation factors ( Lee et al . , 2009; Szabowski et al . , 2000 ) . The proliferative phase of the healing program rests on the ability of various epithelial stem cell pools , such as interfollicular epidermal ( IFE ) stem cells and hair follicle stem cells ( HfSC ) , to contribute progenitor cells to seal the breached epidermis rapidly ( Blanpain and Fuchs , 2009; Lau et al . , 2009 ) . As a model , we used the epidermal caspase-8 knockout , which has a uniform wound-healing phenotype throughout the skin ( Lee et al . , 2009 ) , to investigate the mechanisms by which IL-1 signaling mobilizes different epithelial stem cell pools to mount an effective wound-healing response . An understanding of the regulation of stem cell proliferation within the context of an organ will advance the goal of enhancing the regenerative process or tempering diseases that have features of a chronic wound-healing response ( i . e . diseases with a ‘wound signature’ ) ( Schäfer and Werner , 2008 ) .
As we previously observed , IL-1-dependent signaling is crucial for a normal wound-healing response as mice deficient in the interleukin-1 receptor type 1 ( IL1R KO ) displayed a 2-day delay in wound closure ( Lee et al . , 2009; Werner and Smola , 2001 ) . Interestingly , a closer analysis of the delay in wound-closure kinetics reveals a defect that manifests itself significantly at day 3 post-wounding , suggesting a role for IL-1 signaling in the proliferative phase of the wound-healing response ( Figure 1—figure supplement 1 ) . This observation is consistent with previous findings that IL-1 mediates a double paracrine signaling loop wherein keratinocyte–fibroblast crosstalk generates growth factors that stimulate epidermal stem cell proliferation ( Lee et al . , 2009; Werner and Smola , 2001 ) . Extending upon this concept , we observed that the interfollicular stem cell proliferation induced by a wound is significantly impaired in the absence of IL-1 signaling ( Figure 1A and Figure 1—figure supplement 2 ) . In light of these observations , we investigated whether other epithelial stem cell pools that are mobilized upon wounding were likewise dependent upon IL-1 signaling . In particular , we focused on the hair follicle stem cells ( HFSCs ) residing in their niche within the hair follicle known as the bulge . These bulge stem cells are responsible for the cyclical regeneration of the hair under homeostatic conditions , but are stimulated to proliferate and migrate into the epidermis following a cutaneous wound ( Ito et al . , 2005 ) . We found that CD34+ HFSCs were highly proliferative ( as noted by Ki67+/CD34+ cells ) in hair follicles proximal to the wound ( Figure 1B ) , but were relatively quiescent in follicles distal to the wound site ( Figure 1—figure supplement 3 ) . Interestingly , this increase in HFSC proliferation was also significantly dependent upon IL-1 signaling . As shown in Figure 1B , there was a substantial decrease in the number of CD34+ HFSCs that were expressing the proliferating antigen Ki67 3 days post-wounding . Upon tracking the status of HFSC proliferation over the course of the wound-healing program , we observed that there was a quantitative decrease in proliferating HFSCs as early as one day post-wounding ( Figure 1C and Figure 1—figure supplement 4 ) . This decrease in HFSC proliferation perdured throughout the proliferative phase of the wound-healing program . Given the contribution of epithelial stem cell proliferation to the repair of the epidermis , we examined whether the impairment of both IFE stem cell and HFSC proliferation in the IL-1R KO background would manifest as a hindrance to the expansion of the epidermis upon wounding . As shown in Figure 1D ( and Figure 1—figure supplement 5 ) , the epidermal region proximal to the wound is hyperplastic , but this response is diminished in the absence of IL-1 signaling . A major obstacle in dissecting the signaling pathways that regulate the wound response is the limited amount of cells that respond to the trauma . The heterogeneous response of cells up to 200 μm from the wound site makes it difficult to identify the extracellular cytokines and intracellular signaling pathways that orchestrate the wound-healing response in this physiological system . We addressed this constraint by employing a genetic wound-healing model based on the epidermal ablation of caspase-8 ( Casp8 cKO ) that converts the entire skin to a wound-like state ( Lee et al . , 2009; Li et al . , 2010 ) . Similar to the observations made with excisional wounds , the Casp8 cKO mouse model of wound healing displayed a thicker epidermis compared to the skin of WT mice ( Figure 1E ) . Interestingly , desensitization of the cells to IL-1 signaling ( via the removal of the IL-1 receptor ) significantly reduced this epidermal thickening . These results confirm our previous finding that one mechanism through which epidermal stem cell proliferation is achieved is through IL-1α-mediated induction of keratinocyte proliferation factors from dermal fibroblasts ( Lee et al . , 2009 ) . The thickened epidermal phenotype was partly due to enhanced proliferation of Keratin-5+ IFE cells , which is elevated in the Casp8 cKO skin and reduced in the absence of IL-1 signaling ( Figure 1F and G ) . In order to determine whether HFSCs were also contributing to the epidermal thickening in the Casp8 cKO , the proliferation of these cells was measured . The Casp8 cKO skin had more than a two-fold increase in proliferating HFSC when compared to WT skin ( Figure 1H ) . In the absence of IL-1 signaling , the number of proliferating HFSCs in the Casp8 cKO skin decreased by ~30% ( Figure 1H ) . These findings were further corroborated upon detecting HFSCs with an antibody recognizing Sox9 ( Adam et al . , 2015 ) . There was a substantial increase in Sox9 +HFSCs in the wounded skin and this increase was substantially reduced in the absence of the IL-1 receptor ( Figure 1—figure supplement 6 ) . Despite the significant contributions of IL-1 signaling to the development of the epidermal thickening in the Casp8 cKO line , removing IL-1 signaling was not sufficient to reduce the epidermal thickness to WT levels ( Figure 1E ) . This is probably due to the contribution of other factors , such as leukocyte-derived signals that are capable of promoting stem cell proliferation , in a wound microenvironment ( Eming et al . , 2007 ) . An important contributor to wound-closure kinetics during the proliferative phase of wound healing is cell migration . Moreover , it has previously been reported that IL-1α can stimulate epidermal keratinocyte migration ( Chen et al . , 1995 ) . We therefore explored whether IL-1α can similarly serve as a chemoattractant to stimulate HFSC emigration from the bulge to the wound site . To separate the effect of IL-1α on HFSC migration from proliferation unambiguously , we reconstituted the migration in a transwell assay ( Figure 1—figure supplement 7 ) . When treated with conditioned media ( CM ) from the Casp8 cKO epidermis , primary mouse HFSCs showed a >3 fold increase in chemotaxis relative to cells exposed to CM from wild-type epidermis . Interestingly , HFSCs lacking the IL-1R remained responsive to the chemotactic cue in the Casp8 cKO CM . This finding is consistent with the observation that recombinant IL-1 was incapable of inducing the transwell migration of HFSCs . Altogether , this provides evidence that IL-1α is neither necessary nor sufficient to induce HFSC migration . As IL-1 signaling is classically associated with the establishment of localized inflammation , we interrogated the inflammatory response in the wound lacking the IL-1R . Among the earliest inflammatory cells that respond to injury are macrophages and granulocytes , which are known to enhance the proliferation of epidermal stem cells ( Martin , 1997 ) . In the Casp8 cKO skin , the numbers of macrophages and granulocytes were increased and a marked reduction in the numbers of these cells was observed in the absence of IL-1 signaling ( Figure 2A ) . Similarly , other well-established contributors to the wound-healing program are epidermal resident γδT-cells , also known as dendritic epidermal T-cells ( DETCs ) . Following a stress signal such as wounding , γδT-cells become activated and promote epidermal stem cell proliferation by secreting keratinocyte growth factor ( KGF , also known as FGF7 ) ( Jameson and Havran , 2007 ) . Wound healing is impaired in γδT-cell deficient mice , which underscores the important role of these immune cells in the repair process ( Jameson et al . , 2002 ) . Furthermore , γδT-cells play an important role in the recruitment of other immune cells such as macrophages during wound healing ( Jameson et al . , 2005 ) . In the Casp8 cKO/Tcrd−/− ( γδT-cell KO ) mouse , the number of granulocytes and macrophages were decreased ( Figure 2A ) . This impairment of immune cell recruitment is similar to the effect observed upon removal of IL-1 signaling in the Casp8 cKO ( Figure 2A ) . On the basis of their shared effect on immune cell recruitment , we investigated whether there is an epistatic relationship between IL-1 signaling and γδT-cell activity . Interestingly , γδT-cell numbers are increased in the Casp8 cKO mice ( Lee et al . , 2009 ) , but in the absence of IL-1 signaling , there is a dramatic reduction of the numbers of these cells in the skin ( Figure 2B ) . Given the ability of γδT-cells to stimulate epidermal stem cell proliferation , we hypothesized that the removal of these cells would reduce the epidermal hyperplasia in the Casp8 cKO mouse as does the loss of IL-1 signaling . We found that the epidermal thickness in the Casp8 cKO/Tcrd−/− mouse was significantly reduced in comparison to that in the Casp8 cKO mouse ( Figure 2C ) . We then assessed the status of HFSCs in the Casp8 cKO background and its dependence on the activity of γδT-cells . Proliferating HFSCs were marked by a pulse of EdU , which is incorporated into the replicating DNA of dividing cells . Owing to antibody incompatibility , we visualized the arrector pili muscle ( APM ) with α-smooth muscle actin as a landmark for the HFSC niche ( the bulge ) , which resides at the terminus of the APM ( Poblet et al . , 2002 ) . In the wild-type skin , there is a minimal amount of EdU , whereas the Casp8 cKO skin exhibits robust EdU signal in the bulge region ( Figure 2D ) . However , this elevated proliferation is markedly reduced in the Casp8 cKO skin lacking functional γδT-cells . Interestingly , the impact of γδT-cell activity is more pronounced upon HFSC proliferation than in IFE cells in the Casp8 cKO skin ( Figure 2E ) . This same reliance on γδT-cells activity for optimized stem cell proliferation was also observed when the stimulus was an excisional wound ( Figure 2—figure supplements 1–3 ) . Likewise , the impairment of epithelial stem cell proliferation in the skin resulting from compromised γδT-cells drastically reduces the epidermal hyperplasia that accompanies the wound-healing program ( Figure 2—figure supplement 4 ) . We also tested the possibility that IL-1 signaling exerts its effect separately from γδT-cells . We thus examined whether the expression of IL-1α is affected in the absence of γδT-cells . As shown in Figure 2—figure supplement 5 , the amount of extracellular IL-1α was comparable in Casp8 cKO skin and Casp8 cKO/Tcrd−/− skin . This was verified by quantitation of IL-1 secretion in wounded samples from wild-type and Tcrd−/− skin , wherein the IL-1α levels were not significantly different ( Figure 2—figure supplements 6–7 ) . These data point to the fact that , despite normal levels of IL-1α in the wound , active γδT-cells are necessary to elicit the early stimulation of HFSC proliferation . Altogether , these observations suggest that IL-1 signaling converges upon γδT-cells to mediate epithelial stem cell proliferation in the skin . As IL-1 signaling elicits many of the same effects as activated γδT-cells , we then focused on interrogating whether there is a direct relationship between IL-1 signaling and γδT-cell function . The remarkable ability of γδT-cells to perform a variety of effector functions depends on their ability to recognize stress signals coming from keratinocytes ( Jameson and Havran , 2007 ) . Unlike αβ T-cells , which recognize protein fragments processed by antigen presenting cells , the effector function of γδT-cells depends on a combination of a yet unknown antigen recognized by the γδ-TCR and a variety of co-stimulatory molecules ( Witherden et al . , 2010 ) . In particular , keratinocytes that are stressed during wound healing increase the expression of the coxsakie and adenovirus receptor ( CAR ) , which acts as a co-stimulatory ligand that is recognized by the junctional adhesion molecule-like protein ( JAML ) on the surface of γδT-cells ( Witherden et al . , 2010 , Witherden and Havran , 2011 ) . Upon activation by a wound , γδT-cells undergo a variety of changes that are hallmarks of an activated cell , including the loss of dendritic extensions , increased proliferation and secretion of cytokines and growth factors that are essential for wound healing ( Jameson and Havran , 2007 ) . Among the functional consequences of γδT-cell activation is enhanced proliferation of epidermal stem cells ( Jameson et al . , 2002 ) and , as shown in Figure 2E , a stronger effect on HFSC proliferation . As epithelial stem cell proliferation was decreased in the caspase-8/IL1R dKO ( Figure 1H ) , we hypothesized that IL-1 signaling is required for optimal activation of γδT-cell , which , in turn , promotes the proliferation of cutaneous epithelial stem cells . Using the Casp8 cKO and caspase-8/IL1R dKO mice , we investigated whether a parameter of γδT-cell activation , namely proliferation , is dependent upon IL-1 signaling . We assayed γδT-cell proliferation and found that the number of proliferating γδT-cells was increased three-fold in the Casp8 cKO skin compared to WT controls , whereas a significant decrease was observed in the absence of IL-1 signaling ( Figure 3A ) . Similarly , in excisional wounds , elevated γδT-cell proliferation is dependent on IL-1 signaling ( Figure 3—figure supplements 1 and 2 ) . It is noteworthy , however , that γδT-cells in the caspase-8/IL1R dKO still displayed increased proliferation compared to wild-type cells , which may reflect the redundancy in this system . We also observed increased γδT-cell activation in the caspase-8/IL1R dKO mice , as observed by TNFα expression ( Figure 3B and C ) . The frequency of these activated γδT-cells was markedly reduced in the absence of IL-1 signaling . In the newborn thymus , IL-1 signaling has been shown to have a synergistic effect with IL-7 in enhancing the proliferation of γδT-cells ( Lynch and Shevach , 1992 ) . Epidermal keratinocytes constitutively express IL-7 and this expression is required for γδT-cell development ( Jameson and Havran , 2007 ) . In the Casp8 cKO , IL-7 expression is upregulated and this is not affected by the absence of IL-1 signaling ( Figure 3—figure supplement 3 ) . Similarly , IL-7 expression is not affected in Casp8 cKO skin or excisional wounds that lack γδT-cells ( Figure 3—figure supplements 4 and 5 ) . On the basis of this observation , we hypothesized that IL-1α released from the keratinocytes in the Casp8 cKO mouse can act synergistically with IL-7 to enhance γδT-cell proliferation . In order to validate the contributions of IL-1 and IL-7 signaling to γδT-cell proliferation , we established short-term γδT-cell in vitro cultures from WT mice ( Witherden et al . , 2010 ) . The use of this in vitro approach allowed us to reconstitute the different components required to stimulate γδT-cell proliferation . We collected conditioned media ( CM ) from WT and Casp8 cKO keratinocytes ( Lee et al . , 2009 ) , applied them to WT or IL1R–/– γδT-cells and monitored the proliferation rates of these cells . γδT-cells were cultured in activating conditions using anti-CD3 and anti-JAML as previously described ( Witherden et al . , 2010 ) , and then incubated for 2 days in the presence of CM , recombinant human IL-1α ( rhIL-1α ) , recombinant human IL-7 ( rhIL-7 ) , or an IL-7 inhibitory antibody . The number of γδT-cells increased 2 . 5-fold when they were treated with Casp8 cKO CM rather than WT CM ( Figure 3D ) . Conversely , removing IL-1 signaling by treating IL1R−/− γδ T-cells with Casp8 cKO CM resulted in significantly reduced proliferation . Similarly , blocking IL-7 signaling in the Casp8 cKO CM with an anti-IL-7 inhibitory antibody blocked γδT-cell proliferation . This effect was enhanced when both IL-1 and IL-7 signaling were perturbed in tandem . As epidermal keratinocytes constitutively secrete IL-7 , we predicted that adding recombinant human IL-1α ( rhIL1α ) to WT CM would be sufficient to cause an increase in γδT-cell proliferation . Consistent with this hypothesis , the addition of rhIL-1α to WT CM elicited an increase in γδT-cell proliferation comparable to the effect seen with Casp8 cKO CM . In fact , simply adding recombinant IL-1 and IL-7 to normal growth media is sufficient to reconstitute the γδT-cell proliferation observed in the Casp8 cKO CM ( Figure 3D ) . Altogether , these results support the model in which IL-1 and IL-7 signaling , in conjunction with γδT-cell activation , amplifies the proliferation of these cells in response to stresses that alter epidermal homeostasis . We have demonstrated that stem cells residing in distinct niches of the skin ( i . e . the interfollicular epidemis and the hair follicle bulge region ) are modulated by IL-1 signaling in a wound-like environment . The data presented to date and reported previously ( Lee et al . , 2009 ) suggest that the effect of IL-1 signaling on different stem cell pools is mediated through the dichotomous activation of dermal fibroblasts and γδT-cells . However , in the skin , where multiple cellular interactions are occurring simultaneously in an intricate manner , it is difficult to resolve the direct effects of IL-1 signaling on each subset of stem cells . As IL-1 signaling can stimulate different pools of epithelial stem cells in the skin , we sought to interrogate whether activated fibroblasts and γδT-cells can induce proliferation within distinct epithelial stem cell niches . Owing to a lack of definitive markers to distinguish IFE cells from HFSCs , assaying the proliferation of these cells within the skin is a challenge . On the other hand , in vitro cultures of IFE cells or HFSC are well established , and grafting experiments have shown that these cells maintain their progenitor properties even after several passages in vitro ( Blanpain et al . , 2004; Ghazizadeh and Taichman , 2005 ) . In order to delineate the contributions of keratinocytes–fibroblast interactions to epithelial stem cell proliferation , we activated dermal fibroblasts ( df ) with Casp8 cKO CM or rhIL-1α , as evidenced by their expression of FGF7 and GM-CSF ( Figure 4A ) . CM was collected from these activated df and applied to IFE cells or HFSC to test their effect on the proliferation of these two different epithelial stem cells . Treatment with CM from activated df caused a dramatic increase in the proliferation of IFE cells and a modest increase in HFSC proliferation ( Figure 4B ) . In the absence of IL-1 signaling , however , the proliferation of both IFE cells and HFSCs was reduced to that of control levels ( Figure 4B , Figure 4—figure supplement 1 ) . These observations suggest that IFE cells preferentially respond to signals from mesenchymal cells . The treatment of primary γδT-cell cultures with Casp8 cKO CM resulted in their activation , as demonstrated by their increased expression of FGF7 and TNFα ( Figure 4C ) . Moreover , abrogating IL-1 or IL-7 signaling was able to reduce FGF7 and TNFα expression , whereas rhIL-1α or rhIL-7 was sufficient to induce maximal FGF7 expression . Exposure of HFSCs to CM from activated γδT-cells caused a significant increase in the proliferation of the HfSC ( Figure 4D ) . Although activated γδT-cells can also stimulate IFE cell proliferation , the numbers of these cells were 40% smaller than those of HFSCs . Similarly , CM from rhIL-1α- or rhIL-7-treated γδT-cells was sufficient to recapitulate the increased proliferation of the HFSCs ( Figure 4D ) . As the CM from df and γδT-cells can enhance the proliferation of the different stem cell pools , we sought to investigate whether the effects on epithelial stem cell proliferation would be additive . Indeed , treatment of both IFE cells and HFSCs with a combination of CM from activated df and γδT-cells substantially increased their proliferation rates ( Figure 4E ) . One probable reason for the differential proliferation of the IFE cells and HFSCs is the difference in components present in the cytokine cocktail secreted from either df or γδT-cells . For instance , even though both activated df and γδT-cells can secrete the mitogenic factor FGF7 , only γδT-cells secrete IL1F8 or IL-36β ( Figure 4—figure supplements 2 and 3 ) , which were recently shown to cause proliferation of stem cells ( Yang et al . , 2010 ) . IL1F8 signals through the IL-36 receptor and it is possible that HFSCs are more susceptible to the mitogenic effects of this cytokine . On the basis of these observations , we conclude that IL-1-mediated activation of γδT-cells preferentially promotes HFSC proliferation .
One of the goals of regenerative medicine is to restore the functional state of the tissue swiftly following trauma and/or disease . A major obstacle to manipulating this process in mammalian tissues is the large number of cells that carry out a synchronized effort in the regenerative/repair process . Therefore , understanding the coordinated interactions between the multiple cell types that orchestrate a successful repair program is a high priority . The efficient repair of damaged tissue depends upon the interactions between the inflammatory , proliferative and remodeling phases of the wound-healing response ( Gurtner et al . , 2008 ) . In this study , we employed a genetic mouse model of wound healing , namely the conditional knockout of epidermal caspase-8 . Our previous work has demonstrated that the downregulation of epidermal caspase-8 is a normal phenomenon upon wounding of the mouse skin ( Lee et al . , 2009 ) . Moreover , we demonstrated that the conditional ablation of epidermal caspase-8 was able to recapitulate many of the cardinal features of a cutaneous wound-healing response . This genetic mouse model is particularly useful in efforts to unravel the signaling network of the wound-healing program , given that the entire skin essentially behaves as a wound . By contrast , cellular responses to an excisional wound to the skin are limited to the cells immediately proximal to the trauma . As a consequence , there is poor signal to noise ratio given the low number of cells that are responding to the wound surrounded by a large number of cells that are in their normal homeostatic mode . This is particularly relevant to studies of hair follicle stem cell activation during wound healing . Only a handful of hair follicles exhibit activated stem cells upon trauma to the skin , whereas the caspase-8 conditional knockout animals exhibit stem cell activation in the vast majority of the hair follicles within the knockout skin . The focus of this study was to understand how one of these signaling nodes that is an important mediator of inflammation ( IL-1 signaling ) manages to enhance the proliferation of different stem-cell pools within the skin . IL1α is released immediately upon wounding , downstream of reduction of caspase-8 expression ( Lee et al . , 2009 ) , and mediates the activation of dermal fibroblasts which then stimulate epidermal stem cells ( Figure 5 ) . In the present study , we demonstrate that IL-1 signaling can partner with IL-7 ( which is constitutively secreted from epidermal keratinocytes ) to expand the population of active γδT-cells . These activated γδT-cells can then stimulate the proliferation of stem cells within the bulge of the hair follicle and can mobilize them for epidermal wound repair . As the wound-healing response progresses , given the redundancy of the system , it is likely that other infiltrating cells can contribute to ( or take over the duties of ) stimulating epithelial stem cell proliferation . One illustrative case of this redundancy is in the activation of HFSCs by multiple routes . We hypothesize that the IL-1 +IL-7/γδTcell/HFSC proliferation module described herein is the rapid mechanism that stimulates HFSC proliferation using cells already residing in the tissue . Interestingly , wounded keratinocytes can also produce CCL2 to attract macrophages to a wound ( Osaka et al . , 2007 ) . These activated macrophages in turn secrete IL-1β ( a more potent relative of IL-1α ) to induce hair regrowth , and it is tempting to postulate that this mechanism maintains and potentiates the effects of IL-1 +IL-7/γδTcells on HFSC proliferation . IL-1 signaling has been studied in depth and found to play critical roles in a variety of cellular processes from immune responses to the proliferation of epidermal stem cells ( Dinarello , 2009 ) . Despite the pleiotropic effects of IL-1 signaling in various cell types and its important function during wound healing , blocking IL-1Receptor-1-dependent signaling does not completely impair the repair process . This can be attributed in part to compensatory mechanisms involving other IL-1 family members . IL-1 family members such as IL-1F6 , IL-1F8 and IL-1F9 are largely found in epithelial tissues . Moreover , transgenic mice overexpressing IL-1F6 in the basal layer of the epidermis develop severe cutaneous inflammation , hyperkeratosis and acanthosis ( Blumberg et al . , 2007 ) . Recently , work by Yang et al . showed that activated γδT-cells released IL-1F8 , which had the ability to promote the proliferation of stem cells ( Blumberg et al . , 2007; Yang et al . , 2010 ) . As IL-1F6 and IL-1F8 signal through the IL-36 receptor and are able to induce the production of mitogenic factors , it is likely that abrogating IL-1R1 signaling alone may not be sufficient to block the epidermal hyperproliferation observed in the Casp8 cKO skin ( Towne et al . , 2004 ) . Moreover , in a system as complex as the wound-healing program , removal of an individual component such as IL-1 signaling ( Kovalenko et al . , 2009 ) will impede the kinetics of the process rather than completely inhibit it ( Figure 1—figure supplement 1 ) . It is worth noting that γδT-cells have been found to stimulate hair neogenesis in wounds via Fgf9 ( Gay et al . , 2013 ) . Interestingly , it is the dermal γδT-cells that secrete Fgf9 whereas the epidermal pool of γδT-cells ( DETCs ) secrete IL-1F8 , which does not possess hair-neogenesis-promoting properties ( Yang et al . , 2010 ) . Consequently , it appears that there is heterogeneity among cutaneous γδT-cells that can secrete unique repertoires of cytokines with differential effects on the hair follicle . Consistent with our observations in the skin , IL-1 and IL-7 can also work together to increase γδT-cell proliferation in the newborn thymus , where these cells compete with γδT-cells for survival ( Lynch and Shevach , 1992 ) . In the case of injury , this synergistic interaction is most probably a response to seal the wound quickly . Our findings suggest that in the presence of a wound-like state , the potentiation of γδT-cellproliferation may be a means to increase the pools of cutaneous epithelial stem cells . Our results are consistent with those of Keyes et al . , 2016 , which showed that optimal activation of γδT-cells is required for proliferation in hair follicle progenitors and efficient sealing of wounds ( Keyes et al . , 2016 ) . Our results provide a mechanistic basis for their observation and it is interesting to note that the age-related decline that they found in wounding correlates with the documented changes in the IL-1 family of cytokines that occur with aging ( Ye et al . , 2002 ) . Owing to the fact that HFSCs are highly activated in the Casp8 cKO skin , it is tempting to speculate that the hair regeneration cycle would be faster in this genetic background relative to the wild-type controls . Unfortunately , the truncated lifespan of the Casp8 cKO mouse ( up to ~20 days ) and the chronic inflammatory phenotype prevent an investigation of this possibility . Given the inherent differences between IFE cells and HFSCs , it is perhaps not too surprising to find that they respond differently to signals from activated dermal fibroblasts and γδT-cells , respectively . Furthermore , the location of the proliferating inducing cell may impact which stem-cell pool it affects . For instance , in the backskin of WT mice , γδT-cells are found in close association with the HFSC niche ( Lee et al . , 2009 ) . On the other hand , dermal fibroblasts are in close apposition to the IFE cells in the basal layer of the epidermis , which would be subjected to the highest concentration of pro-proliferation factors secreted by the activated fibroblasts . There is an emerging paradigm that signals classically associated with immune cells are involved in regulating stem-cell homeostasis ( Chen et al . , 2015 ) . The results of our experiments shed new insight into the complex nature of the repair process , which is a recurring theme in some pathological conditions such as cancer . We showed that the increased proliferation observed in the Casp8 cKO can be attained through reciprocal keratinocyte–fibroblast or keratinocyte–γδT-cell interactions . The keratinocyte–fibroblast-mediated proliferation is akin to the interaction between the tumor cell and stromal cells ( McAllister and Weinberg , 2010 ) . With regards to the keratinocytes–γδT-cell interaction , it has recently been shown that the activation of γδT-cells promotes tumorigenesis ( Arwert et al . , 2010 ) . Thus an understanding of the molecular mechanisms underlying the proliferation of stem cells in a wound microenvironment holds the promise of shedding light on the complex signaling pathways that mediate tumor initiation .
Epidermis-specific knockouts were obtained by crossing mice carrying the floxed caspase-8 allele ( Casp8 fl/fl ) ( Beisner et al . , 2005 ) to Krt14-Cre mice ( Vasioukhin et al . , 1999 ) . IL1R–/– mice were purchased from Jackson Laboratory . γδTCR–/– mice have been described previously ( Jameson et al . , 2002 ) . All animals were on a C57BL/6 background . Animal work carried out at UCSD was approved and adhered to the guidelines of the Institutional Animal Care and Use Committee . Animal work conducted in the NCBS/inStem Animal Care and Resource Center was approved by the inStem Institutional Animal Ethics Committee following norms specified by the Committee for the Purpose of Control and Supervision of Experiments on Animals , Government of India . Mouse skin samples were processed and immunohistochemistry was performed as described previously ( Du et al . , 2010; Lee et al . , 2009 ) . Ki67 quantification was performed as described previously ( Arwert et al . , 2010 ) . At least 12 frames per sample from four mice per genotype were used in counts . Epidermis and dermis from WT ( n = 6 ) , caspase-8 cKO ( n = 9 ) , caspase-8/IL1R dKO ( n = 7 ) and caspase-8/γδT dKO ( n = 5 ) P4 mice were separated with dispase treatment for 1 hr at 37°C and RNA was isolated using Trizol reagent ( Invitrogen ) according to manufacturer’s instructions . cDNA was synthesized by reverse transcription using the iScript kit ( Biorad ) , and real-time quantitative PCR analysis was performed using the Ssofast EvaGreen mix in a Biorad CFX96 system with the primers listed in the supplemental section . Experiments were carried out in triplicate with cDNA isolated from five different animals . Data are presented as the fold difference ± SEM . 8-week-old male IL1R–/– and WT control ( C57Bl/6 ) mice were anesthesized by intraperitoneal injections with pentobarbital at 50 mg/kg . 5 mm punch biopsies were used to make full-thickness excisional wounds . Each day , images of the wounds were taken and analyzed using ImageJ software to measure wound area . Postnatal day 6 ( P6 ) mice were injected intraperitoneally with 50 µg/body weight EdU ( 5-ethynyl-2´-deoxyuridine , Invitrogen ) dissolved in sterile PBS . Skins were collected 4 hr afterwards , embedded and frozen in OCT . 10 µm thick sections were fixed in 4% paraformaldehyde for 10 min , blocked in donkey serum for 1 hr and stained overnight with primary antibodies , followed by 30 min incubation with secondary antibodies . Proliferating HFSCs are recognized by co-staining CD34 and Ki67 . However , CD34 is not expressed in skin at P6 . Therefore , the location of HFSCs in the bulge region in P6 skin is marked by smooth muscle actin ( SMA ) -positive arrector pilli muscle , which joins the hair follicle at the bulge region where HFSCs reside ( Poblet et al . , 2002 ) . γδT-cells were detected with anti-γδTCR ( GL3 , BD Biosciences ) . Short-term cultures of WT and IL1R–/– γδT-cells were established and 30 , 000 cells were plated in 96-well plates coated with 0 . 1 μg/ml anti-CD3 and 10 μg/ml anti-JAML ( eBioscience , clone eBio4E10 ) as described previously ( Witherden et al . , 2010 ) . Cells were treated with conditioned media from WT and caspase 8 cKO keratinocytes collected in RPMI medium . γδT-cells were treated with KO CM or KO CM with 4 μg/ml anti-IL-7 inhibitory antibody ( R&D Systems ) or IL1R–/– γδT-cells were treated with KO CM . γδT-cells were incubated with WT CM , WT CM treated with 300 pg/ml rhIL-1α , or media supplemented with rhIL-1α and 2 ng/ml rhIL-7 . Cell numbers were assessed by cell counting using trypan blue exclusion . Unipotent interfollicular epidermal ( IFE ) stem cells were isolated as described previously Lee et al . , 2009 . Hair follicle stem cells were generated by DrsEve Kandyba and Krzysztof Kobielak at USC and prepared as described previously ( Blanpain et al . , 2004 ) . Dermal fibroblasts were isolated from 6-week-old C57bl/6 WT and IL1R–/– mice . The hairs were removed by shaving and treatment with a hair removal agent ( Nair , Church and Dwight Co . , Princeton , NJ , USA ) . Epidermis and dermis were separated after overnight incubation with trypsin solution at 4°C . Dermal portions were then incubated in collagenase IV for 1 hr at 37°C with shaking , and collagenase was neutralized with 2 mM EDTA . Cell suspensions were filtered through a 70 µm cell strainer and pelleted by centrifugation at 400 x g for 10 min at 25°C . The cells were then plated in DMEM with 10% FBS , 1% penicillin/streptomycin and 2 mM L-glutamine . WT and IL1R–/– dermal fibroblasts were treated with WT and caspase 8 KO CM in the presence of rhIL-7 , rhIL-1α or anti-IL-7 inhibitory antibody for 24 hr . 5000 IFE and HF stem cells were plated in 48-well plates and incubated with CM from treated dermal fibroblasts for 4 days . Cell counts were assessed by trypan blue exclusion . P4 WT , caspase 8 cKO and caspase 8/IL1R dKO mice were injected with 0 . 2 mg of Brefeldin A ( BFA , Sigma-Aldrich , St . Louis , MO , USA ) subcutaneously and samples collected after 5 hr . Skins were frozen with OCT compound ( Tissue-Tek ) and 10 μm sections were made . Tissues were fixed for 10 min in 4% PFA , blocked ( in 2 . 5% goat serum , 2 . 5% donkey serum , 1% BSA , and 0 . 3% triton-X in PBS ) and incubated for 1 hr at 25°C with antibodies against γδTCR-FITC ( GL3 , BD Biosciences ) and TNFα-PE ( eBiosciences , San Diego , CA ) . Images were obtained as described previously ( Du et al . , 2010 ) . TCRVγ3-F: GCAGCTGGAGCAAACTGAAT TCRVγ3-R: GTTTTTGCCGGTACCAATGT FGF7-F: GTGAGAAGACTGTTCTGTCGC FGF7-R: CCACGGTCCTGATTTCCATGA FGF10-F: GTGTCCTGGAGATAACATCAGTG FGF10-R: AGCCATAGAGTTTCCCCTTCTT TNF⟨-F: CTGTGAAGGGAATGGGTGTT TNF⟨-R: GGTCACTGTCCCAGCATCTT IL1F6-F: CACGTACATGGGAGTGCAAA IL1F6-R: GCAGCTCCCTTTAGAGCAGA IL1F8-F: GGTATGGGTCCTGACTGGAA IL1F8-R: CCTCCATCTCAACACAGCAG IL7-F: TGGAATTCCTCCACTGATCC IL7-R: TGGTTCATTATTCGGGCAAT 8-week-old WT ( C57BL6 ) and IL1R−/− mice were used for wounding and conditioning of epidermal stem cell media lacking serum . 5 mm punch biopsies was used to make full-thickness excisional wounds . Wound samples were collected from an 8-mm biopsy surrounding the wound . 200 μl of epidermal stem cell media lacking serum was added to the wound samples and incubated at 37°C for 1 hr . The medium was replaced with another 200 μl of epidermal stem cell media lacking serum and incubated at 37°C for 16 hr , and this was used as conditioned media . Conditioned media was collected and stored at −80°C after snap-freezing in liquid N2 . IL1α ELISA was performed using the mouse IL1-alpha ELISA kit ( eBiosciences , San Diego , CA , USA ) according to the manufacturer’s protocol . Conditioned media from wounds were diluted 1:5 and 1:15 to detect IL1α secretion . Absorbance was detected using the Infinite 200 Pro ( Tecan , Männedorf , Switzerland ) microplate reader . Total IL1α secretion was estimated by comparing the absorbance values of samples to the standard curve . All in vivo experiments were done on three mice per genotype and samples in in vitro assays were run in triplicate . Results were generated by average ± SEM from three independent experiments . For comparison of means between two groups , Student t test was performed . For multigroup comparisons , ANOVA test with a Bonferroni’s multiple comparison correction was used . All p-values < 0 . 05 were considered significant . Further details regarding reagents are provided in the Research Resource Identifiers document . | The skin is a physical barrier that protects the body from the outside world . If the skin is injured , the body mounts a “wound healing” response to rapidly mend and restore this protective barrier . Wound healing is a complex process and relies on the different types of cells in the skin communicating with each other . Stem cells provide tissues , like the skin , with new cells . Normally , stem cells are in a resting or inactive state . Yet , during wound healing , stem cells near the injured area are awakened and start producing more cells to repair the wound . Understanding how stem cells become activated in a wound has proved challenging because only a small number of cells near a damaged site will respond , and it is difficult to distinguish their response from that of other cells slightly further away . Now , Lee et al . overcome this hurdle by analyzing a genetically engineered mouse in which the entire skin displays a wound healing response , even without any injury or trauma . In these mice , most of the stem cells in the skin are awakened from their normal resting state and behave as if there is a wound to heal . It turns out that a protein called interleukin-1 , which is released from damaged skin cells known as keratinocytes , can activate two different groups of stem cells in the skin to help repair the injured tissue . One group lives in the hair follicle and is normally responsible for replacing the hair that falls from the body . Lee et al . found that when the skin is wounded interleukin-1 activates certain immune cells ( called γδT-cells ) . These immune cells then awaken the resting stem cells in the hair follicle to multiply and travel to the wound site to repair the injury . The other group of stem cells resides in the outermost layer of the skin . Interleukin-1 can also activate so-called fibroblast cells , which then stimulate this second group of stem cells to divide and cover the open wound . Quickly healing wounds has many health benefits such as preventing infection and shortening the time to recover from an injury . These new findings may help to repair injured skin in diseases such as diabetes , where wounds can take months to heal and often leads to permanent tissue damage . The next challenge is to identify the cues that instruct the stem cells to travel to the wound site and turn into the specific cells that are required to replace the damaged cells . | [
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] | 2017 | Stimulation of hair follicle stem cell proliferation through an IL-1 dependent activation of γδT-cells |
Many environmental stimuli present a quasi-rhythmic structure at different timescales that the brain needs to decompose and integrate . Cortical oscillations have been proposed as instruments of sensory de-multiplexing , i . e . , the parallel processing of different frequency streams in sensory signals . Yet their causal role in such a process has never been demonstrated . Here , we used a neural microcircuit model to address whether coupled theta–gamma oscillations , as observed in human auditory cortex , could underpin the multiscale sensory analysis of speech . We show that , in continuous speech , theta oscillations can flexibly track the syllabic rhythm and temporally organize the phoneme-level response of gamma neurons into a code that enables syllable identification . The tracking of slow speech fluctuations by theta oscillations , and its coupling to gamma-spiking activity both appeared as critical features for accurate speech encoding . These results demonstrate that cortical oscillations can be a key instrument of speech de-multiplexing , parsing , and encoding .
The physical complexity of biological and environmental signals poses a fundamental problem to the sensory systems . Sensory signals are often made of different rhythmic streams organized at multiple timescales , which require to be processed in parallel and recombined to achieve unified perception . Speech constitutes an example of such a physical complexity , in which different rhythms index linguistic representations of different granularities , from phoneme to syllables and words ( Rosen , 1992; Zion Golumbic et al . , 2012 ) . Before meaning can be extracted from continuous speech , two critical pre-processing steps need to be carried out: a de-multiplexing step , i . e . , the parallel analysis of each constitutive rhythm , and a parsing step , i . e . , the discretization of the acoustic signal into linguistically relevant chunks that can be individually processed ( Stevens , 2002; Poeppel , 2003; Ghitza , 2011 ) . While parsing is presumably modulated in a top-down way , by knowing a priori through developmental learning ( Ngon et al . , 2013 ) where linguistic boundaries should lie , it is likely largely guided by speech acoustic dynamics . It has recently been proposed that speech de-multiplexing and parsing could both be handled in a bottom-up way by the combined action of auditory cortical oscillations in distinct frequency ranges , enabling parallel computations at syllabic and phonemic timescales ( Ghitza , 2011; Giraud and Poeppel , 2012 ) . Intrinsic coupling across cortical oscillations of distinct frequencies , as observed in electrophysiological recordings of auditory cortex ( Lakatos et al . , 2005; Fontolan et al . , 2014 ) , could enable the hierarchical combination of syllabic- and phonemic-scale computations , subsequently restoring the natural arrangement of phonemes within syllables ( Giraud and Poeppel , 2012 ) . The most pronounced energy fluctuations in speech occur at about 4 Hz ( Zion Golumbic et al . , 2012 ) and can serve as an acoustic guide for signalling the syllabic rhythm ( Mermelstein , 1975 ) . Since the syllabic rate coincides with the auditory cortex theta rhythm ( 3–8 Hz ) , syllable boundaries could be viably signalled by a given phase in the theta cycle . The relevance of speech tracking by the theta neural rhythm ( Henry et al . , 2014 ) is highlighted by experimental data showing that speech intelligibility depends on the degree of phase-locking of the theta-range neural activity in auditory cortex ( Ahissar et al . , 2001; Luo and Poeppel , 2007; Peelle et al . , 2013; Gross et al . , 2013 ) . By analogy with the spatial and mnemonic oscillatory processes that take place in the hippocampus ( Jensen and Lisman , 1996; Lisman and Jensen , 2013; Lever et al . , 2014 ) , the theta oscillation may orchestrate gamma neural activity to facilitate its subsequent decoding ( Canolty et al . , 2007 ) : the phase of theta-paced neural activity could regulate faster neural activity in the low-gamma range ( >30 Hz ) involved in linguistic coding of phonemic details ( Ghitza , 2011; Giraud and Poeppel , 2012 ) . The control of gamma by theta oscillations could hence both modulate the excitability of gamma neurons to devote more processing power to the informative parts of syllabic sound patterns , and constitute a reference time frame aligned on syllabic contours for interpreting gamma-based phonemic processing ( Shamir et al . , 2009; Ghitza , 2011; Kayser et al . , 2012; Panzeri et al . , 2014 ) . Compelling as this hypothesis may sound , direct evidence for neural mechanisms linking speech constituents and oscillatory components is still lacking . One way to address a causal role of oscillations in speech processing is computational modelling , as it permits to directly test the efficiency of cross-coupled theta and gamma oscillations as an instrument of speech de-multiplexing , parsing , and encoding . Previous models of speech processing involved only gamma oscillations in the context of isolated speech segments ( Shamir et al . , 2009 ) or did not involve neural oscillations at all ( Gütig and Sompolinsky , 2009; Yildiz et al . , 2013 ) . On the other hand , previous models of cross-frequency coupled oscillations did not address sensory functions as parsing and de-multiplexing ( Jensen and Lisman , 1996; Tort et al . , 2007 ) . Here , we examined how a biophysically inspired model of coupled theta and gamma neural oscillations can process continuous speech ( spoken sentences ) . Specifically , we determined: ( i ) whether theta oscillations are able to accurately parse speech into syllables , ( ii ) whether syllable-related theta signal may serve as a reference time frame to improve gamma-based decoding of continuous speech; ( iii ) whether this decoding requires theta to modulate the activity of the gamma network . To address the last two points , we compared speech decoding performance of the model with two control versions of the network , in which we removed the neural connection entraining the theta neurons by speech fluctuations or the link that couples them to the gamma neurons .
The model proposed here ( Figure 1A ) is inspired from cortical architecture ( Douglas and Martin , 2004; da Costa and Martin , 2010 ) and function ( Lakatos et al . , 2007 ) as well as from previous biophysical models of cross-frequency coupled oscillation generation ( Tort et al . , 2007; Kopell et al . , 2010; Vierling-Claassen et al . , 2010 ) . We used the well documented Pyramidal Interneuron Gamma ( PING ) model for implementing a gamma network: bursts of inhibitory neurons immediately follow bursts of excitatory neurons ( Jadi and Sejnowski , 2014 ) , creating the overall spiking rhythm . Given that gamma and theta oscillations are both locally present in superficial cortical layers ( Lakatos et al . , 2005 ) , we assume similar local generation mechanisms for theta and gamma with a direct connection between them . Direct evidence for a local generation of theta oscillations in auditory cortex is still scarce ( Ainsworth et al . , 2011 ) and we cannot completely rule out that they might spread from remote generators ( e . g . , in the hippocampus; Tort et al . , 2007; Kopell et al . , 2010 ) . Yet , we built the case for local generation from the following facts: ( 1 ) neocortical ( somatosensory ) theta oscillations are observed in vitro ( Fanselow et al . , 2008 ) , ( 2 ) MEG , EEG , and combined EEG/FMRI recordings in humans show that theta activity phase-locks to speech amplitude envelope in A1 and immediate association cortex—but not beyond— ( Ahissar et al . , 2001; Luo and Poeppel , 2007; Cogan and Poeppel , 2011; Morillon et al . , 2012 ) , and ( 3 ) theta phase-locking to speech is not accompanied by power increase , arguing for a phase restructuring of a local oscillation ( Luo and Poeppel , 2007 ) . We assumed a similar generation mechanism for theta and gamma oscillations , with slower excitatory and inhibitory synaptic time constants for theta ( Kopell et al . , 2010; Vierling-Claassen et al . , 2010 ) . The distinct dynamics for the two modules reflect the diversity of inhibitory synaptic timescales observed experimentally , with Martinotti cells displaying slow synaptic inhibition ( Ti neurons ) , and basket cells showing faster inhibition decay ( Gi neurons ) ( Silberberg and Markram , 2007 ) . We refer to the theta network as Pyramidal Interneuron Theta ( PINTH ) , by analogy with PING . The full model is hence composed of a theta-generating module with interconnected spiking excitatory ( Te ) and inhibitory ( Ti ) neurons that spontaneously synchronize at theta frequency ( 6–8 Hz ) through slow decaying inhibition; and of a gamma-generating module with excitatory ( Ge ) and inhibitory ( Gi ) neurons that burst at a faster rate ( 25–45 Hz ) synchronized by fast decaying inhibition ( PING; Figure 1B ) ( Börgers and Kopell , 2005 ) . The firing pattern of our simulated neurons is sparse and weakly synchronous at rest , consistent with the low spiking rate of cortical neurons ( Brunel and Wang , 2003 ) ( Figure 1—figure supplement 1D ) . Unlike the classical 50–80 Hz PING seen in in vitro preparations of rat auditory cortex ( Ainsworth et al . , 2011 ) , our network produced a lower gamma frequency around 30 Hz , as observed in human auditory cortex in response to speech ( Nourski et al . , 2009; Pasley et al . , 2012 ) . 10 . 7554/eLife . 06213 . 003Figure 1 . Network architecture and dynamics . ( A ) Architecture of the full model . Te excitatory neurons ( n = 10 ) and Ti inhibitory neurons ( n = 10 ) form the PINTH loop generating theta oscillations . Ge excitatory neurons ( n = 32 ) and Gi inhibitory neurons ( n = 32 ) form the PING loop generating gamma oscillations . Te neurons receive non-specific projections from all auditory channels , while Ge units receive specific projection from a single auditory channel , preserving tonotopy in the Ge population . PING and PINTH loops are coupled through all-to-all projections from Te to Ge units . ( B ) Network activity at rest and during speech perception . Raster plot of spikes from representative Ti ( dark green ) , Te ( light green ) , Gi ( dark blue ) , and Ge ( light blue ) . Simulated LFP is shown on top and the auditory spectrogram of the input sentence "Ralph prepared red snapper with fresh lemon sauce for dinner" is shown below . Ge spikes relative to theta burst ( red boxes ) form the output of the network . Gamma synchrony is visible in Gi spikes . ( C ) Evoked potential ( ERP ) and Post-stimulus time histograms ( PSTH ) of Te and Ge population from 50 simulations of the same sentence: ERP ( i . e . , simulated LFP averaged over simulations , black line ) , acoustic envelope of the sentence ( red line , filtered at 20 Hz ) , PSTH for theta ( green line ) and gamma ( blue line ) neurons . Vertical bars show scale of 10 spikes for both PSTH . The theta network phase-locks to speech slow fluctuations and entrains the gamma network through the theta–gamma connection . ( D ) Theta/gamma phase-amplitude coupling in Ge spiking activity . Top panel: LFP gamma envelope follows LFP theta phase in single trials . Bottom-Left panel: LFP phase-amplitude coupling ( measured by Modulation Index ) for pairs of frequencies during rest , showing peak in theta–gamma pairs . Bottom-right panel: MI phase-amplitude coupling at the spiking level for the intact model and a control model with no theta–gamma connection ( red arrow on A panel ) , during rest ( blue bars ) and speech presentation ( brown bars ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06213 . 00310 . 7554/eLife . 06213 . 004Figure 1—figure supplement 1 . Spectral analysis . ( A ) Theta phase pattern ( left panels ) and theta power pattern ( right panels ) for 50 presentations of the same sentence in the uncoupled theta–gamma control model ( top panels ) and intact panels ( bottom panels ) . Phase/power is binned into 4 different bins and colour coded . Theta phase is much more reliably imprinted by speech stimulus than power . ( B ) ( Left panel ) Spike phase-amplitude coupling: mean value for PING amplitude ( defined as the number of Gi neurons spiking within a gamma burst ) as a function of PINTH phase ( defined from interpolation between successive theta bursts ) . Intact model is shown in black while the uncoupled theta–gamma model is shown in blue . Data for rest ( thick dashed lines ) and during processing of speech ( full thick lines ) almost perfectly match . Thin dashed lines represent s . e . m . Spike PAC was very strong in the full model but quasi-absent when the theta–gamma connection was removed . ( Right panel ) Spontaneous spike phase-frequency coupling: mean value for PING frequency ( defined from the duration between successive gamma bursts ) as a function of PINTH phase . Same legend as left panel . Spike PFC is strong when and only when the theta–gamma connection is present ( significant coupling p < 10−9 for both speech and rest ) . ( C ) Phase-locking of the theta and gamma oscillations to speech . Phase concentration of the filtered LFP theta ( top panel ) and gamma ( bottom panel ) signals through time for 200 presentations of the same sentence ( same as Figure 1B , C ) . The horizontal orange bar indicates the presentation of the sentence . There is a rapid transition from uniform theta distribution before sentence onset to perfectly phase-locked theta . Phase-locking vanishes at the end of sentence presentation . ( D ) Spike pattern Coefficient of Variation ( left ) and spike count Fano factors ( right ) during speech presentation . Both measures were computed from the response of the network to 100 presentations of the same one-second speech segment . Bars and error bars represent mean and standard deviation over distinct neural populations . ( E ) LFP average ( ERP ) and standard deviation computed from the 100 repeats of presentation of the same sentence to the network . Note that the LFP variability is greatly reduced at speech onset , mainly due to phase-locking of theta and gamma oscillations . DOI: http://dx . doi . org/10 . 7554/eLife . 06213 . 004 At rest the PINTH population activity synchronizes at the theta timescale , and the PING population at the gamma time scale . Both the Te and Ge populations receive projections from a ‘subcortical’ module that mimics the nonlinear filtering of acoustic input by subcortical structures , which primarily includes a signal decomposition into 32 auditory channels ( Chi et al . , 2005 ) . Individual excitatory neurons in the theta module received channel-averaged input while those in the gamma module received frequency selective input . Such a differential selectivity was motivated by experimental observations from intracranial recordings ( Morillon et al . , 2012; Fontolan et al . , 2014 ) suggesting that unlike the gamma one , the theta response does not depend on the input spectrum . It also mirrors the dissociation in primate auditory cortex between a population of 'stereotyped' neurons responding very rapidly and non-selectively to any acoustic stimulus ( putatively Te neurons ) and a population of 'modulated' neurons responding selectively to specific spectro-temporal features ( putatively Ge neurons ) ( Brasselet et al . , 2012 ) . Each Ge neuron receives input from one specific channel , preserving the auditory tonotopy , so that the whole Ge population represents the rich spectral structure of the stimulus . Each Te neuron receives input from all the channels , i . e . , the Te population conveys a widely tuned temporal signal capturing slow stimulus fluctuations . Importantly , the two oscillating modules are connected through all-to-all connections from Te neurons to Ge neurons allowing the theta oscillations to control the activity of the faster gamma oscillations . This structure enables syllable boundary detection ( through the theta module ) to constrain the decoding of faster phonemic information . The output of the network is taken from the Ge neurons as we assume that the Ge neurons provide the input to higher-level cortical structures performing operations like phoneme categorization and providing access to lexicon . Accordingly , in the model the Ge neurons receive more spectral details about speech than the Te neurons ( Figure 1B ) . Ge spiking is then referenced with respect to timing of theta spikes , and submitted to decoding algorithms . We first explored the dynamic behaviour of the model . As expected from its architecture and biophysical parameters ( see ‘Materials and methods’ ) , the neural network produced activity in theta ( 6–8 Hz ) and low gamma ( 25–45 Hz ) ranges , both at rest and during speech presentation . Consistent with experimental observations ( Luo and Poeppel , 2007 ) there was no notable increase in theta spiking during speech presentation , but sentence onsets induced a phase-locking of theta oscillations as shown by the Post-stimulus time histograms of theta neurons , which was further enhanced by all edges in speech envelope . Consequently , the resulting global evoked activity followed the acoustic envelope of the speech signal ( Figure 1C ) ( Abrams et al . , 2008 ) . Local Field Potential ( LFP ) indexes the global synaptic activity over the network ( excitatory neurons of both networks ) and its dynamics closely followed spiking dynamics . Unlike the LFP theta power pattern , the LFP theta phase pattern was robust across repetitions of the same sentence ( Figure 1—figure supplement 1A , C , E ) , replicating LFP behaviour from the primate auditory cortex ( Kayser et al . , 2009 ) , and human MEG data ( Luo and Poeppel , 2007; Luo et al . , 2010 ) . In line with other empirical data from human auditory cortex ( Nourski et al . , 2009 ) gamma oscillations followed the onset of sentences ( Figure 1C ) . Owing to the feed-forward connection from the theta to the gamma sub-circuits , the gamma amplitude was coupled to the theta phase both at rest and during speech ( Figure 1D ) . The coupling was visible both in the spiking ( Figure 1—figure supplement 1B ) and LFP signal ( Figure 1D ) . Critically , this coupling disappeared when the theta/gamma connection was removed , showing that a common input to Te and Ge cells is not sufficient to couple the two oscillations . Before testing the speech decoding properties of the model , we explored whether syllable boundaries could reliably be detected at the cortical level by a theta network ( see Methods ) . This first study was based on a corpus consisting of 4620 phonetically labelled English sentences ( TIMIT Linguistic Data Consortium , 1993 ) . The acoustic analysis of these sentences confirmed a correspondence between the dominant peak of the speech modulation spectrum and the mean syllabic rate ( 3–6 Hz ) ( Figure 2—figure supplement 1A ) , whereby syllabic boundaries correspond to trough in speech slow fluctuations ( Peelle et al . , 2013 ) . The theta network in the model ( Figure 2—figure supplement 1B ) was explicitly designed to exploit such regularities and infer syllable boundaries . When presenting sentences to the theta module , we observed a consistent theta burst within 50 ms following syllable onset followed by a locking of theta oscillations to theta acoustic fluctuations in the speech signal ( Figure 2—figure supplement 1C , D ) . More importantly , neuronal theta bursts closely aligned to the timing of syllable boundaries in the presented sentences ( Figure 2A ) . We compared the performance of the theta network to that of two alternative models also susceptible to predict syllable boundaries: a simple linear-nonlinear acoustic boundary detector ( Figure 2—figure supplement 1E ) and Mermelstein algorithm , a state-of-the-art model which , unlike the model developed here , only permits ‘off-line’ syllable boundary detection ( Mermelstein , 1975 ) . The theta network performed better than both the linear model and the Mermelstein algorithm ( Figure 2B , all p-values <10−12 ) . Similar to results from behavioural studies of human perception ( Miller et al . , 1984; Nourski et al . , 2009; Mukamel et al . , 2011 ) the theta network could adapt to different speech rates . The model performed better than other algorithms , with a syllabic alignment accuracy remaining well above chance levels ( p < 10−12 ) in the twofold and threefold time compression conditions . ( Figure 2B ) . 10 . 7554/eLife . 06213 . 005Figure 2 . Theta entrainment by syllabic structure . ( A ) Theta spikes align to syllable boundaries . Top graph shows the activity of the theta network at rest and in response to a sentence , including the LFP traces displaying strong theta oscillations , and raster plots for spikes in the Ti ( light green ) and Te ( dark green ) populations . Theta bursts align well to the syllable boundaries obtained from labelled data ( vertical black lines shown on top of auditory spectrogram in graph below ) . ( B ) Performance of different algorithms in predicting syllable onsets: Syllable alignment score indexes how well theta bursts aligned onto syllable boundaries for each sentence in the corpus , and the score was averaged over the 3620 sentences in the test data set ( error bars: standard error ) . Results compare Mermelstein algorithm ( grey bar ) , linear-nonlinear predictor ( LN , pink ) and theta network ( green ) , both for normal speed speech ( compression factor 1 ) and compressed speech ( compression factors 2 and 3 ) . Performance was assessed on a different subsample of sentences than those used for parameter fitting . DOI: http://dx . doi . org/10 . 7554/eLife . 06213 . 00510 . 7554/eLife . 06213 . 006Figure 2—figure supplement 1 . TIMIT corpus and models used for syllable boundary detection . ( A ) Acoustic analysis of TIMIT corpus . Left panel: speech modulation frequency increases with syllabic rate . All 4620 sentences of the TIMIT corpus ( Test data set ) were sorted into quartiles according to syllabic rate ( i . e . , number of syllables per second ) . Speech envelope spectrum ( with 1/f correction ) was averaged over all sentences within each quartile , and the four averages are plotted . Colour bars on top of the graphs represent the syllabic rate range for all four quartiles , showing a correspondence between the modal frequency and the syllabic rate over the corpus . Middle panel: average channel spectrum . Spectrum was taken for each 128 auditory channels of the Chi and colleagues pre-cortical auditory model ( Chi et al . , 2005 ) , averaged over all sentences in the corpus . All channels show a clear peak in the same 4–8 Hz range , showing that the theta modulation is very present in the input to auditory cortex . Right panel: syllable onset corresponds to a dip in spectrogram . Average of auditory spectrogram channels of sentences phase-locked to syllable onsets . t = 0 ( green line ) corresponds to syllable onset . Red colours correspond to high value , blue colours to low values . Dip at syllable onset is particularly pronounced over medium frequencies corresponding to formants . Auditory channels were averaged over all syllable onsets over the entire corpus ( 4620 sentences ) . This plot shows the connection between syllable boundaries and fluctuations of auditory channels that the auditory cortex may take advantage of in order to predict syllable boundaries . ( B ) Theta network model . Left panel: the architecture of the theta model is the same as the full model network without the PING component . Speech data are decomposed into auditory channels as in the LN model and projected non-specifically onto 10 Te excitatory neurons . The Te population interacts reciprocally with 10 Ti inhibitory neurons , generating theta oscillations . Theta bursts provide the model prediction for syllable boundary timing . ( C ) Te neurons burst at speech onset: Te neurons provide onset-signalling neurons that respond non-specifically to the onset of all sentences . The spikes from one Te neuron were collected over presentation of 500 distinct sentences , and then referenced in time with respect to sentence onset . Here , sentence onset was defined as the time when speech envelope first reached a given threshold ( 1000 a . u . ) . Spikes counts are then averaged in 20 ms bins , showing that this neuron displays a strong activity peak 0–60 ms after sentence onset . A secondary burst occurs around 200 ms after onset , as present in the example neuron shown in Brasselet et al . , 2012 . ( D ) Model of linear-nonlinear ( LN ) predictor of syllable boundaries . Auditory channels are filtered , summed , and passed through a nonlinear function: the output determines the expected probability of syllable onset . A negative feedback loop prevents repeated onset at close timings . Values for filters , nonlinear function , and feedback loops are optimized through fitting to a sub-sample of sentences . ( E ) Stimulus-network coherence . Theta phase ( 4–8 Hz ) was extracted from both the simulated LFP and speech input . Coherence at each data point was computed as the Phase-Locking Value of the phase difference computed from 100 simulations with a distinct sentence . Coherence established in the 0–200 ms following sentence onset to a stable high coherence value of about 0 . 4 . DOI: http://dx . doi . org/10 . 7554/eLife . 06213 . 006 This first study demonstrates that theta activity provides a reliable , syllable-based , internal time reference that the neural system could use when reading out the activity of gamma neurons . Our next step was to test whether the theta-based syllable chunks of output spike trains ( Ge neurons ) for the different input types could be properly classified . We first quantified the model's ability to encode stimuli designed as simple temporal patterns . We used 50 ms sawtooth stimuli whose shape was parametrically varied by changing the peak position ( Figure 3A ) , with interstimulus interval between 50 and 250 ms . This toy set of stimuli was previously used in a gamma-based speech encoding model and argued to represent idealized formant transitions ( Shamir et al . , 2009 ) . We extracted spike patterns from all the Ge ( output ) neurons from −20 ms before each sawtooth onset to 20 ms after its offset . This procedure is referred to as ‘stimulus timing’ since it uses the stimulus onset as time reference . Using a clustering method ( see ‘Materials and methods’ ) , we observed that the identity of the presented sawtooth could be decoded from the output spike patterns ( Figure 3A ) with over 60% accuracy ( Figure 3C , light grey bar ) . We also computed the decoding performance when we used an internal time reference provided by the theta timing rather than by the stimulus timing . When spike patterns were analysed within a window defined by two successive theta bursts ( Figure 3C , dark grey bar ) , sawtooth decoding was still possible and even relatively well preserved ( mean decoding rate of 41 . 7% ) . Noise in the theta module allows the alignment of theta bursts to stimulus onset and thus improves detection performance by enabling consistent theta chunking of spike patterns . 10 . 7554/eLife . 06213 . 007Figure 3 . Sawtooth classification . ( A ) Gamma spiking patterns in response to simple stimuli . The model was presented with 50 ms sawtooth stimuli , where peak timing was parameterized between 0 ( peak at onset ) and 1 ( peak at offset ) . Spiking is shown for different Ge neurons ( y axis ) in windows phase-locked to theta bursts ( −20 to +70 ms around the burst , x-axis ) . Neural patterns are plotted below the corresponding sawtooths . ( B ) Simulated networks . The analysis was performed on simulated data from three distinct networks: ‘Undriven-theta model’ ( no speech input to Te units , top ) , ‘Uncoupled theta/gamma model’ ( no projection from Te to Ge units , middle ) , full intact model ( bottom ) . ( C ) Classification performance using stimulus vs . theta timing for the three simulated networks . The stimulus timing ( light bars ) is obtained by extracting Ge spikes in a fixed-size window locked to the onset of the external stimulus; the theta timing ( dark bars ) is obtained by extracting Ge spikes in a window defined by consecutive theta bursts ( theta chunk , see Figure 3A ) . Classification was repeated 10 times for each network and neural code , and mean values and standard deviation were extracted . Average expected chance level is 10% . ( D ) Stimulus detection performance , for the intact and control models . Rest neural patterns were discriminated against any of the 10 neural patterns defined by the 10 distinct temporal shapes . ( E ) Confusion matrices for stimulus- and theta-timing and the two control models ( using theta-timing code ) . The colour of each cell represents the number of trials where a stimulus parameter was associated with a decoded parameter ( blue: low numbers; red: high numbers ) . Values on the diagonal represent correct decoding . DOI: http://dx . doi . org/10 . 7554/eLife . 06213 . 007 We then compared the decoding performance from the full model with that of two control models: one in which the theta module was not driven by the stimulus ( undriven theta model ) and one in which the theta module was not connected with the gamma module ( uncoupled theta/gamma model ) ( Figure 3B , green and blue ) . Decoding performance of both control models , as revealed by the mean performance ( Figure 3C ) and confusion matrices ( Figure 3E ) , was degraded for either neural code ( theta onset and stimulus timing , all p-values <10−9 ) . The details of the raw confusion matrices show that the temporal patterns are decoded correctly or as a neighbouring temporal shape only in the intact version of the model ( Figure 3E ) . Furthermore , the intact model achieved better signal vs rest discrimination than the two control models , notably avoiding false alarms ( Figure 3D ) . In summary , these analyses show that gamma-spiking neurons within theta bursts provide a reliable internal code for characterizing simple temporal patterns , and that this ability is granted by the time-locking of theta neurons ( Te units ) to stimulus and the modulation they exert on the fast-scale output ( Ge ) units . The overarching goal of this theoretical work was to assess whether coupled cortical oscillations can achieve on-line speech decoding from continuous signal . We therefore set out to classify syllables from natural sentences . To decode Ge spiking , we used similar procedures as for the encoding/decoding of simple temporal patterns . Output Ge spikes were parsed into spike patterns based on the theta chunks , and the decoding analysis was used to recover syllable identity ( Figure 4A ) . To evaluate the importance of the precise spike timing of gamma neurons , we compared decoding ( see ‘Materials and methods’ ) using spike patterns ( i . e . , spikes labelled with their precise timing w . r . t . chunk onset ) vs those obtained from plain spike counts ( i . e . , unlabelled spikes ) . When using spike patterns syllable decoding reached a high level of accuracy in the intact model: 58% of syllables were correctly classified within a set of 10 possible ( randomly chosen ) syllables ( Figure 4B ) . Syllable decoding dropped when using spike counts instead of spike patterns ( p < 10−12 ) . Critically , decoding was poor in both control models ( undriven theta and uncoupled theta/gamma ) using either spike counts or spike patterns ( significantly lower than decoding using spike patterns in the full model , all p-values < 10−12 , and non-significantly higher than decoding using spike counts in the full model , all p-values > 0 . 08 uncorrected ) . 10 . 7554/eLife . 06213 . 008Figure 4 . Continuous speech parsing and syllable classification . ( A ) Decoding scheme . Output spike patterns were built by extracting Ge spikes occurring within time windows defined by consecutive theta bursts ( red boxes ) during speech processing simulations . Each output pattern was then labelled with the corresponding syllable ( grey bars ) . ( B ) Syllable decoding average performance for uncompressed speech . Performance for the three simulated models ( Figure 3B ) using two possible neural codes: spike count and spike pattern . ( C ) Syllable decoding average performance across speakers , using the spike pattern code . Syllable decoding was optimal when syllable duration was within the 100–300 ms range , i . e . , corresponded to the duration of one theta cycle . The intact model performed better than the two controls irrespective of syllable duration range . Chance level is 10% . Colour code same as B . ( D ) Syllable decoding performance for compressed speech for the intact model using the spike pattern code ( same speaker , as in B ) . Compression ranges from 1 ( uncompressed ) to 3 . Average chance level is 10% ( horizontal line in the right plot ) . DOI: http://dx . doi . org/10 . 7554/eLife . 06213 . 00810 . 7554/eLife . 06213 . 009Figure 4—figure supplement 1 . Syllable classification across speakers . ( A ) Distribution of syllable duration across sentences and 462 speakers . The shaded area ( 100–300 ms ) indicates region of maximal density . Extreme values probably correspond to ill-defined syllables . DOI: http://dx . doi . org/10 . 7554/eLife . 06213 . 009 We also explored the model performance for encoding syllables spoken by different speakers . We used a similar decoding procedure as above , but here the classifier was trained on different speakers pronouncing the same two sentences . Theta chunks were classified into syllables based on the network response to the two sentences uttered by 99 other speakers . The material included sentences spoken by 462 speakers of various ethnic and geographical origins , showing a marked heterogeneity in phonemic realization and syllable durations ( as labelled by phoneticians ) . The syllable duration distribution was skewed with the median at 200 ms and tail values ranging from a few ms to over 800 ms ( Figure 4—figure supplement 1A ) . Given that theta activity is meant to operate in a 3–9 Hz range , i . e . , integrate speech chunks of about 100–300 ms ( Ghitza , 2011 , 2014 ) , we did not expect the model to perform equally well along the whole syllable duration range . Accordingly , decoding accuracy was not uniform across the whole syllable duration range . When decoding from spike pattern , the intact model allowed 24% accuracy ( chance level at 10% ) . It showed a peak in performance in the range in which it is expected to operate , i . e . , for syllables durations between 100 and 300 ms . Given the cross-speaker phonemic variability such a performance is fairly good . Critically , the intact model outperformed control models both within the 100 to 300 ms range ( p < 0 . 001 ) , and throughout the whole syllable duration span ( p < 0 . 001 ) . These analyses overall show that the model can flexibly track syllables within a physiological operating window , and that syllable decoding relies on the integrity of the model architecture . Lastly , we tested more directly the resilience of the spike pattern code to speech temporal compression and found that while degrading the decoding performance remained above chance for compression rates of 2 and 3 ( Figure 4D ) , mimicking humans decoding performance ( Ahissar et al . , 2001 ) . Altogether , the decoding of syllables from continuous speech showed that coupled theta and gamma oscillations provide a viable instrument for syllable parsing and decoding , and that its performance relies on the coupling between the two oscillation networks . We finally assessed the physiological plausibility of the model by comparing the encoding properties of the simulated neurons , without further parameter fitting , with those of neurons recorded from primate auditory cortex ( Kayser et al . , 2009; 2012 ) . The first analysis of neural encoding properties consisted of comparing the ability to classify neural codes from the model into arbitrary speech segments of fixed duration ( as opposed to classification into syllables as in previous section ) . We simulated data using natural speech and studied the spiking activity of Ge neurons by implementing the same methods of analysis as in the original experiment . We extracted fixed-size windows of spike patterns activity for individual Ge neurons , and assessed neural encoding characteristics using different neural codes . Speech encoding was first evaluated using a nearest-mean classifier and then using mutual information techniques ( Kayser et al . , 2009 ) .
Like most complex natural patterns , speech contains rhythmic activity at different scales that conveys different and sometimes non-independent categories of information . Using a biophysically inspired model of auditory cortex function , we show that cortical theta–gamma cross-frequency coupling provides a means of using the timing of syllables to orchestrate the readout of speech-induced gamma activity . The current modelling data demonstrate that theta bursts generated by a theta ( PINTH ) network can predict ‘on-line’ syllable boundaries at least as accurately as state-of-the-art offline syllable detection algorithms . Syllable boundary detection by a theta network hence provides an endogenous time reference for speech decoding . Our simulated data further show that a gamma biophysical network , receiving a spectral decomposition of speech as input , can take advantage of the theta time reference to encode fast phonemic information . The central result of our work is that the gamma network could efficiently encode temporal patterns ( from simple sawtooths to natural speech ) , as long as it was entrained by the theta rhythm driven by syllable boundaries . The proposed theta/gamma network displayed sophisticated spectral and encoding properties that compared both qualitatively and quantitatively to existing neurophysiological evidence including cross-frequency coupling properties ( Schroeder and Lakatos , 2009 ) and theta-referenced stimulus encoding ( Kayser et al . , 2009; 2012 ) . The projections from the Te to Ge neurons endowed the network with phase-amplitude and phase-frequency coupling between gamma and theta oscillations , at both the spike and the LFP levels ( Jensen and Colgin , 2007 ) . This closely reproduces the theta/gamma phase-amplitude coupling observed from intracortical recordings ( Giraud and Poeppel , 2012; Lakatos et al . , 2005 ) . Importantly , due to the dissociation of excitatory populations we obtained denser gamma spiking immediately after the theta burst evoked by the syllable onset . This validates a critical point of theta/gamma parsing system , namely that a more in-depth encoding is carried-out by the auditory cortex during the early phase of syllables , when more information needs to be extracted ( Schroeder and Lakatos , 2009; Giraud and Poeppel , 2012 ) . The human auditory system , like other sensory systems , is able to produce invariant responses to different physical presentations of the same input . Importantly , it is relatively insensitive to the speed at which speech is being produced . Speech can double in speed from one speaker to another and yet remain intelligible up to an artificial compression factor of 3 ( Ahissar et al . , 2001 ) . In the current model , theta bursts could still signal syllable boundaries when speech was compressed by a factor 2 and this alignment deteriorated for higher compression factors . Syllable decoding was significantly degraded for compressed speech , yet remained twice as accurate as chance . Our network is purely bottom-up and does not include high level linguistic processes and representations , which in all likelihood plays an important role in speech perception ( Davis et al . , 2011; Peelle et al . , 2013; Gagnepain et al . , 2012 ) : its relative resilience to speech compression is thus a fairly good performance . A previous model ( Gütig and Sompolinsky , 2009 ) proposed a neural code that was robust to speech warping , based on the notion that individual neurons correct for speech rate by their overall level of activity . While this model achieved very good speech categorization performance , it relied on extremely precise spiking behaviour ( neurons spiked only once , when their associated channel reached a certain threshold ) , for which neurophysiological evidence is scarce . Another model developed by Hopfield proposes that a low gamma external current provides encoding neurons with reliable timing and dynamical memory spanning up to 200 ms , a long enough window to integrate information over a full syllable ( Hopfield , 2004 ) . The utility of gamma oscillations for precise spiking is arguably similar in both Hopfield's model and ours , whereas the syllable integration process is irregularly ensured by intermittent traces of recent ( ∼200 ms ) neural activity in Hopfield's , and in ours by regularly spaced theta bursts that are locked to the speech signal . The advantage of our model is that integration over long speech segments is permanently enabled by the phase of output spikes with respect to the ongoing theta oscillation . Our approach shows that accurate encoding can be achieved using a system that does not require explicit memory processes , and in which the temporal integration buffer is only emulated by a slow neural oscillator aligned to speech dynamics . In the current combined theta/gamma model , theta oscillations do not only act as a syllable-scale integration buffer , but also as a precise neural timer . Because syllabic contours are reflected in the slow modulations of speech , the theta oscillator can flexibly entrain to them ( 3–7 Hz , Figure 2—figure supplement 1A ) and signal syllable boundaries . The spiking behaviour of theta neurons parallels experimental observations that a subset of neurons in A1 respond to the onset of naturalistic sounds ( Fishbach et al . , 2001; Phillips et al . , 2002; Wang et al . , 2008 ) , providing an endogenous time reference that serves as a landmark to decode from other neurons ( Kayser et al . , 2012; Brasselet et al . , 2012; Panzeri and Diamond , 2010; Panzeri et al . , 2014 ) . This parallels the dissociation between Ge and Te units in our model: while Ge units are channel specific , Te units cover the whole acoustic spectrum , which allow them to respond quickly and reliably to the onset of all auditory stimuli ( Brasselet et al . , 2012 ) . In the model , however , theta neurons did not only discharge at stimulus onset but at regular landmarks along the speech signal , the syllable boundaries ( Zhou and Wang , 2010 ) . These neurons , hence , tie together the fast neural activity of gamma excitatory neurons into strings of linguistically relevant chunks ( syllables ) , acting like punctuation in written language ( Lisman and Buzsáki , 2008 ) . This mechanism for segmentation is conceptually similar to the segmentation of neural codes by theta oscillations in the hippocampus during spatial navigation ( Gupta et al . , 2012 ) . From an evolutionary viewpoint , because the theta rhythm is neither auditory- nor human-specific , it might have been incorporated as a speech-parsing tool in the course of language evolution . Likewise , human language presumably optimized the length of its main constituents , syllables , to the parsing capacity of the auditory cortex . As a result , syllables have the ideal temporal format to interface with , e . g . , hippocampal memory processes , or with motor routines reflecting other types of rhythmic mechanical constrains , e . g . , the natural motion rate of the jaw ( 4Hz ) ( Lieberman , 1985 ) . Although conceptually promising , syllable tracking and speech encoding by a theta/gamma network , as proposed here , also show some limitations . While our current model is purely bottom-up , top-down predictions play a significant role in guiding speech perception ( Arnal and Giraud , 2012; Gagnepain et al . , 2012; Poeppel et al . , 2008 ) presumably across different frequency channels and processing timescales ( Wang , 2010; Bastos et al . , 2012; Fontolan et al . , 2014 ) . How these predictions interplay with theta- and gamma-parsing activity remain unclear ( Lee et al . , 2013 ) . Experimental findings suggest that theta activity might be at the interface of bottom-up and top-down processes ( Peelle et al . , 2013 ) . Theta auditory activity is better synchronized to speech modulations when speech is intelligible , irrespective of its temporal or spectral structure ( Luo and Poeppel , 2007; Peelle et al . , 2013 ) . In the present model , theta activity bears an intrinsic temporal predictive function: it is driven by speech modulations , but is also resilient enough to syllable length variations to stay tuned to the global statistics of speech ( average syllable duration ) . The model performed well above chance level when decoding syllables from a new speaker , showing flexibility in syllable tracking within a 3 to 9 Hz range . A natural follow-up of this work will hence be to explore how the intrinsic dynamics of theta and gamma activity interact not only with sensory input but also with linguistic top-down signals , e . g . , word , sentence level predictions ( Gagnepain et al . , 2012 ) , and even cross-modal predictions ( Arnal et al . , 2009 ) . The trade-off between the autonomous functioning of theta and gamma oscillatory activity on one hand and their entrainment to sensory input on the other hand are at the core of future experimental and theoretical challenges . In conclusion , our model provides a direct evidence that theta/gamma coupled oscillations can be a viable instrument to de-multiplex speech , and by extension to analyse complex sensory scenes at different timescales in parallel . By tying the gamma-organized spiking to the syllable boundaries , theta activity allows for decoding individual syllables in continuous speech streams . The model demonstrates the computational value of neural oscillations for parsing sensory stimuli based on their temporal properties and offers new perspectives for syllable-based automatic speech recognition ( Wu et al . , 1997 ) and brain-machine interfaces using oscillation-based neuromorphic algorithms .
The model is composed of 4 types of cells: theta inhibitory neurons ( Ti , 10 neurons ) , theta excitatory cells ( Te , 10 neurons ) , gamma inhibitory neurons ( Gi , 32 neurons ) , and gamma excitatory neurons ( Ge , 32 neurons ) also called output neurons . All neurons were modeled as leaky integrate-and-fire neurons , where the dynamics of the membrane potential Vi of the neurons followed:CdVi/dt=gL ( VL−Vi ) +IiSYN ( t ) +IiINP ( t ) +IiDC+η ( t ) , where C is the capacitance of the membrane potential; gL and VL are the conductance and equilibrium potential of the leak current; ISYN , IINP and IDC are the synaptic and constant currents , respectively; η ( t ) is a Gaussian noise term of σi variance . Whenever Vi reached the threshold potential VTHR , the neuron emitted a spike and Vi was turned back to VRESET . ISYN is the sum of all synaptic currents from all projecting neurons in the network:IiSYN ( t ) =∑jgijsij ( t ) ( VjSYN−Vi ( t ) ) , where gij is the synaptic conductance of the j-to-i synapse , sij ( t ) is the corresponding activation variable , and VSYN is the equilibrium potential of synaptic current ( 0 mV for excitatory neurons , −80 mV for inhibitory neurons ) . The activation variable sij ( t ) varies as follow:dxjR/dt=−1/τjR+δ ( t−tjSPK ) , dsij/dt=−1/τjD , where τjR and τjD are the time constants for synaptic rise and synaptic decay , respectively . The connectivity among the cells is the following:Te and Ti are reciprocally connected with all-to-all connections , generating the PINTH rhythm . There were also all-to-all connections within Ti cells . Ge and Gi are also reciprocally connected with all-to-all connections , generating the PING rhythm . Te projected with all-to-all connections to Ge cells , enabling cross-frequency coupling . Input current IiINP ( t ) is non-null only for Te and Ge cells and follows the equation:IiINP ( t ) =∑cωcixc ( t ) , where xc ( t ) is the signal from channel c and ωci is the weight of the projection from channel c to unit i . Input to Te units is computed by filtering the auditory spectrogram by an optimized 2D spectro-temporal kernel ( see section LN model below ) . LFP signal was simulated by summing the absolute values of all synaptic currents to all excitatory cells ( both Ge and Te ) , as in Mazzoni et al . ( 2008 ) . All simulations were run on Matlab . Differential equations were solved using Euler method with a time step of 0 . 005 ms . Values for all parameters are provided in Tables 1 and 2 . 10 . 7554/eLife . 06213 . 012Table 1 . Full network parameter setDOI: http://dx . doi . org/10 . 7554/eLife . 06213 . 012ParameterCVTHRVRESETVKVLgLgGe , GigGi , GegTe , GeValue1 F/cm2−40 mV−87 mV−100 mV−67 mV0 . 15/NGe5/NGi0 . 3/NTeParameterτGeRτTeRτGiRτTiRτGeDτGiDIGeDCIGiDCValue0 . 2 ms4 ms0 . 5 ms5 ms2 ms20 ms3110 . 7554/eLife . 06213 . 013Table 2 . Optimal parameters for the LN modelDOI: http://dx . doi . org/10 . 7554/eLife . 06213 . 013ParametertspnextτIhDCValue0 . 07481 . 4330 . 4672 We used oral recordings of English sentences produced by male and female speakers from the TIMIT database ( Linguistic Data Consortium , 1993 ) . The sentences were first processed through a model of subcortical auditory processing ( Chi et al . , 2005 ) to the sentences . The model decomposes the auditory input into 128 channels of different frequency bands , reproducing the cochlear filterbank ( http://www . isr . umd . edu/Labs/NSL/Software . htm ) . The frequency-decomposed signals undergo a series of nonlinear filters reflecting the computations taking place in the auditory nerve and other subcortical nuclei . We then reduced the number of channels from 128 to 32 by averaging the signal of each group of four consecutive channels , and used these 32 channels as input to the network . Each channel projected onto a distinct Ge cell ( i . e . , specific connections , ωci=0 . 25δ ( c , i ) ) . As for Te input , each channel was convolved by the temporal filter and projected to all Te cells ( all-to-all connections ) . Such a convolution can be implemented by a population of relay neurons that transmit their input with a certain delay , here between 0 and 50 ms . Phoneme identity and boundaries have been labelled by phoneticians in every sentence of the corpus . We used the Tsylb2 program ( Fisher , 1996 ) that automatically syllabifies phonetic transcriptions ( Kahn , 1976 ) to merge these sequences of phonemes into sequences of syllables according to English grammar rules and thus get a timing for syllable boundaries . To address the resilience of the model to speech compression , we produced compressed sentences by applying a pitch-synchronous , overlap and add ( PSOLA ) procedure implemented by PRAAT , a speech analysis and modification software ( http://www . fon . hum . uva . nl/praat/ ) . The procedure retains all spectral properties from the original speech data in the compressed process . The same precortical filters were then applied as for uncompressed data before feeding into the network . Syllable boundaries triggered average ( STAs ) were computed as follow: for each syllable boundary ( syllable onsets excluding the first of each sentence ) , we extracted a 700 ms window of the corresponding locked to the syllable boundary and averaged over all syllable boundaries . STAs were computed for speech envelope and for each channel of the Chi et al . ( 2005 ) model . We first explored the model's performance using simple sawtooth signals ( Shamir et al . , 2009 ) , representing prototypical realizations of formant transitions in a given frequency band . Each stimulus consisted of a rising component between 0 and 1 , followed by a decay component from 1 back to 0 . The overall length of the sawtooth was 50 ms , and the relative position of the maximal point tMAX between the starting point tSTART and end point tEND was defined by a variable a = ( tMAX − tSTART ) / ( tEND − tSTART ) . The input connectivity had to be slightly modified since sawtooths are one-dimensional signals in contrast to the multi-dimensional channel signals that we have to use for speech stimuli: for Te units , we used ITeEXT = 20; and for the connections to Ge units in line with the original model ( Shamir et al . , 2009 ) , we used different input levels across the population , ranging from 0 . 125 to 4 in 0 . 125 intervals . The rest of the model remained unchanged . We simulated the response of the network to a series of 500 sawtooths with parameter a taking one of 10 equally spaced values within the [0 1] interval . Interstimulus interval varied randomly between 50 and 250 ms . We compared the model's performance for different neural codes . For the ‘stimulus timing’ code ( see ‘Results’ section ) , we extracted the spike pattern of output ( Ge ) neurons between 20 ms before and 70 ms after of each sawtooth onset . We computed the distance between all output spike patterns using a spike train distance measure ( Victor and Purpura , 1997 ) , implemented in the Spike Train Analysis Toolkit ( http://neuroanalysis . org/toolkit/ ) . We used a shift cost of 200 s−1 corresponding to a timing resolution of 5 ms . We decoded the peak parameter using the simple leave-one-out clustering procedure of the STA toolkit , using a clustering exponent of −10 . By comparing the ‘decoded parameter’ , i . e . , the parameter corresponding to the closest cluster , to the input sawtooth parameter , we built confusion matrices and computed decoding performance . In the ‘theta-timing’ code , we extracted the spike pattern of output neuron in windows starting 20 before a theta burst and finishing 20 ms after the next theta burst ( ‘theta chunks’ , Figure 4A ) . Spike times within each chunk were referenced with respect to the onset of the window . Each spike pattern was labelled with the corresponding value of the stimulus if the theta burst occurred during the presentation of the stimulus , or with the label ‘rest’ if the theta burst occurred during an interstimulus interval . The same decoding analysis was applied on such internally referenced neural patterns , yielding a 11 × 11 confusion matrix ( 10 stimulus shapes and rest ) . Detection theory measures ( hits , misses , correct rejections , and false alarms ) were computed by summing values in blocks of the confusion matrix ( of size 10 × 10 , 10 × 1 , 1 × 10 , and 1 × 1 , respectively ) . A classification confusion matrix was obtained by removing the last row and last column of that confusion matrix . We run the same decoding analysis on variants of the network: the full network; a control model where Te units do not receive the sawtooth input ( undriven theta network ) and another control where theta–gamma connections were removed ( uncoupled theta–gamma network ) . The classification procedure was similar for syllable decoding , where we tried to decode the identity of syllables within continuous stream of speech ( full sentences ) from the activity of output neurons . We stimulated the network by presenting 25 sentences from the TIMIT corpus repeated 100 times each . We extracted theta chunks of Ge spike patterns as explained previously . Each chunk was labelled with the identity of the syllable being presented at the time of the first theta burst of the chunk . We randomly selected 10 syllables from the whole set of syllables within the 25 sentences . As in some cases there were several consecutive theta chunks corresponding to the same syllable , we equated the total number of theta chunks per syllable by randomly selecting 100 theta chunks labelled with each of the 10 syllables . Syllable classification of theta-chunked Ge spike patterns was performed using two different neural codes . For the spike pattern code , we applied the same procedure as for sawtooth classification , using a smaller value of spike shift cost corresponding to a timing resolution of 60 ms . For the spike count code , we measured the number of spikes emitted by each Ge neuron within a theta chunk . We then ran a simple nearest-mean classification procedure to decode syllable identity corresponding to each theta chunk from the spike counts of all Ge neurons ( see ‘Classification analysis’ below ) . Both methods relied on the leave-one-out procedure that consists in identifying a chunk after the decoder was trained on all chunks but the to-be-decoded one . Decoding was repeated 200 times using each time a different set of 10 random syllables , and the analysis was performed over all three variants of the network . For syllable classification across speakers , we used the two sentences from the TIMIT corpus that have been recorded for each of the 462 speakers ( 'She had your dark suit in greasy wash water all year' and 'Don't ask me to carry an oily rag like that' ) and trained the network to classify syllables based on the neural output from other speakers , thus testing generalization across speakers . There is a wide variability of pronunciations over speakers as attested by the variability of chain of phonemes labelled of phoneticians , but the two sentences could nonetheless be parsed into 25 syllables overall for each speaker . We simulated the network presenting these 924 sentences and used the theta-chunked output to decode syllable identity . The method used was very similar to the syllable decoding analysis , where we classified theta-chunked neural patterns into one of 10 possible syllables ( drawn randomly from the set of 25 syllables ) , with the only difference that here the classifier was based on theta chunks coming from different speakers . The classification was repeated 100 times for different subsets of syllables . The first analysis of neural encoding properties consisted in comparing the ability to classify neural codes from the model into arbitrary speech segments ( as opposed to syllables as in previous section ) . The methods , as detailed below , were inspired by the decoding of neural auditory cortical activity recorded in monkeys in response to naturalistic sounds ( Kayser et al . , 2012 ) . We simulated the network by presenting 25 different sentences from the TIMIT corpus repeated 50 times each . For a given window size ( ranging from 80 to 480 ms in 80 ms intervals ) , we randomly extracted 10 windows ( defined as stimuli ) from the overall set of 25 sentences . We then retrieved stimulus identity based on the activity of a neuron that was randomly drawn from the Ge population using three different neural codes . In the neural count code , we counted the number of spikes emitted by that neuron within each window . In the time-partitioned code , we divided each window into N equally size bins , and computed the number of spikes for each of the 8 bins separately . In the phase-partitioned code , we divided the window based on theta-phase- rather time-intervals: each spike was labelled with the phase of the theta oscillation at the corresponding spike time , and we computed the number of spikes falling into each of the N subdivisions of the [−π;π] interval . We then used a nearest-mean template matching procedure to decode the stimuli . To classify each stimulus exemplar using each neural code , we averaged the vectors over all presentations of each stimulus using a leave-one-out procedure; we then computed the Euclidian distance from the current vector to each of the 10 stimulus-averaged template . Finally , we ‘decoded’ the neural code by assigning it to the stimulus class with minimal distance to template . A more detailed explanation of the procedure is provided in the original experiment article ( Kayser et al . , 2012 ) . The procedure was repeated 1000 times , each time with a different set of 10 random stimuli , and performed the 3 variants of network . We complemented the stimulus classification with a similar analysis using mutual information between the acoustic ‘stimulus’ and response of individual Ge neurons to further characterize the encoding properties of the network . Mutual Information ( MI ) estimates the reduction of uncertainty about the acoustic ‘stimulus’ that is obtained from the knowledge of a single trial of neural response . The data set was identical to the one previously used for stimulus classification analysis , where each stimulus was again segmented into non-overlapping windows of length T ( here 8 to 48 ms ) ( Kayser et al . , 2009; de Ruyter van Steveninck et al . , Strong , 1997 ) . Mutual Information was computed for the same neural codes as in Kayser et al . ( 2009 ) . We used Spike count code and Time-partitioned code as described above ( for the Time-partitioned code the size of the bins was kept constant to 8 bins; the number of bins in a window hence increased with window size . As slow LFP phase was more reliable over sentence repetitions than power , we combined spike count and LFP theta phase to get a Spike count & Phase-partitioned code ( Montemurro et al . , 2008 ) . For this code , the phase of slow LFP was divided into N = 4 bins , and the firing rate in each window was labelled according to the phase at which the first spike occurred . Finally , we explored the influence of slow LFP phase on MI when combined with temporal spiking patterns . Thus , in the Time- & Phase-partitioned code spikes carry two distinct tags , the first one referring to the position of the spike inside one of the four subdivisions of the stimulus window , the second indicating the phase of the underlying LFP at the moment of the spike occurrence . We corrected for sampling bias ( Kayser et al . , 2009 ) first by using a shuffling method ( Panzeri et al . , 2007 ) , then the quadratic extrapolation method ( Strong et al . , 1998 ) . We further reduced the residual bias using a bootstrapping technique ( 200 resampled data ) ( Montemurro et al . , 2008 ) . | Some people speak twice as fast as others , while people with different accents pronounce the same words in different ways . However , despite these differences between speakers , humans can usually follow spoken language with remarkable ease . The different elements of speech have different frequencies: the typical frequency for syllables , for example , is about four syllables per second in speech . Phonemes , which are the smallest elements of speech , appear at a higher frequency . However , these elements are all transmitted at the same time , so the brain needs to be able to process them simultaneously . The auditory cortex , the part of the brain that processes sound , produces various ‘waves’ of electrical activity , and these waves also have a characteristic frequency ( which is the number of bursts of neural activity per second ) . One type of brain wave , called the theta rhythm , has a frequency of three to eight bursts per second , which is similar to the typical frequency of syllables in speech , and the frequency of another brain wave , the gamma rhythm , is similar to the frequency of phonemes . It has been suggested that these two brain waves may have a central role in our ability to follow speech , but to date there has been no direct evidence to support this theory . Hyafil et al . have now used computer models of neural oscillations to explore this theory . Their simulations show that , as predicted , the theta rhythm tracks the syllables in spoken language , while the gamma rhythm encodes the specific features of each phoneme . Moreover , the two rhythms work together to establish the sequence of phonemes that makes up each syllable . These findings will support the development of improved speech recognition technologies . | [
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] | 2015 | Speech encoding by coupled cortical theta and gamma oscillations |
SMC complexes , such as condensin or cohesin , organize chromatin throughout the cell cycle by a process known as loop extrusion . SMC complexes reel in DNA , extruding and progressively growing DNA loops . Modeling assuming two-sided loop extrusion reproduces key features of chromatin organization across different organisms . In vitro single-molecule experiments confirmed that yeast condensins extrude loops , however , they remain anchored to their loading sites and extrude loops in a ‘one-sided’ manner . We therefore simulate one-sided loop extrusion to investigate whether ‘one-sided’ complexes can compact mitotic chromosomes , organize interphase domains , and juxtapose bacterial chromosomal arms , as can be done by ‘two-sided’ loop extruders . While one-sided loop extrusion cannot reproduce these phenomena , variants can recapitulate in vivo observations . We predict that SMC complexes in vivo constitute effectively two-sided motors or exhibit biased loading and propose relevant experiments . Our work suggests that loop extrusion is a viable general mechanism of chromatin organization .
The SMC condensin complex in metazoan cells plays a central role in mitotic chromosome compaction and segregation ( Charbin et al . , 2014; Hagstrom et al . , 2002; Hirano , 2016; Hirano et al . , 1997; Hirano and Mitchison , 1994; Hudson et al . , 2003; Nagasaka et al . , 2016; Ono et al . , 2003; Piskadlo et al . , 2017; Saka et al . , 1994; Shintomi et al . , 2017; Shintomi et al . , 2015; Steffensen et al . , 2001; Strunnikov et al . , 1995 ) . In mitotic chromosomes , electron microscopy reveals that chromatin is arranged in arrays of loops ( Earnshaw and Laemmli , 1983; Maeshima et al . , 2005; Marsden and Laemmli , 1979; Paulson and Laemmli , 1977 ) . This results in dramatic linear compaction of the chromatin fiber into a polymer brush with a > 100 fold shorter backbone ( Guacci et al . , 1994; Lawrence et al . , 1988; Trask et al . , 1989 ) . Fluorescence imaging and Hi-C show that these loops maintain the linear ordering of the genome ( Gibcus et al . , 2018; Naumova et al . , 2013; Strukov and Belmont , 2009; Trask et al . , 1993 ) . Together , these features may facilitate the packaging , resolution , and segregation of chromosomes during mitosis by effectively shortening and disentangling chromatids ( Brahmachari and Marko , 2019; Eykelenboom et al . , 2019; Goloborodko et al . , 2016a; Green et al . , 2012; Marko , 2009; Nagasaka et al . , 2016; Sakai et al . , 2018; Sakai et al . , 2016 ) . Each of these experimental observations is reproduced by the two-sided loop extrusion model , in which dynamic loop-extruding condensins collectively form arrays of reinforced loops by locally extruding chromatin until encountering another condensin ( Goloborodko et al . , 2016a; Goloborodko et al . , 2016b ) . The simplest one-sided loop extrusion process , in contrast , can only linearly compact chromosomes 10-fold because it leaves unlooped ( and thus , uncompacted ) polymer gaps between loop extruders ( Banigan and Mirny , 2019; it is unclear whether 10-fold compaction is sufficient for robust chromosome segregation . Nonetheless , variants of one-sided loop extrusion in which loop extruders are effectively two-sided may robustly compact mitotic chromosomes ( Banigan and Mirny , 2019 ) . This raises the question of what abilities an individual one-sided loop extruder must possess to compact and spatially resolve chromosomes . In interphase in vertebrate cells , Hi-C reveals that the SMC cohesin complex is responsible for frequent but transient loop formation , which results in regions of high intra-chromatin contact frequency referred to as TADs ( Dixon et al . , 2012; Gassler et al . , 2017; Haarhuis et al . , 2017; Nora et al . , 2012; Rao et al . , 2017; Rao et al . , 2014; Schwarzer et al . , 2017; Sexton et al . , 2012; Sofueva et al . , 2013 ) . These regions are bordered by convergently oriented CTCF protein binding sites ( de Wit et al . , 2015; Guo et al . , 2015; Rao et al . , 2014; Sanborn et al . , 2015; Vietri Rudan et al . , 2015 ) , which act as obstacles to loop extrusion and translocation of cohesin ( Busslinger et al . , 2017; de Wit et al . , 2015; Fudenberg et al . , 2016; Nora et al . , 2017; Sanborn et al . , 2015; Wutz et al . , 2017 ) . The two-sided loop extrusion model explains the emergence of TADs and their ‘corner peaks’ ( or ‘dots’ ) and ‘stripes’ ( sometimes called ‘lines’ , ‘tracks’ or ‘flames’ ) in Hi-C maps as an average collective effect of multiple cohesins dynamically extruding chromatin loops and stopping at the CTCF boundaries ( Fudenberg et al . , 2016; Sanborn et al . , 2015; reviewed in Fudenberg et al . , 2017 ) . Existing models for loop extrusion during interphase have assumed LEFs with two mobile subunits , whether they be active or inactive ( Alipour and Marko , 2012; Benedetti et al . , 2017; Brackley et al . , 2017; Fudenberg et al . , 2016; Sanborn et al . , 2015; Yamamoto and Schiessel , 2017 ) . While it is clear that a one-sided LEF will necessarily leave an unlooped gap between its initial loading site and one of the CTCF boundary elements , the extent to which one-sided loop extrusion can recapitulate the experimental observations remains entirely unexplored . In bacteria , SMC complexes and homologs play an important role in the maintenance of proper chromosome organization and efficient chromosomal segregation ( Britton et al . , 1998; Jensen and Shapiro , 1999; Moriya et al . , 1998; Sullivan et al . , 2009 and others ) . In Bacillus subtilis and Caulobacter crescentus , the circular chromosome exhibits enhanced contact frequency between its two chromosomal arms ( often called ‘replichores’ ) , as shown by Hi-C ( Le et al . , 2013; Marbouty et al . , 2015 ) . This signal is dependent on the bacterial SMC complex ( bSMC ) ( Marbouty et al . , 2015; Wang et al . , 2015 ) . Experiments show that bSMC is loaded at a bacterial parS site near the origin of replication , and then , while bridging the two arms , actively and processively moves along the chromosome , thus juxtaposing or ‘zipping’ the arms together ( Minnen et al . , 2016; Tran et al . , 2017; Wang et al . , 2018; Wang et al . , 2017 ) . The symmetry of the juxtaposed chromosome arms implies that bSMC should be a two-sided LEF ( Brandão et al . , 2019; Wang et al . , 2017 ) . Indeed , previous modeling has shown that pure one-sided loop extrusion produces contact maps that differ from experimental observations ( Miermans and Broedersz , 2018 ) . However , it is unknown whether variations of one-sided extrusion can properly juxtapose the arms of a circular bacterial chromosome . Two-sided loop extrusion models ( Brandão et al . , 2019; Fudenberg et al . , 2017; Fudenberg et al . , 2016; Goloborodko et al . , 2016a; Goloborodko et al . , 2016b; Sanborn et al . , 2015 ) can account for the various chromosome organization phenomena described above , but in vitro single-molecule experiments suggest that at least some SMC complexes are one-sided LEFs . We therefore investigate whether a mechanism of one-sided loop extrusion can account for in vivo observations of 3D chromatin organization , as listed above , namely metazoan mitotic chromosome compaction and resolution , interphase chromatin organization in vertebrate cells , and juxtaposition of bacterial chromosome arms . To study these processes , we construct a model for one-sided loop extrusion and simulate the collective dynamics of SMC complexes and chromatin in these three distinct scenarios . We also explore several one-sided extrusion variants . By comparing our results to experimental data , we find that pure one-sided loop extrusion fails to capture most of the in vivo phenomenology . However , simple variants of the one-sided model that make loop extrusion effectively two-sided or otherwise suppress the formation of unlooped chromatin gaps can restore the emergent features of chromatin organization observed in experiments .
We focused on several variations of the one-sided loop extrusion model and investigated the consequences for 3D chromosome organization ( Table 1 ) . Our aim was not to exhaustively enumerate all possible model variations of one-sided extrusion . Instead , we sought to obtain and evaluate a set of minimalistic requirements to explain experimental data . We modeled SMC complexes as LEFs with two subunits with distinct dynamics; subunits could be either active ( i . e . , moving processively ) , inactive and anchored , or inactive but diffusive . Within this framework of varying the dynamics of the subunits , we primarily focused on the following models for LEFs: 1 ) one subunit active , the other subunit inactive and anchored ( ‘pure one-sided’ ) , 2 ) one subunit active , the other subunit inactive but diffusive ( ‘semi-diffusive’ ) , 3 ) one subunit active , the other subunit anchored , with kinetic interchange of active and anchored subunits ( ‘switching’ ) . We also considered several related variants for each chromosome organization scenario , such as preferential loading at CTCF by one-sided cohesins during interphase . As a point for comparison , we quantitatively compared all results with those of two-sided extrusion , which previous works have shown to recapitulate key experimental observations ( Alipour and Marko , 2012; Brandão et al . , 2019; Fudenberg et al . , 2016; Goloborodko et al . , 2016a; Goloborodko et al . , 2016b; Miermans and Broedersz , 2018; Sanborn et al . , 2015 ) . Our modeling demonstrates that the ability to robustly eliminate unlooped gaps is essential to the chromosome-organizing role of LEFs . As a result , models in which gaps persist in steady state , such as the pure one-sided model , fail to reproduce hallmarks of chromosome organization found in several physiological scenarios . One-sided extrusion generally does not reproduce mitotic chromosome compaction and chromatid segregation or hallmarks of interphase Hi-C maps , without further assumptions beyond what has been observed experimentally . Importantly , even dynamic LEF turnover ( i . e . , allowing dynamic chromatin unbinding with uniform rebinding ) does not eliminate gaps because LEF unbinding ( and even LEF binding ) can introduce new gaps . Instead , chromosome compaction , resolution , and interphase organization can readily be explained by physical mechanisms that either eliminate gaps by turning one-sided extrusion into effectively two-sided extrusion ( e . g . , as in the switching model ) or suppress the creation of gaps ( e . g . , by biased loading at boundaries ) . In the case of mitotic chromosome compaction , linear compaction by pure one-sided loop extrusion is limited to ~10 fold because it unavoidably leaves gaps between SMC complexes ( Figure 2c ( i ) , ( ii ) and [Banigan and Mirny , 2019] ) . By simulations , we showed that 10-fold linear compaction is not sufficient to reproduce the classical 3D shapes of mitotic chromatids and chromosomes are volumetrically compacted at most twofold in 3D ( Figure 2 b , c ( iii ) ) . This defect in 3D compaction leads to defects in mitotic chromosome resolution ( Figure 2 b , c ( iv ) ) . Allowing the SMC complexes’ anchor points to diffuse ( i . e . , slide ) along chromosomes also does not close gaps because loop formation is opposed by the conformational entropy of the formed loop ( Figure 2d ( ii ) and Figure 2—figure supplement 3 ) . Therefore , the LEFs cannot generate a sufficient increase in linear compaction for any diffusive stepping rate , vdiff ( or diffusion coefficient , D ) ( Figure 2d ( i ) ) ; in vitro experiments also show that one-sided condensins with diffusing safety belts do not grow large DNA loops ( Ganji et al . , 2018 ) . More generally , with one-sided LEFs , uncompacted gaps are pervasive , so simply adding a small fraction of two-sided LEFs is unable to sufficiently compact chromosomes; in vivo levels of compaction requires >80% two-sided LEFs ( Figure 2—figure supplement 1; Banigan and Mirny , 2019 ) . Similarly , a model in which LEFs are effectively two-sided , such as the switching model in which the active and inactive subunits dynamically switch , can generate greater than twofold 3D compaction and clear resolution of sister chromatids ( Figure 2e ( iii ) , ( iv ) ) , as observed in vivo . Such a switching mechanism could be achieved in vivo by a stochastic strand switching mechanism in which both upstream and downstream DNA can be captured by the loop extruder ( Hassler et al . , 2018; Marko et al . , 2019 ) . For interphase organization in vertebrate cells , the ability of one-sided loop extrusion to reproduce major features of Hi-C maps is more complicated . We found that one-sided extrusion with uniform association and dissociation of LEFs can generate TADs ( Figure 3b , right ) and ‘stripes’ ( or ‘flames , ’ ‘tracks , ’ or ‘lines’ ) ( Fudenberg et al . , 2017; Fudenberg et al . , 2016; Vian et al . , 2018 ) on Hi-C maps ( Figure 3a ) . However , one-sided extrusion cannot reliably bring CTCF barriers together , and thus , cannot generate the dots ( corner peaks ) that are prominent features of Hi-C and micro-C maps ( Krietenstein et al . , 2020 ) and are reproduced by two-sided extrusion ( Figure 3b , right and Figure 3—figure supplement 2 ) . The presence of unavoidable gaps between LEFs and between LEFs and barriers is the reason for this deficiency . This can be remedied by introducing a comparable number of two-sided LEFs to close gaps ( Figure 3d , right ) . One-sided extrusion alone , however , can reproduce dots when undergoing frequent stochastic switches in translocation direction , turning one-sided into effectively two-sided extrusion . Additional mechanisms to generate two-sided or effectively two-sided extrusion have also been proposed ( Davidson et al . , 2019; Golfier et al . , 2020; Kim et al . , 2020; Kim et al . , 2019; Kong et al . , 2020; Moevus , 2019 ) , and gap closure may be achieved by several other mechanisms , as we discuss below in the subsection ‘Molecular evidence and plausibility of different modes of SMC function . ’ Another strategy to eliminate gaps between boundaries and generate dots is to have strongly ( >100 fold ) biased loading of LEFs at barriers . Loading of cohesin at CTCF sites has been proposed since the two were found to colocalize ( Nichols and Corces , 2015; Rubio et al . , 2008 ) . Available experimental evidence , however , argues against loading at CTCF sites; it was previously shown that CTCF is dispensable for cohesin loading ( Parelho et al . , 2008; Wendt et al . , 2008 ) , and more recently , CTCF-degradation experiments appear to have little effect on the levels of chromatin-associated cohesin ( Busslinger et al . , 2017; Nora et al . , 2019; Nora et al . , 2017 ) and the extent of loop extrusion ( Fudenberg et al . , 2017 ) . In many bacteria , bSMCs loaded near the origin of replication ( by the parABS system ) generate contacts centered about the ori-ter axis , which is visible in Hi-C maps as a secondary diagonal ( Böhm et al . , 2020; Le et al . , 2013; Marbouty et al . , 2014; Umbarger et al . , 2011; Wang et al . , 2017; Wang et al . , 2015 ) . The challenge for one-sided loop extrusion models in bacteria is to explain how one-sided ( i . e . , asymmetric ) LEF translocation might generate symmetrically aligned contacts between chromosome arms . Pure one-sided extrusion does not work because it creates a ‘+”-shape on the contact map instead of a secondary diagonal ( Figure 4c and [Miermans and Broedersz , 2018] ) . Furthermore , we find that allowing diffusion of the anchor point does not help because this type of asymmetric extrusion cannot promote symmetric juxtaposition of the chromosome arms . The switching model , however , with a switching time on the order of seconds ( <10 s for B . subtilis and <20 s for C . crescentus , i . e . , rates kswitch≳0 . 1 s−1; Figure 4d ) exhibits the desired effectively two-sided property and naturally creates the desired symmetry of contacts between left and right chromosome arms . Interestingly , if bSMCs function by one-sided extrusion with switching , this constraint suggests that bSMCs can switch their direction of extrusion within a few ATPase cycles ( the B . subtilis SMC complex has an ATPase rate of 0 . 7 ATP/s [Wang et al . , 2018] ) . Switching , however , has not been observed in single-molecule experiments with yeast condensin SMC complexes , and such fast switching may appear as two-sided extrusion in vitro . We note that it was recently suggested that B . subtilis SMCs have two independent motor activities for extrusion ( Brandão et al . , 2019; Wang et al . , 2017 ) ; this observation is consistent with either two-sided extrusion or one-sided extrusion with rapid switching . Thus , our model suggests that microscopically one-sided extrusion can explain juxtaposition of chromosome arms , provided that bSMCs act as effectively two-sided extruders . One-sided loop extrusion was first imaged for budding yeast ( S . cerevisiae ) condensins ( Ganji et al . , 2018 ) . Yeast chromosomes are organized differently from chromosomes of higher eukaryotes . In budding yeast , cohesin is responsible for moderate compaction of mitotic chromosomes , while condensin compacts rDNA and proximal regions into insulated domains ( Lazar‐Stefanita et al . , 2017; Schalbetter et al . , 2017 ) and , in quiescent cells , forms 10–60 kb chromatin domains that silence transcription ( Swygert et al . , 2019 ) . In fission yeast ( S . pombe ) , cohesin forms small ( <100 kb ) domains ( Kim et al . , 2016; Mizuguchi et al . , 2014; Tanizawa et al . , 2017 ) , while during mitosis , condensin compacts chromatin by forming larger ( 100’s of kb ) domains ( Kakui et al . , 2017; Kim et al . , 2016; Tanizawa et al . , 2017 ) . The ~10 fold linear compaction achievable by pure one-sided loop extrusion is consistent with fluorescence in situ hybridization imaging of yeast mitotic chromosomes ( Guacci et al . , 1994; Kruitwagen et al . , 2018 ) . Moreover , previous modeling of budding yeast mitotic chromosomes indicated that just ~30–40% coverage by cohesin-extruded loops ( i . e . , ~2 fold linear compaction , Figure 2—figure supplement 7 ) produces chromosome contact maps consistent with those obtained from Hi-C experiments ( Schalbetter et al . , 2017 ) . This lesser degree of compaction generally leads to poorly resolved sister chromatids in our model ( Figure 2b , c ( iii ) , and c ( iv ) ) , but chromatid resolution in yeast could be facilitated by spindle tension ( Lazar‐Stefanita et al . , 2017 ) and the shorter length of yeast chromosomes . These observations could be consistent with compaction by cohesins performing one-sided loop extrusion . In contrast , one-sided extrusion could account for some , but not all , of the observations of chromatin domains in yeast Hi-C , micro-C , and ChIA-PET experiments . Yeast condensins compact pre- and post-rDNA genomic regions ( in S . cerevisiae ) ( Lazar‐Stefanita et al . , 2017; Schalbetter et al . , 2017 ) and mitotic chromosomes ( S . pombe ) ( Kakui et al . , 2017; Tanizawa et al . , 2017 ) into insulated domains that do not exhibit the dots that are indicative of bringing boundaries together . In a similar manner , fission yeast cohesins organize small chromatin domains without dots ( Kim et al . , 2016; Mizuguchi et al . , 2014; Tanizawa et al . , 2017 ) . As shown in Figure 3b , pure one-sided loop extrusion can generate domains without dots . Nonetheless , recent observations of chromatin domains with dots under certain conditions in budding yeast challenge the viability of one-sided extrusion by both condensin and cohesin . In quiescent cells , condensins generate dots at the corners of small ( 10–60 kb ) , transcription-silencing domains in micro-C maps ( Swygert et al . , 2019 ) . In exponentially growing cells arrested during mitosis , cohesins can also generate dots in S phase ( Ohno et al . , 2019 ) . This observation suggests that budding yeast condensins and/or cohesins are either effectively two-sided loop extruders or loaded at specific sites because one-sided extrusion alone cannot generate dots ( Figure 3b ) . However , a mix of two-sided cohesins and one-sided condensins ( e . g . , similar to Figure 3d , right panels ) could generate dots as in micro-C/Hi-C experiments , while remaining consistent with single-molecule experiments . Cohesin-dependent dots have also been observed at sites of convergent transcription in Hi-C maps when cohesin is overexpressed in G1 ( Dauban et al . , 2020 ) . While such dots can be explained by two-sided extrusion , we also considered the possibility that one-sided extrusion assisted by RNA polymerases that can push one side of an SMC complex ( Lengronne et al . , 2004; Ocampo-Hafalla and Uhlmann , 2011 ) . For one-sided extrusion , this effect could in principle generate effectively two-sided ( but asymmetric ) extrusion , where the slower extruding subunit moves at the speed of transcription ( ~1 kb/min ) . For typical cohesin residence times ( Gerlich et al . , 2006b; Hansen et al . , 2017; Kueng et al . , 2006; Tedeschi et al . , 2013; Wutz et al . , 2017 ) , this model suggests that small loops of 10–60 kb ( Dauban et al . , 2020; Ohno et al . , 2019 ) could be generated by the combined activity of loop extrusion and transcription ( Figure 3—figure supplement 8 ) . In summary , one-sided extrusion by condensin and cohesin can reproduce some , but not all , of the chromosome organization phenomena observed in yeast . The lower degree of mitotic chromosome compaction ( Guacci et al . , 1994; Kruitwagen et al . , 2018; Schalbetter et al . , 2017 ) and formation of chromatin domains without dots ( Kakui et al . , 2017; Lazar‐Stefanita et al . , 2017; Mizuguchi et al . , 2014; Schalbetter et al . , 2017; Tanizawa et al . , 2017 ) is consistent with one-sided extrusion by yeast SMC complexes . However , pure one-sided extrusion alone is insufficient to form dots in Hi-C and micro-C ( Dauban et al . , 2020; Ohno et al . , 2019; Swygert et al . , 2019 ) . Consistent with single-molecule experiments , budding yeast condensins could be one-sided , but then cohesins must be two-sided or effectively two-sided in order to generate Hi-C patterns in quiescent cells . In metaphase , budding yeast cohesins may be one-sided extruders , but their interphase activity during exponential growth requires two-sided or effectively two-sided extrusion . Our work identifies two requirements for loop extrusion by SMC complexes to generate known chromosome structures . First , unlooped chromatin gaps between SMC complexes must be closed in order to compact mitotic chromosomes , and they occasionally must be closed between extrusion barriers during interphase to generate enrichment of CTCF-CTCF interactions . Second , particularly in prokaryotes , we find that extrusion must be two-sided or effectively two-sided in order to juxtapose bacterial chromosome arms . Although we studied the switching model in detail , we note that several molecular mechanisms can give rise to such effectively two-sided , gap-closing extrusion . Based on the available experimental evidence , we also considered several physical factors and additional models , discussed below . In Table 1 , we list possible mechanisms of loop extrusion and whether they are able to reproduce in vivo experimental observations; however , many of these mechanisms have not yet been observed or tested . Single-molecule experiments ( Davidson et al . , 2019; Ganji et al . , 2018; Golfier et al . , 2020; Kim et al . , 2020; Kim et al . , 2019; Kong et al . , 2020 ) could assay different types of SMC complexes from a range of organisms in order to establish which loop extrusion models are applicable . We predict that SMC complexes in vivo may constitute effectively two-sided motors or exhibit biased loading in order to robustly organize and compact chromatin . However , a variety of microscopic ( molecule-level ) modes of extrusion may achieve the same macroscopic organization of the chromosomal DNA . We make several testable predictions . First , if switching of extrusion direction is observed , switching should be fast ( occurring at least once per 10 s for bSMCs and at least once per minute for human SMC complexes cohesins and condensins ) . In addition , we predict that if a mixture of one-sided and two-sided extrusion is observed for a population of SMC complexes , then the fraction of two-sided extrusion should be at least 50% for cohesin and at least 80% for condensin ( Table 1 ) . We also predict that bSMCs from eubacteria are either two-sided monomeric complexes or a dimer of complexes that translocate in opposing directions , enlarging a loop and resulting in two-sided extrusion . A few other types of experiments are critical to perform at the single-molecule level in vitro; these would be difficult to test in vivo by microscopic and biochemical methods . We suggest: 1 ) testing how SMC complexes interact with one another when they meet on the same chromatin/DNA substrate in vivo , as we show that LEF traversal can lead to effective compaction; 2 ) testing whether/what fraction of SMC complexes do one-sided or two-sided extrusion under different conditions , such as at various salt concentrations and/or with molecular crowding agents; and 3 ) testing whether specific factors , such as chromatin conformations ( e . g . , supercoils or Holliday junctions ) or proteins ( e . g . , other SMC complexes or CTCF ) , affect mechanisms of extrusion . Finally , we note that there may be differences in functionality among condensins of different species or physiological scenarios . For example , it has been hypothesized that yeast condensins could be one-sided because they do not need to linearly compact mitotic chromosomes 1000-fold ( Banigan and Mirny , 2019 ) . If yeast condensin is fundamentally different from human condensin in function , its use in cell-free chromosome assembly systems ( Shintomi et al . , 2017; Shintomi et al . , 2015 ) should result in long , poorly folded chromosomes relative to those with condensin II only . Similarly , mutations that bias condensin activity towards one-sided extrusion could lead to catastrophic under-compaction of human chromosomes , failure to decatenate chromosomes ( Martin et al . , 2016 ) , DNA damage , aneuploidy , developmental disorders ( Martin et al . , 2016 ) , and cancer ( Mazumdar et al . , 2015; Woodward et al . , 2016 ) . The loop extrusion model has been hypothesized to explain a variety of chromosome organization phenomena , but until recently had remained a hypothesis . Experimental work on yeast condensins ( Ganji et al . , 2018; Kim et al . , 2020 ) has observed that loop extrusion by yeast condensins occurs in a one-sided manner . Theory and simulations of one-sided loop extrusion ( Banigan and Mirny , 2019; Miermans and Broedersz , 2018 ) challenge the generality of this observation . We have shown that pure one-sided loop extrusion generally is unable to reproduce a variety of chromosome organization phenomena in different organisms and scenarios . Instead , loop extrusion should be ‘effectively two-sided’ and/or have the ability to robustly eliminate unlooped chromatin gaps to organize chromosomes; in accord with this , recent experimental data indicate that human condensins and human and Xenopus cohesins are capable of acting in a two-sided manner ( Davidson et al . , 2019; Golfier et al . , 2020; Kim et al . , 2019; Kong et al . , 2020 ) . Additionally , among the models we explored , the switching model is an example that meets these requirements . Nonetheless , experimental evidence suggests that different organisms are likely to achieve macroscopic chromosome organization through diverse microscopic mechanisms . While loop extrusion remains a unifying model for chromosome organization across different domains of life , various to-be-determined microscopic mechanisms could underlie these phenomena .
Stochastic simulations of loop-extrusion dynamics are performed with N LEFs on a lattice of length L . There are several types of events . LEFs bind to the chromatin lattice at rate kbind by occupying two adjacent lattice sites and LEFs unbind at rate kunbind . When an active subunit of a LEF makes a step , it occupies the site that was immediately adjacent to it , which frees the lattice site that it previously occupied . Directional stepping by an active subunit occurs at speed v and proceeds in the direction away from the other LEF subunit . Diffusive stepping occurs in either direction at loop-size-dependent rate v±diff ( ℓ ) . When a one-sided LEF switches its active extrusion direction , the active subunit becomes passive and vice versa . Switches occur at a rate kswitch . In interphase simulations , LEF subunits may stall upon encountering a correctly oriented CTCF site . This occurs with probability pstall . Each simulation consists of a chromatin polymer with L sites and a fixed number , Nb , of LEFs that populate the sites at low density , Nb/L ≤ 0 . 05 . The simulation code is publicly available at https://github . com/mirnylab/one_sided_extrusion ( Banigan et al . , 2020; copy archived at https://github . com/elifesciences-publications/one_sided_extrusion ) . 1D stochastic simulations of loop-extrusion dynamics modeling mitotic chromosome compaction for pure one-sided , two-sided , switching , and pushing models are performed with N LEFs on a lattice of length L , with L = 60000 sites and 100 < N < 3000 . Each site is taken to be a = 0 . 5 kb . We use the Gillespie algorithm to determine the time that each kinetic event -- binding , unbinding , directional stepping , and switching -- occurs ( Gillespie , 1977; Goloborodko et al . , 2016b ) . Events are executed in temporal order , and after an event occurs , we compute the lifetimes of new events that become permissible ( e . g . , a LEF step that becomes possible because another LEF has moved ) . Simulations are run for tsim = 400 max ( ( 1/kunbind+1/kbind ) , L/v+1/kbind ) , and data is recorded for the second half of the simulation , long after the onset of the steady-state , for at least three simulations per parameter combination . For 1D simulations of chromosome compaction in the semi-diffusive model , 1D simulations of compaction with LEF traversal , 3D polymer simulations of chromosome compaction with all models , interphase TAD formation , and 1D simulations of LEF dynamics on bacterial chromosomes , we use a fixed-time-step Monte Carlo algorithm instead of the Gillespie algorithm . This algorithm facilitates coupling of LEF kinetics to the loop architecture ( for the semi-diffusive model ) and/or 3D polymer conformation ( for polymer simulations ) . Here , each event is modeled as a Poisson process; at each LEF time step dt , an event is executed with probability kidt , where ki is the rate of event i . In the semi-diffusive model , the passive diffusive stepping rate for a LEF is v±diff ( ℓ ) =vdiff e∓ ( 3/2 ) ( a / ℓ ) , which is updated when the size of either the loop associated with the LEF or any loop in which the LEF is nested changes in size . The expression for v±diff ( ℓ ) is a discretization of v±diff ( ℓ ) =vdiff e∓f a / kT . Here , f = -dU/dℓ = ( 3/2 ) kT ln ( ℓ/a ) defines the entropic force arising from loop configurational entropy ( e . g . , see Brackley et al . ( 2017 ) . For fixed-time-step simulations of mitotic chromosomes , L = 30000 , N = 750 , and a = 0 . 5 kb , which is assumed to be 30 nm in diameter ( ~3 nucleosomes ) . At least three simulations per parameter combinations are run for >40 residence times , and linear compaction is measured after 20 residence times . Probe radius rhull = 600 nm was used to calculate concave hulls . For simulations of interphase , we simulate a chain with three different TAD sizes of 100 , 200 , and 400 monomers . This system of 700 monomers in total is repeated 6 or eight times , giving a total size of 4200 monomers ( for computing dot strengths ) or 5600 monomers ( for computing contact maps and scalings ) . When LEFs encounter a CTCF site , they are stalled ( i . e . they stop moving until they are unloaded ) , with a probability of 80% ( Fudenberg et al . , 2016 ) . From the scalings , we determined that one monomer corresponds to 2 kb ( Figure 3—figure supplement 1 ) . We used a total of 4000 conformations to compute contact maps , scalings or dot strengths . For computing the contact maps , we used a contact radius of 5 monomers . Dot strengths are computed as follows: first , we compute observed-over-expected of a contact map ( we divide out the distance dependence , by dividing each diagonal by its average [Lieberman-Aiden et al . , 2009] ) , then we compute the strength of a dot of a particular TAD ( Figure 3—figure supplement 2 ) and last , we compute the average of all the dots ( each of which appears six times on one map ) . In contrast to mitotic compaction , λ and d are varied separately for interphase chromosomes , because the dot strengths depend on λ and d separately , as well as the distance between two CTCF sites , dCTCF . Based on contact probability scalings ( Figure 3—figure supplement 1 ) and experimental observations , we consider a separation between loop extruders of d = 200 kb and a processivity of λ = 200 kb ( Cattoglio et al . , 2019; Fudenberg et al . , 2016; Holzmann et al . , 2019 ) in the main text , and we consider other parameter values in the figure supplements . Furthermore , we choose typical TAD sizes of 200 and 400 kb ( Rao et al . , 2014 ) . For simulations of Wapl depletion conditions , we use d = 200 kb and λ = 2 Mb ( Gassler et al . , 2017; Nuebler et al . , 2018 ) . We simulate loop extrusion on bacterial chromosomes using the fixed-time-step simulations for LEF dynamics described above . LEFs are allowed to randomly load on a lattice of L = 4000 sites , where each lattice site corresponds to ~1 kb of DNA . LEFs have a strong bias to bind one site at the center of the lattice to mimic the effect of a single parS site near the origin of replication in bacterial chromosomes . The relative probability of loading at the simulated parS site was ~40 , 000 times stronger than that of every other site , i . e . , if the relative probability of loading at the simulated parS is 1 , then the total relative probability to load on any other site is 0 . 1 L . As a result , the overall preference to bind the parS site over all other genomic loci is approximately 10-fold . Bacterial LEFs were simulated as deterministic extruders with a stochastic dissociation rate kunbind = 2/L to approximate the steady decrease in bSMC density away from the ori observed via ChIP-seq ( i . e . , bSMC density at the ter region is ~1/3 of the value at ori ) ( Wang et al . , 2017 ) . In addition to a stochastic ( position-independent ) dissociation rate , LEFs automatically unbind if one of the subunits reached the edge of the lattice , i . e . , the ter region; ter was set to lattice positions 0–3 and 3996–3999 ( i . e . , diametrically opposite to the parS site at lattice site 2000 ) . To model the 3D dynamics of polymers loaded with LEFs , we performed polymer molecular dynamics simulations in OpenMM ( Eastman et al . , 2017; Eastman et al . , 2013; Eastman and Pande , 2010 ) using a custom , publicly available library , openmm-polymer ( available at https://github . com/mirnylab/openmm-polymer-legacy; ( Imakaev et al . , 2020 ) , coupled with the fixed-time-step LEF simulations described above and in Fudenberg et al . ( 2016 ) ; Goloborodko et al . ( 2016a ) . In the polymer simulation , a LEF crosslinks the sites that it occupies together . LEF positions are evolved as described above . After each time step of LEF dynamics , the polymer simulation is evolved via Langevin dynamics for 200 or 250 time steps ( for interphase and mitosis , respectively ) with dt = 80 . Polymers are constructed of L consecutive subunits bonded via the pairwise potential:Ub ( r ) =k2 ( r-b ) 2where r = ri rj is the displacement between monomers i and j , k = 2 kT / δ2 is the spring constant , δ = 0 . 1 , and b is the diameter of a monomer . For mitotic chromosome simulations , b = 30 nm; for other scenarios , it is unnecessary to assign a value to b . Monomers crosslinked by a LEF are held together by the same potential . Weakly repulsive excluded volume interactions between monomers are modeled as:Uexc ( r ) =εexc εm ( r σrm ) 12 ( ( r σrm ) 2 -1 ) + εexc , for r<σ with σ=1 . 05b , rm=6/7 , εm=46656/823543 , and εexc=1 . 5 kT . For simulations of mitotic chromosomes with 3D attractive interactions , monomers interact through the potential:Uatt ( r ) = -ε εm ( r σrm ) 12 ( ( r σrm ) 2 -1 ) + ε , for σ < r < 2b and ε is a parameter to be varied . At the beginning of each simulation , the polymer is initialized as a random walk and monomers are initialized with normally distributed velocities , so that the temperature is T . The system is thermostatted by intermittent rescaling of velocities to maintain temperature T . To compute contact maps for bacterial chromosomes , the contact frequency was calculated from the equilibrium contact probability for a Gaussian chain . This theoretical model agrees well with polymer molecular dynamics simulations ( Appendix 3 and Figure 4—figure supplements 6 , 7 ) . Briefly , contact probability between two sites on a Gaussian chain scales with s-3/2 , where s is the linear distance between the sites , excluding any loops between the two sites . Sites within the same loop obey this scaling relation with an effective s , seff , substituted for s in the scaling relation; seff = s ( 1 s/ℓ ) , where ℓ is the loop size . For sites in different loops , s in the scaling relation is replaced by the sum of the effective lengths of the regions connecting the two sites ( see Appendix 3 for details ) . These relative contact probabilities are used to compute the contact maps for bacterial chromosome simulations . Contact maps are generated using contacts from 50 , 000 to 100 , 000 different LEF conformations . | The different molecules of DNA in a cell are called chromosomes , and they change shape dramatically when cells divide . Ordinarily , chromosomes are packaged by proteins called histones to make thick fibres called chromatin . Chromatin fibres are further folded into a sparse collection of loops . These loops are important not only to make genetic material fit inside a cell , but also to make distant regions of the chromosomes interact with each other , which is important to regulate gene activities . The fibres compact to prepare for cell division: they fold into a much denser series of loops . This is a remarkable physical feat in which tiny protein machines wrangle lengthy strands of DNA . A process called loop extrusion could explain how chromatin folding works . In this process , ring-like protein complexes known as SMC complexes would act as motors that can form loops . SMC complexes could bind a chromatin fibre and reel it in to form the loops , with the density of loops increasing before cell division to further compact the chromosomes . Looping by SMC complexes has been observed in a variety of cell types , including mammalian and bacterial cells . From these studies , loop extrusion is generally assumed to be ‘two-sided’ . This means that each SMC complex reels in the chromatin on both sides of it , thus growing the chromatin loop . However , imaging individual SMC complexes bound to single molecules of DNA showed that extrusion can be asymmetric , or ‘one-sided’ . These observations show the SMC complex remains anchored in place and the chromatin is reeled in and extruded by only one side of the complex . So Banigan , van den Berg , Brandão et al . created a computer model to test whether the mechanism of one-sided extrusion could produce chromosomes that are organised , compact , and ready for cell division , like two-sided extrusion can . To answer this question , Banigan , van den Berg , Brandão et al . analysed imaging experiments and data that had been collected using a technique that captures how chromatin fibres are arranged inside cells . This was paired with computer simulations of chromosomes bound by SMC protein complexes . The simulations and analysis found that the simplest one-sided loop extrusion complexes generally cannot reproduce the same patterns of chromatin loops as two-sided complexes . However , a few specific variations of one-sided extrusion can actually recapitulate correct chromatin folding and organisation . These results show that some aspects of chromosome organization can be attained by one-sided extrusion , but many require two-sided extrusion . Banigan , van den Berg , Brandão et al . explain how the simulated mechanisms of loop extrusion could be consistent with seemingly contradictory observations from different sets of experiments . Altogether , they demonstrate that loop extrusion is a viable general mechanism to explain chromatin organisation , and that it likely possesses physical capabilities that have yet to be observed experimentally . | [
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